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Results for “Energy Exascale Earth System Model (E3SM)”

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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
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 OSTI2021

The Polar System Analysis Package (Ver. 1 Specifications)

The Polar System Analysis Package (POLeSTAr) is a modern data fusion tool being designed to benchmark,evaluate and compare the polar physics and biogeochemistry of Earth System Models (ESMs). It is being written in Python to make use of libraries including Xarray, Matplotlib, SciPy, NumPy and PyTorch within algorithms developed specifically for polar system modeling. Initial POLeSTAr algorithms will be adapted from the MATLAB Ridgepack package that was designed to analyze the Model for Prediction Across Scales (MPAS) sea ice component of the Energy Exascale Earth System Model (E3SM) and sea ice model CICE within the Community Earth System Model (CESM).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Combining artificial intelligence, Earth observations, and climate models to improve predictability of ice-biogeochemistry interactions

Focal Area: Predictive Modeling. We describe how artificial intelligence (AI) can be combined with state-of-the-science Earth system models to better predict future regional climate responses. To demonstrate, we describe a case study in biogeochemical interactions with sea ice. Science Challenge: Biogeochemical models are poorly constrained for high latitude systems. Machine learning methods and edge computing can be combined with Earth system models, such as the Energy Exascale Earth System Model (E3SM), to gain insight into ice-biogeochemical interactions and improve sea ice extent prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Hybrid (PDE+ML) models in the context of land ice modeling

Focal Area(s): Primary focal areas are: predictive modeling through the use of AI-derived model components; advanced methods including network design/optimization/deep learning. Science Challenge: The atmospheric, ocean and ice dynamics components of the Energy Exascale Earth System Model (E3SM) are governed by Partial Differential Equations (PDEs) and significant efforts have been made during the last decades to develop such computational models. Here we propose to fundamentally improve these PDE-based codes by enhancing them with Machine Learning (ML) sub-models for complex, poorly understood physical processes in the context of ice sheet modeling. We propose to train these models with a novel approach that allows the assimilation of the different sources of data available (direct/indirect observations and possibly simulation data), improving on existing simplified models. We also highlight computational challenges originating from the coexistence of PDE-based and ML-based models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Physics-Informed Learning for Predictive Multi-Scale Modeling of Water Cycle and Extreme Events

Climate and earth system models have limited skill in predicting water cycle and associated extremes such as intense rain- and wind-producing storms and resulting hazards across all timescales. Water cycle predictions are inherently limited by the predictability of the chaotic system, but large potential for improving predictions remains untapped due to limited use of data-model integration to improve predictive modeling. This white paper proposes a hybrid data-physics paradigm based on physics-informed neural networks (PINNs) to improve the Energy Exascale Earth System Model (E3SM). In particular, we aim to predict and quantify uncertainties of water cycle processes and low probability high impact events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Causal Evaluations for Identifying Differences between Observations and Earth System Models

We use a nascent data-driven causal discovery method to find and compare causal relationships in observed data and climate model output. We consider ten different features in the Arctic climate collected from public databases on observational and Energy Exascale Earth System Model (E3SM) data. In identifying and analyzing the resulting causal networks, we make meaningful comparisons between observed and climate model interdependencies. This work demonstrates our ability to apply the PCMCI causal discovery algorithm to Arctic climate data, that there are noticeable similarities between observed and simulated Arctic climate dynamics, and that further work is needed to identify specific areas for improvement to better align models with natural observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Simulating physical and biogeochemical dynamics of the Arctic Ocean [Slides]

Ocean physics (currents, mixing, air-sea transfer of heat and gases) and biogeochemistry (carbon and oxygen cycles, ecosystem dynamics, nutrient distributions) are tightly coupled. We used the DOE-developed Energy Exascale Earth System Model (E3SM) to simulate physical and biogeochemical dynamics of the global ocean, with an emphasis on the western North Pacific. In particular, we used the grid refinement capability of E3SM’s ocean component (LANL-developed MPASOcean) to quantify the effect of model resolution on the carbon and oxygen cycles using a pair of century-long simulations that differ only in the western North Pacific grid. Regional grid refinement has a noticeable impact on biogeochemistry. Distribution of important nutrients (such as nitrate) changes the amount of photosynthesis by phytoplankton, which in turn affects air-sea transfer of CO2 and the local carbon cycle.

58 GEOSCIENCES↗
DOE OSTI2022

Porting a fast-running wildfire plume model for use in national security and climate applications

In this project, the code for the fast-running Freitas wildfire plume model was successfully ported into the NARAC system, creating an operations-ready capability for predicting the transport of hazardous smoke from large wildfires. “Porting” refers to copying a section of code from one software package to another while accounting for unique dependencies in each software package. In this case, the code was ported from the Weather Research and Forecasting (WRF) Model, which is used extensively by the project team. To verify correct implementation, model results from the updated NARAC system were compared to those from WRF and the High-Resolution Rapid Refresh (HRRR) forecast model, which also employs the Freitas scheme. The NARAC User Interface (UI) and code documentation were also updated to include the new wildfire plume option, and a wildfire case was added to the NARAC test suite. The code was used in a real-time, semi-operational setup to predict smoke transport during the Cerro Pelado fire near Los Alamos National Laboratory in May 2022. The Freitas plume model was also tested for LLNL climate applications. Although the code was not ported into the Energy Exascale Earth System Model (E3SM), it was tested offline using E3SM atmospheric inputs, and results were compared to existing high-resolution plume predictions from large-eddy simulations in WRF. The code implementation and testing completed during this project will enable future work predicting smoke transport from large wildfires in LLNL national security and climate mission spaces.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Model Coupling Through Learned Representations

Reliable climate predictions are important for making robust decisions in response to the changing climate. This project aims to reduce mis-modeling uncertainties arising from the representation of the land-atmosphere coupling in the Energy Exascale Earth System Model (E3SM) by using a machine learning approach. This approach will use an encoder-decoder architecture to represent the information that is developed in the land model and given to the atmosphere model. The simulated data will be taken from the E3SM simulation. However, the incorporation of observed data into the simulated dataset reduces mis-modeling uncertainties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Simulating Extreme Precipitation in the United States in the Energy Exascale Earth System Model: Investigating the Importance of Representing Convective Intensity versus Dynamic Structure (Final Report)

Weather events that produce extreme precipitation are associated with severe flooding and winds that result in thousands of deaths and billions of dollars in damages annually in the United States (U.S.). This project aims to improve understanding of the small- and large-scale processes that govern these events and improve our ability to project changes in these extreme events under the influences of natural variability and human activities. We focus on two promising directions in the development of the U.S. Department of Energy’s Energy Exascale Earth System Model (E3SM) to investigate the tradeoffs between resolving the convective-scale processes that control the intensity versus the intermediate-scale processes that control the dynamic structure of the most prominent extreme precipitation events that impact the U.S. throughout the year (i.e., mesoscale convective systems, tropical cyclones, and extratropical cyclone).

58 GEOSCIENCES↗
DOE OSTI2021

Science & Technology Review: Climate Change Comes into Focus

At Lawrence Livermore National Laboratory, we focus on science and technology research to ensure our nation’s security. We also apply that expertise to solve other important national problems in energy, bioscience, and the environment. Science & Technology Review is published eight times a year to communicate, to a broad audience, the Laboratory’s scientific and technological accomplishments in fulfilling its primary missions. The publication’s goal is to help readers understand these accomplishments and appreciate their value to the individual citizen, the nation, and the world. The cover image shows a simulation run on the Department of Energy’s (DOE’s) state-of-the-science Energy Exascale Earth Systems Model (E3SM), which leverages DOE’s advanced supercomputers for climate and earth science research. As the article beginning on p. 4 describes, the newly released version 2 will be used to simulate aspects of Earth’s variability at weather-scale resolution and investigate decadal changes in climate that will critically impact the United States in coming years.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Properties and Controlling Processes of Aerosol and Cloud Condensation Nuclei in Marine Boundary Layer Over Eastern North Atlantic (Final Report)

Remote marine low cloud systems have large spatial and temporal coverages. Because of their relatively low optical thickness and background aerosol concentrations, marine low clouds are particularly susceptible to perturbations in aerosols associated with anthropogenic emissions. Recent studies show that a large fraction of the global aerosol indirect forcing can be attributed to changes in marine low clouds, despite their relatively long distance from most anthropogenic sources. Currently, there remain large uncertainties in the magnitude of the global aerosol indirect radiative forcing. One major contribution to this large uncertainty derives from poor understanding of the aerosols under natural conditions, the perturbation by anthropogenic emissions, and the processes that drive them. In this project, we took advantage of the comprehensive, multi-dimensional characterization of meteorological parameters, trace gases, aerosol, cloud, and drizzle fields during the Aerosol and Cloud Experiments in Eastern North Atlantic (ACE-ENA) and the long-term measurements at the Atmospheric Radiation Measurement (ARM) Eastern North Atlantic (ENA) site on Graciosa Island. By synergistically combining in situ measurements, remote sensing retrievals, and model simulations, we quantified aerosol properties in the ENA, their vertical profiles, and variations with season, synoptic state, and airmass. We investigated key aerosol processes, including entrainment of free troposphere aerosol into marine boundary layer, new particle formation, growth of Aitken mode particles, and the influences of these processes on the cloud condensation nuclei (CCN) population in marine boundary layer. The representation of key aerosol processes in the Energy Exascale Earth System Model (E3SM) was evaluated using the observations. This project has led to 26 publications, which are listed at the end of this report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Developing a simulator-based satellite dataset for using machine learning techniques to derive aerosol-cloud-precipitation interactions in models and observations in a consistent framework

Aerosol-cloud-precipitation interactions (ACPI) remain a major uncertainty in understanding the Earth’s radiation budget and water cycle (including extremes). After decades of active research, various observationally based metrics have been developed to constrain ACPI in Earth System Models (ESMs), but direct comparison of model and data estimates can confound scientific understanding because limitations and uncertainties in sampling and retrieval procedures may combine with model deficiencies in process representations of ACPI to obstruct understanding. Furthermore, conventional ACPI metrics often vary from one regime to another, and the ACPI process representation in ESMs is also typically derived based on only a limited area/regime (even though the parameterization applies globally). To bridge the gap between models and data and to correctly describe ACPI in all regimes, we propose to construct a new CALIPSO-CloudSat merged dataset that is produced by the same algorithms used in satellite simulators in ESMs, and to use machine learning techniques to derive new ACPI metrics that can be accurately estimated by satellites and can provide meaningful constraints on cloud microphysical process representations in ESMs. The dataset will include measured and retrieved variables for aerosol, cloud, and precipitation from CALIPSO and CloudSat, and environmental variables from meteorological reanalysis. The data will be used to train a neural network to construct the ACPI metrics as a function of environmental conditions. The new ACPI formula will be used to constrain the ACPI in the Energy Exascale Earth System Model (E3SM), and to augment/reformulate the ACPI process representation in the E3SM to improve the simulation of the evolution of the atmosphere under different environmental conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Peatland hydrology across scale: a probabilistic framework for confronting variability, heterogeneity, and uncertainty (Final Report)

Our project aimed to guide the representations of peatland hydrology and water-carbon feedbacks within Earth System Models, by improving process understanding and developing parsimonious models that elucidate the effects of hydroclimatic variability and spatial heterogeneity. Our research questions centered on the three key drivers of peatland hydrology: (1) seasonal and interannual hydroclimatic fluctuations, (2) spatial heterogeneity of peatland microtopography, and (3) hydrological connectivity across landscape units within a peatland watershed. Our analyses and model testing used existing long-term datasets within the Marcell Experimental Forest (MEF), with new data collected whenever necessary. To date, we have demonstrated the strong influence of water table elevations on the temperature sensitivity of CH 4 emissions, using a newly developed eddy covariance dataset spanning eleven years at MEF. Specifically, higher water tables dampen the increase in CH 4 emissions in the spring as well as their decrease in the fall, resulting in hysteresis. These results imply that any hydroclimatological changes in peatlands that shift seasonal water availability from winter to summer will increase annual CH 4 emissions, even if soil temperature remains unchanged. To further investigate water table elevation change within the peatland microtopography, we installed automated water table gauges across four bog-forest transects (the “lagg”) in two peatland watersheds at MEF, which are hotspots of intense biogeochemical activity. These measurements will give us new information about the extent of lagg expansion and contraction during snowmelt and high intensity rainfall events, as well as the directionality of water and nutrient flow into the bog from the surrounding forests. Finally, we investigated the hydrological connectivity across the peatland watershed complex during early spring – a critical period where water table is elevated from snowmelt – using extended hydrological records from MEF (e.g., snow and frost depths, water table elevations, streamflow). The importance of thermal and wintertime processes within the Energy Exascale Earth System Model (E3SM) was determined through sensitivity analyses. Results from data analyses show that (i) streamflow has decreased over decades likely due to increased evapotranspiration rates (despite no detectable trends in precipitation), (ii) spring streamflow generation is controlled by a sequence of fill-and-spill pathways from the snowpack to the stream through peatland water stores, and (iii) frost depth is a key explanatory variable for the timing and magnitude of streamflow. These results suggest that frost plays an important role in connecting surface water storage in peatlands to stream outlets, and that the hydrological connectivity across the peatland watershed complex can mediate the sensitivity of hydrological responses to climate variations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Ice-shelf freshwater triggers for the Filchner–Ronne Ice Shelf melt tipping point in a global ocean–sea-ice model

Some ocean modeling studies have identified a potential tipping point from a low to a high basal melt regime beneath the Filchner–Ronne Ice Shelf (FRIS), Antarctica, with significant implications for subsequent Antarctic ice sheet mass loss. To date, investigation of the climate drivers and impacts of this possible event have been limited because ice-shelf cavities and ice-shelf melting are only now starting to be included in global climate models. Using a global ocean–sea-ice configuration of the Energy Exascale Earth System Model (E3SM) that represents both ocean circulations and melting within ice-shelf cavities, we explore freshwater triggers (iceberg melt and ice-shelf basal melt) of a transition to a high-melt regime at FRIS in a low-resolution (30 km in the Southern Ocean) global ocean–sea-ice model. We find that a realistic spatial distribution of iceberg melt fluxes is necessary to prevent the FRIS melt regime change from unrealistically occurring under historical-reanalysis-based atmospheric forcing. Further, improvement of the default parameterization for mesoscale eddy mixing significantly reduces a large regional fresh bias and weak Antarctic Slope Front structure, both of which precondition the model to melt regime change. Using two different stable model con figurations, we explore the sensitivity of FRIS melt regime change to regional ice-sheet freshwater fluxes. Through a series of sensitivity experiments prescribing incrementally increasing melt rates from the smaller, neighboring ice shelves in the eastern Weddell Sea, we demonstrate the potential for an ice-shelf melt “domino effect” should the upstream ice shelves experience increased melt rates. The experiments also reveal that modest ice-shelf melt biases in a model, especially at coarse ocean resolution where narrow continental shelf dynamics are not well resolved, can lead to an unrealistic melt regime change at downstream ice shelves. Thus, we find that remote connections between melt fluxes at different ice shelves are sensitive to baseline model conditions. Our results highlight both the potential and the peril of simulating prognostic Antarctic ice-shelf melt rates in a low-resolution global model.

58 GEOSCIENCES↗
DOE OSTI2024

Improving North American Wildfire Prediction by Integrating a Machine-Learning Fire Model in a Land Surface Model

Wildfires have shown increasing trends in both frequency and severity across the Contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth System Models (ESMs). Alternatively, machine learning (ML) based fire models, which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ML4Fire-XGB) that integrates a pretrained eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM) version 2.1. A Fortran-C-Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001-2020, the ML4Fire-XGB model outperforms process-based fire models in terms of spatial distribution and seasonal variations. Sensitivity analysis confirms that the ML4Fire-XGB well captures the responses of the burned area to rising temperatures. The ML4Fire-XGB model has proved to be a new tool for studying vegetation-fire interactions, and more importantly, enables seamless exploration of climate-fire feedback, working as an active component in E3SM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Advancing the Understanding of Cloud Microphysical Processes and Aerosol Indirect Effects in High-Latitude Mixed-Phase Clouds by Linking ARM Measurements with Climate Model Simulations (Final Report)

The key objectives of this project were to advance our understanding of cloud microphysical characteristics and aerosol indirect effects on mixed-phase clouds in high latitudes. To improve the representation of ice and mixed-phase clouds in Earth System Models (ESMs), we propose an integrated observation and modeling study of cloud macro- and microphysical properties, including spatial heterogeneities, mass partitioning between ice crystals and supercooled liquid water, effects of ice nucleating particles (INPs), and efficiency of secondary ice production (SIP), etc. Specifically, we took four main approaches in this project: (1) examining macro- and microphysical properties of ice and mixed-phase clouds based on in-situ and ground-based observations from multiple field campaigns funded by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program, including the Mixed-Phase Arctic Cloud Experiment (M-PACE), Indirect and Semi-Direct Aerosol Campaign (ISDAC), Ice Nucleating Particle Sources at Oliktok Point (INPOP), ARM West Antarctic Radiation Experiment (AWARE), Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS), and Macquarie Island Cloud and Radiation Experiment (MICRE); (2) evaluating the DOE Energy Exascale Earth System Model (E3SM) simulations based on observations, particularly for ice and mixed-phase cloud microphysical properties; (3) examining the impacts of INPs on ice and mixed-phase clouds. Specifically, a series of comparisons were conducted using observations over the Arctic, Southern Ocean, and Antarctica, including comparisons between the lower and higher southern latitudes as well as comparisons between the northern and southern hemispheres. In addition, aerosol indirect effects from distinct sources of dust particles were examined; and (4) investigating the impacts of SIP. Ultimately, these results helped to improve cloud microphysics and aerosol-cloud interaction parameterizations in the E3SM model. Overall, the project provided improved understanding regarding various factors, including thermodynamic, dynamic, and aerosol conditions, on the micro- and macrophysical properties of ice and mixed-phase clouds in the high latitudes. Resulting analysis helped to provide an improved physical basis for refining the current cloud microphysics parameterizations related to ice and mixed-phase clouds in E3SM.

54 ENVIRONMENTAL SCIENCES↗