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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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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Carbon stored in live ponderosa pines in the Sierra Nevada will not return to pre-drought (2012) levels during the 21st century due to bark beetle outbreaks

Outbreaks of several bark beetle species can develop rapidly in response to drought and may result in large transfers of carbon (C) stored in live trees to C stored in dead trees (10s of Tg C yr -1 in the western U.S. alone), which over time will be released back to the atmosphere. The western pine beetle (WPB) outbreak incited by the 2012–2015 mega-drought in the Sierra Nevada, California, U.S., could portend more frequent and/or severe bark beetle outbreaks as the temperature warms and drought frequency and intensity increase in the future. However, changes in the frequency and/or severity (resultant levels of host tree mortality) of beetle outbreaks are difficult to predict as outbreaks are complex with non-linear and eruptive processes primarily driven by interactions among beetle populations, the demography of hosts and other tree species, and climate and weather. Using an insect phenology and tree defense model, we projected the future likelihood of WPB outbreaks in the Sierra Nevada with climate drivers from different Earth System Models. Our goal was to understand how host (ponderosa pine, PIPO) recovery and future warming and drought affect the frequency and severity of WPB outbreaks and their C consequences. Our projections suggested that by 2100 the C stored in live PIPO (mean: 1.98 kg C m -2 , 95% CI: 1.74–2.21 kg C m -2 ) will not return to levels that occurred before the 2012–2015 drought (2012: ~2.30 kg C m –2 ) due to future WPB outbreaks. However, differences in climate models indicate a wide range of possible WPB outbreak frequencies and severities. Our results suggest that total plot basal area is the most significant factor in the mortality rate of PIPO by WPB in any given year, followed by drought severity and temperature. High levels of host basal area, higher temperature, and extreme drought all contribute to the frequency and severity of future WPB outbreaks. While PIPO basal area may decline under increased drought and warming, limiting high-stand basal area (>60 m 2 ha -1 ) may reduce the severity of future WPB outbreaks in the Sierra Nevada.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

HIRM v1.0: a hybrid impulse response model for climate modeling and uncertainty analyses

Simple climate models (SCMs) are frequently used in research and decision-making communities because of their flexibility, tractability, and low computational cost. SCMs can be idealized, flexibly representing major climate dynamics as impulse response functions, or process-based, using explicit equations to model possibly nonlinear climate and Earth system dynamics. Each of these approaches has strengths and limitations. Here we present and test a hybrid impulse response modeling framework (HIRM) that combines the strengths of process-based SCMs in an idealized impulse response model, with HIRM's input derived from the output of a process-based model. This structure enables the model to capture some of the major nonlinear dynamics that occur in complex climate models as greenhouse gas emissions transform to atmospheric concentration to radiative forcing to climate change. As a test, the HIRM framework was configured to emulate the total temperature of the simple climate model Hector 2.0 under the four Representative Concentration Pathways and the temperature response of an abrupt 4 times CO 2 concentration step. HIRM was able to reproduce near-term and long-term Hector global temperature with a high degree of fidelity. Additionally, we conducted two case studies to demonstrate potential applications for this hybrid model: examining the effect of aerosol forcing uncertainty on global temperature and incorporating more process-based representations of black carbon into a SCM. The open-source HIRM framework has a range of applications including complex climate model emulation, uncertainty analyses of radiative forcing, attribution studies, and climate model development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The future of Earth system prediction: Advances in model-data fusion

Predictions of the Earth system, such as weather forecasts and climate projections, require models informed by observations at many levels. Some methods for integrating models and observations are very systematic and comprehensive (e.g., data assimilation), and some are single purpose and customized (e.g., for model validation). We review current methods and best practices for integrating models and observations. We highlight how future developments can enable advanced heterogeneous observation networks and models to improve predictions of the Earth system (including atmosphere, land surface, oceans, cryosphere, and chemistry) across scales from weather to climate. As the community pushes to develop the next generation of models and data systems, there is a need to take a more holistic, integrated, and coordinated approach to models, observations, and their uncertainties to maximize the benefit for Earth system prediction and impacts on society.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Accelerated Climate Model for Energy (ACME): Ocean and Sea-Ice Processes

The primary goal of this project was to understand the role of the ocean and sea-ice components in the evolution of the climate in both a high-resolution fully-coupled 1850 preindustrial control and present day transient climate simulations run using the Energy Exascale Earth System Model (E3SM), formerly known as the Accelerated Climate Model for Energy (ACME). Two distinct approaches to the initialization of the present day transients were tested: in the first, an ensemble of three transients used atmospheric reanalysis forced 0.1-degree ocean/sea-ice states, while a fourth transient was branched off the preindustrial control. Biases from the control were inherited by its successor transient, in particular, sea surface temperatures (SSTs) at high latitudes were too warm and too much northward meridional heat transport occurred in the North Atlantic Ocean. SSTs in the Northern Hemisphere and Atlantic meridional heat transport increased in the transients initialized from the ocean/sea-ice simulation over a 40-50 year period to largely resemble the transient branched off the control simulation. Excessive absorbed solar radiation (ASR) is considered to be responsible for the SST biases as well as the formation of mode waters in lighter density classes than in reality both in the Southern Ocean and the North Atlantic Ocean subtropical gyre. These mode waters ventilate the thermocline waters to the north of their formation sites explaining the excessive Atlantic meridional heat transport and some of the warming in the high-latitude North Atlantic Ocean after multiple decades.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

gdess: A framework for evaluating simulated atmospheric CO 2 in Earth System Models

Atmospheric carbon dioxide (CO 2 ) plays a key role in the global carbon cycle and global warming. Climate-carbon feedbacks are often studied and estimated using Earth System Models (ESMs), which couple together multiple model components—including the atmosphere, ocean, terrestrial biosphere, and cryosphere—to jointly simulate mass and energy exchanges within and between these components. Despite tremendous advances, model intercomparisons and benchmarking are aspects of ESMs that warrant further improvement (Fer et al., 2021; Smith et al., 2014). Such benchmarking is critical because comparing the value of state variables in these simulations against observed values provides evidence for appropriately refining model components; moreover, researchers can learn much about Earth system dynamics in the process (Randall et al., 2019). We introduce `gdess` (a.k.a., Greenhouse gas Diagnostics for Earth System Simulations), which parses observational datasets and ESM simulation output, combines them to be in a consistent structure, computes statistical metrics, and generates diagnostic visualizations. In its current incarnation, `gdess` facilitates evaluating a model's ability to reproduce observed temporal and spatial variations of atmospheric CO 2 . The diagnostics implemented modularly in `gdess` support more rapid assessment and improvement of model-simulated global CO 2 sources and sinks associated with land and ocean ecosystem processes. We intend for this set of automated diagnostics to form an extensible, open source framework for future comparisons of simulated and observed concentrations of various greenhouse gases across Earth system models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Description of historical and future projection simulations by the global coupled E3SMv1.0 model as used in CMIP6

Abstract. This paper documents the experimental setup and general features of the coupled historical and future climate simulations with the first version of the US Department of Energy (DOE) Energy Exascale Earth System Model (E3SMv1.0). The future projected climate characteristics of E3SMv1.0 at the highest emission scenario (SSP5-8.5) designed in the Scenario Model Intercomparison Project (ScenarioMIP) and the SSP5-8.5 greenhouse gas (GHG) only forcing experiment are analyzed with a focus on regional responses of atmosphere, ocean, sea ice, and land. Due to its high equilibrium climate sensitivity (ECS of 5.3 K), E3SMv1.0 is one of the Coupled Model Intercomparison Project phase 6 (CMIP6) models with the largest surface warming by the end of the 21st century under the high-emission SSP5-8.5 scenario. The global mean precipitation change is highly correlated with the global temperature change, while the spatial pattern of the change in runoff is consistent with the precipitation changes. The oceanic mixed layer generally shoals throughout the global ocean. The annual mean Atlantic meridional overturning circulation (AMOC) is overly weak with a slower change from ∼ 11 to ∼ 6 Sv (Sverdrup) relative to other CMIP6 models. The sea ice, especially in the Northern Hemisphere, decreases rapidly with large seasonal variability. We detect a significant polar amplification in E3SMv1.0 from the atmosphere, ocean, and sea ice. Comparing the SSP5-8.5 all-forcing experiment with the GHG-only experiment, we find that the unmasking of the aerosol effects due to the decline of the aerosol loading in the future projection period causes transient accelerated warming in the all-forcing experiment in the first half of the 21st century. While the oceanic climate response is mainly controlled by the GHG forcing, the land runoff response is impacted primarily by forcings other than GHG over certain regions, e.g., southern North America, southern Africa, central Africa, and eastern Asia. However, the importance of the GHG forcing on the land runoff changes grows in the future climate projection period compared to the historical period.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A hierarchy of global ocean models coupled to CESM1

We develop a hierarchy of simplified ocean models for coupled ocean, atmosphere, and sea ice climate simulations using the Community Earth System Model version 1 (CESM1). The hierarchy has four members: a slab ocean model, a mixed-layer model (MLM) with entrainment and detrainment, an Ekman MLM, and an ocean general circulation model (OGCM). Flux corrections of heat and salt are applied to the simplified models ensuring that all hierarchy members have the same climatology. We diagnose the needed flux corrections from auxiliary simulations in which we restore the temperature and salinity to the daily climatology obtained from a target CESM1 simulation. The resulting three-dimensional corrections contain the interannual variability fluxes that maintain the correct vertical gradients of temperature and salinity in the tropics. We find that the inclusion of mixed-layer entrainment and Ekman flow produces sea surface temperature and surface air temperature fields whose means and variances are progressively more similar to those produced by the target CESM1 simulation. We illustrate the application of the hierarchy to the problem of understanding the response of the climate system to the loss of Arctic sea ice. We find that the shifts in the positions of the mid-latitude westerly jet and of the Inter-tropical Convergence Zone (ITCZ) in response to sea-ice loss depend critically on upper ocean processes. Specifically, heat uptake associated with the mixed-layer entrainment influences the shift in the westerly jet and ITCZ. Moreover, the shift of ITCZ is sensitive to the form of Ekman flow parameterization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Towards provision of regularly updated climate data from the Coupled Model Intercomparison Project

The Coupled Model Intercomparison Project (CMIP) is a flagship of the World Climate Research Programme (WCRP). CMIP has become a recognised ‘brand’ in climate circles evolving over the last thirty years from a targeted research activity by a small number of climate modelling centres intercomparing their Earth System Model (ESM) simulations to a broad international coordinated research effort (Durack et al, 2025). CMIP is organized as a research activity leveraging funded and in-kind contributions from experts within modelling centres and the broader scientific community supported more recently by a fully-funded International Project Office. Within CMIP, Model Intercomparison Projects (MIPs) are community-designed to understand past, present and future climate. CMIP data provides a valuable resource for climate research and is routinely used to assess model representation of climate processes and test scientific hypotheses in the context of model uncertainty and (forced and internal) variability as evident from its prolific use in scientific publications1 . The impact relies on enabling infrastructure (most prominently via the Earth System Grid Federation (ESGF)), which allows sharing of simulation output, provision of the boundary conditions used in each simulation, and definition of the data standards that are essential to facilitating wide use of the data. The impact is supplemented by the wide-ranging scrutiny to which model simulations are subjected. Beyond its use in research, CMIP data is a key resource for communities producing derived climate information from downscaling and impact studies, such as the Coordinated Regional Downscaling Experiment (CORDEX; Gutowski et al., 2016) and the Intersectoral Impacts MIP (ISIMIP; Frieler et al., 2024). Government, academic and commercial entities also increasingly rely on CMIP and its downstream data for climate risk assessments and climate services (for example, Copernicus Climate Change Service and World Bank portal). This means that, although CMIP is a research activity, it increasingly serves a secondary and very relevant role as a provider of climate data – a long-recognised dichotomy (Stevens, 2024). Research and applications have distinct needs, with the former requiring flexibility and generality and the latter consistency. Here we explain how the design of the research activity has been adapted to reduce the burdens imposed by applications and how the research infrastructure might evolve to further enable scientific inquiry. We propose one possible approach to consistently providing model information and projections for applications in the future.

Environmental sciences↗
DOE OSTI2025

Scalable Multi-Facility Workflows for Artificial Intelligence Applications in Climate Research

Earth observation satellites and earth system models are sources of vast, multi-modal datasets that are invaluable for advancing climate and environmental research. However, their scale and complexity pose significant challenges for processing and analysis. In this paper we discuss our experiences in developing and using a scientific research application using an automated multi-facility workflow that orchestrates data collection, preprocessing, artificial intelligence (AI) inferencing, and data movement across diverse computational resources, leveraging the Advanced Computing Ecosystem Testbed at the Oak Ridge Leadership Computing Facility (OLCF). We demonstrate that our workflow can be seamlessly integrated and orchestrated across research facilities managed by different federal agencies, thus allowing users to extract new scientific insights from climate datasets. The experimental results indicate that the multi-facility workflow significantly reduces processing time, enhances scalability, and maintains high efficiency across varying workloads. Notably, our workflow processes 12,000 high-resolution satellite images in just 44 seconds using 80 workers distributed across 10 nodes on the OLCF systems. Such high throughput is essential for dynamic tokenization and sharding of petascale satellite data for distributed AI model training and inferencing at scale across thousands of GPUs.

Kurihana, Takuya [ORNL] (ORCID:0000000156698565)↗
DOE OSTI2021

Identifying the sources of uncertainty in climate model simulations of solar radiation modification with the G6sulfur and G6solar Geoengineering Model Intercomparison Project (GeoMIP) simulations

We present here results from the Geoengineering Model Intercomparison Project (GeoMIP) simulations for the experiments G6sulfur and G6solar for six Earth system models participating in the Climate Model Intercomparison Project (CMIP) Phase 6. The aim of the experiments is to reduce the warming that results from a high-tier emission scenario (Shared Socioeconomic Pathways SSP5-8.5) to that resulting from a medium-tier emission scenario (SSP2-4.5). These simulations aim to analyze the response of climate models to a reduction in incoming surface radiation as a means to reduce global surface temperatures, and they do so either by simulating a stratospheric sulfate aerosol layer or, in a more idealized way, through a uniform reduction in the solar constant in the model. We find that over the final two decades of this century there are considerable inter-model spreads in the needed injection amounts of sulfate (29 ± 9 Tg-SO 2 /yr between 2081 and 2100), in the latitudinal distribution of the aerosol cloud and in the stratospheric temperature changes resulting from the added aerosol layer. Even in the simpler G6solar experiment, there is a spread in the needed solar dimming to achieve the same global temperature target (1.91 ± 0.44 %). The analyzed models already show significant differences in the response to the increasing CO 2 concentrations for global mean temperatures and global mean precipitation (2.05 K ± 0.42 K and 2.28 ± 0.80 %, respectively, for SSP5-8.5 minus SSP2-4.5 averaged over 2081–2100). With aerosol injection, the differences in how the aerosols spread further change some of the underlying uncertainties, such as the global mean precipitation response (–3.79 ± 0.76 % for G6sulfur compared to –2.07 ± 0.40 % for G6solar against SSP2-4.5 between 2081 and 2100). These differences in the behavior of the aerosols also result in a larger uncertainty in the regional surface temperature response among models in the case of the G6sulfur simulations, suggesting the need to devise various, more specific experiments to single out and resolve particular sources of uncertainty. The spread in the modeled response suggests that a degree of caution is necessary when using these results for assessing specific impacts of geoengineering in various aspects of the Earth system. However, all models agree that compared to a scenario with unmitigated warming, stratospheric aerosol geoengineering has the potential to both globally and locally reduce the increase in surface temperatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A Hybrid Climate Modeling System Using AI-assisted Process Emulators

This white paper addresses Focus Area II. We advocate developing a hybrid modeling system to improve the understanding of decadal- and longer-scale predictability of high impact water cycle components. This hybrid model combines a partial differential equation (PDE)-based dynamic core with AI/ML based emulators to represent many of the computationally expensive processes in Earth’s climate models. The hybrid modeling system has the potential to exploit emerging graphics processing unit (GPU)-accelerated architectures and allows for the generation of large ensemble (~1000’s) simulations to better characterize the model uncertainty and understand predictability.

58 GEOSCIENCES↗
DOE OSTI2021

Significant impact of forcing uncertainty in a large ensemble of climate model simulations

Forcing due to solar and volcanic variability, on the natural side, and greenhouse gas and aerosol emissions, on the anthropogenic side, are the main inputs to climate models. Reliable climate model simulations of past and future climate change depend crucially upon them. Here we analyze large ensembles of simulations using a comprehensive Earth System Model to quantify uncertainties in global climate change attributable to differences in prescribed forcings. The different forcings considered here are those used in the two most recent phases of the Coupled Model Intercomparison Project (CMIP), namely CMIP5 and CMIP6. We show significant differences in simulated global surface air temperature due to volcanic aerosol forcing in the second half of the 19th century and in the early 21st century. The latter arise from small-to-moderate eruptions incorporated in CMIP6 simulations but not in CMIP5 simulations. We also find significant differences in global surface air temperature and Arctic sea ice area due to anthropogenic aerosol forcing in the second half of the 20th century and early 21st century. These differences are as large as those obtained in different versions of an Earth System Model employing identical forcings. In simulations from 2015 to 2100, we find significant differences in the rates of projected global warming arising from CMIP5 and CMIP6 concentration pathways that differ slightly but are equivalent in terms of their nominal radiative forcing levels in 2100. Our results highlight the influence of assumptions about natural and anthropogenic aerosol loadings on carbon budgets, the likelihood of meeting Paris targets, and the equivalence of future forcing scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Improved Understanding of Multicentury Greenland Ice Sheet Response to Strong Warming in the Coupled CESM2‐CISM2 With Regional Grid Refinement

The simulation of ice sheet‐climate interactions, such as surface mass balance fluxes, is sensitive to model grid resolution. Here we simulate the multi‐century evolution of the Greenland Ice Sheet (GrIS) and its interaction with the climate using the Community Earth System Model version 2.2 (CESM2.2) including an interactive GrIS component (the Community Ice Sheet Model v2.1 [CISM2.1]) under an idealized warming scenario (atmospheric CO 2 increases by 1% yr -1 until quadrupling the pre‐industrial level and then is held fixed). A variable‐resolution (VR) grid with 1/4° regional refinement over the broader Arctic and 1° resolution elsewhere is applied to the atmosphere and land components, and the results are compared with conventional 1° lat‐lon grid simulations to investigate the impact of grid refinement. Compared with the 1° runs, the VR run features a slower rate of surface melt, especially over the western and northern GrIS, where the ice surface slopes gently toward the periphery. This difference pattern originates primarily from higher snow albedo and, thus, weaker albedo feedback in the VR run. The VR grid better captures the CISM ice sheet topography by reducing elevation discrepancies between CAM and CISM and is, therefore, less reliant on the downscaling algorithm, which is known to underestimate albedo gradients. The sea level rise contribution from the GrIS in the VR run is 53 mm by year 150 and 831 mm by year 350, approximately 40% and 20% less than that of the 1° runs, respectively.

Earth System Model↗
DOE OSTI2022

A Hierarchy of Global Ocean Models Coupled to CESM1

Abstract We develop a hierarchy of simplified ocean models for coupled ocean, atmosphere, and sea ice climate simulations using the Community Earth System Model version 1 (CESM1). The hierarchy has four members: a slab ocean model, a mixed‐layer model (MLM) with entrainment and detrainment, an Ekman MLM, and an ocean general circulation model (OGCM). Flux corrections of heat and salt are applied to the simplified models ensuring that all hierarchy members have the same climatology. We diagnose the needed flux corrections from auxiliary simulations in which we restore the temperature and salinity to the daily climatology obtained from a target CESM1 simulation. The resulting three‐dimensional corrections contain the interannual variability fluxes that maintain the correct vertical gradients of temperature and salinity in the tropics. We find that the inclusion of mixed‐layer entrainment and Ekman flow produces sea surface temperature and surface air temperature fields whose means and variances are progressively more similar to those produced by the target CESM1 simulation. We illustrate the application of the hierarchy to the problem of understanding the response of the climate system to the loss of Arctic sea ice. We find that the shifts in the positions of the mid‐latitude westerly jet and of the Inter‐tropical Convergence Zone (ITCZ) in response to sea‐ice loss depend critically on upper ocean processes. Specifically, heat uptake associated with the mixed‐layer entrainment influences the shift in the westerly jet and ITCZ. Moreover, the shift of ITCZ is sensitive to the form of Ekman flow parameterization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Projected land ice contributions to twenty-first-century sea level rise

The land ice contribution to global mean sea level rise has not yet been predicted using ice sheet and glacier models for the latest set of socio-economic scenarios, nor using coordinated exploration of uncertainties arising from the various computer models involved. Two recent international projects generated a large suite of projections using multiple models, but primarily used previous-generation scenarios and climate models, and could not fully explore known uncertainties. Here we estimate probability distributions for these projections under the new scenarios using statistical emulation of the ice sheet and glacier models. In this work, we find that limiting global warming to 1.5 degrees Celsius would halve the land ice contribution to twenty-first-century sea level rise, relative to current emissions pledges. The median decreases from 25 to 13 centimetres sea level equivalent (SLE) by 2100, with glaciers responsible for half the sea level contribution. The projected Antarctic contribution does not show a clear response to the emissions scenario, owing to uncertainties in the competing processes of increasing ice loss and snowfall accumulation in a warming climate. However, under risk-averse (pessimistic) assumptions, Antarctic ice loss could be five times higher, increasing the median land ice contribution to 42 centimetres SLE under current policies and pledges, with the 95th percentile projection exceeding half a metre even under 1.5 degrees Celsius warming. This would severely limit the possibility of mitigating future coastal flooding. Given this large range (between 13 centimetres SLE using the main projections under 1.5 degrees Celsius warming and 42 centimetres SLE using risk-averse projections under current pledges), adaptation planning for twenty-first-century sea level rise must account for a factor-of-three uncertainty in the land ice contribution until climate policies and the Antarctic response are further constrained.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evaluation of native Earth system model output with ESMValTool v2.6.0

Earth system models (ESMs) are state-of-the-art climate models that allow numerical simulations of the past, present-day, and future climate. To extend our understanding of the Earth system and improve climate change projections, the complexity of ESMs heavily increased over the last decades. As a consequence, the amount and volume of data provided by ESMs has increased considerably. Innovative tools for a comprehensive model evaluation and analysis are required to assess the performance of these increasingly complex ESMs against observations or reanalyses. One of these tools is the Earth System Model Evaluation Tool (ESMValTool), a community diagnostic and performance metrics tool for the evaluation of ESMs. Input data for ESMValTool needs to be formatted according to the CMOR (Climate Model Output Rewriter) standard, a process that is usually referred to as “CMORization”. While this is a quasi-standard for large model intercomparison projects like the Coupled Model Intercomparison Project (CMIP), this complicates the application of ESMValTool to non-CMOR-compliant climate model output. In this paper, we describe an extension of ESMValTool introduced in v2.6.0 that allows seamless reading and processing of “native” climate model output, i.e., operational output produced by running the climate model through the standard workflow of the corresponding modeling institute. This is achieved by an extension of ESMValTool's preprocessing pipeline that performs a CMOR-like reformatting of the native model output during runtime. Thus, the rich collection of diagnostics provided by ESMValTool is now fully available for these models. For models that use unstructured grids, a further preprocessing step required to apply many common diagnostics is regridding to a regular latitude–longitude grid. Extensions to ESMValTool's regridding functions described here allow for more flexible interpolation schemes that can be used on unstructured grids. Currently, ESMValTool supports nearest-neighbor, bilinear, and first-order conservative regridding from unstructured grids to regular grids. Example applications of this new native model support are the evaluation of new model setups against predecessor versions, assessing of the performance of different simulations against observations, CMORization of native model data for contributions to model intercomparison projects, and monitoring of running climate model simulations. For the latter, new general-purpose diagnostics have been added to ESMValTool that are able to plot a wide range of variable types. Currently, five climate models are supported: CESM2 (experimental; at the moment, only surface variables are available), EC-Earth3, EMAC, ICON, and IPSL-CM6. As the framework for the CMOR-like reformatting of native model output described here is implemented in a general way, support for other climate models can be easily added.

58 GEOSCIENCES↗
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↗