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DOE OSTI2025

Demography, dynamics and data: building confidence for simulating changes in the world's forests

Vegetation demographic models (VDMs) are advanced tools for simulating forest responses to climate and land-use changes, and are essential for projecting carbon cycling and large-scale forest management strategies. Despite their increasing incorporation into Earth System Models, VDMs differ in their demographic assumptions, with no prior quantitative comparison of their performance. We benchmarked nine VDMs against observational data from boreal, temperate and tropical sites, assessing their accuracy in predicting tree growth, carbon turnover, biomass stocks and size distributions. Models were simulated under consistent climate conditions with postdisturbance recovery monitored for at least 420 yr. Postdisturbance carbon recovery trajectories showed significant variability while remaining within observational ranges. Initial regrowth rates varied substantially (0.03-0.60, 0.18-0.70 and 0.35-1.10 kgCm-2 yr-1 for boreal, temperate and tropical sites, respectively), influenced by each model's initial forest state. Models captured mature forest carbon content but showed compensating effects between overestimated growth and underestimated mortality rates. This first multi-model benchmarking identifies growth and mortality rates as critical calibration targets and highlights the need to refine postdisturbance establishment conditions for model development. We outline specific benchmarking variables needed to improve predictions of forest responses to environmental change.

demographic vegetation model benchmarking↗

DOE OSTI2023

The winter central Arctic surface energy budget: A model evaluation using observations from the MOSAiC campaign

This study evaluates the simulation of wintertime (15 October, 2019, to 15 March, 2020) statistics of the central Arctic near-surface atmosphere and surface energy budget observed during the MOSAiC campaign with short-term forecasts from 7 state-of-the-art operational and experimental forecast systems. Five of these systems are fully coupled ocean-sea ice-atmosphere models. Forecast systems need to simultaneously simulate the impact of radiative effects, turbulence, and precipitation processes on the surface energy budget and near-surface atmospheric conditions in order to produce useful forecasts of the Arctic system. This study focuses on processes unique to the Arctic, such as, the representation of liquid-bearing clouds at cold temperatures and the representation of a persistent stable boundary layer. It is found that contemporary models still struggle to maintain liquid water in clouds at cold temperatures. Given the simple balance between net longwave radiation, sensible heat flux, and conductive ground flux in the wintertime Arctic surface energy balance, a bias in one of these components manifests as a compensating bias in other terms. This study highlights the different manifestations of model bias and the potential implications on other terms. Three general types of challenges are found within the models evaluated: representing the radiative impact of clouds, representing the interaction of atmospheric heat fluxes with sub-surface fluxes (i.e., snow and ice properties), and representing the relationship between stability and turbulent heat fluxes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Nuclear Energy in Long-Term System Models: A Multi-Model Perspective

Long-term energy system models–including electric sector capacity expansion models–are widely used tools for informing planning, technology assessment, and policy analysis. Recent decarbonization goals and rapid technological change have increased the need to appropriately represent economic characteristics and technical details of energy system resources, including variable renewable energy, energy storage technologies, carbon-capture-equipped capacity, and nuclear energy. Nuclear power represents about 20% of electricity generation and 50% of carbon-free electricity in the United States as of 2021. However, there are many perspectives on the role of existing and new nuclear in the future U.S. energy system, which is reflected in the broad range of potential contributions reported in the literature. This project aims to understand how issues central to nuclear energy are represented in long-term energy models. Building on earlier collaborations that focused on variable renewable energy and energy storage, this project convenes four modeling teams that use national-scale long-term energy system models from the Electric Power Research Institute, the National Renewable Energy Laboratory, the U.S. Energy Information Administration, and the U.S. Environmental Protection Agency to share methods and data, update models, run coordinated scenarios, and identify research needs. Improving tools can provide more insightful analyses and ensure that methods are more transparent.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Data for Bistline, et al. (2025) "Policy Implications of Net-Zero Emissions: A Multi-Model Analysis of United States Emissions and Energy System Impacts"

These files contain input assumptions, results, and figures associated with the Bistline, et al. (2025) article "Policy Implications of Net-Zero Emissions: A Multi-Model Analysis of United States Emissions and Energy System Impacts" in Energy and Climate Change as part of the Energy Modeling Forum 37 study. Please refer to the original paper for details.

climate policy↗

DOE OSTI2022

Nuclear's Role in the U.S. Electricity System: A Multi-Model Inter-Comparison Analysis

Multiple capacity expansion models (CEMs) for the U.S. power system represent the balance of options among generation, transmission, and storage assets that can satisfy electric loads, operating and planning reserves, and policy requirements. These models are typically set up to find the least-cost portfolio of assets that meet specified requirements, and model decisions can include both investments in new, and retirement of existing, resources. The scenarios explored by CEMs can help inform strategies for meeting future electricity and energy needs under a range of future conditions. However, projections can differ between models, sometimes dramatically, for a seemingly similar scenario. Differences in model coverage, structure, and input assumptions contribute to the range of model outcomes. Understanding what drives the biggest differences in model outputs improves model insights and provides context for interpreting results. This summary presents analysis that was performed through a forum of analysts who own, update, and apply CEMs, as well as nuclear experts from national laboratories, industry, and the research community. The following sections describe methods, results, and findings from an original, innovative inter-model comparison that provides insights into what drives the greatest differences in nuclear retirement and deployment projections across models and a range of technology, market, and policy conditions.

capacity expansion model↗

DOE OSTI2023

Nuclear's Role in the U.S. Electricity System: A Multi-Model Inter-Comparison Analysis

A variety of US electric power sector capacity expansion models are used by decision makers and analysts to evaluate competition among generation, transmission, and storage technologies to meeting the demands of the system. CEMs use least-cost optimization to identify optimal portfolios of investments capable of satisfying all specified requirements. While CEMs are a useful tool to inform pathways to meet future needs, projections can differ significantly between tools for apparently similar scenario assumptions. Differences in model structure, scope, and input assumption contribute to this issue. This work compares model response with harmonization between four modeling teams on issues significant to the representation and development of nuclear energy.

capacity expansion model↗

DOE OSTI2023

Insights on Nuclear Energy from Long-Term System Models: Inter-Model Comparison Results

capacity expansion model↗

DOE OSTI2025

Radiochronometric discordance in cast uranium metal: a multi-laboratory intercomparison exercise

Here, the model age of a nuclear material is crucial in nuclear forensic analysis. Uranium metals with complex production histories often exhibit discordant model ages from the 230 Th– 234 U and 231 Pa– 235 U chronometers. Recent studies involving targeted uranium metal castings have enhanced our understanding of decay product behavior during casting, aiding nuclear forensic interpretation. Building on this prior work, forensics laboratories at Atomic Weapons Establishment (AWE), Lawrence Livermore National Laboratory (LLNL), and Los Alamos National Laboratory (LANL) conducted an interlaboratory comparison to investigate spatial heterogeneity in uranium metal cast under controlled conditions. Each laboratory measured samples of a mixed feedstock and its corresponding cast product. This work furthers our understanding of discordant model ages and the use of discordance as a signature to enhance confidence in interpretations of radiochronometric data for nuclear forensics.

230Th/234U↗

DOE OSTI2020

Identifying emergent agent types and effective practices for portability, scalability, and intercomparison in water resource agent-based models

Not Available

Kaiser, Kendra E.↗

DOE OSTI2022

Radiosonde Intercomparison and Validation (RIVAL) Field Campaign Report

Radiosondes provide the primary source of high-vertical-resolution temperature, humidity, and pressure data from the surface through to the lower stratosphere, and are a key component of the sustained measurement program at U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility observatories. At the end of 2017 the radiosonde manufacturer Vaisala ceased sales and production of the RS92 radiosondes, which were in use at the ARM field sites since 2005, and transitioned to the RS41 model. The Radiosonde Intercomparison and Validation (RIVAL) field campaign was proposed in order to undertake a sustained intercomparison and validation campaign at the Eastern North Atlantic (ENA), North Slope of Alaska (NSA) at Barrow, and Southern Great Plains (SGP) observatories in order to fully quantify the RS92-RS41 sonde differences and their dependence on all relevant influencing variables, and to understand the effects of the sonde model transition.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Metrics for Intercomparison of Remapping Algorithms (MIRA) protocol applied to Earth system models

Abstract. Strongly coupled nonlinear phenomena such as those described by Earth system models (ESMs) are composed of multiple component models with independent mesh topologies and scalable numerical solvers. A common operation in ESMs is to remap or interpolate component solution fields defined on their computational mesh to another mesh with a different combinatorial structure and decomposition, e.g., from the atmosphere to the ocean, during the temporal integration of the coupled system. Several remapping schemes are currently in use or available for ESMs. However, a unified approach to compare the properties of these different schemes has not been attempted previously. We present a rigorous methodology for the evaluation and intercomparison of remapping methods through an independently implemented suite of metrics that measure the ability of a method to adhere to constraints such as grid independence, monotonicity, global conservation, and local extrema or feature preservation. A comprehensive set of numerical evaluations is conducted based on a progression of scalar fields from idealized and smooth to more general climate data with strong discontinuities and strict bounds. We examine four remapping algorithms with distinct design approaches, namely ESMF Regrid (Hill et al., 2004), TempestRemap (Ullrich and Taylor, 2015), generalized moving least squares (GMLS) (Trask and Kuberry, 2020) with post-processing filters, and WLS-ENOR (Li et al., 2020). By repeated iterative application of the high-order remapping methods to the test fields, we verify the accuracy of each scheme in terms of their observed convergence order for smooth data and determine the bounded error propagation using challenging, realistic field data on both uniform and regionally refined mesh cases. In addition to retaining high-order accuracy under idealized conditions, the methods also demonstrate robust remapping performance when dealing with non-smooth data. There is a failure to maintain monotonicity in the traditional L2-minimization approaches used in ESMF and TempestRemap, in contrast to stable recovery through nonlinear filters used in both meshless GMLS and hybrid mesh-based WLS-ENOR schemes. Local feature preservation analysis indicates that high-order methods perform better than low-order dissipative schemes for all test cases. The behavior of these remappers remains consistent when applied on regionally refined meshes, indicating mesh-invariant implementations. The MIRA intercomparison protocol proposed in this paper and the detailed comparison of the four algorithms demonstrate that the new schemes, namely GMLS and WLS-ENOR, are competitive compared to standard conservative minimization methods requiring computation of mesh intersections. The work presented in this paper provides a foundation that can be extended to include complex field definitions, realistic mesh topologies, and spectral element discretizations, thereby allowing for a more complete analysis of production-ready remapping packages.

58 GEOSCIENCES↗

DOE OSTI2025

A Practical Probabilistic Benchmark for AI Weather Models

Since the weather is chaotic, it is necessary to forecast an ensemble of future states. Recently, multiple AI weather models have emerged claiming breakthroughs in deterministic skill. Unfortunately, it is hard to fairly compare ensembles of AI forecasts because variations in ensembling methodology become confounding and the baseline data volume is immense. We address this by scoring lagged initial condition ensembles—whereby an ensemble can be constructed from a library of deterministic hindcasts. This allows the first parameter‐free intercomparison of leading AI weather models' probabilistic skill against an operational baseline. Lagged ensembles of the two leading AI weather models, GraphCast and Pangu, perform similarly even though the former outperforms the latter in deterministic scoring. These results are elaborated upon by sensitivity tests showing that commonly used multiple time‐step loss functions damage ensemble calibration.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Tropical Cirrus in Global Storm-Resolving Models: 1. Role of Deep Convection

Pervasive cirrus clouds in the upper troposphere and tropical tropopause layer (TTL) influence the climate by altering the top-of-atmosphere radiation balance and stratospheric water vapor budget. These cirrus are often associated with deep convection, which global climate models must parameterize and struggle to accurately simulate. By comparing high-resolution global storm-resolving models from the Dynamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) intercomparison that explicitly simulate deep convection to satellite observations, we assess how well these models simulate deep convection, convectively generated cirrus, and deep convective injection of water into the TTL over representative tropical land and ocean regions. The DYAMOND models simulate deep convective precipitation, organization, and cloud structure fairly well over land and ocean regions, but with clear intermodel differences. All models produce frequent overshooting convection whose strongest updrafts humidify the TTL and are its main source of frozen water. Intermodel differences in cloud properties and convective injection exceed differences between land and ocean regions in each model. We argue that, with further improvements, global storm-resolving models can better represent tropical cirrus and deep convection in present and future climates than coarser-resolution climate models. To realize this potential, they must use available observations to perfect their ice microphysics and dynamical flow solvers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

The Climate Response to Emissions Reductions Due to COVID-19: Initial Results From CovidMIP

Many nations responded to the COVID-19 pandemic by restricting travel and other activities during 2020, resulting in temporarily reduced emissions of CO 2 , other greenhouse gases and ozone and aerosol precursors. We perform a coordinated Intercomparison, CovidMIP, of Earth System model simulations to assess the impact on climate of these emissions reductions. Eleven models performed multiple initial-condition ensembles to produce over 280 simulations spanning both initial condition and model structural uncertainty. We find model consensus on reduced aerosol amounts (particularly over East Asia) and associated increases in surface shortwave radiation levels. However, any impact on near-surface temperature or rainfall during 2020-2024 is extremely small and is not detectable in this initial analysis. Regional analyses on a finer scale, and closer attention to extremes (especially linked to changes in atmospheric composition and air quality) are required to test the impact of COVID-19-related emission reductions on near-term climate.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Comparing storm resolving models and climates via unsupervised machine learning

Global storm-resolving models (GSRMs) have gained widespread interest because of the unprecedented detail with which they resolve the global climate. However, it remains difficult to quantify objective differences in how GSRMs resolve complex atmospheric formations. This lack of comprehensive tools for comparing model similarities is a problem in many disparate fields that involve simulation tools for complex data. To address this challenge we develop methods to estimate distributional distances based on both nonlinear dimensionality reduction and vector quantization. Our approach automatically learns physically meaningful notions of similarity from low-dimensional latent data representations that the different models produce. This enables an intercomparison of nine GSRMs based on their high-dimensional simulation data (2D vertical velocity snapshots) and reveals that only six are similar in their representation of atmospheric dynamics. Furthermore, we uncover signatures of the convective response to global warming in a fully unsupervised way. Our study provides a path toward evaluating future high-resolution simulation data more objectively.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Object-Based Evaluation of Tropical Precipitation Systems in DYAMOND Simulations over the Maritime Continent

The present study uses an object-based evaluation metric to examine the precipitation bias over the Maritime Continent in the global cloud-resolving models. We specifically focus on the difference between the models that directly resolve convection and those using convection parameterization. The 40-day hindcast experiments of the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domain (DYAMOND) intercomparison project are evaluated against the high-resolution satellite rainfall products. The hindcast of the Central Weather Bureau Global Forecast System (CWBGFS) under the DYAMOND protocol is also included. The results indicate that most models simulate insufficient numbers of large precipitation system [object-based precipitation system (OPS), > 370 km in scale], indicating weaker convection organization. The observation indicates that the maximum precipitation within the OPS intensifies with increasing object size. All of the models capture this positive relationship, but most of them overestimate the sensitivity. Most of the models overestimate both the frequency and intensity of small OPS (< 160 km), except for the models with convection parameterization [i.e., CWBGFS, European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS)-9 km]. Although most of the models can reproduce the observed peak time of diurnal precipitation over the land area in the Maritime Continent, the simulated fractional contribution of different sizes of OPS to the total precipitation varies from model to model, and their peak times do not follow the observed ones with delayed peak times as the size of OPS increases from small, mid-size, to large categories. Most of the models reasonably capture the mean diurnal cycle peak time, but only the models with convection parameterization and Model for Prediction Across Scales (MPAS) can represent the diurnal evolution of fractional contribution from different OPSs. The implications of the current results to the upscale processes of the tropical convection systems in the global models are also discussed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

input4MIPs.CMIP6Plus.PCMDI.PCMDI-AMIP-1-1-9

CMIP6Plus Forcing Datasets (input4MIPs). This dataset PCMDI-AMIP-1-1-9 is part of input4MIPs under dataset_category: "['SSTsAndSeaIce']". More information about the dataset can be found at the following links: https://pcmdi.llnl.gov/mips/amip https://input4mips-controlled-vocabularies-cvs.readthedocs.io/en/stable/database-views/input4MIPs_source-id_CMIP7.html https://input4mips-controlled-vocabularies-cvs.readthedocs.io/en/stable/dataset-overviews/ The dataset is available at: https://esgf-node.ornl.gov/search/input4mips/?mip_era=CMIP6Plus&activity_id=input4MIPs&institution_id=PCMDI&source_id=PCMDI-AMIP-1-1-9

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Climate response to off-equatorial stratospheric sulfur injections in three Earth system models – Part 1: Experimental protocols and surface changes

There is now substantial literature on climate model studies of equatorial or tropical stratospheric SO 2 injections that aim to counteract the surface warming produced by rising concentrations of greenhouse gases. Here we present the results from the first systematic intercomparison of climate responses in three Earth system models wherein the injection of SO 2 occurs at different latitudes in the lower stratosphere: CESM2-WACCM6, UKESM1.0 and GISS-E2.1-G. The first two use a modal aerosol microphysics scheme, while two versions of GISS-E2.1-G use a bulk aerosol (One-Moment Aerosol, OMA) and a two-moment (Multiconfiguration Aerosol TRacker of mIXing state, MATRIX) microphysics approach, respectively. Our aim in this work is to determine commonalities and differences between the climate model responses in terms of the distribution of the optically reflective sulfate aerosols produced from the oxidation of SO 2 and in terms of the surface response to the resulting reduction in solar radiation. A focus on understanding the contribution of characteristics of models transport alongside their microphysical and chemical schemes, and on evaluating the resulting stratospheric responses in different models, is given in the companion paper (Bednarz et al., 2023). The goal of this exercise is not to evaluate these single-point injection simulations as stand-alone proposed strategies to counteract global warming; instead we determine sources and areas of agreement and uncertainty in the simulated responses and, ultimately, the possibility of designing a comprehensive intervention strategy capable of managing multiple simultaneous climate goals through the combination of different injection locations. We find large disagreements between GISS-E2.1-G and the CESM2-WACCM6 and UKESM1.0 models regarding the magnitude of cooling per unit of aerosol optical depth (AOD) produced, which varies from 4.7 K per unit of AOD in CESM2-WACCM6 to 16.7 K in the GISS-E2.1-G version with two-moment aerosol microphysics. By normalizing the results with the global mean response in each of the models and thus assuming that the amount of SO 2 injected is a free parameter that can be managed independently, we highlight some commonalities in the overall distributions of the aerosols, in the inter-hemispheric surface temperature response and in shifts to the Intertropical Convergence Zone, as well as some areas of disagreement, such as the extent of the aerosol confinement in the equatorial region and the efficiency of the transport to polar latitudes. In conclusion, we demonstrate that it is possible to use these simulations to produce more comprehensive injection strategies in multiple climate models. However, large differences in the injection magnitudes can be expected, potentially increasing inter-model spreads in some stratospheric quantities (such as aerosol distribution) while reducing the spread in the surface response in terms of temperature and precipitation; furthermore, the selection of the injection locations may be dependent on the models' specific stratospheric transport.

54 ENVIRONMENTAL SCIENCES↗