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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 OSTI2021

Evaluation of extreme sub-daily precipitation in high-resolution global climate model simulations

We examine the resolution dependence of errors in extreme sub-daily precipitation in available high-resolution climate models. We find that simulated extreme precipitation increases as horizontal resolution increases but that appropriately constructed model skill metrics do not significantly change. We find little evidence that simulated extreme winter or summer storm processes significantly improve with the resolution because the model performance changes identified are consistent with expectations from scale dependence arguments alone. We also discuss the implications of these scale-dependent limitations on the interpretation of simulated extreme precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Understanding processes that control dust spatial distributions with global climate models and satellite observations

Dust aerosol is important in modulating the climate system at local and global scales, yet its spatiotemporal distributions simulated by global climate models (GCMs) are highly uncertain. In this study, we evaluate the spatiotemporal variations of dust extinction profiles and dust optical depth (DOD) simulated by the Community Earth System Model version 1 (CESM1) and version 2 (CESM2), the Energy Exascale Earth System Model version 1 (E3SMv1), and the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) against satellite retrievals from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer (MODIS), and Multi-angle Imaging SpectroRadiometer (MISR). We find that CESM1, CESM2, and E3SMv1 underestimate dust transport to remote regions. E3SMv1 performs better than CESM1 and CESM2 in simulating dust transport and the northern hemispheric DOD due to its higher mass fraction of fine dust. CESM2 performs the worst in the Northern Hemisphere due to its lower dust emission than in the other two models but has a better dust simulation over the Southern Ocean due to the overestimation of dust emission in the Southern Hemisphere. DOD from MERRA-2 agrees well with CALIOP DOD in remote regions due to its higher mass fraction of fine dust and the assimilation of aerosol optical depth. The large disagreements in the dust extinction profiles and DOD among CALIOP, MODIS, and MISR retrievals make the model evaluation of dust spatial distributions challenging. Our study indicates the importance of representing dust emission, dry/wet deposition, and size distribution in GCMs in correctly simulating dust spatiotemporal distributions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Refined Understanding of the Ice Cloud Longwave Scattering Effects in Climate Model

Abstract Because longwave (LW) absorption by greenhouse gases and clouds is more significant than the LW scattering effect by clouds, most climate models neglect cloud LW scattering to save computational costs. Ignoring cloud LW scattering directly overestimates outgoing longwave radiation (OLR). This study included ice‐cloud LW scattering treatment in the Exascale Energy Earth System Model (E3SM) version 2 and ran fully‐coupled simulations, prescribed sea surface temperature simulations, and offline radiative transfer calculations to comprehensively assess the impact of ice‐cloud LW scattering on global climate simulation. The instantaneous effect due to ice‐cloud LW scattering reduces the OLR by ∼1 W/m 2 on the global average and 2 W/m 2 on the tropical average. Tropospheric warming and high cloud amount reduction act to partially compensate for such instantaneous OLR reduction caused by the inclusion of LW scattering. When the simulation reaches the equilibrium, the surface warms by 0.66 K on average with respect to the simulation without LW scattering, with the Arctic surface temperature differences more than twice as large as that of the global mean. The impact of including LW scattering on the simulated climate change in response to 4 × CO 2 is also assessed. While including the cloud LW scattering does not significantly modify radiative forcing and total radiative feedback under such a scenario, it results in a 10% more positive cloud feedback.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling

AI models are criticized as being black boxes, potentially subjecting climate science to greater uncertainty. Explainable artificial intelligence (XAI) has been proposed to probe AI models and increase trust. In this review and perspective paper, we suggest that, in addition to using XAI methods, AI researchers in climate science can learn from past successes in the development of physics-based dynamical climate models. Dynamical models are complex but have gained trust because their successes and failures can sometimes be attributed to specific components or sub-models, such as when model bias is explained by pointing to a particular parameterization. We propose three types of understanding as a basis to evaluate trust in dynamical and AI models alike: (1) instrumental understanding, which is obtained when a model has passed a functional test; (2) statistical understanding, obtained when researchers can make sense of the modeling results using statistical techniques to identify input–output relationships; and (3) component-level understanding, which refers to modelers' ability to point to specific model components or parts in the model architecture as the culprit for erratic model behaviors or as the crucial reason why the model functions well. We demonstrate how component-level understanding has been sought and achieved via climate model intercomparison projects over the past several decades. Such component-level understanding routinely leads to model improvements and may also serve as a template for thinking about AI-driven climate science. Currently, XAI methods can help explain the behaviors of AI models by focusing on the mapping between input and output, thereby increasing the statistical understanding of AI models. Yet, to further increase our understanding of AI models, we will have to build AI models that have interpretable components amenable to component-level understanding. We give recent examples from the AI climate science literature to highlight some recent, albeit limited, successes in achieving component-level understanding and thereby explaining model behavior. The merit of such interpretable AI models is that they serve as a stronger basis for trust in climate modeling and, by extension, downstream uses of climate model data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Pacific Tropical Decadal Variability Challenge for Climate Models

Understanding and forecasting Tropical Pacific Decadal-scale Variability (TPDV) strongly rely on climate model simulations. Using a Linear Inverse Modeling (LIM) diagnostic approach, we reveal Coupled Model Intercomparison Project Phase 6 models have significant challenges in reproducing the spatial structure and dominant mechanisms of TPDV. Specifically, while the models' ensemble mean pattern of TPDV resembles that of observations, the spread across models is very large and most models show significant differences from observations. In observations, removing the coupling between extratropics and tropics reduces TPDV by ~60%–70%, and removing the tropical thermocline variability makes the central tropical Pacific a key center of action for TPDV and El Niño Southern Oscillation variability. These characteristics are only confirmed in a subset of models. Differences between observations and simulations are outside the range of natural internal TPDV noise and pose important questions regarding our ability to model the impacts of natural internal low-frequency variability superimposed on long-term climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

High-Resolution Meteorology with Climate Change Impacts from Global Climate Model Data Using Generative Machine Learning

As renewable energy generation increases, the impacts of weather and climate on energy generation and demand become critical to the reliability of the energy system. However, these impacts are often overlooked. Global climate models (GCMs) can be used to understand possible changes to our climate, but their coarse resolution makes them difficult to use in energy system modelling. Here we present open-source generative machine learning methods that produce meteorological data at a nominal spatial resolution of 4 km at an hourly frequency based on inputs from 100 km daily-average GCM data. These methods run 40 times faster than traditional downscaling methods and produce data that have high-resolution spatial and temporal attributes similar to historical datasets. We demonstrate that these methods can be used to downscale projected changes in wind, solar and temperature variables across multiple GCMs including projections for more frequent low-wind and high-temperature events in the Eastern United States.

climate change↗
DOE OSTI2024

Statistical Downscaling of Climate Models for Solar Resource Assessment

This study presents the development of statistical models to efficiently downscale future projections of solar irradiance for solar energy applications. A climate data set simulated from a Regional Climate Model (RCM) obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) is selected as input to the statistical models to create high-resolution global horizontal irradiance (GHI) over the contiguous United States (CONUS). Our approach builds statistical downscaling models that (1) regrid RCM data (0.22 degree and daily spatiotemporal resolution), (2) correct bias of GHI projections, (3) downscale the future GHI project from daily-scale to hourly-scale, and (4) spatially downscale to generate GHI at 8-km resolution. To calibrate and validate the statistical models, we adapt and use the National Solar Radiation Database (NSRDB). Preliminary results show that the statistical downscaling approach downscales future projections of GHI under two climate scenarios (RCP4.5 and RCP8.5) with a nBIAS of 3%, nMAE of 34% and nRMSE of 46% estimated against NSRDB for the contiguous United State. This presentation will summarize the implemented methodology and validation results as well as future extension of this research.

climate data↗
DOE OSTI2022

Evaluating seasonal and regional distribution of snowfall in regional climate model simulations in the Arctic

In this study, we investigate how the regional climate model HIRHAM5 reproduces the spatial and temporal distribution of Arctic snowfall when compared to CloudSat satellite observations during the examined period of 2007–2010. For this purpose, both approaches, i.e., the assessments of the surface snowfall rate (observation-to-model) and the radar reflectivity factor profiles (model-to-observation), are carried out considering spatial and temporal sampling differences. The HIRHAM5 model, which is constrained in its synoptic representation by nudging to ERA-Interim, represents the snowfall in the Arctic region well in comparison to CloudSat products. The spatial distribution of the snowfall patterns is similar in both identifying the southeastern coast of Greenland and the North Atlantic corridor as regions gaining more than twice as much snowfall as the Arctic average, defined here for latitudes between 66 and 81°N. Excellent agreement (difference less than 1%) in the Arctic-averaged annual snowfall rate between HIRHAM5 and CloudSat is found, whereas ERA-Interim reanalysis shows an underestimation of 45% and significant deficits in the representation of the snowfall rate distribution. From the spatial analysis, it can be seen that the largest differences in the mean annual snowfall rates are an overestimation near the coastlines of Greenland and other regions with large orographic variations as well as an underestimation in the northern North Atlantic Ocean. To a large extent, the differences can be explained by clutter contamination, blind zone or higher resolution of CloudSat measurements, but clearly HIRHAM5 overestimates the orographic-driven precipitation. The underestimation of HIRHAM5 within the North Atlantic corridor south of Svalbard is likely connected to a poor description of the marine cold air outbreaks which could be identified by separating snowfall into different circulation weather type regimes. By simulating the radar reflectivity factor profiles from HIRHAM5 utilizing the Passive and Active Microwave TRAnsfer (PAMTRA) forward-modeling operator, the contribution of individual hydrometeor types can be assessed. Looking at a latitude band at 72–73°N, snow can be identified as the hydrometeor type dominating radar reflectivity factor values across all seasons. The largest differences between the observed and simulated reflectivity factor values are related to the contribution of cloud ice particles, which is underestimated in the model, most likely due to the small sizes of the particles. The model-to-observation approach offers a promising diagnostic when improving cloud schemes, as illustrated by comparison of different schemes available for HIRHAM5.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Sensitivity of Arctic Clouds to Ice Microphysical Processes in the NorESM2 Climate Model

Abstract Ice formation remains one of the most poorly represented microphysical processes in climate models. While primary ice production (PIP) parameterizations are known to have a large influence on the modeled cloud properties, the representation of secondary ice production (SIP) is incomplete and its corresponding impact is therefore largely unquantified. Furthermore, ice aggregation is another important process for the total cloud ice budget, which also remains largely unconstrained. In this study, we examine the impact of PIP, SIP, and ice aggregation on Arctic clouds, using the Norwegian Earth System Model, version 2 (NorESM2). Simulations with both prognostic and diagnostic PIP show that heterogeneous freezing alone cannot reproduce the observed cloud ice content. The implementation of missing SIP mechanisms (collisional breakup, drop shattering, and sublimation breakup) in NorESM2 improves the modeled ice properties, while improvements in liquid content occur only in simulations with prognostic PIP. However, results are sensitive to the description of collisional breakup. This mechanism, which dominates SIP in the examined conditions, is very sensitive to the treatment of the sublimation correction factor, a poorly constrained parameter that is included in the utilized parameterization. Finally, variations in ice aggregation treatment can also significantly impact cloud properties, mainly through their impact on collisional breakup efficiency. Overall, enhancement in ice production through the addition of SIP mechanisms and the reduction in ice aggregation (in line with radar observations of shallow Arctic clouds) result in enhanced cloud cover and decreased TOA radiation biases, compared to satellite measurements, especially during the cold months. Significance Statement Arctic clouds remain a large source of uncertainty in projections of the future climate due to the poor representation of the microphysical processes that govern their life cycle. Ice formation is among the least understood processes. While it is widely recognized that better constraints on primary ice production (PIP) are needed to improve existing parameterizations, we show that secondary ice production (SIP) and ice aggregation can have also a significant impact on ice number concentrations. Constraining ice formation through the addition of missing SIP mechanisms and reducing ice aggregation can improve the representation of the cloud macrophysical properties and enhance total cloud cover in the Arctic region, which in turn contributes to decreased TOA radiation biases in the cold months.

Meteorology & Atmospheric Sciences↗
DOE OSTI2020

Azimuthally averaged wind and thermodynamic structures of tropical cyclones in global climate models and their sensitivity to horizontal resolution

Characteristics of tropical cyclones (TCs) in global climate models (GCMs) are known to be influenced by details of the model configurations, including horizontal resolution and parameterization schemes. Understanding model-to-model differences in TC characteristics is a prerequisite for reducing uncertainty in future TC activity projections by GCMs. This study performs a process-level examination of TC structures in eight GCM simulations that span a range of horizontal resolutions from 1° to 0.25°. A recently developed set of process-oriented diagnostics is used to examine the azimuthally averaged wind and thermodynamic structures of the GCM-simulated TCs. Results indicate that the inner-core wind structures of simulated TCs are more strongly constrained by the horizontal resolutions of the models than are the thermodynamic structures of those TCs. As expected, the structures of TC circulations become more realistic with smaller horizontal grid spacing, such that the radii of maximum wind (RMW) become smaller, and the maximum vertical velocities occur off the center. However, the RMWs are still too large, especially at higher intensities, and there are rising motions occurring at the storm centers, inconsistently with observations. The distributions of precipitation, moisture, radiative and surface turbulent heat fluxes around TCs are diverse, even across models with similar horizontal resolutions. At the same horizontal resolution, models that produce greater rainfall in the inner-core regions tend to simulate stronger TCs. Further, when TCs are weak, the radial gradient of net column radiative flux convergence is comparable to that of surface turbulent heat fluxes, emphasizing the importance of cloud-radiative feedbacks during the early developmental phases of TCs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Direct and semi-direct radiative forcing of biomass-burning aerosols over the southeast Atlantic (SEA) and its sensitivity to absorbing properties: a regional climate modeling study

Simulations are performed for the period 2000-2015 by two different regional climate models, ALADIN and RegCM, to quantify the direct and semi-direct radiative effects of biomass-burning aerosols (BBAs) in the southeast Atlantic (SEA) region. Different simulations have been performed using strongly absorbing BBAs in accordance with recent in situ observations over the SEA. For the July-August-September (JAS) season, the single scattering albedo (SSA) and total aerosol optical depth (AOD) simulated by the ALADIN and RegCM models are consistent with the MACv2 climatology and MERRA-2 and CAMS-RA reanalyses near the biomass-burning emission sources. However, the above-cloud AOD is slightly underestimated compared to satellite (MODIS and POLDER) data during the transport over the SEA. The direct radiative effect exerted at the continental and oceanic surfaces by BBAs is significant in both models and the radiative effects at the top of the atmosphere indicate a remarkable regional contrast over SEA (in all-sky conditions), with a cooling (warming) north (south) of 10°S, which is in agreement with the recent MACv2 climatology. In addition, the two models indicate that BBAs are responsible for an important shortwave radiative heating of ~0.5-1K per day over SEA during JAS with maxima between 2 and 4 km a.m.s.l. (above mean sea level). At these altitudes, BBAs increase air temperature by ~0.2-0.5 K, with the highest values being co-located with low stratocumulus clouds. Vertical changes in air temperature limit the subsidence of air mass over SEA, creating a cyclonic anomaly. The opposite effect is simulated over the continent due to the increase in lower troposphere stability. The BBA semi-direct effect on the lower troposphere circulation is found to be consistent between the two models. Changes in the cloud fraction are moderate in response to the presence of smoke, and the models differ over the Gulf of Guinea. Finally, the results indicate an important sensitivity of the direct and semi-direct effects to the absorbing properties of BBAs. Over the stratocumulus (Sc) region, DRE varies from +0.94 W m -2 (scattering BBAs) to +3.93 W m -2 (most absorbing BBAs).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A new metrics framework for quantifying and intercomparing atmospheric rivers in observations, reanalyses, and climate models

We present a new atmospheric river (AR) analysis and benchmarking tool, namely Atmospheric River Metrics Package (ARMP). It includes a suite of new AR metrics that are designed for quick analysis of AR characteristics via statistics in gridded climate datasets such as model output and reanalysis. This package can be used for climate model evaluation in comparison with reanalysis and observational products. Integrated metrics such as mean bias and spatial pattern correlation are efficient for diagnosing systematic AR biases in climate models. For example, the package identifies the fact that, in CMIP5 and CMIP6 (Coupled Model Intercomparison Project Phases 5 and 6) models, AR tracks in the South Atlantic are positioned farther poleward compared to ERA5 reanalysis, while in the South Pacific, tracks are generally biased towards the Equator. For the landfalling AR peak season, we find that most climate models simulate a completely opposite seasonal cycle over western Africa. This tool can also be used for identifying and characterizing structural differences among different AR detectors (ARDTs). For example, ARs detected with the Mundhenk algorithm exhibit systematically larger size, width, and length compared to the TempestExtremes (TE) method. The AR metrics developed from this work can be routinely applied for model benchmarking and during the development cycle to trace performance evolution across model versions or generations and set objective targets for the improvement of models. They can also be used by operational centers to perform near-real-time climate and extreme event impact assessments as part of their forecast cycle.

58 GEOSCIENCES↗
DOE OSTI2023

Observed humidity trends in dry regions contradict climate models

Arid and semi-arid regions of the world are particularly vulnerable to greenhouse gas–driven hydroclimate change. Climate models are our primary tool for projecting the future hydroclimate that society in these regions must adapt to, but here, we present a concerning discrepancy between observed and model-based historical hydroclimate trends. Over the arid/semi-arid regions of the world, the predominant signal in all model simulations is an increase in atmospheric water vapor, on average, over the last four decades, in association with the increased water vapor–holding capacity of a warmer atmosphere. In observations, this increase in atmospheric water vapor has not happened, suggesting that the availability of moisture to satisfy the increased atmospheric demand is lower in reality than in models in arid/semi-arid regions. This discrepancy is most clear in locations that are arid/semi-arid year round, but it is also apparent in more humid regions during the most arid months of the year. Here, it indicates a major gap in our understanding and modeling capabilities which could have severe implications for hydroclimate projections, including fire hazard, moving forward.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

The Impact of Parameterized Lateral Mixing on the Antarctic Circumpolar Current in a Coupled Climate Model

This study examines the impact of changing the lateral diffusion coefficient ARedi on the transport of the Antarctic Circumpolar Current (ACC). The lateral diffusion coefficient ARedi is poorly constrained, with values ranging across an order of magnitude in climate models. The ACC is difficult to accurately simulate, and there is a large spread in eastward transport in the Southern Ocean (SO) in these models. This paper examines how much of that spread can be attributed to different eddy parameterization coefficients. A coarse-resolution, fully coupled model suite was run with A Redi = 400, 800, 1200, and 2400 m 2 s -1 . Additionally, two simulations were run with two-dimensional representations of the mixing coefficient based on satellite altimetry. Relative to the 400 m 2 s -1 case, the 2400 m 2 s -1 case exhibits 1) an 11% decrease in average wind stress from 50° to 65°S, 2) a 20% decrease in zonally averaged eastward transport in the SO, and 3) a 14% weaker transport through the Drake Passage. The decrease in transport is well explained by changes in the thermal current shear, largely due to increases in ocean density occurring on the northern side of the ACC. In intermediate waters these increases are associated with changes in the formation of intermediate waters in the North Pacific. We hypothesize that the deep increases are associated with changes in the wind stress curl allowing Antarctic Bottom Water to escape and flow northward.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A new spatially distributed added value index for regional climate models: the EURO-CORDEX and the CORDEX-CORE highest resolution ensembles

The added value of using regional climate models (RCMs) to downscale data from general circulation models (GCMs) has often been questioned and researched. Although several studies have used different methods to identify (and in some cases quantify) the added value, there is still a need to find a general metric that quantifies the added value of any variable. This paper builds on past studies to propose a new metric of added value in the simulation of present-day climate which measures the difference in the probability density functions (PDFs) at each grid-cell between a model and an observation source, and then compares the results of the RCM and GCM in order to spatially compute the added value index. The same method is also adapted to quantify the climate change downscaling signal in a way that is consistent with the present-day metric. These new metrics are tested on the daily precipitation output from the EURO-CORDEX and CORDEX-CORE projection ensembles and reveal an overall positive added value of RCMs, especially at the tail-end of the distribution. Higher added value is obtained in areas of complex topography and coast-lines, as well as in tropical regions. Areas with large added value in present-day climate are consistent with areas of significant climate change downscaling signal in the RCP 8.5 far future simulations, and when the analysis is repeated at a low-resolution. The use of different resolution observations shows that the added value tends to decrease when models are compared to low-resolution observation datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

MJO Teleconnections over the PNA Region in Climate Models. Part II: Impacts of the MJO and Basic State

In an assessment of 29 global climate models (GCMs), Part I of this study identified biases in boreal winter MJO teleconnections in anomalous 500-hPa geopotential height over the Pacific–North America (PNA) region that are common to many models: an eastward shift, a longer persistence, and a larger amplitude. In Part II, we explore the relationships of the teleconnection metrics developed in Part I with several existing and newly developed MJO and basic state (the mean subtropical westerly jet) metrics. The MJO and basic state diagnostics indicate that the MJO is generally weaker and less coherent and propagates faster in models compared to observations. The mean subtropical jet also exhibits notable biases such as too strong amplitude, excessive eastward extension, or southward shift. The following relationships are found to be robust among the models: 1) models with a faster MJO propagation tend to produce weaker teleconnections; 2) models with a less coherent eastward MJO propagation tend to simulate more persistent MJO teleconnections; 3) models with a stronger westerly jet produce stronger and eastward shifted MJO teleconnections; 4) models with an eastward extended jet produce an eastward shift in MJO teleconnections; and 5) models with a southward shifted jet produce stronger MJO teleconnections. The results are supported by linear baroclinic model experiments. Our results suggest that the larger amplitude and eastward shift biases in GCM MJO teleconnections can be attributed to the biases in the westerly jet, and that the longer persistence bias is likely due to the lack of coherent eastward MJO propagation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Implementing and Improving CBMZ-MAM3 Chemistry and Aerosol Modules in the Regional Climate Model WRF-CAM5: An Evaluation over the Western US and Eastern North Pacific

The representation of aerosols in climate-chemistry models is important for air quality and climate change research, but it can require significant computational resources. The objective of this study was to improve the representation of aerosols in climate–chemistry models, specifically in the carbon bond mechanism, version Z (CBMZ), and modal aerosol modules with three lognormal modes (MAM3) in the WRF-CAM5 model. The study aimed to enhance the model’s chemistry capabilities by incorporating biomass burning emissions, establishing a conversion mechanism between volatile organic compounds (VOCs) and secondary organic carbons (SOCs), and evaluating its performance against observational benchmarks. The results of the study demonstrated the effectiveness of the enhanced chemistry capabilities in the WRF-CAM5 model. Six simulations were conducted over the western U.S. and northeastern Pacific region, comparing the model’s performance with observational benchmarks such as reanalysis, ground-based, and satellite data. The findings revealed a significant reduction in root-mean-square errors (RMSE) for surface concentrations of black carbon (BC) and organic carbon (OC). Specifically, the model exhibited a 31% reduction in RMSE for BC concentrations and a 58% reduction in RMSE for OC concentrations. These outcomes underscored the importance of accurate aerosol representation in climate-chemistry models and emphasized the potential for improving simulation accuracy and reducing errors through the incorporation of enhanced chemistry modules in such models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

How Do Climate Model Resolution and Atmospheric Moisture Affect the Simulation of Unprecedented Extreme Events Like the 2021 Western North American Heat Wave?

Abstract Although the 2021 Western North America (WNA) heat wave was predicted by weather forecast models, questions remain about whether such strong events can be simulated by global climate models (GCMs) at different model resolutions. Here, we analyze sets of GCM simulations including historical and future periods to check for the occurrence of similar events. High‐ and low‐resolution simulations both encounter challenges in reproducing events as extreme as the observed one, particularly under the present climate. Relatively stronger amplitudes are observed during the future periods. Furthermore, high‐ and low‐resolution short initialized GCM simulations are both able to reasonably predict such strong events and their associated high‐pressure ridge over the WNA with a 1 week forecast lead time. Moisture sensitivity experiments further indicate a drier atmospheric moisture condition results in substantially higher near‐surface temperatures in the simulated heat events.

54 ENVIRONMENTAL SCIENCES↗