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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Data and scripts associated with “Sequential Precipitation Input Tagging (SPIT) to Estimate Water Transit Times and Hydrologic Tracer Dynamics within Water-Tagging Enabled Hydrologic Models” (v3)

This data package is associated with the publication “Sequential Precipitation Input Tagging (SPIT) to Estimate Water Transit Times and Hydrologic Tracer Dynamics within Water-Tagging Enabled Hydrologic Models” submitted to Journal of Advances in Modeling Earth Systems (Butler et al. 2025). This study developed the Sequential Precipitation Input Tagging (SPIT) framework to tag input precipitation and estimate water transit times and hydrologic tracers. SPIT tags all precipitation events at regular intervals over an extended period (monthly tags over seven years) in a hydrologic model from 2016-2022. SPIT is applied at six National Ecological Observatory Network (NEON) sites across the continental United States to calculate transit time distributions (TTD) and derive from these mean transit times (MTT), fractions of young water (Fyw), and hydrologic tracer concentrations in stream water (δ18O) within a water-tagging enabled version of the Weather Research and Forecast (WT-WRF-Hydro) model with national water model (NWM) configurations. We go on to validate WT-WRF-Hydro estimates against Butler et al. (2023), who analyzed the same NEON sites using stable water isotope data to estimate water transit times. This new tracking method provides a detailed picture of water movement and helps improve predictions about water availability in the future. This data package was originally published in January 2025. It was updated May 2025 (v2; new and modified files) and October 2025 (v3; new and modified files). File and folder names were not revised to indicate changes. See the change history section in the readme for more details. This data package contains the data and scripts used to develop the SPIT framework WT-WRF-Hydro (Water Tagging Weather Research and Forecasting Hydrologic) model and is associated with the following GitHub repository: https://github.com/zbutler33/SPIT-Framework. This data package contains five parent folders: (1) “Manipulated_outputs”, (2) “Metadata”, (3) “Observed”, (4) “Outputs”, and (5) “Scripts”. Each of these parent folders contains additional subfolders and files. Please see the FLMD (“v*_Butler_2024_WT_WRF_Hydro_flmd.csv”) for a list of all the files contained in this data package and descriptions for each. See the data dictionary (“v*_Butler_2024_WT_WRF_Hydro_dd.csv”) for definitions and units of all of the tabular (files ending in “.csv” and ".tsv") column headers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems

Abstract Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Improving the interface processes in the DOE/ACME model (Final Report)

This is the Final Report of our 4-year Energy Exascale Earth System Model (E3SM) project (3 years for the original project + 1-year no-cost extension). The overall objective of our project is to improve the interface processes in the E3SM. Two overarching questions have been addressed: 1) What are the major deficiencies of the (land-atmosphere, ocean-atmosphere, land-ocean, and snow-sea ice) interface processes in E3SM? 2) How can we improve the treatment of these deficiencies in E3SM? Four tasks have been carried out: to evaluate and improve the land–atmosphere coupling in E3SM; to evaluate and improve the ocean–atmosphere coupling in E3SM; to evaluate and improve the land–ocean coupling in E3SM; and to evaluate and improve the snow–sea ice coupling in E3SM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Using probability distribution function as a scaling approach to incorporate soil heterogeneity into biogeochemical models for greenhouse gas predictions (Final Technical Report)

The project investigated biogeochemical processes at terrestrial-aquatic interfaces (TAIs), focusing on soil microsite heterogeneity and its impact on greenhouse gas (GHG) fluxes. Using laboratory experiments, modeling, and data integration, researchers explored redox-driven microbial processes under fluctuating hydrological conditions. Key advancements included modifying the DAMM-GHG model to incorporateelectron acceptor availability and enhancing the AquaMEND model for improved microbial metabolism representation. Results highlighted microsite redox variability as a key driver of GHG fluxes, informing Earth system models. The project fostered interdisciplinary collaborations, student training, and the development of novel modeling frameworks to improve Earth'senergy budget.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Large-scale offshore wind farm effects on weather and climate in Puerto Rico (Final Technical Report)

Puerto Rico’s current electricity generation heavily relies on imported fossil fuels. This results in an average cost of electricity higher than the U.S. mainland average in all sectors (residential, commercial, and industrial), despite abundant local offshore wind resources, which have the potential to provide secure, low-cost energy generation and consequent economic prosperity. However, effects on atmospheric and oceanic circulation resulting from large-scale deployments of offshore wind farms have not been previously studied at tropical latitude. This project addressed this knowledge gap through a computational modeling effort designed to capture the coupled dynamics of the atmosphere and the ocean in presence of offshore wind farms. Results indicate that wind farm wakes can alter wind stress, generate Ekman-driven vertical transport, and potentially affect nutrient distribution. While full model coupling remains challenging, progress in parameterization and large-eddy simulations provides a foundation for future research. The project contributes to DOE’s Earth System modeling efforts and supports STEM workforce development.

17 WIND ENERGY↗
DOE OSTI2022

An integrated supply chain analysis for cobalt and rare earth elements under global electrification and constrained resources

As global electrification becomes more prominent, demand is projected to increase for electric vehicles (EVs), their components (EV batteries, rare earth traction motors), and their raw materials (cobalt, rare earths). To analyze the supply chain constraints associated with these components and raw materials, a system dynamics model was developed to simulate the interactions between the rare earth and cobalt supply chains. The results showed that under current understanding of raw material resources, production of the projected volume of EVs could be inadequate. To meet the aggressive EV volume sale projections set by the International Energy Agency, EV manufacturers will need to implement a large portion of traction motors without rare earths and utilize non-cobalt batteries as much as possible. On the supply side, a much faster ramp-up than what being modeled would be needed to meet much higher demand than current market forecasts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Interpretable Deep Learning for the Earth System with Fractal Nets

Focal Area 3: Explainable AI Our confidence in the projections made by Earth System Models (ESMs) depends on understanding them to be, in some important respects, faithful representations of the Earth system. Here we present an “explainable Artificial Intelligence (AI)” method that allows us to uncover the dynamical structure of the observed and modeled Earth system, discover hidden links across wide spatiotemporal scales, target model development efforts at poorly-represented dynamics, and optimize observed or modeled data collection to maximize predictive information. Science Challenge: Dynamical system science for the Earth system poses unique challenges given the large degree of internal climate variability. Thus, tools that help us understand how ESMs succeed and fail at representing these dynamics are crucial, particularly in relation to the observed system. Furthermore, the computational and memory constraints on ESM data output motivate in situ analysis of ESM dynamics, including automatic detection of dynamical shifts. Also, of key importance are procedures that leverage ESMs to optimize observational campaigns for improving process representation, reducing structural uncertainty and improving model skill.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Advancing Sea Ice Predictability in E3SM with Machine Learning

Focal area(s): To improve predictions of sea ice in E3SM we propose to develop a hierarchy of data-driven models using observational and simulation data to investigate the most important Earth system drivers of sea ice variability and loss, develop surrogates that build on the reduced parameter space of important drivers, and, where appropriate, couple machine learning models with standard PDE models to capture important physical behavior at different scales. This work falls under Focal Area 2. Predictive modeling through the use of AI techniques.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

UFNet: Joint U-Net and Fully Connected Neural Network to Bias Correct Precipitation Predictions from

Paper information. Shuang Yu, Indrasis Chakraborty, Gemma J. Anderson, Donald D. Lucas, Yannic Lops, and Daniel Galea. UFNet: Joint U-Net and fully connected neural network to bias correct precipitation predictions from climate models. Artificial Intelligence for the Earth Systems, 2024. Overview. This work develops the UFNet methodology to correct E3SM historical precipitation projection bias. The UFNet deep learning framework consists of a two-part architecture: a U-Net convolutional network to capture the spatiotemporal distribution of precipitation and a fully connected network to capture the distribution of higher-order statistics. The joint network, termed UFNet, can simultaneously improve the spatial structure of the modeled precipitation and capture the distribution of extreme precipitation values. Below we provide guidance for applying UFNet to correct the Energy Exascale Earth System Model (E3SM; Golaz et al. 2019) daily precipitation projection over the contiguous United States (CONUS). Getting started 1. Obtain the historical climate simulation and observation data. The E3SM historical simulation data are available through https://aims2.llnl.gov/search/cmip6/. The CPC unified gauge-based analysis of daily precipitation can be found through https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html. The ECMWF atmospheric reanalysis of the 20th century (ERA-20C) data are available through https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-20c. The spatial resolution of E3SM and observed datasets are both regridded to a common 1° resolution grid using conservative interpolation. The regridded E3SM, CPC and ERA-20C with 1° resolution can be found throught ./data/. 2. Train the fully connected network (DNN) Python train_dnn.py 3. Train the UFNet Python train_ufnet.py 4. Evaluation and compared with the baseline Python evaluation.py

Lucas, DonaldD↗
DOE OSTI2020

Documenting numerical experiments in support of the Coupled Model Intercomparison Project Phase 6 (CMIP6)

Abstract. Numerical simulation, and in particular simulation of the earth system, relies on contributions from diverse communities, from those who develop models to those involved in devising, executing, and analysing numerical experiments. Often these people work in different institutions and may be working with significant separation in time (particularly analysts, who may be working on data produced years earlier), and they typically communicate via published information (whether journal papers, technical notes, or websites). The complexity of the models, experiments, and methodologies, along with the diversity (and sometimes inexact nature) of information sources, can easily lead to misinterpretation of what was actually intended or done. In this paper we introduce a taxonomy of terms for more clearly defining numerical experiments, put it in the context of previous work on experimental ontologies, and describe how we have used it to document the experiments of the sixth phase for the Coupled Model Intercomparison Project (CMIP6). We describe how, through iteration with a range of CMIP6 stakeholders, we rationalized multiple sources of information and improved the clarity of experimental definitions. We demonstrate how this process has added value to CMIP6 itself by (a) helping those devising experiments to be clear about their goals and their implementation, (b) making it easier for those executing experiments to know what is intended, (c) exposing interrelationships between experiments, and (d) making it clearer for third parties (data users) to understand the CMIP6 experiments. We conclude with some lessons learnt and how these may be applied to future CMIP phases as well as other modelling campaigns.

Pascoe, Charlotte (ORCID:0000000223616778)↗
DOE OSTI2021

A science paradigm shift is needed for Earth and Environmental Systems Sciences (EESS) to integrate Knowledge-Guided Artificial Intelligence (KGAI) and lead new EESS-KGAI theories

The focal area of this white paper is learning from complex data through the use of AI techniques and AI-derived model components. Specifically, we advocate for research programs to develop knowledge-guided AI (KGAI) in the Earth and Environmental Systems sciences (EESS) as a basic research paradigm that is separate from (but supports) any specific Earth system model, modeling components, and modeling workflows, and even separates from specific hypothesis-driven questions about individual Earth system processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Benchmark Analysis

Tremendous progress has been achieved in the development of land models and their inclusion in Earth system models (ESMs). However, we still have very limited knowledge on the performance skills of these land models. This chapter introduces benchmark analysis, which is a procedure to measure performance of models against a set of defined standards. The benchmark analysis includes: (1) defining targeted aspects of model performance to be evaluated; (2) testing model performance in comparison with a set of benchmarks; (3) measuring model performance skill through quantitative metrics; and (4) evaluating model performance and offering suggestions for future model improvement.

Luo, Yiqi↗
DOE OSTI2020

Biogeochemical Processes Across Aquatic Interfaces

The aquatic interfaces exposing terrestrial soils to oxic-anoxic regime shifts represent biogeochemical “hotspots” that are extremely sensitive to climate and environmental change. However, processes and interaction across theses aquatic interfaces are poorly understood and underrepresented in current Earth system models. In this project, we aim to develop predictive understanding of the feedbacks between microbial systems and geochemical environments that determine emergent ecosystem behaviors and resilience in response to disturbances. We use experimental, mechanistic modeling and meta-analysis tools to elucidate interactions among soil, water, geomorphology and microbiology that regulate the molecular transformations and fluxes of carbon, nutrients, and redox-sensitive compounds across aquatic interfaces.

58 GEOSCIENCES↗
DOE OSTI2023

Coast-Urban-Rural Atmospheric Gradient Experiment

Accurately representing the climate and weather variability within cities and providing well-tested representations of the impacts of urban systems on the atmospheric environment are challenges for current earth system models (ESMs). Field data for testing and developing these models are critical to addressing these limitations. The Atmospheric Radiation Measurement (ARM) user facility will support a field campaign aimed at understanding how different surface-atmosphere interactions around a city are influencing its climate. The Coast-Urban-Rural Atmospheric Gradient Experiment (CoURAGE) is expected to operate from December 2024 to November 2025 in and around Baltimore, Maryland. With its aging infrastructure, growing susceptibility to heat and flooding, and ongoing issues with air and water pollution, Baltimore is characteristic of many large industrial cities in the Eastern United States. ARM, a U.S. Department of Energy (DOE) Office of Science user facility, plans to deploy one of its three mobile observatories at Morgan State University’s Clifton Park site in downtown Baltimore. Ancillary ARM sites are also planned for rural Maryland, northwest of the city; and to the southern end of Kent Island in Chesapeake Bay. Data from CoURAGE will complement measurements from the Baltimore Social-Environmental Collaborative (BSEC), one of four DOE Urban Integrated Field Laboratories that will operate in cities across the United States to study urban climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Building collaboration to advance our understanding of regional climate impacts of dust in California's San Joaquin Valley

This project successfully achieved its central objective of building collaborative research capabilities at UC Merced, a Hispanic-Serving Institution, to advance understanding of the regional climate impacts of dust in California's San Joaquin Valley. Through strategic partnerships with three DOE national laboratories (PNNL, LLNL, and LBNL), we developed critical expertise in the Energy Exascale Earth System Model (E3SM) and Atmospheric Radiation Measurement (ARM) facilities. Among the project's scientific contributions, one key publication includes demonstrating that fallowed agricultural lands are the primary source of anthropogenic dust in California's Central Valley, with dust activities increasing substantially between 2008 and 2022, in correlation with drought severity and expanded fallowed land coverage. This finding suggests that current climate models, including E3SM, likely underestimate the dust burden due to inadequate representation of agricultural land-use changes. Beyond the scientific contributions, the project successfully trained a PhD student, established ongoing collaborations resulting in multiple manuscripts in preparation, and positioned UC Merced to participate in the DUSTIEAIM campaign for 2026-2027, thereby building sustainable research capacity while addressing climate science questions directly relevant to the California Central Valley.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Tracking carbon dioxide in the Southern Ocean (Final Technical Report)

We investigated the discrepancy in observed and modeled Southern Ocean carbon dioxide (CO 2 ) absorption through the lens of the Energy Exascale Earth System Model (E3SM), leveraging recent and upcoming E3SM development advances to simulate Lagrangian Biogeochemical-Argo floats. E3SM output, together with observational data streams and output from the evolving CMIP6 archive, are being used to study the spatiotemporal bias in modeled Southern Ocean CO 2 flux.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Identifying Climate Patterns Using Clustering Autoencoder Techniques

Abstract The complexity of growing spatiotemporal resolution of climate simulations produces a variety of climate patterns under different projection scenarios. This paper proposes a new data-driven climate classification workflow via an unsupervised deep learning technique that can dimensionally reduce the vast volume of spatiotemporal numerical climate projection data into a compact representation. We aim to identify distinct zones that capture multiple climate variables as well as their future changes under different climate change scenarios. Our approach leverages convolutional autoencoders combined with k -means clustering (standard autoencoder) and online clustering based on the Sinkhorn–Knopp algorithm (clustering autoencoder) across the conterminous United States (CONUS) to capture unique climate patterns in a data-driven fashion from the Geophysical Fluid Dynamics Laboratory Earth System Model with GOLD component (GFDL-ESM2G). The developed approach compresses 70 years of GFDL-ESM2G simulation at 0.125° spatial resolution across the CONUS under multiple warming scenarios to a lower-dimensional space by a factor of 660 000 and then tested on 150 years of GFDL-ESM2G simulation data. The results show that five climate clusters capture physically reasonable and spatially stable climatological patterns matched to known climate classes defined by human experts. Results also show that using a clustering autoencoder can reduce the computational time for clustering by up to 9.2 times when compared to using a standard autoencoder. Our five unique climate patterns resulting from the deep learning–based clustering of the lower-dimensional space thereby enable us to provide insights on hydrometeorology and its spatial heterogeneity across the conterminous United States immediately without downloading large climate datasets. Significance Statement This paper presents a data-driven climate classification approach using unsupervised deep learning to dimensionally reduce climate model outputs and to identify distinct climate regions for their future changes. Our approach compresses climate information for 70 years of Geophysical Fluid Dynamics Laboratory Earth System Model data across the conterminous United States (CONUS) at 0.125° spatial resolution. The results reveal that five climate clusters capture reasonable and stable climatological patterns matched to known climate patterns. The embedded clustering process in deep learning provides ×9.2 times faster execution than the k -means clustering technique. These results give us insight about climate spatial patterns and heterogeneity of hydrological patterns across the conterminous United States without downloading large climate datasets.

Kurihana, Takuya↗
DOE OSTI2024

Developing a Redox Network for Coastal Saltmarsh Systems in the PFLOTRAN Reaction Model

Coastal ecosystems have been largely ignored in Earth system models but are important zones for carbon and nutrient processing. Interactions between water, microbes, soil, sediments, and vegetation are important for mechanistic representation of coastal processes and ecosystem function. To investigate the role of these feedbacks, we used a reactive transport model (PFLOTRAN) that has the capability to be connected to the Energy Exascale Earth System Model (E3SM). PFLOTRAN was used to incorporate redox reactions and track chemical species important for coastal ecosystems as well as define simple representations of vegetation dynamics. Here our goal was to incorporate oxygen flux, salinity, pH, sulfur cycling, and methane production along with plant-mediated transport of gases and tidal flux. Using porewater profile and incubation data for model calibration and evaluation, we were able to create depth-resolved biogeochemical soil profiles for saltmarsh habitat and use this updated representation to simulate direct and indirect effects of elevated CO 2 and temperature on subsurface biogeochemical cycling. We found that simply changing the partial pressure of CO 2 or increasing temperature in the model did not fully reproduce observed changes in the porewater profile, but the inclusion of plant or microbial responses to CO 2 and temperature manipulations was more accurate in representing porewater concentrations. This indicates the importance of characterizing tightly coupled vegetation-subsurface processes for developing predictive understanding and the need for measurement of plant-soil interactions on the same time scale to understand how hotspots or moments are generated.

54 ENVIRONMENTAL SCIENCES↗