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DOE OSTI2021

A Grand Challenge "Uncertainty Project" to Accelerate Advances in Earth System Predictability: AI-Enabled Concepts and Applications

This proposal is emerging from GISS ModelE3 ESM development in the area of cloud physics, so we begin with an example of research needs/gaps from that work. Here, some of our greatest development concerns arise where we lack fundamental process-level understanding, as in ice formation. Namely, it is currently unclear what is the main process that is forming the majority of ice crystals in commonly occurring convection, apparently via secondary ice production at warm temperatures. We are keenly awaiting laboratory data for candidate mechanisms, which is not yet in hand to crucially establish their efficiency. Our progress is also hampered by a lack of uncertainty characterization in currently available measurements of ice crystal number size distributions. Furthermore, the same multiplication process may be responsible for a majority of ice crystals in many extratropical mixed-phase clouds, whose variable representation in CMIP6 ESMs may be a leading cause of differences in cloud phase feedback and ECS. Yet we have been required to deliver an ESM with the cloud physics knowledge at hand. The proposed grand challenge project is AI-enabled via application of machine learning (ML) to climate model and observational data streams (focal area 3), and applications include AI-guided observing system design and model/component/parameterization selection (areas 1 and 2). The project is structurally agnostic as to whether model or observing system components use AI approaches or not, but uncertainties must be estimated and propagatable in both.

58 GEOSCIENCES↗

DOE OSTI2026

Examining the Impacts of Microphysical-Dynamical Feedbacks on Convective Clouds in Different Aerosol Environments Using Enhanced Observational and Modeling Strategies

This project investigated how microphysical–dynamical feedbacks influence deep convective clouds across a range of aerosol conditions, storm lifecycles, and meteorological regimes, and how these sensitivities depend on the modeling framework used to represent convection and microphysics. Using the Aerosol, Cloud, Precipitation, and Climate (ACPC) Working Group model intercomparison simulations of isolated deep convection in the Houston region, we found a robust warm-phase aerosol response in most cloud-resolving models. Increased aerosol loading tended to suppress warm-rain production, increase cloud water, and reduce surface rainfall and near-surface evaporation. These impacts were typically strongest early in the convective lifecycle, with the largest aerosol-driven differences occurring during the first half of storm evolution. The ice-phase response, on the other hand, varied widely across modeling frameworks, and differed in sign, timing, and vertical structure. Ice microphysical pathways and parameterizations are therefore leading sources of uncertainty in aerosol–deep convection interactions. Theoretical analyses further suggested that aerosol-driven invigoration through cold-phase processes was much weaker than previously hypothesized for cold-based storms, and in warm-based storms could even reduce updraft strength. These results imply that any invigoration signal is more likely linked to warm-phase processes and peaks early in storm development.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2017

Using Satellite Observations of Cloud Vertical Distribution to Improve Global Model Estimates of Cloud Radiative Effect on Key Tropospheric Oxidants

Clouds directly affect tropospheric photochemistry through modification of solar radiation that determines photolysis frequencies. This effect is an important component of global tropospheric chemistry-climate interaction, and its understanding is thus essential for predicting the feedback of climate change on tropospheric chemistry.

Liu, Hongyu↗

NASA NTRS1995

The role of sea ice in 2 x CO2 climate model sensitivity. Part 1: The total influence of sea ice thickness and extent

As a first step in investigating the effects of sea ice changes on the climate sensitivity to doubled atmospheric CO2, the authors use a standard simple sea ice model while varying the sea ice distributions and thicknesses in the control run. Thinner ice amplifies the atmospheric temperature senstivity in these experiments by about 15% (to a warming of 4.8 C), because it is easier for the thinner ice to be removed as the climate warms. Thus, its impact on sensitivity is similar to that of greater sea ice extent in the control run, which provides more opportunity for sea ice reduction. An experiment with sea ice not allowed to change between the control and doubled CO2 simulations illustrates that the total effect of sea ice on surface air temperature changes, including cloud cover and water vapor feedbacks that arise in response to sea ice variations, amounts to 37% of the temperature sensitivity to the CO2 doubling, accounting for 1.56 C of the 4.17 C global warming. This is about four times larger than the sea ice impact when no feedbacks are allowed. The different experiments produce a range of results for southern high latitudes with the hydrologic budget over Antarctica implying sea level increases of varying magnitude or no change. These results highlight the importance of properly constraining the sea ice response to climate perturbations, necessitating the use of more realistic sea ice and ocean models.

Rind, D.↗

NASA NTRSDate not supplied

Parameterization of Vertical Cloud Distribution from C3M and MERRA Data Using ML Method

Clouds play a key role in regulating the hydrological cycle and the Earth's radiative energy budget. However, global climate models (GCMs) with a horizontal grid spacing on the order of 100 km have limitations in representing sub-grid cloud dynamics with spatial scales on the order of 1 km, leading to potential uncertainties in cloud radiative feedback on the global scale. In our research, we will leverage the capabilities of Deep Machine Learning (DML) methods to construct parameterizations of sub-grid volumetric cloud fraction (VCF), which is the frequency of occurrence on a grid volume accumulated in the horizontal and vertical directions. Our investigation delves into the intricate relationship between VCF obtained from the NASA CALIPSO-CloudSat-CERES-MODIS (CCCM) satellite observation data and 3-D MERRA-2 reanalysis meteorological profiling data (e.g., wind, relative humidity, temperature). Through a comprehensive one-year data training utilizing the Sequence to Sequence DML method, we have successfully disentangled the complicated cloud formation dynamics across diverse meteorological conditions through a day-to-day analysis framework. Preliminary findings reveal promising statistical agreements in geographical and vertical distributions and seasonal variations of volumetric cloud fraction between ML prediction and satellite measurements. These results underscore the aptitude of our DML model to discern underlying cloud physical processes and accurately represent sub-grid cloud formation dynamics. Additionally, we have also employed trained neural network to analyze uncertainties arising from errors in meteorological data, further enhancing the robustness of our VCF parameterization.

Shan Zeng↗

NASA NTRSDate not supplied

Observing System Simulations for the AOS Mission

The Earth System Observatory (ESO) is NASA’s response to the recommendations of the 2017 Earth Sciences Decadal Survey conducted by the US National Academy of Sciences, Engineering and Medicine. The ESO is being conceived as a set of fully integrated missions addressing 4 main Earth science focus areas including aerosols, clouds, convection and precipitation (jointly re-ferred to as AOS, the Atmosphere Observing System). ESO ground breaking observations will provide critical measurements to address societally relevant problems in climate change, natural hazard mitiga-tion, fighting forest fires, and improving real-time agricultural processes. A critical element of the AOS observing strategy is to make extensive use of new passive and active sen-sors as well as of the so-called Program-of-Record (PoR), complemented by a fully integrated sub-orbital component. In order to achieve maximum benefit, all these observations need to be integrated into comprehensive observing and modeling/data assimilation systems. Such an approach requires compre-hensive model-data synthesis capabilities that needs to be conceived in conjunction with the space-based and suborbital components of AOS. In this presentation we will summarize the major science goals of AOS including cloud feedbacks, at-mospheric convection, emphasizing aerosol processes and aerosol radiative effects, and the synergistic aspects of clouds-precipitation-aerosol interactions. We will describe examples of the observing system simulation capabilities being developed for AOS, including global storm resolving nature runs, detailed instrument and retrieval simulators, as well as fast retrieval emulators for instrumenting climate models. This simulation environment, being developed under NASA’s open-source science initiative, will permit us to explore how AOS data will be used across space and time to better initialize forecasts and train modeling systems, and to infuse models and data assimilation systems with AOS data, well before launch.

Arlindo da Silva↗

NASA NTRS2004

Warm Rain Processes over the Tropical Oceans and Implications on Climate Change: Results from TRMM and GEOS GCM

In this talk, we will first show results from TRMM data regarding the characteristics of warm rains over the tropical oceans, and the dependence of rate of warm rain production on sea surface temperature. Results lead to the hypothesis that warm rain production efficiency, i.e., autoconversion, may be increased in a warm climate. We use the GEOS-II GCM to test this hypothesis. Our modeling results show that in a climate with increased rate of autoconversion, the total rain amount is increased, with warm rain contributing to larger portion of the increase. The abundant rainout of warm precipitation causes a reduction of low and middle cloud amount due to rainout, and reduced high clouds due to less water vapor available for ice-phase convection. However, clod radiation feedback caused by the increased rainfall efficiency, leads to differential vertical heating/cooling producing a more unstable atmosphere, allowing, more intense, but isolated penetrative convection, with contracted anvils to develop. Results also show that increased autoconversion reduces the convective adjustment time scale, resulting in faster recycling of atmospheric water. Most interestingly, the increased low level heating associated with warm rain leads to more energetic Madden and Julian oscillations in the tropics, with well-defined eastward propagation. While reducing the autoconversion leads to an abundant mix of westward and eastward tropical disturbances on daily to weekly time scales. The crucial link of precipitation microphysical processes to climate change including the effects of aerosols will be discussed.

Lau, William K. M.↗

NASA NTRS2004

Warm Rain Processes Over the Tropical Oceans and Implications on Climate Change: Results from TRMM and GOES GCM

In this talk, we will first show results from TRMM data regarding the characteristics of warm rains over the tropical oceans, and the dependence of rate of warm rain production on sea surface temperature. Results lead to the hypothesis that warm rain production efficiency, i.e., autoconversion, may be increased in a warm climate. We use the GEOS-II GCM to test this hypothesis. Our modeling results show that in a climate with increased rate of autoconversion, the total rain amount is increased, with warm rain contributing to a larger portion of the increase. The abundant rainout of warm precipitation causes a reduction of low and middle cloud amount due to rainout, and reduced high clouds due to less water vapor available for ice-phase convection. However, clod radiation feedback caused by the increased rainfall efficiency, leads to differential vertical heating/cooling producing a more unstable atmosphere, allowing, more intense, but isolated penetrative convection, with contracted anvils to develop. Results also show that increased autoconversion reduces the convective adjustment time scale, resulting in faster recycling of atmospheric water. Most interestingly, the increased low level heating associated with warm rain leads to more energetic Madden and Julian oscillations in the tropics, with well-defined eastward propagation. While reducing the autoconversion leads to an abundant mix of westward and eastward tropical disturbances on daily to weekly time scales. The crucial link of precipitation microphysical processes to climate change including the effects of aerosols will be discussed.

Lau, William K. M.↗

NASA NTRSDate not supplied

The Surface Albedo of Sea Ice in CMIP6 and the Implications for the Surface Albedo Feedback

The Arctic has experienced rapid sea ice loss and a substantial decline in surface albedo, significantly impacting its radiation budget. Climate models from the Coupled Model Intercomparison Project (CMIP6) reproduce these changes. However, inconsistencies remain among models regarding the magnitude, spatial distribution, and seasonal patterns of Arctic surface albedo evolution. This study investigates these discrepancies by comparing model outputs with observation from the Clouds and the Earth's Radiant Energy System (CERES). We develop a decomposition method to assess the contributions of sea ice albedo, sea ice concentration, and sea ice extent to Arctic surface albedo. Over land, differences in snow cover account for the substantial inter-model spread in surface albedo, while over the ocean, sea ice albedo, concentration, and extent all contribute. Comparisons between CMIP6 and the Atmospheric Model Intercomparison Project (AMIP) simulations, which use identically prescribed sea ice concentrations, reveals considerable inter-model spread in Arctic Ocean surface albedo due to differences in sea ice albedo. Applying the decomposition method to projections shows that models predicting larger decreases in sea ice concentration and extent, especially in the Central Arctic, exhibit lower surface albedo and stronger sea ice albedo feedback. Beyond 2050, Arctic Ocean surface albedo decline is mainly influenced by sea ice extent indicating that the retreat of the ice edge is the most important process to constrain the surface albedo feedback. This study provides insights into factors contributing to the spread and changes in Arctic surface albedo and the associated sea ice albedo feedback.

Patrick C Taylor↗

NASA NTRS2012

Using Satellites to Investigate the Sensitivity of Longwave Downward Radiation to Water Vapor at High Elevations

Many studies suggest that high-elevation regions may be among the most sensitive to future climate change. However, in situ observations in these often remote locations are too sparse to determine the feedbacks responsible for enhanced warming rates. One of these feedbacks is associated with the sensitivity of longwave downward radiation (LDR) to changes in water vapor, with the sensitivity being particularly large in many high-elevation regions where the average water vapor is often low. We show that satellite retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Clouds and the Earth's Radiant Energy System (CERES) can be used to expand the current ground-based observational database and that the monthly averaged clear-sky satellite estimates of humidity and LDR are in good agreement with the well-instrumented Center for Snow and Avalanche Studies ground-based site in the southwestern Colorado Rocky Mountains. The relationship between MODIS-retrieved precipitable water vapor and surface specific humidity across the contiguous United States was found to be similar to that previously found for the Alps. More important, we show that satellites capture the nonlinear relationship between LDR and water vapor and confirm that LDR is especially sensitive to changes in water vapor at high elevations in several midlatitude mountain ranges. Because the global population depends on adequate fresh water, much of which has its source in high mountains, it is critically important to understand how climate will change there. We demonstrate that satellites can be used to investigate these feedbacks in high-elevation regions where the coverage of surface-based observations is insufficient to do so.

Naud, Catherine M.↗

NASA NTRS2013

The Role of Long-Lived Greenhouse Gases as Principal LW Control Knob that Governs the Global Surface Temperature for Past and Future Climate Change

The climate system of the Earth is endowed with a moderately strong greenhouse effect that is characterized by non-condensing greenhouse gases (GHGs) that provide the core radiative forcing. Of these, the most important is atmospheric CO2. There is a strong feedback contribution to the greenhouse effect by water vapor and clouds that is unique in the solar system, exceeding the core radiative forcing due to the non-condensing GHGs by a factor of three. The significance of the non-condensing GHGs is that once they have been injected into the atmosphere, they remain there virtually indefinitely because they do not condense and precipitate from the atmosphere, their chemical removal time ranging from decades to millennia. Water vapor and clouds have only a short lifespan, with their distribution determined by the locally prevailing meteorological conditions, subject to Clausius-Clapeyron constraint. Although solar irradiance is the ultimate energy source that powers the terrestrial greenhouse effect, there has been no discernible long-term trend in solar irradiance since precise monitoring began in the late 1970s. This leaves atmospheric CO2 as the effective control knob driving the current global warming trend. Over geological time scales, volcanoes are the principal source of atmospheric CO2, and the weathering of rocks is the principal sink, with the biosphere participating as both a source and a sink. The problem at hand is that human industrial activity is causing atmospheric CO2, to increase by 2 ppm per year, whereas the interglacial rate has been 0.005 ppm per year. This is a geologically unprecedented rate to turn the CO2 climate control knob. This is causing the global warming that threatens the global environment.

Greenhouse effect↗

DOE OSTI2022

UAS remote sensing (DJI Phantom 4 RTK platform): RGB orthomosaic, digital surface and canopy height models, plant functional type map, Seward Peninsula, Alaska, 2019

Airborne remote sensing data collected using a DJI Phantom 4 RTK unoccupied aerial system (UAS) ? operated by the University of Maine Wheatland Geospatial Lab (https://wheatlandlab.org/). This package includes data from 17 flights flown over the NGEE-Arctic Council, Kougarok, Kougarok Mile80, Teller, and Teller Mile32 sites in July 2019. The Phantom 4 RTK is a drone platform that collects very high spatial resolution optical red/green/blue (RGB) imagery. Derived image products include point cloud, ortho-mosaiced RGB, a digital surface model (DSM) using the structure from motion (SfM) technique, and a canopy height model (CHM). Unprocessed and processed data products (1,000+ files) are included in this package (processing levels 0-3). Data and metadata are provided as text (*.txt, *.json, *hdr,), ENVI image file (*.dat), point cloud (*.laz) and image (*.jpg, *.tif, *png) formats. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1990

Observed effects of landscape variability on convective clouds

A case is presented in which clouds are observed to form first over a mesoscale-size area (100 x 300 km) of harvested wheat in Oklahoma, where the ground temperature is warmer than adjoining areas dominated by growing vegetation. In addition, clouds are suppressed over relatively long bands downwind of small man-made lakes and areas characterized by heavy tree cover. The observed variability of cloud relative to landscape type is compared with that simulated with a one-dimensional boundary-layer model. Clouds form earliest over regions characterized by high, sensible heat flux, and are suppressed over regions characterized by high, latent heat flux during relatively dry atmospheric conditions. This observation has significance in gaining understanding of the feedback mechanisms of land modification on climate, as well as understanding relatively short-range weather forecasting.

Rabin, Robert M.↗

NASA NTRSDate not supplied

Role of Arctic Low Clouds in the Arctic Climate System: Isolating the Surface Type Influence on Arctic Low-Clouds

Interactions between sea ice and clouds represent a mechanism through which sea ice influences climate. Understanding the cloud response to the rapidly changing Arctic surface properties is necessary for improving climate simulations and projection of future change inside and outside the Arctic. Towards the goal of understanding the cloud response to sea ice, we composite cloud properties for ice-free, marginal ice zone (MIZ), and ice-covered surfaces during MIZ crossing events to analyze the cloud property differences between surface types. Restricting the analysis to MIZ crossing events enables the isolation of the sea ice effect on clouds from meteorological factors. We find larger cloud fraction and total water concentration below ~1.5 km over ice-free relative to ice-covered surfaces during non-summer months. During summer, the results suggest larger cloud fraction and total water concentration over ice-free surfaces, however differences do not exceed observational uncertainty. Cloud property differences are linked to atmospheric thermodynamic profile differences, namely ice-free surfaces are warmer, moister, less stable, and have more positive surface turbulent fluxes than ice-covered surfaces. Ice-free minus ice-covered cloud property differences scale with surface temperature differences and are only found in the presence of a surface temperature difference. Our results suggest a 0.02 cloud fraction and 0.005 g m 3 total water concentration increase (~5%) at the level of maximum cloud fraction between 2000-2021 due to the observed Arctic sea ice decline in fall, corresponding to ~2 Wm 2 increase in the net surface radiative flux supporting a positive sea ice-cloud radiative feedback in fall and winter and a negative sea ice-cloud radiative feedback in spring. We propose an updated conceptual model where the average surface type influence on cloud properties is mediated by surface temperature differences between ice-free and ice-covered surfaces.

Arctic climate↗

NASA NTRS1989

Observational determination of the greenhouse effect

Satellite measurements are used to quantify the atmospheric greenhouse effect, defined here as the infrared radiation energy trapped by atmospheric gases and clouds. The greenhouse effect is found to increase significantly with sea surface temperature. The rate of increase gives compelling evidence for the positive feedback between surface temperature, water vapor and the greenhouse effect; the magnitude of the feedback is consistent with that predicted by climate models. This study demonstrates an effective method for directly monitoring, from space, future changes in the greenhouse effect.

Raval, A.↗

NASA NTRS2002

The Sun's Impact on Climate

We provide an overview of the impact of the Sun on the Earth atmosphere and climate system, focused on heating of Earth's atmosphere and oceans. We emphasize the importance of the spectral measurements of SIM and SOLSTICE- that we must know how solar variations are distributed over ultraviolet, visible, and infrared wavelengths, since these have separate characteristic influences on Earth's ozone layer, clouds, and upper layers of the oceans. Emphasis is also given to understanding both direct and indirect influences of the Sun on the Earth, which involve feedbacks between Earth's stratosphere, troposphere, and oceans, each with unique time scales, dynamics, chemistry, and biology, interacting non-linearly. Especially crucial is the role of all three phases of water on Earth, water vapor being the primary greenhouse gas in the atmosphere, the importance of trace gases such as CO2 arising from their absorption in the "water vapor window" at 800 - 1250/cm (12.5 to 8 microns). Melting of polar ice is one major response to the post-industrial global warming, enhanced due to "ice-albedo" feedback. Finally, water in liquid form has a major influence due to cloud albedo feedback, and also due to the oceans' absorption of solar radiation, particularly at visible wavelengths, through the visible "liquid water window" that allows penetration of visible light deep into the mixed layer, while nearby ultraviolet and infrared wavelengths do not penetrate past the upper centimeter ocean surface skin layer. A large fraction of solar energy absorbed by the oceans goes into the latent heat of evaporation. Thus the solar heating of the atmosphere-ocean system is strongly coupled through the water cycle of evaporation, cloud formation, precipitation, surface runoff and ice formation, to Earth's energy budget and climate, each different climate component responding to variations in different solar spectral bands, at ultraviolet, visible and infrared wavelengths.

Cahalan, Robert↗

NASA NTRS1993

Statistical dependence of albedo and cloud cover on sea surface temperature for two tropical marine stratocumulus regions

The relationship between sea surface temperature (SST) and albedo or cloud cover is examined for two tropical regions with high values of cloud radiative forcing and persistent marine stratocumulus (mSc)-one off the west coast of Peru, the other off the west coast of Angola. The data span five years, from December 1984 to November 1989. Albedos are from the Earth Radiation Budget Experiment (ERBE), cloud covers are from the International Satellite Cloud Climatology Project (ISCCP), and SSTS are from the Climate Analysis Center. Negative correlation coefficients between albedo and SST are found to be about -0.8 when the seasonal variation of the entire dataset is analyzed. The interannual variation and the spatial variation of individual months also yields correlation coefficients that are negative. The correlation between cloud cover and SST is found to be similar to but weaker than the correlation between albedo and SST, suggesting a decrease in cloud amount and a decrease in cloud albedo with increasing SST for these regions. The corresponding albedo sensitivity averages -0.018/K with local values reaching -0.04/K. These findings are valid from 19 C to 25 C for the Peru mSc and 22 C to 27 C for the Angola mSc. These temperatures approximately bound the domains over which mSc is the prevalent cloud type within each region. These results imply a potential positive feedback to global warming by marine stratocumulus that ranges from approximately 0.14 W/sq m/K to approximately 1 W/sq m/K, depending on whether or not our results apply to all marine stratocumulus. While these values are uncertain to at least +/- 50%, the sensitivity of albedo to sea surface temperature in the present climate may serve as a useful diagnostic tool in monitoring the performance of global climate models.

Oreopoulos, Lazaros↗

DOE OSTI2023

UAS remote sensing (3DR SOLO platform): multispectral reflectance and normalized difference vegetation index, Seward Peninsula, Alaska, 2022

Airborne remote sensing data collected using a Parrot Sequoia+ multispectral sensor installed on a 3DR SOLO unoccupied aerial system (UAS) – operated by the Terrestrial Ecosystem Science & Technology group https://www.bnl.gov/envsci/testgroup/ at Brookhaven National Laboratory. This package includes data from 19 flights flown over the NGEE-Arctic, Kougarok Mile Marker (MM) 80, Kougarok Fire Complex (KFC) and Teller MM 27 sites in July 2022. Derived image products include point cloud, ortho-mosaiced multispectral image, a digital surface model (DSM) using the structure from motion (SfM) technique, and a normalized difference vegetation index (NDVI) map. Unprocessed and processed data products are included in this package (processing levels 0-2). Data and metadata are provided as text (*.txt, *.json, *hdr,), tabular (*.dat, *.csv), point cloud (*.laz), Cloud Optimized GeoTIFF (COG, *.tif), and image (*.jpg, *.tif, *png) formats.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗