U.S. Dept. of Commerce / NOAA / OAR / PMEL / Publications
The Tropical Ocean-Global Atmosphere observing system: A decade of progress
Michael J. McPhaden,1 Antonio J. Busalacchi,2 Robert Cheney,3 Jean-René
Donguy,4 Kenneth S. Gage,5 David Halpern,6 Ming Ji,7 Paul
Julian,8 Gary Meyers,9 Gary T. Mitchum,10 Pearn P. Niiler,11 Joel
Picaut,12,13 Richard W. Reynolds,7 Neville Smith,14 and Kensuke Takeuchi15
1Pacific Marine Environmental Laboratory, NOAA, Seattle, Washington
2NASA Goddard Space Flight Center, Greenbelt, Maryland
3National Ocean Service, NOAA, Silver Spring, Maryland
4Institut Français de Recherche Scientifique pour le Développement en Coopération,
Plouzane, France
5Aeronomy Laboratory, NOAA, Boulder, Colorado
6Jet Propulsion Laboratory, California Institute of Technology, Pasadena
7National Centers for Environmental Prediction, NOAA, Camp Springs, Maryland
8Suitland, Maryland
9Commonwealth Scientific and Industrial Research Organization, Tasmania, Australia
10Department of Marine Science, University of South Florida, Saint Petersburg
11Scripps Institution of Oceanography, La Jolla, California
12Institut Français de Recherche Scientifique pour le Développement on Coopération
13Now at NASA Goddard Space Flight Center, Greenbelt, Maryland
14Bureau of Meteorology Research Centre, Melbourne, Victoria, Australia
15Institute of Low Temperature Science, Hokkaido University, Sapporo, Japan
Journal of Geophysical Research, 103(C7), 14,169-14,240 (1998).
Copyright ©1998 by the American Geophysical Union. Further electronic distribution is not allowed.
Abstract
A major accomplishment of the recently completed Tropical Ocean-Global Atmosphere (TOGA) Program was the development of an
ocean observing system to support seasonal-to-interannual climate studies. This paper reviews the scientific motivations for
the development of that observing system, the technological advances that made it possible, and the scientific advances that
resulted from the availability of a significantly expanded observational database. A primary phenomenological focus of TOGA
was interannual variability of the coupled ocean-atmosphere system associated with El Niño and the Southern
Oscillation (ENSO). Prior to the start of TOGA, our understanding of the physical processes responsible for the ENSO cycle
was limited, our ability to monitor variability in the tropical oceans was primitive, and the capability to predict ENSO was
nonexistent. TOGA therefore initiated and/or supported efforts to provide real-time measurements of the following key
oceanographic variables: surface winds, sea surface temperature, subsurface temperature, sea level and ocean velocity.
Specific in situ observational programs developed to provide these data sets included the Tropical Atmosphere-Ocean (TAO)
array of moored buoys in the Pacific, a surface drifting buoy program, an island and coastal tide gauge network, and a
volunteer observing ship network of expendable bathythermograph measurements. Complementing these in situ efforts were
satellite missions which provided near-global coverage of surface winds, sea surface temperature, and sea level. These new
TOGA data sets led to fundamental progress in our understanding of the physical processes responsible for ENSO and to the
development of coupled ocean-atmosphere models for ENSO prediction.
And thorough this distemperature we see the seasons alter...
Shakespeare's "A Midsummer Night's Dream"
Act 2, Scene 1
1. Introduction
El Niño (EN) is characterized by a large-scale weakening of the trade winds and warming of the surface layers in
the eastern and central equatorial Pacific Ocean. El Niño events occur irregularly at intervals of roughly
27 years, although the average is about once every 34 years [Quinn et al., 1987]. They typically
last 1218 months, and are accompanied by swings in the Southern Oscillation (SO), an interannual seesaw in
tropical sea level pressure between the eastern and western hemispheres [Walker, 1924]. During El Niño,
unusually high atmospheric sea level pressures develop in the western tropical Pacific and Indian Ocean regions, and
unusually low sea level pressures develop in the southeastern tropical Pacific. Bjerknes [1966, 1969] was the first to link
swings in the Southern Oscillation to El Niño events, proposing that the two phenomena were generated by coupled
ocean-atmosphere interactions. SO tendencies for unusually low pressures west of the date line and high pressures east
of the date line have also been linked to periods of anomalously cold equatorial Pacific sea surface temperatures (SSTs)
sometimes referred to as La Niña [Philander, 1990]. The full range of SO variability,
including both anomalously warm and cold equatorial SSTs, is often referred to as ENSO.
ENSO is associated with shifts in the location and intensity of deep convection and rainfall in the tropical Pacific.
During El Niño events, drought conditions prevail in northern Australia, Indonesia, and the Philippines, and
excessive rains occur in the island states of the central tropical Pacific and along the west coast of South America.
Shifts in the pattern of deep convection in the tropical Pacific also affect the general circulation of the atmosphere
and extend the impacts of ENSO to other tropical ocean basins and to midlatitudes [Rasmusson and Wallace, 1983; Ropelewski and Halpert, 1986, 1987; Halpert and Ropelewski, 1992;
Trenberth et al., this issue]. During El Niño most of Canada and the northwestern
United States tend to experience mild winters, and the states bordering the Gulf of Mexico tend to be cooler and wetter
than normal. California has experienced a disproportionate share of episodes of heavy rainfall during El Niño
winters such as 19821983, 19911992, and 19941995. Atlantic hurricanes tend to be less frequent during
warm events and more frequent during cold events [Gray et al., 1993]. El Niño events also
disrupt the marine ecology of the tropical Pacific and the Pacific coast regions of the Americas, affecting the
mortality and distribution of commercially valuable fish stocks and other marine organisms [Barber and Chavez, 1983;
Dessier and Donguy, 1987; Pearcy and Schoener, 1987; Lehodey et
al., 1997]. Thus, though originating in the tropical Pacific, ENSO has socioeconomic consequences that are felt
worldwide.
The widespread and systematic influence of ENSO on the ocean-atmosphere system, and the potential that it might be
predictable seasons to years in advance, led to initiation of the international Tropical Ocean-Global Atmosphere (TOGA)
Program, a 10-year study (19851994) of seasonal-to-interannual (also referred to as short-term) climate
variability. The goals of the TOGA program were [World Climate Research Program, 1985, p. vii].
[1.] to gain a description of the tropical oceans and the global atmosphere as a time dependent system, in order to
determine the extent to which this system is predictable on time scales of months to years, and to understand the
mechanisms and processes underlying that predictability;
[2.] to study the feasibility of modeling the coupled ocean-atmosphere system for the purpose of predicting its
variability on timescales of months to years; and
[3.] to provide the scientific background for designing an observing and data transmission system for operational
prediction if this capability is demonstrated by the coupled ocean-atmosphere system.
The scientific background and rationale for TOGA was spelled out in several planning documents [e.g., World Climate Research
Program, 1985; National Research Council, 1983, 1986]. Prior to TOGA, a basic description of oceanic and
atmospheric variability associated with El Niño existed [e.g., Rasmusson and Carpenter, 1982], as did a
basic description of tropical/extratropical atmospheric teleconnections in the northern hemisphere [e.g., Horel and
Wallace, 1981]. Atmospheric general circulation models had shown a sensitivity both in the tropics and at higher
latitudes to underlying equatorial Pacific SST anomalies, and theories were emerging on how tropical forcing gave rise
to observed teleconnection patterns [e.g., Hoskins and Karoly, 1981]. Relatively simple
wind-forced ocean models prior to TOGA were capable of simulating some aspects of seasonal-to-interannual variability
associated with sea level variations in the Pacific [e.g., Busalacchi and O'Brien, 1980; Busalacchi
and O'Brien, 1981; Busalacchi et al., 1983]. Initial attempts to quantitatively assess the role of
ocean dynamics in controlling interannual variations in SST were underway [Gill, 1983]. Also, ocean general circulation
models with explicit mixed layer thermodynamics were being developed for improved simulations of SST variability [e.g.,
Schopf
and Cane, 1983]. Coupled tropical ocean-atmosphere models were in their infancy prior to TOGA. They showed
promise though in their ability to elucidate possible mechanisms responsible for ocean-atmosphere feedbacks and in their
ability to crudely simulate aspects of the ENSO cycle [McCreary, 1983; Philander et al., 1984].
Theories regarding the mechanisms responsible for El Niño variations in the ocean were likewise developing [e.g.,
Wyrtki,
1975; McCreary, 1976; Hurlburt et al., 1976]. The roles of ocean
dynamics and, in particular, wind-forced equatorial Kelvin and Rossby waves in affecting large-scale redistribution of
mass and heat in the equatorial band were widely regarded as crucial aspects of the ocean's role in the ENSO cycle. The
rapid response of the equatorial ocean to wind forcing and the ability of equatorial waves to affect remote parts of the
basin on relatively short timescales distinguish the tropics from higher latitudes where planetary scale waves propagate
much more slowly. Substantial responses in equatorial currents and sea surface heights to relatively short-duration wind
events were evident in observations before the start of TOGA [Knox and Halpern, 1982; Eriksen et al.,
1983]. These observations suggested the potential for remotely forced changes in SST due to wave-induced changes in
horizontal and vertical advection and upper ocean mixing. Thus understanding the oceanic processes giving rise to SST
variability in the tropical Pacific was a more challenging problem than at midlatitudes, where SST variations on
seasonal and interannual timescales are generated primarily by local air-sea heat exchange [Gill and Niiler, 1973].
Much of the progress in oceanographic studies related to El Niño in the 1970s and early 1980s was stimulated by
fieldwork and modeling efforts as part of the Equatorial Pacific Ocean Climate Studies (EPOCS) program [Hayes et al.,
1986], the North Pacific Experiment (NORPAX) [Wyrtki et al., 1981], and the Pacific Equatorial
Ocean Dynamics (PEQUOD) experiment [Eriksen, 1987]. These programs provided new data for basic description of
phenomenology, for developing and testing dynamical hypotheses, and for model development and validation [Halpern,
1996]. Impressive though the scientific advances were during this period, they were still inadequate in many
respects. To quote from the document U.S. Participation in the TOGA Program [National Research Council, 1986,
p. 67]:
[1.] The subsurface signature of El Niño events and the time-dependent fluxes of momentum and energy at the
air-sea interface are known only qualitatively, and existing observations are inadequate to define them with the
accuracy needed for initializing and verifying models.
[2.] Major uncertainties still exist concerning the tropical and southern hemisphere atmospheric circulations and their
interannual variability.
[3.] The processes that determine the sea surface temperature distribution and the surface wind field over the tropics
are not yet well understood.
[4.] The fundamental behavior and predictability of the coupled climate system are just beginning to be understood.
TOGA, initiated by the World Climate Research Program [1985], provided a framework for coordinated, sustained
international efforts aimed at addressing these shortcomings. Implementation of TOGA was to be carried out with major
new initiatives in modeling, process-oriented field studies, and long-term observations. Efforts in these areas were to
be highly interactive and mutually reinforcing. Models and the results of process studies would be used to help guide
the development of long-term observational systems. Long-term observations in turn would provide a large-scale,
long-term framework in which to interpret the results of shorter-duration, geographically focused, intensive process
studies. Long-term observations would also be used to validate models, to aid in the development of parameterization
schemes for subgrid scale model physics, and to initialize dynamical model-based climate forecasting schemes.
The need for an improved observing system was underscored during the planning stages of TOGA in the early 1980s, when
the scientific community was caught completely off guard by the 19821983 El Niño, the strongest in over a
hundred years (see Appendix A for details). This El Niño was neither predicted nor even detected until several
months after it had started. The lesson from this experience was obvious: an in situ observing system capable of
delivering data in real time was urgently needed for improved monitoring, understanding, and prediction of El
Niño and related phenomena. To meet these requirements, the TOGA Implementation Plan called for the development
of a "thin monitoring" array of in situ measurements based on the enhancement of existing capabilities [International TOGA
Project Office, 1992]. This observing system was to provide data on a basin scale for at least 10 years
without significant temporal gaps, so that a continuous record of climate variability could be assembled. Ten years was
considered the minimum length of time needed for a comprehensive study of interannual variability, the dominant mode of
which was ENSO cycle.
The purpose of this paper is to describe the development of the TOGA observing system, to highlight scientific advances
that have resulted from implementation of this system, and to summarize how data from this system have contributed to
progress in developing models for improved climate analysis and prediction. We will emphasize oceanic, rather than
atmospheric, components of the observing system, reflecting relative levels of effort expended on implementation during
the TOGA decade. However, we will discuss TOGA efforts to augment the World Weather Watch for atmospheric measurements
and to establish a specialized network of island-based wind profilers.
We will also emphasize in situ rather than satellite data. Satellite missions were generally initiated for purposes
other than, or only partially motivated by, short-term climate research (e.g., operational weather prediction, national
defense, general oceanographic and/or meteorological applications). Also, delays in satellite missions and/or temporal
discontinuities in satellite data coverage heightened reliance on in situ measurements during the TOGA decade. For
example, launch of the National Aeronautics and Space Administration's scatterometer (NSCAT) for surface wind velocity
estimates, originally scheduled for 1989, was repeatedly delayed until August 1996, almost 2 years after the end of
TOGA. The satellite carrying NSCAT then failed prematurely, in June 1997, after being operational for only 8 months.
Similarly, there was a 2-year hiatus in satellite sea level altimetry measurements between the end of the U.S. Navy's
Geodetic Satellite (Geosat) mission in 1989 and the launch of European Space Agency's European Remote Sensing Satellite
(ERS-1) in 1991. Nonetheless, we will discuss those satellite missions that contributed directly to TOGA objectives,
particularly with regard to oceanic variability. Satellite measurements targeted more toward documenting and
understanding atmospheric variability during TOGA, namely those for precipitation, water vapor, clouds, radiation, and
evaporation [Lau and Busalacchi, 1993], are discussed in work by Wallace et al. [this issue].
Originally, it was anticipated that TOGA would develop a balanced research agenda with significant levels of effort
directed at variations in all three tropical oceans [World Climate Research Program, 1985]. Important
dynamical linkages between ENSO and climate variability in the other tropical ocean basins were evident [e.g., Barnett,
1983; Horel et al., 1986]. In addition, phenomena significantly impacting regional climate, such as
the Indian monsoon [Webster et al., this issue], the Indian Ocean dipole [Nicholls, 1989], El Niño-like warm
episodes in the equatorial Atlantic [Philander, 1986], and the so-called "Atlantic SST dipole" [Moura and Shukla,
1981], were not well understood in terms of underlying physical processes or potential predictability. However, the
strength of the ENSO signal and its global impacts, coupled with limited financial resources, tended to concentrate most
efforts in the Pacific. This review therefore focuses primarily on the Pacific. Recognizing that some elements of the
observing system (satellite and in situ) are more global in character, this broader geographic coverage will be noted as
appropriate.
Before concluding this introduction, we note that there is a range of interpretations in the literature on use of the
terms El Niño, La Niña, and ENSO [Scientific Committee on Ocean Research (SCOR),
1983; Deser and Wallace, 1987; Enfield, 1989; Aceituno, 1992; Glantz, 1994; Trenberth,
1997]. Originally, the term El Niño (in reference to the Christ child) denoted a warm southward flowing ocean
current that occurred every year around Christmas time off the west coast of Peru and Ecuador. The term was later
restricted to unusually strong warmings that disrupted local fish and bird populations every few years. However, as a
result of the frequent association of South American coastal temperature anomalies with interannual basin-scale
equatorial warm events, El Niño has also become synonymous with larger-scale, climatically significant, warm
events. There is not, however, unanimity in the use of the term El Niño. In this paper, therefore, we will adopt
a standard of referring interchangeably to El Niño, ENSO warm event, or the warm phase of ENSO as those times of
warm eastern and central equatorial Pacific SST anomalies. Conversely, the terms La Niña, ENSO cold event, or
cold phase of ENSO will be used interchangeably to describe those times of cold eastern and central equatorial Pacific
SST anomalies. As noted earlier, the terms ENSO and ENSO cycle will be used to describe the full range of variability
observed in the Southern Oscillation Index, including both warm and cold events.
The rest of the paper is organized as follows. We begin in section 2 with a brief overview of El Niño as the
primary phenomenological target of the TOGA observing system and then describe the observing system design in terms of
primary variables measured and platforms used for implementation. Scientific progress through descriptive and diagnostic
studies is reviewed in section 3. Section 4 describes how the TOGA observing system contributed to the development of
dynamical models for seasonal-to-interannual climate analysis and prediction. The paper concludes in section 5 with a
summary and a brief discussion of future directions for climate observations based on the successes of TOGA. Four
appendices are included, the first of which (Appendix A) describes the failure to observe the onset of the
19821983 El Niño. Appendices B, C, and D provide historical background and technical information related to
development of the in situ oceanographic components, the ocean-related satellite components, and the in situ
meteorological components, respectively, of the observing system. A partial list of current World Wide Web sites for
access to data and data analysis products engendered by the TOGA observing system can be found in the National Research
Council's [1996] report on TOGA. In addition, reports on the TOGA observing system at various stages in its
development can be found in work by McPhaden and Taft [1984], U.S. TOGA Office [1988], Nova University [1989], World Climate Research
Program [1990b], and the National Research Council [1990].
2. An Overview of the TOGA Observing System
2.1 El Niño: A Primary Focus of TOGA
We begin with a brief overview of El Niño, which was the primary phenomenological focus of TOGA, in order to
highlight physical principles that helped to guide development of the TOGA observing system. This overview parallels
what was known at the start of TOGA with the caveat that, as a conceptual model, many of its key mechanisms were poorly
understood or not yet critically tested from observations. Progress beyond this simple description is taken up in
sections 3 and 4.
In the tropical Pacific, net heat gain from the atmosphere tends to create a warmer surface layer near the equator than
at higher latitudes. Under normal conditions (Figure 1, top), easterly trade wind forcing
drives near-equatorial surface flow westward in the South Equatorial Current (SEC), piling up this warm surface layer in
the western Pacific to create a deep warm pool. Conversely, warm water is drained from the eastern Pacific, leading to
an upward tilt of the thermocline to the east. The relative shallowness of the thermocline in the eastern Pacific
increases the efficiency of local trade-wind-driven equatorial upwelling to cool the surface, creating a cold tongue in
SST that extends from the coast of South America to near the international date line. The easterly trade winds are
reinforced by the east-west SST contrast, which is associated with low atmospheric surface pressure over the warm pool
in the west and high surface pressure over the cooler waters of the eastern Pacific. Atmospheric circulation on the
equatorial plane (that is, the Walker circulation) is closed by ascent of warm moist air over the warm pool (associated
with deep convection and precipitation), westerly winds aloft, and subsidence in the high-pressure zone of the eastern
Pacific. In the ocean, westward flow in the surface SEC is in part compensated by a return flow to the east in the
thermocline, i.e., the Equatorial Undercurrent (EUC). This current flows down the zonal pressure gradient associated
with the east-west tilt of the thermocline and provides a source of water for upwelling in the east [Bryden and
Brady, 1985].
Figure 1: Schematic of normal and El Niño conditions in the equatorial Pacific. See
section 2 for discussion.
During El Niño (Figure 1, bottom), the trade winds weaken in the central and
western Pacific, leading to a local eastward acceleration of the surface currents. In addition, weakening of the trade
winds excites downwelling equatorial Kelvin waves, which propagate into the eastern equatorial Pacific, where they
depress the thermocline, and upwelling equatorial Rossby waves, which propagate into the western Pacific, where they
elevate the thermocline [Wyrtki, 1975; McCreary, 1976; Hurlburt et al., 1976]. Anomalously warm sea
surface temperatures appear from the coast of South America to west of the international date line, and the pattern of
deep convection and precipitation shifts eastward with the warmest SSTs [Gill and Rasmusson, 1983]. Deep
convection is the principal driving force for atmospheric circulation through the release of latent heat at
midtropospheric levels, and these shifts in the centers of deep convection during El Niño affect the atmospheric
circulation on a global basis [Horel and Wallace, 1981].
2.2 Key Variables and Sampling Requirements
The physical basis for ENSO and related phenomena provided a rationale for the development of an observing system to
measure key oceanographic and meteorological variables. Prioritization of these variables was based on the need not only
to better document and understand but also to predict short-term climate variability. Foremost were fields of surface
wind stress and sea surface temperature since, as evident from discussion in the preceding section, it is these two
variables by which the ocean and atmosphere most immediately interact in the tropics.
Of next highest priority was the upper ocean thermal field. The basic periodicity of ENSO is controlled in part by the
vast thermal inertia of the upper ocean through the propagation of planetary-scale equatorial waves. These waves mediate
coupling to the atmosphere on interannual timescales by redistributing upper ocean heat not only zonally along the
equator, as evident in Figure 1, but also meridionally [Wyrtki, 1985a]. Thus the
"memory" for the ENSO cycle is to be found in the ocean rather than in the atmosphere, where (excluding the mean
seasonal cycle, which is externally forced by variations in insolation) intrinsic timescales are much shorter and are
primarily associated with 35-day weather variability. Also, the slow evolution of upper ocean heat content on
seasonal-to-interannual timescales suggested a logic for initializing ocean models used in climate prediction with
subsurface temperature data.
Sea level variability was likewise deemed to be a crucial variable because it is a proxy for upper ocean heat content.
The tropical oceans behave in many ways as a two-layer fluid, with thermocline variations reflected in sea level heights
[Rebert
et al., 1985]. For example, during ENSO, sea level is elevated when the thermocline deepens in the eastern
Pacific, and it is depressed when the thermocline shoals in the western Pacific. Sea level thus provides a convenient
measure of the vertically integrated oceanic response to atmospheric forcing.
Measurement of ocean currents was deemed to be essential for meeting the goals of TOGA because of the strong control
ocean dynamics plays in creating ENSO SST anomalies. In most parts of the world ocean, seasonal-to-interannual changes
in SST are controlled simply by variations in heat flux across the air-sea interface. In the equatorial Pacific, on the
other hand, changes in three-dimensional ocean circulation play a crucial role in generating ENSO SST anomalies through
horizontal advection and through changes in intensity of upwelling in the cold tongue region. To a certain extent, the
need for information on the horizontal flow field could be met via estimates from the thermal field via geostrophy.
However, it was also considered essential to directly measure horizontal currents along the equator, where pure
geostrophy breaks down, and in the surface mixed layer, where frictional Ekman flows were expected to be large and
likewise inaccessible via the geostrophic approximation.
Surface winds, SST, upper ocean thermal structure, sea level, and ocean currents, though of central importance in
motivating the development of an observing system for TOGA, were of course not the only variables considered to be of
value for studies of ENSO and related phenomena. It was appreciated that a quantitative understanding of SST variability
required improved estimates of surface heat fluxes, that salinity variability needed to be better documented in the
tropical oceans for a variety of reasons (e.g., its contribution to static stability and dynamic height, and its
potential role in the surface heat balance in regions of heavy rainfall), and that studies of atmospheric circulation
would benefit from an improved definition of precipitation (an integral measure of latent heat release) over the ocean.
TOGA-sponsored research activities thus addressed measurement issues aimed at variables other than winds, SST, upper
ocean thermal structure, sea level, and currents. However, these five key variables were viewed as a sine qua non both
for improved understanding of short-term climate variability (section 3) and for the development of climate
forecast models with significant predictive skill (section 4).
It was also recognized at the start of TOGA that, although ENSO is predominantly a large-scale, interannual perturbation
of the climate system, it could not be effectively observed without taking into account smaller-scale, higher-frequency
fluctuations. There is a broad spectrum of variability in both the ocean and the atmosphere that represents a potential
source of geophysical noise in estimates of climate signals. Noise contamination can arise because of inadequate
sampling in space and/or time, which will alias energy from high-frequency, small-scale fluctuations into the lower
frequencies and larger scales of climatic interest. The existence of this broad spectrum of variability imposes
stringent sampling requirements for climate observations. As an example, Halpern [1988a] and Mangum et
al. [1992] determined that about one sample per day would be required at a given location in the equatorial
Pacific to estimate monthly mean winds with an accuracy of 0.51.0 m s-1. Much of the
equatorial Pacific was significantly undersampled relative to this criterion by volunteer observing ships (VOS), the
main source of information on surface winds prior to and during the early stages of TOGA. Furthermore, some
high-frequency variations were likely to be dynamically relevant in the evolution of El Niño. Potential scale
interactions result from nonlinearities in the ocean-atmosphere system through processes such as atmospheric convection,
ocean mixing, heat and momentum advection, etc. Considerable debate, for example, revolved around the role of episodic
110-day westerly wind bursts and the 3060-day intraseasonal Madden and Julian Oscillation [Madden and
Julian, 1971, 1972] in either triggering or sustaining El Niño events or in accounting for the irregular
periodicity of El Niño [e.g., Keen, 1982; Luther et al., 1983; Harrison and Schopf,
1984; Lau and Chan, 1986].
Resolution and accuracy requirements established by TOGA for the measurements discussed in this study are presented in
Table 1, as excerpted from the fourth edition of the "TOGA International
Implementation Plan" [International TOGA Project Office, 1992]. These requirements evolved during the program as
understanding of the climate system and technical capabilities improved. Table 1
represents the final assessment of the TOGA community, taking into account developments up to 1992. No specific
requirements were set for subsurface temperature. For this variable it was felt that available observational techniques
would fall short of expectations, especially in terms of resolution, except in certain well-sampled regions. Note that
as a practical matter, surface wind velocity rather than wind stress is measured over the oceans, with stress estimated
from wind velocity using bulk turbulent transfer formulae. As specified in Table 1, an accuracy of 0.01 Pa (1 Pa = 1 N m-2)
for surface stress translates roughly into an accuracy requirement of 0.5 m s-1 for surface winds
in regions of trade wind forcing.
Table 1. TOGA Data Requirements
The requirements in Table 1 were generally cast in terms of mapping and/or
documenting variability, rather than in terms of requirements for initialization of climate forecast models. These
latter requirements are still a matter of ongoing research. Nonetheless, by the standards of Table 1, it could be claimed that by the end of TOGA the observing system met many of the
data requirements in the equatorial Pacific Ocean between 8°N and 8°S. This is partly because that was where
most in situ resources were concentrated and partly because TOGA data requirements in some instances (e.g., subsurface
temperature and sea level) were based on what was considered technically feasible. Outside the latitude band
8°N8°S in the tropical Pacific, and in the tropical Atlantic and Indian oceans, the observing system fell
short of specific requirements in Table 1.
In the following subsection we provide a brief summary of the observing system, element by element. Additional technical
details such as instrumental design and instrumental accuracies are elaborated on in Appendices B, C, and D.
2.3 TOGA Observing System Components
2.3.1 In situ oceanographic measurements
In situ elements of the oceanographic observing system developed and implemented in support of TOGA objectives are
illustrated in and summarized in Figures 2 and 3 and
summarized in Tables 2 and 3. These elements include
an island and coastal tide gauge network to provide sea level measurements; drifting buoy arrays to provide mixed layer
velocity and SST measurements; the TOGA Tropical Atmosphere-Ocean (TAO) array of moored buoys to provide surface wind,
SST, upper ocean temperature, and current measurements; and a volunteer observing ship (VOS) expendable bathythermograph
(XBT) program for upper ocean temperature profiles. The XBT program was embedded in the ongoing program of VOS surface
marine meteorological measurements, which provided wind, SST, and other surface data. TOGA also inherited a decade-long
VOS sea surface salinity network in 1985. In addition, repeat hydrographic sections from regularly scheduled research
cruises, most notably along 110°W [McPhaden and Hayes, 1990b; Hayes et
al., 1991c], 165°E [Delcroix et al., 1992], and 137°E [Shuto, 1996], provided valuable information
on upper ocean water mass structures in the Pacific during TOGA.
Figure 2: The in situ Tropical Pacific Ocean Observing System developed under the auspices of
the TOGA program. (top) The observing system in January 1985 at the start of TOGA; (middle) the observing system in July
1990 at the time of the TOGA midlife conference in Honolulu [World Climate Research Program, 1990b]; (bottom) the
observing system in December 1994 at the end of TOGA. The four major elements of this observing system are (1) a
volunteer observing ship expendable bathythermograph program (shown by schematic ship tracks); (2) an island and coastal
tide gauge network (circles); (3) a drifting buoy program (shown schematically by curved arrows); and (4) a moored buoy
program consisting of wind and thermistor chain moorings (shown by diamonds) and current meter moorings (shown by
squares). Thick ship tracks indicate expendable bathythermograph sampling with 11 or more transects per year; thin ship
tracks indicate sampling with 610 transects per year. Although emphasis is on 30°N30°S, termini of
VOS XBT lines originating outside these limits are nonetheless shown. One drifting buoy schematic represents 10 actual
drifters. Only those tide gauge stations are shown that reported their data to the TOGA Sea Level Center in Honolulu
within 2 years of collection. Some tide gauge stations are so close as to be overplotted on one another. By December
1994 most measurements made as part of this four-element observing system were being reported in real time, with data
relay via either geostationary or polar orbiting satellites.
Figure 3: The in situ TOGA Ocean Observing System in its final configuration in December
1994. (top) Pacific Ocean, (bottom) Indian and Atlantic Oceans. Symbols are as in Figure 2.
Table 2. In Situ Elements of the TOGA Ocean Observing System
Table 3. Growth of Various In Situ Observing Arrays
A key feature of the array elements shown in Figures 2 and 3 was that by the end of TOGA most of the data were transmitted to shore via satellite relay in
real time. In addition, each array element had unique measurement capabilities that were advantageous for TOGA (Table 2). However, none of these elements by themselves would have been adequate for TOGA
purposes, because each has certain disadvantages in terms of cost and/or sampling characteristics that limit its
applicability for short-term climate studies. These array components were therefore viewed as complementary to one
another, providing a synergistic framework in which to document and analyze climate fluctuations in the tropical oceans.
Design of the observing system was guided by general circulation model simulations of wind-forced oceanic variability
and by empirical studies of space-time correlation scales. Model design studies indicated, for example, that basin-scale
wind measurements were required within at least ~7° of the equator to simulate accurately the
seasonal-to-interannual evolution of SST variability in the cold tongue region of the equatorial Pacific, and that the
ocean responds most sensitively to zonal wind rather than meridional wind forcing on these timescales [Harrison,
1989]. Empirical studies indicated that zonal wind field variations are minimally coherent over 2°3°
latitude and 10°15° longitude [Harrison and Luther, 1990], and that
approximately one sample per day would be required to meet TOGA accuracy requirements [Halpern, 1988a; Mangum et
al., 1992]. The space scales and timescales of upper ocean thermal structure are depth dependent and
nonstationary in time. However, the most stringent thermal field sampling requirements (for thermocline temperature
during non-ENSO periods) are comparable to those for zonal winds [e.g., Meyers et al., 1991; Hayes and
McPhaden, 1992; Kessler et al., 1996]. Scales of variability and sampling requirements for velocity were
described in work by Hansen and Herman [1989], World Climate Research Program [1990b], and McPhaden et
al. [1991].
Design of the observing system was constrained by logistical considerations, such as the availability of islands
suitable for tide gauge installation and the availability of commercial shipping routes. It was also constrained by the
practicalities of cost, since financial resources were limited. Implementation was based on existing technologies,
although measurement capabilities and cost efficiencies were greatly enhanced by two significant technological
breakthroughs. One was the development of a low-cost Autonomous Temperature Line Acquisition System (ATLAS) wind and
thermistor chain mooring capable of telemetering its data in real time [Hayes et al., 1991a]. The second was
the development of a low-cost, long-lived drifting buoy with accurate water-following characteristics [Niiler et
al., 1995].
The in situ observing system was much better developed in the Pacific than in the Atlantic and Indian Oceans, as evident
in Figure 3 and Table 3. In the Atlantic and
Indian Oceans, fewer VOS XBT tracks and tide gauge stations were instrumented, and no long-term moorings were deployed
for TOGA purposes. Drifter deployments were occasionally made in the tropical Atlantic and Indian Oceans during TOGA
[e.g., Integrated Global Ocean Services System (IGOSS), 1992], but there was no program of
sustained drifter deployments undertaken in either basin specifically by TOGA investigators until near the end of the
program.
2.3.1.1 The TAO array
The full TAO array of ~70 moorings is situated between 8°N and 8°S, 95°W and 137°E and spans over one
third the circumference of the globe at the equator (Figure 2). The backbone of the
array is the low-cost ATLAS wind and thermistor chain mooring [Hayes et al., 1991a]. Five long-term current meter
mooring sites are also maintained along the equator [World Climate Research Program, 1990a]. The array was
built up primarily during the second half of TOGA (Figure 2 and Table 3) and was completed only at the very end of TOGA in December 1994 [McPhaden,
1995]. A major advantage of the TAO array was its finely resolved (daily or higher temporal resolution) time series
data of key variables, particularly winds, which significantly reduced the amount of aliased high-frequency energy in
the climate signals of interest. Data were transmitted in real time to shore via Service Argos then retransmitted on the
Global Telecommunications System (GTS). Financial support was derived mainly from the United States, France, Japan,
Taiwan, and Korea.
2.3.1.2 The Surface Velocity Program
A TOGA/World Ocean Circulation Experiment (WOCE) Surface Velocity Program (SVP) was organized at the beginning of TOGA
to seek broad international support for drifter acquisitions and deployments. At the time, there were several competing
designs of unknown water-following characteristics. Several years of engineering and design work led to the Global
Lagrangian Drifter with a mean lifetime (defined in terms of drogue retention) of roughly 300400 days. Position
information, SST, and other drifter data were telemetered to shore in real time via Service Argos then retransmitted on
the GTS. In TOGA, drifters were deployed from research vessels, VOS, and airplanes. The objective was to maintain
drifter arrays with enough samples in 2° latitude × 8° longitude areas to define the mean
15-m circulation, the seasonal cycle [Reverdin et al., 1994], and ENSO-related
anomalies [Frankignoul et al., 1996]. SST data from the drifters have also proven to be critical for
operational SST analyses (see Appendix C). By the end of TOGA, over 700 drifters were operational in the global oceans,
over one third of which were deployed in the tropical Pacific. The SVP emerged from TOGA as the Global Drifter Program,
maintained with resources from 16 countries.
2.3.1.3 The Tide Gauge Network
TOGA inherited a substantial Pacific tide gauge network that was largely installed during NORPAX. Though design of the
tide gauge network was constrained by the availability of islands where gauges could be placed (Figures 2 and 3), efforts in the Pacific during TOGA were
focused on expanding and refining this network, under the direction of the University of Hawaii Sea Level Center. By the
end of TOGA the number of stations in the Pacific had more than doubled (Table 3).
Relative growth was equally impressive in the Atlantic and Indian Oceans, although the number of sites instrumented in
those oceans was fewer than in the Pacific. Many sites were linked to the Hawaii Center via data channels on
geostationary satellites. In addition, many of the TOGA tide gauges contributed to the Integrated Global Ocean Services
System (IGOSS) Sea Level Project in the Pacific, for which data were made available via GTS with a delay of 1 month.
2.3.1.4 The VOS Program
There are currently around 7000 VOS worldwide, operated by about 50 countries. They collect observations on sea surface
pressure, wind velocity, sea state, humidity, and SST as part of the World Weather Watch (WWW). On a few routes, surface
salinity is also sampled. Each month, typically 100,000 or more surface observations are collected and transmitted in
real time to national meteorological centers via satellite communication systems or via coastal radio stations, then
entered onto the GTS for general use. Prior to the establishment of TAO and other dedicated TOGA observing systems, data
from VOS marine reports and from island weather stations constituted the bulk of the available information on seasonal
and interannual variability in tropical surface marine meteorological fields. Important data sets and products such as
the Florida State University (FSU) wind analysis [Stricherz et al., 1992] and Comprehensive
Ocean-Atmosphere Data Set (COADS) [Woodruff et al., 1987] derive largely from VOS surface marine observations.
A subset of VOS ships also collect XBT data, and ~150,000 temperature profiles to a depth of 400 m or more were
added to the climatological database during TOGA in the tropical Pacific. Design of the VOS XBT array for TOGA was based
on a strategy of low-density sampling to provide broad-scale, widely dispersed coverage in areas of routine merchant
shipping on a monthly-to-quarterly cycle for description of large-scale thermal field signals. Recommended low-density
XBT sampling was prescribed as one XBT drop per 1.5° latitude by 7.5° longitude per month. TOGA also recognized
a need to observe seasonal and interannual variations of major geostrophic currents in the tropical oceans. A strategy
of frequently repeated sampling with higher along-track resolution was devised for a few transequatorial VOS lines to
meet this need [Meyers et al., 1991]. On some routes, expendable conductivity-temperature-depth (XCTD) data
were also collected [Roemmich et al., 1994]. By the end of TOGA most VOS XBT data were telemetered to shore in real
time via Service Argos or via geostationary satellites, then retransmitted on the GTS.
2.3.2 Satellite measurements
Complementing in situ oceanographic observations were satellite missions to measure SST, sea level, and winds (Table 4). Sea level measurements were provided from altimeters flown on the Geosat mission, the
ERS-1 mission, and the joint National Aeronautics and Space Administration (NASA)/Centre National d'Études
Spatiales (CNES) TOPEX/POSEIDON mission. SST measurements were derived principally from multichannel advanced very high
resolution radiometers (AVHRR) carried aboard the National Oceanic and Atmospheric Administration (NOAA) series of polar
orbiting weather satellites. Wind speeds were measured by the special sensor microwave imager (SSM/I) deployed on the
Defense Meteorological Satellite Program (DMSP) sponsored by the U.S. Department of Defense. Remotely sensed wind
velocities were first available during TOGA beginning in 1991 from a scatterometer aboard the ERS-1 satellite. Note that
Table 4 does not list all the wind speed and SST data available during TOGA from
satellite platforms. For example, SST information was available from the along-track scanning radiometer on ERS-1, and
wind speed was available from altimeter missions. The emphasis in Table 4 is on
those satellite data sets which for technical reasons were most widely applied in TOGA studies.
Table 4. Key Satellite Contributions to the TOGA Ocean Observing System
Satellite measurements have the advantage of being global, or nearly so, in coverage and quasi-synoptic in time, and
they often have better spatial and/or temporal resolution than in situ data. The increased use of satellite data did not
diminish the need for in situ oceanographic measurements, however. In situ techniques are required for measurements of
variability below the surface of the ocean. Also, satellite systems rely on complicated algorithms to convert
measurements of electromagnetic radiation into geophysically meaningful variables. To be useful, satellite data must be
calibrated and validated against in situ observations in order to detect and remove potential biases induced by orbital
errors, instrumental errors, and/or atmospheric effects (e.g., water vapor, clouds, and aerosols).
Considerable effort was devoted to calibration and validation during TOGA for satellite-derived estimates of SST [e.g.,
Liu,
1988; Allen et al., 1995], SSM/I surface wind speed [e.g., Bates, 1991; Halpern, 1993; Boutin and
Etcheto, 1996], surface wind velocity from the ERS-1 scatterometer [Bentamy et al., 1996; Rufenach,
1995], sea level from Geosat and TOPEX/POSEIDON [Cheney et al., 1989, 1994; Busalacchi et al.,
1994; Delcroix et al., 1991, 1994; Katz et al., 1995a; Picaut et
al., 1995], and surface zonal geostrophic currents derived from satellite altimetry [Picaut et al., 1990; Menkes et
al., 1995]. The accuracies achieved depended on the particular satellite sensor and the level of data processing
(Appendix C). Also, blended satellite/in situ products were developed during TOGA to take advantage of the strengths of
both types of data. These products include the SSM/I-based wind analysis merged with in situ data and European Center
for Medium-Range Weather Forecasts (ECMWF) model output [Atlas et al., 1991, 1996] and the National Centers for
Environmental Prediction (NCEP) blended satellite/in situ SST analysis, an example of which is shown in Figure 4 for the last week of TOGA [Reynolds and Smith, 1994, 1995] (see also Appendix
C, section C1).
Figure 4: (top) SST weekly mean and (bottom) anomaly for December 2531, 1994. The contour interval is
1°C, except there are two extra contours at ±0.5°C in Figure 4
(bottom). Negative contours are dashed. Heavy contour lines are used every 5°C in Figure 4 (top) and every 2°C in Figure 4 (bottom).
In Figure 4 (top) the heavy shading at values < 1.75°C approximates the
sea ice coverage. The anomalies are computed as departures from the monthly climatology of Reynolds and Smith
[1995], which was interpolated to the weekly time period.
2.3.3 In situ meteorological measurements
Most long atmospheric time series available for climate research derive from the operational activities of the WWW. At
the start of TOGA, there were about 400 upper air reporting stations between 30°N and 30°S as part of the WWW,
of which TOGA identified 150 as a minimal network for documenting planetary-scale variations in atmospheric circulation.
Thus the basic elements of an upper air observing system existed at the outset of TOGA. Even so, this WWW network of
stations was not adequate for TOGA purposes. As a consequence, initial planning for TOGA by the various scientific
bodies noted the strong desirability of expanding the network of WWW rawinsonde sites in the tropics, especially in the
Pacific and Indian Ocean sectors. Sites eventually instrumented under TOGA auspices included Tarawa, Kanton, Penrhyn,
and San Cristóbal (in the Galápagos Islands) in the Pacific (Figure 5) and
the island of Gan in the Indian Ocean. Unfortunately, the WWW network in the tropics in general underwent significant
declines in data collection and exchange through the GTS during the TOGA decade for a variety of technological,
political, and economic reasons [National Research Council, 1994a].
Figure 5: Map of the tropical Pacific Ocean basin showing the locations of wind profilers and
conventional upper air sounding systems used for enhanced atmospheric observations during TOGA. Shown are VHF and UHF
profiler sites at Biak (Indonesia) and Christmas Island (Kiribati); stand-alone VHF sites at Pohnpei (Federated States
of Micronesia) and Piura (Peru); stand-alone UHF profiler sites at Tarawa (Kiribati) and San Cristobal (Galapagos
Islands); and integrated sounding systems (ISS) at Manus Island (Papua New Guinea), Kapingamarangi (Kiribati), and the
island Republic of Nauru. The ISS system consists of a UHF profiler integrated with a balloon sounding system and
surface meteorological instruments; the ISS sites at Manus Island, Nauru, and Kapingamarangi were established as part of
TOGA COARE. World Weather Watch sites using conventional sounding systems were maintained at Tarawa, Kanton (Kiribati),
San Cristobal, and Penrhyn. Not shown is the World Weather Watch (WWW) upper air sounding station site established by
TOGA at Gan (0.5°16.1S, 73°16.1E) in the
Maldive Islands.
TOGA also supported the establishment of wind profilers at several sites throughout the Pacific Basin (Figure 5), beginning with the 50-MHz very high frequency (VHF) wind profiler that commenced
operation at Christmas Island in April 1986 [Gage et al., 1990, 1991a]. This Transpacific Profiler
Network provides measurements of tropospheric winds between altitudes of 1.8 and 18 km height. Four times per day,
hourly averaged VHF profiler data are telemetered via geostationary satellite and incorporated into the GTS for
worldwide distribution. In addition, 915-MHz ultrahigh frequency (UHF) wind profilers were installed at Biak, Indonesia;
Tarawa, Kiribati; and San Cristóbal, in the Galápagos Islands of Ecuador to provide more information on
boundary layer wind variability.
3. Scientific Progress: Improved Description and Understanding
3.1 Long-Term Mean and Mean Seasonal Cycle
The long-term mean and mean seasonal cycle are crucial for understanding interannual variations in the coupled system.
Background stratification, for example, affects the length scales, timescales, and phase speeds of planetary equatorial
waves thought to be important in the ENSO cycle. Likewise, zonal asymmetries in the background state of the equatorial
ocean due to mean trade wind forcing, e.g., the mean zonal slope of the equatorial thermocline and zonal SST gradient
associated with it (shown schematically in Figure 1), establish conditions necessary
for the growth of ENSO-related SST anomalies [e.g., Battisti and Hirst, 1989]. El Niño
anomalies also tend to be phase locked to the seasonal cycle, with warmest El Niño SST anomalies often occurring
in boreal winter in the equatorial cold tongue, when SST is seasonally at its coldest [Rasmusson and
Carpenter, 1982]. Empirical and modeling studies have indicated that persistence and predictability of ENSO
anomalies is seasonally modulated, being highest in boreal summer and winter and falling off through the boreal spring
[Latif
and Graham, 1992; Webster and Yang, 1992; Latif et al., 1994; Balmaseda et al., 1995].
Some theories also suggest that the mean seasonal cycle determines the basic periodicity and irregularity of the ENSO
cycle via chaotic nonlinear self-interaction [e.g., Jin et al., 1994; Tziperman et al., 1994;
Chang et
al., 1995]. However, few, if any, coupled ocean general circulation models (GCMs) are capable of simulating both
the mean seasonal cycle and interannual ENSO-like variability with equal degrees of veracity [Mechoso et al.,
1995]. Finally, seasonal variations for some variables (e.g., SST in the eastern Pacific) are as large as, or larger
than, ENSO-related interannual anomalies. Therefore, at minimum, one requires a clear definition of the climatological
mean seasonal cycle for model validation and in order to accurately define interannual climate anomalies. Climatologies
existed prior to TOGA, but in some cases, especially for subsurface oceanographic variables, they were of poor quality
because of the sparsity of data on which they were based.
3.1.1 Long-term mean
Key features important in characterizing the coupled ocean-atmosphere system in the equatorial Pacific include the
western Pacific warm pool with SSTs > 28°C and the equatorial cold tongue of the eastern and central equatorial
Pacific (Figure 4). These structures, evident in all long-term mean SST
climatologies, are modulated in intensity and areal coverage on seasonal, interannual, and decadal timescales.
Understanding how these features relate to surface winds and subsurface ocean hydrodynamics is critical to understanding
climate variability related to ENSO.
An example of the improved definition from the TOGA observing system of mean upper ocean temperature, surface dynamic
height, and wind stress along the equator is shown in Figure 6. The mean temperature
section, on the basis of all available TAO data between 2°N and 2°S, is similar to that presented by Kessler et
al. [1996]. It shows the increase in SST from east to west, the warm pool of 28°C water in the upper
100 m of the western Pacific, the downward sloping thermocline in the upper 300 m, and the existence of a
weakly stratified "thermostad" of 13°C water in the eastern Pacific [Stroup, 1969]. Situated in the middle of
the highly stratified upper thermocline is the 20°C isotherm; for this reason this isotherm is often used as an
index for the depth of the thermocline in the tropical Pacific. The mean surface dynamic height associated with the
temperature field rises by 40 dynamic centimeters (dyn. cm) between 95°W and 170°E, after which it
decreases slightly to the west. Zonal variations in dynamic height and thermocline depth along the equator are a
response to steady easterly trade wind forcing in the eastern and central Pacific [McPhaden and Taft, 1988]; reversal
of these gradients in the western Pacific is associated with local westerly winds [see also Wyrtki, 1984; Mangum et
al., 1990; McPhaden et al., 1990a]. The zonal section in Figure 6 has
many features in common with sections composited from different individual cruises prior to TOGA [e.g., Philander,
1973; Halpern, 1980] but is more representative of long-term mean conditions.
Figure 6: Zonal section of mean temperature averaged between 2°N and 2°S on the basis of available TAO
time series data in 19801996. Also shown is the corresponding mean zonal wind stress (computed using a constant
drag coefficient of 1.2 × 10-3) and dynamic height 0500 dbar (computed using mean
temperature/salinity relationships based on work by Levitus and Boyer [1994] and Levitus et
al. [1994a]). Crosses indicate depths and longitudes where temperature data were available. An average at a
particular location was computed only if a minimum of 2 years of data was available.
The mean thermal structure of the Pacific along quasi-meridionally oriented VOS XBT lines (Figure 7) also shows the downward slope of the thermocline toward the west in response to mean
trade wind forcing. In addition, the meridional structure of ridges and troughs in the thermocline, which are related to
major zonal currents [e.g., Donguy and Meyers, 1996a], is also clearly delineated. Evidence of
trade-wind-driven equatorial upwelling (local minima in temperatures near the equator in the surface layer) is apparent
in the central and eastern Pacific sections.
Figure 7: Mean temperature for the period 19851994 on four well-sampled XBT lines. Typically, 120 or more
realizations of the quasi-synoptic temperature field were obtained during the decade for each section. The standard
deviation of seasonal-to-interannual temperature variability during 19851994 from the Australian ocean thermal
analysis system [Smith, 1995b] is indicated by shading. Westernmost section is at the top, easternmost at the
bottom.
Methods to estimate the volume transport of the major equatorial currents from monthly, synoptic VOS XBT sections, as in
Figure 7, were developed by Kessler and Taft [1987], Taft and Kessler
[1991], Picaut and Tournier [1991], and Donguy and Meyers [1996a]. A comparison of
transports from VOS XBT data to research vessel data (Table 5) shows that all of
the geostrophic current transports can be reasonably well monitored by the VOS program. Differences between means based
on research vessel and VOS data are of the order of only 720% (Tables 5a and 5b). The temporal variation inferred from research cruise data is highly correlated to
the VOS estimates [Picaut and Tournier, 1991]. Although somewhat different methods were used to calculate XBT
transports by Kessler and Taft [1987] and Picaut and Tournier [1991], the mean and
standard deviation of transports over a 7-year period are only slightly different (Table 5c).
Table 5a. Mean Current Transports During the Hawaii-Tahiti Shuttle From March 1979 to June 1980
Table 5b. Mean Current Transports During the Line Islands Profiling Projects (LIPP) From March 1982 to June
1983
Table 5c. Mean Current Transports From January 1979 to June 1985
Drifter data allow for a definition of the surface circulation (combined Ekman and geostrophic components) across the
entire basin, rather than just along prevailing shipping routes. The average velocity at 15-m depth from the drifter
data for 19881994 (Figure 8) shows the persistent and well-documented surface
current systems of the tropical Pacific: the North Equatorial Current (NEC), South Equatorial Current (SEC), North
Equatorial Countercurrent (NECC), and a vestigial South Equatorial Countercurrent (SECC) (in the region
6°10°S, 160°176°E). The standard error of the velocity shows that the general circulation of
the tropical Pacific is well defined everywhere, even to the extent that divergence and relative vorticity fields can be
computed from this data with a high degree of confidence.
Figure 8: Mean surface layer (15 m) circulation in the tropical Pacific based on Surface Velocity Program
drifter data for the period 19881994. The ellipse at the end of each vector is the 95% confidence
interval.
Significant departures from the patterns that have been reported by ship drift charts, or from interpretation of the
gradients of dynamic height as an index of the surface current, emerge from the drifter data. For example, dynamic
height maps show that there should be a geostrophic flow toward the equator nearly everywhere, while drifter data
indicate that there is a flow toward the pole nearly everywhere. Thus the meridional Ekman flows are strong enough not
only to cancel the near-surface geostrophic currents but also to transport surface layer water in the opposite
direction. Surface layer Ekman divergence near the equator in particular is important in determining the equatorial
upwelling circulation [Wyrtki, 1981]. Also, compared to ship drift charts, the drifter data show a splitting and
divergence of the South Equatorial Current between 110° and 136°W, with maxima in westward flow to the north and
south of the equator.
3.1.2 Mean seasonal cycle
The seasonal cycle of SST in the equatorial Pacific has been well documented from COADS and other VOS-based analyses
[e.g., Reynolds and Smith, 1995]. Warmest SSTs in the cold tongue occur in boreal spring, and coolest
SSTs occur in boreal autumn. The amplitude of these annual period variations diminishes from east to west as the
thermocline deepens (Figure 9); similarly, the timing of maximum temperatures occurs
later in the boreal spring progressing from west to east [e.g., Horel, 1981; Enfield, 1986; Chao and Philander,
1991]. The westward progression of the annual cycle of SST along the equator in the Pacific is related to the
westward progression in the zonal winds [Chang, 1994; Xie, 1994]. Annual variations in SST in turn set up
atmospheric boundary layer pressure gradients which drive annual period zonal wind variations [Nigam and Chao, 1996].
Figure 9: Mean seasonal cycles of temperature and zonal velocity at four sites along the
equator based on multiyear analyses (19801994 at 110°W, 19831994 at 140°W, 19881994 at
170°W, and 19861993 at 165°E). The 110°W, 140°W, and 165°E analyses are updated versions of
those found in work by McPhaden and McCarty [1992] and McCarty and McPhaden [1993]. The 170°W
analysis is based on data presented by Weisberg and Hayes [1995], extended through
1994.
Although solar forcing near the equator is predominantly at semiannual periods, SST in the equatorial cold tongue of the
eastern and central Pacific is dominated by annual period variations because of the importance of ocean dynamics and the
influence of land masses bordering the Pacific [Li and Philander, 1996]. Recent diagnostic
studies and model results illustrate the complex mix of ocean processes in accounting for the amplitude and phase of
seasonal SST variations in this region [Hayes et al., 1991b; Köberle and
Philander, 1994; Chang, 1993, 1994; Chen et al., 1994a]. The shallow mean thermocline depth in the eastern
Pacific, which is due to large-scale wind forcing (Figure 6), is important in
facilitating upwelling and vertical mixing to cool the surface. Zonal advection associated with seasonally varying
currents is also important, particularly in the central Pacific [Chen et al., 1994a; Minobe and Takeuchi,
1995]. Variations in surface heat fluxes (mainly solar irradiance and latent heat flux) are significant at all
locations. These fluxes assume a dominant role as ocean dynamical processes diminish poleward away from the equator and
in the western equatorial Pacific where the thermocline is deep. In this latter region the semiannual period in solar
irradiance forcing leads to the dominant semiannual period in SST (Figure 9).
Studies using XBT and conductivity-temperature-depth (CTD) data have described the seasonal cycle of upper ocean thermal
structure based on the dynamics of Ekman pumping and Rossby waves [Delcroix and Henin, 1989; Kessler, 1990; Kessler and
McCreary, 1993]. Seasonal variations in transports of major currents have also been documented using XBT and
tide gauge data by Taft and Kessler [1991], Picaut and Tournier [1991], and Donguy and
Meyers [1996a]. Mitchum and Lukas [1990] used a set of sea level data lying along the North Equatorial
Countercurrent trough to show that annual variations propagate to the west as a Rossby wave resonantly forced by
westward propagating components in the wind field. Recent model simulations of the seasonal cycle, validated against
TOGA observations [e.g., Minobe and Takeuchi, 1995], confirm the results of these empirical studies on the
importance of wind stress forcing and equatorial wave processes.
Reverdin et al. [1994], developed a climatology of the surface currents in the tropical
Pacific from TOGA drifter and mooring data. A notable aspect of the mean seasonal cycle along the equator is the
"springtime reversal" of the normally westward flowing South Equatorial Current [Halpern, 1987b]. It is most evident in
the eastern Pacific where, for example, eastward flow of over 30 cm s-1 occurs in AprilMay at 110°W
(Figure 9). This reversal in flow propagates westward along the equator [McPhaden and
Taft, 1988], as do zonal winds and SST [Horel, 1981; Lukas and Firing, 1985], with
variations at 140° and 170°W lagging those farther to the east. The springtime reversal in the SEC had been
known for nearly a century [Puls, 1895], though its magnitude was underestimated because of contamination of ship drift
estimates by windage on ship's hulls [McPhaden et al., 1991]. Model simulations
suggest that the springtime reversal results from the seasonal relaxation of the zonal component of trade winds, causing
flow to accelerate eastward down the zonal pressure gradient [Chao and Philander, 1991; Yu et al., 1997].
The mean seasonal cycle of the Equatorial Undercurrent along the equator has been described in several reports [Halpern,
1987b; McPhaden and McCarty, 1992; McCarty and McPhaden, 1993; Weisberg and
Hayes, 1995]. Juxtaposing seasonal analyses based on these studies (Figure 9) helps to highlight some of the important characteristics of variability on this
timescale. The EUC, on average, is located in the upper thermocline and is therefore found at greater depths in the west
than in the east. Zonal current variations are confined principally to above the Undercurrent core, with a maximum
eastward flow in the thermocline occurring in boreal spring at all longitudes.
Recent analyses suggest that the seasonal cycle is nonstationary in the eastern equatorial Pacific [Gu et al., 1998].
Specifically, at 110°W the annual period in thermocline depth variations was much more pronounced in the 1990s than
in the 1980s, presumably because of changes in the annual cycle of zonal wind forcing farther to the west.
Interestingly, amplification of thermocline depth variations was not reflected in amplified annual SST variations at
110°W. The mean depth of the thermocline remained sufficiently shallow in the eastern Pacific that, consistent with
the theories of Köberle and Philander [1994] and Xie [1994], the efficiency of ocean-atmosphere
interactions and ocean dynamical processes to cool the surface would not have been significantly impacted.
3.2 ENSO Variability
Some of the hallmark manifestations of the ENSO cycle are illustrated in Plate 1,
which shows time series of the Southern Oscillation Index (SOI) and of surface zonal wind stress anomalies and sea
surface temperature anomalies along the equator. The period shown (19821995) encompasses the 19821983 El
Niño and interannual variability during the TOGA decade (19851994). Each warm episode (19821983,
19861987, 19911992, 1993, and 19941995) is associated with negative SOI values and weaker than normal
trade winds over about 60° of longitude in the central and western Pacific. In the case of the intense
19821983 El Niño the trade winds weakened progressively from west to east all the way across the basin.
Conversely, the 19881989 cold La Niña event was associated with high SOI values and a strengthening of the
trade winds over roughly 60° of longitude. Also noteworthy in Plate 1 is the persistence of warm SST anomalies
near the date line and the occurrence of three distinct warm episodes in the eastern Pacific in concert with
consistently low Southern Oscillation Index values between 1991 and 1995. Although it is known that the frequency and
intensity of ENSO events are modulated on decadal and longer timescales [Gu and Philander, 1995], the duration
of warm phase ENSO conditions over 5 calendar years is unparalleled in this century [Trenberth and Hoar,
1996].
Plate 1: Time-longitude plots of zonal pseudostress (in m2 s-2) and SST (in °C)
between 2°N and 2°S along the equator from 19821995. Pseudostress time series are from the Florida State
University (FSU) analyses [Stricherz et al., 1992], and the SST is from Reynolds and Smith [1994]. Also
shown is the Southern Oscillation Index (SOI) for the same time period. The SOI, defined as the normalized difference in
surface pressure between Tahiti, French Polynesia and Darwin, Australia is a measure of the strength of the trade winds,
which have a component of flow from regions of high to low pressure in the tropical marine boundary layer. High SOI
(large pressure difference) is associated with stronger than normal trade winds and La Niña conditions, and low
SOI (smaller pressure difference) is associated with weaker than normal trade winds and El Niño conditions. All
time series have been smoothed with a 5-month triangle filter (roughly equivalent to a seasonal average). The FSU
pseudostress and Reynolds SST have also been smoothed zonally over 10° longitude.
The relationship between surface winds and SST for December 1994 (Figure 10)
illustrates another important aspect of ENSO variability. Deep atmospheric convection typically occurs over the warmest
SSTs in the tropical Pacific [e.g., Graham and Barnett, 1987]. Warmest SSTs (>
30°C) in December 1994 were situated just south of the equator near the date line in a region of strongly convergent
surface winds and active deep atmospheric convection [Climate Analysis Center, 1994]. Converging winds act to
sustain both deep convection (via moisture convergence) and warm SSTs (via ocean dynamics) [Philander et al., 1984].
These processes tend to locally reinforce one another, and representing them properly in coupled ocean-atmosphere models
has been one of the challenges of ENSO modeling [e.g., Zebiak and Cane, 1987; Battisti, 1988; Battisti and
Hirst, 1989; Schopf and Suarez, 1988].
Figure 10: Wind vectors and SSTs from the TAO array for December 1994. (top) Monthly means; (bottom) monthly
anomalies from the COADS wind climatology and NCEP SST climatology (19501979). SSTs warmer than 29°C and
colder than 27°C are shaded; SST anomalies >1°C and <-1°C are shaded.
An important oceanic feature of the ENSO cycle is the zonal redistribution of warm surface layer water masses [White et al.,
1985; Donguy, 1987; Donguy et al., 1989; McPhaden et al., 1990a;
McPhaden and Hayes, 1990b; Kessler and McPhaden, 1995a]. In the western
Pacific the thermocline (as indicated by the depth of the 20°C isotherm) shoals 2050 m in the latitude
band 15°S to 20°N during El Niño, whereas in the eastern Pacific the thermocline deepens by a comparable
amount but in a narrower band of latitudes than in the west. These thermocline depth variations, illustrated along the
equator in Figure 11 for the 19911993 El Niño, are correlated with
changes in the strength of major currents. The westward SEC weakens significantly during El Niño episodes, while
in some events the NECC intensifies [Taft and Kessler, 1991; Kessler and McPhaden, 1995a]. Thus there is
an anomalous eastward mass transport of warm water by the equatorial surface currents during the onset of warm events.
Figure 11: Time-longitude sections of anomalies in surface zonal winds (in m s-1), sea surface
temperature (in °C), and 20°C isotherm depth (in meters) for January 1991 to December 1993. Analysis is based on
5-day averages between 2°N and 2°S of moored time series data from the TAO Array. Anomalies are relative to
monthly climatologies cubic spline fitted to 5-day intervals (COADS winds, Reynolds and Smith [1995] SST,
CTD/XBT 20°C depths). Shading indicates anomaly magnitudes > 2 m s-1, 1°C, and 20 m
for winds, temperatures, and 20°C depths, respectively. Positive winds are westerly. Squares on the top abscissa
indicate longitudes where data were available at the start of the time series, and squares on the bottom abscissa
indicate where data were available at the end of the time series.
Changes in the zonal distribution of upper ocean heat content are reflected in sea level variations [e.g., Rebert et
al., 1985; Delcroix and Gautier, 1987] because of the vertically coherent structure of the upper ocean
thermal field on seasonal-to-interannual timescales. In other words, anomalously deep thermocline tends to be associated
with anomalously high sea level and vice versa. Wyrtki [1984] described the sea surface height gradient
along the equator during the 19821983 El Niño assuming that the long-term mean sea level at tide gauges
along the equator was equal to the long-term surface dynamic height relative to a deep reference level. He showed that
the normal upward slope of sea level from east to west (Figure 7) was sharply
reduced and at times reversed in the eastern and central Pacific during 19821983. Reduction and reversal of the
sea surface slope also occurred in the 19861987 and 19911992 El Niño events (Figure 12). Variations were weaker at these times than in 19821983 though, as expected
from the weaker and less zonally extensive westerly wind anomalies along the equator (Plate
1). Conversely, during the 19881989 cold La Niña event the sea level slope along the equator
intensified, in association with stronger than normal trade winds (Figure 12).
Figure 12: Zonal slope of sea surface height along the equator. Sea level anomalies from the 19751987 mean
seasonal cycle were taken from seven locations near the equator: Rabaul (4°S, 152°E), Kapingamarangi (1°N,
155°E), Nauru (0.5°S, 167°E), Tarawa (1°N, 173°E), Kanton (3°S, 172°W), Christmas Island
(2°N, 157°W), and the Galapagos Islands (0.5°S, 90°W). These anomalies were added to the mean dynamic
topography difference (01000 dbar) computed from the Levitus and Boyer [1994] and Levitus et
al. [1994a] temperature and salinity climatologies in order to calculate absolute heights. (top) Mean
conditions during three warm events are shown as solid circles (June 1982 to May 1983), crosses (January to December
1987), and open circles (June 1991 to May 1992). The heavy solid line is the long-term mean conditions taken from the
Levitus climatology. (bottom) Warm and cold conditions are contrasted by showing the difference (the vertical bars) of
the mean sea level anomaly in 1988 (cold) minus the mean sea level anomaly in 1987 (warm).
Sea level slope along the equator is an index for the strength of the zonal pressure gradient, which is the driving
force for the Equatorial Undercurrent [Philander and Pacanowski, 1980; McCreary,
1980; McPhaden, 1981]. Reduction and reversal of this sea level slope were associated with a
significant weakening and disappearance of the EUC in the thermocline during the 19821983 El Niño [Firing et
al., 1983; Halpern, 1987b] and the 19861987 El Niño [McPhaden et al., 1990a]. The EUC,
though it did not disappear during the 19911993 El Niño, was greatly reduced in strength in the central
Pacific for several months [Kessler and McPhaden, 1995a]. El Niño related reductions in Undercurrent
strength have significant implications for the heat balance of the surface layer, since the Undercurrent is normally a
source of cold water to feed equatorial upwelling [Bryden and Brady, 1985].
Near the equator, adjustment of the upper ocean heat and mass is strongly influenced by excitation and propagation of
equatorial Kelvin and long Rossby waves, which are the primary mechanisms by which the winds communicate their influence
to other parts of the ocean basin. The Kelvin waves most prominent in equatorial time series data are associated with
forcing by westerly wind bursts and the atmospheric Madden and Julian Oscillation [Miller et al., 1988; McPhaden et
al., 1988a; Kessler et al., 1995]. These waves are clearly evident in 20°C isotherm depth variations
(e.g., Figure 11), as well as in time series of sea level, dynamic height, and zonal
currents within 2° latitude of the equator. Using TAO data and Geosat-derived sea level data, Cheney et
al. [1987], Miller et al. [1988], McPhaden et al. [1988a], McPhaden and
Hayes [1990b], Delcroix et al. [1991, 1994], Johnson and McPhaden [1993a],
and Picaut and Delcroix [1995] clearly documented equatorial Kelvin waves propagating eastward
with first baroclinic mode phase speeds of 23 m s-1 prior to and during the 19861987 El
Niño. Similarly, analysis of TAO data and TOPEX/POSEIDON sea level data indicated prominent oceanic variability
due to equatorial Kelvin waves generated by wind forcing west of the date line during 19911995 [Busalacchi
et al., 1994; Kessler et al., 1995; Boulanger and Menkes, 1995].
Weakening of the trade winds near the equator in the central and western Pacific at the onset of warm ENSO events leads
to a pattern of upwelling favorable wind stress curl which elevates the thermocline locally at extraequatorial latitudes
[e.g., Kessler, 1990]. Weakening of the trade winds also excites upwelling long Rossby waves [White et
al., 1985, 1987; Kessler, 1990; Boulanger and Menkes, 1995; Kessler
and McPhaden, 1995b], the fastest of which propagates westward at phase speeds of one third the Kelvin wave
speed. The slower propagation speed of these waves compared to equatorial Kelvin waves implies that elevation of the
thermocline in the west lags depression of the thermocline in the east by several months as evident in thermal field and
sea level analyses (e.g., for 20°C along the equator between late 1991 to early 1992 in Figure 11). The Geosat analysis of Delcroix et al. [1991] and subsequent
modeling study of du Penhoat et al. [1992] for the 19861987 El Niño suggest that, in addition
to wind forcing, eastern boundary reflections of equatorial Kelvin waves can generate equatorial Rossby waves that
affect the evolution of ENSO.
Empirical studies of the surface layer heat balance emphasize the complex mix of processes controlling SST variability
on ENSO timescales. For example, the importance of remotely forced equatorial waves in mediating SST variability in the
eastern and central Pacific can be inferred from Plate 1. Largest ENSO SST anomalies during 19801995 were located
significantly to the east of the largest zonal wind anomalies; moreover, large SST anomalies were found in the far
eastern Pacific where zonal wind anomalies were weak. Waves affect SST in the cold tongue region by inducing changes in
thermocline depth which affect upwelling and vertical mixing rates [e.g., Hayes et al., 1991b; Kessler
and McPhaden, 1995a, b]. Waves can also advect temperature fields meridionally and, more importantly, zonally
along the equator. Wave- and current-induced zonal advection of the eastern edge of the warm pool produces large
interannual SST anomalies in the central Pacific [McPhaden and Picaut, 1990; Picaut and
Delcroix, 1995; Picaut et al., 1996].
Local air-sea heat exchanges are also important in the surface layer heat balance of the tropical Pacific on interannual
time scales [Liu and Gautier, 1990; Hayes et al., 1991b; Kessler and McPhaden,
1995a]. The most strongly varying components of the surface energy balance are solar irradiance, which is modulated
by changes in cloudiness, and latent heat flux which is modulated by changes in wind speed, SST, and relative humidity
[Liu,
1988; Waliser et al., 1994]. East of the date line, where ocean dynamics are crucial for generating
SST anomalies on interannual time scales, latent heat flux tends to increase with increasing SST, and therefore acts as
a negative feedback on developing SST anomalies [Kessler and McPhaden, 1995a; Weisberg and
Wang, 1997]. In the western Pacific warm pool, the thermocline is deep, mean horizontal SST gradients are weak,
and ocean dynamical processes are less capable of generating large scale SST anomalies than further east. In this region
air-sea turbulent heat exchange is an important generating mechanism for SST anomalies, through enhanced evaporation
during periods of strong westerly winds [Meyers et al., 1986]. Variations in short wave
radiation tend to damp developing SST anomalies throughout the tropical Pacific since high cloudiness, which reduces
insolation, tends to occur over the warmest surface waters [Waliser et al., 1994].
Data from the TOGA observing system have been used to test various theories of El Niño and the ENSO cycle. An
early theory espoused by Wyrtki [1975] suggested that prior to El Niño, the trade winds strengthened, and there
was a increase in sea level (a proxy for heat content) in the western Pacific warm pool. When the trade winds weakened,
the overcharged warm water pool would collapse and surge eastward in the form of a Kelvin wave to initiate a warm event.
The importance of Kelvin waves in the development of El Niño has been confirmed by many studies. However, other
aspects of Wyrtki's theory were undermined when prior to the 19821983 El Niño, the strongest of the
century, there was no anomalous rise in sea level in the western Pacific or intensification of the easterly trades [Cane,
1984]. Similarly, prior to the equatorial warming in 1993, there was no buildup of heat content in the western
Pacific warm pool or intensification of the easterlies [Kessler and McPhaden, 1995b].
Wyrtki
[1985a] proposed another hypothesis, namely that warm water accumulated in the tropical Pacific prior to an El
Niño on a zonally averaged basis between 15°N and 15°S. In this scenario, El Niño represents a
mechanism whereby excess heat is purged to higher latitudes. Cane et al. [1986] interpreted the interannual
oscillations in their coupled ocean-atmosphere model in terms of this mechanism. Springer et al. [1990], in a
wind-forced ocean model simulation, found a buildup of heat content near the equator prior to the 19821983 El
Niño as hypothesized by Wyrtki, but only between 5°N and 5°S. The difference in latitude bands over which
the buildup was assumed to occur resulted from Wyrtki's use of tide gauge station data which had to be interpolated over
great distances zonally beyond 5°N5°S [Springer et al., 1990]. Miller and Cheney
[1990], however, did not find a buildup at all prior to the 19861987 El Niño event using Geosat data.
Thus Wyrtki's [1985a] mechanism, modified to a narrower band of longitudes, may be operative during
some but not all El Niño events.
McCreary [1983] proposed a theory for ENSO in which the timescale between warm events was set
by the slow westward propagation of long extraequatorial Rossby waves and their reflection off the western boundary as
equatorial Kelvin waves. The reflected Kelvin waves would alter thermocline depths (and by proxy SST) in the eastern
Pacific, thereby affecting the strength of the trade winds. In order to get a realistic 34-year periodicity for
the ENSO cycle, Rossby waves with significant amplitudes at roughly 20° latitude from the equator were required.
Using XBT data, Graham and White [1988] argued for the existence of extraequatorial Rossby waves along
12°N and 12°S and their reflection into equatorial Kelvin waves at the western boundary. However, Kessler
[1990] offered alternative explanations for the observed variability along the equator in terms of direct wind
forcing rather than Rossby wave reflection, and Kessler [1991] showed that only Rossby waves
equatorward of about 8° latitude could reflect into equatorial Kelvin waves with significant amplitudes.
The delayed oscillator theory of ENSO [Battisti, 1988; Battisti and Hirst, 1989; Schopf and Suarez,
1988] also involves the reflection of Rossby waves into equatorial Kelvin waves at the western Pacific boundary. In
contrast to McCreary's [1983] theory though, equatorial Rossby waves closely trapped to the equator,
rather than extraequatorial Rossby waves at higher latitudes, are most relevant. Thermocline changes associated with
reflected Kelvin waves lead to SST anomalies in the eastern Pacific cold tongue by altering upwelling rates. The SST
anomalies affect the atmospheric convection and circulation, giving rise to local positive feedbacks that reinforce the
SST and wind anomalies (e.g., Figure 10). The anomalous surface winds in turn excite
equatorial oceanic waves of opposite sign to those that generated the original SST anomalies. The timescale for the ENSO
cycle in this theory is set by the competition between the local positive feedbacks and delayed negative feedbacks
associated with remotely forced equatorial waves and their western boundary wave reflections.
Tests of the delayed oscillator have focused primarily on the question of whether equatorial Rossby waves can reflect
from the irregular and gappy coastal geometry of the western Pacific. Theories suggest coastal irregularities should not
be a fundamental limitation to this reflection process [Clarke, 1991; du Penhoat and Cane, 1991].
However, although in principle western boundary reflections should work equally well to both initiate and terminate El
Niño events, it appears that they are most effective in terminating events [Li and Clarke, 1994; Mantua and
Battisti, 1994]. In this situation, reflection of an upwelling Rossby wave at the western boundary excites an
upwelling equatorial Kelvin wave train which erodes the warm SST anomaly in the cold tongue, eventually leading to cool
La Niña SST anomalies. Even so, not all warm events appear to be terminated by western boundary reflections. Boulanger and Menkes [1995], for example, found that wind-forced upwelling Kelvin waves,
rather than boundary-reflected Kelvin waves, led to cooling along the equator in the eastern Pacific in late 1993. Also,
Picaut and Delcroix [1995] argued that the 19861987 El Niño was terminated by
Rossby waves emanating from the eastern boundary, rather than Kelvin waves emanating from the western boundary.
Few, if any, El Niño events of the TOGA decade appear to have been initiated by delayed oscillator physics.
Through extended empirical orthogonal function (EOF) analysis of Geosat data during the 19861989 El Niño-La
Niña cycle, White and Tai [1992] suggested that an equatorial Rossby wave reflected into an equatorial
Kelvin wave at the western boundary, consistent with delayed oscillator theory. However, a detailed projection of Geosat
sea level and derived surface currents on individual equatorial wave modes indicated very little evidence of first
meridional Rossby wave reflection into Kelvin waves during this time [Delcroix et al., 1994]. Similarly, Kessler
and McPhaden [1995b], using TAO and XBT data during 19881993, and Boulanger and Menkes [1995],
using TAO and TOPEX/POSEIDON data during 19921993, found little evidence for the initiation of warm events via
Rossby wave reflections at the western boundary. Boulanger and Fu [1996], using TOPEX/POSEIDON
altimeter data and ERS-1 wind data, detected wind-forced downwelling equatorial Rossby waves that reflected into
downwelling Kelvin waves prior to warming along the equator in middle to late 1994. They interpreted these reflections
as evidence for delayed oscillator physics as a trigger for the 19941995 El Niño. In contrast, however, Goddard
and Graham [1997] argued that this same 19941995 warm event in the NCEP reanalysis [Ji and Smith, 1995; see
also section 4.4] was not initiated Rossby wave reflection at the western boundary, but rather direct wind forcing near
the equator.
Another perspective of the ENSO cycle was proposed by Picaut and Delcroix [1995] and Picaut et
al. [1996]. Using hypothetical drifters moved by current fields derived from Geosat and TOPEX/POSEIDON
data, TAO mooring data and SVP drifter data, and three different classes of ocean models, these authors found that
ENSO-related SST anomalies in the central western Pacific were primarily the result of zonal advection (Figure 13). Picaut and Delcroix [1995] and Picaut et
al. [1997] argued that Rossby waves excited by eastern boundary reflections, in addition to the direct effects
of wind forcing, were instrumental in generating these currents. Since the impacts of SST variations on the atmosphere
are most pronounced in the central and western equatorial Pacific [Geisler et al., 1985], Picaut et
al. [1997] argue for a revision of the delayed oscillator theory to provide more weight to oceanic
processes affecting this region, including eastern boundary wave reflections. It is evident from this wide variety of
theoretical, modeling, and empirical studies that, despite progress made during TOGA on understanding the ENSO cycle,
there are many as-of-yet unresolved issues related to the coupled ocean-atmosphere interactions that require further
investigation.
Figure 13: (left) Longitude-time distribution of 4°N4°S averaged SST. Contour interval is 1°C,
except for the 28.5°C isotherm. Superimposed as thick lines are the trajectories of two hypothetical drifters moved
by 4°N4°S averaged surface current anomalies derived from Geosat data (thick solid lines correspond to the
total currents; thick dashed lines correspond to the Kelvin and Rossby wave contributions). (right) Longitude-time
distribution of 4°N4°S averaged surface current anomaly derived from Geosat. Contour interval is
10 cm s-1. Solid (dashed) lines denote eastward (westward) current anomalies. Thick solid and thick
dashed lines are as in Figure 13 (left). From Picaut and Delcroix
[1995].
3.3 Intraseasonal Kelvin Waves
The Kelvin waves most prominent in equatorial Pacific time series data have energy across a broad band of periods
spanning roughly 40120 days, with maximum energy concentrated near periods of 6090 days. Sea level,
thermocline depth, and zonal currents associated with these waves propagate eastward with
23 m s-1 phase speeds [Enfield, 1987; McPhaden and Taft, 1988; Johnson
and McPhaden, 1993a, b]. Vertical structures suggest significant energy in both the first and second vertical
modes [Kessler and McPhaden, 1995b], consistent with model simulations [e.g., Busalacchi and Cane,
1985; Giese and Harrison, 1990; Kindle and Phoebus, 1995]. There is also
evidence that the wave structures are modified by wave-mean flow interactions [Johnson and McPhaden, 1993a, b].
Upon reaching the eastern boundary, the waves can be traced along the coasts of North and South America as coastal
Kelvin waves [Spillane et al., 1987].
These Kelvin waves are forced primarily by surface zonal wind variations associated with westerly wind bursts and the
Madden and Julian Oscillation in the western Pacific (Figure 11). The amplitude of
the ocean wave response depends on the structure of the wind forcing, namely its temporal evolution, zonal fetch, and
meridional structure [Knox, 1987; Harrison and Giese, 1991; Giese and Harrison,
1991]. In terms of frequency content, wave energy is concentrated at periods decidedly longer than the dominant
3060-day period of the wind forcing itself [McPhaden and Taft, 1988]. Kessler et
al. [1995] explain this "red shift" as the result of a scale selection process related to wind fetch, which
favors excitation of the lower-frequency Kelvin waves in response to wind forcing in the intraseasonal band. Their
results are analogous to Knox's [1987] analysis in the time domain, which indicated that an equatorial wind event of
duration T and zonal fetch L, would lead to a Kelvin pulse of longer duration T +
L/c, where c is the zonal phase speed of the Kelvin wave.
Intraseasonal Kelvin waves affect SST in the equatorial Pacific in a variety of ways. They can warm SST by zonal
advection in the equatorial cold tongue as documented for the 19861987 El Niño [Johnson and McPhaden,
1993a] and the 19911993 El Niño [Kessler and McPhaden, 1995a]. Downwelling
Kelvin waves also depress the thermocline [McPhaden and Hayes, 1990b; Kessler et
al., 1995], which can lead to surface warming by reducing the efficiency of local wind-driven upwelling to cool
the surface. Lien et al. [1995] found that the passage of a downwelling Kelvin wave during the
19911992 El Niño led to a reduction in upper ocean turbulent mixing in the central equatorial Pacific,
which would likewise favor the development of warm SST anomalies.
There is a notable relationship between enhanced intraseasonal variability and El Niño in both the ocean and
atmosphere [e.g., Keen, 1982; Luther et al., 1983; Lau and Chan, 1986; Enfield, 1987; McPhaden and
Hayes, 1990b; Kessler et al., 1995; Kindle and Phoebus, 1995]. During El
Niño westerly wind bursts tend to be more prominent, deep convection associated with the Madden and Julian
Oscillation tends to be stronger and extend farther eastward along the equator in the Pacific, and intraseasonal
equatorial Kelvin waves tend to be of larger amplitude. These findings have led to suggestions that intraseasonal
variability, rather than chaotic interactions of the seasonal cycle with itself (see section 3.1), may be responsible
for the irregularity of the ENSO cycle [e.g., Zebiak, 1989].
Nonlinear interactions between the ocean and the atmosphere are necessary to couple intraseasonal variations to the ENSO
cycle. Harrison and Schopf [1984] proposed a mechanism whereby zonal advection by short-period Kelvin
waves could initiate low-frequency warming in the equatorial cold tongue of the eastern and central Pacific, and some
coupled models bear out the potential for this mechanism to trigger an El Niño [Latif et al., 1988].
Likewise, Kessler et al. [1995] described how intraseasonal Kelvin waves can contribute to the slow
eastward displacement of the western Pacific warm pool, which would favor the development of warm El Niño SST
anomalies.
3.4 Local Response to Westerly Wind Burst Forcing
The importance of the local response to strong westerly wind burst forcing in the western Pacific warm pool was first
highlighted by Lukas and Lindstrom [1991]. That study and related work ultimately contributed to the design
and implementation of the TOGA Coupled Ocean Atmosphere Response Experiment (COARE) [Godfrey et al., this
issue]. Westerly wind bursts typically occur during the westerly phase of the Madden and Julian Oscillation [Sui and
Lau, 1992], during which surface westerlies may attain speeds of 510 m s-1. These
wind events lead to dramatic zonal current reversals in time and depth in the upper 100150 m of the water
column [McPhaden et al., 1988a, 1992; Delcroix et al., 1993; Kuroda and
McPhaden, 1993; Kutsuwada and Inaba, 1995; Ralph et al., 1997]. The surface flow accelerates
eastward and can reach speeds of over 100 cm s-1 in the course of a week. The resultant jet may
extend over 40° of longitude, with anomalous eastward transports of 50 Sverdrups
(1 Sv = 106 m3 s-1) between 5°N and 5°S. Westerly wind
bursts and the westerly phase of the Madden and Julian Oscillation are usually associated with a drop in SST due to
increased latent heat flux and reduced insolation [McPhaden and Hayes, 1991; Weller and Anderson,
1996; Cronin and McPhaden, 1997]. Strong wind forced currents advecting fresher water from the west,
in combination with enhanced precipitation, generally lead to a freshening of the surface layer near the equator in the
warm pool region. These processes can lead to barrier layer formation [Sprintall and McPhaden, 1994;
Roemmich et al., 1994; Anderson et al., 1996], which Lukas and
Lindstrom [1991] hypothesized as important for understanding the evolution of ENSO warm events. Wind burst
forcing also excites downwelling equatorial Kelvin waves which propagate into the eastern Pacific as discussed in the
previous section.
3.5 Instability Waves
Tropical instability waves, first observed in the Pacific in satellite SST imagery [Legeckis, 1977], typically
propagate westward with zonal wavelengths of 8002000 km and periods of 2030 days. They have been observed in
ocean currents, temperatures, and salinity [Philander et al., 1985; Pullen et al., 1987;
Halpern
et al., 1988; McPhaden et al., 1990c; Kessler and McPhaden, 1995a; Qiao and
Weisberg, 1995; McPhaden, 1996; Flament et al., 1996]. They are also detectable
in Geosat and TOPEX/POSEIDON altimetry data [Perigaud, 1990; Giese et al., 1994; Busalacchi
et al., 1994] despite the relatively coarse temporal resolution of altimeters compared to the basic frequency of
the waves. Instability waves are seasonally and interannually modulated, being weakest during boreal spring and during
the warm phase of ENSO. The waves derive their energy from the large-scale, seasonally varying zonal equatorial currents
through shear instability [Philander, 1978; Cox, 1980; Philander et al., 1986; Luther and Johnson,
1990] and possibly through SST frontal instabilities [Yu et al., 1995]. As such, they are a significant
source of drag on the South Equatorial Current and Equatorial Undercurrent, and they heat the cold tongue through large
downgradient (i.e., equatorward) eddy heat transports [Hansen and Paul, 1984; Bryden and Brady, 1989].
The waves also affect the stability of the atmospheric boundary layer [Hayes et al., 1989b], the distribution
of cloudiness [Deser et al., 1993], latent heat fluxes [Zhang and McPhaden, 1995], and the distribution
of nutrients, pCO2, and other chemical species in the eastern equatorial Pacific [Feely et al., 1994].
Instability waves of similar character have been documented in the equatorial Atlantic, where they are evident during
the boreal summer season [e.g., Weisberg and Weingartner, 1988; Musman,
1992]. They are a potentially significant source of aliased energy which, if unresolved (as in infrequently sampled
shipboard data), add noise contamination to lower-frequency signals of climatic interest [Hayes and McPhaden,
1992; Kessler et al., 1996].
3.6 ENSO and the Indo-Pacific Throughflow
Wyrtki
[1987] first attempted to monitor the variations of the throughflow by computing the large-scale pressure gradient
between the western Pacific and eastern Indian Oceans. Davao in the Philippines was used for the western Pacific, and
Darwin in Australia was used for the eastern Indian Ocean. He found that this difference was dominated by seasonal
variations but that the two records were coherent at interannual timescales, resulting in a small difference on ENSO
timescales. Later, Clarke [1991] modeled the reflection and transmission of large-scale, low-frequency waves at a
gappy western Pacific boundary and found that the interannual sea level variations along northern Australia were in fact
of Pacific origin. These results implied that the Davao-Darwin sea level difference was not an appropriate index for the
throughflow at interannual timescales. Clarke and Liu [1993, 1994] argued that a better index of the throughflow would
be based on differences between northern and southeastern Indian Ocean sea levels. Their index suggested that the
throughflow increased during cold ENSO events and decreased during warm events.
Thermal structure associated with the Indonesian throughflow in the eastern Indian Ocean has marked interannual
variations, which have been documented on a frequently repeated XBT line between Shark Bay (northwestern Australia) and
Sunda Strait (Java) [Meyers, 1996]. The largest variations of dynamic height and depth of the thermocline are near
the coast of Australia (Figure 14, left), and they are highly correlated to the ENSO
signal in the western equatorial Pacific (Figure 14, right). The XBT observations
are consistent with the study by Clarke and Liu [1994] and with their model of the generation of the variations by
wind forcing with long timescales. The XBT measurements also document how the signal extends into the ocean interior and
how it is related to variations on the coast of Indonesia. The observations and model consistently indicate that
variations near the coast of western Australia are generated by winds over the equatorial Pacific, while variations near
the coast of Indonesia are generated by winds over the equatorial Indian Ocean. The differences in vertically integrated
dynamic height between the coasts of Australia and Indonesia are a measure of the transport of Indonesian throughflow.
The estimated mean transport, based on XBT data, is 7 Sv [Meyers et al., 1995]. Consistent with the tide
gauge measurements, the throughflow is weaker during El Niño, with a peak-to-trough amplitude on interannual
timescales of transport in the upper 400 m of about
5 × 106 m3 s-1. What impact ENSO timescale variations in
throughflow have on the climate of the Indian Ocean region is, however, unclear.
Figure 14: Joint empirical orthogonal functions (EOFs) of anomalies of SST, dynamic height
(0400 dbar) and depth of the 20° isotherm on a frequently repeated XBT line between Shark Bay
(westernmost point of Australia) and Sunda Strait (western end of Java). (left) The first EOF (34% of the variance)
shows the ENSO signal entering the Indian Ocean along the coast of Australia. (right) The temporal coefficients of the
first EOF are highly correlated with the Southern Oscillation Index (SOI). From Meyers [1996].
3.7 ENSO and Global Oceanic Variability
Although the TOGA observing system focused primarily on the ENSO phenomenon in the tropical Pacific, satellite and some
in situ measurement programs (e.g., VOS, tide gauges, and drifters) provided a global perspective on climate variations
during the TOGA decade. In this section we briefly review studies of climate phenomena facilitated by measurements
outside the tropical Pacific, with emphasis on variability related to ENSO.
Atmospheric teleconnections associated with the ENSO cycle affect oceanic variability in wide-ranging parts of the
globe. Over the North Pacific Ocean, for example, the Aleutian Low becomes anomalously strong during the late fall and
winter of an El Niño year. Associated with these changes in atmospheric pressure, the axis of the subtropical jet
stream splits, one branch displaced southward, steering storms into the southwestern United States, and another branch
displaced northward into the Pacific Northwest. Air-sea heat exchange is enhanced at midlatitudes by these changes in
atmospheric circulation [Alexander, 1992], leading to cold open ocean SST anomalies during El Niño years [Wallace
et al., this issue]. Along the west coast of the United States, on the other hand, anomalous alongshore
southerly winds during El Niño can lead to reduced coastal upwelling, which contributes to warmer coastal SSTs
and higher coastal sea level [Enfield and Allen, 1980; Ramp et al., 1997, and references therein].
In addition to this atmospheric teleconnection pathway between the tropical and midlatitude Pacific Ocean, equatorial
oceanic Kelvin waves impinge on the eastern boundary, forcing poleward propagating coastal Kelvin waves in both
hemispheres [Enfield and Allen, 1980; Chelton and Davis, 1982; Clarke, 1992; Clarke and
Van Gorder, 1994; Ramp et al., 1997; Shaffer et al., 1997]. Roach et
al. [1989] concluded that these signals dominate sea level variability as far north as San Francisco. These
waves are particularly energetic at intraseasonal periods [Spillane et al., 1987]. Recently, Jacobs et
al. [1994] found that Rossby wave signals forced at the eastern boundary by the passage of El
Niño-related coastal Kelvin waves associated with the 19821983 El Niño could be detected in the
central and western North Pacific a decade later. Jacobs et al. [1994] speculated that these
Rossby waves contributed to the development of SST anomalies in the midlatitude North Pacific by rerouting the warm,
normally eastward flowing Kuroshio Extension off Japan to a more northeasterly course in the early 1990s.
White
and Peterson [1996] have recently detected a 45-year eastward propagating, zonal wave number two
oscillation encircling the globe in the Antarctic Circumpolar Current. The wave is characterized by coherent
oscillations in SST, sea level pressure, meridional winds, and sea ice extent. White and Peterson [1996]
hypothesized that this wave may be related to forcing associated with El Niño through atmospheric teleconnections
between the tropical Pacific and the Southern Ocean.
The tropical Atlantic is characterized by a prominent mean seasonal cycle in surface winds, sea level upper ocean
currents, and temperatures [e.g., Carton and Katz, 1990; Reverdin et al., 1991a, b; Molinari and Johns,
1994; Katz et al., 1995b]. In addition, two important modes of interannual-to-decadal variability
are evident around this seasonal cycle, one of which consists of warm events with variability concentrated near the
equator [Philander, 1986; Houghton, 1991; Zebiak, 1993; Carton and Huang, 1994]
and another of which consists of interhemispheric variations in tropical SST [Moura and Shukla, 1981; Servain,
1991; Houghton, 1991; Houghton and Tourre, 1992]. Dynamics intrinsic
to the ocean-atmosphere-land system in the Atlantic basin are important in determining the variability associated with
these low-frequency climate signals. However, ENSO teleconnections through the atmosphere influence their evolution as
well, as discussed by Servain [1991], Delecluse et al. [1994], and Enfield and
Mayer [1997].
Variability in the Indian Ocean is dominated by a pronounced seasonal cycle related to monsoon wind forcing [Rao et al.,
1989; Molinari et al., 1990; Perigaud and Delecluse, 1992; Mizuno et
al., 1995; Donguy and Meyers, 1995, 1996a; Meyers et al., 1995]. However,
interannual anomalies on ENSO timescales are detectable as well [e.g., Perigaud and Delecluse, 1993;
Tourre
and White, 1995]. Tourre and White's [1995] simultaneous analysis of upper ocean thermal data in all
three tropical ocean basins indicated what appeared to be a coherent eastward propagating interannual wave in upper
ocean heat content near the equator. On the strength of this result they suggested the possibility of oceanic precursors
to ENSO in the Indian Ocean thermal field, in addition to atmospheric precursors believed to be important in association
with the monsoons [Webster and Yang, 1992]. Latif and Barnett [1995], on the other hand,
argued that the Pacific forces the tropical Indian and Atlantic Oceans remotely through atmospheric teleconnections on
ENSO timescales and that this forcing accounts for a significant percentage of the observed thermal variability
described by Tourre and White [1995].
3.8 Salinity Variations
For the three tropical oceans, long-term averaged sea surface salinity (SSS) exhibits well-documented minima associated
with the Intertropical Convergence Zones as well as relatively high salinities, mainly where evaporation significantly
exceeds precipitation. Maximum seasonal SSS variations are found primarily in the Intertropical Convergence Zones and in
the South Pacific Convergence Zone, in close relation to seasonal variations in rainfall [Delcroix and Henin,
1991; Dessier and Donguy, 1994; Donguy and Meyers, 1996b]. There is also
notable ENSO-related SSS variability. During El Niño periods the SSS field west of about 150°W is
characterized by fresher than average SSS within 8°N8°S; conversely, saltier than average SSS is found
poleward of 8° latitude [Delcroix and Henin, 1991; Delcroix et al., 1996]. There is also
significant freshening of the surface layer in the eastern Pacific within 10° of the equator during El Niño,
particularly east of 110°W [Ando and McPhaden, 1997]. SSS anomalies of reverse sign are observed during La
Niña periods. In the equatorial band these interannual modifications in the salinity field result mainly from the
combined effects of rainfall and horizontal salt advection, the latter process apparently dominating west of about
165°E [Picaut et al., 1996; Delcroix and Picaut, 1998; Ando and
McPhaden, 1997; Henin et al., 1998].
Lukas and Lindstrom [1991] proposed that salinity variability of the upper ocean may be an
important determinant in the evolution of ENSO. They hypothesized that in regions of heavy rainfall, thin surface mixed
layers form which are isolated from the upper thermocline by salt stratified "barrier layers." The creation of these
barrier layers potentially reduces the efficiency of vertical turbulent mixing to entrain cold thermocline water into
the surface layer, except during periods of strong winds. Thus, barrier layer formation would favor warm SSTs in regions
of heavy rainfall, thereby coupling the hydrologic cycle to the upper ocean heat balance.
Barrier layers have been detected in all three tropical oceans. They vary in thickness and location seasonally [Sprintall
and Tomczak, 1992] and on ENSO time scales in the Pacific [Delcroix et al., 1992; Sprintall and
McPhaden, 1994; Ando and McPhaden, 1997]. Processes responsible for their formation, and how the salt and heat
balances of the upper ocean are coupled in the western Pacific warm pool, were major research themes of TOGA-COARE [Godfrey
et al., this issue]. The results of TOGA-COARE, combined with ENSO predictability studies (e.g., Ji, M., R. W.
Reynolds, and D. W. Behringer, Use of TOPEX/POSEIDON sea level data of ocean analyses and ENSO prediction: some early
results. submitted to the Journal of Climate, 1998), may indicate the need for an improved network of long-term
sustained ocean salinity observations for both ENSO prediction and climate diagnostics.
3.9 Atmospheric Variability
During the decade of TOGA several studies have shown that the ENSO signal extends through the tropical troposphere into
the lower stratosphere [Gage and Reid, 1987; Reid et al., 1989; Gage et al., 1993]. In other
words, the atmospheric response to changing patterns of sea surface temperature extends to high altitudes owing to the
influence of tropical convection. Diabatic heating associated with latent heat release and radiative effects of clouds
have a profound influence on even the largest-scale circulation systems in the atmosphere [Hartmann et al., 1984; Houze,
1989; Mapes and Houze, 1995].
The Christmas Island wind profiler (Figure 5) has been in place long enough to
observe many annual cycles and a few ENSO cycles of the zonal winds. A time-height cross section of zonal winds observed
at Christmas Island is shown in Figure 15. While the mean zonal winds in the tropics
are usually easterly, we observe substantial westerlies recurring periodically in the upper troposphere. These
westerlies develop on an annual basis during the northern hemisphere winter months. Note that the strongest upper
tropospheric westerlies are seen during the La Niña or cold event of 19881989. By way of contrast the upper
tropospheric westerlies are relatively weak during the El Niño years of 19861987, 19911992, and
19941995. These observations are consistent with a strengthening of the Walker circulation during cold events and
a weakening of the Walker circulation during warm events.
Figure 15: Time-height cross section of Christmas Island zonal winds, April 1986 to April 1995. After Gage et
al. [1996b].
The mean annual variation of tropospheric zonal winds observed at Christmas Island is reproduced in Figure 16. The upper tropospheric westerlies are seen to occur above about 7 km and are
seen to be strongest during MarchMay and NovemberDecember. Zonal winds over Christmas Island are typically
easterly at all heights during the northern summer. The annual variation of the zonal winds observed at Christmas Island
is in phase with the annual cycle of tropical convection over the western Pacific and is consistent with a strengthening
and weakening of the Walker circulation driven by convective heating over the western Pacific warm pool region [Gage et al.,
1996b]. The depth of the upper tropospheric westerlies is likely due to the deep tropical heating associated with
mesoscale convective systems [Hartmann et al., 1984].
Figure 16: Low-pass-filtered composite annual cycle of zonal winds observed at Christmas Island. After Gage et
al. [1996b].
Vertical motions are rarely observed directly in the atmosphere [Balsley et al., 1988]. This is partly due to the
difficulty in measuring very small motions, but the measurement problem is complicated by the presence of internal
gravity waves that can mask the small long-term mean vertical motions or otherwise bias observations [Nastrom and
VanZandt, 1994]. Wind profiler direct measurements of vertical velocities in the tropics have confirmed some
expectations at the same time they have raised new questions. The principal finding is that in the absence of convection
the troposphere is generally subsiding at a fraction of a cm s-1. The adiabatic warming consistent with
the observed magnitude of subsidence is what is required to balance radiative cooling to space [Gage et al., 1991b].
While they have not been in use as long as the VHF profilers, the UHF profilers have already proven to be valuable tools
for atmospheric research [Angevine et al., 1993, 1994; Rogers et al., 1993; Gage et al.,
1994b, 1996a]. High-resolution time and height observations by UHF profilers have improved our knowledge of
vertical structure and temporal variability of lower tropospheric winds in the tropics [Gutzler et al., 1994; Parsons,
1994]. For example, Deser [1994] and Gutzler and Hartten [1995] have used the
profiler observations to obtain a more complete picture of the daily variability of the lower tropospheric winds at a
number of locations in the Pacific.
Recently, it has become evident that UHF profilers can provide valuable information about precipitating cloud systems
[Gossard, 1988; Rogers et al., 1993; Gage et al., 1994b, 1996a; Ecklund et
al., 1995; Williams et al., 1995]. In the presence of precipitating loud systems the height coverage of
the profilers is greatly increased. With the large amounts of data obtained from the tropics using UHF profilers at a
number of locations, it is now possible to begin to construct the climatology of precipitating cloud systems in the
western Pacific. Used in conjunction with a VHF profiler, the UHF profiler can provide precipitation fall speeds
relative to background vertical air motions [Currier et al., 1992].
TAO data have also been of value in studies of atmospheric dynamics. For example, Hayes et al. [1989b] found that
in addition to forcing of boundary layer winds by horizontal pressure gradients, as hypothesized by Lindzen and Nigam
[1987], stabilization of the boundary layer over the cold tongue tends to reduce mixing of wind momentum downward
from aloft, particularly in the meridional direction as hypothesized by Wallace et al. [1989].
Accounting for these variations in vertical stability in diagnostic studies allows for a more dynamically consistent
interpretation of oceanic effects on boundary layer winds in the equatorial Pacific [Nigam and Chao, 1996].
Zhang
[1996] used TAO data to document surface manifestations of the Madden and Julian Oscillation in the atmospheric
boundary layer of the western Pacific. He found inconsistencies, as did Jones and Gautier [1995] and Flatau et
al. [1997], between observations from the western Pacific and theories for these oscillations. As a result,
Flatau
et al. [1997] proposed a new theory involving interactive SST feedbacks on convection at intraseasonal time
scales. Their modified theory allowed for time varying SST feedbacks to the atmosphere in response to intraseasonal heat
flux forcing of the ocean, which led to a better simulation of the Madden and Julian Oscillation in a simple coupled
ocean-atmosphere model.
TAO data have been used to examine the role of mesoscale enhancement of surface turbulent fluxes [Zhang, 1995; Esbensen
and McPhaden, 1996] and the related issue of convection-evaporation feedbacks [Zhang et al., 1995]. The
role of evaporation in limiting long-term mean SST in the western Pacific warm pool was described by Zhang and
McPhaden [1995]. They found that above 29°C, latent heat flux decreases with increasing SST, lending
credence to the "thermostat" hypothesis [Ramanathan and Collins, 1991], which
suggests cloud-radiative feedbacks are the primary limiting factor in determining maximum warm pool SSTs.
3.10 Relation to Process-Oriented Studies
Process-oriented studies embedded in the TOGA observing system included the Tropical Pacific Upper Ocean Heat and Mass
Budgets (TROPIC HEAT) Experiments (I in 19841985 and II in 1987) to examine the processes controlling SST in the
equatorial eastern Pacific [Eriksen, 1985; Hebert et al., 1991], the Western Equatorial
Pacific Ocean Circulation Study (WEPOCS) in 19851988 to examine complex current structures in a relatively poorly
explored part of the tropics [Lindstrom et al., 1987], the Tropical Instability Wave Experiment (TIWE) in
19901991 to study the life cycle and energy sources for tropical instability waves in the eastern Pacific [Qiao and
Weisberg, 1995; Flament et al., 1996], TOGA COARE in 19921994 to study ocean-atmosphere interactions in
the western equatorial Pacific [Godfrey et al., this issue], the Joint Global Ocean Flux Studies (JGOFS) Equatorial
Pacific (EqPac) experiment in 1992 to study biogeochemical cycling in the equatorial Pacific [Murray et al., 1994],
and the Central Equatorial Pacific Experiment (CEPEX) in 1993 to study cloud radiative feedbacks and their impacts on
SST [Ramanathan et al., 1995].
The TOGA observing system provided a broad geographical perspective and long time history to aid in the interpretation
of the measurements from these shorter-duration, regional-scale field programs. For example, the intensive observing
period of TOGA COARE took place in the western Pacific from November 1992 to February 1993, during a hiatus in El
Niño conditions in the eastern equatorial Pacific (Plate 1 and Figure 11). The JGOFS experiment, on the other hand, started during the 19911992 warm event
but concluded during near-normal conditions at the end of 1992.
In many cases the TOGA observing system was enhanced to facilitate these process studies. During the enhanced monitoring
phase of TOGA COARE in 19921994 additional TAO moorings were deployed west of the date line to provide finer than
10° zonal resolution of surface winds, upper ocean temperatures, and currents along the equator [Webster and
Lukas, 1992]. Several TAO moorings in the western Pacific were also equipped with special sensors to measure
salinity, rainfall, and incoming shortwave radiation in an effort to better understand surface fluxes in relation to
upper ocean variability [Cronin and McPhaden, 1997]. After TOGA COARE ended, some of these measurements
continued in the western Pacific warm pool [e.g., Koehn et al., 1996]. Drifting buoy deployments also
increased west of the date line during TOGA COARE [Ralph et al., 1997], and some of these drifters
were equipped with salinity sensors. Enhancements of the WWW included installation of integrated sounding systems (ISS)
at Manus Island, Nauru, and Kapingamarangi in 1992 prior to TOGA COARE [Gutzler and Hartten, 1995], with
the Manus Island and Nauru sites continuing after COARE ended.
Specific enhancements to other process studies included specially instrumented TAO current meter moorings at 0°,
140°W during TROPIC HEAT, and TIWE provided support for the hypothesis that internal waves mediate the diurnal cycle
of vertical mixing along the equator [McPhaden and Peters, 1992; Moum et al.,
1992; Lien et al., 1996]. During TIWE, moored data were used to estimate diurnally varying vertical
heat fluxes associated with that mixing [Bond and McPhaden, 1995], and a large number of
drifters were deployed to provide additional information on the structure of instability waves [Flament et al.,
1996]. Moored and drifting buoys were deployed with bio-optical sensors during JGOFS to document physical controls
on primary productivity in the equatorial Pacific [Foley et al., 1998].
4. Role of the TOGA Observing System in the Development of Improved Model-Based
Analyses and Prediction Products
4.1 Introduction
At the outset of TOGA the modeling and observational activities were relatively separate components of the program.
However, as the program matured, a number of factors contributed to the development of a mutual dependency between TOGA
models and observations. At a very basic level, data were needed for the development and validation of oceanic,
atmospheric, and coupled models. Moreover, as experimental forecasts of ENSO became more routine and as initialization
and assimilation techniques for coupled models took on greater importance, the modeling and observational components of
TOGA developed a more intricate relationship. Overviews of the interaction between tropical models and data can be found
in work by Latif et al. [this issue] and Stockdale et al. [this issue] and in the
reviews by Knox and Anderson [1985], Philander [1990], and McCreary and Anderson
[1991]. We concentrate here specifically on the evolution of this partnership toward improved model-based analyses
and better coupled model initial conditions for predictions.
When considering the initialization of tropical ocean models and coupled prediction models, there are several factors
that are critical. First, the tropical oceans are to a certain extent deterministic, by which we mean that adequate
knowledge of past forcing in principle allows us to largely determine the state of the ocean. Knowledge of the surface
wind stress is paramount in this determination. For example, Busalacchi and O'Brien [1980, 1981]
demonstrated that, with a reduced gravity model and surface stress, one could capture key aspects of sea level
variability associated with ENSO. Studies with ocean general circulation models (OGCMs) [e.g., Philander and
Seigel, 1985; Harrison et al., 1990] also emphasized the paramount importance of wind forcing in model
simulations. This fact makes the analysis and initialization problem quite different from that of numerical weather
prediction, where there is no counterpart to the external forcing (and its associated errors) that are imposed through
surface wind stress.
While in theory it is feasible that coupled tropical forecast models could be initialized with wind stress alone,
practical considerations suggest that ocean thermal data will also be important. This is because wind stress and upper
ocean thermal structure are partially redundant, so that observing and initializing baroclinic equatorial wave modes
with subsurface temperature data could help correct some of the deficiencies in the imposed wind forcing. SST
observations, either through assimilation or via surface boundary constraints, have also been important for the
development of both the atmospheric and oceanic components of coupled prediction models. The ready availability, spatial
coverage, and accuracy of SST analyses makes this variable particularly valuable for model validation and development
[e.g., Stockdale et al., 1993].
From a historical perspective, sea level data has made one of the more significant contributions to ocean model
development, particularly as equatorial theory was developing prior to TOGA. In situ sea level data continue to provide
important model validation, particularly as sea level variations represent an integral, low baroclinic mode response to
wind forcing and thermodynamic adjustments. With the advent of satellite altimetry, giving the spatial coverage not
possible with in situ instrumentation, sea level may well assume far greater importance for model initialization.
Early studies in TOGA pointed to the advantages of thermal (mass) information vis-a-vis velocity information for ocean
model initialization [Moore et al., 1987; Philander et al., 1987]. Hence, in a modeling
context, velocity data have been used mostly for validation purposes [e.g., Leetmaa and Ji, 1989; Brady and
Gent, 1994; Chen et al., 1994b; Fukumori, 1995; Halpern et al., 1995; World Climate Research
Program, 1995a]. Various other data sets, such as those for salinity and surface heat fluxes, have also played
important though somewhat less critical roles in model development. Consistent with these considerations and with the
discussion in section 2.2 the National Research Council [1994a] ranked measurements in the following order of importance for
the purpose of short-term climate prediction: (1) wind stress and SST, (2) subsurface thermal data, (3) sea level and
ocean current data, and (4) salinity and atmospheric boundary layer data.
4.2 Improved Wind Analyses for Use in Modeling Studies
4.2.1 Numerical Weather Prediction Products
One of the focuses through the early part of TOGA was the assessment of the quality of various wind stress products. It
was known that VOS winds would be useful but likely inadequate, but it was not immediately clear whether improved
analysis techniques and improved numerical weather prediction schemes would make up for some of these inadequacies
[e.g., Reynolds et al., 1989a]. Harrison et al. [1989] used a tropical
ocean general circulation model to diagnose the impact of differences in various wind stress products. They compared
simulations of the 1982-1983 El Niño forced by the Sadler and Kilonsky [1985] wind analysis
(produced from VOS wind data and cloud drift winds), the FSU wind analysis [Goldenberg and O'Brien, 1981],
and three analyses based on numerical weather prediction models (ECMWF, National Meteorological Center (NMC), and Fleet
Numerical Oceanography Center (FNOC)). Overall, the research analyses [Goldenberg and O'Brien, 1981; Sadler
and Kilonsky, 1985] produced more realistic dynamic responses but less convincing SST results for the equatorial
waveguide. Simulations of the mean seasonal cycle and the 1982-1983 El Niño using linear dynamical ocean models
[McPhaden et al., 1988b; Busalacchi et al., 1990] yielded similar
results with regard to ocean dynamical responses, namely that the research products led to more realistic results.
Details aside, one of the most important conclusions of these studies as far as TOGA was concerned was that improved
knowledge of the surface wind stress was essential.
Operational atmospheric weather analysis and forecast models routinely merge observations of different parameters (e.g.,
temperature, winds, etc.) made at different levels in the atmosphere using different instruments. These analysis
and forecast systems produce a dynamically consistent model atmosphere with high temporal and spatial resolution. For
this reason the surface wind fields from such systems are often used to force ocean models like that run at NCEP for
near-real-time tropical ocean analyses. Improving the quality of operational atmospheric model-based wind analyses is
therefore an issue of some importance to climate modelers.
Operational centers now routinely use either wind speeds from the DMSP SSM/I instrument and/or vector winds from the
ERS-1 and recently launched ERS-2 scatterometers. For example, the U.S. Navy [Phoebus and Goerss, 1991] and NCEP
[Yu and
Deaven, 1991] use the SSM/I wind speeds, while ECMWF [Gaffard and Roquet, 1995] and NCEP [Peters et
al., 1994] use the ERS-1 and ERS-2 vector winds. The SSM/I winds are converted to vector winds using directions
assigned from either the model forecast or a combination of the forecast and available data. Phoebus et al. [1994]
reported that the greatest impact of the SSM/I was in the tropics and at higher latitudes along the meteorological storm
tracks. Gaffard and Roquet [1995] found that the ERS-1 and ERS-2 vector winds improved the analyses in
the southern hemisphere and had some positive impact in the short-range forecast.
TAO data are also used in operational weather forecast systems. Impact studies done at ECMWF, as reported by Anderson
[1994], showed that differences between ECMWF analyses with and without TAO winds could exceed
3 m s-1, although typical differences were less. In addition, the impact of TAO observations tended
to weaken significantly if the model was not reinforced with new TAO observations every 6 hours. Anderson [1994] pointed
out that, in general, single level surface data like those from TAO buoys can be expected to have a relatively low
impact on the atmospheric weather analyses. Reynolds et al. [1989a] reached a similar
conclusion in a comparison of surface winds from the buoys with the winds from several different operational analyses.
They found that the analyses looked more like each other than like the data. However, the models themselves have
problems, as pointed out in a study by Williams et al. [1992]. They compared wind
profile data at Christmas Island with the ECMWF forecast model and found that the model and the data were consistent
above 1.5 km but not below this level. The model winds at these lower elevations were too weak and did not properly
turn with height. This result suggests that there are problems in the model tropical boundary layer and that model and
analysis systems need to be improved to optimize assimilation of tropical surface winds.
Recently, TAO and other TOGA-related data sets have been incorporated into atmosphere reanalyses at NCEP, ECMWF, and
NASA Goddard Space Flight Center. These decade-long, internally consistent model analyses are produced using
state-of-the-art numerical models, assimilation systems, and the most complete data sets available from historical
archives [e.g., Schubert et al., 1993; Kalnay et al., 1996]. These analyses are valuable
for providing initialization and validation fields for coupled model predictability studies, for determining the
sensitivity of atmospheric models to slow variations in the surface boundary conditions, and for diagnostic studies of
atmospheric variability. Evaluations of these reanalyses products are currently underway [e.g., Saha et al., 1995; Smull
and McPhaden, 1996].
4.2.2 Blended products using buoy, ship, satellite winds, and/or model output
Availability of TAO data has led to efforts to develop improved surface wind analyses for ocean modeling through
blending of buoy data with ship winds, satellite winds, and/or model output. Two studies illustrate this approach and
the impact that TAO data make on such analyses. Menkes and Busalacchi [1995] performed a
series of linear ocean model hindcasts for the equatorial Pacific using two baseline forcing functions over the period
19821993. The first, denoted CMP9, was based on winds derived from the NCEP medium-range forecast model as forced
by observed SST but without incorporating any surface wind data or other meteorological data via an
assimilation/analysis cycle. The other baseline wind product was the FSU winds. Beginning in November 1992, the FSU
analyses incorporated TAO observations in increasing numbers (see Appendix B, section B1), but it is difficult to
quantify the weight they were given in the subjective FSU analysis. Two combined data sets, CMP9 plus TAO and FSU plus
TAO, were constructed by optimally interpolating the monthly TAO wind observations to each baseline forcing. Wind
observations at each TAO location were converted to wind stress using the stability dependent parameterization of Liu et
al. [1979]. Four sea level simulations were then performed and evaluated against tide gauge sea level
measurements, gridded fields of TOPEX/POSEIDON sea level, and TOGA-TAO dynamic height anomalies across the equatorial
Pacific Ocean.
The impact of TAO winds was characterized as a function of the increasing number of TAO observations with time. It was
shown that the incorporation of a few TAO observations into the CMP9 wind product from 1987 onward compensated for the
erroneously weak winds in the central and eastern equatorial Pacific and subsequently led to improved simulations (Figure 17). Similarly, the TAO observations also had a positive impact on the FSU
simulation, both in terms of phase and amplitude, suggesting that the TAO observations be given greater weight in the
FSU analysis. The impact of TAO observations in the 1990s, when the TAO array was reaching full deployment, was such
that the improved simulations forced by FSU plus TAO and CMP9 plus TAO winds were quite similar, in contrast to earlier
periods in the 1980s when the FSU and CMP9 simulations were very different.
Figure 17: Modeled sea level anomaly (SLA) versus observation at Santa Cruz, Galapagos Islands. (top left) CMP9
(dotted line) simulation versus observations (solid line). (top right) Same as Figure 17
(top left), but for CMP9 plus TAO (dashed line). (middle left) Same as Figure 17
(top left), with FSU (thin line). (middle right) Same as Figure 17 (top left), with
FSU plus TAO (dot-dash line). (bottom) The correlation coefficient between the modeled and observed sea level anomalies
as the time over which the correlation is computed progressively reduced by 1 year from its starting date (labeled on
abscissa) to 1993. For example, the point labeled 1987 represents the cross correlation from 1987 through 1993.
A similar study was done by Reynolds et al. [1995] for the period April 1992 to April 1994. However, in
this study they used the FSU product as well as two different monthly products: the lowest sigma level winds (roughly
40 m in height) from the NCEP operational medium-range forecast model with atmospheric data assimilation and ERS-1
wind stresses computed at a height of 10 m using the algorithm of Freilich and Dunbar [1993]. An
objective analysis procedure [see Lorenc, 1981] was used to correct each of the wind fields with TAO data. Comparison
of the corrections showed that all analyses tended to have zonal wind stresses that were too weak relative to TAO in the
eastern tropical Pacific (Figure 18). Of the three wind products, however, the FSU
analysis was in best agreement with TAO. NCEP stresses were too weak (i.e., consistently negative differences with TAO)
during roughly the first half of the comparison period, although there appeared to be some improvement in the NCEP winds
over the second half of the record. Conversely, the ERS-1 stresses were consistently too weak relative to TAO for the
entire period.
Figure 18: Zonal wind stress differences relative to TOGA-TAO for three products: NCEP, FSU, and ERS-1. The
differences are averaged over 10°N to 10°S and 180° to 100°W. After Ji and Leetmaa
[1997].
Reynolds et al. [1995] also used an ocean model to evaluate the impact of these different
wind products. However, in contrast to Menkes and Busalacchi [1995], they used the
general circulation model reported by Ji et al. [1995] both with and without the assimilation of thermal data.
Results showed that assimilation was able to compensate for wind stress differences. Without assimilation, though, the
ocean model was more affected by the different wind stress forcing. In particular, it was possible to clearly determine
that ERS-1 zonal wind stresses were too weak in the eastern equatorial Pacific. However, the differences in the model
fields compared to observations could not clearly identify which of the remaining three products (NCEP, FSU, and NCEP
corrected by TAO) was superior. The Menkes and Busalacchi [1995] and Reynolds et
al. [1995] studies differ because different wind stress fields and different models were used. However, in
combination these studies indicate that TAO data have the strongest positive impact on the wind stress fields that are
most independent of the mooring data.
4.3 Assimilation of Temperature Data Into Ocean Models
Implementation of the TOGA observing system provided unprecedented opportunity for studying large-scale, low-frequency
climate variability through the application of data assimilation techniques in combination with simple and complex
tropical ocean models. Keys to achieving this were the vastly improved data coverage from the TOGA observing system,
more effective data management strategies allowing rapid access to observations, order-of-magnitude improvements in
computing capacity and resources, and improvements in ocean models.
Prior to TOGA, most oceanic observations were obtained from VOS lines, a handful of moorings and circulation drifters,
and occasional research cruises. With the exception of SST, which could also be retrieved from satellite, it was
essentially impossible to produce basin-scale ocean analyses from observations alone. With increased data coverage
during TOGA, including a greatly enhanced volunteer observing ship network and the TAO array in the equatorial Pacific,
regular and routine subsurface ocean analyses became possible. Several centers, including the El Niño Monitoring
Center of the Japan Meteorological Agency (JMA), NCEP, the Joint Environmental Data Analysis Center in the United
States, and the Bureau of Meteorology Research Center (BMRC) in Australia, began routinely producing monthly subsurface
maps, particularly for the tropical Pacific.
All the data analysis and assimilation systems depend on knowledge of the amplitude and spatial and temporal scales of
variability. Scale analyses, such as those of Meyers et al. [1991], Festa and Molinari
[1992], and Kessler et al. [1996], provided estimates of signal levels versus unresolvable noise as
well as estimates of the spatial and temporal covariance of the resolvable signal (which allow realistic scales for
interpolation to be set). While the practical application of such information is not always straightforward,
particularly when the first guess is provided by a dynamical model, it does nevertheless represent the fundamental basis
for most of the applications described below.
One example is the subsurface ocean analysis system developed at BMRC of Australia [Smith et al., 1991; Smith,
1995a]. This system uses optimal interpolation and a simple statistical forecast model to produce global upper ocean
temperature analyses at periods from 10 days to 2 months, utilizing data from XBTs and TOGA-TAO. All quality control is
objective [Smith, 1991] on the basis of information derived from the statistical interpolation. The
shorter-period analyses were shown to retain all the important low-frequency, large-scale information of the bimonthly
analyses (the analysis period upon which much of the TOGA observations were planned) as well as much of the interesting
high-frequency fluctuations [Smith, 1995b]. Over monthly periods for the last half of TOGA, the estimated root-mean-square
(rms) error variance in the 20°C isotherm analysis was typically 46 m (equivalent to around 0.3°C).
Achieving such accuracy was a remarkable accomplishment, considering the low expectations for the measurement of
subsurface thermal structure during TOGA as reflected in Table 4.
Dynamic ocean models have been used to simulate basin-scale ocean circulations long before TOGA. While such simulations
did not usually ingest ocean information, they did represent an alternative route to ocean analyses, whereby information
in the applied surface boundary forcing (principally the wind stress) was used to indirectly infer the state of the
ocean. The main problems with model simulations were the poor quality of surface forcing, because of a lack of wind
observations over the open ocean, and errors in the ocean model physical parameterizations. Limited in situ observations
were primarily used for validation of model results. Although the quality of surface winds has improved steadily,
especially since the TOGA observing system increased surface wind observations in the tropical Pacific, errors in the
winds and in ocean models still significantly limit the accuracy of the simulations [Ji and Smith, 1995]. One way
to compensate for errors in wind stress forcing and ocean model physics is to use data assimilation techniques to
combine observations and model fields to yield the best possible estimate of the ocean state.
Data assimilation has been an active area of research from well before TOGA, although most practical applications were
in the field of meteorology. Advances in ocean data collection, communication, and modeling in the late 1970s and early
1980s made ocean data assimilation a feasible option. Several studies have examined the problem of ingesting ocean
subsurface data into simpler, linear, shallow water models of the tropical ocean [e.g., Moore, 1989, 1990, 1992; Moore et al., 1987; Moore and
Anderson, 1989; Sheinbaum and Anderson, 1990a, b; Hao and Ghil, 1994; see also Busalacchi,
1996; Stockdale et al., this issue]. All these studies showed that subsurface sampling as practiced
during TOGA could be used to correct model and wind-forcing errors and that the time taken for correction was only a
month or so, owing to the rapid communication of information by equatorial waves.
An early attempt to produce routine ocean analyses utilizing an ocean data assimilation technique was a system developed
by Leetmaa and Ji [1989] for the tropical Pacific. This system used wind-forced ocean model
simulation as a first guess and combined the observations collected during a period of 1 month with the model field
using the optimal interpolation. The data assimilation procedure was done monthly.
The main advantage to the model-based analyses is that large areas of data void are filled in by model dynamics. The
main drawback to the sequential initialization method is that the data assimilation can introduce a strong shock when
corrections are applied to the model fields, as discussed by Moore [1990]. Also, for models integrated forward in
time until the next data assimilation cycle without continuous constraint by observations, model fields will drift
toward the model's own equilibrium state. Hence a "sawtooth" pattern in the time history of the analyses is sometimes
obvious [e.g., Hayes et al., 1989a].
A data assimilation system developed by Derber and Rosati [1989] was a significant
improvement over earlier ocean analyses. This system is based on a variational method in which assimilation is done
continuously during the model integration. Corrections to the model are spread over a long period of time; thus change
to the model temperature field during each model time step is incremental. This significantly reduces the impact to the
dynamical balances of the model fields and also keeps model fields from drifting toward their own climate. Further, an
observation is retained in the model for a long period of time (24 weeks), weighted by the difference between the
model time and the observation time during each assimilation time step. This procedure significantly increases the
influence of observations to compensate for the lack of spatial and temporal data coverage in many areas. The drawback
in doing this is that it tends to limit the analyses to resolving only large spatial scales and low-frequency phenomena
[Halpern
and Ji, 1993].
An operational model-based ocean analysis system based on the data assimilation system of Derber and Rosati [1989]
has been implemented at the NCEP [Ji et al., 1995]. Real-time observations from satellite, VOS ships, and drifting
and moored buoys are assimilated into an ocean general circulation model to produce near-real-time (weekly mean) Pacific
and Atlantic analyses. The near-real-time NCEP ocean analysis system is forced with weekly averaged surface winds
produced by the NCEP operational atmospheric analyses. Retrospective monthly Pacific Ocean reanalyses have also been
generated at NCEP by forcing the ocean model with historical monthly wind-stress analyses produced at Florida State
University [Stricherz et al., 1992] and incorporating additional delayed mode data not available in real
time [Ji
and Smith, 1995].
Shown in Figure 19 is the time history of the depth of 20°C isotherm anomalies
along the equator in the Pacific for 19821995. The thermocline anomalies produced by the ocean analysis system (Figure 19, middle) showed variability in the central and western Pacific stronger than
that produced by a model forced with the FSU winds without data assimilation (Figure 19, right). Comparisons with in situ observations of moorings and tide gauges suggest
that the model-based analyses are of higher accuracy than the wind-forced simulation [Ji and Smith, 1995]. These
studies show that even when using a high-quality wind stress forcing and a state-of-the-art ocean general circulation
model, ocean data assimilation can still further improve the quality of analyses by compensating for errors in the
forcing and model.
Figure 19: Anomalous depth of the 20° isotherm along the equator for the Pacific produced (left) by BMRC,
(middle) by the NCEP ocean analysis system, and (right) by an ocean model simulation forced with monthly surface wind
analyses from FSU. The contour interval is 10 m. Anomalies greater (less) than 20 m (-20 m) are indicated
by dark (light) shading. From Ji and Leetmaa [1997].
Also shown in Figure 19 (left) are the 20°C isotherm depth anomalies from the
BMRC subsurface analysis system, which is based on statistical analyses rather than dynamical model analyses [Smith,
1995b]. It should be noted that the NCEP and BMRC systems have quite different approaches to quality control
(subjective versus objective) and to assimilation/inter- polation (continual insertion with variational con-straints
versus sequential single-period optimal interpolation). The analyzed peaks and depressions of the thermocline depth from
the NCEP and BMRC systems are generally similar (e.g., the peak anomalies of the 19821983 and 19911992 warm
and 1984 cool events), as should be expected since they are essentially based on the same data sets. There are, however,
some significant differences; the NCEP analysis of the 1984 cooling is characterized by a coherent west-to-east
evolution, whereas the BMRC analysis shows essentially in-place cooling. Such differences reflect the different modes of
interpolation; the dynamic system has theoretical advantages for transferring information within the equatorial
waveguide but at the same time may be hampered by errors in the wind and/or the model.
A promising way of improving tropical ocean model-based analyses is through the assimilation of altimetry data [see,
e.g., Arnault and Perigaud, 1992]. This requires projection of sea level variability onto baroclinic
ocean thermal structure, which can be readily done by developing empirical relationships between the two variables
[e.g., Rebert et al., 1985; Carton et al., 1996]. Advanced techniques such as
the Kalman filter and the adjoint method have been used to assimilate Geosat and TOPEX/POSEIDON altimetry data into
simple reduced-gravity models [e.g., Gourdeau et al., 1997; Greiner and
Perigaud, 1994, 1996; Fu et al., 1993; Fukumori, 1995]. Impact studies of altimetry
assimilation on ocean general circulation model-based analyses have also been performed Carton et al. [1996], and
Fischer
et al., 1997].
Assimilation of observations obtained from the TOGA observing system not only provides means to produce much improved
ocean analyses, it also provides a great opportunity for improving the definition of the initial ocean fields for
prediction of ENSO using coupled models. This is discussed in section 4.4. Analyses such as those described above have
also found a wide range of other applications. For example, Lukas et al. [1995] studied the large-scale
variations of the Pacific Ocean during TOGA COARE using the NCEP subsurface analyses, providing a context for the
analysis of observations of air-sea interaction in the intensive flux array. The use of model-based analyses for process
studies is now quite common in meteorology, and the advances in ocean analysis and assimilation during TOGA will assist
in making such applications more common in climate studies.
Finally, analysis systems have been used to examine the design of the TOGA subsurface observing system. Miller [1990]
investigated the impact that ocean thermal data (processed to estimates of dynamic height) might have in hindcasts of
sea level in the equatorial Pacific. His results suggested that the TAO array would positively impact on hindcasts of
monthly mean sea level. Smith and Meyers [1996] have examined the relative impact of XBT and TOGA-TAO data for
monitoring tropical Pacific Ocean thermal variability. They concluded that for the last half of the TOGA period, over
the region 20°S20°N, the net information content of the systems were comparable in magnitude, each
contributing the equivalent of around 300 independent subsurface samples per month.
4.4 Initialization of Coupled Ocean-Atmosphere Models for Climate Forecasting
ENSO prediction depends strongly on the accuracy of the ocean initial conditions. Three different methods are presently
used for initialization of the ocean for ENSO predictions using coupled ocean-atmosphere models. The first method, used
by Cane et
al. [1986], is to spin up the ocean using the observed surface wind history prior to the initiation of a
forecast. A second method uses assimilation of subsurface temperature data together with surface wind forcing to achieve
better defined subsurface ocean states. This is done at the NCEP [Ji et al., 1994] and at the Geophysical Fluid Dynamics
Laboratory (GFDL) [Rosati et al., 1997]. A third method developed at BMRC utilizes both wind and subsurface data
jointly to initialize a coupled model through an adjoint data assimilation method [Kleeman et al., 1995].
Assimilation experiments described in the previous section illustrated the need to assimilate data in such a way that
initialization "shock" is minimized. On the other hand, these studies demonstrated the potential impact of data
assimilation on the forecast of eastern equatorial Pacific SSTs several seasons into the future. Ji and Leetmaa [1997],
for example, compared results from forecast experiments initiated from ocean initial conditions produced with data
assimilation and produced with wind forcing alone, using the NCEP coupled ocean-atmosphere forecast model [Ji et al.,
1994]. Shown in Figure 20 are temporal anomaly correlation coefficients (ACC)
and root-mean-square (rms) errors as a function of forecast lead times for area-averaged SST anomalies between forecasts
and observations for an eastern equatorial Pacific region (120°170°W, 5°S5°N). The forecasts
were initiated monthly for the period of 19831993. This comparison demonstrates convincingly that data
assimilation has a significant positive impact on improving ENSO forecast skill. Ji and Leetmaa [1997] also showed
forecast skills using ocean initial conditions produced with assimilation of XBT data alone and with assimilation of
both XBT and TAO buoy data. The results indicate significant positive impact of the TAO buoy data, largely due to the
vastly improved spatial and temporal data coverage by the TAO array in the tropical Pacific.
Figure 20: (left) Anomaly correlation coefficients and (right) rms errors between forecasts and observations for
area-averaged SST anomalies in the eastern equatorial Pacific region between 170°120°W and
5°S5°N. Solid (dash-dot) lines are for forecasts initiated from ocean initial conditions produced with
(without) subsurface data assimilation.
Kleeman
et al. [1995] also demonstrated how enhanced forecast skill could be achieved in an intermediate coupled
model by improving the initial conditions for upper ocean heat content. In this study the adjoint for the ocean
component of the coupled model was used to improve the ocean initial conditions by finding a condition that was
consistent with both the wind forcing and the subsurface ocean thermal data. Two sets of experiments were performed for
the period January 1982 through October 1991. In the first experiment the ocean initial conditions were obtained by
forcing the ocean model with the FSU winds. This initialization procedure was consistent with that of Cane et
al. [1986], and similar forecast skill scores were obtained. In the second experiment improved initial
conditions were obtained by using analyzed subsurface temperature anomalies averaged over the upper 400 m of the
water column [Smith, 1995b] for the 12 months prior to initiating a coupled forecast. The use of the ocean
data assimilation in this case led to notable increases in forecast skill.
Altimetry data, in addition to upper ocean thermal data, likewise have the potential for improving the skill of
short-term climate predictions. [Fischer et al., 1997; M. Ji, R. W. Reynolds, and D. W. Behringer, Use of
TOPEX/POSEIDON sea level data of ocean analyses and ENSO prediction: Some early results, submitted to the Journal of
Climate, 1998]. In one set of experiments, for example, sea level data from TOPEX/POSEIDON were added to the XBT and
TAO ocean model assimilation system of Ji et al. [1995]. The sea level data improved the agreement of the model sea
level with independent tide gauge data and led to a more realistic forecast of tropical Pacific SSTs. On the other hand,
predictability experiments using the Zebiak and Cane [1987] coupled model indicated that forecast errors were not
reduced by using altimetry data for ocean model initialization [Cassou et al., 1996]. Thus, the utility of
altimetry for initialization is model dependent, so that more research will be required to fully exploit altimetry for
ENSO prediction.
In the previous initialization studies the oceanic component was first forced by observed wind stress and adjusted by
assimilating subsurface thermal observations. Subsequently, the model-simulated SST was used to force the atmospheric
component. However, a potential problem with this common approach is that since there are no interactions allowed
between the oceanic and atmospheric components during initialization, the coupled system is not well balanced initially
and may experience a shock when the forecast starts. Further, the imbalances between the mean states of the oceanic
initial conditions and the coupled model contribute to systematic error of the forecast fields [Leetmaa and Ji, 1996].
In the study by Chen et al. [1995], initial conditions for the Cane and Zebiak [1985] model are
generated in a self-consistent manner using a coupled data assimilation procedure. Initial conditions for each forecast
are obtained by running the coupled model for the period from January 1964 up to the forecast starting time. At each
time step prior to the forecast a simple data assimilation procedure is used whereby the coupled model wind stress
anomalies are nudged toward the FSU wind stress observations. In this manner the coupled model itself is used to
dynamically filter the initial conditions. Initialization shocks are reduced by providing a better balanced set of
ocean-atmosphere initial conditions for the coupled forecast. Previously, the ocean initial conditions contained
considerable high-frequency energy when forced by the FSU wind stress anomalies. The influence of the coupled model in
the new initialization preferentially selects the low-frequency, interannual variability. This approach also results in
a shallower thermocline in the western equatorial Pacific during most ENSO warm events with important implications for
improved forecasts of warm event termination. Moreover, the coupled approach to initialization eliminates the springtime
barrier to prediction that characterizes most coupled forecast schemes.
Recently, decadal-scale variability in the forecast skill has been noted in coupled models. Chen et al. [1995], for
example, found that for the period 19821992, forecast skill was generally high for lead times of 1224
months. Conversely, for the period 19721981, forecast skill was generally low for lead times longer than a few
months. Balmaseda et al. [1995] also found generally higher predictability in the 1980s compared to
the 1970s, and Goddard and Graham [1997] described reduced predictability associated with the 1993 and
19941995 El Niños relative to El Niños during 19821992. ENSO variations in the 1980s were
generally stronger than those during the 1970s or during 19931995, suggesting that stronger ENSO events may be
easier to predict than weaker events. It is also likely that the present generation of prediction models does not
adequately represent the full range of physical processes responsible for the ENSO cycle or the interaction of ENSO with
decadal time scale variations. These limitations could contribute to decadal fluctuations in predictability as well.
Although most data assimilation efforts in support of coupled models have focused on improving initial conditions, data
assimilation techniques such as the Kalman filter have also been used as a means of parameter estimation in simple
coupled models. In idealized versions of intermediate coupled models, there exist key parameters that govern the
coupling strength between SST and the surface winds and the relation between the depth of the thermocline and the
temperature of the water entrained into the ocean mixed layer. The particular values of these coefficients tend to
determine the behavior of the coupled mode characteristic of the system. Similar to the way in which assimilation
techniques have been used to estimate parameters such as the phase speed in shallow water models, the work of Hao and Ghil
[1994] demonstrated how subsurface thermal data from the TAO array could be assimilated into coupled models to guide
the proper estimation of key model parameters.
5. Discussion and Conclusion
The preceding sections have described the evolution of the TOGA observing system and how it has contributed to
scientific progress in studies of short-term climate variability during the TOGA decade. Development of this observing
system was a major technological achievement, which revolutionized climate monitoring programs by stimulating increased
demand for real-time ocean data delivery. The data from this observing system were essential to fostering advances in
many aspects of TOGA research, including the following: (1) documentation of the ENSO cycle and related phenomena, such
as the mean seasonal cycle and intraseasonal variability, with unparalleled resolution and accuracy; (2) testing of ENSO
theories, such as the delayed oscillator; (3) development of new theoretical concepts relating to ocean-atmosphere
interactions on seasonal-to-interannual timescales; (4) development of oceanic, atmospheric, and coupled
ocean-atmosphere models; and (5) development of ocean data assimilation systems for improved climate analyses and for
initializing climate prediction models. In short, measured against the goals of TOGA stated in section 1, the TOGA
observing system was a tremendous success.
It is fortuitous that TOGA spanned a decade in which there was both a large swing from El Niño to La Niña
conditions (19861989) and a period of prolonged anomalous warming (19911995). The dramatic change from El
Niño to La Niña during the first half of TOGA heightened awareness about the importance of the cold phase
of the ENSO cycle [e.g., Trenberth and Branstator, 1992; Halpert and Ropelewski, 1992] and afforded
the opportunity to examine sharp contrasts between extreme climatic conditions in the Pacific and their impacts
worldwide [e.g., Palmer et al., 1992]. On the other hand, the period 19911995 was unprecedented when
viewed in the context of modern instrumental records dating back to the last century. The warm conditions evident during
19911995 have been interpreted as a single warm phase ENSO event, in which case it would be the longest in the
past 100 years [Trenberth and Hoar, 1996]. An alternative interpretation is that 19911995 was
characterized by three distinct warm events [Goddard and Graham, 1997], implying a
recurrence rate significantly higher than the average 3-4 years expected from historical records. Either interpretation
identifies 19911995 as unique in the modern record.
It is interesting to compare the evolution of warm events in Plate 1 with the Rasmusson and Carpenter [1982]
composite, which was based on El Niño events from the 1950s to the 1970s. Rasmusson and Carpenter [1982]
suggested that anomalous surface warming occurs first off the South American coast, peaking in MarchMay, then
progresses westward along the equator into the interior basin, reaching a "mature phase" in DecemberFebruary.
Subsequently, warm SST anomalies and associated westerly wind anomalies weaken and eventually disappear by the following
May. There were features common among the El Niño events observed during TOGA, such as anomalous warming in the
equatorial cold tongue and large-scale weakening of the trade winds in the central and western Pacific. However, like
the 19821983 El Niño prior to TOGA, none of these warm events evolved strictly according to the canonical
Rasmusson and Carpenter [1982] composite.
Significant differences in duration, phasing, and spatial warming patterns observed during events of the 1980s and early
1990s defy easy categorization. Most pronounced warmings in the eastern and central Pacific in the 1990s, for example,
occurred in boreal winter 19911992, boreal spring 1993, and boreal fall 1994. This disparate timing of maximum
warm anomalies raises questions about the dynamical links between the seasonal cycle and the evolution of El
Niño. Moreover, South American coastal warming did not generally precede maximum SST anomalies in the equatorial
cold tongue, as in the Rasmusson and Carpenter [1982] composite. Deser and Wallace [1987] had earlier
found that coastal warmings appear to be only loosely coupled to the broader basin-scale manifestations of El
Niño, a result that appears also to apply to warm events observed during the TOGA decade. Also, considering the
1993 and 19941995 warmings as separate events, their duration was significantly shorter than the norm of
1218 months for El Niños of the past.
Consistent with the complexity of the observed interannual variability, tests of ENSO theories using data prior to and
during the TOGA decade suggest that more than one set of mechanisms can give rise to ENSO timescale warm and cold events
in the tropical Pacific. The delayed oscillator theory, for example, can often, but not always, be invoked to explain
the termination of ENSO warm events. On the other hand, delayed oscillator physics cannot generally account for the
onset of warm ENSO events. New physical hypotheses are being formulated regarding the ENSO cycle, based on the failure
of existing theories to explain the full range of observed variability.
The unusual warm conditions prevailing near the date line in the equatorial Pacific during 19911995 raise
questions about the relationship between the ENSO cycle and decadal timescale variability. The persistent warm anomalies
are the reflection of a decadal timescale variation that has higher latitude manifestations in North and South Pacific
SSTs [e.g., Latif et al., 1997; Wallace et al., this issue; Zhang et al.,
1997]. This decadal mode may result from decadal modulations in the intensity and/or frequency of ENSO events, or it
may be a mode of coupled ocean-atmosphere variability with dynamics distinctly different from those of ENSO. In either
case the decadal timescale of this variation and its manifestations at higher latitudes suggest a link to decadal
timescale processes that maintain the equatorial thermocline [Fine et al., 1987; McPhaden and Fine, 1988].
These processes involve the ocean thermohaline circulation which couples the tropical ocean to the subtropical and
higher-latitude North and South Pacific Ocean [e.g., McCreary and Lu, 1994; Lu and McCreary, 1995].
Decadal timescale variations in the overlying atmospheric circulation at midlatitudes [Trenberth and Hurrell,
1994; Latif and Barnett, 1995; Zhang et al., 1997] alter patterns of air-sea heat
exchange, providing a mechanism by which the formation of thermocline water masses can be affected in density surface
outcrop regions [Miller et al., 1994]. A theory for self-sustaining decadal time scale oscillations involving
ocean-atmosphere interactions and heat transports between the tropical and extra-tropical oceans has been proposed
recently by Gu and Philander [1997].
Observed variability during TOGA also suggests a possible connection between El Niño and global warming. Average
SSTs in the tropical Pacific were unusually high during the 1980s and 1990s, at the same time that there was a trend for
warmer global surface air temperature. The tropical Pacific SSTs were warmer because of a greater intensity, frequency,
and/or duration of warm ENSO events. Two recent studies [Kumar et al., 1994; Graham, 1995] based on
atmospheric model simulations forced with observed SSTs for the 1980s and 1990s suggested that the warming of global
surface air temperature for this period may have been induced by the warming of SST in the tropical Pacific. Tropical
Pacific SSTs in these simulations were prescribed from observations, however. It is possible that the character of ENSO
changed and that SSTs were warmer because of anthropogenic greenhouse gas warming [Trenberth and Hoar, 1996]. There is
no consensus on this issue, and recently, Cane et al. [1997] argued that global warming should
lead to a cooling of the tropical Pacific. Clearly, resolution of the questions concerning ENSO, decadal variability,
and anthropogenic greenhouse gas warming will require considerably more research.
TOGA demonstrated the synergy that can emerge from the combined use of data and dynamical models. As a measure of
progress, prior to TOGA, there was no system of routine data assimilation for tropical ocean climate analyses and no
routine short-term climate prediction efforts. However, during TOGA, models were used to help design the observing
system, and data from the observing system were then used to foster model development and to initialize models for
short-term climate prediction. Now many ENSO prediction modeling groups have been established [National Weather Service,
1997], and prediction models, initialized with TOGA data sets, show significant skill for lead times of up to 1
year. The skill of these predictions is likely to improve as we learn more about the underlying dynamical processes
involved in ENSO and as models and assimilation systems improve.
TOGA also demonstrated the synergy that can emerge from the combined analysis of satellite and in situ measurements. In
situ measurement systems provide high-accuracy information on both surface and subsurface ocean variability, the latter
of which is not directly accessible to satellites. In situ measurement systems also provide necessary data for ongoing
calibration and validation of satellite retrievals. The strength of the satellite data, on the other hand, is their
near-global coverage and uniform time-space sampling characteristics. Unfortunately, the full potential for satellite
missions for climate research during TOGA was not realized in part because most of the satellite missions were sponsored
for reasons other than climate research and some (like TOPEX/POSEIDON) were originally intended as one-time experimental
missions. Similarly, the launch of NSCAT was so often delayed that eventually it fell outside the TOGA time frame.
Coordination between agencies and countries sponsoring satellite missions did not always succeed because of
uncertainties in funding, payload development, and launch dates. This lack of coordination led to a 2-year gap in
altimeter measurements between the U.S. Navy Geosat mission and the ERS-1 mission. Nonetheless, the tremendous value of
those satellite data that were acquired during TOGA bodes well for the future application of satellite measurements to
ocean climate studies.
As a result of TOGA, we are now entering a new era of climate research and forecasting. The World Climate Research
Program (WCRP) has embarked on a 15-year (19952010) study of Climate Variability and Predictability (CLIVAR), one
element of which, the Global Ocean-Atmosphere-Land Studies (GOALS) program focuses on seasonal-to-interannual
variability [National Research Council, 1994b; World Climate Research Program, 1995b]. Also, a newly
instituted International Research Institute for Climate Prediction (IRICP) will begin to issue routine short-term ENSO
forecasts, conduct research on ways to improve those forecasts, and help to coordinate the use of the forecast products
for various socioeconomic applications [International Research Institute for Climate Prediction Task Group, 1992].
Likewise, some national meteorological centers are already routinely issuing climate forecasts [e.g., National Centers for
Environmental Prediction, 1996], and others intend to do so in the near future.
The success of these research and forecasting activities requires that essential elements of the TOGA observing system
be continued for the foreseeable future. Explicit guidance on the development of post-TOGA climate observing systems is
contained in the reports of various planning committees that have considered the observational needs of future climate
programs [e.g., National Research Council, 1994b; Ocean Observing System Development Panel, 1995]. These
reports are unanimous in their recommendations to continue the observing system developed under TOGA in support of
short-term climate prediction. For some components of the observing system this may require transfer of the
responsibility for long-term, systematic measurements from the research community to the operational oceanographic
and/or meteorological communities. Effecting this transition will be challenging because there is no precedent for
institutionalizing an observing system built entirely within the framework of a climate research program.
The need for long-term support of critical climate measurements has motivated planning for the Global Climate Observing
System (GCOS) as well as the climate module of the Global Ocean Observing System (GOOS). These emerging international
programs, modeled loosely on the World Weather Watch for weather forecasting, are intended to foster and coordinate
measurements for a wide range of climate applications. As national commitments were essential in developing the TOGA
observing system, so will they be essential in maintaining the observing system after TOGA. GOOS and GCOS are at
different stages of evolution in different countries involved in supporting climate observations, complicating
coordination at the international level. However, CLIVAR and GCOS/GOOS have recognized the merits of collaboration to
ensure that an effective post-TOGA observing system is maintained. Therefore, in the near term, it is almost inevitable
that the post-TOGA observing system will be maintained under a mix of research and operational support.
In the meantime it is of paramount importance that the existing data stream not be interrupted. Tremendous effort was
expended in developing an adequate infrastructure to support the collection of critical data sets during TOGA. This
infrastructure, involving cooperative relationships between research institutions and government agencies in several
countries, was established through painstaking evaluation and oversight by the international scientific community over
the course of 10 years. This infrastructure is fragile; premature curtailment or disruption of observational efforts
could have disastrous and long-lived effects on the development of future climate observing systems. Thus a conservative
approach must be adopted in recommending changes to either observational strategies or to the organizational framework
in which the observations are supported. Conservatism does not imply that the observing systems for post-TOGA climate
studies should be static in their design, though. On the contrary, the observing system should be flexible enough to
take advantage of new advances in technology. Likewise, it is essential that there be ongoing assessments of the
observing system design and that these assessments be guided by scientific priorities.
Much of this paper has dealt with the TOGA observing system in the tropical Pacific, where TOGA focused its effort as a
first priority. Clearly, adequately observing the tropical Pacific was a sine qua non for making progress on
understanding and predicting ENSO. In contrast, scientific questions relating to the climatic impacts of
ocean-atmosphere interactions were not as thoroughly explored in the other two ocean basins, and resources were too
limited to allow for uniform development of observing system components throughout the global tropics during the TOGA
decade. Nonetheless, as a consequence of TOGA, our understanding of ocean-atmosphere interactions in the Indian and
Atlantic Oceans has significantly improved. New hypotheses have emerged, such as the role of the Indian and east Asian
monsoons in ENSO [e.g., Webster and Yang, 1992] and the role of both Pacific and Atlantic SST variations in affecting
climate in the Atlantic basin [e.g., Servain, 1991; Zebiak, 1993; Delecluse et al., 1994]. Also,
while there is ongoing debate about the origin of ENSO-related SST anomalies in the North Pacific and their effects on
climate variability over North America [e.g., Lau and Nath, 1994], even stronger decadal timescale
variations in North Pacific SSTs have recently been documented [e.g., Zhang et al., 1997]. The relationship of these
decadal variations to ENSO and to global climate variability, in general, needs to be better understood. Thus geographic
expansion of in situ observational efforts should be carefully considered as part of the post-TOGA climate research
agenda.
Appendix A: A Rude Awakening
The need for an improved climate observing system was underscored during the planning stages of TOGA in the early 1980s,
when the scientific community was caught completely off guard by the 19821983 El Niño, the strongest in
over a hundred years. This El Niño was neither predicted nor even detected until several months after it had
started. At the time, most in situ oceanographic data were available for analysis only months, or in some cases years,
after they had been collected. So only a handful of scattered reports from islands and volunteer observing ships were
available to track conditions in the equatorial Pacific in real time (delay of less than a day) or near-real time (delay
of less than a month). Some SST reports were extraordinarily high and suggestive that an El Niño event might be
underway. However, they were discounted as erroneous for several reasons. One reason was that there had been no
"buildup" of sea level in the western Pacific by stronger than normal trade winds prior to 1982, presumed to be a
necessary precursor of El Niño [Wyrtki, 1975]. Also, there had been no warming off the west coast of South America
in early 1982, considered to be part of the normal sequence of events characterizing the evolution of El Niño [Rasmusson and Carpenter, 1982].
To complicate matters, in situ data were rejected from blended satellite/in situ SST analyses produced operationally by
the U.S. National Centers for Environmental Prediction (NCEP), then known as the National Meteorological Center (NMC).
These analyses indicated that the equatorial Pacific SSTs were near normal, or even slightly colder than normal, during
much of 1982. However, the effect of the MarchApril 1982 eruptions of the Mexican volcano El Chichon on satellite
SST retrievals was not fully appreciated at the time. These eruptions injected a cloud of aerosols into the lower
stratosphere, where prevailing winds spread it around the globe at low latitudes within 3 weeks. The aerosols, whose
effects were not included in algorithms to convert observed satellite radiances to SSTs, led to cold biases of several
degrees centigrade in the satellite SST retrievals. Cloud detection algorithms interpreted these retrievals as cloud
contaminated and replaced them with climatological mean SSTs. In situ data were then rejected because they differed so
greatly from the satellite analyses, which were strongly biased toward climatology. It was only after reports from the
R/V Conrad in SeptemberOctober 1982 that the thermocline in the eastern equatorial Pacific was
50100 m deeper than normal [Toole and Borges, 1984] that the scientific
community realized to what extent existing data sources had misinformed and misled them and likewise how misguided was
the notion of a "canonical" El Niño with a fixed pattern of stages from event to event.
Appendix B: In Situ Oceanographic Components of the Observing System--Technical and Historical
Background
B1. Tropical Atmosphere Ocean (TAO) Array
The history of moored measurements for climate studies in the equatorial Pacific dates back to the 1970s, when surface
current meter moorings were first deployed along the equator as part of the EPOCS program [Halpern, 1987b] and the NORPAX
Hawaii-Tahiti Shuttle [Wyrtki et al., 1981; Knox and Halpern, 1982]. Based in part on these
early successes, original plans in TOGA called for a small number of moorings to be deployed near the equator and in
gaps between widely spaced XBT lines [U.S. TOGA Office, 1988].
However, the 19821983 El Niño highlighted the inadequacy of existing ocean observational networks for
climate studies, in part because of the lack of high-quality real-time data by which to monitor evolving conditions in
the tropical Pacific Ocean. This realization spurred attempts to develop telemetry systems for deep ocean moorings at
NOAA's Pacific Marine Environmental Laboratory. The most notable development in this regard was the Autonomous
Temperature Line Acquisition System (ATLAS) mooring [Milburn and McLain, 1986; Hayes et al., 1991a],
which incorporated many proven design concepts from taut-line current meter moorings used in earlier equatorial ocean
studies [Halpern, 1996]. However, significant cost savings were achieved by eliminating current meters
from the suite of instrumentation and by targeting temperature rather than velocity as the primary oceanographic
variable. Elimination of current meters, whose moving parts (rotors, vanes, or propellers) were sensitive to mechanical
wear and biofouling in the energetic and biologically productive upper layers of the equatorial Pacific, also extended
the expected lifetime of the mooring from 6 months to 12 months. Equally significant, the ATLAS mooring was designed to
telemeter air temperature, SST, and subsurface temperature data to shore in real time via Service Argos. In 1986,
real-time winds were added to the ATLAS system, adapting earlier design concepts developed for real-time wind
measurements from current meter moorings [Halpern et al., 1984]. Relative humidity sensors
were added to ATLAS moorings in 1989.
ATLAS sampling and data transmission schemes have evolved with time. The current generation ATLAS telemeters all data as
daily averages and, in addition, as hourly values for SST and surface meteorology coincident with three to four
satellite overpasses per day. Data are also internally recorded and available upon recovery of the mooring system. A
recent assessment of instrumental accuracies indicates errors of about 0.03°C for SST, 0.2°C for air
temperature, < 0.1°C for subsurface temperature, 0.2 m s-1 for wind speed, and 4% for
relative humidity [Mangum et al., 1994; Freitag et al., 1995]. The estimate of wind speed
error (unlike the other estimates) does not take into account possible calibration drift for instruments deployed at sea
for up to one year. An assessment of this drift is presently underway, and preliminary results suggest that including it
may lead to an overall accuracy of about 0.5 m s-1 for wind speed.
The early technical successes of the ATLAS mooring program and the recognized value of the data for short-term climate
studies led to multinational plans for a basin-scale expansion of the array during the second half of TOGA [National Research
Council, 1990]. This expansion was feasible because the relatively low cost of the ATLAS mooring allowed for its
deployment in large numbers and because the 1-year ATLAS design lifetime made for manageable long-term maintenance costs
and ship time requirements. The array, dubbed TOGA-TAO [Hayes et al., 1991a], far exceeded in scope what
had been originally anticipated as a moored buoy component of the TOGA observing system [U.S. TOGA Office, 1988].
Coordinated with the early ATLAS mooring program, but separate from it, was a parallel effort to develop a long-term
array of current meter moorings for TOGA studies in the Pacific [World Climate Research Program, 1990a]. These moorings
were concentrated on the equator where direct measurements would be most valuable in view of the limited applicability
of the geostrophic approximation. Siting was based in part on historical precedent (i.e., where long records already
existed) and the need to sample different hydrodynamic regimes (e.g., cold tongue, warm pool). It became apparent,
however, as the ATLAS program expanded, that the current meter mooring and ATLAS mooring programs should be integrated
more fully for a variety of technical, logistic, and scientific reasons. Thus, during the second half of TOGA, TAO was
configured to include both ATLAS and current meter moorings in a single unified mooring program [McPhaden, 1993a].
Details of current meter mooring design, sampling characteristics, and instrumental accuracies can be found in work by
Halpern
[1987a, c], McPhaden et al. [1990b], McCarty and McPhaden [1993], Lien et
al. [1994], Freitag et al. [1995], Plimpton et al. [1995], and Weisberg and
Hayes [1995].
Design criteria for the TAO array were based on general circulation model simulations of wind-forced oceanic variability
and on empirical studies of space-time correlation scales as described in section 2. The array was built up over time
and maintained through a series of research cruises at roughly 6-month intervals. These cruises were necessary to deploy
new mooring systems and recover old mooring systems that were close to or past their design lifetimes. The array was
completed at the very end of TOGA in December 1994, with deployment of an ATLAS mooring at 8°N, 156°E [McPhaden,
1995].
A subset of the real-time TAO data stream, formatted by Service Argos into World Meteorological Organization (WMO) code,
is retransmitted on the GTS. The data are then available to operational meteorological and oceanographic centers around
the world. The availability of TAO data on the GTS increased significantly in November 1992 after long-standing problems
with the Argos-GTS link were finally resolved; data throughput increased at that time from 1030% to 8090%
[McPhaden, 1993b].
The rapid growth of the TAO array during the second half of TOGA has led to improvements in the quality of several
important operational climate analysis and prediction products. For example, at present approximately half the wind
observations used in FSU monthly Pacific wind analyses in the band 10°N10°S are from TAO buoys (Figure B1). TAO data are also included in the weekly NCEP SST analysis (see
section C1). The Comprehensive Ocean Atmosphere Data Set (COADS) [Woodruff et al., 1987], a global
compilation of marine observations since 1854, also incorporates TAO data. As of this writing, only those TAO data
available from the GTS have been included in COADS. A COADS reanalysis of data collected after 1980, including a more
comprehensive set of delayed-mode TAO data, is planned for the near future (S. Worley, personal communication, 1996).
Figure B1: GTS ship (small solid dot) and buoy (open circles) wind reports used in the Florida State University
Pacific surface wind analysis for the month of December 1994. In the latitude band 10°N10°S a total of
6970 observations were reported; 3809 of these reports (55%) were from TAO buoys (data courtesy of D. Legler,
1997).
Development of the TAO array required an extraordinary effort from individuals and institutions in several countries, at
the core of which was sustained support provided by the United States, Japan, France, Taiwan, and Korea. As one measure
of effort, accumulated over the 10 years between 1985 and 1994, more than 400 TAO moorings were deployed on 83 research
cruises involving 17 ships from six different countries, requiring a total of 5.7 years of shiptime. At present, nearly
1 year of dedicated shiptime per calendar year is required to maintain the fully implemented array of nearly 70
moorings. Overall, data return has been > 80%, with many sites providing over 90% data return. Regions of data return
< 80% are found in the far eastern and western Pacific, where vandalism by fishing fleets has been an ongoing problem
[e.g., Koehn et al., 1995, 1996]. Scientific use of TAO data has been encouraged by
the development of sophisticated data management, display, and dissemination capabilities. These include the TAO
workstation and access to the data via anonymous file transfer protocols and the World Wide Web [Soreide et al.,
1996].
B2. Drifters
In the early 1970s the Argos Doppler ranging system became operational on National Oceanic and Atmospheric
Administration (NOAA) polar orbiting weather satellites, and a cost-effective technique of listening to and locating
radio transmitters on the global ocean surface was made available to oceanographers. This spawned the design and
construction of a large number of ocean surface drifters, both for measuring ocean circulation as well as for use as
platforms for deploying a variety of meteorological sensors. Throughout the 1970s, typical drifter configurations
consisted of a 100200-kg aluminum surface float and a World War II surplus parachute or
2- × 6-m rectangular window-shade drogue attached with rope and chain to a depth of 1030 m.
Over 150 of these drifters were released into the Gulf Stream system [Richardson, 1983], and the largest array deployment
was in the Antarctic Circumpolar Current, where about 180 drifters were operational during the intensive observation
phase of the Global Atmospheric Research Program (GARP) First Global GARP Experiment (FGGE) in 19791980 [Hofmann,
1985]. Small arrays of FGGE drifters with drogues were also deployed in the tropical Pacific as part of the EPOCS
program between 1979 and 1987, with the main purpose of understanding eastern tropical circulation.
During the planning phase of TOGA it became clear that accurate global fields of SST, atmospheric pressure and ocean
basin-wide patterns of surface circulation were required [World Climate Research Program, 1985] (see also Table 1). A potentially valuable tool was the Argos-tracked drifter, but several serious
questions arose regarding the feasibility of designing an affordable instrument that could be deployed in global arrays.
The drifters used during FGGE were too heavy to be routinely deployed from merchant ships or by air; they were very
costly to build and did not retain their drogues longer than several months. No mechanical design improvements had been
made to them since 1975. There were no engineering standards or field-verified hydrodynamic models by which to design a
Lagrangian drifter in order that its water-following capability could be determined to the accuracy required by TOGA.
Finally, the tariffs charged by Service Argos, the firm which had the exclusive right to decode Argos location data,
would severely limit the extent of a global, long-term deployment. To meet TOGA objectives, a two-pronged program of
drifter deployments was developed, as described below.
B2.1. Surface Velocity Program (SVP)
In 1982 a group of oceanographers and engineers met at the National Center for Atmospheric Research to consider the
challenges presented by the WCRP requirements for global ocean and atmosphere monitoring and to determine how a variety
of newly designed ocean Lagrangian tools could be used to meet these needs. It was decided that a low-cost, lightweight
surface drifter should be developed. Funding for the new drifter development came first from the Office of Naval
Research and then from NOAA and the National Science Foundation.
By 1985, competing drifter designs had emerged from the Draper Laboratory, NOAA/Atlantic Oceanographic and
Meteorological Laboratory (AOML), and Scripps Institution of Oceanography. The field measurements of the water-following
capability of the drifters, with vector-measuring current meters attached to the top and bottom of the drogue, were done
over the period of 19851989 [Niiler et al., 1987, 1995]. Several modeling studies of drifter behavior in
steady upper layer shear and linear gravity wave fields were also done [Chabbra et al., 1987; Chereskin et
al., 1989]. These studies provided a rational basis for the interpretation of the drogue slip measurements in
the field. A TOGA/WOCE Surface Velocity Program (SVP) was organized to seek broad international support for drifter
acquisitions and deployments.
By 1986 several SVP drifter designs had emerged and were being used in research programs in the Atlantic and Pacific. In
1988 a Pacific basin-wide TOGA process study, the Pan-Pacific Surface Current Study [World Climate Research Program,
1988], became operational. Its technical objectives were to use VOS ships to maintain an array of 120 drifters for a
3-year period and to select from the competing SVP drifter designs the most robust elements. Its scientific objectives
were to obtain a tropical Pacific basin-wide field of surface currents and SST for the purpose of studying a variety of
processes that determine SST evolution. Barometers were added to the SVP drifters in 1991, and in 1992, salinity sensors
became an operational system on drifters in TOGA COARE. SVP drifters were deployed for WOCE in significant numbers in
the Pacific by 1992, in the Atlantic as part of the Atlantic Climate Change Program in 1992, and in the Southern and
Indian Oceans in 1994. By the end of TOGA, over 700 drifters were operational, and SVP emerged as the Global Drifter
Program, maintained by resources from 16 countries.
The evolution of the SVP drifter to the Global Lagrangian Drifter took nearly 5 years of design and evaluation. It now
consists of a spherical surface float that carries the electronics, SST, barometer, and drogue-on sensors. This float is
tethered with plastic-coated wire to a holey sock drogue centered at 15-m depth [Sybrandy and Niiler, 1991; Sybrandy
et al., 1995]. In the subtropical oceans the mean lifetime of a buoy (defined in terms of drogue retention) is
440 days; in the Southern Ocean the mean lifetime is 280 days. The accuracy of the water-following capability is
dependent upon the winds and the "drag area ratio," the ratio of the frontal drag areas of the drogue relative to the
surface float and tether [Niiler et al., 1995]. These drifters were designed to slip < 1 cm s-1
in 10-m s-1 winds and have a drag area ratio about 5 times larger than was used in FGGE drifters [Niiler
and Paduan, 1995]. At the time of deployment the calibrated accuracy of the SST sensor is 0.1°C, and the
accuracy of the barometer is 1 mbar. Location data provided by Service Argos have a minimum error of 300 m. To
reduce Service Argos fees, these drifters transmit one third of the time in a 24- or 72-hour period.
Service Argos processes these data for location, SST, and sea level pressure and places it on GTS within 2 hours of
reception. The GTS data are quality controlled and used on an operational basis by the meteorological agencies for
weather and climate prediction and in a variety of data products that assess the nature of the variability of the oceans
and lower atmosphere. For example, NCEP uses the raw drifter barometer data in real time off the west coast of the
United States to aid in marine weather broadcasts and forecasts. Scientific quality data are processed at the Global
Drifter Center at NOAA/AOML, and on a 6-month interval, they are deposited at Marine Environmental Data Service (MEDS),
Canada, for release to the scientific and operational communities.
In TOGA the SVP drifters were deployed from research vessels, VOS, and airplanes. The average failure rate upon ship
deployment was 5% and from airplanes 15%. In the tropical Pacific most of the drifters were released near the equator.
The objective was to maintain drifter arrays with enough samples in 2°
latitude × 8° longitude areas to define the 15-m circulation. The sample size (Figure B2) depends more on the nature of the deformation field of the circulation than upon
where drifters were released. For example, there were many more deployments in the eastern Pacific equatorial waveguide
than in the North Equatorial Countercurrent, although the data density was much larger in the latter because of the
nature of the surface flow and its variability.
Figure B2: Number of 5-day observations of velocity observed in 2° latitude × 8° longitude areas
from surface drifters between January 1, 1979, and December 31, 1995, in the tropical Pacific. The total number of 5-day
observations is 81,589. The maximum number of 5-day observations possible in any given box is 1098.
B2.2. Southern Ocean drifters
While the SVP drifter was being developed, the second part of the two-pronged program for drifter deployment was getting
underway. The U.S. TOGA Scientific Steering Group in 1983 authorized a program to begin deployment of FGGE-type drifters
in the Southern Oceans, managed by NOAA's National Ocean Service and National Data Buoy Center. These drifters carried
barometers and SST sensors inside the metal hulls, and the data were reported routinely via Argos through the GTS as an
operational system. Some drifters had a long rope or cable attached to the base, while others drifted freely in the wind
and waves. In 1984, about 40 FGGE-type drifters were deployed, but escalating costs, inflation, a noncompetitive
environment for industrial construction, and fixed budgets reduced the array to 20 drifters by 1994. Several operational
meteorological agencies contributed drifters to this Southern Ocean FGGE drifter program through the Drifting Buoy
Cooperation Panel (which later became the Data Buoy Cooperation Panel). The data reported on GTS is stored in MEDS,
Canada.
B3. TOGA Tide Gauge Network
TOGA inherited a substantial Pacific tide gauge network that was largely installed during NORPAX. Recognition of the
importance of the El Niño phenomenon and many of the early diagnostic studies of it may not have been possible
without the sea level data. For example, the sea level changes associated with the El Niño events of 1972, 1976,
and 19821983 were described prior to the beginning of TOGA in a series of papers by Wyrtki [1975, 1977, 1979, 1984, 1985b].
Efforts in the Pacific during TOGA were focused on expanding and refining this network. As the network was expanded, new
gauges were generally placed at least 500 km from existing ones. In the Indian and Atlantic Oceans, however, few gauges
existed or were reporting data regularly at the start of TOGA. Hence significant effort was undertaken to remedy this
situation. At present the Indian Ocean network is nearly complete, in the sense that most of the available sites have
been instrumented. The network in the Atlantic Ocean, on the other hand, remains limited by the scarcity of islands
suitable for gauges. Early in TOGA, it was determined that the problem in the Atlantic was basically one of collecting
available data, rather than attempting to place new instruments. This data collection effort was also largely
successful, with 21 sites reporting data by the end of 1994 (Table 3).
The University of Hawaii Sea Level Center was responsible for coordinating, maintaining, and expanding the tide gauge
network in the tropical regions for TOGA purposes. Instrumentation used in the network consists of a heterogenous blend
of instruments ranging from bubbler-type pressure gauges to state-of-the-art acoustic gauges, as described by Carter et
al. [1987]. The majority of the sites, however, have traditional float gauges in stilling wells. Some
general information on the instrumentation available for sea level measurements is given by Pugh [1987], and more specific
information about the equipment used by the University of Hawaii Sea Level Center can be found in work by Kilonsky
and Caldwell [1991] and Mitchum et al. [1994].
The heterogeneity of the instrumentation is due in part to the logistics involved in maintaining gauges over a wide
geographical area. Simple bubbler gauges are favored at very remote locations, whereas sophisticated acoustic gauges
have been installed at several readily accessible sites. At most sites, however, the float-type gauge is favored because
of its relatively low cost, which allows the placement of redundant systems at each site. This philosophy has been
proven successful, in that the University of Hawaii Sea Level Center network typically has a data return exceeding 97%,
even with maintenance trips spaced at 23-year intervals.
The redundant nature of the University of Hawaii Sea Level Center installations allows an estimate of the instrumental
accuracy of the float-type gauges. There are typically two independent stilling wells with completely separate
instrumentation at each site, and these wells are within 12 m of one another. Differences between the time series
are taken to be an estimate of the instrumental accuracy. These intercomparisons show that for timescales longer than 2
days the redundant float-type gauges agree to ~0.5 cm. Bubbler gauges are typically noisier, with differences of
the order of 2 cm. Frequency spectra of the differences show that the noise is approximately white and will thus be
negligible on monthly mean timescales.
A more significant concern is possible contamination of the tide gauge time series by local island effects distorting
the large-scale open ocean pressure field. This type of error is more difficult to estimate, but recent intercomparisons
with sea surface heights from satellite altimeters suggest that it is not a very significant error source at low
frequencies. Mitchum [1994] and Cheney et al. [1994] have shown that the sea
levels from the tide gauges agree with the heights from the TOPEX altimeter to ~4 cm for timescales longer than 20
days and to 2 cm for timescales longer than 2 months. These estimates are comparable to what is expected from the
error budget for the altimeter alone, which implies that the tide gauges cannot be contributing a large amount of
variance to the differences.
For the stations in that portion of the TOGA tide gauge network maintained directly by the University of Hawaii Sea
Level Center, data are returned for many stations via the data channels on the geostationary satellites, and this
real-time delivery is backed up by delayed transmission of tapes from the stations to the Sea Level Center on a monthly
basis. For stations using only the delayed mode data delivery, data are processed and available to the community within
several months of collection. Many of the TOGA tide gauges contribute to the operational data flow handled by the IGOSS
Sea Level Project in the Pacific and to the near-real-time data set provided by the WOCE "Fast Delivery" Sea Level
Center, both of which are also operated by the University of Hawaii Sea Level Center.
B4. Volunteer Observing Ship (VOS) Network
The international system of voluntary observation ships, initiated in the last century [Maury, 1859], is still a
critical element of modern meteorological and oceanographic observation networks. This section treats different
components of the VOS program separately. Surface marine meteorological data are reviewed first (section B4.1), followed
by a discussion of the VOS XBT program, which was one of the major observational initiatives of TOGA (section B4.2). For
completeness we also provide a brief discussion of the VOS sea surface salinity effort in the Pacific (section B4.3).
B4.1. VOS surface marine observations
There are currently around 7000 VOS worldwide, operated by about 50 countries. They collect observations on sea surface
pressure, wind velocity, sea state, humidity, and SST, as part of the World Weather Watch (WWW). Each month, typically,
100,000 or more surface observations are collected and transmitted in real time to national meteorological centers via
satellite communication systems or via coastal radio stations. The meteorological centers are responsible for entering
the data on the GTS for general use. VOS coverage is excellent in the vicinity of the well-traveled shipping routes
(e.g., the North Pacific and North Atlantic) but has serious gaps in the southern oceans and in parts of the tropical
oceans [Weller and Taylor, 1993].
Prior to the establishment of TAO and other dedicated TOGA observing systems, data from VOS marine reports and from
island weather stations constituted the bulk of the available information on seasonal and interannual variability in
tropical surface marine meteorological fields. The TOGA data requirements for surface fields and fluxes (Table 1) were based almost entirely on knowledge derived from analyses of VOS data [e.g.,
Taylor,
1984]. The International TOGA Project Office [1992] made several suggestions for improving the quality
and density of VOS observations, but by and large, these were not implemented. Nonetheless, analyses of VOS data were
extremely valuable for TOGA.
The creation of COADS [Woodruff et al., 1987] was a development based largely on VOS surface data that had a
significant impact on climate research during TOGA. Prior to 1985, scientists who wished to work with the conventional
surface marine data set often had to go through a laborious process of data extraction from the archives, followed by
extensive quality control and analysis. COADS substantially reduced this impediment by creating a single data set of all
available archived marine observations. The data were quality controlled and made widely available. This compilation was
the basis for the Oberhuber [1988] and da Silva et al. [1994] climatologies and has been
the basis for many recent studies of longer-term variability [e.g., Shriver and O'Brien, 1995].
Perhaps the most significant development in terms of the TOGA scientific history was the application of surface marine
wind observations to produce time-varying winds. Wyrtki and Meyers [1975, 1976] produced the first
such maps of wind and wind stress over the tropical Pacific Ocean, though on a coarse 2° latitude × 10°
longitude grid. Esbensen and Kushnir [1981], Han and Lee [1983], and Hellerman and
Rosenstein [1983] also exploited the marine data set to produce global analyses of wind stress and marine
fields, but only for the seasonal and annual means. The Mesoscale Air-Sea Interaction Group at Florida State University
(FSU), motivated by the need to produce a wind field data set suitable for forcing tropical ocean models, reanalyzed the
Wyrtki and Meyers wind data [Goldenberg and O'Brien, 1981]. These analyses were for pseudostress (the product of
the wind velocity times wind speed) and were originally restricted to the tropical Pacific Ocean
(30°S30°N) on a 2° × 2° grid. Focusing on pseudostress allowed Goldenberg
and O'Brien [1981] to avoid complications due to uncertainties in specification of drag coefficients while at
the same time including at least some of the nonlinearity of the wind stress formulation, which is quadratic in wind
speed.
The FSU wind analysis evolved considerably through the period of TOGA [Legler and O'Brien, 1984; Legler, 1991;
Stricherz et al., 1992]. Monthly analyses are now performed routinely in near-real time for
the Pacific Ocean using data available from the GTS. The fact that these analyses have been made available in near-real
time allowed the development of timely and useful prediction systems like that of Cane et al. [1986]. Recently, full
development of the TAO array during the second half of TOGA has approximately doubled the number of wind estimates used
in the FSU analyses between 10°N and 10°S in the equatorial Pacific (Figure B1). Yearly reanalyses are performed augmenting GTS data with delayed mode data from
National Climate Data Center (NCDC) archives and COADS.
Legler
et al. [1989] extended the FSU analysis system to the Indian Ocean using techniques that allow information
from various platforms, including satellites, to be merged. This technique has been extended to include surface fluxes
over the Indian Ocean for the period 19601989 [Jones et al., 1995]. Rao et al. [1991] also
analyzed the COADS data for the tropical Indian Ocean region to produce a consistent set of heat flux fields. These
fields have been used in various numerical models of the Indian Ocean [e.g., McCreary et al., 1993].
In the tropical Atlantic Ocean one of the first time-varying analyses of surface marine data was produced by the
Institut Français de Recherche Scientifique pour le Développement en Coopération (ORSTOM) group at
Brest, France, following the methodology developed by the FSU [Servain et al., 1984, 1985]. Also, Reverdin et
al. [1991a] analyzed the wind stress between 20°S and 30°N in the tropical Atlantic, using merchant
ship wind observations. These analyses have been used in several numerical modeling studies, including Blanke and
Delecluse [1993] and Braconnot and Frankignoul [1994].
The accuracy of surface analyses based on merchant ship marine data is dependent on the quality and sampling density of
the input data. For example, Weare [1989] cataloged a number of different systematic errors in surface marine observations,
including conversion of Beaufort wind force observations to wind speeds and spurious warming in SSTs from engine intake
temperatures. Systematic errors like these are significant and cannot be removed by increased sampling density, as can
random errors. As an example of the magnitude of these effects, Weare [1989] also concluded that uncertainties in latent
heat flux computed from VOS data exceeded 30 W m-2 everywhere. Cardone et al. [1990]
also cautioned that differing interpretations of Beaufort wind observations in the historical data set can lead to
artificial trends in surface analyses, such as that of Legler and O'Brien [1984].
B4.2. VOS/XBT measurements
Major events in the evolution of XBT sampling since the instrument was invented were discussed by Meyers et
al. [1991]. The XBT is a temperature profiler commonly dropped from VOS recruited from the merchant
shipping, fishing, and military fleets [e.g., Baker, 1981; Sy, 1991]. The most commonly used models (T4
and T7) measure to a depth of 460 and 760 m. The instrument was developed during the 1960s and over the years
has perhaps been more extensively used than any other single oceanographic instrument. Among its advantages are that the
measurements can be carried out quickly, while the ship is underway, without in most cases having to reduce speed;
operation of the instrument is easily learned by a new observer.
According to the manufacturer's specifications the temperature accuracy of the XBT is ±0.15°C. Some studies
have shown that probe-to-probe thermistor temperature variability can be <±0.06°C at the 95% confidence
level [Sy,
1991]. The measurement of relative, vertical temperature differences is also more accurate than the specifications
[Roemmich and Cornuelle, 1987] so that small inversions and finestructure are detectable in the
profile. The depth is estimated from a drop-rate equation using the time elapsed after the probe enters the water. It
has been demonstrated, however, that temperature profiles made using the T4, T6, and T7 probes exhibit a systematic
error with depth that is associated with an inadequate drop-rate equation supplied by the manufacturer [Hanawa et
al., 1995]. After correction for the systematic error the depth accuracy is within the manufacturer's
specifications (±2% of depth or ±5 m) in ~82% of XBT drops. Quality control of XBT data is a major
task because the instrument malfunctions before reaching 250 m in about 15% of the probe launches. The modes of
instrument failure have been carefully documented [Bailey et al., 1994] and distinguished from real
temperature inversions and other structure so that a data quality expert can recognize and flag most faulty data in
postcruise processing.
Design of the VOS XBT array for TOGA recognized the need to map large-scale variations in thermal structure to a known
accuracy on a grid that would barely detect the smallest scales of interest. A strategy of low-density sampling was
devised to provide broad-scale, widely dispersed coverage in areas that have routine merchant shipping on a
monthly-to-quarterly cycle. Sampling error due to unresolved small-scale variability such as eddies, tropical
instability waves, and internal waves is a source of geophysical noise. Many studies since the late 1970s have shown
that the noise variance is about equal to the variance of the large-scale signals [Meyers et al., 1991; White, 1995;
Kessler
et al., 1996]. Maps of large-scale signals are produced using optimal interpolation (OI) as a filter to remove
(or reduce) the smaller-scale variability. The mapping error variance after OI is typically 0.3 to 0.5 times the signal
variance, in the areas that are best sampled. In dimensional units this translates to mapping errors of about
46 m in the depth of isotherms [Smith and Meyers, 1996].
Using the method of OI to design a sampling strategy required a prior knowledge of the statistical structure of the
subsurface temperature field. Of particular importance are the so-called "decorrelation scales of variability," which
represent the typical spatial and temporal extent in latitude, longitude, and time of the most energetic features. The
scales for the tropical oceans were estimated [Meyers et al., 1991; Meyers and Phillips,
1992] by fitting a Gaussian curve to the covariance function of temperature variability estimated from observations.
Recognizing that the scales show considerable regional differences as well as differences in depth and time, a uniform
set of e-folding scales was recommended for application throughout the tropical oceans, 2° latitude ×
15° longitude × 2 months. Maps of the temperature field were found to be rather insensitive to the exact value
of the decorrelation scales in regions with good data coverage; however, mapping errors changed considerably with
changes in the assumed scales.
On the basis of the above considerations the recommended low-density sampling strategy was prescribed as one XBT drop
per 1.5° latitude × 7.5° longitude per month. Experience has shown that the recommended low-density
sampling can be achieved in regions with good VOS coverage by dropping an XBT every 6 hours along the regular shipping
tracks. A shortcoming of the VOS XBT network is that merchant shipping does not cover all areas of the global ocean, so
that XBT sampling must be combined with other observations from in situ instruments or altimetric data to achieve global
coverage.
In addition to the description of large-scale signals and initialization of ocean models the design of VOS XBT sampling
for TOGA also recognized a need to observe seasonal-to-interannual variations of major geostrophic currents in the
tropical oceans. A strategy of frequently repeated sampling was devised for a few transequatorial VOS lines in each
ocean, with a recommended sample rate of three observations per decorrelation scale in latitude and time [Meyers et
al., 1991]. The frequently repeated sampling rate can usually be achieved with 4-hour sampling on 18 voyages per
year. Some noise due to spatial aliasing may be introduced into analyses of repeat transect data, if the ship tracks are
spread out in a swath, but are treated as having been exactly repeated [McPhaden et al., 1988c]. In most
cases this error is much smaller than the signals of interest along frequently traversed lines in the tropical Pacific.
On some routes, XCTD data are also collected [Roemmich et al., 1994].
Since the 1980s most shipboard XBT systems have recorded data on a personal (desktop or laptop) computer. Real-time
delivery is achieved for most installations by sending data to the GTS via Argos or geostationary satellites. Some data
still are sent to the GTS by coastal radio stations. Upper Ocean Thermal Data Assembly Centers provide expert quality
control of delayed mode data which are archived at the WOCE/TOGA Subsurface Data Center in Brest, France. Based in part
on VOS XBT data collected during TOGA and WOCE, the annual and seasonal mean upper ocean thermal structure of the global
ocean has been documented with the most comprehensive data set available in the World Ocean Atlas 1994 [Levitus and
Boyer, 1994].
B4.3. VOS sea surface salinity (SSS) measurements
Salinity data collected as part of VOS programs have provided valuable insights into near-surface water mass variability
and its relation to atmospheric forcing in the tropics. Though not among the highest-priority measurements during TOGA,
surface salinity nonetheless exhibits strong seasonal-to-interannual timescale variations that are important to
understand in the context of coupled ocean-atmosphere interactions associated with ENSO. For this reason and to present
a complete picture of the overall VOS effort in TOGA we briefly discuss SSS measurements in this section.
The history of SSS measurements based on VOS networks dates at least back to the early 1950s in the Gulf of Guinea [Berrit,
1961]. On the basis of this early effort, VOS SSS networks were initiated by ORSTOM in the Pacific in 1969 and in
the Atlantic and Indian Oceans in 1977 [see Donguy, 1994, and references therein]. SSS measurements
are obtained from water samples bottled by ship officers about every 60 nautical miles and later analyzed on shore by
laboratory salinometers. As compared with CTD measurements, the accuracy of bucket measurements is estimated to be of
the order of 0.10.2 psu.
TOGA inherited a decade-long VOS SSS network in 1985, but because of various obstacles the bucket sampling rate
decreased dramatically in 1994 to about 25% of the 1985 rate. From the second half of TOGA and during the COARE Enhanced
Monitoring Period, efforts were focused on complementary arrays of thermosalinographs installed on board merchant ships
[Henin
and Grelet, 1996], on TAO moorings [McPhaden et al., 1990c; Koehn et al., 1996],
and on drifting buoys [Swenson et al., 1991]. When deployed, the accuracy of temperature and conductivity sensors on
these platforms results in an accuracy of about 0.02 psu in salinity. Owing to the disproportionate availability of
surface to subsurface salinity measurements, most TOGA-related salinity studies concern SSS only.
Appendix C: Satellite Components of the Observing System--Technical and Historical Background
C1. AVHRR and Blended Sea Surface Temperature Analyses
Errors in the 1982 sea surface temperature (SST) analyses discussed in Appendix A led to improved analyses at the NCEP,
formerly NMC (Figure C1). These analyses used both in situ and satellite data. The
satellite observations are infrared measurements from the AVHRR on the NOAA polar orbiting satellites. These data were
processes operationally by NOAA's Environmental Satellite, Data, and Information Service (NESDIS) until 1993, when the
responbility for operational processing was transferred to the Naval Oceanographic Office of the U.S. Navy [May et al.,
1998]. The satellite SST retrieval algorithms are "tuned" by regression against quality-controlled drifting buoy
data using the multichannel SST technique of McClain et al. [1985] and Walton
[1988]. This procedure converts the satellite measurement of the "skin" SST (roughly a millimeter in depth) to a
buoy "bulk" SST (roughly at 0.5 m depth). The tuning is done when a new satellite becomes operational or when
verification with the buoy data shows increasing errors. The algorithms are computed globally and are not a function of
position or time. Although the AVHRR cannot retrieve SSTs in cloud-covered regions, the spatial coverage of satellite
data is much more uniform than the coverage for in situ data. As an example, the distribution of AVHRR retrievals for
the last week of TOGA is shown in Figure C1, where the number of daytime and
nighttime observations has been averaged onto a 1° spatial grid. Day and night have been separated because the cloud
detection algorithms are different for day and night.
Figure C1: Number of SST observations for the week of December 2531. (top) Regions on a 1° grid where
the number of daytime or nighttime AVHRR retrievals is three or more. (bottom) The distribution of ship, buoy, and
simulated ice SSTs. In Figure C1 (bottom right) the moored buoys are indicated by a
circle, the drifting buoys by a dot, and the ice by a plus.
In situ SST data used in the NCEP analyses are obtained from two different sources. The data source from 1990 to present
consists of all ship and buoy observations available to NCEP on the GTS within 10 hours of observation time. Prior to
1990, the data were obtained from the COADS [Woodruff et al., 1987]. COADS adds additional
delayed data to the GTS data. After a wait of several years the procedure can roughly double the number of in situ
observations. The distribution of real-time in situ data for the last week of TOGA is shown in Figure C1. Figure C1 (top) shows the distribution of
observations from ships. These observations are surface marine observations, which are roughly 20 times more frequent
than XBT observations. This distribution depends on ship traffic and is most dense in the midlatitude northern
hemisphere. Figure C1 (bottom) shows the in situ observations from drifting and
moored buoys. The deployment of the buoys has partially been designed to fill in some areas with little ship data. This
process has been most successful in the tropical Pacific and southern hemisphere. However, it should be noted that there
are areas, such as the tropical Atlantic, that have almost no buoy SST observations.
In situ and satellite observations are sparse near the ice edge. To supplement these data, sea ice information is used
on a 2° grid. If a grid box is ice covered (concentration of 50% or greater), an SST value is generated with a value
of -1.8°C, which is the freezing point of seawater with a salinity of 3334 psu. This range of salinity is
typical near the ice edge in the open ocean.
The superior coverage and greater density of satellite SST data would tend to overwhelm the in situ data in most
conventional analyses. This would only be a problem if the satellite data have biases on large timescales and space
scales. These biases have occurred in the operational satellite data set. The most severe cases occurred following the
MarchApril 1982 eruptions of El Chichon [Reynolds et al., 1989b] and the June 1991
eruptions of Mount Pinatubo [Reynolds, 1993]. The stratospheric aerosols from these eruptions resulted in strong negative
biases in the satellite algorithms.
To illustrate the effect of one of these events, the average weekly anomaly from in situ, daytime, and nighttime
satellite observations was computed between 20°S and 20°N during the period with strong stratospheric aerosols
from Mount Pinatubo [see Reynolds, 1993]. The results (Figure C2) show that the SST
anomalies were all tightly grouped during May and June 1991. After this period the in situ anomaly remained relatively
constant while the day and night satellite anomalies became more negative. The nighttime anomalies reached a minimum
during September; the daytime retrievals reached a minimum during August. The difference between the in situ and
satellite anomalies shows that the satellite observations had average negative biases with magnitudes > 1°C in
the tropics in August and September 1991. An attempt was made on October 3, 1991, to correct the nighttime algorithm.
However, as shown in Figure C2, this correction was only partially effective. As
discussed by Reynolds [1993], this correction led to other satellite biases in the southern hemisphere
midlatitudes. The aerosols and the associated tropical biases gradually became weaker until the biases became negligible
in April 1992.
Figure C2: SST anomalies obtained from weekly in situ, daytime, and nighttime satellite observations. The
anomalies are averaged between 20°S and 20°N from May 1981 to May 1992 [from Reynolds, 1993].
The NCEP analysis of Reynolds [1988] and Reynolds and Marsico [1993] used Poisson's
equation to remove any satellite biases relative to the in situ data before combining the two types of data. This
analysis, henceforth called the blend, was produced monthly from January 1982 to December 1994 on a 2° grid with an
effective spatial resolution of 6°. In this procedure the analysis resolution was degraded to a resolution that
could be supported by the in situ data.
To improve this resolution, an optimum interpolation (OI) analysis was developed [Reynolds and Smith, 1994]. The OI
is done weekly on a 1° grid and uses the same data that were used by the blend. To correct for satellite biases, a
preliminary step using the blended method provides a smooth correction with 12° resolution for each week. The
satellite data are adjusted by this correction and used in the OI along with the in situ data. In the next step, OI
error statistics are assigned to each type of data (ship, buoy, etc.). The random in situ and satellite data errors are
comparable. Hence, because the satellite distribution is so much better than the in situ distribution, the satellite
data overwhelm the in situ data in the OI. The OI also weights the nighttime temperatures more since the diurnal cycle
is not fully resolved and the daytime temperatures tend to be noisier.
The OI has now been computed from November 1981 to the present. As an example, the analysis corresponding to the data
coverages shown in Figure C1 was presented earlier in Figure 4. November 1981 was selected as the starting point of the OI analysis because that
is the date the AVHRR data first became operational. For comparison the OI has also been computed without the
preliminary satellite bias correction. This analysis will be referred to as OI-UC, where UC stands for uncorrected.
To verify the accuracy of the differences among the blend and the two versions of the OI, monthly SST anomalies from the
analyses are compared with independent data. These data are the monthly averaged SST anomalies from TOGA-TAO equatorial
current moorings [McPhaden, 1993a]. Three locations have been selected with the longest records: 110°W,
140°W, and 165°E. The monthly root-mean-square (rms) difference between buoys and each of the three analyses
(blend, OI, and OI-UC) are computed for the period January 1982 to January 1993 and for each month. The results are
summarized in Table C1 for a high aerosol year, 1991, and for the entire period.
In all cases the OI is superior to both the OI-UC and the blend. The OI is superior to the blend because of its better
resolution. The spatial gradients are greater in the eastern than in the western Pacific, so analysis differences
between the blend and the OI are greater at 110°W and 140°W than at 165°E. In years without strong satellite
biases the OI and OI-UC analyses behave similarly. However, the large biases during periods such as 1991 cause the
degradation of OI-UC analysis relative to both the OI and the blend.
Table C1. Monthly rms Differences Between SSTs From Current Meter Moorings and From NCEP Analyses
The OI analysis with the satellite bias correction yields high-quality global SST fields. These SST fields are widely
used for climate monitoring, prediction, and research as well as specifying the surface boundary condition for numerical
weather prediction. They appear in many publications, e.g., the NCEP Climate Prediction Center's Climate Diagnostic
Bulletin, and are freely available to any user. The SST fields have also been used in atmospheric reanalyses at
NCEP, ECMWF, and the U.S. Navy.
In addition, the OI fields have also been used to improve SST analyses from 1950 to 1981 when satellite data were not
available. In this method, spatial patterns from empirical orthogonal functions (EOFs) are obtained from the OI fields.
The dominant EOF modes (which correspond to the largest variance) are used as basis functions and are fit in a least
squares sense to the in situ data to determine the time dependence of each mode. A complete field of SST is then
reconstructed from these spatial and temporal modes as described by Smith et al. [1996].
C2. Satellite Altimetry
At the beginning of the TOGA project in 1985 it seemed unlikely that satellite altimetry would play much of a role in
the ocean observing system. No altimeters had flown since Seasat 7 years earlier. The proposed Seasat-like Navy Remote
Ocean Sensing System (NROSS) collapsed under the weight of its enormous budget. NASA had struggled for years, without
success, to obtain approval for its dedicated ocean topography altimeter TOPEX, and the French were having similar
problems with their counterpart, known as POSEIDON. The U.S. Navy was preparing for the launch of Geosat in March 1985,
but this was to be a classified geodetic mission, and it was doubtful that any of the data would be available to the
scientific community. On a positive note the European Space Agency (ESA) had just begun building ERS-1 with its
altimeter, but the mission was several years behind its original 1987 launch schedule. Given this background, it is easy
to understand why TOGA planned to rely so heavily on in situ observations rather than remote sensing.
Despite these inauspicious beginnings, satellite altimetry ultimately provided global observations during 8 of the 10
TOGA years, the gap occurring during 19891991 between Geosat and ERS-1. Some of the Geosat data were initially
classified, but today they are available in their entirety, spanning the first 4 years of the TOGA project. The Geosat
era turned out to be one of the more interesting times in the tropical Pacific cycle because it included a normal period
(1985 through mid-1986) followed by distinct ENSO warm and cold events in 19861987 and 19881989,
respectively. ERS-1 became operational in 1991 just as another warm event was beginning, and the TOPEX/POSEIDON
observations began in 1992. With the successful launch of ERS-2 in 1995, three altimeters were collecting data
simultaneously by middecade, with excellent prospects for a continuous series of altimeters to be in place for the
foreseeable future. As shown in Figure C3, it has been possible to connect the
various altimeter missions to generate a consistent, long-term record of sea level variations throughout the tropics.
Figure C3: Sea level time series computed from Geosat, ERS-1, and TOPEX/POSEIDON altimeter data (solid line)
near the Honiara tide gauge (dashed line) in the western tropical Pacific (taken from Lillibridge et
al. [1994]).
The spatial and temporal sampling patterns have varied among these missions as summarized by Koblinsky et
al. [1992], but of more fundamental importance is their relative accuracies. It is useful to begin with
TOPEX/POSEIDON, as this highly accurate altimeter system has set the standard by which all others are being measured.
Primarily because of advances in orbit determination, TOPEX/POSEIDON is able to measure sea level with an absolute
accuracy of 4 cm for 1-s averages [Fu et al., 1994; Tapley et al., 1996]. For monthly means in 2°
squares the figure is closer to 2 cm [Cheney et al., 1994], and global sea level is
being monitored at the level of a few millimeters [Nerem et al., 1997]. Geosat and ERS-1, even after
recent orbit improvements [Scharoo et al., 1994; Williamson and Nerem, 1994] are only accurate
to 1015 cm in an absolute sense for the 1-s data. But simple adjustments [Lillibridge et al.,
1994] and other sophisticated processing techniques [Tai and Kuhn, 1995] have increased the net accuracy
to 5 cm or less for determination of monthly mean sea level variations. Furthermore, much of the ERS-1 error can be
removed by adjusting the profiles relative to concurrent TOPEX/POSEIDON data [Le Traon et al., 1995]. For most
tropical ocean applications the result is a nearly continuous altimetric record of sea level variability, which can be
assimilated in ocean models to improve initial conditions for climate forecasting [Fu and Cheney, 1995].
Special altimeter validation efforts were undertaken during TOGA in recognition of the fact that accuracy requirements
might be higher for sea level near the equator than elsewhere in the world ocean. The primary goal of satellite
altimetry missions is the study of large-scale ocean circulation, through estimation of the surface geostrophic
currents. Geostrophic estimates of surface flow will be very sensitive to small sea level errors near the equator,
however, because of the vanishing of the horizontal component of the Coriolis force [e.g., Picaut et al., 1989]. A
rigorous open-ocean validation experiment was therefore conducted in the western equatorial Pacific Ocean during the
verification phase of the TOPEX/POSEIDON mission to examine the accuracy of the altimetry measurements in the TOGA
domain. Two TAO moorings were outfitted with additional temperature, salinity, and pressure sensors to measure within
1 cm the dynamic height from the surface to the bottom at 5-min intervals directly beneath two TOPEX/POSEIDON
crossovers; bottom pressure sensors and inverted echo sounders were deployed as well [Katz et al., 1995a; Picaut et
al., 1995]. Instantaneous comparisons with the 1-s TOPEX/POSEIDON altimeter retrievals and the 5-min dynamic
height resulted in a root-mean-square difference as low as 3.3 cm at 2°S164°E and 3.7 cm at
2°S156°E. After the use of a 30-day low-pass filter, in situ and satellite data were found to be highly
correlated, with rms differences of < 2 cm.
The applicability of satellite altimeter data for estimating zonal surface current variability at the equator was also
assessed using the meridionally differenced form of the geostrophic momentum balance [Picaut et al., 1990; Delcroix
et al., 1991, 1992; Menkes et al., 1995]. These studies indicated that altimetry-derived geostrophic zonal current
estimates agreed well with near-surface zonal currents observed from TAO moorings along the equator. Given the
sensitivity of the geostrophic approximation to small sea level variations near the equator, these results represent the
most stringent test of using altimetry observations to estimate sea level and surface currents anywhere in the world
ocean.
C3. Satellite Surface Winds
For several years before the beginning of TOGA, there was optimism that the 1978 success of Seasat, which, for the first
time, recorded surface wind velocity over the global ocean [Chelton et al., 1989], would be followed by
another satellite wind velocity measuring system. In 1986, cancellation of the NROSS mission, which was to have carried
a NSCAT to measure surface wind velocity, created a requirement to implement an in situ surface wind velocity
measurement system throughout the equatorial Pacific. This requirement was in part met by development of the TOGA-TAO
array [Hayes et al., 1991a; McPhaden, 1993a] using moored wind measurement
technology developed in earlier Pacific climate studies [Halpern, 1988b].
The inability during TOGA to launch NSCAT, which was intended to provide global coverage of 25-km-resolution surface
wind velocity every 3 days, was partially mitigated with the July 1991 launch of ERS-1. Monthly mean ERS-1 wind speed
and direction are accurate to about 1 m s-1 and 35°. However, at wind speeds below
23 m s-1, accuracy is poor because the intensity of Bragg scattering has little variation
with wind speed. However, ERS-1 data yielded the first opportunity to learn about the detailed space-time structures of
intraseasonal surface westerly wind bursts along the Pacific equator [Liu et al., 1996].
In the interim from the beginning of TOGA to the launch of ERS-1, special sensor microwave imager (SSM/I) surface wind
speed measurements, which have been recorded since July 1987, have been combined with wind directions [Atlas et al.,
1991]. The Atlas et al. [1991] SSM/I surface wind velocity data product, which Busalacchi et
al. [1993] demonstrated to be an alternate source of wind vector information, yielded sea surface
temperatures, simulated from an ocean general circulation model, that were more representative than those created with a
numerical weather prediction surface wind data product [Liu et al., 1996].
The ERS-1 scatterometer and the SSM/I represent active and passive microwave wind-measuring tech- niques, respectively.
Another active microwave method is produced with the radar altimeter, which is of secondary importance for studies of
large-scale ocean circulation because of the very small coverage in the cross-track direction. SSM/I and ERS-1 wind data
are determined over distances > 500 km perpendicular to the ground track, with an areal coverage nearly 100
times greater than that of an altimeter.
Appendix D: In Situ Meteorological Components of the Observing System--Technical and Historical
Background
D1. TOGA Upper Air Network
One of the objectives of TOGA was to resolve the three-dimensional structure of planetary-scale disturbances along the
equator so that daily wind profiles taken at a sufficiently dense horizontal scale were required. The distribution of
existing WWW sites was of particular concern in the equatorial Pacific, having gaps which needed attention. Owing to the
lack of suitable island sites and considering the logistic difficulty and expense of some of the candidate islands, only
some of these gaps could be filled.
During the first few years of TOGA the International TOGA Project Office, with the assistance of many countries,
concentrated on setting up observing capability at a number of sites: Kanton Island (Republic of Kiribati) and San
Cristobal in the Galapagos, Gan in the Maldive Islands, and Penrhyn Islands. In addition, wind profilers were planned
for a number of Pacific Islands (see section D2). These plans were set out in the first edition of the "TOGA
International Implementation Plan" and were revised in later editions as circumstances changed. The final list of
upper-air sites labeled as Key Stations for TOGA is given in the fourth and final edition of the "Implementation Plan"
[International TOGA Project Office, 1992].
Two data sets were produced as a result of the data management of TOGA observations. Upper air reports transmitted on
the GTS of the WWW were incorporated in the synoptic-time analyses and forecasts made by the operational forecast
centers. The official TOGA archive of these data and the resulting analyses are those produced by the ECMWF. Another
data set now exists at NCDC in Asheville, North Carolina, as a result of the Comprehensive Aerological Reference Data
Set (CARDS) Program. This data set consists of all soundings made by the WWW and supplemental sites as forwarded by GTS
and delayed mode to NCDC. Unfortunately, the CARDS rawinsonde data are spotty and concentrated in the last years of the
TOGA experiment. The CARDS archive is available for use in research programs and, in particular, for current and future
reanalysis efforts.
D2. Island Wind Profilers
In 1985 the NOAA Aeronomy Laboratory in Boulder, Colorado, began to apply newly developed wind profiler technology to
the TOGA program to support studies of the tropical atmosphere and climate system [Gage et al., 1990, 1991a]. The first step was
taken at Christmas Island in the central Pacific Ocean island republic of Kiribati. A 50-MHz VHF wind profiler was
constructed on Christmas Island in 1985 and has been operated nearly continuously since April 1986 [Gage et al., 1994a].
The VHF wind profiler observes horizontal and vertical velocities in the altitude range 1.818 km.
Christmas Island is located just north of the equator in the Line Islands south of Hawaii, as shown in Figure 5. The weather on Christmas Island is influenced by its location in the equatorial
dry zone associated with the cold tongue of equatorial waters extending from the eastern Pacific across the central
Pacific. Substantial rain occurs at Christmas Island only during ENSO warm events when the trade wind circulation
relaxes and the cold tongue disappears. Some rain occurs in most years during MarchMay, when the Intertropical
Convergence Zone (ITCZ) makes its closest approach to the equator. The wind profiler was placed at Christmas Island to
determine the climatology of tropical wind fields and to observe the natural variability of winds over the central
Pacific on the ENSO timescale.
Wind-profiling radars observe weak backscatter from turbulent irregularities in the atmospheric radio refractive index
[see, e.g., Gage et al., 1990]. Wind velocity is inferred from the Doppler shift of the backscattered
power in the direction of the radar beam. Most wind profilers operate with several fixed beams. Vertically directed
beams are utilized for the measurement of vertical motions. Oblique beams (typically directed 15° off-zenith in
orthogonal vertical planes) are utilized for the measurement of horizontal motions, since vertical motions are typically
very small. In routine operation, orthogonal wind components are sampled every few minutes and processed to yield a
consensus mean hourly wind. Typical precision expected for individual wind profiler measurements of horizontal
velocities is close to 1 m s-1 [Strauch et al., 1987]. Four times per day, hourly
averaged Christmas Island wind data are telemetered via geostationary satellite and incorporated onto the GTS for
worldwide distribution.
The Christmas Island wind profiler has served as a prototype for a complementary array of VHF wind profilers that was
designed to span the Pacific basin from Indonesia to Peru. This Trans-Pacific Profiler Network was constructed with
support from the National Science Foundation. It is comprised of VHF wind profilers at Pohnpei, Federated States of
Micronesia; Biak, Indonesia; and Piura, Peru.
Island-based profilers are subject to local influences that may affect their ability to measure representative samples
of the large-scale wind field. The magnitude of the local effects generally can be expected to decrease with height and
depend on the location of the profiler and the size and topography of the island. For example, Balsley and Carter
[1989] found lee waves in the vertical velocities that were pronounced at Pohnpei, an island with substantial
topography, but absent at Christmas Island, which is very flat. Additional research is needed to quantify island
influences on island-based profiler observations.
In order to observe the winds in the tropical lower troposphere the Aeronomy Laboratory developed an UHF boundary layer
wind profiler to complement the VHF wind profiler [Ecklund et al., 1988, 1990]. The new UHF profiler
operates at 915 MHz and observes winds in the lower troposphere up to 56 km with good vertical
resolution. The 915-MHz profiler was installed at Christmas Island in 1990. Together the two profilers observe the
entire tropical troposphere. UHF wind profilers are much more sensitive to hydrometeors than are VHF wind profilers; it
is necessary to account for the fall speed of hydrometeors with UHF profilers to obtain accurate wind velocities when
hydrometeors dominate the radar returns. Since the UHF profilers are very sensitive to hydrometeors [Gage et al.,
1994b, 1996a; Ecklund et al., 1995], they are increasingly being used for precipitation studies
in the tropics [Williams et al., 1995].
Technical aspects of the development of the 915-MHz UHF profiler are reviewed by Carter et al. [1995]. The
profiler was adapted to shipboard operation [Carter et al., 1992] and integrated with a balloon
sounding system and a suite of surface instruments to create an integrated sounding system (ISS), which formed a major
part of the upper air sounding system used for TOGA COARE [Webster and Lukas, 1992; Parsons, 1994]. ISSs were
operated for COARE on Kavieng and Manus Islands in Papua New Guinea, on Kapingamarangi in the Federated States of
Micronesia, and on the Island Republic of Nauru in the central Pacific. Intercomparisons of UHF profiler wind and
temperature measurements with balloon soundings at ISS sites show very good agreement during COARE [Riddle et al., 1996].
Acknowledgments. The authors would like to thank three anonymous reviewers for their helpful comments on an
earlier version of this manuscript. Also, Todd Mitchell of the Joint Institute for the Study of the Atmosphere and
Ocean, University of Washington, provided many valuable suggestions on improving the readability of the paper.
Scientific oversight for development of the TOGA Observing System was provided by the International TOGA Scientific
Steering Group, and implementation was coordinated by International TOGA Project Office (ITPO). We would like to
acknowledge John Marsh, director of the ITPO, for his dedicated and enthusiastic service in support of the TOGA program
and in particular for his unflagging efforts on behalf of the many individuals and institutions involved in implementing
the TOGA Observing System. Financial and other national contributions to TOGA were coordinated through the International
TOGA Board. Sponsors for the TOGA Observing System included, in the United States, the National Oceanic and Atmospheric
Administration (Office of Global Programs and Office of Oceanic and Atmospheric Research), the National Aeronautics and
Space Administration, and the National Science Foundation; in France, L'Institut Français de Recherche pour le
Développement en Coopération (ORSTOM), the Programme National de Dynamique du Climat, the Ministère
de la Recherche et de l'Enseignement Supérieur, and the Institut Français de Recherche pour l'Exploitation
des Océans; in Japan, the Science and Technology Agency and the Japan Marine Science and Technology Center; in
Korea, the Ministry of Science and Technology; in Taiwan, the National Science Council; in Australia, the Commonwealth
Scientific and Industrial Research Organization, the Royal Australian Navy, and the Bureau of Meteorology; and
organizations too numerous to list in many other countries that participated in the 10-year TOGA program. This is PMEL
contribution 1720.
References
Aceituno, P., El Niño, Southern Oscillation and ENSO: Confusing names for a complex ocean-atmosphere interaction,
Bull. Am. Meteorol. Soc., 73, 483485, 1992.
Alexander, M. A., Mid-latitude ocean-atmosphere interaction during El Niño, 1, The North Pacific Ocean, J.
Clim., 5, 944958, 1992.
Allen, M. R., S. P. Lawrence, M. J. Murray, C. T. Mutlow, T. N. Stockdale, D. T. Llewellyn-Jones, and D. L. T. Anderson,
Control of tropical instability waves in the Pacific, Geophys. Res. Lett., 22, 25812584,
1995.
Anderson, D., TAO data assimilation at ECMWF, in Proceedings of the Second Workshop of the TOGA-TAO Implementation
Panel, M. J. McPhaden (Ed.), Bali, Indonesia, October 1820, 1993, ITPO Publication No. 10,
Pac. Mar. Environ. Lab., Seattle, Wash., p. 2021, 1994.
Anderson, S. P., R. A. Weller, and R. B. Lukas, Surface buoyancy forcing and the mixed layer of the western Pacific warm
pool: Observations and 1D model results, J. Clim., 9, 30563085, 1996.
Ando, K., and M. J. McPhaden, Variability of the surface layer hydrography in the tropical Pacific Ocean, J.
Geophys. Res., 102, 23,06323,078, 1997.
Angevine, W. M., S. K. Avery, W. L. Ecklund, and D. A. Carter, Fluxes of heat and momentum measured with a boundary
layer wind profiler radar/RASS, J. Appl. Meteorol., 32, 7380, 1993.
Angevine, W. M., A. B. White, and S. K. Avery, Boundary layer depth and entrainment zone characterization with a
boundary layer profiler, Boundary Layer Meteorol., 68, 375385, 1994.
Arnault, S., and C. Perigaud, Altimetry and models in the tropical oceans: A review, Oceanol. Acta,
15, 411430, 1992.
Atlas, R., S. C. Bloom, R. N. Hoffman, J. V. Ardizzone, and G. Brin, Space-based surface wind vectors to aid
understanding of air-sea interactions, Eos Trans. AGU, 72, 201208, 1991.
Atlas, R., R. N. Hoffman, S. C. Bloom, J. C. Jusem, and J. Ardizzone, A multi-year global surface wind velocity data set
using SSM/I wind observations, Bull. Am. Meteorol. Soc., 77, 869882, 1996.
Bailey, R., A. Gronell, H. Phillips, E. Tanner, and G. Meyers, Quality control cookbook for XBT data,
Rep. 221, Mar. Lab., Div. of Oceanogr., Commonw. Sci. and Ind. Res. Organ.,
Hobart, Tasmania, Australia, 1994.
Baker, D. J., Ocean instruments and experiment design, in Evolution of Physical Oceanography, edited by B. A.
Warren and C. Wunsch, pp. 396433, MIT Press, Cambridge, Mass., 1981.
Balmaseda, M. A., M. K. Davey, and D. L. T. Anderson, Decadal and seasonal dependence of ENSO prediction skill, J.
Clim., 8, 27052715, 1995.
Balsley, B. B., and D. A. Carter, Mountain waves in the tropical Pacific atmosphere: A comparison of vertical wind
fluctuations over Pohnpei and Christmas Island using VHF wind profilers, J. Atmos. Sci., 46,
26982715, 1989.
Balsley, B. B., W. L. Ecklund, D. A. Carter, A. C. Riddle, and K. S. Gage, Average vertical motions in the tropical
atmosphere observed by a radar wind profiler on Pohnpei (7°N latitude, 157°E longitude), J.
Atmos. Sci., 45, 396405, 1988.
Barber, R. T., and F. P. Chavez, Biological consequences of El Niño, Science, 222, 12031210,
1983.
Barnett, T. P., Interaction of the monsoon and Pacific trade wind system on interannual time scales, I, The equatorial
zone, Mon. Weather Rev., 111, 756773, 1983.
Bates, J., High-frequency variability of special sensor microwave/imager derived wind speed and moisture during an
intraseasonal oscillation, J. Geophys. Res., 96, 34113423, 1991.
Battisti, D. S., Dynamics and thermodynamics of a warming event in a coupled atmosphere-ocean model, J.
Atmos. Sci., 45, 28892919, 1988.
Battisti, D. S., and A. C. Hirst, Interannual variability in a tropical atmosphere-ocean model: Influence of the basic
state, ocean geometry and nonlinearity, J. Atmos. Sci., 46, 16871712, 1989.
Bentamy, A., Y. Quilfen, F. Gohin, N. Grima, M. Lenaour, and J. Servain, Determination and validation of average wind
fields from ERS-1 scatterometer measurements, J. Global Atmos. Ocean Syst., 4, 129, 1996.
Berrit, G., Contribution à la connaissance des variations saisonnières dans le golf de Guinée,
Observations de surface le long des lignes de navigation, 1, generalités, Cah. Oceanogr., 13,
715727, 1961.
Bjerknes, J., A possible response of the atmospheric Hadley circulation to equatorial anomalies of ocean temperature,
Tellus, 18, 820829, 1966.
Bjerknes, J., Atmospheric teleconnections from the equatorial Pacific, Mon. Weather Rev., 97,
163172, 1969.
Blanke, B., and P. Delecluse, Variability of the tropical Atlantic Ocean simulated by a general circulation model with
two different mixed layer physics, J. Phys. Oceanogr., 23, 13631388, 1993.
Bond, N. A., and M. J. McPhaden, An indirect estimate of the diurnal cycle in upper ocean turbulent heat fluxes at the
equator, 140°W, J. Geophys. Res., 100, 18,36918,378, 1995.
Boulanger, J.-P., and L.-L. Fu, Evidence of boundary reflection of Kelvin and first-mode Rossby waves from
TOPEX/POSEIDON sea level data, J. Geophys. Res., 101, 16,36116,371, 1996.
Boulanger, J.-P., and C. Menkes, Propagation and reflection of long equatorial waves in the Pacific Ocean during the
19921993 El Niño, J. Geophys. Res., 100, 25,04124,059, 1995.
Boutin, J., and J. Etcheto, Consistency of Geosat, SSM/I and ERS1 global surface wind speeds: Comparison with in-situ
data, J. Atmos. Oceanic Technol., 13, 183187, 1996.
Braconnot, P., and C. Frankignoul, On the ability of the LODYC GCM to simulate the thermocline depth in the equatorial
Atlantic, Clim. Dyn., 9, 221234, 1994.
Brady, E. C., and P. R. Gent, The seasonal cycle of meridional heat transport in a numerical model of the Pacific
equatorial upwelling zone, J. Phys. Oceanogr., 24, 26582673, 1994.
Bryden, H. L., and E. C. Brady, Diagnostic model of the three-dimensional circulation in the upper equatorial Pacific
Ocean, J. Phys. Oceanogr., 15, 12551273, 1985.
Bryden, H. L., and E. L. Brady, Eddy momentum and heat fluxes and their effects on the circulation of the equatorial
Pacific Ocean, J. Mar. Res., 47, 5579, 1989.
Busalacchi, A. J., Data assimilation in support of tropical ocean circulation studies, in Modern Approaches to Data
Assimilation in Ocean Modeling, pp. 235270, Elsevier Sci., New York, 1996.
Busalacchi, A. J., and M. A. Cane, Hindcasts of sea level variations during the 198283 El Niño, J.
Phys. Oceanogr., 15, 213221, 1985.
Busalacchi, A. J., and J. J. O'Brien, Seasonal variability in a model of the tropical Pacific, J.
Phys. Oceanogr., 10, 19291952, 1980.
Busalacchi, A. J., and J. J. O'Brien, Interannual variability of the equatorial Pacific in the 1960s, J.
Geophys. Res., 86, 10,90110,907, 1981.
Busalacchi, A. J., K. Takeuchi, and J. J. O'Brien, Interannual variability of the equatorial Pacific-Revisited, J.
Geophys. Res., 88, 75517562, 1983.
Busalacchi, A. J., M. J. McPhaden, J. Picaut, and S. Springer, Sensitivity of wind-driven tropical Pacific Ocean on
seasonal and interannual time-scales, J. Mar. Syst., 1, 119154, 1990.
Busalacchi, A. J., R. M. Atlas, and E. C. Hackert, Comparison of SSM/I vector wind stress with model-derived and
subjective products for the tropical Pacific, J. Geophys. Res., 98, 69616977, 1993.
Busalacchi, A. J., M. J. McPhaden, and J. Picaut, Variability in equatorial Pacific sea surface topography during the
verification phase of the TOPEX/POSEIDON mission, J. Geophys. Res., 99, 24,72524,738, 1994.
Cane, M. A., Oceanographic events during El Niño, Science, 222, 11891195, 1984.
Cane, M. A., and S. E. Zebiak, A theory for El Niño and the Southern Oscillation, Science, 228,
10851087, 1985.
Cane, M. A., S. C. Dolan, and S. E. Zebiak, Experimental forecasts of the 1982/83 El Niño, Nature,
321, 827832, 1986.
Cane, M. A., A. Clement, A. Kaplan, Y. Kushnir, D. Pozdnyakov, R. Seager, S. Zebiak, and R. Murtugudde, 20th century sea
surface temperature trends, Science, 275, 957960, 1997.
Cardone, V. J., J. G. Greenwood, and M. A. Cane, On trends in historical marine wind data, J. Clim., 3,
113127, 1990.
Carter, D. A., W. L. Ecklund, K. S. Gage, M. Spowart, H. L. Cole, E. F. Chamberlain, W. F. Dabberdt, and J. Wilson,
First test of a shipboard wind profiler, Bull. Am. Meteorol. Soc., 73, 15871592,
1992.
Carter, D. A., K. S. Gage, W. L. Ecklund, W. M. Angevine, P. E. Johnston, A. C. Riddle, J. Wilson, and C. R. Williams,
Developments in lower tropospheric wind profiling at NOAA's Aeronomy Laboratory, Radio Sci., 30,
9771001, 1995.
Carter, W., W. Scherer, and J. Diamante, Measuring absolute sea level, Sea Technol., 28, 5254, 1987.
Carton, J. A., and B. Huang, Warm events in the tropical Atlantic, J. Phys. Oceanogr., 24, 888903,
1994.
Carton, J. A., and E. J. Katz, Estimates of the zonal slope and the seasonal transport of the Atlantic North Equatorial
Countercurrent, J. Geophys. Res., 95, 30913100, 1990.
Carton, J. A., B. S. Giese, X. Cao, and L. Miller, Impact of altimeter, thermistor, and expendable bathythermograph data
on retrospective analyses of the tropical Pacific Ocean, J. Geophys. Res., 101, 14,14714,159,
1996.
Cassou, C., C. Perigaud, L. L. Fu, and J. P. Boulanger, El Niño events over 19801995 simulated and
forecasted with a simple coupled ocean-atmosphere model, Eos Trans. AGU, 77(46), Fall Meet. Suppl.,
F385, 1996.
Chabbra, N. K., J. M. Dahlen, J. R. Scholten, Calibration of the Draper Laboratory low cost drifter (LCD),
Rep. CSDL-R-1906, 137 pp., Charles Stark Draper Lab., Inc., Cambridge, Mass., 1987.
Chang, P., Seasonal cycle of sea surface temperature and mixed layer heat budget in the tropical Pacific Ocean,
Geophys. Res. Lett., 20, 20792082, 1993.
Chang, P., A study of the seasonal cycle of sea surface temperature in the tropical Pacific Ocean using reduced gravity
models, J. Geophys. Res., 99, 77257741, 1994.
Chang, P., L. Ji, B. Wang, and T. Li, Interactions between the seasonal cycle and El Niño-Southern Oscillation in
an intermediate coupled ocean atmosphere model, J. Atmos. Sci., 52, 23532372, 1995.
Chao, Y., and S. G. H. Philander, On the contrast between the seasonal cycles of the equatorial Atlantic and Pacific
Oceans, J. Phys. Oceanogr., 21, 13991406, 1991.
Chelton, D., and R. Davis, Monthly mean sea level variability along the west coast of North America, J.
Phys. Oceanogr., 12, 757784, 1982.
Chelton, D., M. Freilich, and J. Johnson, Evaluation of unambiguous vector winds from the Seasat scatterometer, J.
Atmos. Oceanic Technol., 6, 10241039, 1989.
Chen, D., A. J. Busalacchi, and L. M. Rothstein, The roles of vertical mixing, solar radiation, and wind stress in a
model simulation of the sea surface temperature seasonal cycle in the tropical Pacific Ocean, J. Geophys. Res.,
99, 20,34520,359, 1994a.
Chen, D., L. W. Rothstein, and A. J. Busalacchi, A hybrid vertical mixing scheme and its application to tropical ocean
models, J. Phys. Oceanogr., 24, 21562179, 1994b.
Chen, D., S. E. Zebiak, A. J. Busalacchi, and M. A. Cane, An improved procedure for El Niño forecasting,
Science, 269, 16991702, 1995.
Cheney, R. E., L. Miller, B. C. Douglas, and R. W. Agreen, Monitoring equatorial Pacific sea level with Geosat, Johns
Hopkins APL Tech. Dig., 8, 245250, 1987.
Cheney, R. E., B. C. Douglas, and L. Miller, Evaluation of Geosat data with application to tropical Pacific sea level
variability, J. Geophys. Res., 94, 47374747, 1989.
Cheney, R. E., L. Miller, R. W. Agreen, N. S. Doyle, and J. L. Lillibridge, TOPEX/POSEIDON: The 2-cm solution, J.
Geophys. Res., 99, 24,55524,563, 1994.
Chereskin, T., P. P. Niiler, and P. Poulain, A numerical study of the effects of upper ocean shear on flexible drogue
drifters, J. Atmos. Oceanic Technol., 6, 243253, 1989.
Clarke, A., On the reflection and transmission of low-frequency energy at the irregular western Pacific Ocean boundary,
J. Geophys. Res., 96, 32893305, 1991.
Clarke, A., Low-frequency reflection from a nonmeridional eastern ocean boundary and the use of coastal sea level to
monitor eastern Pacific equatorial Kelvin waves, J. Phys. Oceanogr., 22, 163183, 1992.
Clarke, A., and X. Liu, Observations and dynamics of semiannual and annual sea levels near the eastern equatorial Indian
Ocean boundary, J. Phys. Oceanogr., 23, 386399, 1993.
Clarke, A. J., and X. Liu, Interannual sea level in the northern and eastern Indian Ocean, J.
Phys. Oceanogr., 24, 12241235, 1994.
Clarke, A. J., and S. Van Gorder, On ENSO coastal currents and sea levels, J. Phys. Oceanogr., 24,
661680, 1994.
Climate Analysis Center, Near real-time analyses, December 1994, U.S. Clim. Diagnostics Bull., 80 pp., U.S.
Dep. of Comm., Washington, D.C., 1994.
Cox, M. D., Generation and propagation of 30-day waves in a numerical model of the Pacific, J.
Phys. Oceanogr., 10, 11681186, 1980.
Cronin, M., and M. J. McPhaden, The upper ocean heat balance in the western equatorial Pacific warm pool during
SeptemberDecember 1992, J. Geophys. Res., 102, 85338553, 1997.
Currier, P. E., S. K. Avery, B. B. Balsley, and K. S. Gage, Use of two wind profilers for precipitation studies,
Geophys. Res. Lett., 19, 10171020, 1992.
da Silva, A. M., C. C. Young, and S. Levitus, Atlas of surface marine data 1994, vol. 1: Algorithms and procedures,
NOAA Atlas NESDIS 6, U.S. Dept. of Commerce, Washington, D.C., 83 pp., 1994.
Delcroix, T., and C. Gautier, Estimate of heat content variations from sea level measurements in the central and western
tropical Pacific from 1979 to 1985, J. Phys. Oceanogr., 17, 725734, 1987.
Delcroix, T., and C. Henin, Mechanisms of subsurface thermal structure and sea surface thermohaline variabilities in the
southwest Pacific during 197585, J. Mar. Res., 47, 777812, 1989.
Delcroix, T., and C. Henin, Seasonal and interannual variations of sea surface salinity in the tropical Pacific Ocean,
J. Geophys. Res., 96, 22,13522,150, 1991.
Delcroix, T., and J. Picaut, Zonal displacement of the western equatorial Pacific "fresh pool," J.
Geophys. Res., 103, 10871098, 1998.
Delcroix, T., J. Picaut, and G. Eldin, Equatorial Kelvin and Rossby waves evidenced in the Pacific Ocean through sea
level and surface current anomalies, J. Geophys. Res., 96, suppl., 32493262, 1991.
Delcroix, T., G. Eldin, M. H. Radenac, J. Toole, and E. Firing, Variations of the western equatorial Pacific Ocean,
19861988, J. Geophys. Res., 97, 54235445, 1992.
Delcroix, T., G. Eldin, M. J. McPhaden, and A. Morliere, Effects of westerly wind bursts upon the western equatorial
Pacific Ocean, FebruaryApril 1991, J. Geophys. Res., 98, 16,37916,386, 1993.
Delcroix, T., J.-P. Boulanger, F. Masia, and C. Menkes, Geosat-derived sea level and surface current anomalies in the
equatorial Pacific during the 19861989 El Niño and La Niña, J. Geophys. Res., 99,
25,09325,107, 1994.
Delcroix, T., C. Henin, V. Porte, and P. Arkin, Precipitation and sea-surface salinity in the tropical Pacific, Deep
Sea Res., Part I, 43, 11231141, 1996.
Delecluse, P., J. Servain, C. Levy, K. Arpe, and L. Bengsston, On the connection between the 1984 Atlantic warm event
and the 198283 ENSO, Tellus, Ser. A, 46, 448464, 1994.
Derber, J. D., and A. Rosati, A global oceanic data assimilation system, J. Phys. Oceanogr., 19,
13331347, 1989.
Deser, C., Daily surface wind variations over the equatorial Pacific Ocean, J. Geophys. Res., 99,
23,07123,078, 1994.
Deser, C., and J. M. Wallace, El Niño events and their relation to the Southern Oscillation: 19251986,
J. Geophys. Res., 92, 14,18914,196, 1987.
Deser, C., J. J. Bates, and S. Wahl, The influence of sea surface temperature gradients on stratiform cloudiness along
the equatorial front in the Pacific Ocean, J. Clim., 6, 11721180, 1993.
Dessier, A., and J. R. Donguy, Response to El Niño signals of the epiplanktonic copepod populations in the
eastern tropical Pacific, J. Geophys. Res., 92, 14,39314,403, 1987.
Dessier, A., and J. R. Donguy, The sea surface salinity in the tropical Atlantic between 10°S and 30°N-seasonal
and interannual variations (19771989), Deep Sea Res., Part I, 41, 81100, 1994.
Donguy, J. R., Recent advances in the knowledge of the climatic variations in the tropical Pacific Ocean,
Prog. Oceanogr., 19, 4985, 1987.
Donguy, J. R., Surface and subsurface salinity in the tropical Pacific Ocean: Relations with climate,
Prog. Oceanogr., 34, 4578, 1994.
Donguy, J. R., and G. Meyers, Observations of geostrophic transport variability in the western tropical Indian Ocean,
Deep Sea Res., Part I, 42, 10071028, 1995.
Donguy, J. R., and G. Meyers, Mean annual variation of transport of major currents in the tropical Pacific Ocean,
Deep Sea Res., Part I, 43, 11051122, 1996a.
Donguy, J. R., and G. Meyers, Seasonal variations of sea-surface salinity and temperature in the tropical Indian Ocean,
Deep Sea Res., Part I, 43, 117138, 1996b.
Donguy, J. R., A. Dessier, and Y. du Penhoat, Heat content displacement in the Pacific during the 198283 El
Niño event, Oceanol. Acta, 12, 149157, 1989.
du Penhoat, Y., and M. A. Cane, Effect of low-latitude western boundary gaps on the reflection of equatorial motions,
J. Geophys. Res., 96, suppl., 33073322, 1991.
du Penhoat, Y., T. Delcroix, and J. Picaut, Interpretation of Kelvin/Rossby waves in the equatorial Pacific from
model-Geosat data intercomparison during the 19861987 El Niño, Oceanol. Acta, 15,
545554, 1992.
Ecklund, W. L., D. A. Carter, and B. B. Balsley, A UHF wind profiler for the boundary layer: Brief description and
initial results, J. Atmos. Oceanic Technol., 5, 432441, 1988.
Ecklund, W. L., D. A. Carter, B. B. Balsley, P. E. Currier, J. L. Green, B. L. Weber, and K. S. Gage, Field tests of a
lower tropospheric wind profiler, Radio Sci., 25, 899906, 1990.
Ecklund, W. L., K. S. Gage, and C. R. Williams, Tropical precipitation studies using 915-MHz wind profiler, Radio
Sci., 30, 10551064, 1995.
Enfield, D. B., Zonal and seasonal variations in the near-surface heat balance of the equatorial ocean, J.
Phys. Oceanogr., 16, 10381054, 1986.
Enfield, D. B., The intraseasonal oscillation in eastern Pacific sea levels: How is it forced?, J.
Phys. Oceanogr., 17, 18601876, 1987.
Enfield, D. B., El Niño, past and present, Rev. Geophys., 27, 159187, 1989.
Enfield, D., and J. Allen, On the structure and dynamics of monthly mean sea level anomalies along the Pacific coast of
North and South America, J. Phys. Oceanogr., 10, 557578, 1980.
Enfield, D. B., and D. A. Mayer, Tropical Atlantic sea surface temperature variability and its relation to El
Niño-Southern Oscillation, J. Geophys. Res., 102, 929945, 1997.
Eriksen, C. C., The Tropic Heat Program: An overview, Eos Trans. AGU, 66, 50, 1985.
Eriksen, C., A review of PEQUOD, in Further Progress in Equatorial Oceanography, edited by E. J. Katz and J. M.
Witte, pp. 2946, Nova Univ. Press, Fort Lauderdale, Fla., 1987.
Eriksen, C. C., M. B. Blumenthal, S. P. Hayes, and P. Ripa, Wind-generated equatorial Kelvin waves observed across the
Pacific Ocean, J. Phys. Oceanogr., 13, 16221640, 1983.
Esbensen, S. K., and Y. Kushnir, The heat budget of the global ocean: An atlas based on estimates from surface marine
observations, Rep. 29, Clim. Res. Inst., Oreg. State Univ., Corvallis, 1981.
Esbensen, S. K., and M. J. McPhaden, Enhancement of tropical ocean evaporation and sensible heat flux by atmospheric
mesoscale systems, J. Clim., 9, 23072325, 1996.
Feely, R. A., R. Wanninkhof, C. E. Cosca, M. J. McPhaden, R. H. Byrne, F. J. Millero, F. P. Chavez, T. Clayton, D. M.
Campbell, and P. P. Murphy, The effect of tropical instability waves on CO2 species distributions along the
equator in the eastern equatorial Pacific during the 1992 ENSO event, Geophys. Res. Lett., 21,
277280, 1994.
Festa, J. F., and R. L. Molinari, An evaluation of the WOCE volunteer observing ship XBT network in the Atlantic, J.
Atmos. Oceanic Technol., 9, 305317, 1992.
Fine, R. A., W. H. Peterson, and H. G. Ostlund, The penetration of tritium into the tropical Pacific, J.
Phys. Oceanogr., 17, 553564, 1987.
Firing, E., R. Lukas, J. Sadler, and K. Wyrtki, Equatorial undercurrent disappears during 198283 El Niño,
Science, 222, 11211122, 1983.
Fischer, M., M. Latif, M. Flügel, and M. Ji, The impact of data assimilation on ENSO simulations and predictions,
Mon. Weather Rev., 125, 819829, 1997.
Flament, P., S. Kennan, R. Knox, P. Niiler, and R. Bernstein, Observations of the three-dimensional structure of
tropical instability, Nature, 383, 610613, 1996.
Flatau, M., P. J. Flatau, P. Phoebus, and P. P. Niiler, The feedback between equatorial convection and local radiative
and evaporative processes: The implications for intraseasonal oscillations, J. Atmos. Sci., 54,
23732386, 1997.
Foley, D. G., T. D. Dickey, M. J. McPhaden, R. R. Bidigare, M. R. Lewis, R. T. Batber, S. T. Lindley, C. Garside, and D.
V. Manov, Time series of physical, bio-optical, and geochemical properties in the central equatorial Pacific Ocean at
0°, 140°W, February 1992March 1993, Deep Sea Res., in press, 1998.
Frankignoul, C., F. Bonjean, and G. Reverdin, Interannual variability of surface currents in the tropical Pacific during
19871993, J. Geophys. Res., 101, 36293647, 1996.
Freilich, M., and R. Dunbar, A preliminary C-band scatterometer model function for the ERS-1 AMI instrument, in
Proceedings of the First ERS-1 Symposium, Eur. Space Agency Spec. Publ., ESA SP-359, 7984,
1993.
Freitag, H. P., Y. Feng, L. J. Mangum, M. P. McPhaden, J. Neander, and L. D. Stratton, Calibration procedures and
instrumental accuracy estimates of TAO temperature, relative humidity and radiation measurements,
Tech. Memo. ERL PMEL-104, 32 pp., Natl. Oceanic and Atmos. Admin., Silver Spring, Md., 1995.
Fu, L.-L., and R. E. Cheney, Application of satellite altimetry to ocean circulation studies: 19871994, U.S.
Natl. Rep. Int. Union Geod. Geophys. 19911994, Rev. Geophys., 33,
213223, 1995.
Fu, L.-L., I. Fukumori, and R. N. Miller, Fitting dynamic models to the Geosat sea level observations in the tropical
Pacific Ocean, II, A linear, wind-driven model, J. Phys. Oceanogr., 23, 21622181, 1993.
Fu, L.-L., E. J. Christensen, and C. A. Yamarone, TOPEX/POSEIDON mission overview, J. Geophys. Res.,
99, 24,36924,381, 1994.
Fukumori, I., Assimilation of TOPEX sea level measurements with a reduced-gravity, shallow water model of the tropical
Pacific Ocean, J. Geophys. Res., 100, 25,02725,039, 1995.
Gaffard, C., and H. Roquet, Impact of the ERS-1 scatterometer wind data on the ECMWF 3D-Var assimilation system,
Tech. Memo. 217, 21 pp., Eur. Cent. for Medium-Range Weather Forecasts, Reading, England,
1995.
Gage, K.S., Radar observation of the free atmosphere: Structure and dynamics, in Radar in Meteorology, edited by
D. Atlas, pp. 534565, American Meteorological Society, Boston, Mass., 1990.
Gage, K. S., and G. C. Reid, Longitudinal variations in tropical tropopause properties in relation to tropical
convection and El Niño-Southern Oscillation events, J. Geophys. Res., 92, 14,19714,203,
1987.
Gage, K. S., B. B. Balsley, W. L. Ecklund, R. F. Woodman, and S. K. Avery, Wind-profiling Doppler radars for tropical
atmospheric research, Eos Trans. AGU, 71, 18511854, 1990.
Gage, K. S., B. B. Balsley, W. L. Ecklund, D. A. Carter, and J. R. McAfee, Wind profiler related research in the
tropical Pacific, J. Geophys. Res., 96, 32093220, 1991a.
Gage, K. S., J. R. McAfee, D. A. Carter, A. C. Riddle, G. C. Reid, and B. B. Balsley, Direct measurements of long-term
mean vertical motions over the tropical Pacific using wind-profiling Doppler radar, Science, 254,
17711773, 1991b.
Gage, K. S., J. R. McAfee, D. A. Carter, W. L. Ecklund, G. C. Reid, A. C. Riddle, P. E. Johnston, and B. B. Balsley,
Wind profiler yields observations of ENSO signal, Eos Trans. AGU, 74, 137, 142, 1993.
Gage, K. S., J. R. McAfee, W. L. Ecklund, D. A. Carter, C. Williams, P. E. Johnston, and A. C. Riddle, The Christmas
Island wind profiler: A prototype VHF wind-profiling Doppler radar for the tropics, J. Atmos. Oceanic
Technol., 11, 2231, 1994a.
Gage, K. S., C. R. Williams, and W. L. Ecklund, UHF wind profilers: A new tool for diagnosing tropical convective cloud
systems, Bull. Am. Meteorol. Soc., 75, 22892294, 1994b.
Gage, K. S., C. R. Williams, and W. L. Ecklund, Application of the 915 MHz profiler for diagnosing and classifying
tropical precipitating cloud systems, Meteorol. Atmos. Phys., 59, 141151, 1996a.
Gage, K. S., J. R. McAfee, and C. R. Williams, On the annual variation of tropospheric zonal winds observed above
Christmas Island in the central equatorial Pacific, J. Geophys. Res., 101, 15,06115,070, 1996b.
Geisler, J. E., M. L. Blackmon, G. T. Bates, and S. Munoz, Sensitivity of January climate response to the magnitude and
position of equatorial Pacific sea surface temperature anomalies, J. Atmos. Sci., 42, 10371149,
1985. Giese, B. S., and D. E. Harrison, Aspects of Kelvin wave response to episodic wind forcing, J.
Geophys. Res., 95, 72897312, 1990.
Giese, B. S., and D. E. Harrison, Eastern equatorial Pacific response to three composite westerly wind types, J.
Geophys. Res., 96, suppl., 32393248, 1991.
Giese, B. S., J. A. Carton, and L. J. Holl, Sea level variability in the eastern tropical Pacific as observed by TOPEX
and Tropical Ocean-Global Atmosphere Tropical Atmosphere-Ocean Experiment, J. Geophys. Res., 99,
24,73924,748, 1994.
Gill, A. E., An estimation of sea-level and surface-current anomalies during the 1972 El Niño and consequent
thermal effects, J. Phys. Oceanogr., 13, 586606, 1983.
Gill, A. E., and P. P. Niiler, A theory of the seasonal variability in the ocean, Deep Sea
Res. Oceanogr. Abstr., 20, 141177, 1973.
Gill, A. E., and E. M. Rasmusson, The 198283 climate anomaly in the equatorial Pacific, Nature, 305,
229234, 1983.
Glantz, M. H. (Ed.), Usable Science: Food Security, Early Warning, and El Niño, Proceedings of the Workshop on
ENSO/FEWS, Budapest, Hungary, October 2528, 1993, Nat. Cent. for Atmos. Res., Boulder, Colo.,
1994.
Goddard, L., and N. E. Graham, El Niño in the 1990s, J. Geophys. Res., 102, 10,42310,436, 1997.
Godfrey, J. S., R. A. Houze Jr., R. H. Johnson, R. Lukas, J.-L. Redelsperger, A. Sumi, and R. Weller, Coupled
Ocean-Atmosphere Response Experiment (COARE): An interim report, J. Geophys. Res., this issue.
Goldenberg, S. B., and J. J. O'Brien, Time and space variability of tropical Pacific wind stress, Mon. Weather
Rev., 109, 11901207, 1981.
Gossard, E. E., Measuring drop-size distributions in clouds with clear-air-sensing Doppler radar, J.
Atmos. Oceanic Technol., 5, 640649, 1988.
Gourdeau, L., J. F. Minster, and M. C. Gennero, Sea level anomalies in the tropical Atlantic from Geosat data
assimilated in a linear model, 19861988, J. Geophys. Res., 102, 55835594, 1997.
Graham, N. E., Simulation of recent global temperature trends, Science, 267, 666671, 1995.
Graham, N. E., and T. P. Barnett, Sea surface temperature, surface wind divergence, and convection over tropical oceans,
Science, 238, 657659, 1987.
Graham, N. E., and W. B. White, The El Niño cycle: A natural oscillator of the Pacific ocean-atmosphere system,
Science, 240, 12931302, 1988.
Gray, W. M., C. W. Landsea, P. Mielke, and K. Berry, Predicting Atlantic seasonal hurricane activity 611 months in
advance, Weather Forecasting, 7, 440455, 1993.
Greiner, E., and C. Perigaud, Assimilation of Geosat altimetric data in a nonlinear reduced-gravity model of the Indian
Ocean, I, Adjoint approach and model-data consistency, J. Phys. Oceanogr., 24, 17831804, 1994.
Greiner, E., and C. Perigaud, Assimilation of Geosat altimetric data in a nonlinear shallow-water model of the Indian
Ocean, II, Validation and interpretation of assimilated results, J. Phys. Oceanogr., 26,
17351746, 1996.
Gu, D., and S. G. H. Philander, Secular changes of annual and interannual variability in the tropics during the past
century, J. Clim., 8, 864876, 1995.
Gu, D., and S. G. H. Philander, Interdecadal climate fluctuations that depend on exchanges between the tropics and
extratropics, Science, 275, 805807, 1997.
Gu, D., S. G. H. Philander, and M. J. McPhaden, The seasonal cycle and its modulation in the eastern Pacific, J.
Phys. Oceanogr., in press, 1998.
Gutzler, D. S., and L. M. Hartten, Daily variability of lower tropospheric winds over the tropical western Pacific,
J. Geophys. Res., 100, 22,99923,008, 1995.
Gutzler, D. S., G. N. Kiladis, G. A. Meehl, K. M. Weickmann, and M. Wheeler, The global climate of December
1992February 1993, II, Large-scale variability across the tropical western Pacific during TOGA COARE, J.
Clim., 7, 16061622, 1994.
Halpern, D., A Pacific equatorial temperature section from 172°E to 110°W during winter-spring 1979, Deep Sea
Res., Part A, 27, 931940, 1980.
Halpern, D., Comparison of upper ocean VACM and VMCM observations in the equatorial Pacific, J. Atmos. Oceanic
Technol., 4, 8493, 1987a.
Halpern, D., Observations of annual and El Niño thermal and flow variations at 0°, 110°W and 0°,
95°W during 19801985, J. Geophys. Res., 92, 81978212, 1987b.
Halpern, D., Comparison of moored wind measurements from a spar and toroidal buoy in the eastern equatorial Pacific
during FebruaryMarch 1981, J. Geophys. Res., 92, 83038306, 1987c.
Halpern, D., On the accuracy of monthly mean wind speeds over the equatorial Pacific, J. Atmos. Oceanic
Technol., 5, 362367, 1988a.
Halpern, D., Moored surface wind observations at four sites along the Pacific equator between 140°W and 95°W,
J. Clim., 1, 12511260, 1988b.
Halpern, D., Validation of special sensor microwave imager monthly-mean wind speed from July 1987 to December 1989,
IEEE Trans. Geosci. Remote Sens., 31, 692699, 1993.
Halpern, D., Visiting TOGA's past, Bull. Am. Meteorol. Soc., 77, 233242, 1996.
Halpern, D., and M. Ji, An evaluation of the National Meteorological Center weekly hindcast of upper-ocean temperature
along the eastern Pacific equator in January 1992, J. Clim., 6, 12211226, 1993.
Halpern, D., H. P. Freitag, and A. Shepherd, Applications of ARGOS and RAMS measurements in equatorial Pacific
ocean-atmosphere interaction studies, in Proceedings of Oceans `84, pp. 736740,
Mar. Technol. Soc., Washington, D.C., 1984.
Halpern, D., R. A. Knox, and D. S. Luther, Observations of 20-day period meridional current oscillations in the upper
ocean along the Pacific equator, J. Phys. Oceanogr., 18, 15141534, 1988.
Halpern, D., Y. Chao, C.-C. Ma, and C. R. Mechoso, Comparison of tropical Pacific temperature and current simulations
with two vertical mixing schemes embedded in an ocean general circulation model and reference to observations, J.
Geophys. Res., 100, 25152522, 1995.
Halpert, M. S., and C. F. Ropelewski, Surface temperature patterns associated with the Southern Oscillation, J.
Clim., 5, 577593, 1992.
Han, Y.-J., and S.-W. Lee, An analysis of monthly mean wind stress over the global ocean, Mon. Weather Rev.,
111, 15541566, 1983.
Hanawa, K., P. Rual, R. Bailey, A. Sy, and M. Szabados, A new depth-time equation for Sippican or TSK T-7, T-6, and T-4
expendable bathythermographs (XBT), Deep Sea Res., Part I, 42, 14231451, 1995.
Hansen, D. V., and A. Herman, Temporal sampling requirements for surface drifting buoys in the tropical Pacific, J.
Atmos. Ocean Technol., 6, 599607, 1989.
Hansen, D. V., and C. A. Paul, Genesis and effects of long waves in the equatorial Pacific, J.
Geophys. Res., 89, 10,43110,440, 1984.
Hao, Z., and M. Ghil, Data assimilation in a simple tropical ocean model with wind stress errors, J.
Phys. Oceanogr., 24, 21112128, 1994.
Harrison, D. E., Local and remote forcing of ENSO ocean waveguide response, J. Phys. Oceanogr., 19,
691695, 1989.
Harrison, D. E., and B. S. Giese, Episodes of westerly winds as observed from islands in the western tropical Pacific,
J. Geophys. Res., 96, suppl., 32213237, 1991.
Harrison, D. E., and D. S. Luther, Surface winds from tropical Pacific islands: Climatological statistics, J.
Clim., 3, 251271, 1990.
Harrison, D. E., and P. S. Schopf, Kelvin wave induced advection and the onset of SST warming in El Niño events,
Mon. Weather Rev., 112, 923933, 1984.
Harrison, D. E., W. S. Kessler, and B. Giese, Hindcasts of the 198283 El Niño: Thermal and dynamic
variability along the ship of opportunity XBT tracks, J. Phys. Oceanogr., 19, 397418, 1989.
Harrison, D. E., B. S. Giese, and E. S. Sarachik, Mechanisms of SST change in the equatorial waveguide during the
198283 ENSO, J. Clim., 3, 173188, 1990.
Hartmann, D. L., H. H. Hendon, and R. A. Houze Jr., Some implications of the mesoscale circulations in tropical cloud
clusters for large-scale dynamics and climate, J. Atmos. Sci., 41, 113121, 1984.
Hayes, S. P., and M. J. McPhaden, Temporal sampling requirements for low frequency temperature variability in the
eastern equatorial Pacific Ocean, Tech. Memo. ERL PMEL-96, 17 pp., Natl. Oceanic and
Atmos. Admin., Silver Spring, Md., 1992.
Hayes, S. P., et al., The Equatorial Pacific Ocean Climate Studies (EPOCS) Plans: 19861988, Eos
Trans. AGU, 67, 442444, 1986.
Hayes, S. P., M. J. McPhaden, and A. Leetmaa, Observational verification of a quasi real-time simulation of the tropical
Pacific Ocean, J. Geophys. Res., 94, 21472157, 1989a.
Hayes, S. P., M. J. McPhaden, and J. M. Wallace, The influence of sea surface temperature on surface wind in the eastern
equatorial Pacific: Weekly to monthly variability, J. Clim., 2, 15001506, 1989b.
Hayes, S. P., L. J. Mangum, J. Picaut, A. Sumi, and K. Takeuchi, TOGA TAO: A moored array for real-time measurements in
the tropical Pacific Ocean, Bull. Am. Meteorol. Soc., 72, 339347, 1991a.
Hayes, S. P., P. Chang, and M. J. McPhaden, Variability of the sea surface temperature in the eastern equatorial Pacific
during 19861988, J. Geophys. Res., 96, 10,55310,566, 1991b.
Hayes, S. P., L. J. Mangum, and K. E. McTaggart, Repeat CTD measurements along PR16, I, WOCE Notes 3,
Tex. A&M Univ., College Station, 1991c.
Hebert, D., J. N. Moum, C. A. Paulson, D. R. Caldwell, T. K. Chereskin, and M. J. McPhaden, Detailed structure of the
upper ocean in the central equatorial Pacific during April 1987, J. Geophys. Res., 96, 71277136, 1991.
Hellerman, S., and M. Rosenstein, Normal monthly wind stress over the world ocean with error estimates, J.
Phys. Oceanogr., 13, 10931104, 1983.
Henin, C., and J. Grelet, A merchant ship thermo-salino- graph network in the Pacific Ocean, Deep Sea Res., Part I,
43, 18331855, 1996.
Henin, C., Y. du Penhoat, and M. Ioualalen, Observations of sea surface salinity in the western Pacific fresh pool:
Large-scale changes in 19921995, J. Geophys. Res., in press, 1998.
Hofmann, E. E., The large-scale horizontal structure of the Antarctic Circumpolar Current from FGGE drifters, J.
Geophys. Res., 90, 14,00414,012, 1985.
Horel, J. D., On the annual cycle in the tropical Pacific atmosphere and ocean, Mon. Weather Rev.,
110, 18631878, 1981.
Horel, J. D., and J. M. Wallace, Planetary scale atmospheric phenomena associated with the Southern Oscillation,
Mon. Weather Rev., 109, 813829, 1981.
Horel, J. D., V. E. Kousky, and M. T. Kagano, Atmospheric conditions in the Atlantic sector, Nature, 322,
248251, 1986.
Hoskins, B. J., and D. Karoly, The linear steady response of a spherical atmosphere to thermal and orographic forcing,
J. Atmos. Sci., 38, 11791196, 1981.
Houghton, R. W., The relationship of sea surface temperature to thermocline depth at annual and interannual timescales
in the tropical Atlantic Ocean, J. Geophys. Res., 96, 15,17315,185, 1991.
Houghton, R. W., and Y. Tourre, Characteristics of low-frequency sea surface temperature fluctuations in the tropical
Atlantic, J. Clim., 5, 765771, 1992.
Houze, R. A., Jr., Observed structure of mesoscale convective systems and implications for large-scale heating, Q. J.
R. Meteorol. Soc., 115, 425461, 1989.
Hurlburt, H. E., J. C. Kindle, and J. J. O'Brien, A numerical simulation of the onset of El Niño, J.
Phys. Oceanogr., 6, 621631, 1976.
Integrated Global Ocean Services System (IGOSS), Products bulletin, OctoberDecember 1992, report, Bundesampt
für Seeschiffahrt und Hydrogr., Hamburg, Germany, 28 pp., 1992.
International Research Institute for Climate Prediction Task Group, International Research Institute for Climate
Prediction: A Proposal, 51 pp., Off. of Global Programs, Natl. Oceanic and Atmos. Admin., Silver
Spring, Md., 1992.
International TOGA Project Office, Tropical Ocean Global Atmosphere (TOGA) International Implementation Plan, ITPO
Rep. 1, World Clim. Res. Programme, World Meteorol. Organ., Geneva, 1992.
Jacobs, G., H. Hurlburt, J. Kindle, E. Metzger, J. Mitchell, W. Teague, and A. Wallcraft, Decade-scale trans-Pacific
propagation and warming effects of an El Niño anomaly, Nature, 370, 360363, 1994.
Ji, M., and A. Leetmaa, Impact of data assimilation on ocean initialization and El Niño prediction,
Mon. Weather Rev., 125, 742753, 1997.
Ji, M., and T. M. Smith, Ocean model responses to temperature data assimilation and varying surface wind stress:
Intercomparisons and implications for climate forecast, Mon. Weather Rev., 123, 18111821, 1995.
Ji, M., A. Kumar, and A. Leetmaa, An experimental coupled forecast system at the national meteorological center: Some
early results, Tellus, Ser. A, 46, 398418, 1994.
Ji, M., A. Leetmaa, and J. Derber, An ocean analysis system for seasonal to interannual climate studies,
Mon. Weather Rev., 123, 460481, 1995.
Jin, F.-F., J. D. Neelin, and M. Ghil, El Niño on the devil's staircase: Annual subharmonic steps to chaos,
Science, 264, 7072, 1994.
Johnson, E. S., and M. J. McPhaden, On the structure of intraseasonal Kelvin waves in the equatorial Pacific Ocean,
J. Phys. Oceanogr., 23, 608625, 1993a.
Johnson, E. S., and M. J. McPhaden, Effects of a three-dimensional mean flow on intraseasonal Kelvin waves in the
equatorial Pacific Ocean, J. Geophys. Res., 98, 10,18510,194, 1993b.
Jones, C., and C. Gautier, Coupled modes of air-sea interaction and the Madden and Julian Oscillation, paper presented
at the TOGA95 Symposium, World Meteorol. Organ., Melbourne, Victoria, Australia, April 27, 1995.
Jones, C. S., D. M. Legler, and J. J. O'Brien, Variability of surface fluxes over the Indian Ocean: 19601989,
Global Atmos. Ocean Syst., 3, 249272, 1995.
Kalnay, E., et al., The NCEP/NCAR 40-year reanalysis project, Bull. Am. Meteorol. Soc., 77,
437471, 1996.
Katz, E. J., A. J. Busalacchi, M. Bushnell, F. Gonzalez, L. Gourdeau, M. J. McPhaden, and J. Picaut, A comparison of
coincidental time series of the ocean surface height by satellite altimeter, mooring, and inverted echo sounder, J.
Geophys. Res., 100, 25,10125,108, 1995a.
Katz, E. J., J. A. Carton, and A. Chakraborty, Dynamics of the equatorial Atlantic from altimetry, J.
Geophys. Res., 100, 25,06125,068, 1995b.
Keen, R. A., The role of cross-equatorial tropical cyclone pairs in the Southern Oscillation, Mon. Weather
Rev., 110, 14051416, 1982.
Kessler, W. S., Observations of long Rossby waves in the northern tropical Pacific, J. Geophys. Res.,
95, 51835218, 1990.
Kessler, W. S., Can reflected extra-equatorial Rossby waves drive ENSO?, J. Phys. Oceanogr., 21,
444452, 1991.
Kessler, W. S., and J. P. McCreary Jr., The annual wind-driven Rossby wave in the subthermocline equatorial Pacific,
J. Phys. Oceanogr., 23, 11921207, 1993.
Kessler, W. S., and M. J. McPhaden, The 199193 El Niño in the central Pacific, Deep Sea Res., Part
II, 42, 295334, 1995a.
Kessler, W. S., and M. J. McPhaden, Oceanic equatorial waves and the 199193 El Niño, J. Clim.,
8, 17571774, 1995b.
Kessler, W. S., and B. A. Taft, Dynamic heights and zonal geostrophic transports in the central equatorial Pacific
during 197984, J. Phys. Oceanogr., 17, 97122, 1987.
Kessler, W. S., M. J. McPhaden, and K. M. Weickmann, Forcing of intraseasonal Kelvin waves in the equatorial Pacific,
J. Geophys. Res., 100, 10,61310,631, 1995.
Kessler, W. S., M. C. Spillane, M. J. McPhaden, and D. E. Harrison, Scales of variability in the equatorial Pacific
inferred from the Tropical Atmosphere-Ocean (TAO) array, J. Clim., 9, 29993204, 1996.
Kilonsky, B., and P. Caldwell, In the pursuit of high-quality sea level data, IEEE Oceans Proc., 2,
669675, 1991.
Kindle, J. C., and P. A. Phoebus, The ocean response to operational westerly wind bursts during the 19911992 El
Niño, J. Geophys. Res., 100, 48934920, 1995.
Kleeman, R., A. M. Moore, and N. R. Smith, Assimilation of subsurface thermal data into a simple ocean model for
initialization of an intermediate tropical coupled ocean-atmosphere forecast model, Mon. Weather Rev.,
123, 31033113, 1995.
Knox, R. A., Equatorial response to impulsive wind, in Further Progress in Equatorial Oceanography, edited by E.
J. Katz and J. M. Witte, pp. 323334, Nova Univ. Press, Fort Lauderdale, Fla., 1987.
Knox, R. A., and D. L. T. Anderson, Recent advances in the study of the low-latitude circulation,
Prog. Oceanogr., 14, 259317, 1985.
Knox, R., and D. Halpern, Long range Kelvin wave propagation of transport variations in Pacific Ocean equatorial
currents, J. Mar. Res., 40, suppl., 329339, 1982.
Köberle, C., and S. G. H. Philander, On the processes that control seasonal variations of sea surface temperatures
in the tropical Pacific Ocean, Tellus, Ser. A, 46, 481496, 1994.
Koblinsky, C. J., P. Gaspar, and G. Lagerloef (Eds.), The Future of Spaceborne Altimetry: Oceans and Climate
Change, 75 pp., Joint Oceanogr. Inst. Inc., Washington, D.C., 1992.
Koehn, M., L. Mangum, and M. McPhaden (Eds.), Proceedings of the Third Meeting of the TOGA-TAO Implementation Panel,
Seoul, Oct. 1820, 1994, International TOGA Project Office Rep. 12, 61 pp.,
Pac. Mar. Environ. Lab., Seattle, Wash., 1995.
Koehn, M. P., L. J. Mangum, and M. J. McPhaden (Eds.), Proceedings of the Fourth Workshop of the TAO Implementation
Panel, Fortaleza, Brazil, Sept. 1214, 1995, WCRP Informal Rep. 1/1996, 61 pp.,
Pac. Mar. Environ. Lab., Seattle, Wash., 1996.
Kumar, A., A. Leetmaa, and M. Ji, Simulations of atmospheric variability induced by sea surface temperatures and
implications for global warming, Science, 266, 632634, 1994.
Kuroda, Y., and M. J. McPhaden, Variability in the western equatorial Pacific Ocean during JAPACS cruises in 1989 and
1990, J. Geophys. Res., 98, 47474759, 1993.
Kutsuwada, K., and H. Inaba, Long-range measurement of surface oceanic current in the western equatorial Pacific by
acoustic doppler current profiler, J. Meteorol. Soc. Jpn., 73(1), 111, 1995.
Latif, M., and T. P. Barnett, Interactions of the tropical oceans, J. Clim., 8, 952964, 1995.
Latif, M., and N. E. Graham, How much predictive skill is contained in the thermal structure of an oceanic GCM?, J.
Phys. Oceanogr., 22, 951962, 1992.
Latif, M., J. Biercamp, and H. von Storch, The response of a coupled ocean-atmosphere general circulation model to wind
bursts, J. Atmos. Sci., 45, 964979, 1988.
Latif, M., T. P. Barnett, M. A. Cane, M. Fluegel, N. E. Graham, H. von Storch, J.-S. Xu, and S. E. Zebiak, A review of
ENSO prediction studies, Clim. Dyn., 9, 167179, 1994.
Latif, M., R. Kleeman, and C. Eckert, Greenhouse warming, decadal variability, or El Niño: An attempt to
understand the anomalous 1990s, J. Clim., 10, 22212239, 1997.
Latif, M., D. Anderson, T. Barnett, M. Cane, R. Kleeman, A. Leetmaa, J. O'Brien, A. Rosati, and E. Schneider, A review
of the predictability and prediction of ENSO, J. Geophys. Res., this issue.
Lau, K.-M., and A. J. Busalacchi, El Niño Southern Oscillation: A view from space, in Atlas of Satellite
Observations Related to Global Change, edited by R. J. Gurney, J. L. Foster, and C. L. Parkinson,
pp. 281294, Cambridge Univ. Press, New York, 1993.
Lau, K.-M., and P. H. Chan, The 4050 day oscillation and the El Niño/Southern Oscillation: A new
perspective, Bull. Am. Meteorol. Soc., 67, 533534, 1986.
Lau, N.-C., and N. J. Nath, A modeling study of the relative roles of tropical and extratropical SST anomalies in the
variability of the global atmosphere-ocean system, J. Clim., 7, 11841207, 1994.
Leetmaa, A., and M. Ji, Operational hindcasting of the tropical Pacific, Dyn. Atmos. Oceans, 13,
465490, 1989.
Leetmaa, A., and M. Ji, Ocean data assimilation as a component of a climate forecast system, in Modern Approaches to
Data Assimilation in Ocean Modeling, edited by P. Malanotte-Rizzoli, pp. 271293, Elsevier, New York,
1996.
Legeckis, R., Long waves in the eastern equatorial Pacific Ocean: A view from a geostationary satellite, Science,
197, 11791181, 1977.
Legler, D. M., Producing surface wind products for oceanographers, paper presented at the IGOSS/IOC Ocean Products
Workshop, International Oceanographic Commission, Tokyo, April 1991.
Legler, D. M., and J. J. O'Brien, Atlas of Tropical Pacific Wind Stress Climatology 19711980, 182 pp.,
Dep. of Meteorol., Fl. State Univ., Tallahassee, 1984.
Legler, D. M., I. M. Navon, and J. J. O'Brien, Objective analysis of pseudo-stress over the Indian Ocean using a
direct-minimisation approach, Mon. Weather Rev., 117, 709720, 1989.
Lehodey, P., M. Bertignac, J. Hampton, A. Lewis, and J. Picaut, El Niño Southern Oscillation and tuna in the
western Pacific, Nature, 389, 715718, 1997.
Le Traon, P. Y., P. Gaspar, F. Bouyssel, and H. Makhmara, Using TOPEX/POSEIDON data to enhance ERS-1 orbit, J.
Atmos. Oceanic Technol., 12, 161170, 1995.
Levitus, S., and T. P. Boyer, World Ocean Atlas 1994, vol. 4, Temperature, NOAA Atlas NESDIS 4,
Washington D.C., 117 pp., Natl. Oceanic and Atmos. Admin., 1994.
Levitus, S., R. Burgett, and T. P. Boyer, World Ocean Atlas 1994, vol. 3, Salinity, NOAA Atlas NESDIS
3, 99 pp., Natl. Oceanic and Atmos. Admin., Washington, D.C., 1994a.
Levitus, S., T. P. Boyer, and J. Antonov, World Ocean Atlas 1994, vol. 5, Interannual Variability of Upper Ocean
Thermal Structure, NOAA Atlas NESDIS 5, 176 pp., Natl. Oceanic and Atmos. Admin., Washington, D.C.,
1994b.
Li, B., and A. J. Clarke, An examination of some ENSO mechanisms using interannual sea level at the eastern and western
equatorial boundaries and the zonally averaged equatorial wind, J. Phys. Oceanogr., 24, 681690,
1994.
Li, T., and S. G. H. Philander, On the annual cycle of the eastern equatorial Pacific, J. Clim., 9,
29862998, 1996.
Lien, R.-C., M. J. McPhaden, and D. Hebert, Intercomparison of ADCP measurements at 0°, 140°W, J.
Atmos. Oceanic Technol., 11, 13341349, 1994.
Lien, R.-C., D. R. Caldwell, M. C. Gregg, and J. N. Moum, Turbulence variability at the equator in the central Pacific
at the beginning of the 19911993 El Niño, J. Geophys. Res., 100, 68816898, 1995.
Lien, R.-C., M. J. McPhaden, and M. C. Gregg, High-frequency internal waves in the upper central equatorial Pacific and
their possible relationship to deep-cycle turbulence, J. Phys. Oceanogr., 26, 581600, 1996.
Lillibridge, J. L., R. E. Cheney, and N. S. Doyle, The 199193 Los Niños from ERS-1 altimetry, in
Proceedings of the Second ERS-1 Symposium, Eur. Space Agency Spec. Publ., ESA SP-361,
495499, 1994.
Lindstrom, E., R. Lukas, R. Fine, E. Firing, S. Godfrey, G. Meyers, and M. Tsuchiya, The Western Equatorial Pacific
Ocean Circulation Study, Nature, 330, 533537, 1987.
Lindzen, R. S., and S. Nigam, On the role of sea surface temperature gradients in forcing low-level winds and
convergence in the tropics, J. Atmos. Sci., 44, 24182436, 1987.
Liu, W. T., Moisture and latent heat flux variabilities in the tropical Pacific derived from satellite data, J.
Geophys. Res., 93, 67496760, 1988.
Liu, W. T., and C. Gautier, Thermal forcing of the tropical Pacific from satellite data, J. Geophys. Res.,
95, 13,20913,217, 1990.
Liu, W. T., K. B. Katsaros, and J. A. Businger, Bulk parameterization of air-sea exchanges of heat and water vapor
including molecular constraints at the interface, J. Atmos. Sci., 36, 17221735, 1979.
Liu, W. T., W. Tang, and R. Atlas, Responses of the tropical Pacific to wind forcing as observed by spaceborne sensors
and simulated by an ocean general circulation model, J. Geophys. Res., 101, 16,34516,359, 1996.
Lorenc, A. C., A global three-dimensional multivariate statistical interpolation scheme, Mon. Weather Rev.,
109, 701721, 1981.
Lu, P., and J. P. McCreary Jr., Influence of the ITCZ on the flow of thermocline water from the subtropical to the
equatorial Pacific Ocean, J. Phys. Oceanogr., 25, 30763088, 1995.
Lukas, R., and E. Firing, The annual Rossby wave in the central equatorial Pacific Ocean, J.
Phys. Oceanogr., 15, 5567, 1985.
Lukas, R., and E. Lindstrom, The mixed layer in the western equatorial Pacific Ocean, J. Geophys. Res.,
96, 33433357, 1991.
Lukas, R., P. J. Webster, M. Ji, and A. Leetmaa, The large-scale context for the TOGA Coupled Ocean-Atmosphere Response
Experiment, Meteorol. Atmos. Phys., 56, 316, 1995.
Luther, D. S., and E. S. Johnson, Eddy energetics in the upper equatorial Pacific during the Hawaii-to-Tahiti Shuttle
Experiment, J. Phys. Oceanogr., 20, 913944, 1990.
Luther, D. S., D. E. Harrison, and R. A. Knox, Zonal winds in the central equatorial Pacific and El Niño,
Science, 222, 327330, 1983.
Madden, R. A., and P. R. Julian, Detection of a 4050 day oscillation in the zonal wind in the tropical Pacific,
J. Atmos. Sci., 28, 702708, 1971.
Madden, R. A., and P. R. Julian, Description of global-scale circulation cells in the tropics with a 4050 day
period, J. Atmos. Sci., 29, 11091123, 1972.
Mangum, L. J., S. P. Hayes, J. M. Toole, Z. Wang, S. Pu, and D. Hu, Thermohaline structure and zonal pressure gradient
in the western equatorial Pacific, J. Geophys. Res., 95, 72797288, 1990.
Mangum, L. J., S. P. Hayes, and L. D. Stratton, Sampling requirements for the surface wind field over the tropical
Pacific Ocean, J. Clim., 9, 668679, 1992.
Mangum, L. J., H. P. Freitag, and M. J. McPhaden, TOGA-TAO array sampling schemes and sensor evaluations, paper
presented at OCEANS 94 OSATES, Institute of Electrical and Electronics Engineers/Ocean Engineering Society, Parc de
Penfeld, Brest, France, Sept. 1316, 1994.
Mantua, N. J., and D. S. Battisti, Evidence for the delayed oscillator mechanism for ENSO: the "observed" oceanic Kelvin
mode in the far western Pacific, J. Phys. Oceanogr., 24, 691699, 1994.
Mapes, B. E., and R. A. Houze Jr., Diabatic divergence profiles in western Pacific mesoscale convective systems, J.
Atmos. Sci., 52, 18071828, 1995.
Maury, M. F., The Physical Geography of the Sea, Harper Collins, New York, 1859.
May, D. A., M. M. Parmeter, D. S. Olszewski, and B. D. McKenzie, Operational processing of satellite sea surface
temperature retrievals at the Naval Oceanographic Office, Bull. Am. Meteorol. Soc., 79, 397407, 1998.
McCarty, M. E., and M. J. McPhaden, Mean seasonal cycles and interannual variations at 0°, 165°E during
19861992, Tech. Memo. ERL PMEL-98, 64 pp., Natl. Oceanic and Atmos. Admin., Silver
Spring, Md., 1993.
McClain, E. P., W. G. Pichel, and C. C. Walton, Comparative performance of AVHRR-based multichannel sea surface
temperatures, J. Geophys. Res., 90, 11,58711,601, 1985.
McCreary, J., Eastern tropical ocean response to changing wind systems, with application to El Niño, J.
Phys. Oceanogr., 6, 632645, 1976.
McCreary, J. P., Jr., A linear stratified ocean model of the equatorial undercurrent, Philos. Trans. R.
Soc. London A, 298, 603635, 1980.
McCreary, J. P., Jr., A model of tropical ocean-atmosphere interaction, Mon. Weather Rev., 111,
370387, 1983.
McCreary, J. P., Jr., and D. L. T. Anderson, An overview of coupled ocean-atmosphere models of El Niño and the
Southern Oscillation, J. Geophys. Res., 96, suppl., 31253150, 1991.
McCreary, J. P., Jr., and P. Lu, On the interaction between the subtropical and the equatorial oceans: The subtropical
cell, J. Phys. Oceanogr., 24, 466497, 1994.
McCreary, J. P., P. K. Kundu, and R. L. Molinari, A numerical investigation of dynamics, thermodynamics and mixed-layer
processes in the Indian Ocean, Prog. Oceanogr., 31, 181244, 1993.
McPhaden, M. J., Continuously stratified models of the steady-state equatorial ocean, J. Phys. Oceanogr.,
11, 337354, 1981.
McPhaden, M. J., TOGA-TAO and the 199193 El Niño-Southern Oscillation event, Oceanography, 6,
3644, 1993a.
McPhaden, M. J. (Ed.), Proceedings of the First Workshop of the TOGA-TAO Implementation Panel, Joint Institute for
Marine and Atmospheric Research, East-West Center, University of Hawaii, Honolulu, Nov. 910, 1992, technical
report, 26 pp., Joint Institute for Marine and Atmospheric Research, Honolulu, Hawaii, 1993b.
McPhaden, M. J. (Ed.), Proceedings of the Second Workshop of the TOGA-TAO Implementation Panel, Bali, Indonesia, October
1820, 1993, ITPO Publ. 10, 53 pp., Pac. Mar. Environ. Lab., Seattle, Wash., 1994.
McPhaden, M. J., The Tropical Atmosphere-Ocean array is completed, Bull. Am. Meteorol. Soc.,
76, 739741, 1995.
McPhaden, M. J., Monthly period oscillations in the Pacific North Equatorial Countercurrent, J. Geophys. Res.,
101, 63376359, 1996.
McPhaden, M. J., and R. A. Fine, A dynamical interpretation of the tritium maximum in the central equatorial Pacific,
J. Phys. Oceanogr., 18, 14541457, 1988.
McPhaden, M. J., and S. P. Hayes, Moored velocity, temperature and wind measurements in the equatorial Pacific Ocean: A
review of scientific results, 19851990, paper presented at the International TOGA Scientific Conference, World
Clim. Res. Program, Honolulu, Hawaii, July 1620, 1990a.
McPhaden, M. J., and S. P. Hayes, Variability in the eastern equatorial Pacific during 19861988, J.
Geophys. Res., 95, 13,19513,208, 1990b.
McPhaden, M. J., and S. P. Hayes, On the variability of winds, sea surface temperature, and surface layer heat content
in the western equatorial Pacific, J. Geophys. Res., 96, suppl., 33313342, 1991.
McPhaden, M. J., and M. E. McCarty, Mean seasonal cycles and interannual variations at 0°, 110°W and 0°,
140°W during 19801991, Tech. Memo. ERL PMEL-95, 118 pp., Natl. Oceanic and
Atmos. Admin., Silver Spring, Md., 1992.
McPhaden, M. J., and H. Peters, Diurnal cycle of internal wave variability in the equatorial Pacific Ocean: Results from
moored observations, J. Phys. Oceanogr., 22, 13171329, 1992.
McPhaden, M. J., and J. Picaut, El Niño-Southern Oscillation displacements of the western equatorial Pacific warm
pool, Science, 250, 13851388, 1990.
McPhaden, M. J., and B. A. Taft, Proceedings of the First International TOGA Workshop on Thermal Sampling,
Rep. USTOGA-3, 53 pp., Univ. Corp. for Atmos. Res., Boulder, Colo., 1984.
McPhaden, M. J., and B. A. Taft, On the dynamics of seasonal and intraseasonal variability in the eastern equatorial
Pacific, J. Phys. Oceanogr., 18, 17131732, 1988.
McPhaden, M. J., H. P. Freitag, S. P. Hayes, B. A. Taft, Z. Chen, and K. Wyrtki, The response of the equatorial Pacific
Ocean to a westerly wind burst in May 1986, J. Geophys. Res., 93, 10,58910,603, 1988a.
McPhaden, M. J., A. J. Busalacchi, and J. Picaut, Observations and wind-forced model simulations of the mean seasonal
cycle in tropical Pacific sea surface topography, J. Geophys. Res., 93, 81318146, 1988b.
McPhaden, M. J., A. J. Busalacchi, and J. Picaut, A model study of potential sampling errors due to data scatter around
expendable bathythermograph transects in the tropical Pacific Ocean, J. Geophys. Res., 93,
81198130, 1988c.
McPhaden, M. J., S. P. Hayes, L. J. Mangum, and J. M. Toole, Variability in the western equatorial Pacific during the
198687 El Niño-Southern Oscillation event, J. Phys. Oceanogr., 20, 190208, 1990a.
McPhaden, M. J., H. B. Milburn, A. I. Nakamura, and A. J. Shepherd, PROTEUS-Profile Telemetry of Upper Ocean Currents,
in Proceedings of the Marine Technology Society Conference, Sept. 2528, 1990, pp. 353357,
Mar. Technol. Soc., Washington, D.C., 1990b.
McPhaden, M., H. P. Freitag, and A. Shepherd, Moored salinity time series measurements at 0°140°W, J.
Atmos. Oceanic Technol., 7, 568575, 1990c.
McPhaden, M. J., D. V. Hansen, and P. L. Richardson, A comparison of ship drift, drifting buoy, and current meter
mooring velocities in the Pacific South Equatorial Current, J. Geophys. Res., 96, 775782, 1991.
McPhaden, M. J., F. Bahr, Y. du Penhoat, E. Firing, S. P. Hayes, P. P. Niiler, P. L. Richardson, and J. M. Toole, The
response of the western equatorial Pacific Ocean to westerly wind bursts during November 1989 to January 1990, J.
Geophys. Res., 97, 14,28914,303, 1992.
Mechoso, C. R., et al., The seasonal cycle over the tropical Pacific in coupled ocean-atmosphere general circulation
models, Mon. Weather Rev., 123, 28252838, 1995.
Menkes, C., and A. J. Busalacchi, The impact of TOGA-TAO wind observations on tropical Pacific Ocean simulations, paper
presented at the TOGA95 Symposium, World Meteorol. Organ., Melbourne, Victoria, Australia, April 27, 1995.
Menkes, C., J.-P. Boulanger, and A. J. Busalacchi, Evaluation of TOPEX and basin-wide Tropical Ocean and Global
Atmosphere-Tropical Atmosphere Ocean sea surface topographies and derived geostrophic currents, J.
Geophys. Res., 100, 25,08725,100, 1995.
Meyers, G., Variation of Indonesian throughflow and the El Niño-Southern Oscillation, J.
Geophys. Res., 101, 12,25512,263, 1996.
Meyers, G., and H. Phillips, TOGA XBT sampling strategy, TOGA Notes 7, Oceanogr. Cent., Nova
Southeastern Univ., Dania, Fla., 1992.
Meyers, G., J. R. Donguy, and D. Cutchin, Tropical Pacific thermocline topography during 19791983, in Papers
From 1982/83 El Niño Southern Oscillation Workshop, pp. 3352, Atl. Oceanogr. and
Meteorol. Lab., Miami, Fla., 1983.
Meyers, G., J. R. Donguy, and R. K. Reed, Evaporative cooling of the western equatorial Pacific Ocean by anomalous
winds, Nature, 323, 523526, 1986.
Meyers, G., H. Phillips, N. Smith, and J. Sprintall, Space and time scales for optimal interpolation of
temperature-Tropical Pacific Ocean, Prog. Oceanogr., 28, 189218, 1991.
Meyers, G., R. J. Bailey, and A. P. Worby, Geostrophic transport of Indonesian throughflow, Deep Sea Res., Part
I, 42, 11631174, 1995.
Milburn, H. B., and P. D. McLain, ATLAS-A low cost satellite data telemetry mooring developed for NOAA's Climate
Research Mission, paper presented at the Marine Data Systems International Symposium, Institute of Electrical and
Electronics Engineers/Marine Technology Society, New Orleans, La., April 30 to May 2, 1986.
Miller, A. J., R. C. Daniel, T. P. Barnett, N. E. Graham, and J. M. Oberhuber, Interdecadal variability of the Pacific
Ocean: Model response to observed heat flux and wind stress anomalies, Clim. Dyn., 9, 287302, 1994.
Miller, L., and R. E. Cheney, Large-scale meridional transport in the tropical Pacific Ocean during the 19861987
El Niño, J. Geophys. Res., 95, 17,90517,920, 1990.
Miller, L., R. E. Cheney, and B. C. Douglas, Geosat altimeter observations of Kelvin waves and the 198687 El
Niño, Science, 239, 5254, 1988.
Miller, R. N., Tropical data assimilation experiments with simulated data: The impact of the Tropical Ocean and Global
Atmosphere Thermal Array for the Ocean, J. Geophys. Res., 95, 11,46111,482, 1990.
Minobe, S., and K. Takeuchi, Annual period equatorial waves in the Pacific Ocean, J. Geophys. Res.,
100, 18,37918,392, 1995.
Mitchum, G., Comparison of TOPEX sea surface heights and tide gauge sea levels, J. Geophys. Res., 99,
24,54124,554, 1994.
Mitchum, G., and R. Lukas, Westward propagation of annual sea level and wind signals in the western Pacific Ocean, J.
Clim., 10, 11021110, 1990.
Mitchum, G., B. Kilonsky, and B. Miyamoto, Methods for maintaining a stable datum in a sea level monitoring system,
paper presented at OCEANS 94 OSATES, Institute of Electrical and Electronics Engineers/Oceanic Engineering Society, Parc
de Penfeld, Brest, France, Sept. 1316, 1994.
Mizuno, K., B. N. Peter, and T. Watanabe, Observation on subsurface temperature by voluntary ships, in
Jpn. Exp. on Asian Monsoon (JEXAM) Annu. Rep., pp. 4159, Res. and Dev. Bur.,
Sci. and Technol. Agency, Tokyo, 1995.
Molinari, R. L., and E. Johns, Upper layer temperature structure of the western tropical Atlantic, J.
Geophys. Res., 99, 18,22518,233, 1994.
Molinari, R. L., D. Olson, and G. Reverdin, Surface current distributions in the tropical Indian Ocean derived from
compilations of surface buoy trajectories, J. Geophys. Res., 95, 72177238, 1990.
Moore, A. M., Aspects of geostrophic adjustment during tropical ocean data assimilation, J. Phys. Oceanogr.,
19, 435461, 1989.
Moore, A. M., Linear equatorial wave mode initialisation in a model of the tropical Pacific Ocean: An initialisation
scheme for tropical ocean models, J. Phys. Oceanogr., 20, 423445, 1990.
Moore, A. M., The role of dynamic adjustment during tropical ocean data assimilation, in Trends in Physical
Oceanography, Res. Trends Ser., vol. 1, pp. 114, Counc. of
Sci. Res. Integration, Trivandrum, India, 1992.
Moore, A. M., and D. L. T. Anderson, The assimilation of XBT data into a layer model of the tropical Pacific Ocean,
Dyn. Atmos. Oceans., 13, 441464, 1989.
Moore, A. M., N. S. Cooper, and D. L. T. Anderson, Initialisation and data assimilation in models of the Indian Ocean,
J. Phys. Oceanogr., 17, 19651977, 1987.
Moum, J. N., M. J. McPhaden, D. Hebert, H. Peters, C. A. Paulson, and D. R. Caldwell, Internal waves, dynamic
instabilities and turbulence in the equatorial thermocline, J. Phys. Oceanogr., 22, 13571359,
1992.
Moura, A., and J. Shukla, On the dynamics of droughts in northeast Brazil: Observations, theory, and numerical
experiments with a general circulation model, J. Atmos. Sci., 38, 26532675, 1981.
Murray, J. W., R. T. Barber, M. R. Roman, M. P. Bacon, and R. A. Feely, Physical and biological controls on carbon
cycling in the equatorial Pacific, Science, 266, 5865, 1994.
Musman, S., Geosat altimeter observations of long waves in the equatorial Atlantic, J. Geophys. Res.,
97, 35733580, 1992.
Nastrom, G. D., and T. E. VanZandt, Mean vertical motions seen by radar wind profilers, J. Appl. Meteorol.,
33, 984995, 1994.
National Centers for Environmental Prediction, Experimental Long Lead Forecast Bulletin, vol. 5, 54 pp.,
Nat. Oceanic and Atmos. Admin., Washington, D.C., 1996.
National Research Council, El Niño and the Southern Oscillation: A Scientific Plan, 72 pp.,
Nat. Acad. of Sci., Washington, D.C., 1983.
National Research Council, U.S. Participation in the TOGA Program: A Research Strategy, 24 pp.,
Nat. Acad. of Sci., Washington, D.C., 1986.
National Research Council, TOGA: A Review of Progress and Future Opportunities, 51 pp., Nat. Acad. of
Sci., Washington, D.C., 1990.
National Research Council, Ocean-Atmosphere Observations for Short-term Climate Predictions, 51 pp.,
Nat. Acad. of Sci., Washington, D.C., 1994a.
National Research Council, GOALS (Global Ocean-Atmosphere-Land System) for Predicting Seasonal to Interannual
Climate, 103 pp., Nat. Acad. of Sci., Washington, D.C., 1994b.
National Research Council, Learning to Predict Climate Variations Associated with El Niño and the Southern
Oscillation, 171 pp., Nat. Acad. Sci., Washington, D.C., 1996.
National Weather Service, Experimental Long Lead Forecast Bulletin, 63 pp., Washington, D.C., September 1997.
Nerem, R. S., B. J. Haines, J. Hendricks, J. F. Minster, G. T. Mitchum, and W. B. White, Improved determination of
global mean sea level variations using TOPEX/POSEIDON altimeter data, Geophys. Res. Lett., 24,
13311334, 1997.
Nicholls, N., Sea surface temperatures and Australian winter rainfall, J. Clim., 2, 965973, 1989.
Nigam, S., and Y. Chao, Evolution dynamics of tropical ocean-atmosphere annual cycle variability, J. Clim., 9,
31873205, 1996.
Niiler, P. P., and J. Paduan, Wind-driven motions in the northeast Pacific as measured by Lagrangian drifters, J.
Phys. Oceanogr., 25, 28192830, 1995.
Niiler, P. P., R. E. Davis, and H. J. White, Water-following characteristics of a mixed layer drifter, Deep Sea
Res., Part A, 34, 18671881, 1987.
Niiler, P. P., A. Sybrandy, K. Bi, P. Poulain, and D. Bitterman, Measurements of the water-following capability of
Holey-sock and TRISTAR drifters, Deep Sea Res., Part I, 42, 19511964, 1995.
Nova University, U.S. TOGA Observing System Mid-Life Progress Review and Recommendations for Continuation, 102
pp., Ft. Lauderdale, Fla., 1989.
Oberhuber, J. M., An atlas based on the COADS data set: The budget of heat, buoyancy and turbulent kinetic energy at the
surface of the global ocean, Rep. 15, Max Planck Inst. for Meteorol., Hamburg, Germany, 1988.
Ocean Observing System Development Panel, Scientific Design for the Common Module of the Global Ocean Observing
System and the Global Climate Observing System: An Ocean Observing System for Climate, 265 pp., Dep. of
Oceanogr., Tex. A&M Univ., College Station, 1995.
Palmer, T. N., C. Brankovic, P. Viterbo, and M. J. Miller, Modeling interannual variations of summer monsoons,
J. Clim., 5, 399407, 1992.
Parsons, D., The integrated sounding system: Description and preliminary observations from TOGA COARE,
Bull. Am. Meteorol. Soc., 75, 553567, 1994.
Pearcy, W. G., and A. Schoener, Changes in the marine biota coincident with the 19821983 El Niño in the
northeastern subarctic Pacific Ocean, J. Geophys. Res., 92, 14,41714,248, 1987.
Perigaud, C., Sea level oscillations observed with Geosat along the two shear fronts of the Pacific North Equatorial
Countercurrent, J. Geophys. Res., 95, 72397248, 1990.
Perigaud, C., and P. Delecluse, Annual sea level variations in the southern tropical Indian Ocean from Geosat and
shallow-water simulations, J. Geophys. Res., 97, 20,16920,178, 1992.
Perigaud, C., and P. Delecluse, Interannual sea level variations in the tropical Indian Ocean from Geosat and
shallow-water simulations, J. Phys. Oceanogr., 23, 19161934, 1993.
Peters, C. A., V. M. Gerald, P. Woiceshyn, and W. H. Gemmill, Operational processing of ERS-1 scatterometer winds, A
documentation, NMC/OPC Tech. Rep. 96, 12 pp., Camp Springs, Md., 1994.
Philander, S. G. H., Equatorial Undercurrent: Measurements and theories, Rev. Geophys., 11,
513570, 1973.
Philander, S. G. H., Instabilities of zonal equatorial currents, 2, J. Geophys. Res., 83,
36793682, 1978.
Philander, S. G. H., Unusual conditions in the tropical Atlantic Ocean in 1984, Nature, 322, 236238, 1986.
Philander, S. G. H., El Niño, La Niña, and the Southern Oscillation, 293 pp., Academic, San Diego,
Calif., 1990.
Philander, S. G. H., and R. C. Pacanowski, The generation of equatorial currents, J. Geophys. Res., 85,
11231136, 1980.
Philander, S. G. H., and A. D. Seigel, Simulation of the El Niño of 198283, in Coupled Ocean-Atmosphere
Models, Elsevier Oceanogr. Ser., vol. 40, edited by J. C. J. Nihoul, pp. 517542,
Elsevier, New York, 1985.
Philander, S. G. H., T. Yamagata, and R. C. Pacanowski, Unstable air-sea interactions in the tropics, J.
Atmos. Sci., 41, 604613, 1984.
Philander, S. G. H., D. Halpern, D. Hansen, R. Legeckis, L. Miller, C. Paul, R. Watts, R. Weisberg, and M. Wimbush, Long
waves in the equatorial Pacific Ocean, Eos Trans. AGU, 66, 154, 1985.
Philander, S. G. H., W. J. Hurlin, and R. C. Pacanowski, Properties of long equatorial waves in models of the seasonal
cycle in the tropical Atlantic and Pacific Oceans, J. Geophys. Res., 91, 14,20714,211,
1986.
Philander, S., W. Hurlin, and A. Seigel, A model of the seasonal cycle in the tropical Pacific Ocean,
J. Phys. Oceanogr., 17, 19862002, 1987.
Phoebus, P. A., and J. S. Goerss, The operational assimilation of SSM/I wind speed data, paper presented at the Ninth
Conference on Numerical Weather Prediction, Am. Meteorol. Soc., Denver, Colo., Oct. 1418, 1991.
Phoebus, P. A., P. A. Wittmann, and J. S. Goerss, Model comparisons depicting the influence of SSM/I wind observations,
paper presented at the Seventh Conference on Satellite Meteorology and Oceanography, Am. Meteorol. Soc.,
Monterey, Calif., June 610, 1994.
Picaut, J., and T. Delcroix, Equatorial wave sequence associated with warm pool displacements during the 19861989
El Niño-La Niña, J. Geophys. Res., 100, 18,39318,408, 1995.
Picaut, J., and R. Tournier, Monitoring the 19791985 Equatorial Pacific current transports with expendable
bathythermography data, J. Geophys. Res., 96, suppl., 32633277, 1991.
Picaut, J., S. P. Hayes, and M. J. McPhaden, Use of the geostrophic approximation to estimate time-varying zonal
currents at the equator, J. Geophys. Res., 94, 32283236, 1989.
Picaut, J., A. J. Busalacchi, M. J. McPhaden, and B. Camusat, Validation of the geostrophic method for estimating zonal
currents at the equator from Geosat altimeter data, J. Geophys. Res., 95, 30153024, 1990.
Picaut, J., A. J. Busalacchi, M. J. McPhaden, L. Gourdeau, F. I. Gonzalez, and E. Hackert, Open-ocean validation of
TOPEX/POSEIDON sea level in the western equatorial Pacific, J. Geophys. Res., 100,
25,10925,127, 1995.
Picaut, J., M. Ioualalen, C. Menkes, T. Delcroix, and M. J. McPhaden, Mechanisms of the zonal displacements of the
Pacific warm pool, Science, 274, 14861489, 1996.
Picaut, J., F. Masia, and Y. du Penhoat, An advective-reflective conceptual model for the oscillatory nature of ENSO,
Science, 277, 663666, 1997.
Plimpton, P. E., H. P. Freitag, and M. J. McPhaden, Correcting moored ADCP data for fish-bias errors at 0°,
110°W and 0°, 140°W from 1990 to 1993, NOAA Tech. Memo. ERL PMEL-107, 49 pp.,
Pac. Mar. Environ. Lab., Seattle, Wash., 1995.
Pugh, D., Tides, Surges, and Mean Sea Level, John Wiley, New York, 1987.
Pullen, P. E., R. L. Bernstein, and D. Halpern, Equatorial long-wave characteristics determined from satellite sea
surface temperature and in situ data, J. Geophys. Res., 92, 742748, 1987.
Puls, C., Oberflächentemperaturen und Strömungsverhältmisse des Aquatorialgürtels des Stillen
Ozeans, Dtsch. Arch. Seewarte, 18, 138, 1895.
Qiao, L., and R. H. Weisberg, Tropical instability wave kinematics: Observations from the Tropical Instability Wave
Experiment (TIWE), J. Geophys. Res., 100, 86778694, 1995.
Quinn, W. H., V. T. Neal, and S. E. Antunez de Mayolo, El Niño occurrences over the past four and a half
centuries, J. Geophys. Res., 92, 14,44914,461, 1987.
Ralph, E. A., K. Bi, P. P. Niiler, and Y. du Penhoat, A Lagrangian description of the western equatorial Pacific
response to the wind burst of December 1992, J. Clim., 10, 17061721, 1997.
Ramanathan, V., and W. Collins, Thermodynamic regulation of ocean warming by cirrus clouds deduced from observations of
the 1987 El Niño, Nature, 351, 2732, 1991.
Ramanathan, V., B. Subasilar, G. J. Zhang, W. Conant, R. D. Cess, J. T. Kiehl, H. Grassl, and L. Shi, Warm pool heat
budget and shortwave cloud forcing: A missing physics? Science, 267, 499503, 1995.
Ramp, S. R., J. L. McClean, C. A. Collins, A. J. Semtner, and K. A. S. Hayes, Observations and modeling of the
19911992 El Niño signal off central California, J. Geophys. Res., 102, 55535582,
1997.
Rao, R. R., R. L. Molinari, and J. F. Festa, Evolution of the climatological near-surface thermal structure of the
tropical Indian Ocean, I, Description of mean monthly mixed layer depth, and sea surface temperature, surface current,
and surface meteorological fields, J. Geophys. Res., 94, 10,80110,815, 1989.
Rao, R. R., R. L. Molinari, and J. F. Festa, Surface meteorological and near surface oceanographic atlas of the tropical
Indian Ocean, NOAA Tech. Memo. ERL AOML-69, 59 pp., Atl. Oceanogr. and Meteorol. Lab.,
Miami, Fla., 1991.
Rasmusson, E. M., and T. H. Carpenter, Variations in tropical sea surface temperature and surface wind fields associated
with the Southern Oscillation/El Niño, Mon. Weather Rev., 110, 354384, 1982.
Rasmusson, E. M., and J. M. Wallace, Meteorological aspects of the El Niño/Southern Oscillation, Science,
222, 11951202, 1983.
Rebert, J., J. Donguy, G. Eldin, and K. Wyrtki, Relations between sea level, thermocline depth, heat content, and
dynamic height in the tropical Pacific Ocean, J. Geophys. Res., 90, 11,71911,725, 1985.
Reid, G. C., K. S. Gage, and J. R. McAfee, The thermal response of the tropical atmosphere to variations in equatorial
Pacific sea surface temperature, J. Geophys. Res., 94, 14,70514,716, 1989.
Reverdin, G., P. Delecluse, C. Levi, A. Morliere, and J. M. Verstraete, The near surface tropical Atlantic in
198284. Results from a numerical simulation and data analysis, Prog. Oceanogr., 27,
273340, 1991a.
Reverdin, G., P. Rual, Y. du Penhoat, and Y. Gouriou, Vertical structure of the seasonal cycle in the central equatorial
Atlantic Ocean, J. Phys. Oceanogr., 21, 277291, 1991b.
Reverdin, G., C. Frankignoul, E. Kestenare, and M. J. McPhaden, Seasonal variability in the surface currents of the
equatorial Pacific, J. Geophys. Res., 99, 20,32320,344, 1994.
Reynolds, R. W., A real-time global sea surface temperature analysis, J. Clim., 1, 7586, 1988.
Reynolds, R. W., Impact of Mount Pinatubo aerosols on satellite-derived sea surface temperatures, J. Clim.,
6, 768774, 1993.
Reynolds, R. W., and D. C. Marsico, An improved real-time global sea surface temperature analysis, J. Clim.,
6, 114119, 1993.
Reynolds, R. W., and T. M. Smith, Improved global sea surface temperature analysis using optimum interpolation,
J. Clim., 7, 929948, 1994.
Reynolds, R. W., and T. M. Smith, A high resolution global sea surface temperature climatology, J. Clim.,
8, 15721583, 1995.
Reynolds, R. W., K. Arpe, C. Gordon, S. P. Hayes, A. Leetmaa, and M. J. McPhaden, A comparison of tropical Pacific
surface wind analyses, J. Clim., 2, 105111, 1989a.
Reynolds, R. W., C. K. Folland, and D. E. Parker, Biases in satellite derived sea-surface-temperatures, Nature,
341, 728731, 1989b.
Reynolds, R. R., M. Ji, A. Leetmaa, and D. Halpern, Comparison of effects of different wind stress analyses on tropical
Pacific model sea level, paper presented at the TOGA95 International Scientific Symposium, World Meteorol. Organ.,
Melbourne, Victoria, Australia, April 27, 1995.
Richardson, P. L., Eddy kinetic energy in the North Atlantic from surface drifters, J. Geophys. Res.,
88, 43554367, 1983.
Riddle, A. C., W. M. Angevine, W. L. Ecklund, E. R. Miller, D. B. Parsons, D. A. Carter, and K. S. Gage, In situ and
remotely sensed horizontal winds and temperature intercomparisons using integrated sounding systems during TOGA COARE,
Contrib. Atmos. Phys., 69, 4961, 1996.
Roach, D., G. Mitchum, and K. Wyrtki, Length scales of interannual sea level variations along the Pacific margin,
J. Phys. Oceanogr., 19, 122128, 1989.
Roemmich, D., and B. Cornuelle, Digitization and calibration of the expendable bathythermograph, Deep Sea Res., Part
A, 34, 299307, 1987.
Roemmich, D., M. Morris, W. R. Young, and J. R. Donguy, Fresh equatorial jets, J. Phys. Oceanogr., 24,
540558, 1994.
Rogers, R. R., W. L. Ecklund, D. A. Carter, K. S. Gage, and S. A. Ethier, Research applications of a boundary-layer wind
profiler, Bull. Am. Meteorol. Soc., 74, 567580, 1993.
Ropelewski, C. F., and M. S. Halpert, North American precipitation and temperature patterns associated with the El
Niño/Southern Oscillation (ENSO), Mon. Weather Rev., 114, 23522362, 1986.
Ropelewski, C. F., and M. Halpert, Global and regional scale precipitation patterns associated with the El
Niño/Southern Oscillation, Mon. Weather Rev., 115, 16061626, 1987.
Rosati, A., K. Miyakoda, and R. Gudgel, The impact of ocean initial conditions on ENSO forecasting with a coupled model,
Mon. Weather Rev., 125, 754772, 1997.
Rufenach, C., A new relationship between radar cross-section and ocean surface wind speed using ERS-1 scatterometer and
buoy measurements, Int. J. Remote Sens., 16, 36293647, 1995.
Sadler, J., and B. J. Kilonsky, Deriving surface winds from satellite observations of low-level cloud motions,
J. Clim. Appl. Meteorol., 24, 758769, 1985.
Saha, S., G. White, and R. Reynolds, An evaluation of the surface fluxes in the NMC/NCAR re-analysis, paper presented at
the 20th Annual NOAA Climate Diagnostics and Prediction Workshop, Natl. Oceanic and Atmos. Admin., Seattle,
Wash., Oct. 2327, 1995.
Scharoo, R., K. F. Wakker, and G. J. Mets, The orbit determination accuracy of the ERS-1 mission, in Proceedings of
the Second ERS-1 Symposium, Eur. Space Agency Spec. Publ., ESA SP-361, 735740, 1994.
Schopf, P. S., and M. A. Cane, On equatorial dynamics, mixed layer physics, and sea surface temperature,
J. Phys. Oceanogr., 13, 917935, 1983.
Schopf, P. S., and M. J. Suarez, Vacillations in a coupled ocean-atmosphere model, J. Atmos. Sci.,
45, 549566, 1988.
Schubert, S. D., R. B. Rood, and J. Pfaendtner, An assimilated dataset for earth science applications,
Bull. Am. Meteorol. Soc., 74, 23312342, 1993.
Scientific Committee on Ocean Research (SCOR), Prediction of El Niño, in Proc. 19,
pp. 4751, Sci. Comm. on Ocean Res. Working Group, Paris, 1983.
Servain, J., Simple climatic indices for the tropical Atlantic Ocean and some applications, J.
Geophys. Res., 96, 15,13715,146, 1991.
Servain, J., J. Picaut, P. Lecomte, and M. Seva, A 16-year series of observations of sea surface temperature and wind
stress field in the tropical Atlantic, in Time Series of Ocean Measurements, Intergovernmental
Oceanogr. Comm. Tech. Ser., vol. 2, pp. 2932, Intergovernmental Oceanographic
Commission, Paris, France, 1984.
Servain, J., J. Picaut, and A. J. Busalacchi, Interannual and seasonal variability of the tropical Atlantic Ocean
depicted by sixteen years of sea surface temperature and wind stress, in Coupled Ocean-Atmosphere Models,
Elsevier Oceanogr. Ser., vol. 40, edited by J. C. J. Nihoul, pp. 211237, Elsevier, New York,
1985.
Shaffer, G., O. Pizarro, L. Djurfeldt, S. Salinas, and J. Rutllant, Circulation and low-frequency variability near the
Chilean coast: Remotely-forced fluctuations during the 19911992 El Niño, J. Phys. Oceanogr.,
27, 217235, 1997.
Sheinbaum, J., and D. L. T. Anderson, Variational assimilation of XBT data, I, J. Phys. Oceanogr.,
20, 672688, 1990a.
Sheinbaum, J., and D. L. T. Anderson, Variational assimilation of XBT data, II, J. Phys. Oceanogr.,
20, 689704, 1990b.
Shriver, J. F., and J. J. O'Brien, Low-frequency variability of the equatorial Pacific Ocean using a new pseudostress
dataset: 19301989, J. Clim., 8, 27622786, 1995.
Shuto, K., Interannual variations of water temperature and salinity along the 137°E meridian, J. Oceanogr.,
52, 575595, 1996.
Smith, N. R., Objective quality controls in an oceanic subsurface thermal analysis scheme, J. Geophys. Res.,
96, suppl., 32793287, 1991.
Smith, N. R., The BMRC ocean thermal analysis system, Aust. Meteorol. Mag., 44, 93110,
1995a.
Smith, N. R., An improved system for tropical ocean sub-surface temperature analyses, J. Atmos. Oceanic
Technol., 12, 850870, 1995b.
Smith, N. R., and G. Meyers, An evaluation of expendable bathythermograph and Tropical Atmosphere-Ocean Array data for
monitoring tropical ocean variability, J. Geophys. Res., 101, 28,48928,501, 1996.
Smith, N. R., J. E. Blomley, and G. Meyers, A univariate statistical interpolation scheme for subsurface thermal
analyses in the tropical oceans, Prog. Oceanogr., 28, 219256, 1991.
Smith, T. M., R. W. Reynolds, R. E. Livezey, and D. C. Stokes, Reconstruction of historical sea surface temperatures
using empirical orthogonal functions, J. Clim., 9, 14031420, 1996.
Smull, B. F., and M. J. McPhaden, Comparison of NCEP/NCAR reanalyzed fields and surface observations over the TOGA-TAO
Array, paper presented at the 21st Annual NOAA Climate Diagnostics and Prediction Workshop, Natl. Oceanic and
Atmos. Admin., Huntsville, Alabama, October 28 to November 1, 1996.
Soreide, N. N., D. C. McClurg, W. H. Zhu, M. J. McPhaden, D. W. Denbo, and M. W. Renton, World Wide Web access to
real-time and historical data from the TAO array of moored buoys in the tropical Pacific Ocean: Updates for 1996, paper
presented at OCEANS 96, Mar. Technol. Soc., Fort Lauderdale, Fla., Sept. 2326, 1996.
Spillane, M., J. Allen, and D. Enfield, Intraseasonal oscillations in sea level along the west coast of the Americas,
J. Phys. Oceanogr., 17, 313325, 1987.
Springer, S. R., M. J. McPhaden, and A. J. Busalacchi, Oceanic heat content variability in the tropical Pacific during
the 19821983 El Niño, J. Geophys. Res., 95, 22,08922,101, 1990.
Sprintall, J., and M. J. McPhaden, Surface layer variations observed in multiyear time series measurements from the
western equatorial Pacific, J. Geophys. Res., 99, 963979, 1994.
Sprintall, J., and M. Tomczak, Evidence of the barrier layer in the surface layer of the tropics, J.
Geophys. Res., 97, 73057316, 1992.
Stockdale, T., D. Anderson, M. Davey, P. Delecluse, A. Kattenberg, Y. Kitamura, M. Latif, and T. Yamagata,
Intercomparison of tropical models, Rep. WCRP-79, 37 pp., World Clim. Res. Program, Geneva, 1993.
Stockdale, T. N., A. J. Busalacchi, D. E. Harrison, and R. Seager, Ocean modeling for ENSO,
J. Geophys. Res., this issue.
Strauch, R. G., B. L. Weber, A. S. Frisch, C. G. Little, D. A. Merritt, K. P. Moran, and D. C. Welsh, The precision and
relative accuracy of profiler wind measurements, J. Atmos. Oceanic Technol., 4, 563571,
1987.
Stricherz, J., J. J. O'Brien, and D. M. Legler, Atlas of Florida State University Tropical Pacific Winds for TOGA
19661985, 275 pp., Fla. State Univ., Tallahassee, 1992.
Stroup, E. D., The thermostad of 13°C water in the equatorial Pacific Ocean, Ph.D. dissertation, 202 pp., Johns
Hopkins Univ., Baltimore, Md., 1969.
Sui, C.-H., and K.-M. Lau, Multi-scale phenomena in the tropical atmosphere over the western Pacific,
Mon. Weather Rev., 120, 407430, 1992.
Swenson, M., P. P. Niiler, A. L. Sybrandy, and L. Sombardier, Feasibility of attaching Seacats to TOGA drifter,
Ref. Ser. 9130, Scripps Inst. of Oceanogr., La Jolla, Calif., 1991.
Sy, A., XBT measurements, in WOCE Operations Manual, WHP Operations and Methods, vol. 3.1.3., WOCE
Rep. 68/91, pp. 119, WOCE Hydrographic Office, Woods Hole, Mass., 1991.
Sybrandy, A. L., and P. P. Niiler, The WOCE/TOGA Lagrangian drifter construction manual, WOCE Rep. 63,
Scripps Inst. of Oceanogr., La Jolla, Calif., 1991.
Sybrandy, A., C. Martin, P. Niiler, E. Charpentier, and D. T. Meldrum, WOCE Surface Velocity Programme Barometer Drifter
Construction Manual, WOCE Rep. 134/95, Scripps Inst. of Oceanogr., La Jolla, Calif., 1995.
Taft, B. A., and W. S. Kessler, Variations of zonal currents in the central tropical Pacific during 1970 to 1987: Sea
level and dynamic height measurements, J. Geophys. Res., 96, 12,59912,618, 1991.
Tai, C.-K., and J. Kuhn, Orbit and tide error reduction for the first 2 years of TOPEX/POSEIDON data,
J. Geophys. Res., 100, 25,35325,363, 1995.
Tapley, B. D., et al., The Joint Gravity Model 3, J. Geophys. Res, 101, 28,02928,049, 1996.
Taylor, P. K., The determination of surface fluxes of heat and water by satellite microwave radiometry and in situ
measurements, in Large-Scale Oceanography Experiments and Satellites, edited by C. Gautier and M. Fieux,
pp. 223246, D. Reidel, Norwell, Mass., 1984.
Toole, J. M., and M. D. Borges, Observations of horizontal velocities and vertical displacements in the equatorial
Pacific Ocean associated with the early stages of the 1982/83 El Niño, J. Phys. Oceanogr.,
14, 948959, 1984.
Tourre, Y. M., and W. B. White, ENSO signals in global upper ocean temperature, J. Phys. Oceanogr.,
25, 13171332, 1995.
Trenberth, K. E., The definition of El Niño, Bull. Am. Meteor. Soc., 78, 27712777,
1997.
Trenberth, K. E., and G. W. Branstator, Issues in establishing causes of the 1988 drought over North America, J.
Clim., 5, 159172, 1992.
Trenberth, K. E., and T. J. Hoar, The 199095 El Niño-Southern Oscillation event: Longest on record,
Geophys. Res. Lett., 23, 5760, 1996.
Trenberth, K. E., and J. W. Hurrell, Decadal atmosphere-ocean variations in the Pacific, Clim. Dyn., 9,
303319, 1994.
Trenberth, K. E., G. W. Branstator, D. Karoly, A. Kumar, N.-C. Lau, and C. Ropelewski, Progress during TOGA in
understanding and modeling global teleconnections associated with tropical sea surface temperatures,
J. Geophys. Res., this issue.
Tziperman, E., L. Stone, H. Jarosh, and M. A. Cane, El Niño chaos: Overlapping of resonances between the seasonal
cycle and the Pacific ocean-atmosphere oscillator, Science, 264, 7274, 1994.
U.S. TOGA Office, Report of a drafting workshop on elements of a ten-year monitoring component for El Niño and
the Southern Oscillation (ENSO), May 1118, 1983, AOML, Miami, Rep. USTOGA-1, 42 pp.,
Univ. Corp. for Atmos. Res., Boulder, Colo., 1988.
Waliser, D. E., B. Blanke, J. D. Neelin, and C. Gautier, Shortwave feedbacks and El Niño-Southern Oscillation:
Forced ocean and coupled ocean-atmosphere experiments, J. Geophys. Res., 99, 25,10925,125, 1994.
Walker, G. T., Correlation in seasonal variations of weather IX: A further study of world weather, Mem. Indian
Meteorol. Dep., 24, 275332, 1924.
Wallace, J. M., T. P. Mitchell and C. Deser, The influence of sea-surface temperature on surface wind in the eastern
equatorial Pacific: Seasonal and interannual variability, J. Clim., 2, 14921499, 1989.
Wallace, J. M., E. M. Rasmusson, T. P. Mitchell, V. E. Kousky, E. S. Sarachik, and H. von Storch, On the structure and
evolution of ENSO-related climate variability in the tropical Pacific: Lessons from TOGA,
J. Geophys. Res., this issue.
Walton, C. C., Nonlinear multichannel algorithms for estimating sea surface temperature with AVHRR satellite data,
J. Appl. Meteorol., 27, 115124, 1988.
Weare, B. C., Uncertainties in estimates of surface heat fluxes derived from marine reports over the tropical and
subtropical oceans, Tellus, Ser. A, 41, 357370, 1989.
Webster, P. J., and R. Lukas, TOGA COARE: The Coupled-Ocean Atmosphere Response Experiment,
Bull. Am. Meteorol. Soc., 73, 13771416, 1992.
Webster, P. J., and S. Yang, Monsoon and ENSO: Selectively interactive systems, Q. J. R. Meteorol. Soc.,
118, 877926, 1992.
Webster, P. J., T. N. Palmer, V. O. Magaña, J. Shukla, R. A. Tomas, T. M. Yanai, and A. Yasunari, Monsoon:
Processes, predictability, and the prospects for prediction, J. Geophys. Res., this issue.
Weisberg, R. H., and S. P. Hayes, Upper ocean variability on the equator in the Pacific at 170°W, J.
Geophys. Res., 100, 20,48520,498, 1995.
Weisberg, R. H., and C. Wang, Slow variability in the equatorial west-central Pacific in relation to ENSO, J.
Climate, 10, 19982017, 1997.
Weisberg, R. H., and T. J. Weingartner, Instability waves in the equatorial Atlantic Ocean, J. Phys. Oceanogr.,
18, 16411657, 1988.
Weller, R. A., and S. P. Anderson, Surface meteorology and air-sea fluxes in the western equatorial Pacific warm pool
during TOGA COARE, J. Clim., 9, 19591990, 1996.
Weller, R. A., and P. K. Taylor, Surface conditions and surface fluxes OOSDP background, Rep. 3,
Tex. A&M Univ., College Station, 1993.
White, W. B., Design of a global observing system for gyre-scale upper ocean temperature variability,
Prog. Oceanogr., 36, 169217, 1995.
White, W. B., and R. G. Peterson, An antarctic circumpolar wave in surface pressure, wind, temperature and sea ice
extent, Nature, 380, 699702, 1996.
White, W. B., and C.-K. Tai, Reflection of interannual Rossby waves at the maritime western boundary of the tropical
Pacific, J. Geophys. Res., 97, 14,30514,322, 1992.
White, W. B., G. Meyers, J. R. Donguy, and S. E. Pazan, Short term climate variability in the thermal structure of the
Pacific Ocean during 19791982, J. Phys. Oceanogr., 15, 917935, 1985.
White, W. B., S. E. Pazan, and M. Inoue, Hindcast-forecast of ENSO based upon the redistribution of observed and modeled
heat content in the western tropical Pacific, J. Phys. Oceanogr., 17, 264280, 1987.
Williams, C. R., K. S. Gage, and A. Hollingsworth, A diagnostic study of planetary boundary layer winds observed by the
915 MHz lower tropospheric wind profiler at Christmas Island, in Proceedings of the Seventeenth Climate Diagnostics
Workshop, Rep. NTIS-PB94-183895/XAB, pp. 210215, Natl. Tech. Inf. Serv.,
Springfield, Va., 1992.
Williams, C. R., W. L. Ecklund, and K. S. Gage, Classification of precipitating clouds in the tropics using 915 MHz wind
profilers, J. Atmos. Oceanic Technol., 12, 9961012, 1995.
Williamson, R. G., and R. S. Nerem, Improved orbit computations for the Geosat mission: Benefits for oceanographic and
geodynamic studies (abstract), Eos Trans. AGU, 75(44), Fall Meet. Suppl., 155, 1994.
Woodruff, S. D., R. J. Slutz, R. L. Jenne, and P. Steurer, A comprehensive ocean-atmosphere data set,
Bull. Am. Meteorol. Soc., 68, 12391250, 1987.
World Climate Research Program, Scientific plan for the Tropical Ocean and Global Atmosphere Program,
Tech. Doc. WMO/TD-64, 146 pp., World Meteorol. Organ., Geneva, 1985.
World Climate Research Program, Surface Velocity Programme Planning Committee report of the first meeting, SVP-1,
Tech. Doc. WMO/TD-323, World Meteorol. Organ., Geneva, 1988.
World Climate Research Program, JSC/CCCO TOGA Scientific Steering Group, Report of the Ninth Meeting, Kona, Hawaii,
2325 July, 1990, Tech. Doc. WMO/TD-387, 23 pp., World Meteorol. Organ., Geneva, 1990a.
World Climate Research Program, International TOGA Scientific Conference Proceedings, Honolulu, Hawaii, July 1620,
1990, Tech. Doc. WMO/TD-379, 239 pp., World Meteorol. Organ., Geneva, 1990b.
World Climate Research Program, TOGA Numerical Experimentation Group, Report of the Seventh Session, La Jolla, Calif.,
12 November 1994, 8 pp., World Meteorol. Organ., Geneva, 1995a.
World Climate Research Program, CLIVAR, A Study of Climate Variability and Predictability,
Tech. Doc. WMO/TD-690, 157 pp., World Meteorol. Organ., Geneva, 1995b.
Wyrtki, K., El Niño-The dynamic response of the equatorial Pacific Ocean to atmospheric forcing,
J. Phys. Oceanogr., 5, 572584, 1975.
Wyrtki, K., Sea level during the 1972 El Niño, J. Phys. Oceanogr., 7, 779787, 1977.
Wyrtki, K., The response of sea surface topography to the 1976 El Niño, J. Phys. Oceanogr.,
9, 12231231, 1979.
Wyrtki, K., An estimate of equatorial upwelling in the Pacific, J. Phys. Oceanogr., 11,
12051214, 1981.
Wyrtki, K., The slope of sea level along the equator during the 1982/1983 El Niño,
J. Geophys. Res., 89, 10,41910,424, 1984.
Wyrtki, K., Water displacements in the Pacific and the genesis of El Niño cycles,
J. Geophys. Res., 90, 71297132, 1985a.
Wyrtki, K., Sea level fluctuations in the Pacific during the 198283 El Niño,
Geophys. Res. Lett., 12, 125128, 1985b.
Wyrtki, K., Indonesian through flow and the associated pressure gradient, J. Geophys. Res., 92,
12,94112,946, 1987.
Wyrtki, K., and G. Meyers, The trade wind field over the Pacific Ocean, I, The mean field and the mean annual variation,
Rep. HIG-75-1, 26 pp., Univ. of Hawaii, 1975.
Wyrtki, K., and G. Meyers, The trade wind field over the Pacific Ocean, J. Appl. Meteorol., 15,
698704, 1976.
Wyrtki, K., E. Firing, D. Halpern, R. Knox, G. J. McNally, W. C. Patzert, E. D. Stroup, B. A. Taft, and R. Williams, The
Hawaii-to-Tahiti shuttle Experiment, Science, 211, 2228, 1981.
Xie, S.-P., On the genesis of the equatorial annual cycle, J. Clim., 7, 20082013, 1994.
Yu, T. W., and D. G. Deaven, Use of SSM/I wind speed data in NMC's GDAS, paper presented at the Ninth Conference on
Numerical Weather Prediction, Am. Meteorol. Soc., Denver, Colo., October 1418, 1991.
Yu, Z., J. P. McCreary Jr., and J. A. Proehl, Meridional asymmetry and energetics of tropical instability waves, J.
Phys. Oceanogr., 25, 29973007, 1995.
Yu, Z., P. S. Schopf, and J. P. McCreary Jr., On the annual cycle in the eastern Pacific Ocean, J.
Phys. Oceanogr., 27, 309324, 1997.
Zebiak, S., On the 3060 day oscillation and the prediction of El Niño, J. Clim., 2,
13811387, 1989.
Zebiak, S., Air-sea interaction in the equatorial Atlantic region, J. Clim., 6, 15671586, 1993.
Zebiak, S. E., and M. A. Cane, A model of El Niño-Southern Oscillation, Mon. Weather Rev.,
115, 22622278, 1987.
Zhang, C., Atmospheric intraseasonal variability at the surface in the tropical western Pacific Ocean, J.
Atmos. Sci., 53, 739758, 1996.
Zhang, G. J., On the use of monthly mean data to compute surface turbulent fluxes in the tropical Pacific,
J. Clim., 8, 30843090, 1995.
Zhang, G. J., and M. J. McPhaden, On the relationship between sea surface temperature and latent heat flux in the
equatorial Pacific, J. Clim., 8, 589605, 1995.
Zhang, G. J., V. Ramanathan, and M. J. McPhaden, Convection evaporation feedback in the equatorial Pacific,
J. Clim., 8, 30403051, 1995.
Zhang, Y., J. M. Wallace, and D. S. Battisti, ENSO-like interdecadal variability: 19001993, J. Clim.,
10, 10041020, 1997.
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