Background

This project is an attempt to reconstruct past environments in terms compatible with those of modern ecosystem process models, thus providing a functional view of the past and useful data to predict the future of planet Earth. It addresses Astrobiology Goal number 6 ("Understand the principles that will shape the future of life, both on Earth and beyond") by focusing on Astrobiology Objective 6.1 ("environmental changes and the cycling of elements by the biota, communities and ecosystems"). It attempts to link the above goal and objective to those of NASA's Earth System Science.

Most frequently, astrobiologists compose evolutionary scenarios on systems that are progressive, of growing complexity. We are analyzing regressive scenarios where evolution is viewed backward, in a process of decreasing complexity.

In order to implement that view, we embarked in a hypothetical reconstruction of the path followed by the biosphere of a planet that is cooling and drying, i.e. , leaving the habitable zone around its star. South America was chosen as an analog (or a metaphor) to carry out our research. This analog was built on the conversion of geographic space into geologic time. Our research focus is on the effects of short-term, global phenomena such as the "El Niño" Southern Oscillation (ENSO) on long-term processes over the last 40,000 years. A few stratigraphic studies such as that of Hooghiemstra's (1984) on pollen in Sabana de Bogota cover all that process, or a large part of it as in D'Antoni (1983) who worked in Gruta del Indio. However, no pollen profile so far offers the yearly time resolution needed for our current modeling goal. Therefore, we decided to address the problem in the timeframe of the last 2,500 years (the European "Subatlantic" or present geologic time). In this reduced timeframe we have one-year resolution tree-ring data from South America from an effort initiated by LaMarche (1982) and continued to this day by Boninsegna, Holmes, and Villalba (NOAA Paleoclimate Database). We also have rich ancillary data to train our hindcasting model.

Approach

In order to study the environmental past beyond the customary taxonomical analysis of change, we attempted to reconstruct the process of Net Primary Productivity (NPP), a synoptic measure of ecosystem operation. We base our calibration on results obtained by a different model, the NASA-CASA model (Potter et al. 1993), and link our work to the modeling trend started by Monteith (1981) using remote sensing data to predict NPP. Our model (D'Antoni and Skiles 2004) relates to the developments that followed Monteith's logic (Nemani et al. 2003) and lead to the relationship:

Paleo-NPP = paleo-tΣT (σ*Paleo-NDVI)*ε
(model 1)

where paleo-tΣT is a simulation of the surface temperature recorded by the infrared channels 3 and 4 of the AVHRR sensor, truncated by the corresponding values of the vegetation index (NDVI); σ*Paleo-NDVI equals the sum of simulated values of the vegetation index over the growing season (which we successfully derived from pollen records), and ε is the energy conversion efficiency factor in grams of carbon per megajoule. Although all the terms in our model can be hindcast in the future, we abbreviated this step by training our artificial neural network model with NASA-CASA generated NPP data and several other data that are available for our sites. For Guayaquil, Ecuador our model produced a prediction of the NASA-CASA NPP, based on:

NPP = {NDVI (.058); Precip (.263); SolIrr (.121); AtlSST (.098); PacSST (.460)}
(model 2)

where the values in parentheses are the weights assigned to the variable after the neural net generated sufficient neurons as to satisfy our precision requirements. [Figure 1] Ecuador is directly connected to ENSO, receiving the warm current directly on the shores of the Santa Elena Peninsula. In contrast, Lake Surara (in Amazonia), [Figure 2] is under indirect ENSO influence, mostly through changes in the circulation patterns. In this case, NPP was derived by the relationship:

NPP = {NDVI (.120); Temp (.474); SolIrr (.047); AtlSST (.280); PacSST (.079)}
(model 3)

When extended into the past, models such as the ones above (models 1, 2 and 3) need to be adjusted by the three forcings of Holocene climate, which are, according to Bradley (2003), solar radiation (Fröhlich and Lean 2002, Fröhlich 2004) orbital (McElroy 2002), and volcanic activity (Bradley 1999, 2003).

Figure 1 - Model calibration for Guayaquil over the 1982-1993 period

 

Figure 2 - NPP Model Calibration for Lake Surara (Amazonia) for the 1982-1993 period.

 

Our introductory phase used 30 sites in South America where pollen stratigraphy was available. For the current phase of our project, we created a training collection of about 120 sites [Figure 3] to monitor most South American ecosystems (Hueck & Seibert 1982, D'Antoni et al. 2002, D'Antoni & Mendonça, in progress). The sea surface temperature for the tropical Atlantic and "Ship Track 1" in the Pacific has reconstructed at one year resolution (D'Antoni & Mlinarevic 2002) and we are working to move further into the past, perhaps to 300 B.C. as to cover most of the "Subatlantic" timeframe (D'Antoni & Schultz, in progress).

NDVI Average Values 1982

Figure 3 - New monitoring sites. The dots are sized according to the NDVI value and color coded in relation to eight mayor vegetation groups, as follows, Subantarctic and Andean vegetation of high mountains, bush and grass steppes, tropical and subtropical deciduous forests, savannas and palm vegetation, tropical and subtropical evergreen forests, dry forests, southern forests, riparian forests.

Achievements

For the development of this project we collected a number of databases and generated a number of products. Among the latter is worth mentioning, (a) the Advanced Very High Resolution Radiometer (AVHRR)-NDVI average map of South America for the growing season, at 8-km resolution for the 1982-2001 period [Figure 4], (b) the validation of the AVHRR-NDVI map with ground-based data by Hueck and Seibert (1982) [Figure 5], (c) a method to quantify NDVI change linked to ENSO effects, at continental scale [Figures 6 and 7], (d) reconstruction of past SST for the period 1246-1995 (see above) [Figure 8], (e) tentative reconstruction of solar irradiance from 1600 to the present [Figure 9], (f) forecasting and hindcasting tree-ring widths from 300 B.C. to the present using a neural net model training collection of over 1,300 years across and 25 sites with contrasting responses to ENSO and other climate factors [Figure 10].

Figure 4 - AVHRR-NDVI Vegetation map integrated over the growing season for the 1982-2001 period	Figure 5 - Vegetation map of South America (slightly modified from Hueck & Seibert 1982)
Figure 6 - 1993 El Niño deviation map. (from D'Antoni et al. 2002)	Figure 7 - 1985 La Niña deviation map (from D'Antoni et al. 2002)

 

Figure 8 - Past sea surface temperature for Ship Track 1 (Pacific Ocean, Blue) and Tropical Atlantic Ocean (Red) for the period 1246-1995 (from D'Antoni & Mlinarevic 2002)

 

Figure 9 - Reconstruction of Solar Irradiance between 1600 and 2000 (from Brenda Bueno Progress Report 2004)

 

Figure 10 - Measured and predicted tree-ring widths for the 1246-1400 period. The widths are expressed in 1/1000 mm, so a reading of 2,500 means a 2.5 mm ring

 

Current Work

Our efforts are concentrated in correlating our reconstructions with environmental events from many other sources. We are refining our reconstructions of solar irradiance, paleo NDVI, adjusting for anomalies and lags in the 11 year cycle (North, Wu & Stevens 2004), completing our yearly analysis of the orbital forcing of climate, and completing our volcanic activity database. With these and other reconstructions using hard data and the (already well advanced) identification of proxies for the terms in model 1, we will be ready to reconstruct (based on over 120 observation sites) the past 2,500 years of main ecosystem operation in South America.

We expect that these results will serve Goal 6 and Objective 6.1 of Astrobiology Roadmap and provide reliable data to Earth System Science modelers for input to their models. Further, we expect that this contribution will help base their prediction of the future of planet Earth on a much larger timeframe.

References

Bradley, R.S. 1999. Paleoclimatology. Reconstructing Climates of the Quaternary. 2nd Edition. Academic Press. International Geophysiscs Series vol. 64.

Bradley, R.S. 2003. Climate Forcing During the Holocene. In Mackay, Battarbee, Birks and Oldfield (eds. ) Global Change in the Holocene. Arnold/Oxford University Press. Chap 2, 10-19.

D'Antoni, H.L. 1983. Pollen Analysis of Gruta del Indio. Quaternary of South America and Antarctic Peninsula. 1 (1983), 83-104. Rotterdam.

D'Antoni, H.L. and F. Schäbitz. 1995. Remote Sensing and Holocene Vegetation: History of Global Change. World Resource Review 7(2), 2828-288.

D'Antoni, H.L. , D.L. Peterson, and A. Mlinarevic. 2002. Rapid Rates of Change in South American Vegetation linked to "El Niño" Southern Oscillation. Astrobiology Science Conference 2002, Presenters 27. Moffett Field.

D'Antoni H.L. and A. Mlinarevic. 2002. Past Sea Surface Temperature Derived From Tree Rings. Astrobiology Science Conference 2002, Presenters 28. Moffett Field.

D'Antoni, H.L. and J.W. Skiles. 2004. Prediciendo Ecosistemas del Pasado en América del Sur, in Astrobiología en Español. International Journal of Astrobiology. Suppl. 1 (AbSciCon) Cambridge University Press, 10.

Fröhlich, C. 2004. Solar Irradiance Variability. In Pap and Fox (eds. ) Solar Variability and its Effects on Climate. American Geophysical Union. 97-110.

Fröhlich, C. and J. Lean. 2002. Solar Irradiance Variability and Climate. Astronomische Nachtrichten AN 323 (3/4), 203-212.

Hooghiemstra, H. 1984. Vegetation and Climate History of the High Plain of Bogotá, Colombia: a continuous record of the last 3.5 million years. Dissertationes Botanicae No. 79. Vaduz: J. Cramer.

Hueck, K. and H. Seibert. 1982. Vegetationskarte von Südamerika. Gustav Fischer Verlag, Stuttagrt.

LaMarche, V.C. 1982. Sampling Strategies. In Climate from tree rings, Kelly, Picher & LaMarche (Eds. ), Cambridge University Press, 2-6.

McElroy, M.B. 2002. The Atmospheric Environment. Effects of Human Activity. Princeton University Press.

North, G.R. , Q. Wu and M.J. Stevens. 2004. Detecting the 11-year solar cycle in the surface temperature field. In Pap and Fox (eds. ) Solar Variability and its Effects on Climate. American Geophysical Union. 251-260.

Nemani, R. et al. (2003). Climate-Driven Increases in Global Terrestrial Net Primary Production from 1982 to 1999. Science 300, 1560-1563.

Potter, C. S. , J. T. Randerson, C. B. Field, P. A. Matson, P. M. Vitousek, H. A. Mooney, and S. A. Klooster. 1993. Terrestrial ecosystem production: A process model based on global satellite and surface data. Global Biogeochemical Cycles. 7(4):811-841.

Ames Team Members Participating in this Investigation:

NAME	ROLE	ORGANIZATION	EMAIL
D'Antoni, Hector	Lead Co-Investigator	NASA Ames Research Center	hdantoni@mail.arc.nasa.gov
Absy, Maria Lucia	Collaborator	National Institute of Research of the Amazon, Manaus, Brazil	luciabsy@inpa.gov.br
Armstrong, Roy	Collaborator	University of Puerto Rico-Mayagüez	roy@cacique.uprm.edu
Burry, Lidia	Collaborator	National University of Mar del Plata, Argentina	lburry@mdp.edu.ar
Felger, Richard	Collaborator	Drylands Institute, Tucson	rfelger@ag.arizona.edu
Marcos, Jorge	Collaborator	Escuela Superior Politecnica del Litoral, Guayaquil, Ecuador	jgmarcos@es.inter.net
Matta, Jaime	Collaborator	Ponce School of Medicine, Puerto Rico	mattajaime@hotmail.com
Meggers, Betty	Collaborator	Smithsonian Institution, Washington D.C.	Meggers.Betty@NMNH.si.edu
Mendonça, Jennifer	Student	San Jose State University	Jenny_dono@yahoo.com
Mlinarevic, Ante	Collaborator	San Jose State University	amlinare6@hotmail.com
Pérez Gollán, Jose	Collaborator	Ethnography Museum, University of Buenos Aires, Argentina	jagollan@mail.retina.ar
Schmidt, Cynthia	Collaborator	San Jose State University	cschmidt@mail.arc.nasa.gov
Schultz, Cynthia	Student	San Jose State University	cschultz@mail.arc.nasa.gov
Skiles, Jay	Co-Investigator	NASA Ames Research Center	Joseph.W.Skiles@nasa.gov
Trivi, Matilde	Collaborator	National University of Mar del Plata, Argentina	mtrivi@mdp.edu.ar
Weiler, Nilda	Collaborator	National Patagonic Center, Pto. Madryn, Argentina	weiler@cenpat.edu.ar

See the following Ames Team research pages:

Formation and Evolution of Habitable Planets
Prebiotic Organics from Space
Origin and Early Evolution of Proteins and Metabolism
Biosignatures in Chemosynthetic and Photosynthetic Systems
Modeling Ecosystems and Biospheres
Hind-Casting Past Environments
Interplanetary Pioneer