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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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At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Empirical estimation of weather-driven yield shocks using biophysical characteristics for U.S. rainfed and irrigated maize, soybeans, and winter wheat

Agricultural yields are highly susceptible to changes in weather system patterns, including both annual and sub-annual changes in temperature and precipitation. Understanding the impacts of future changes in meteorological variables on crop yields have been widely studied in both empirical and process-based models. This work presents a structural econometric approach that combines historical weather data with information about the biophysical growth cycles of maize, winter wheat, and soy to predict the year-to-year yield responses for rainfed crops. Modeled soil moisture and temperature are taken as key predictors, which allows testing the fitted rainfed model’s ability to predict irrigated yields by assuming irrigation produces the numerically optimal level of soil moisture. This approach is grounded in known biophysical processes, which improves confidence in the model predictions. It produces estimates of interannual variability in yields, which enable study of the effects of extreme events on agricultural productivity. Finally, it enables prediction of the potential impacts of changing weather patterns on irrigated crops in areas that are currently primarily rainfed. We present the results of the empirical model, fitted with rainfed data; out-of-sample validation on irrigated crops; and projections of yield shocks under multiple future climate scenarios. Under a bias-corrected GFDL RCP8.5 scenario, this approach predicts average yield change, relative to 2006-2020 average yields, across U.S. counties in the 2040-2060 period of -16.9% and -13.4% for rainfed and irrigated maize, of -19.1% and -18.4% for rainfed and irrigated soy, and of -4.0% and -2.0% for rainfed and irrigated winter wheat. And in the 2070-2090 period of -30.3% and -28.8 % for rainfed and irrigated maize, of -33.1% and -32.8% for rainfed and irrigated soy, and of -6.5% and -5.2%for rainfed and irrigated winter wheat.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2006

Radio Heating of Lunar Soil to Release Gases

A report proposes the development of a system to collect volatile elements and compounds from Lunar soil for use in supporting habitation and processing into rocket fuel. Prior exploratory missions revealed that H2, He, and N2 are present in Lunar soil and there are some indications that water ice may also be present. The proposed system would include a shroud that would be placed on the Lunar surface. Inside the shroud would be a radio antenna aimed downward. The antenna would be excited at a suitably high power and at a frequency chosen to optimize the depth of penetration of radio waves into the soil. The radio waves would heat the soil, thereby releasing volatiles bound to soil particles. The escaping volatiles would be retained by the shroud and collected by condensation in a radiatively cooled vessel connected to the shroud. It has been estimated that through radio-frequency heating at a power of 10 kW for one day, it should be possible to increase the temperature of a soil volume of about 1 cubic m by about 200 C -- an amount that should suffice for harvesting a significant quantity of volatile material.

Chui, Talso↗
NASA NTRS1992

An annual model of SSM/I radiobrightness for dry soil

An annual model is presented of the temperature structure within a homogeneous, dry soil halfspace that is subject to both diurnal and annual insolation, radiant heating from the atmosphere, sensible heat exchange with the atmosphere, and radiant cooling. The thermal constitutive properties of the soil are assumed to be constant so that the heat flow equation can be solved analytically. For computational economy, a variable time interval Laplace transform method is developed to predict the temperature.

Liou, Yuei-An↗
NASA NTRS2003

Effects of litter addition on ectomycorrhizal associates of a lodgepole pine (Pinus contorta) stand in Yellowstone National Park

Increasing soil nutrients through litter manipulation, pollution, or fertilization can adversely affect ectomycorrhizal (EM) communities by inhibiting fungal growth. In this study, we used molecular genetic methods to determine the effects of litter addition on the EM community of a Pinus contorta stand in Yellowstone National Park that regenerated after a stand-replacing fire. Two controls were used; in unmodified control plots nothing was added to the soil, and in perlite plots perlite, a chemically neutral substance, was added to maintain soil moisture and temperature at levels similar to those under litter. We found that (i) species richness did not change significantly following perlite addition (2.6 +/- 0.3 species/core in control plots, compared with 2.3 +/- 0.3 species/core in perlite plots) but decreased significantly (P < 0.05) following litter addition (1.8 +/- 0.3 species/core); (ii) EM infection was not affected by the addition of perlite but increased significantly (P < 0.001) in response to litter addition, and the increase occurred only in the upper soil layer, directly adjacent to the added litter; and (iii) Suillus granulatus, Wilcoxina mikolae, and agaricoid DD were the dominant organisms in controls, but the levels of W. mikolae and agaricoid DD decreased significantly in response to both perlite and litter addition. The relative levels of S. granulatus and a fourth fungus, Cortinariaceae species 2, increased significantly (P < 0.01 and P < 0.05, respectively) following litter addition. Thus, litter addition resulted in some negative effects that may be attributable to moisture-temperature relationships rather than to the increased nutrients associated with litter. Some species respond positively to litter addition, indicating that there are differences in their physiologies. Hence, changes in the EM community induced by litter accumulation also may affect ecosystem function.

Fungi/classification/growth & development↗
DOE OSTI2022

Data for: Patel et al. Carbon flux estimates are sensitive to data source: A comparison of field and lab temperature sensitivity data

This dataset contains data and code used for the paper "Carbon flux estimates are sensitive to data source: A comparison of field and lab temperature sensitivity data" [DOI COMING SOON]A large literature exists on mechanisms driving soil production of the greenhouse gases CO2 and CH4. Measurements of these gases’ fluxes are often performed using closed-chamber incubations in the laboratory or in situ, i.e., in the field. Although it is common knowledge that measurements obtained through field studies vs. laboratory incubations can diverge because of the vastly different conditions of these environments, few studies have systematically examined these patterns. It is crucial to understand the magnitude and reasons for any differences, as these data are used to parametrize and benchmark ecosystem- to global-scale models, which are then susceptible to the biases of the source data. Here, we specifically examine how greenhouse gas measurements may be influenced by whether the measurement/incubation was conducted in the field vs. laboratory, focusing on CO2 and CH4 measurements. We use Q10 of greenhouse gas flux (temperature sensitivity) for our analyses, because of the ubiquity of this metric in biological and Earth system sciences and its importance to many modeling frameworks. We predicted that laboratory measurements would be less variable, but also less representative of true field conditions. However, there was greater variability in the Q10 values calculated from lab-based measurements of CO2 fluxes, because lab experiments explore extremes rarely seen in situ, and reflect the physical and chemical disturbances occurring during sampling, transport, and incubation. Overall, respiration Q10 values were significantly greater in laboratory incubations (mean = 4.19) than field measurements (mean = 3.05), with strong influences of incubation temperature and climate region/biome. However, this was in part because field measurements typically represent total respiration (Rs), whereas lab incubations typically represent heterotrophic respiration (Rh), making direct comparisons difficult to interpret. Focusing only on Rh-derived Q10, these values showed almost identical distributions across laboratory (n = 1110) and field (n = 581) experiments, providing strong support for using the former as an experimental proxy for the latter, although we caution that geographic biases in the extant data make this conclusion tentative. Due to a smaller sample size of CH4 Q10 data, we were unable to perform a comparable robust analysis, but we expect similar interactions with soil temperature, moisture, and environmental/climatic variables. Our results here suggest the need for more concerted efforts to document and standardize these data, including sample and site metadata. This dataset contains a compressed (.zip) archive of the data and R scripts used for this manuscript. The dataset includes files in .csv format, which can be accessed and processed using MS Excel or R. This archive can also be accessed on GitHub at https://github.com/kaizadp/field_lab_q10 (DOI: 10.5281/zenodo.7106554).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2019

Seasonal Dependence of SMAP Radiometer-Based Soil Moisture Performance as Observed over Core Validation Sites

The NASA SMAP (Soil Moisture Active Passive) mission provides a global coverage of soil moisture measurements based on its L-band microwave radiometer every 2-3 days at about 40 km resolution. The soil moisture retrieval algorithms model the brightness temperature as a function of soil moisture, surface conditions and vegetation. External data sources inform the algorithms about the surface conditions and vegetation, which enable the retrieval of soil moisture. The inversion process contains uncertainties related to radiometer measurements, forward model assumptions and ancillary data sources. This study focuses on the uncertainties that depend on the seasonal evolution of the surface conditions and vegetation. This study compares the SMAP and core validation site (CVS) soil moisture values over a period of three years to extract the evolution of performance metrics over time. The analysis showed that most CVS that include managed agriculture exhibit significant time-dependent seasonal bias. This bias was linked to seasonal temperature cycle, which is a proxy to several features that can cause seasonally dependent errors in the SMAP product.

core validation site↗
DOE OSTI2026

Effects of 9.5 Years of Whole-Soil Warming on the Fatty Acid and n-Alkanes Composition in Bulk Soil and Density Fractions at Blodgett Experimental Forest, California, USA

Original data of molecular data (fatty acids and n-alkanes) including concentrations and calculated molecular proxies in a whole-soil warming experiment at the Blodgett Forest Research Station after 9.5 years of warming. The study site has a Mediterranean climate with annual average temperature of 12.5 ℃ and annual average precipitation of 1774 mm. The study site is characterized by a mesic Ultic Alfisol formed from granitic parent material, corresponding to a Dystric Cambisol under the World Reference Base for Soil Resources (WRB) classification system. Experimental warming is applied throughout the soil profile to a depth of 1 m using vertically embedded heating cables that raise soil temperature by 4 °C relative to ambient conditions. Soil samples were collected on 1 May 2023, after the experiment had been operating continuously for about 9.5 years since its initiation in January 2014.The data has been processed from raw data and cross-validated by other peers. The dataset includes: - Bulk_Fattyacid_9.5-year_Soil_Warming_Blodgett, California, USA: fatty acid concentrations and proxies including Carbon Preference Index (CPI) and Average Chain Length (ACL) of bulk soil organic carbon; - Fractions_Fattyacid_9.5-year_Soil_Warming_Blodgett, California, USA: fatty acid concentrations and proxies including CPI and ACL of free particulate organic matter (fPOM) and mineral-associated organic matter (MAOM); - Bulk_Alkanes_9.5-year_Soil_Warming_Blodgett, California, USA: n-alkanes concentrations and proxies including CPI and ACL of bulk soil organic carbon; - Fractions_Alkanes_9.5-year_Soil_Warming_Blodgett, California, USA: n-alkanes concentrations and proxies including CPI and ACL of fPOM and MAOM; - n-Alkanes_All_Monomer_Concentration_9.5-year_Soil_Warming_Blodgett, California, USA: concentration of all the n-alkane monomers identified and integrated for bulk soil, fPOM and MAOM; - Fattyacid_All_Monomer_Concentration_9.5-year_Soil_Warming_Blodgett, California, USA: concentration of all the fatty acid monomers including diacids identified and integrated for bulk soil, fPOM, and MAOM. All data are provided in CSV format and can be viewed using Microsoft Excel. We specifically look at fatty acids (FA) and n-alkanes in bulk soil, fPOM and MAOM and calculated molecular proxies such as CPI and ACL to understand the source of oragnic carbon (with ACL) and degree of decomposition (CPI) of each soil fraction. Due to lack of long-chain fatty acids (carbon number ⩾ 20), microorganism-derived organic carbon is characterized by shorter ACL in comparison to plant-derived organic carbon. Fresh SOC is characterized by even-over-odd dominance for fatty acids and odd-over-even dominance for n-alkanes. Therefore, CPI indicates whether soil organic carbon (SOC) represents fresh input (CPI > 10) or is strongly decomposed (close to 1). The research questions should be then, after 9.5-year warming: 1. whether the relative contribution between microorganism-derived and plant-derived SOC in each soil fraction? 2. whether fPOM became more decomposed whereas MAOM remained relatively persistent in each soil fraction across the soil depth?

Carbon↗
DOE OSTI2022

Environmental controls on the light use efficiency of terrestrial gross primary production

Abstract Gross primary production (GPP) by terrestrial ecosystems is a key quantity in the global carbon cycle. The instantaneous controls of leaf‐level photosynthesis are well established, but there is still no consensus on the mechanisms by which canopy‐level GPP depends on spatial and temporal variation in the environment. The standard model of photosynthesis provides a robust mechanistic representation for C 3 species; however, additional assumptions are required to “scale up” from leaf to canopy. As a consequence, competing models make inconsistent predictions about how GPP will respond to continuing environmental change. This problem is addressed here by means of an empirical analysis of the light use efficiency (LUE) of GPP inferred from eddy covariance carbon dioxide flux measurements, in situ measurements of photosynthetically active radiation (PAR), and remotely sensed estimates of the fraction of PAR (fAPAR) absorbed by the vegetation canopy. Focusing on LUE allows potential drivers of GPP to be separated from its overriding dependence on light. GPP data from over 100 sites, collated over 20 years and located in a range of biomes and climate zones, were extracted from the FLUXNET2015 database and combined with remotely sensed fAPAR data to estimate daily LUE. Daytime air temperature, vapor pressure deficit, diffuse fraction of solar radiation, and soil moisture were shown to be salient predictors of LUE in a generalized linear mixed‐effects model. The same model design was fitted to site‐based LUE estimates generated by 16 terrestrial ecosystem models. The published models showed wide variation in the shape, the strength, and even the sign of the environmental effects on modeled LUE. These findings highlight important model deficiencies and suggest a need to progress beyond simple “goodness of fit” comparisons of inferred and predicted carbon fluxes toward an approach focused on the functional responses of the underlying dependencies.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1982

The feasibility of thermal inertia mapping for detection of perched water tables in semi-arid irrigated lands

The thermal inertia mapping remote sensing technique is evaluated as a potential method for delineating areas of shallow perched water tables and related salinization problems on a regional basis. This method for detecting shallow water tables is based on the existence of significant differences in soil thermal properties between wet and dry soil profiles. Ground observations were conducted at two different test sites in California along the flight line, with one site located in a perched water table area and the other site in a well drained area. Measurements were taken hourly during a four day period (two days before and two days after the flight) for variables such as radiometric, meteorologic, soil moisture and temperature profiles, and surface characteristics. Results indicate that it is not feasible using present apparent thermal inertia remote sensing techniques to reliably delineate regional patterns of very shallow water tables due to the complex environmental system. However, some features within specific fields which may be directly related to the local presence of shallow water tables, such as differences in evaporative cooling due to soil moisture effects between and within fields, can be detected using the thermal inertia technique.

Ezra, C. E.↗
NASA NTRSDate not supplied

NASA SPoRT-Land Information System and Vegetation Stress Real-Time Products for Drought, Wildfire, and Pluvial Analysis

The NASA SPoRT Center has been producing a near real-time instance of the NASA Land Information System over a CONUS domain (hereafter “SPoRT-LIS”) since ~2015. The SPoRT-LIS runs the legacy Noah LSM in an observations-constrained manner, with outputs of soil moisture and temperature at layered depths along with surface energy fluxes. The unique configuration of SPoRT-LIS enables decision-making on operational timescales since it incorporates near real-time observations such as VIIRS Green Vegetation Fraction and MRMS QPE. An additional in-house Alaska-LIS is produced in real time to help inform end-users on spring snow melt and summer soil moisture trends during the wildfire season. Use of the SPoRT-LIS has gradually expanded in recent years as a component of drought analysis, feedback to the USDM, and is utilized by State Climate Offices. Operational feedback has led to increased applicability for analyses by other end-users in the drought community. This presentation will provide SPoRT-LIS applications for drought, pluvial, and fire weather case-studies. We will also present preliminary results of 2-week SPoRT-LIS forecast percentiles that were recently implemented using GFS model forecast fields. We will additionally discuss the pathway toward extending these forecasts into the future by incorporating ensemble forecasts for probabilistic guidance on soil moisture trends for potential flash drought and subseasonal outlooks.

Soil Moisture↗
NASA NTRS2012

L-Band H Polarized Microwave Emission During the Corn Growth Cycle

Hourly L-band (1.4 GHz) horizontally (H) polarized brightness temperatures (T(sub B))'s measured during five episodes (more than two days of continuous measurements) of the 2002 corn growth cycle are analyzed. These T(sub B)'s measurements were acquired as a part of a combined active/passive microwave field campaign, and were obtained at five incidence and three azimuth angles relative to the row direction. In support of this microwave data collection, intensive ground sampling took place once a week. Moreover, the interpretation of the hourly T(sub B)'s could also rely on the data obtained using the various automated instruments installed in the same field. In this paper, the soil moisture and temperature measured at fixed time intervals have been employed as input for the tau-omega model to reproduce the hourly T(sub B). Through the calibration of the vegetation and surface roughness parameterizations, the impact of the vegetation morphological changes on the microwave emission and the dependence of the soil surface roughness parameter, h(sub r), on soil moisture are investigated. This analysis demonstrates that the b parameter, appearing in the representation of the canopy opacity, has an angular dependence that varies throughout the growing period and also that the parameter hr increases as the soil dries in a portion of the dry-down cycle. The angular dependence of the b parameter imposes the largest uncertainty on T(sub B) simulations near senescence as the response of b to the incidence is also affected by the crop row orientation. On the other hand, the incorporation of a soil moisture dependent h(sub r) parameterization was responsible for the largest error reduction of T(sub B) simulations in the early growth cycle.

Joseph, A. T.↗
DOE OSTI2021

Temporal changes in global soil respiration since 1987

As the second-largest terrestrial carbon (C) flux, soil respiration (R S ) has been stimulated by climate warming. However, the magnitude and dynamics of such stimulations of soil respiration are highly uncertain at the global scale, undermining our confidence in future climate projections. Here, we present an analysis of global R S observations from 1987–2016. R S increased (P < 0.001) at a rate of 27.66 g C m -2 yr -2 (equivalent to 0.161 Pg C yr -2 ) in 1987–1999 globally but became unchanged in 2000–2016, which were related to complex temporal variations of temperature anomalies and soil C stocks. However, global heterotrophic respiration (R h ) derived from microbial decomposition of soil C increased in 1987–2016 (P < 0.001), suggesting accumulated soil C losses. Given the warmest years on records after 2015, our modeling analysis shows a possible resuscitation of global R S rise. This study of naturally occurring shifts in R S over recent decades has provided invaluable insights for designing more effective policies addressing future climate challenges.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Global Precipitation Measurement, Validation, and Applications Integrated Hydrologic Validation to Improve Physical Precipitation Retrievals for GPM

Land surface modeling and data assimilation can provide dynamic land surface state variables necessary to support physical precipitation retrieval algorithms over land. It is well-known that surface emission, particularly over the range of frequencies to be included in the Global Precipitation Measurement Mission (GPM), is sensitive to land surface states, including soil properties, vegetation type and greenness, soil moisture, surface temperature, and snow cover, density, and grain size. In order to investigate the robustness of both the land surface model states and the microwave emissivity and forward radiative transfer models, we have undertaken a multi-site investigation as part of the NASA Precipitation Measurement Missions (PMM) Land Surface Characterization Working Group. Specifically, we will demonstrate the performance of the Land Information System (LIS; http://lis.gsfc.nasa.gov; Peters-Lidard et aI., 2007; Kumar et al., 2006) coupled to the Joint Center for Satellite Data Assimilation (JCSDA's) Community Radiative Transfer Model (CRTM; Weng, 2007; van Deist, 2009). The land surface is characterized by complex physical/chemical constituents and creates temporally and spatially heterogeneous surface properties in response to microwave radiation scattering. The uncertainties in surface microwave emission (both surface radiative temperature and emissivity) and very low polarization ratio are linked to difficulties in rainfall detection using low-frequency passive microwave sensors (e.g.,Kummerow et al. 2001). Therefore, addressing these issues is of utmost importance for the GPM mission. There are many approaches to parameterizing land surface emission and radiative transfer, some of which have been customized for snow (e.g., the Helsinki University of Technology or HUT radiative transfer model;) and soil moisture (e.g., the Land Surface Microwave Emission Model or LSMEM).

Peters-Lidar, Christa D.↗
DOE OSTI2023

Projection of Future Fire Emissions Over the Contiguous US Using Explainable Artificial Intelligence and CMIP6 Models

Abstract Increasing temperature and water cycle changes due to warming climate may increase the frequency and intensity of wildfires. Fire emission projections are useful for informing strategies for adaptation and mitigation of fire impacts on societies and ecosystems. Here, we construct a neural network (NN) model explained by the Shapley Additive explanation to predict fire PM 2.5 emissions change and understand their drivers over the contiguous US (CONUS) in the mid‐21st century under a high greenhouse gas emissions scenario (SSP5‐8.5). Using future meteorology and leaf area index (LAI) simulated by eight global climate models from the Coupled Model Intercomparison Project Phase 6, future population density, and present‐day land use and land cover (LULC) as input to the NN model, the total fire PM 2.5 emissions over CONUS are projected to increase by 4%–75% (model spread). Among different regions, fire emissions in the western US are projected to increase more significantly in June‐July‐August than in other seasons and regions, with the median ratios of future to present‐day fire emissions ranging from 1.67 to 2.86. The increases in fire emissions are mainly driven by increasing normalized temperature (23%–29%) and decreasing soil moisture (2%–10%) in the future. When future LULC change is considered, the projected fire emissions further increase by 58%–83% over the western US compared to projections without LULC change because of future increases in vegetation fraction. The results highlight the important role of warmer temperature, decreasing soil moisture, and LULC change in increasing fire emissions in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Variations in Bedrock and Vegetation Cover Modulate Subsurface Water Flow Dynamics of a Mountainous Hillslope

Abstract Predicting the hydrological response of watersheds to climate disturbances requires a detailed understanding of the processes connecting hillslopes and streams. Using a network of soil moisture and temperature sensors, electrical resistivity tomography monitoring, and a weather station we assess the above and below‐ground processes driving the hydrological response of a hillslope during snowmelt and summer monsoon. The transect covers bedrock and vegetation gradients, with a steep upper part characterized by shallow bedrock, and gentle lower part underlain by colluvium. The main vegetation cover is conifers on the upper, and grass and veratrum on the lower part. Combined with a simplified hydrological model, we show that the thin soil layer of the steep slope acts as a preferential flow path, leading to mostly shallow lateral flow, interrupted by vertical flow, mostly at tree locations, and likely facilitated by flow along fractures and roots. Vertical flow and upstream‐driven groundwater dynamics are prevailing at the colluvium, presenting a very different hydrological behavior compared to the upper part. These results show that subsurface structure and features have a strong control on the hydrological response of a hillslope and that those can create considerably varying hydrological dynamics across small spatial scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

How initial conditions-, structural-, and parameter-based model uncertainty interact and influence predictions in permafrost ecosystems: Modeling Archive

This dataset contains model output and input data, as well as source code examples for the Terrestrial Ecosystem Model with the Dynamic Vegetation Model and Dynamic Organic Soil (DVM-DOS-TEM) for the field sites Imnavait creek and the Bonanza creek Long Term Ecological Research Network (LTER). The data covers simulations from the last glacial maximum (LGM) until 2100 for a selection of paleo scenarios, setting the mean temperature of the LGM up to 10°C lower than pre-industrial conditions. The model structure was modulated to represent various model versions, and this dataset contains the relevant changes in the source code. The raw output data, the processed statistical data, the setup and processing scripts as well as parameter value distribution files from a parameter sensitivity analysis are included as well. Model outputs include active layer depth, organic soil carbon, soil layer depths, gross primary productivity (GPP) with and without nitrogen limitation, net primary productivity (NPP), soil liquid water content, heterotrophic, maintenance, and growth respiration, soil temperature, and vegetation carbon (*.nc files). The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research.Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

54 ENVIRONMENTAL SCIENCES↗