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Explicit representation of soil microbial processes and interactions with biotic and abiotic processes in Earth System Models (ESMs) remains limited, despite their importance in biogeochemical cycles. To address this gap, which hinders prediction of global biogeochemial cycling and responses to atmospheric conditions, we integrated a microbe‐ and mineral‐surface‐explicit model, the Reaction‐network‐based model of soil organic matter and Microbes (ReSOM), into the Energy Exascale ESM (E3SM) land model (ELM). Here, we describe ELM‐ReSOM and show a case study at a conifer forest in California. ELM‐ReSOM accurately simulated surface CO2 fluxes and SOM stocks, demonstrating improved representations of microbial and mineral interactions compared to the default ELM. We examined ELM‐ReSOM sensitivity to microbial traits, enzyme properties, and organo‐mineral interactions. Microbial traits such as the maximum mortality rate, transporter‐density scaling factor, and maximum monomer assimilation rate were strong controllers of heterotrophic respiration, while these microbial traits and enzyme‐related properties collectively influenced SOM stocks. Mineral surfaces primarily affected SOM stocks by adsorbing enzymes, thereby limiting depolymerization. Synergies among processes led to stronger impacts of parameters when evaluated together versus separately (i.e., most parameters had greater indirect than direct effects). For example, due to interactions of microbial necromass with mineral surface adsorption, the indirect effect of the maximum microbial mortality rate was 33% larger than its direct effect on SOM stock. Thus, microbial and enzyme dynamics and their interactions with mineral surfaces play critical roles in SOM cycling. Tackling the challenges of microbe‐explicit models will advance understanding and modeling of SOM dynamics. Plain Language Summary Soils store a vast amount of carbon in organic matter. The activity of soil microbes drives how carbon is released or stored under changing environmental conditions. We introduce E3SM Land Model‐Reaction‐network‐based model of Soil Organic Matter and Microbes (ELM‐ReSOM), a model designed to explicitly represent soil microbial processes and their interactions with soil minerals. By incorporating these detailed mechanisms, ELM‐ReSOM provides accurate predictions of soil carbon and surface CO2 fluxes at a California forest site. Results showed the critical influence of microbial traits like growth rates and enzyme activity in controlling soil carbon cycling. The study also reveals that interactions among microbial processes and soil minerals have a larger effect than do individual traits on soil carbon storage. These findings improve understanding of the mechanisms driving soil carbon responses to environmental change and provide a model foundation for better global climate predictions. Key Points E3SM Land Model‐Reaction‐network‐based model of Soil Organic Matter and Microbes (ReSOM) accurately simulates CO2 fluxes and soil organic matter (SOM) stocks by incorporating explicit microbial and mineral‐surface interaction processes Microbial traits strongly influence heterotrophic respiration, and both microbial traits and enzyme properties drive SOM stock dynamics Synergistic interactions among processes dominate parameter total effects, highlighting the role of interactions in SOM cycling dynamics

Accurate representation of groundwater table depth (GWTD) is crucial for simulating hydrological cycling in Earth system models (ESM). Nevertheless, there is a notable gap in the literature regarding the validation of GWTD simulations in ESMs and their subsequent impact on downstream hydrological components. This study explores the calibration of parameterization of global GWTD using machine learning within the Energy Exascale Earth System Model (E3SM) Land Model (ELM). Despite achieving significant gains in simulating GWTD through calibration, offline ELM simulations unexpectedly show that these improvements do not translate to substantial enhancements in model performance for other key hydrological variables, including soil moisture (SM), runoff, groundwater contribution to runoff or base flow index (BFI), and evapotranspiration and its partitioning. The performance in SM and runoff was even degraded in some regions, while BFI was mostly overestimated. Although there is significant improvement in GWTD within the critical range of 1–5 m, where groundwater traditionally influences land surface energy fluxes, these improvements occurred mostly in humid areas where the impact of GWTD on surface processes is minimal. Although the impacts of model calibration are generally small in offline ELM simulations, coupled land‐atmosphere simulations exhibit much stronger responses to GWTD calibration, highlighting the role of land‐atmosphere feedbacks in Earth system modeling. These findings underscore the need for integrated calibration strategies that simultaneously optimize multiple hydrological variables. However, if a single‐variable approach is necessary, it is crucial to establish clear priorities for calibration, identifying the most critical variables that have the greatest impact on overall model performance. Plain Language Summary Accurately modeling the groundwater table depths is crucial for understanding how water moves through the environment. However, there's a research gap in evaluating how well the Earth system models perform in water table depths and how they affect other important hydrological variables like SM and runoff. In this study, machine learning was used to improve the modeling of groundwater table depth in an Earth System Model. While the improvements in water table depth were notable, they didn't consistently improve other variables. In some cases, the model's performance even worsened for certain variables. However, the improvements had a much greater impact in the coupled land‐atmosphere simulations. This suggests that feedback between land and atmosphere is important for capturing the full impact of groundwater on the climate system. Overall, the study underscores the need for a more holistic approach that considers multiple hydrological variables, rather than focusing on a single target at a time. Key Points Machine learning techniques can be used to effectively calibrate groundwater table depth (GWTD) in the E3SM Land Model Enhancements in GWTD calibration do not significantly improve surface runoff and energy fluxes in offline simulations The amplified effects of GWTD calibration in coupled versus offline simulations underscore the importance of coupled model calibration

Earth system models are essential tools for climate projections, but coarse resolutions limit regional accuracy, especially in the Arctic. Regionally refined meshes (RRMs) enhance resolution in key areas while maintaining computational efficiency. This paper provides an overview of the United States (U.S.) Department of Energy's (DOE's) Energy Exascale Earth System Model version 2.1 with an Arctic RRM, hereafter referred to as E3SMv2.1‐Arctic, for the atmosphere (25 km), land (25 km), and ocean/ice (10 km) components. We evaluate the atmospheric component and its interactions with land, ocean, and cryosphere by comparing the RRM (E3SM2.1‐Arctic) historical simulations (1950–2014) with the uniform low‐resolution (LR) counterpart, reanalysis products, and observational data sets. The RRM generally reduces biases in the LR model, improving simulations of Arctic large‐scale mean fields, such as precipitation, atmospheric circulation, clouds, atmospheric river frequency, and sea ice thickness. However, it introduces a seasonally dependent surface air temperature bias, reducing the LR cold bias in summer but enhancing the LR warm bias in winter, which contributes to the underestimated winter sea ice area and volume. Radiative feedback analysis shows similar climate feedback strengths in both model configurations, with the RRM exhibiting a more positive surface albedo feedback and contributing to a stronger surface warming than LR. These findings underscore the importance of high‐resolution modeling for advancing our understanding of Arctic climate changes and their broader global impacts, although some persistent biases appear to be independent of model resolution at 10–100 km scales. Plain Language Summary Earth system models (ESMs) are essential tools for understanding the climate system and projecting future changes, but standard coarse model resolutions often fail to realistically represent regional processes and topography, particularly in the polar regions, while uniform high‐resolution grids are too computationally expensive. To address this, regionally refined meshes (RRMs) have been developed within ESMs to provide high‐resolution simulations in target areas, improving the accuracy of regional climate simulations while maintaining computational efficiency. This study looks at how well the U.S. DOE's RRM model, E3SMv2.1‐Arctic, performs in simulating Arctic climate. The RRM, which focuses on the Arctic region, is compared to a uniform low‐resolution version, as well as to observations and reanalysis data. The RRM does a better job of simulating important Arctic climate features like precipitation, atmospheric circulation, clouds, sea ice and atmospheric rivers compared to the low‐resolution model. However, it shows some seasonal temperature biases, reducing the cold bias in summer but increasing the warm bias in winter. The RRM also underestimates winter sea ice, consistent with the warm winter bias. While the study demonstrates the advantages of using high‐resolution models to better understand Arctic climate changes, it also notes that some biases remain despite the increased resolution. Key Points Evaluation of atmosphere‐land‐ocean‐ice fully coupled E3SM‐Arctic historical simulations with regionally refined meshes for the Arctic E3SM‐Arctic reduces bias in precipitation, clouds and atmospheric rivers due to better topography compared to its low‐resolution counterpart Higher resolution leads to an increased albedo feedback and more accurate sea ice area, but with faster sea ice melting due to a warm bias

Abstract For decades, Arctic temperatures have increased twice as fast as average global temperatures. As a first step toward quantifying parametric uncertainty in Arctic climate, we performed a variance‐based global sensitivity analysis (GSA) using a fully coupled, ultra‐low resolution (ULR) configuration of version 1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SMv1). Specifically, we quantified the sensitivity of six quantities of interests (QOIs), which characterize changes in Arctic climate over a 75 year period, to uncertainties in nine model parameters spanning the sea ice, atmosphere, and ocean components of E3SMv1. Sensitivity indices for each QOI were computed with a Gaussian process emulator using 139 random realizations of the random parameters and fixed preindustrial forcing. Uncertainties in the atmospheric parameters in the Cloud Layers Unified by Binormals (CLUBB) scheme were found to have the most impact on sea ice status and the larger Arctic climate. Our results demonstrate the importance of conducting sensitivity analyses with fully coupled climate models. The ULR configuration makes such studies computationally feasible today due to its low computational cost. When advances in computational power and modeling algorithms enable the tractable use of higher‐resolution models, our results will provide a baseline that can quantify the impact of model resolution on the accuracy of sensitivity indices. Moreover, the confidence intervals provided by our study, which we used to quantify the impact of the number of model evaluations on the accuracy of sensitivity estimates, have the potential to inform the computational resources needed for future sensitivity studies.

The characteristics of the wintertime Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) and their impacts on climate variability over the Northern Hemisphere are important metrics for evaluating a climate system model. Observational analyses reveal that the horizontal and vertical structures in the AO and NAO exhibit a meridional dipole and a large-scale barotropic pattern between the Arctic and mid-latitudes. Historical model simulations from the Energy Exascale Earth System Model (E3SM-HIST) are used to identify how well it captures these major climate modes. It is found that the simulated AO and NAO modes have spatial structures similar to the observed features. In addition, the observed frequency bands in the AO and NAO-related time variability are captured well in the E3SM-HIST simulation. Associated with the positive phase in wintertime AO and NAO, zonal flow and warm advection in mid-latitude continents are enhanced, along with stronger cold flow from enhanced northerly winds over high latitudes. These features are linked to the atmospheric circulation pattern reflected by lower SLP anomalies over the Arctic and higher SLP anomalies over the mid‐latitudes. In E3SM-HIST, these spatial associations and main structural features are analogous to those in observations. In the time-height evolution related to winter AO and NAO modes, it can also be seen that the simulations reproduce the downward propagating patterns in observations. Nevertheless, the vertical structures associated with AO and NAO in E3SM-HIST exhibit substantial biases in the lower stratosphere. The cause of these stratospheric biases is investigated using the strength of climatological stratospheric polar vortex (SPV) and wave activity fluxes. The results herein suggest that E3SM-HIST has a reasonable skill in reproducing the observed characteristics related to the winter AO and NAO, although there exist systematic biases in the associated climate variability.

For decades, Arctic temperatures have increased twice as fast as average global temperatures. As a first step toward quantifying parametric uncertainty in Arctic climate, we performed a variance‐based global sensitivity analysis (GSA) using a fully coupled, ultra‐low resolution (ULR) configuration of version 1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SMv1). Specifically, we quantified the sensitivity of six quantities of interests (QOIs), which characterize changes in Arctic climate over a 75 year period, to uncertainties in nine model parameters spanning the sea ice, atmosphere, and ocean components of E3SMv1. Sensitivity indices for each QOI were computed with a Gaussian process emulator using 139 random realizations of the random parameters and fixed preindustrial forcing. Uncertainties in the atmospheric parameters in the Cloud Layers Unified by Binormals (CLUBB) scheme were found to have the most impact on sea ice status and the larger Arctic climate. Our results demonstrate the importance of conducting sensitivity analyses with fully coupled climate models. The ULR configuration makes such studies computationally feasible today due to its low computational cost. When advances in computational power and modeling algorithms enable the tractable use of higher‐resolution models, our results will provide a baseline that can quantify the impact of model resolution on the accuracy of sensitivity indices. Moreover, the confidence intervals provided by our study, which we used to quantify the impact of the number of model evaluations on the accuracy of sensitivity estimates, have the potential to inform the computational resources needed for future sensitivity studies. Plain Language Summary Feedbacks associated with Arctic warming are consequential for both the region and the strongly coupled global climate system. To assess the variability of the impacts of global warming and associated feedbacks in model‐based predictions, we quantified the sensitivity of the Arctic climate state to nine uncertain variables parameterizing the U.S. Department of Energy's global climate model known as the Energy Exascale Earth System Model (E3SM). Because the computational cost of repeatedly running high‐resolution configurations of E3SM was prohibitive, we used an ultra‐low resolution (ULR) configuration as a physics‐based surrogate for sensitivity analysis. Our first ever global sensitivity study of version 1 of the E3SM identified that the atmospheric parameters in E3SM's cloud physics model had the most impact on the atmosphere, sea ice, and ocean quantities of interest. This result demonstrates the importance of fully coupled climate analyses, which are necessary to identify such cross‐component influences. While we constructed confidence intervals that quantify the error in our estimates of parameter sensitivity introduced by using a limited number of ULR E3SM model runs, future investigation is needed to quantify the impact of resolution on error. Key Points We perform the first global sensitivity analysis using the fully coupled ultra‐low resolution Energy Exascale Earth System Model Uncertainty in cloud physics parameters is found to most greatly impact Arctic climate predictions Our inferred quantity of interest parameter correlations uncover key physical feedbacks and can guide model tuning