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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Dominant controls of cold-season precipitation variability over the high mountains of Asia

A robust understanding of the sub-seasonal cold season (November–March) precipitation variability over the High Mountains of Asia (HMA) is lacking. Here, we identify dynamic and thermodynamic pathways through which natural modes of climate variability establish their teleconnections over the HMA. First, we identify evaporative sources that contribute to the cold season precipitation over the HMA and surrounding areas. The predominant moisture contribution comes from the mid-latitude regions, including the Mediterranean/Caspian Seas and Mediterranean land. Second, we establish that several tropical and extratropical forcings display a sub-seasonally fluctuating influence on precipitation distribution over the region during the cold season. Many of them varyingly interact, so their impacts cannot be explained independently or at seasonal timescales. Lastly, a single set of evaporative sources is not identifiable as the key determinant in propagating a remote teleconnection because the sources of moisture anomalies depend on the pattern of sub-seasonally varying dynamical forcing in the atmosphere.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Distinctive Pattern of Global Warming in Ocean Heat Content

Abstract Huge heat anomalies in the atmosphere and ocean in recent years are not yet explained. Strong characteristic patterns in temperatures for upper layers of the ocean occurred from 2000 to 2023 in the presence of global warming from increasing atmospheric greenhouse gases. Here, we show that the deep tropics are warming, although sharply modulated by El Niño–Southern Oscillation events, with strong heating in the extratropics near 40°N and 40°–45°S but little heating near 20°N and 25°–30°S. The heating is most clearly manifested in zonal-mean ocean heat content and is evident in sea surface temperatures. The strongest heating is in the Southern Hemisphere, where aerosol effects are small. Estimates are made of the contributions to heating of top-of-atmosphere (TOA) radiation, atmospheric energy transports, surface fluxes of energy, and redistribution of energy by surface winds and ocean currents. The patterns of change are not directly related to TOA radiation but are evident in net surface energy fluxes and inferred ocean heat transports, underscoring their coupled origin. Changes in the atmospheric circulation through a poleward shift in ocean jet streams and storm tracks are reflected in surface wind-driven ocean Ekman transports. As well as human-induced climate change, internal natural variability is likely in play. Hence, the atmosphere and ocean currents are systematically redistributing heat from global warming, profoundly affecting local climates. Significance Statement As the climate changes, it has been difficult to discern meaningful patterns. Distinctive patterns of change have occurred in the ocean when examined as zonal averages around latitude bands. Most excess heat from global warming resides in the ocean and, since 2005, has become focused into bands near 40°N and 40°S, with little net warming in the subtropics. The strongest warming is in the Southern Hemisphere, although sea surface temperatures have increased more in the Northern Hemisphere. Changes in the atmospheric circulation through a poleward shift in the jet stream and storm tracks are primarily responsible along with corresponding changes in ocean currents. These changes are linked through surface exchanges of energy via heat, moisture, and wind stress.

Trenberth, Kevin E. [National Center for Atmospher↗
NASA NTRS2018

Pre-Aerosol, Clouds, and Ocean Ecosystem (PACE) Mission Science Definition Team Report: PACE Technical Report Series - Volume 2

We live in an era in which increasing climate variability is having measurable impact on marine ecosystems within our own lifespans. At the same time, an ever-growing human population requires increased access to and use of marine resources. To understand and be better prepared to respond to these challenges, we must expand our capabilities to investigate and monitor ecological and bio geo chemical processes in the oceans. In response to this imperative, the National Aeronautics and Space Administration (NASA) conceived the Pre-Aerosol, Clouds, and ocean Ecosystem (PACE) mission to provide new information for understanding the living ocean and for improving forecasts of Earth System variability. The PACE mission will achieve these objectives by making global ocean color measurements that are essential for understanding the carbon cycle and its inter-relationship with climate change, and by expanding our understanding about ocean ecology and biogeochemistry. PACE measurements will also extend ocean climate data records collected since the 1990s to document changes in the function of aquatic ecosystems as they respond to human activities and natural processes over short and long periods of time. These measurements are pivotal for differentiating natural variability from anthropogenic climate change effects and for understanding the interactions between these processes and various human uses of the ocean. PACE ocean science goals and measurement capabilities greatly exceed those of our heritage ocean color sensors, and are needed to address the many outstanding science questions developed by the oceanographic community over the past 40 years.

Cetinic, Ivona↗
NASA NTRS2002

Quantifying Fractional Ground Cover on the Climate Sensitive High Plains Using AVIRIS and Landsat TM Data

The High Plains is an economically important and climatologically sensitive region of the United States and Canada. The High Plains contain 100,000 sq km of Holocene sand dunes and sand sheets that are currently stabilized by natural vegetation. Droughts and the larger threat of global warming are climate phenomena that could cause depletion of natural vegetation and make this region susceptible to sand dune reactivation. This thesis is part of a larger study that is assessing the effect of climate variability on the natural vegetation that covers the High Plains using Landsat 5 and Landsat 7 data. The question this thesis addresses is how can fractional vegetation cover be mapped with the Landsat instruments using linear spectral mixture analysis and to what accuracy. The method discussed in this thesis made use of a high spatial and spectral resolution sensor called AVIRIS (Airborne Visible and Infrared Imaging Spectrometer) and field measurements to test vegetation mapping in three Landsat 7 sub-scenes. Near-simultaneous AVIRIS images near Ft. Morgan, Colorado and near Logan, New Mexico were acquired on July 10, 1999 and September 30, 1999, respectively. The AVIRIS flights preceded Landsat 7 overpasses by approximately one hour. These data provided the opportunity to test spectral mixture algorithms with AVIRIS and to use these data to constrain the multispectral mixed pixels of Landsat 7. The comparisons of mixture analysis between the two instruments showed that AVIRIS endmembers can be used to unmix Landsat 7 data with good estimates of soil cover, and reasonable estimates of non-photosynthetic vegetation and green vegetation. Landsat 7 derived image endmembers correlate with AVIRIS fractions, but the error is relatively large and does not give a precise estimate of cover.

Warner, Amanda Susan↗
DOE OSTI2021

Exploring variability in seasonal average and extreme precipitation using unsupervised machine learning.

Focal Area(s): We will use unsupervised machine learning methods to identify and quantify the influence of large scale natural modes of climate variability to gain insight into the observed and simulated seasonal average and extreme precipitation changes. Science Challenge: A recent paper, led by co-PI Mark Risser, finds that although much of the variability in seasonal average and extreme precipitation over CONUS is unforced, the effect of large-scale modes of circulation variability (such as ENSO, AMO, PNA, etc.) can be detected and attributed. However, it is unclear whether or not unsupervised learning methods can (a) replicate this finding or (b) yield insight into possible nonlinear behavior that was not captured in the initial statistical analysis. Further work would entail extending this framework to other global land areas.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2000

Science Writers' Guide to TERRA

The launch of NASA's Terra spacecraft marks a new era of comprehensive monitoring of the Earth's atmosphere, oceans, and continents from a single space-based platform. Data from the five Terra instruments will create continuous, long-term records of the state of the land, oceans, and atmosphere. Together with data from other satellite systems launched by NASA and other countries, Terra will inaugurate a new self-consistent data record that will be gathered over the next 15 years. The science objectives of NASAs Earth Observing System (EOS) program are to provide global observations and scientific understanding of land cover change and global productivity, climate variability and change, natural hazards, and atmospheric ozone. Observations by the Terra instruments will: provide the first global and seasonal measurements of the Earth system, including such critical functions as biological productivity of the land and oceans, snow and ice, surface temperature, clouds, water vapor, and land cover; improve our ability to detect human impacts on the Earth system and climate, identify the "fingerprint" of human activity on climate, and predict climate change by using the new global observations in climate models; help develop technologies for disaster prediction, characterization, and risk reduction from wildfires, volcanoes, floods, and droughts, and start long-term monitoring of global climate change and environmental change.

Source record↗
DOE OSTI2026

Stratospheric aerosol forcing for CMIP7 – Part 1: optical properties for pre-industrial, historical, and scenario simulations

Stratospheric aerosols, most of which originate from explosive volcanic sulfur emissions into the stratosphere, are a key natural driver of climate variability. They are thus a forcing provided by the Coupled Model Intercomparison Project (CMIP) Climate Forcings Task Team to climate modelling groups participating in phase 7 of CMIP. For the historical period, we provide two datasets covering 1750–2023: (i) a volcanic upper tropospheric-stratospheric sulfur emission dataset, documented in a companion paper; and (ii) a stratospheric sulfate aerosol optical property dataset, documented in this paper. For the satellite era (from 1979 onwards), stratospheric aerosol optical properties are derived from the Global Space-based Stratospheric Aerosol Climatology (GloSSAC) dataset. For the pre-satellite era (1750–1978), optical properties are derived from our volcanic SO 2 emission dataset using a new version of the reduced-complexity volcanic aerosol model Easy Volcanic Aerosol (Height) (EVA_H v2). A background, non-volcanic stratospheric aerosol climatology is derived from the 1998–2001 period with a trend over 1850–1978 accounting for increasing anthropogenic aerosols. A monthly stratospheric aerosol climatology is derived from the 1850–2021 average for both pre-industrial and scenario (future) simulations, with a 9-year ramp over 2022–2030 for scenario simulations to ensure a smooth transition from the historical period. CMIP7 simulations should use version 2.2.1 of our pre-industrial and historical datasets, and version 2.2.2 of our scenario dataset. Our methodology to produce historical aerosol optical properties significantly differs from CMIP6 for the pre-satellite era, and the resulting forcings in turn largely differ. In particular, the CMIP6 dataset was mostly based on the sparse and uncertain pyrheliometer record, which resulted in strongly underrepresented emissions from small-to-moderate magnitude eruptions. The resulting bias is addressed in CMIP7, which is entirely emission-derived in the pre-satellite era and uses more recent ice-core-based volcanic sulfur emission inventories than CMIP6. Our approach results in an overall larger volcanic aerosol forcing for CMIP7, with the 1850–2014 mean mid-visible global mean stratospheric aerosol optical depth (SAOD) in CMIP7 (0.0138) being 29 % higher than in CMIP6 (0.0107). The pre-industrial mean of the same variable is 26 % higher in CMIP7 (0.0135, derived from the historical 1850–2021) than CMIP6 (0.0107, derived from the historical 1850–2014 mean). Using a reduced-complexity climate model, we simulate a global mean surface temperature that is 0.07 °C colder for 1850–1900 when using the CMIP7 dataset instead of CMIP6, whereas 2000–2014 is 0.03 °C warmer in CMIP7. Our dataset also exhibits lower forcing for 1960–1980, resulting in temperatures 0.06 °C warmer when averaged over 1960–1990, a period for which CMIP6 climate models exhibit a cold bias. Given the large uncertainties characterizing the dataset, in particular for the pre-satellite era, we advise against treating the CMIP7 or CMIP6 dataset as uniquely superior for any specific year and highlight the need for further evaluation. We conclude the study by discussing sources of uncertainty for the dataset, future research avenues to improve it, as well as requirements to operationalize the production of the dataset, i.e. extend it and update it on an annual basis instead of every 5–7 years following CMIP cycles.

Aubry, Thomas J. [Univ. of Exeter, Devon (United K↗
NASA NTRS1988

Intraseasonal and interannual variations of tropical convection - A possible link between the 40-50 day oscillation and ENSO?

Twelve-years of NOAA data on outgoing longwave radiation (OLR) are used to trace intraseasonal variations of tropical convection by examining the spatial patterns of OLR variance in the 1-5 day, 20-70 day, and longer-than-180 day bands separately for the northern summer and winter. Based on these patterns, the relative importance of these bands are evaluated and the natural variability of the climate signal and the geographical distribution of potential predictability is assessed. The results of empirical orthogonal functional analyses show that multiple time scales exist in the most dominant patterns of tropical convection variation. These time scales can be identified with those of the 40-50-day oscillation, the El Nino/Southern Oscillation, and the annual cycle. Interpretation of the results in terms of possible interaction between these various time scales is presented.

Lau, Ka-Ming↗
NASA NTRS1989

A video atlas of TOMS ozone data, 1978-88

The Total Ozone Mapping Spectrometer (TOMS), on-board NASA's Nimbus-7 weather satellite, has been observing ozone over the earth once daily for the last 10 yr. A time-lapse atlas of 3440 color-coded images drawn from the TOMS archive from 1978 to 1988 has been visualized on a standard VHS videotape that is now available from NASA. The rapid and complex ozone variations presented demonstrate the difficulty of separating man-induced climate changes from natural variability. This article presents a few images from the atlas and describes interesting features in the animation, such as the correlation between ozone and 'the weather', and the recent deepening of the annual ozone hole over the South Pole. Originally intended as a browsing tool for the TOMS digital database, the videotape is a vivid presentation of the earth's atmospheric dynamics and chemistry that is recommended for scientists, educators, policy makers, and citizens concerned about the global environment.

Chesters, D.↗
NASA NTRS2019

Editorial: North Pacific Environment and Paleoclimate from the Late Pleistocene to Present

The vast area of the North Pacific, spanning ~55° longitude, represents a challenge for documenting and understanding the geologic history of ocean, atmosphere, and terrestrial environmental change. This special issue highlights site-specific analyses to address various questions and provides clues that arise in response to continued North Pacific warming today in a rapidly changing climate (Figure 1). The emergence of new methods and novel application of existing methods serve to enhance our fundamental understanding of natural modes of climate variability driven by ocean and atmospheric circulation patterns, including the role of external (e.g., insolation) vs. internal forcing [e.g., El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO)]. Included in this volume are geographically diverse studies that present new and traditional methods for the interpretation of climate records drawn from pollen, macrofossils, tree rings, diatoms, grain size, and glacial studies. Many provide new geographic comparisons, such as Kamchatka and Hawaii, or emerge from novel geologic archives and environments, such as the marine shelf, a thermokarst lake, and a fossil forest.

Jones, Miriam C.↗
DOE OSTI2020

Multi-Frequency Analysis of Simulated versus Observed Variability in Tropospheric Temperature

Studies seeking to identify a human-caused global warming signal generally rely on climate model estimates of the “noise” of intrinsic natural variability. Assessing the reliability of these noise estimates is of critical importance. We evaluate here the statistical significance of differences between climate model and observational natural variability spectra for global-mean mid- to upper-tropospheric temperature (TMT). We use TMT information from satellites and large multimodel ensembles of forced and unforced simulations. Our main goal is to explore the sensitivity of model-versus-data spectral comparisons to a wide range of subjective decisions. These include the choice of satellite and climate model TMT datasets, the method for separating signal and noise, the frequency range considered, and the statistical model used to represent observed natural variability. Of particular interest is the amplitude of the interdecadal noise against which an anthropogenic tropospheric warming signal must be detected. We find that on time scales of 5–20 years, observed TMT variability is (on average) overestimated by the last two generations of climate models participating in the Coupled Model Intercomparison Project. This result is relatively insensitive to different plausible analyst choices, enhancing confidence in previous claims of detectable anthropogenic warming of the troposphere and indicating that these claims may be conservative. A further key finding is that two commonly used statistical models of short-term and long-term memory have deficiencies in their ability to capture the complex shape of observed TMT spectra.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

The Calibration and Characterization of Earth Remote Sensing and Environmental Monitoring Instruments

The use of remote sensing instruments on orbiting satellite platforms in the study of Earth Science and environmental monitoring was officially inaugurated with the April 1, 1960 launch of the Television Infrared Observation Satellite (TIROS) [1]. The first TIROS accommodated two television cameras and operated for only 78 days. However, the TIROS program, in providing in excess of 22,000 pictures of the Earth, achieved its primary goal of providing Earth images from a satellite platform to aid in identifying and monitoring meteorological processes. This marked the beginning of what is now over four decades of Earth observations from satellite platforms. reflected and emitted radiation from the Earth using instruments on satellite platforms. These measurements are input to climate models, and the model results are analyzed in an effort to detect short and long-term changes and trends in the Earth's climate and environment, to identify the cause of those changes, and to predict or influence future changes. Examples of short-term climate change events include the periodic appearance of the El Nino-Southern Oscillation (ENSO) in the tropical Pacific Ocean [2] and the spectacular eruption of Mount Pinatubo on the Philippine island of Luzon in 1991. Examples of long term climate change events, which are more subtle to detect, include the destruction of coral reefs, the disappearance of glaciers, and global warming. Climatic variability can be both large and small scale and can be caused by natural or anthropogenic processes. The periodic El Nino event is an example of a natural process which induces significant climatic variability over a wide range of the Earth. A classic example of a large scale anthropogenic influence on climate is the well-documented rapid increase of atmospheric carbon dioxide occurring since the beginning of the Industrial Revolution [3]. An example of the study of a small-scale anthropogenic influence in climate variability is the Atlanta Land-use Analysis Temperature and Air-quality (ATLANTA) project [4]. This project has found that the replacement of trees and vegetation with concrete and asphalt in Atlanta, Georgia, and its environs has created a microclimate capable of producing wind and thunderstorms. A key objective of climate research is to be able to distinguish the natural versus human roles in climate change and to clearly communicate those findings to those who shape and direct environmental policy.

Butler, James J.↗
NASA NTRS2019

Satellite Ocean Colour: Current Status and Future Perspective

Spectrally resolved water-leaving radiances (ocean colour) and inferred chlorophyll concentration are key to studying phytoplankton dynamics at seasonal and inter-annual scales, for a better understanding of the role of phytoplankton in marine biogeochemistry; the global carbon cycle; and the response of marine ecosystems to climate variability, change and feedback processes. Ocean colour data also have a critical role in operational observation systems monitoring coastal eutrophication, harmful algal blooms, and sediment plumes. The contiguous ocean-colour record reached 21 years in 2018; however, it is comprised of a number of one-off missions such that creating a consistent time-series of ocean-colour data requires merging of the individual sensors (including MERIS, Aqua-MODIS, SeaWiFS, VIIRS, and OLCI) with differing sensor characteristics, without introducing artefacts. By contrast, the next decade will see consistent observations from operational ocean colour series with sensors of similar design and with a replacement strategy. Also, by 2029 the record will start to be of sufficient duration to discriminate climate change impacts from natural variability, at least in some regions. This paper describes the current status and future prospects in the field of ocean colour focusing on large to medium resolution observations of oceans and coastal seas. It reviews the user requirements in terms of products and uncertainty characteristics and then describes features of current and future satellite ocean-colour sensors, both operational and innovative. The key role of in situ validation and calibration is highlighted as are ground segments that process the data received from the ocean-colour sensors and deliver analysis-ready products to end-users. Example applications of the ocean-colour data are presented, focusing on the climate data record and operational applications including water quality and assimilation into numerical models. Current capacity building and training activities pertinent to ocean colour are described and finally a summary of future perspectives is provided.

ocean colour↗
DOE OSTI2020

Time of Emergence and Large Ensemble Intercomparison for Ocean Biogeochemical Trends

Anthropogenically forced changes in ocean biogeochemistry are underway and critical for the ocean carbon sink and marine habitat. Detecting such changes in ocean biogeochemistry will require quantification of the magnitude of the change (anthropogenic signal) and the natural variability inherent to the climate system (noise). Here we use Large Ensemble (LE) experiments from four Earth system models (ESMs) with multiple emissions scenarios to estimate Time of Emergence (ToE) and partition projection uncertainty for anthropogenic signals in five biogeochemically important upper-ocean variables. We find ToEs are robust across ESMs for sea surface temperature and the invasion of anthropogenic carbon; emergence time scales are 20–30 yr. For the biological carbon pump, and sea surface chlorophyll and salinity, emergence time scales are longer (50+ yr), less robust across the ESMs, and more sensitive to the forcing scenario considered. We find internal variability uncertainty, and model differences in the internal variability uncertainty, can be consequential sources of uncertainty for projecting regional changes in ocean biogeochemistry over the coming decades. In combining structural, scenario, and internal variability uncertainty, this study represents the most comprehensive characterization of biogeochemical emergence time scales and uncertainty to date. Our findings delineate critical spatial and duration requirements for marine observing systems to robustly detect anthropogenic change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

How do intermittency and simultaneous processes obfuscate the Arctic influence on midlatitude winter extreme weather events?

Abstract Pronounced changes in the Arctic environment add a new potential driver of anomalous weather patterns in midlatitudes that affect billions of people. Recent studies of these Arctic/midlatitude weather linkages, however, state inconsistent conclusions. A source of uncertainty arises from the chaotic nature of the atmosphere. Thermodynamic forcing by a rapidly warming Arctic contributes to weather events through changing surface heat fluxes and large-scale temperature and pressure gradients. But internal shifts in atmospheric dynamics—the variability of the location, strength, and character of the jet stream, blocking, and stratospheric polar vortex (SPV)—obscure the direct causes and effects. It is important to understand these associated processes to differentiate Arctic-forced variability from natural variability. For example in early winter, reduced Barents/Kara Seas sea-ice coverage may reinforce existing atmospheric teleconnections between the North Atlantic/Arctic and central Asia, and affect downstream weather in East Asia. Reduced sea ice in the Chukchi Sea can amplify atmospheric ridging of high pressure near Alaska, influencing downstream weather across North America. In late winter southward displacement of the SPV, coupled to the troposphere, leads to weather extremes in Eurasia and North America. Combined tropical and sea ice conditions can modulate the variability of the SPV. Observational evidence for Arctic/midlatitude weather linkages continues to accumulate, along with understanding of connections with pre-existing climate states. Relative to natural atmospheric variability, sea-ice loss alone has played a secondary role in Arctic/midlatitude weather linkages; the full influence of Arctic amplification remains uncertain.

Environmental Sciences & Ecology↗
NASA NTRS2013

Human and Natural Influences on the Changing Thermal Structure of the Atmosphere

Since the late 1970s, satellite-based instruments have monitored global changes in atmospheric temperature. These measurements reveal multidecadal tropospheric warming and stratospheric cooling, punctuated by short-term volcanic signals of reverse sign. Similar long- and short-term temperature signals occur in model simulations driven by human-caused changes in atmospheric composition and natural variations in volcanic aerosols. Most previous comparisons of modeled and observed atmospheric temperature changes have used results from individual models and individual observational records. In contrast, we rely on a large multimodel archive and multiple observational datasets. We show that a human-caused latitude/altitude pattern of atmospheric temperature change can be identified with high statistical confidence in satellite data. Results are robust to current uncertainties in models and observations. Virtually all previous research in this area has attempted to discriminate an anthropogenic signal from internal variability. Here, we present evidence that a human-caused signal can also be identified relative to the larger "total" natural variability arising from sources internal to the climate system, solar irradiance changes, and volcanic forcing. Consistent signal identification occurs because both internal and total natural variability (as simulated by state-of-the-art models) cannot produce sustained global-scale tropospheric warming and stratospheric cooling. Our results provide clear evidence for a discernible human influence on the thermal structure of the atmosphere.

Santer, Benjamin D.↗
NASA NTRS2008

Dominance of ENSO-Like Variability in Controlling Tropical Ocean Surface Energy Fluxes in the Satellite Era

Ocean surface turbulent and radiative fluxes are critical links in the climate system since they mediate energy exchange between the two fluid systems (ocean and atmosphere) whose combined heat transport determines the basic character of Earth's climate. Moreover, interannual to decadal climate variability depends crucially on the nature of these exchange processes. For example, addressing the question of the degree to which the global hydrologic cycle is changing depends on our ability to observe and model these fluxes accurately. In this work we investigate the interannual to decadal variation of fluxes over the global tropics, especially the tropical oceans. Recent versions of satellite-derived fresh water flux estimates as well as some reanalyses (e.g. products from Remote Sensing Systems, the Woods Hole Oceanographic Institute, and Global Precipitation Climatology Project) suggest that increases in evaporation and precipitation over the past 20 years exceed those expected on the basis of climate model projected responses to greenhouse gas forcing. At the same time, it is well known that E1 Nino / Southern Oscillation behavior in the Pacific exhibits significant variability at scales longer than interannual. We examine here the degree to which surface fluxes attending these interannual to decadal fluctuations are related to ENSO. We examine consistency between these data sets and explore relationships between SST variations, flux changes and modulation of tropical Walker and Hadley circulations.

Robertson, F. R.↗
NASA NTRS2016

IN11B-1621: Quantifying How Climate Affects Vegetation in the Amazon Rainforest

Amazon droughts in 2005 and 2010 have raised serious concern about the future of the rainforest. Amazon forests are crucial because of their role as the largest carbon sink in the world which would effect the global warming phenomena with decreased photosynthesis activity. Especially, after a decline in plant growth in 1.68 million km2 forest area during the once-in-a-century severe drought in 2010, it is of primary importance to understand the relationship between different climatic variables and vegetation. In an earlier study, we have shown that non-linear models are better at capturing the relation dynamics of vegetation and climate variables such as temperature and precipitation, compared to linear models. In this research, we learn precise models between vegetation and climatic variables (temperature, precipitation) for normal conditions in the Amazon region using genetic programming based symbolic regression. This is done by removing high elevation and drought affected areas and also considering the slope of the region as one of the important factors while building the model. The model learned reveals new and interesting ways historical and current climate variables affect the vegetation at any location. MAIAC data has been used as a vegetation surrogate in our study. For temperature and precipitation, we have used TRMM and MODIS Land Surface Temperature data sets while learning the non-linear regression model. However, to generalize the model to make it independent of the data source, we perform transfer learning where we regress a regularized least squares to learn the parameters of the non-linear model using other data sources such as the precipitation and temperature from the Climatic Research Center (CRU). This new model is very similar in structure and performance compared to the original learned model and verifies the same claims about the nature of dependency between these climate variables and the vegetation in the Amazon region. As a result of this study, we are able to learn, for the very first time how exactly different climate factors influence vegetation at any location in the Amazon rainforests, independent of the specific sources from which the data has been obtained.

global warming↗