esm_watermasses
A water mass analysis package for gridded ocean and atmospheric data and Earth system model output
Moore-Maley, Ben [@SciDAC-ImPACTS @E3SM-Project @M↗
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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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Earth System Model Improvement Pipeline via Uncertainty Attribution and Active Learning
Primary focal area: 2 (Predictive Modeling via AI): We develop methods to formally quantify uncertainties in Earth System models for the land-atmosphere coupled system. Science Challenge: Earth system models still have significant biases in historical predictions of the intensity and frequency of water cycling extremes (e.g., droughts and flood events), leading to low confidence in future projections. Uncertainties arise from incomplete understanding of land and atmospheric processes, and insufficient observational constraints on model parameters. Many observations, including those from key DOE investments such as ARM and AmeriFlux, are used to evaluate model performance but have not been used to formally quantify model uncertainty because of the expense of running ESM simulations. An efficient pipeline engaging cutting-edge machine learning (ML) and uncertainty quantification (UQ) methods is needed to improve the predictive understanding of water cycle extremes in the Earth system.
54 ENVIRONMENTAL SCIENCES↗
Surrogate multi-fidelity data and model fusion for scientific discovery and uncertainty quantification in Earth System Models
This whitepaper addresses the Earth and Environmental Systems Sciences Division (EESSD)’s predictability challenges in modeling the integrated water cycle and data-model integration. Specifically, it focuses on reducing and characterizing the uncertainty in the representation of process models for unresolved physics, either due to model resolution or limited by the physical under standing or computational efficiency, and the use of observational data for in-situ process parameter optimization within ESM. The described methods may also be used to determine the nature of responses (e.g. strength and direction), and hence to identify critical processes that drive the overall ESM responses to perturbation in the forcing
54 ENVIRONMENTAL SCIENCES↗
ELM‐MOSART‐DOC: A Large‐Scale Riverine Dissolved Organic Carbon Model and Its Application Over the United States
Riverine dissolved organic carbon (DOC), primarily sourced from soil organic carbon (SOC), plays a crucial role in regional and global carbon cycles. However, the complexities of the underlying mechanisms and limited observations present significant challenges for predictive understanding of DOC at regional or larger scales. Recently, we developed a machine learning‐based (ML) map of DOC transformation rates, bridging the gap between SOC and DOC leaching flux and simplifying terrestrial DOC representation. Building on this advancement, we introduce ELM‐MOSART‐DOC, a DOC module integrated into the riverine component of the Energy Exascale Earth System Model (E3SM)—the Model for Scale Adaptive River Transport (MOSART). ELM‐MOSART‐DOC simulates DOC transport and transformation across both headwater streams and river networks, including those managed. Model validation demonstrates the ability of ELM‐MOSART‐DOC to accurately capture long‐term average DOC concentrations, with Kling‐Gupta Efficiency (KGE) scores of 0.58 and 0.76 at large and local stations, respectively. We further assess the impact of reservoirs through different simulation schemes, revealing that reservoirs significantly alter DOC fluxes by regulating streamflow patterns and promoting DOC mineralization. Model simulations indicate that reservoirs reduce total DOC flux from the Mississippi River into the ocean by 7.5%, with the long‐term average annual export decreasing from 3.34 to 3.14 teragrams (Tg) per year. ELM‐MOSART‐DOC integrates process‐based modeling with ML parameterization to enhance the predictive understanding of riverine biogeochemical processes. This approach reduces uncertainties in modeling regional and global carbon cycle ESMs and provides new insights into carbon cycling and its implications for global environmental change.
Li, Lingbo [Univ. of Houston, TX (United States); ↗
A Grand Challenge "Uncertainty Project" to Accelerate Advances in Earth System Predictability: AI-Enabled Concepts and Applications
This proposal is emerging from GISS ModelE3 ESM development in the area of cloud physics, so we begin with an example of research needs/gaps from that work. Here, some of our greatest development concerns arise where we lack fundamental process-level understanding, as in ice formation. Namely, it is currently unclear what is the main process that is forming the majority of ice crystals in commonly occurring convection, apparently via secondary ice production at warm temperatures. We are keenly awaiting laboratory data for candidate mechanisms, which is not yet in hand to crucially establish their efficiency. Our progress is also hampered by a lack of uncertainty characterization in currently available measurements of ice crystal number size distributions. Furthermore, the same multiplication process may be responsible for a majority of ice crystals in many extratropical mixed-phase clouds, whose variable representation in CMIP6 ESMs may be a leading cause of differences in cloud phase feedback and ECS. Yet we have been required to deliver an ESM with the cloud physics knowledge at hand. The proposed grand challenge project is AI-enabled via application of machine learning (ML) to climate model and observational data streams (focal area 3), and applications include AI-guided observing system design and model/component/parameterization selection (areas 1 and 2). The project is structurally agnostic as to whether model or observing system components use AI approaches or not, but uncertainties must be estimated and propagatable in both.
58 GEOSCIENCES↗
Development of A High-Resolution Dataset for Solar Resource Adequacy Studies
High-resolution, long-term solar dataset is essential for characterizing the variability of solar energy resources and for informing strategies that ensure grid reliability and resilience in grid systems with high levels of solar energy integration. We present the development of a new 4-km, hourly Earth system dataset for the contiguous United States (CONUS), using a statistical downscaling approach that integrates the National Solar Radiation Database (NSRDB) with regional Earth system model projections. The new high-resolution Earth system dataset includes key variables - GHI, DNI, DHI, surface air temperature, and wind speed - under two future scenarios. Preliminary results show a reasonable agreement with NSRDB observations, with nBias less than 1% for GHI across CONUS. The dataset is expected to support in-depth analyses of extreme weather impacts and provide input to resource adequacy for future energy systems with diverse generation sources.
14 SOLAR ENERGY↗
Initial Land Use/Cover Distribution Substantially Affects Global Carbon and Local Temperature Projections in the Integrated Earth System Model
Initial land cover distribution varies among Earth system models, an uncertainty in initial conditions that can substantially affect carbon and climate projections. We use the integrated Earth System Model to show that a 3.9 M km2 difference in 2005 global forest area (9–14% of total forest area) generates uncertainties in initial atmospheric CO2 concentration, terrestrial carbon, and local temperature that propagate through a future simulation following the Representative Concentration Pathway 4.5. By 2095, the initial 6 ppmv uncertainty range increases to 9 ppmv and the initial 26 PgC uncertainty range in terrestrial carbon increases to 33 PgC. The initial uncertainty range in annual average local temperature of -0.74 to 0.96 °C persists throughout the future simulation, with a seasonal maximum in Dec-Jan-Feb. These results highlight the importance of accurately characterizing historical land use and land cover to reduce overall initial condition uncertainty.
Di Vittorio, Alan↗