Journal of Advances in Modeling Earth Systems最新文献

{"title":"Simulating the Effects of Aerosol-Radiation Interactions on Subseasonal Prediction Using the Coupled Unified Forecast System and CCPP-Chem: Interactive Aerosol Module Versus Prescribed Aerosol Climatology","authors":"S. Sun,&nbsp;G. A. Grell,&nbsp;L. Zhang,&nbsp;J. K. Henderson,&nbsp;S. Wang,&nbsp;D. Heinzeller,&nbsp;H. Li,&nbsp;J. Meixner,&nbsp;P. S. Bhattacharjee","doi":"10.1029/2024MS004392","DOIUrl":"https://doi.org/10.1029/2024MS004392","url":null,"abstract":"<p>This study investigates the effects of aerosol-radiation interactions on subseasonal prediction using the Unified Forecast System, which includes atmosphere, ocean, sea ice, and wave components, coupled with an aerosol module. The aerosol module is from the current NOAA operational GEFSv12-Aerosols model, which is based on the WRF-Chem GOCART with updates to the dust scheme and the biomass burning plume rise module. It simulates five aerosol species: sulfate, dust, black carbon, organic carbon, and sea salt. The modeled aerosol optical depth (AOD) is compared to MERRA-2 reanalysis, MODIS satellite retrievals, and ATom aircraft measurements. Despite biases primarily in dust and sea salt, the AOD shows good agreement globally. The simulated radiative forcing (RF) at the top of the atmosphere (TOA) from the total aerosols is approximately −2.6 W/m² or −16 W/m² per unit AOD globally. In subsequent simulations, the prognostic aerosol module is replaced with climatological aerosol concentrations derived from the preceding experiments. While regional differences in RF at TOA between these two experiments are noticeable in specific events, the multi-year subseasonal simulations reveal consistent patterns in RF at TOA, surface temperature, geopotential height at 500 hPa, and precipitation. These results suggest that given the current capacities of aerosol modeling, adopting a climatology of aerosol concentrations as a cost-effective alternative to a complex aerosol module may be a practical approach for subseasonal applications.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 7","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004392","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chasing Ice Crystals: Interlinking Cloud Microphysics and Dynamics in Cloud Seeding Plumes With Lagrangian Trajectories","authors":"N. Omanovic,&nbsp;S. Ferrachat,&nbsp;C. Fuchs,&nbsp;F. Ramelli,&nbsp;J. Henneberger,&nbsp;A. J. Miller,&nbsp;R. Spirig,&nbsp;H. Zhang,&nbsp;U. Lohmann","doi":"10.1029/2025MS005016","DOIUrl":"https://doi.org/10.1029/2025MS005016","url":null,"abstract":"<p>The ice phase is a major contributor to precipitation formation over continents due to its efficiency in growing hydrometeors to large enough sizes for sedimentation. One prominent growth mechanism is the vapor deposition onto ice crystals. However, its actual growth rates remain ambiguous. In the CLOUDLAB project, we conducted field experiments in supercooled clouds with the goal to infer ice crystal growth rates through local perturbations from cloud seeding. In this study, we combine a high-resolution model setup of 65 m with Lagrangian trajectories to achieve a more straightforward comparison to the observations. We first show that the chosen field experiments can be reproduced in the model in terms of ice crystal number concentration. Second, we perform a series of sensitivity studies by perturbing two parameters in the vapor depositional growth equation. The goal is to understand what change is needed to achieve an agreement between simulated and observed ice crystal growth rates since the default model configuration fails to do so. Increasing the vapor deposition efficiency by a factor of up to three yields comparable growth rates to the observations. Last, we try to quantify the different contributions to the vertical motions within the seeding plume, such as the large-scale forcing, the underlying topography, and latent heat release upon ice nucleation and growth. We show the different factors are superposed with the large-scale forcing being a dominant factor. The Lagrangian trajectories proved to be crucial to bridge dynamics and cloud microphysical processes.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 7","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025MS005016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved Representations of Land-Atmosphere Interactions Over the Continental U.S. Through Dynamic Root Modeling","authors":"Zhao Yang,&nbsp;Guo-Yue Niu,&nbsp;Yun Qian,&nbsp;Larry K. Berg,&nbsp;Jerome Fast,&nbsp;Colleen M. Kaul,&nbsp;Jingyi Chen,&nbsp;Koichi Sakaguchi,&nbsp;Sheng-Lun Tai,&nbsp;Brian Gaudet,&nbsp;Ye Liu,&nbsp;Heng Xiao","doi":"10.1029/2024MS004474","DOIUrl":"https://doi.org/10.1029/2024MS004474","url":null,"abstract":"<p>Previous studies have identified the oversimplified root system representation as a key factor leading to inaccuracies in vegetation-atmosphere feedbacks. In this study, a dynamic root water uptake scheme in the Noah-MP land surface model has been coupled to the Weather Research and Forecasting (WRF) model to investigate its impact on the surface hydroclimate variables and land-atmosphere interactions. To evaluate the impact of the dynamic root, two coupled simulations were conducted, one with the dynamic root water uptake scheme (DynRt) and one with the static root water uptake scheme (StcRt), which is based on the default root representation in Noah-MP, with slight modifications, primarily in vegetation-related parameters. Both DynRt and StcRt simulations were conducted with a small ensemble of three members to account for variations in physical parameterizations, initial and boundary forcing and model setup. When compared with reference data sets, the DynRt simulations show improved results than the StcRt simulations, reducing biases in the simulated leaf area index, surface energy fluxes, soil moisture and precipitation. Two different mechanisms through which roots affect land-atmosphere coupling have been identified. Over the transitional climate zone between the dry and wet climate, the dynamic root scheme affects surface climate and land-atmosphere coupling mainly through changes in soil moisture through hydraulic redistribution by plant root system. Over the energy-limited mesic zone, the dynamic root affects regional land-atmosphere coupling mainly through changes in carbon allocation. This work highlights the importance of dynamic root representation in improving vegetation-atmosphere simulations by enhancing predictions of water, energy, and carbon fluxes.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 7","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004474","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Model-Independent Strategy for the Targeted Observation Analysis and Its Application in ENSO Prediction","authors":"Weixun Rao,&nbsp;Youmin Tang,&nbsp;Yanling Wu,&nbsp;Xiaojing Li","doi":"10.1029/2024MS004742","DOIUrl":"https://doi.org/10.1029/2024MS004742","url":null,"abstract":"<p>The model-dependency has been a challenging issue for traditional data assimilation-based targeted observational method. This study developed a new strategy to address this challenge using multiple-model prediction ensemble. It was found that while the ensemble size reaches a sufficiently large number the optimal observational sites detected tend to stable and model-independent. This new finding answers the long-standing challenge question on the model dependence in targeted observational analysis, offering an efficient and objective way to identify optimal observational sites. With this strategy, we designed an optimal observational array in the tropical Pacific for the El Niño-Southern Oscillation (ENSO) prediction using the multiple historical simulation data sets from Coupled Model Intercomparison Project Phase 6 and reanalysis data sets. Sensitive experiments show that while number of data sets reaches 12, a robust optimal observational array is obtained. The first 10 optimal observational sites, mostly located in the equatorial central eastern Pacific, can reduce initial uncertainties by 67%. This was further confirmed by the observation system simulation experiments, which is implemented by the Ensemble Adjustment Kalman Filter assimilation system developed in the Community Earth System Model. This newly developed model-independent strategy makes it feasible to design a robust oceanic observational network for ENSO prediction even using the current targeted observational algorithm, well serving the goal of international Tropical Pacific Observation System 2020 project.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 7","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004742","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Turbulence Representation on the Relationship Between Cloud Feedback and Aerosol-Cloud Interaction in an E3SMv2 Perturbed Parameter Ensemble","authors":"Yi Qin,&nbsp;Po-Lun Ma,&nbsp;Mark D. Zelinka,&nbsp;Stephen A. Klein,&nbsp;Tao Zhang,&nbsp;Xue Zheng,&nbsp;Vincent E. Larson,&nbsp;Meng Huang","doi":"10.1029/2024MS004756","DOIUrl":"https://doi.org/10.1029/2024MS004756","url":null,"abstract":"<p>Recent studies reveal an anti-correlation between global cloud feedback (CF) and effective radiative forcing due to aerosol-cloud interaction (ERFaci) in Earth system models, but the physical mechanisms underlying it remain uncertain. Here we investigate how different turbulence representations contribute to this relationship over the global ocean using an ensemble of Energy Exascale Earth System Model version 2 simulations with perturbed turbulence parameters. The anti-correlation appears only in the tropical ascent regime. In the Northern Hemisphere midlatitude and high latitude regimes, there is no significant correlation, and in the tropical marine low cloud and Southern Ocean regimes, the correlation is positive. These opposite correlations are primarily driven by opposing CF responses to perturbed parameters. We find that the mean-state turbulent mixing strength affects both CF and ERFaci, enabling strong correlations in certain regimes. This study highlights the complex linkages between CF and ERFaci through turbulent processes across diverse cloud regimes.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004756","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resolving Low Cloud Feedbacks Globally With E3SM High-Res MMF: Agreement With LES but Stronger Shortwave Effects","authors":"Liran Peng,&nbsp;Peter N. Blossey,&nbsp;Walter M. Hannah,&nbsp;Christopher S. Bretherton,&nbsp;Christopher R. Terai,&nbsp;Andrea M. Jenney,&nbsp;Savannah L. Ferretti,&nbsp;Hossein Parishani,&nbsp;Michael S. Pritchard","doi":"10.1029/2025MS005003","DOIUrl":"https://doi.org/10.1029/2025MS005003","url":null,"abstract":"<p>This study investigates low cloud feedback in a warmer climate using global simulations from the High-Resolution Multi-scale Modeling Framework (HR-MMF), which explicitly simulates small-scale eddies globally. Two 5-year simulations—one with present-day sea surface temperatures (SSTs) and a second with SSTs warmed uniformly by 4 K—reveal a positive global shortwave cloud radiative effect (SWCRE = 0.3 W/<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mi>m</mi>\u0000 <mn>2</mn>\u0000 </msup>\u0000 </mrow>\u0000 <annotation> ${mathrm{m}}^{2}$</annotation>\u0000 </semantics></math>/K), comparable to estimates from CMIP models. As the climate warms, significant reductions in low cloud cover occur over stratocumulus regions. This study is the first attempt to compare HR-MMF results with predictions from idealized large-eddy simulations from the CGILS intercomparison. Despite different underlying assumptions, we find qualitative agreement in SWCRE and inversion height changes between HR-MMF and CGILS predictions. This suggests reasonable credibility for the CGILS framework in predicting cloud responses under the out-of-sample conditions found in HR-MMF. However, the HR-MMF exhibits stronger SWCRE changes than predicted by CGILS. We explore potential causes for this discrepancy, examining variations in cloud-controlling factors (CCFs) and cloud conditions. Our results show a fairly homogeneous SWCRE response, with little systematic variation tied to the variations in CCFs. This reveals a dominant role for SST forcing in modulating SWCRE.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025MS005003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"E3SM-Arctic: Regionally Refined Coupled Model for Advanced Understanding of Arctic Systems Interactions","authors":"Yiling Huo,&nbsp;Hailong Wang,&nbsp;Milena Veneziani,&nbsp;Darin Comeau,&nbsp;Robert Osinski,&nbsp;Benjamin R. Hillman,&nbsp;Erika Roesler,&nbsp;Wieslaw Maslowski,&nbsp;Philip J. Rasch,&nbsp;Wilbert Weijer,&nbsp;Ian Baxter,&nbsp;Qiang Fu,&nbsp;Oluwayemi A. Garuba,&nbsp;Weiming Ma,&nbsp;Mark W. Seefeldt,&nbsp;Aodhan Sweeney,&nbsp;Mingxuan Wu,&nbsp;Jing Zhang,&nbsp;Xiangdong Zhang,&nbsp;Yu Zhang,&nbsp;Xylar Asay-Davis,&nbsp;Anthony P. Craig,&nbsp;Younjoo J. Lee,&nbsp;Wuyin Lin,&nbsp;Andrew F. Roberts,&nbsp;Jonathan D. Wolfe,&nbsp;Shixuan Zhang","doi":"10.1029/2024MS004726","DOIUrl":"https://doi.org/10.1029/2024MS004726","url":null,"abstract":"<p>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.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004726","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inferring Parameters in a Complex Land Surface Model by Combining Data Assimilation and Machine Learning","authors":"L. T. Keetz,&nbsp;K. Aalstad,&nbsp;R. A. Fisher,&nbsp;C. Poppe Terán,&nbsp;B. Naz,&nbsp;N. Pirk,&nbsp;Y. A. Yilmaz,&nbsp;O. Skarpaas","doi":"10.1029/2024MS004542","DOIUrl":"https://doi.org/10.1029/2024MS004542","url":null,"abstract":"<p>Complex Land Surface Models (LSMs) rely on a plethora of parameters. These parameters and the associated process formulations are often poorly constrained, which hampers reliable predictions of ecosystem dynamics and climate feedbacks. Robust and uncertainty-aware parameter estimation with observations is complicated by, for example, the high dimensionality of the model parameter space and the computational cost of LSM simulations. Herein, we adapt a novel Bayesian data assimilation (DA) and machine learning framework termed “calibrate, emulate, sample” (CES) to infer parameters in a widely-used LSM coupled with a demographic vegetation model (CLM-FATES). First, an iterative ensemble Kalman smoother provides an initial estimate of the posterior distribution (“calibrate”). Subsequently, a machine-learning-based emulator is trained on the resulting model-observation mismatches to predict outcomes for unseen parameter combinations (“emulate”). Finally, this emulator replaces CLM-FATES simulations in an adaptive Markov Chain Monte Carlo approach enabling computationally feasible posterior sampling with enhanced uncertainty quantification (“sample”). We test our implementation with synthetic and real observations representing a boreal forest site in southern Finland. We estimate a total of six plant-functional-type-specific photosynthetic parameters by assimilating evapotranspiration (ET) and gross primary production (GPP) flux data. CES provided the best estimates of the synthetic truth parameters when compared to data-blind emulator sampling designs while all approaches reduced model-observation errors compared to a default parameter simulation (GPP: <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>10</mn>\u0000 </mrow>\u0000 <annotation> ${-}10$</annotation>\u0000 </semantics></math>% to <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>30</mn>\u0000 </mrow>\u0000 <annotation> ${-}30$</annotation>\u0000 </semantics></math>%, ET: <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>4</mn>\u0000 </mrow>\u0000 <annotation> ${-}4$</annotation>\u0000 </semantics></math>% to <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>6</mn>\u0000 </mrow>\u0000 <annotation> ${-}6$</annotation>\u0000 </semantics></math>%). Although errors were also consistently reduced with real data, comparing the emulator designs was less conclusive, which we mainly attribute to equifinality, structural uncertainty within CLM-FATES, and/or unknown errors in the data that are not accounted for.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004542","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144332015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"E3SM-GCAM: A Synchronously Coupled Human Component in the E3SM Earth System Model Enables Novel Human-Earth Feedback Research","authors":"Alan V. Di Vittorio,&nbsp;Eva Sinha,&nbsp;Dalei Hao,&nbsp;Balwinder Singh,&nbsp;Katherine V. Calvin,&nbsp;Tim Shippert,&nbsp;Pralit Patel,&nbsp;Ben Bond-Lamberty","doi":"10.1029/2024MS004806","DOIUrl":"https://doi.org/10.1029/2024MS004806","url":null,"abstract":"<p>Modeling human-environment feedbacks is critical for assessing the effectiveness of climate change mitigation and adaptation strategies under a changing climate. The Energy Exascale Earth System Model (E3SM) now includes a human component, with the Global Change Analysis Model (GCAM) at its core, that is synchronously coupled with the land and atmosphere components through the E3SM coupling software. Terrestrial productivity is passed from E3SM to GCAM to make climate-responsive land use and CO₂ emission projections for the next 5-year period, which are interpolated and passed to E3SM annually. Key variables affected by the incorporation of these feedbacks include land use/cover change, crop prices, terrestrial carbon, local surface temperature, and climate extremes. Regional differences are more pronounced than global differences because the effects are driven primarily by differences in land use. This novel system enables a new type of scenario development and provides a powerful modeling framework that facilitates the addition of other feedbacks between these models. This system has the potential to explore how human responses to climate change impacts in a variety of sectors, including heating/cooling energy demand, water management, and energy production, may alter emissions trajectories and Earth system changes.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004806","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Key Gaps in Models' Physical Representation of Climate Intervention and Its Impacts","authors":"Sebastian D. Eastham,&nbsp;Amy H. Butler,&nbsp;Sarah J. Doherty,&nbsp;Blaž Gasparini,&nbsp;Simone Tilmes,&nbsp;Ewa M. Bednarz,&nbsp;Ulrike Burkhardt,&nbsp;Gabriel Chiodo,&nbsp;Daniel J. Cziczo,&nbsp;Michael S. Diamond,&nbsp;David W. Keith,&nbsp;Thomas Leisner,&nbsp;Douglas G. MacMartin,&nbsp;Johannes Quaas,&nbsp;Philip J. Rasch,&nbsp;Odran Sourdeval,&nbsp;Isabelle Steinke,&nbsp;Chelsea Thompson,&nbsp;Daniele Visioni,&nbsp;Robert Wood,&nbsp;Lili Xia,&nbsp;Pengfei Yu","doi":"10.1029/2024MS004872","DOIUrl":"https://doi.org/10.1029/2024MS004872","url":null,"abstract":"<p>Solar radiation modification (SRM) is increasingly discussed as a potential method to ameliorate some negative effects of climate change. However, unquantified uncertainties in physical and environmental impacts of SRM impede informed debate and decision making. Some uncertainties are due to lack of understanding of processes determining atmospheric effects of SRM and/or a lag in development of their representation in models, meaning even high-quality model intercomparisons will not necessarily reveal or address them. Although climate models at multiple scales are advancing in complexity, there are specific areas of uncertainty where additional model development (often requiring new observations) could significantly advance understanding of SRM's effects, and improve our ability to assess and weigh potential risks against those of choosing to not use SRM. We convene expert panels in the areas of atmospheric science most critical to understanding the three most widely discussed forms of SRM. Each identifies three key modeling gaps relevant to either stratospheric aerosols, cirrus, or low-altitude marine clouds. Within each area, key challenges remain in capturing impacts due to complex interactions in aerosol physics, atmospheric chemistry/dynamics, and aerosol-cloud interactions. Across all three, in addition to arguing for more observations, the panels argue that model development work to either leverage different capabilities of existing models, bridge scales across which relevant processes operate, or address known modeling gaps could advance understanding. By focusing on these knowledge gaps we believe the modeling community could advance understanding of SRM's physical risks and potential benefits, allowing better-informed decision-making about whether and how to use SRM.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004872","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}