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DOE OSTI2014

A Subbasin-based framework to represent land surface processes in an Earth System Model

Realistically representing spatial heterogeneity and lateral land surface processes within and between modeling units in earth system models is important because of their implications to surface energy and water exchange. The traditional approach of using regular grids as computational units in land surface models and earth system models may lead to inadequate representation of lateral movements of water, energy and carbon fluxes, especially when the grid resolution increases. Here a new subbasin-based framework is introduced in the Community Land Model (CLM), which is the land component of the Community Earth System Model (CESM). Local processes are represented assuming each subbasin as a grid cell on a pseudo grid matrix with no significant modifications to the existing CLM modeling structure. Lateral routing of water within and between subbasins is simulated with the subbasin version of a recently-developed physically based routing model, Model for Scale Adaptive River Routing (MOSART). As an illustration, this new framework is implemented in the topographically diverse region of the U.S. Pacific Northwest. The modeling units (subbasins) are delineated from high-resolution Digital Elevation Model while atmospheric forcing and surface parameters are remapped from the corresponding high resolution datasets. The impacts of this representation on simulating hydrologic processes are explored by comparing it with the default (grid-based) CLM representation. In addition, the effects of DEM resolution on parameterizing topography and the subsequent effects on runoff processes are investigated. Limited model evaluation and comparison showed that small difference between the averaged forcing can lead to more significant difference in the simulated runoff and streamflow because of nonlinear horizontal processes. Topographic indices derived from high resolution DEM may not improve the overall water balance, but affect the partitioning between surface and subsurface runoff. More systematic analyses are needed to determine the relative merits of the subbasin representation compared to the commonly used grid-based representation, especially when land surface models are approaching higher resolutions.

Subbasin-based representation, Community Land Mode↗

DOE OSTI2017

231 Pa and 230 Th in the ocean model of the Community Earth System Model (CESM1.3)

The sediment 231 Pa / 230 Th activity ratio is emerging as an important proxy for deep ocean circulation in the past. In order to allow for a direct model–data comparison and to improve our understanding of the sediment 231 Pa / 230 Th activity ratio, we implement 231 Pa and 230 Th in the ocean component of the Community Earth System Model (CESM). In addition to the fully coupled implementation of the scavenging behavior of 231 Pa and 230 Th with the active marine ecosystem module (particle-coupled: hereafter p-coupled), another form of 231 Pa and 230 Th have also been implemented with prescribed particle flux fields of the present climate (particle-fixed: hereafter p-fixed). The comparison of the two forms of 231 Pa and 230 Th helps to isolate the influence of the particle fluxes from that of ocean circulation. Under present-day climate forcing, our model is able to simulate water column 231 Pa and 230 Th activity and the sediment 231 Pa / 230 Th activity ratio in good agreement with available observations. In addition, in response to freshwater forcing, the p-coupled and p-fixed sediment 231 Pa / 230 Th activity ratios behave similarly over large areas of low productivity on long timescales, but can differ substantially in some regions of high productivity and on short timescales, indicating the importance of biological productivity in addition to ocean transport. Thus, our model provides a potentially powerful tool to help the interpretation of sediment 231 Pa / 230 Th reconstructions and to improve our understanding of past ocean circulation and climate changes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Technical note: Deep learning for creating surrogate models of precipitation in Earth system models

Abstract. We investigate techniques for using deep neural networks to produce surrogatemodels for short-term climate forecasts. A convolutional neural network istrained on 97 years of monthly precipitation output from the 1pctCO2 run (theCO 2 concentration increases by 1 % per year) simulated by the second-generation Canadian Earth System Model (CanESM2). The neural network clearly outperforms a persistence forecast anddoes not show substantially degraded performance even when the forecast lengthis extended to 120 months. The model is prone to underpredicting precipitationin areas characterized by intense precipitation events. Scheduled sampling(forcing the model to gradually use its own past predictions rather than groundtruth) is essential for avoiding amplification of early forecasting errors.However, the use of scheduled sampling also necessitates preforecasting(generating forecasts prior to the first forecast date) to obtain adequateperformance for the first few prediction time steps. We document the trainingprocedures and hyperparameter optimization process for researchers who wish toextend the use of neural networks in developing surrogate models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Integrated parameter and process learning for hydrologic and biogeochemical modules in Earth System Models

Focus area: Primary focal area #2; secondary focal area #3: Learning about parameters and processes of land surface hydrologic and biogeochemical models in Earth System models by integrating machine learning, physics, and big data. Science challenges: How do we maximally leverage big-data observations to improve hydrobiogeochemical process description and parameterization so that such modules more realistically capture hydrologic and vegetation responses and feedbacks under the future climate? For example, how can we leverage physics, limited observations of vegetation and streamflow to better estimate evapotranspiration, and, relatedly, net primary productivity, especially for drought areas? Vegetation plays a critical role in regional and global water cycles; however, existing vegetation models have failed to predict vegetation response to droughts (McDowell & Xu, 2017) , arctic greening (Keenan & Riley, 2018) , and critical transitions between forest and savanna (Hirota et al., 2011) . These studies suggest that when we build process-based models (PBM) parameterized from regional and global plant traits, we tend to poorly describe plant adaptation and local-scale competition processes. The models and their associated parameters assigned for different regions in the world are not capturing essential heterogeneity in vegetation responses at finer spatial scales. Many parameters of the land surface models control hydrology and vegetation dynamics at the same time. The heterogeneity in vegetation response is a function of (i) plant type, (ii) plant size, (iii) competition and succession, (iv) environmental controls, and (v) local variations due to the unique ecological community that are very difficult to describe (e.g., the size of gaps resulting from fire that facilitated the coexistence of pioneering species). In the demographic models, only factors (i) and (iv) were captured, and plant types were generally described only by leaf phenology and climate zones. With current demographic models, we generally consider more traits to define plant types (i) and calibrate these traits to consider factors (ii), (iii) and (iv); however, it is substantially challenging to scale to regional and global simulations due to trait variations across space (Ali et al., 2016). Moreover, it has been noted that hillslope processes, including ridge-to-valley flow and sunny vs. shady slopes are primary organizers of water, energy, and vegetation (Clark et al., 2015; Fan et al., 2019) . Although gradual improvements in the hydrologic model component in earth system models may reduce this error (at a remarkably slow pace), the long-term, gradual impact of hydrology on plant traits are not well captured. Recent work showed that the hydrologic controls exerted by groundwater and lateral flow are primary regulators of rooting depth (Fan et al., 2017) . Such hydrologic controls have seldom been reflected in vegetation model parameterizations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

United States multi-sector land use and land cover base maps to support human and Earth system models

Abstract Earth System Models (ESMs) require current and future projections of land use and landcover change (LULC) to simulate land-atmospheric interactions and global biogeochemical cycles. Among the most utilized land systems in ESMs are the Community Land Model (CLM) and the Land-Use Harmonization 2 (LUH2) products. Regional studies also use these products by extending coarse projections to finer resolutions via downscaling or by using multisector dynamic (MSD) models. One such MSD model is the Global Change Analysis Model (GCAM), which has its own independent land module, but often relies on CLM or LUH2 as spatial inputs for its base years. However, this requires harmonization of thematically incongruent land systems at multiple spatial resolutions, leading to uncertainty and error propagation. To resolve these issues, we develop a thematically consistent LULC system for the conterminous United States adaptable to multiple MSD frameworks to support research at a regional level. Using empirically derived spatial products, we developed a series of base maps for multiple contemporary years of observation at a 30-m resolution that support flexibility and interchangeability amongst LUH2, CLM, and GCAM classification systems.

Oliver, Jay↗

DOE OSTI2014

Cu(Ir1-xCrx)(2)S-4: a model system for studying nanoscale phase coexistence at the metal-insulator transition

N/A

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2017

Improving the Representation of Polar Snow and Firn in the Community Earth System Model

Abstract In Earth system models, terrestrial snow is usually modeled by the land surface component. In most cases, these snow models have been developed with an emphasis on seasonal snow. Questions about future sea level rise, however, prompt the need for a realistic representation of perennial snow, as snow processes play a key role in the mass balance of glaciers and ice sheets. Here we enhance realism of modeled polar snow in the Community Land Model (CLM), the land component of the Community Earth System Model (CESM), by implementing (1) new parametrizations for fresh snow density, destructive metamorphism, and compaction by overburden pressure, (2) by allowing for deeper snow packs, and (3) by introducing drifting snow compaction, with a focus on the ice sheet interior. Comparison with Greenlandic and Antarctic snow density observations show that the new physics improve model skill in predicting firn and near‐surface density in the absence of melt. Moreover, compensating biases are removed and spurious subsurface melt rates at ice sheets are eliminated. The deeper snow pack enhances refreezing and allows for deeper percolation, raising ice temperatures up to 15°C above the skin temperature.

van Kampenhout, Leonardus↗

DOE OSTI2015

Development of hybrid 3-D hydrological modeling for the NCAR Community Earth System Model (CESM)

58 GEOSCIENCES↗

DOE OSTI2015

Development of hybrid 3-D hydrological modeling for the NCAR Community Earth System Model (CESM)

This is the Final Report of our four-year (3-year plus one-year no cost extension) collaborative project between the University of Arizona (UA) and the National Center for Atmospheric Research (NCAR). The overall objective of our project is to develop and evaluate the first hybrid 3-D hydrological model with a horizontal grid spacing of 1 km for the NCAR Community Earth System Model (CESM). We have made substantial progress in model development and evaluation, computational efficiencies and software engineering, and data development and evaluation, as discussed in Sections 2-4. Section 5 presents our success in data dissemination, while Section 6 discusses the scientific impacts of our work. Section 7 discusses education and mentoring success of our project, while Section 8 lists our relevant DOE services. All peer-reviewed papers that acknowledged this project are listed in Section 9. Highlights of our achievements include: • We have finished 20 papers (most published already) on model development and evaluation, computational efficiencies and software engineering, and data development and evaluation • The global datasets developed under this project have been permanently archived and publicly available • Some of our research results have already been implemented in WRF and CLM • Patrick Broxton and Michael Brunke have received their Ph.D. • PI Zeng has served on DOE proposal review panels and DOE lab scientific focus area (SFA) review panels

58 GEOSCIENCES↗

DOE OSTI2017

integrated Earth System Model

The integrated Earth system model (iESM) has been developed as a new tool for projecting the joint human/climate system. The iESM is based upon coupling an integrated assessment model (IAM) and an Earth system model (ESM) into a common modeling infrastructure. IAMs are the primary tool for describing the human-Earth system, including the sources of global greenhouse gases (GHGs) and short-lived species (SLS), land use and land cover change (LULCC), and other resource-related drivers of anthropogenic climate change. ESMs are the primary scientific tools for examining the physical, chemical, and biogeochemical impacts of human-induced changes to the climate system. The iESM project integrates the economic and human-dimension modeling of an IAM and a fully coupled ESM within a single simulation system while maintaining the separability of each model if needed. Both IAM and ESM codes are developed and used by large communities and have been extensively applied in recent national and international climate assessments. By introducing heretofore-omitted feedbacks between natural and societal drivers, we can improve scientific understanding of the human-Earth system dynamics. Potential applications include studies of the interactions and feedbacks leading to the timing, scale, and geographic distribution of emissions trajectories and other human influences, corresponding climate effects, and the subsequent impacts of a changing climate on human and natural systems.

Jones, Andew↗

DOE OSTI2021

System Modeling of the HTTR and Economic Dispatch Model of the Secondary System

High Temperature Gas-cooled Reactors (HTGRs) can be used for the generation of electricity and their process heat can be used to improve the efficiency of chemical processes such as hydrogen production. The JAEA-operated High-Temperature engineering Test Reactor (HTTR-GT/H2) is exploring using the reactor for electricity and hydrogen production. A RELAP5-3D model of the HTTR-GT/H2 secondary system has been developed using design information. The various components and heat exchangers in the secondary system were modeled and results were compared to the design conditions. The results for the sole-power generation mode were shown to fit the design conditions very well. The largest temperature difference was on the order of 7 K, and the largest pressure difference was on the order of 0.05 MPa. The results for the hydrogen cogeneration mode did not match the design conditions nearly as well. The largest temperature difference was about 39 K and the largest pressure difference was about 0.27 MPa at the compressor outlet. The larger differences for the hydrogen cogeneration mode are attributed to the various complex components and the flow being split in the secondary loop. A transient reduction in heat removal capability of the secondary system was investigated. Reactor temperatures are anticipated to rise as a result. The core reactivity response due to this increase in temperature is investigated and is expected to add negative reactivity to the reactor. An economic dispatch model was developed for a nuclear-driven iodine-sulfur cycle system to determine hydrogen sale prices that would make such a system profitable. The study focuses on the development of the economic model and the role that input data plays on final calculated values. It was found that the input electricity prices, whether using historical data or a host of synthetic time histories, produce significantly different breakeven hydrogen sale prices. As such, great care should be used in these economic dispatch analyses to select reasonable input assumptions.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Building a machine learning surrogate model for wildfire activities within a global Earth system model

Abstract. Wildfire is an important ecosystem process, influencing land biogeophysical and biogeochemical dynamics and atmospheric composition. Fire-driven loss of vegetation cover, for example, directly modifies the surface energy budget as a consequence of changing albedo, surface roughness, and partitioning of sensible and latent heat fluxes. Carbon dioxide and methane emitted by fires contribute to a positive atmospheric forcing, whereas emissions of carbonaceous aerosols may contribute to surface cooling. Process-based modeling of wildfires in Earth system land models is challenging due to limited understanding of human, climate, and ecosystem controls on fire counts, fire size, and burned area. Integration of mechanistic wildfire models within Earth system models requires careful parameter calibration, which is computationally expensive and subject to equifinality. To explore alternative approaches, we present a deep neural network (DNN) scheme that surrogates the process-based wildfire model with the Energy Exascale Earth System Model (E3SM) interface. The DNN wildfire model accurately simulates observed burned area with over 90 % higher accuracy with a large reduction in parameterization time compared with the current process-based wildfire model. The surrogate wildfire model successfully captured the observed monthly regional burned area during validation period 2011 to 2015 (coefficient of determination, R2=0.93). Since the DNN wildfire model has the same input and output requirements as the E3SM process-based wildfire model, our results demonstrate the applicability of machine learning for high accuracy and efficient large-scale land model development and predictions.

58 GEOSCIENCES↗

DOE OSTI2014

Diagnosis of the Marine Low Cloud Simulation in the NCAR Community Earth System Model (CESM) and the NCEP Global Forecast System (GFS)-Modular Ocean Model v4 (MOM4) coupled model

We present a diagnostic analysis of the marine low cloud climatology simulated by two state-of-the-art coupled atmosphere-ocean models: the NCAR Community Earth System Model (CESM) and the NCEP Global Forecasting System (GFS). In both models, the shallow convection and boundary layer turbulence parameterizations have been recently updated: both models now use a mass-flux scheme for the parameterization of shallow convection, and a turbulence parameterization capable of handling Stratocumulus (Sc)-topped Planetary Boundary Layers (PBLs). For shallow convection, both models employ a convective trigger function based on the concept of convective inhibition and both include explicit convective overshooting/penetrative entrainment formulation. For Sc-topped PBL, both models treat explicitly turbulence mixing and cloud-top entrainment driven by cloud-top radiative cooling. Our focus is on the climatological transition from Sc to shallow Cumulus (Cu)-topped PBL in the subtropical eastern oceans. We show that in the CESM the coastal Sc-topped PBLs in the subtropical Eastern Pacific are well-simulated but the climatological transition from Sc to shallow Cu is too abrupt and happens too close to the coast. By contrast, in the GFS coupled simulation the coastal Sc amount and PBL depth are severely underestimated while the transition from Sc to shallow Cu is ³delayed² and offshore Sc cover is too extensive in the subtropical Eastern Pacific. We discuss the possible connections between such differences in the simulations and differences in the parameterizations of shallow convection and boundary layer turbulence in the two models.

Xiao, Heng↗

DOE OSTI2026

The Energy Exascale Earth System Model Version 3: 2. Overview of the Coupled System

The Energy Exascale Earth System Model version 3 (E3SMv3) represents the latest advancement in Earth system modeling developed by the U.S. Department of Energy (DOE). Building upon previous versions, E3SMv3 introduces significant updates across its coupled components to enhance capability and improve fidelity. The atmosphere component incorporates advancements in chemistry, aerosol-cloud interactions, convection, and microphysics. The ocean features a new time-stepping scheme and a higher-resolution unstructured mesh with sub-ice-shelf cavities, while the sea ice model integrates advanced snow and ice physics for more realistic cryospheric simulations. The land model introduces prognostic vegetation dynamics and a new sub-grid topographic treatment of solar radiation. A new tri-grid configuration harmonizes the horizontal grids of the land and river components for improved process coupling. It is enabled by a new non-linear remapping between the atmosphere and land. E3SMv3 underwent extensive testing through a comprehensive simulation campaign, including pre-industrial control, idealized CO 2 experiments, and historical simulations spanning 1850–2024. The model demonstrates significant improvements in simulating the evolution of the historical surface temperature, particularly addressing the “pothole cooling” bias in earlier versions. Reduced aerosol-related forcing contributes to more realistic radiative forcing and better alignment with the observational record. Ocean heat content (OHC) and sea ice trends are also improved as a result.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Systems and methods for enhanced sequential power system model parameter estimation

A system for enhanced sequential power system model calibration is provided. The system is programmed to store a model of a device. The model includes a plurality of parameters. The system is also programmed to receive a plurality of events associated with the device, receive a first set of calibration values for the plurality of parameters, generate a plurality of sets of calibration values for the plurality of parameters, for each of the plurality of sets of calibration values, analyze a first event of the plurality of events using a corresponding set of calibration values to generate a plurality of updated sets of calibration values, analyze the plurality of updated sets of calibration values to determine a current updated set of calibration values, and update the model to include the current updated set of calibration values.

Wang, Honggang↗

DOE OSTI2016

ReEDS™ 2.0 Model (Regional Energy Deployment System Model) Version 2.0 [SWR-20-36 and SWR-09-14]

ReEDS™ is a capacity planning and dispatch model for the U.S. electricity system. As the National Laboratory of the Rockies (NLR's) flagship long-term power sector model, ReEDS has served as the primary analytic tool for many studies of electricity sector research questions. Example model results are available in the Scenario Viewer here: https://scenarioviewer.nlr.gov/. Quick-start guide The ReEDS model is written in Python, GAMS, and Julia. Python and Julia are free, open-source languages; GAMS requires a software license from the vendor. A step-by-step guide for getting started with ReEDS is available here: https://pages.github.nrel.gov/ReEDS/ReEDS-2.0/setup.html.

Short, Walter↗

DOE OSTI2014

Domain wall motion and electromechanical strain in lead-free piezoelectrics: Insight from the model system (1 - x)Ba(Zr 0.2 Ti 0.8 )O 3 -x(Ba 0.7 Ca 0.3 )TiO 3 using in situ high-energy X-ray diffraction during application of electric fields

The piezoelectric compositions (1 - x)Ba(Zr 0.2 Ti 0.8 )O 3 –x(Ba 0.7 Ca 0.3 )TiO 3 (BZT-xBCT) span a model lead-free morphotropic phase boundary (MPB) between room temperature rhombohedral and tetragonal phases at approximately x = 0.5. In the present work, in situ X-ray diffraction measurements during electric field application are used to elucidate the origin of electromechanical strain in several compositions spanning the tetragonal compositional range 0.6 ≤ x ≤ 0.9. As BCT concentration decreases towards the MPB, the tetragonal distortion (given by c/a-1) decreases concomitantly with an increase in 90° domain wall motion. The increase in observed macroscopic strain is predominantly attributed to the increased contribution from 90° domain wall motion. The results demonstrate that domain wall motion is a significant factor in achieving high strain and piezoelectric coefficients in lead-free polycrystalline piezoelectrics.

Tutuncu, Goknur↗

DOE OSTI2018

A New Global Storage-Area-Depth Data Set for Modeling Reservoirs in Land Surface and Earth System Models

Reservoir storage-area-depth relationships are the most important factors controlling thermal stratification in reservoirs and, more broadly, the water, energy and biogeochemical dynamics in the reservoirs and subsequently their impacts on downstream rivers. However, most land surface or earth system models do not account for the gradual changes of reservoir surface area and storage with the changing depth, inhibiting a consistent and accurate representation of mass, energy and biogeochemical balances in reservoirs. Here we present a physically coherent parameterization of reservoir storage-area-depth dataset at the global scale. For each reservoir, the storage-area-depth relationships were derived from an optimal geometric shape selected iteratively from five possible regular geometric shapes that minimizes the error of total storage and surface area estimation. We applied this algorithm to 6,800 reservoirs included in the Global Reservoir and Dam database (GRanD). The relative error between the estimated and observed total storage is no more than 5% and 50% for 66% and 99% of all GRanD reservoirs, respectively. More importantly, the storage-depth profiles derived from the approximated reservoir geometry compared well with remote sensing based estimation at 40 major reservoirs from previous studies, and ground-truth measurements for 34 reservoirs in the United States and China. Furthermore, the new global reservoir storage-area-depth dataset is critical for advancing future modeling and understanding of reservoir processes and subsequent effects on the terrestrial hydrological, ecological and biogeochemical cycles at the regional and global scales.

13 HYDRO ENERGY↗