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DOE OSTI2025

Implementation and Evaluation of Emission‐Driven Land‐Atmosphere Coupled Simulation in E3SMv2.1

Emissions-driven (prognostic CO 2 ) simulations are essential for representing two-way carbon-climate feedback in Earth System Models. We present an emissions-driven land–atmosphere coupled biogeochemistry (BGC) configuration (BGCLNDATM_progCO2) in version 2.1 of the Energy Exascale Earth System Model (E3SMv2.1). This is the first E3SM configuration that performs land-atmosphere emission-hindcasts. Here, we document its implementation, evaluate the model's performance against observations and other models, and propose a structured evaluation protocol for such emissions-driven simulations. We conducted transient historical simulations (1850–2014) with BGCLNDATM_progCO2 and compare them to reference simulations—a land-atmosphere coupled simulation without BGC and a standalone land simulation with BGC, both using prescribed CO 2 concentrations—and to observations. BGCLNDATM_progCO2 overestimates atmospheric CO 2 concentrations by 11–23 ppm yet stays within the 40-ppm spread CMIP6 emission-driven models and retains physical climate properties comparable to the reference runs. The CO 2 biases are partly attributed to underrepresented oceanic CO 2 uptake and inadequate representations of some terrestrial processes. In general, introducing prognostic CO 2 did not change physical climate metrics at the global scale but had larger regional effects, particularly over land where spatially heterogeneous CO 2 and prognostic leaf area index influenced surface energy balance. Finally, we propose a general evaluation protocol including spin-up assessment, atmospheric CO 2 benchmarking, physical climate evaluation, and land biogeochemical analysis to support scientific rigor and facilitate inter-model comparisons. The new configuration lays the groundwork for future enhancements, including improved terrestrial biogeochemical processes, integrated marine biogeochemistry, and additional human–Earth system interactions. These developments advance E3SM toward fully coupled emissions-driven simulations, enabling more accurate carbon–climate feedback projections and informing mitigation policy by providing physically consistent carbon-budget metrics for mitigation scenarios.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Earth System Model Improvement Pipeline via Uncertainty Attribution and Active Learning

Primary focal area: 2 (Predictive Modeling via AI): We develop methods to formally quantify uncertainties in Earth System models for the land-atmosphere coupled system. Science Challenge: Earth system models still have significant biases in historical predictions of the intensity and frequency of water cycling extremes (e.g., droughts and flood events), leading to low confidence in future projections. Uncertainties arise from incomplete understanding of land and atmospheric processes, and insufficient observational constraints on model parameters. Many observations, including those from key DOE investments such as ARM and AmeriFlux, are used to evaluate model performance but have not been used to formally quantify model uncertainty because of the expense of running ESM simulations. An efficient pipeline engaging cutting-edge machine learning (ML) and uncertainty quantification (UQ) methods is needed to improve the predictive understanding of water cycle extremes in the Earth system.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2022

Evaluating Twenty-Year Trends in Earth’s Energy Flows from Observations and Reanalyses

Satellite, reanalysis, and ocean in situ data are analyzed to evaluate regional, hemispheric and global mean trends in Earth’s energy fluxes during the first twenty years of the 21st century. Regional trends in net top-of-atmosphere (TOA) radiation from the Clouds and the Earth’s Radiant Energy System (CERES), ECMWF Reanalysis 5 (ERA5), and a model similar to ERA5 with prescribed sea surface temperature (SST) and sea ice differ markedly, particularly over the Eastern Pacific Ocean, where CERES observes large positive trends. Hemispheric and global mean net TOA flux trends for the two reanalyses are smaller than CERES, and their climatological means are half those of CERES in the southern hemisphere (SH) and more than nine times larger in the northern hemisphere (NH). The regional trend pattern of the divergence of total atmospheric energy transport (TEDIV) over ocean determined using ERA5 analyzed fields is similar to that inferred from the difference between TOA and surface fluxes from ERA5 short-term forecasts. There is also agreement in the trend pattern over ocean for surface fluxes inferred as a residual between CERES net TOA flux and ERA5 analysis TEDIV and surface fluxes obtained directly from ERA5 forecasts. Robust trends are observed over the Gulf Stream associated with enhanced surface-to-atmosphere transfer of heat. Within the ocean, larger trends in ocean heating rate are found in the NH than the SH after 2005, but the magnitude of the trend varies greatly among datasets.

Norman G Loeb↗

NASA NTRS2015

Technical Report Series on Global Modeling and Data Assimilation, Volume 41 : GDIS Workshop Report

The workshop "An International Global Drought Information System Workshop: Next Steps" was held on 10-13 December 2014 in Pasadena, California. The more than 60 participants from 15 countries spanned the drought research community and included select representatives from applications communities as well as providers of regional and global drought information products. The workshop was sponsored and supported by the US National Integrated Drought Information System (NIDIS) program, the World Climate Research Program (WCRP: GEWEX, CLIVAR), the World Meteorological Organization (WMO), the Group on Earth Observations (GEO), the European Commission Joint Research Centre (JRC), the US Climate Variability and Predictability (CLIVAR) program, and the US National Oceanic and Atmospheric Administration (NOAA) programs on Modeling, Analysis, Predictions and Projections (MAPP) and Climate Variability & Predictability (CVP). NASA/JPL hosted the workshop with logistical support provided by the GEWEX program office. The goal of the workshop was to build on past Global Drought Information System (GDIS) progress toward developing an experimental global drought information system. Specific goals were threefold: (i) to review recent research results focused on understanding drought mechanisms and their predictability on a wide range of time scales and to identify gaps in understanding that could be addressed by coordinated research; (ii) to help ensure that WRCP research priorities mesh with efforts to build capacity to address drought at the regional level; and (iii) to produce an implementation plan for a short duration pilot project to demonstrate current GDIS capabilities. See http://www.wcrp-climate.org/gdis-wkshp-2014-objectives for more information.

GEO↗

NASA NTRS1988

Precise orbit determination for NASA's Earth Observing System using GPS

A precision orbit determination technique for NASA's Earth Observing System (EOS) is discussed. The technique exploits the redundant geometric information from GPS carrier phase and P-code pseudorange measurements and minimizes the requirements for precision dynamical modeling. Performance estimates for the application of the technique to the orbit geometry planned for the EOS platforms show that decimeter accuracies of orbit position may be obtained. The sensitivity of the predicted orbit uncertainties to model errors for station locations, nongravitational platform accelerations, and earth gravity are presented.

Williams, Bobby G.↗

DOE OSTI2021

A model-independent data assimilation (MIDA) module and its applications in ecology

Abstract. Models are an important tool to predict Earth system dynamics. An accurate prediction of future states of ecosystems depends on not only model structures but also parameterizations. Model parameters can be constrained by data assimilation. However, applications of data assimilation to ecology are restricted by highly technical requirements such as model-dependent coding. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module. MIDA works in three steps including data preparation, execution of data assimilation, and visualization. The first step prepares prior ranges of parameter values, a defined number of iterations, and directory paths to access files of observations and models. The execution step calibrates parameter values to best fit the observations and estimates the parameter posterior distributions. The final step automatically visualizes the calibration performance and posterior distributions. MIDA is model independent, and modelers can use MIDA for an accurate and efficient data assimilation in a simple and interactive way without modification of their original models. We applied MIDA to four types of ecological models: the data assimilation linked ecosystem carbon (DALEC) model, a surrogate-based energy exascale earth system model: the land component (ELM), nine phenological models and a stand-alone biome ecological strategy simulator (BiomeE). The applications indicate that MIDA can effectively solve data assimilation problems for different ecological models. Additionally, the easy implementation and model-independent feature of MIDA breaks the technical barrier of applications of data–model fusion in ecology. MIDA facilitates the assimilation of various observations into models for uncertainty reduction in ecological modeling and forecasting.

58 GEOSCIENCES↗

NASA NTRS2000

Remote sensing and human health: new sensors and new opportunities

Since the launch of Landsat-1 28 years ago, remotely sensed data have been used to map features on the earth's surface. An increasing number of health studies have used remotely sensed data for monitoring, surveillance, or risk mapping, particularly of vector-borne diseases. Nearly all studies used data from Landsat, the French Systeme Pour l'Observation de la Terre, and the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer. New sensor systems are in orbit, or soon to be launched, whose data may prove useful for characterizing and monitoring the spatial and temporal patterns of infectious diseases. Increased computing power and spatial modeling capabilities of geographic information systems could extend the use of remote sensing beyond the research community into operational disease surveillance and control. This article illustrates how remotely sensed data have been used in health applications and assesses earth-observing satellites that could detect and map environmental variables related to the distribution of vector-borne and other diseases.

Satellite Communications↗

DOE OSTI2020

NGEE Arctic CO2, CH4 and Energy Eddy-Covariance (EC) Flux Tower Auxiliary Measurements, Utqiagvik (Barrow), Alaska, 2012 - 2019

Measurements began in September 2012 and are generally made throughout the snow-free growing season starting from April (pre-thawing) until November (freeze-up) when the instrumentation is taken down for maintenance and instrument calibration. CO2, CH4 and energy fluxes using the Eddy Covariance (EC) technique (Baldocchi 2003) and meteorological measurements are reported as 30-minute averages. The tower site is registered with AmeriFlux as US-NGB. See http://ameriflux.lbl.gov/sites/siteinfo/US-NGB for more information. Reported data include: (1) additional/extra meteorological and eddy covariance CO2, CH4 and energy flux data, (2) binned spectra for the three wind components, the sonic temperature and gas densities together with the binned cospectra for covariances of w (vertical wind component) and gas densities, (3) binned ogives (cumulative (co)spectra) for the three wind components, the sonic temperature and gas densities, and (4) tower site footprint matrices. Meteorological and eddy covariance CO2, CH4 and energy flux data (AmeriFlux BASE data product) are available from AmeriFlux https://doi.org/10.17190/AMF/1436326. The tower site is adjacent to the NGEE Arctic Tram system with several complementary time-series measurements https://doi.org/10.5440/1558782.This dataset supersedes the previously released dataset "Eddy-Covariance and auxiliary measurements, NGEE-Barrow, 2012-2013" DOI:10.5440/1124200 as this new product includes data for the longer time period processed using consistent methods for the entire record. 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) near Utqiagvik (Barrow), Alaska and 2) multiple areas in 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↗

DOE OSTI2020

NGEE Arctic CO2, CH4 and Energy Eddy-Covariance (EC) Flux Tower Auxiliary Measurements, Council Road Mile Marker 71, Seward Peninsula, Alaska, 2017 - 2019

Measurements began in July 2017 and are made year-round (2017 seasonal only) on the tussock tundra with discontinuous permafrost at the Council Road Site on the Seward Peninsula. CO2, CH4 and energy fluxes using the Eddy Covariance (EC) technique (Baldocchi 2003) and meteorological measurements are reported as 30-minute averages. The tower site is registered with AmeriFlux as US-NGC. See http://ameriflux.lbl.gov/sites/siteinfo/US-NGC for more information. Reported data include: (1) additional/extra meteorological and eddy covariance CO2, CH4 and energy flux data, (2) binned spectra for the three wind components, the sonic temperature and gas densities together with the binned cospectra for covariances of w (vertical wind component) and gas densities, (3) binned ogives (cumulative (co)spectra) for the three wind components, the sonic temperature and gas densities, and (4) tower site footprint matrices. The majority of the files are *.csv with paired visualization footprint files as *.jpg. Meteorological and eddy covariance CO2, CH4 and energy flux data (AmeriFlux BASE data product) are available from AmeriFlux BASE US-NGC NGEE Arctic Council (Torn, 2020) https://doi.org/10.17190/AMF/1634883. 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) near Utqiagvik (Barrow), Alaska and 2) multiple areas in 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 NTRSDate not supplied

Trade-Offs of Simplified Versus Comprehensive Representation of Mineralogy When Studying Dust Impacts on Earth’s Climate Systems

The intensity and direction of dust impacts on Earth’s climate systems depend on mineral composition. For example, the presence or absence of a few percent of iron oxides in dust will determine if dust is warming or cooling the atmosphere. Similarly, feldspar will enhance ice cloud formation, while acid gases in the atmosphere will react on the surface of dust calcite limiting acid rain. Still, most climate models use a simplified representation of dust mineralogy. They assume a fixed composition at emission which stays invariant during transport and removal. Such simplification assumes spatially and temporally constant physical and chemical properties of dust, and appears to provide satisfactory results when comparing some properties with observations. The trade-off is their lack of spatial gradients, which will fail to induce circulation, cloud and precipitation changes. The two reasons to omit mineral variations are the uncertainty of current atlases of soil mineral composition in arid regions, and, more practically, an improved runtime efficiency. The former reason is losing ground with the recent launch (July 2022) of a dedicated mission (NASA/JPL EMIT) to retrieve global soil mineralogy of dust sources at high spatial resolution. While the EMIT science team is finalizing a satisfactory global map of mineral composition of dust sources, we analyzed the interaction of dust mineralogy on radiation and its impact on the fast temperature response using different representations of mineral composition from detailed and spatially varying to simplified and globally uniform, assuming different hematite contents and methods to calculate optical properties. Our results show that resolving dust mineralogy reduces dust absorption, and results in improved agreement with observation-based single scattering albedo (SSA), radiative fluxes from CERES (the Clouds and the Earth’s Radiant Energy System), and land surface temperature from CRU (Climatic Research Unit), compared to the baseline bulk dust model version. It also results in distinct radiative impacts on Earth’s climate over North Africa. From our 19-year simulation, we will show that it leads to a reduction of over 50% in net downward radiation at top of atmosphere (TOA) across the Sahara and an approximately 20% reduction over the Sahel. We will explain how the surface temperature response affects the monsoon flow from the Gulf of Guinea. Interestingly, we find similar results by simply fixing the hematite content of dust to a globally uniform value of 0.9% by volume. We will discuss the underlying reasons for such results and show that they may be unrelated to the distribution of soil mineralogy. Still, an accurate representation of soil mineralogy is necessary to better understand dust impacts on the Earth’s climate systems.

dust impacts↗

NASA NTRS2000

A Method for Assessing the Quality of Model-Based Estimates of Ground Temperature and Atmospheric Moisture Using Satellite Data

A method is developed for validating model-based estimates of atmospheric moisture and ground temperature using satellite data. The approach relates errors in estimates of clear-sky longwave fluxes at the top of the Earth-atmosphere system to errors in geophysical parameters. The fluxes include clear-sky outgoing longwave radiation (CLR) and radiative flux in the window region between 8 and 12 microns (RadWn). The approach capitalizes on the availability of satellite estimates of CLR and RadWn and other auxiliary satellite data, and multiple global four-dimensional data assimilation (4-DDA) products. The basic methodology employs off-line forward radiative transfer calculations to generate synthetic clear-sky longwave fluxes from two different 4-DDA data sets. Simple linear regression is used to relate the clear-sky longwave flux discrepancies to discrepancies in ground temperature ((delta)T(sub g)) and broad-layer integrated atmospheric precipitable water ((delta)pw). The slopes of the regression lines define sensitivity parameters which can be exploited to help interpret mismatches between satellite observations and model-based estimates of clear-sky longwave fluxes. For illustration we analyze the discrepancies in the clear-sky longwave fluxes between an early implementation of the Goddard Earth Observing System Data Assimilation System (GEOS2) and a recent operational version of the European Centre for Medium-Range Weather Forecasts data assimilation system. The analysis of the synthetic clear-sky flux data shows that simple linear regression employing (delta)T(sub g)) and broad layer (delta)pw provides a good approximation to the full radiative transfer calculations, typically explaining more thin 90% of the 6 hourly variance in the flux differences. These simple regression relations can be inverted to "retrieve" the errors in the geophysical parameters, Uncertainties (normalized by standard deviation) in the monthly mean retrieved parameters range from 7% for (delta)T(sub g) to approx. 20% for the lower tropospheric moisture between 500 hPa and surface. The regression relationships developed from the synthetic flux data, together with CLR and RadWn observed with the Clouds and Earth Radiant Energy System instrument, ire used to assess the quality of the GEOS2 T(sub g) and pw. Results showed that the GEOS2 T(sub g) is too cold over land, and pw in upper layers is too high over the tropical oceans and too low in the lower atmosphere.

Wu, Man Li C.↗

NASA NTRS2010

The Kepler End-to-End Model: Creating High-Fidelity Simulations to Test Kepler Ground Processing

The Kepler mission is designed to detect the transit of Earth-like planets around Sun-like stars by observing 100,000 stellar targets. Developing and testing the Kepler ground-segment processing system, in particular the data analysis pipeline, requires high-fidelity simulated data. This simulated data is provided by the Kepler End-to-End Model (ETEM). ETEM simulates the astrophysics of planetary transits and other phenomena, properties of the Kepler spacecraft and the format of the downlinked data. Major challenges addressed by ETEM include the rapid production of large amounts of simulated data, extensibility and maintainability.

Bryson, Stephen T.↗

DOE OSTI2022

Internal variability and forcing influence model–satellite differences in the rate of tropical tropospheric warming

Climate-model simulations exhibit approximately two times more tropical tropospheric warming than satellite observations since 1979. The causes of this difference are not fully understood and are poorly quantified. Here, we apply machine learning to relate the patterns of surface-temperature change to the forced and unforced components of tropical tropospheric warming. This approach allows us to disentangle the forced and unforced change in the model-simulated temperature of the midtroposphere (TMT). In applying the climate-model-trained machine-learning framework to observations, we estimate that external forcing has produced a tropical TMT trend of 0.25 ± 0.08 K⋅decade −1 between 1979 and 2014, but internal variability has offset this warming by 0.07 ± 0.07 K⋅decade −1 . Using the Community Earth System Model version 2 (CESM2) large ensemble, we also find that a discontinuity in the variability of prescribed biomass-burning aerosol emissions artificially enhances simulated tropical TMT change by 0.04 K⋅decade −1 . The magnitude of this aerosol-forcing bias will vary across climate models, but since the latest generation of climate models all use the same emissions dataset, the bias may systematically enhance climate-model trends over the satellite era. Our results indicate that internal variability and forcing uncertainties largely explain differences in satellite-versus-model warming and are important considerations when evaluating climate models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Summertime Carbonaceous Aerosol in Interior Versus Coastal Northern Alaska

Abstract Rapid warming is likely increasing primary production and wildfire occurrence in the Arctic. Projected changes in carbonaceous aerosols during the summer will impact atmospheric chemistry and climate, but our understanding of these processes is limited by sparse observations. Here, we characterize carbonaceous aerosol in Alaska, USA: Toolik Field Station in the Interior and the Atmospheric Radiation Measurement facility at Utqiaġvik on the Arctic coast, during the summers of 2022 and 2023. We estimated PM 2.5 and PM 10 concentrations using laser light scattering (PurpleAir sensors) and examined total carbon (TC) and its organic carbon (OC) and elemental carbon (EC) fractions in total suspended particles (TSP). We investigated the dominant sources of carbonaceous aerosol using air mass backward‐trajectories from the NOAA HYSPLIT model and radiocarbon source apportionment of TC. TC concentrations were about twice as high in the Interior compared to the coast, with contemporary sources dominating at both Toolik (95%–99%) and Utqiaġvik (86%–89%) over minor contributions from fossil sources. Elevated PM, TC, OC, and EC concentrations coincided with major boreal forest fire activity in North America that brought smoke to the region. The radiocarbon signature of EC measured at Toolik during these wildfire events indicated that over 90% of the EC came from contemporary sources. Our measurements demonstrate the potential for Arctic aerosol concentrations to respond significantly to climate warming‐induced changes to the landscape and emphasize the need for continuous atmospheric monitoring to advance our understanding of this rapidly changing environment.

Welch, Allison M. [Department of Earth System Scie↗

NASA NTRS1990

The first geocenter estimation results using GPS measurements

The center of mass of the Earth is the natural and unambiguous origin of a geocentric satellite dynamical system. A geocentric reference frame assumes that the origin of its coordinate axes is at the geocenter, in which all relevant observations and results can be referred and in which geodynamic theories or models for the dynamic behavior of Earth can be formulated. In practice, however, a kinematically obtained terrestrial reference frame may assume an origin other than the geocenter. A fast and accurate method of determining origin offset from the geocenter is highly desirable. Global Positioning System (GPS) measurements, because of their abundance and broad distribution, provide a powerful tool to obtain this origin offset in a short period of time. Two effective strategies have been devised. Data from the first Central and South America (Casa Uno) global GPS experiment were studied to demonstrate the ability of recovering the geocenter location with present-day GPS satellites and receivers.

Malla, R. P.↗

NASA NTRS1981

Atmospheric neutral points outside of the principal plane

It is noted that the positions in the sky where the skylight is unpolarized, that is, the neutral points, are in most cases located in the vertical plane through the sun (the principal plane). Points have been observed outside the principal plane (Soret, 1888) when the plane intersected a lake or sea. Here, the neutral points were located at an azimuth of about 15 deg from the sun and near the almucantar through the sun. In order to investigate the effects of water surface and aerosols in the neutral point positions, the positions are computed for models of the earth-atmosphere system that simulate the observational conditions. The computed and measured positions are found to agree well. While previous observations provided only qualitative information on the degree of polarization, it is noted that the computations provide details concerning the polarization parameters.

Fraser, R. S.↗

DOE OSTI2021

Vegetation Warming Experiment: 15N Uptake Experiment Environmental Observations and Thaw Depth, Utqiagvik (Barrow), Alaska, 2018

This dataset consists of measured soil thaw depth, soil temperature, soil moisture, and Arctagrostis latifolia height in vegetation warming experiment enclosures and paired control plots. Vegetation warming chambers (Zero Power) were deployed on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska. These chambers (Figure 1) consistently elevated air temperatures by approximately 4°C using a self-venting system described by Lewin et al (2017). Five chambers were deployed from June 17, 2018 to September 24, 2018 on the BEO within a 1 km2 area centered on 71.275N, -156.641W. Each chamber was co-located with an ambient plot where temperatures were not manipulated on patches of tundra containing the target species Arctagrostis latifolia. An intensive field campaign in late July investigated the impact of warming had on A. latifolia biomass, chemistry, and uptake of 15N labeled ammonia that was injected into the surface soils for one week. Initial measurements were taken on July 21, 2018. Harvest occurred on July 27, 2018. Water-extractable nutrients in soils were measured in July following harvests of A. latifolia plants and underlying soils. Availability of ammonia, nitrate, and phosphate throughout the growing season was measured by extracting nutrients bound to anion and cation binding resins deployed from July through September. Environmental variables (thaw depth, surface soil temperatures, surface soil moisture) were measured. Leaf traits and root traits of A. latifolia were also measured. Included are two *.csv data files and one *.pdf user guide. 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↗

DOE OSTI2021

High-Accuracy Module Emulators from Physically-Constrained AI Algorithms

Focal Area(s): How do we use AI tools to integrate observations, simulated data and physical and chemical fundamentals (Focal Area 3) into model components (Focal Area 2) that have high accuracy and stability and low computational burden to improve Earth System Predictability? Science Challenge: Earth system modeling of the hydrological cycle involves compute-intensive modules representing complex chemical and physical process. Recently, AI tools that are far less compute intensive have been developed that emulate these modules, but many of these efforts are not yet sufficiently accurate or even stable. We know a lot about the physics and chemistry of earth system processes. The Science Challenge is developing AI tools that not only incorporate observations and simulated data, but also incorporate the physics and chemistry of the process, while still maintaining the compute efficiency.

54 ENVIRONMENTAL SCIENCES↗