Journal of Advances in Modeling Earth Systems最新文献

{"title":"The Energy Exascale Earth System Model Version 3: 2. Overview of the Coupled System","authors":"Jean-Christophe Golaz,&nbsp;Wuyin Lin,&nbsp;Xue Zheng,&nbsp;Shaocheng Xie,&nbsp;Andrew F. Roberts,&nbsp;Luke P. Van Roekel,&nbsp;Peter E. Thornton,&nbsp;Alice M. Barthel,&nbsp;Andrew M. Bradley,&nbsp;Jonathan D. Wolfe,&nbsp;Chengzhu Zhang,&nbsp;Kai Zhang,&nbsp;Shixuan Zhang,&nbsp;Xylar S. Asay-Davis,&nbsp;Carolyn B. Begeman,&nbsp;Gautam Bisht,&nbsp;Susannah M. Burrows,&nbsp;Chih-Chieh-Jack Chen,&nbsp;Yan Feng,&nbsp;Elizabeth C. Hunke,&nbsp;Robert L. Jacob,&nbsp;Ziming Ke,&nbsp;Salil Mahajan,&nbsp;Naser G. Mahfouz,&nbsp;Mathew E. Maltrud,&nbsp;Xiaoying Shi,&nbsp;Qi Tang,&nbsp;Christopher R. Terai,&nbsp;Erin E. Thomas,&nbsp;Hailong Wang,&nbsp;Jinbo Xie,&nbsp;Tian Zhou,&nbsp;Tony Bartoletti,&nbsp;James J. Benedict,&nbsp;Michael A. Brunke,&nbsp;Darin S. Comeau,&nbsp;Jiwen Fan,&nbsp;Ryan M. Forsyth,&nbsp;James G. Foucar,&nbsp;Oksana Guba,&nbsp;Walter M. Hannah,&nbsp;Dalei Hao,&nbsp;Xianglei Huang,&nbsp;Nicole Jeffery,&nbsp;Hyun-Gyu Kang,&nbsp;Noel D. Keen,&nbsp;Hsiang-He Lee,&nbsp;Jiwoo Lee,&nbsp;Xiaohong Liu,&nbsp;Azamat Mametjanov,&nbsp;Johannes Mülmenstädt,&nbsp;Mark R. Petersen,&nbsp;Michael J. Prather,&nbsp;Stephen F. Price,&nbsp;Yun Qian,&nbsp;Andrew G. Salinger,&nbsp;Sean P. Santos,&nbsp;Yunpeng Shan,&nbsp;Balwinder Singh,&nbsp;Katherine M. Smith,&nbsp;Xiaoliang Song,&nbsp;Sarat Sreepathi,&nbsp;Adrian K. Turner,&nbsp;Tom Vo,&nbsp;Hui Wan,&nbsp;Mingxuan Wu,&nbsp;Wandi Yu,&nbsp;Charles S. Zender,&nbsp;Xubin Zeng,&nbsp;Guang J. Zhang,&nbsp;Meng Zhang,&nbsp;Tao Zhang,&nbsp;Yuying Zhang,&nbsp;Renata B. McCoy,&nbsp;Mark A. Taylor,&nbsp;L. Ruby Leung,&nbsp;Peter M. Caldwell,&nbsp;David C. Bader","doi":"10.1029/2025MS005302","DOIUrl":"https://doi.org/10.1029/2025MS005302","url":null,"abstract":"<p>The Energy Exascale Earth System Model version 3 (E3SMv3) represents the latest advancement in Earth system modeling developed by the U.S. Department of Energy (DOE). Building upon previous versions, E3SMv3 introduces significant updates across its coupled components to enhance capability and improve fidelity. The atmosphere component incorporates advancements in chemistry, aerosol-cloud interactions, convection, and microphysics. The ocean features a new time-stepping scheme and a higher-resolution unstructured mesh with sub-ice-shelf cavities, while the sea ice model integrates advanced snow and ice physics for more realistic cryospheric simulations. The land model introduces prognostic vegetation dynamics and a new sub-grid topographic treatment of solar radiation. A new tri-grid configuration harmonizes the horizontal grids of the land and river components for improved process coupling. It is enabled by a new non-linear remapping between the atmosphere and land. E3SMv3 underwent extensive testing through a comprehensive simulation campaign, including pre-industrial control, idealized <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mtext>CO</mtext>\u0000 <mn>2</mn>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${text{CO}}_{2}$</annotation>\u0000 </semantics></math> experiments, and historical simulations spanning 1850–2024. The model demonstrates significant improvements in simulating the evolution of the historical surface temperature, particularly addressing the “pothole cooling” bias in earlier versions. Reduced aerosol-related forcing contributes to more realistic radiative forcing and better alignment with the observational record. Ocean heat content (OHC) and sea ice trends are also improved as a result.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"18 4","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025MS005302","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708071","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 Generic Spinup Procedure to Initialize European Forest State Variables in Land Surface Models","authors":"Marine Remaud,&nbsp;Jina Jeong,&nbsp;Guillaume Marie,&nbsp;Omar Flores,&nbsp;Kim Naudts,&nbsp;Ana Peregon,&nbsp;Sebastiaan Luyssaert","doi":"10.1029/2025MS005584","DOIUrl":"10.1029/2025MS005584","url":null,"abstract":"<p>Forest structure is shaped by forest management practices, land-use changes, and natural disturbances, including droughts, fires, storms, and insect outbreaks that drive species-specific and size-specific mortality. By modifying the carbon-water-energy exchanges with the atmosphere, it influences a stand's capacity to buffer against or succumb to extreme weather events, which in turn determines the long-term stability of the terrestrial carbon stocks. Given the importance of forest structure for forest land sink, land surface models are moving toward explicit representations of forest structure and management strategies. We present a new procedure to initialize forest diameters over Europe and document its implications for simulations of future forest carbon sinks. The simulated diameters for each grid cell covered by forests are initialized toward the diameter from a forest inventory. To this end, a 300-year semi-analytical spinup was carried out to bring the soil carbon pools into equilibrium. A lookup table with the simulated diameter and plant functional type as its entries was built by clearcutting all forests followed by a 200-year simulation over Europe. For each grid point, the year associated with the simulated diameter that is the closest to the observation is selected, enabling the production of new initial state files over Europe. The new initialization procedure makes the initial state of forest more realistic and therefore significantly modifies the evolution of the forest carbon sink. The method could be further extended to initialize other forest state variables such as height or aboveground biomass.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"18 4","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025MS005584","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708092","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 Generic Spinup Procedure to Initialize European Forest State Variables in Land Surface Models","authors":"Marine Remaud,&nbsp;Jina Jeong,&nbsp;Guillaume Marie,&nbsp;Omar Flores,&nbsp;Kim Naudts,&nbsp;Ana Peregon,&nbsp;Sebastiaan Luyssaert","doi":"10.1029/2025MS005584","DOIUrl":"https://doi.org/10.1029/2025MS005584","url":null,"abstract":"<p>Forest structure is shaped by forest management practices, land-use changes, and natural disturbances, including droughts, fires, storms, and insect outbreaks that drive species-specific and size-specific mortality. By modifying the carbon-water-energy exchanges with the atmosphere, it influences a stand's capacity to buffer against or succumb to extreme weather events, which in turn determines the long-term stability of the terrestrial carbon stocks. Given the importance of forest structure for forest land sink, land surface models are moving toward explicit representations of forest structure and management strategies. We present a new procedure to initialize forest diameters over Europe and document its implications for simulations of future forest carbon sinks. The simulated diameters for each grid cell covered by forests are initialized toward the diameter from a forest inventory. To this end, a 300-year semi-analytical spinup was carried out to bring the soil carbon pools into equilibrium. A lookup table with the simulated diameter and plant functional type as its entries was built by clearcutting all forests followed by a 200-year simulation over Europe. For each grid point, the year associated with the simulated diameter that is the closest to the observation is selected, enabling the production of new initial state files over Europe. The new initialization procedure makes the initial state of forest more realistic and therefore significantly modifies the evolution of the forest carbon sink. The method could be further extended to initialize other forest state variables such as height or aboveground biomass.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"18 4","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025MS005584","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708250","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":"Toward an Analytical Solution of the Liouville Equation via Data-Driven Methods: Applications to Ensemble Forecasting","authors":"Kai-Chih Tseng,&nbsp;Ray Kuo,&nbsp;Yi-An Feng","doi":"10.1029/2024MS004688","DOIUrl":"https://doi.org/10.1029/2024MS004688","url":null,"abstract":"<p>Solving probabilistic weather forecasts is challenging due to computational constraints and the nonlinear nature of Earth atmosphere. This study proposes a proof-of-concept to address these challenges by solving the Liouville equation, that is, the analytical solution for probabilistic forecasts, with data-driven method. Using the sparse identification of nonlinear dynamics (SINDy) algorithm, our research demonstrates that data-driven models can achieve accuracy levels in probabilistic forecasts comparable to analytical solutions. Through various experiments, including Bernoulli differential equations, the Lorenz 84 model, and subseasonal forecasts of tropical intraseasonal variability, we show that the data-driven Liouville equations yield simple functional forms or smoothness across physical space when predictability is present. These findings suggest the potential of these advancements in tackling higher-dimensional weather forecasting problems. Additionally, we discuss potential applications and future challenges.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"18 4","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004688","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708146","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":"TOMO4D Operator for Assimilation of GNSS Tomography-Derived Water Vapor Fields Into the WRFDA 4DVAR System to Improve Regional Rainfall Forecasting","authors":"Arash Tayfehrostami,&nbsp;Yazdan Amerian,&nbsp;Saeed Izanlou,&nbsp;Majid Azadi","doi":"10.1029/2025MS005610","DOIUrl":"https://doi.org/10.1029/2025MS005610","url":null,"abstract":"<p>Accurate representation of atmospheric water vapor is crucial for improving numerical weather prediction, particularly over regions with complex topography and sparse observation networks. Although assimilation of Global Navigation Satellite System (GNSS)-derived integrated products such as zenith total delay or precipitable water vapor can improve humidity analyses, these approaches are limited by their lack of vertical resolution. This study introduces TOMO4D, a new four-dimensional (4D) observation operator developed to assimilate GNSS tomography-derived voxel-based wet refractivity (<i>N</i>_<i>w</i>) fields directly into the WRFDA four-dimensional variational (4DVAR) system. Performance is evaluated for two heavy rainfall events over northern and northwestern Iran (23–24 October 2022). Three experiments are conducted: CTRL (no assimilation), TOMO3DVAR (3DVAR tomography assimilation), and TOMO4D (4DVAR tomography assimilation). Radiosonde (RS) validation at Tehran and Tabriz shows that TOMO4D improves the thermodynamic structure of the troposphere, reducing relative humidity RMSE by up to 38% and temperature RMSE by 9%–11% compared to CTRL, while decreasing temperature bias to within ±0.3K. Verification against 16 SYNOP stations further indicates that TOMO4D reduces accumulated precipitation RMSE by ∼17% and increases correlation by 35.3%. TOMO3DVAR results consistently fall between CTRL and TOMO4D, confirming that voxel-based tomography improves moisture initialization even in 3DVAR, while the additional TOMO4D gains arise from time-consistent assimilation within the 4DVAR window. Overall, GNSS tomography assimilation provides a promising pathway for improving humidity analyses and short-range precipitation forecasting in data-sparse regions such as Iran.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"18 4","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025MS005610","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708145","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":"TOMO4D Operator for Assimilation of GNSS Tomography-Derived Water Vapor Fields Into the WRFDA 4DVAR System to Improve Regional Rainfall Forecasting","authors":"Arash Tayfehrostami,&nbsp;Yazdan Amerian,&nbsp;Saeed Izanlou,&nbsp;Majid Azadi","doi":"10.1029/2025MS005610","DOIUrl":"10.1029/2025MS005610","url":null,"abstract":"<p>Accurate representation of atmospheric water vapor is crucial for improving numerical weather prediction, particularly over regions with complex topography and sparse observation networks. Although assimilation of Global Navigation Satellite System (GNSS)-derived integrated products such as zenith total delay or precipitable water vapor can improve humidity analyses, these approaches are limited by their lack of vertical resolution. This study introduces TOMO4D, a new four-dimensional (4D) observation operator developed to assimilate GNSS tomography-derived voxel-based wet refractivity (<i>N</i>_<i>w</i>) fields directly into the WRFDA four-dimensional variational (4DVAR) system. Performance is evaluated for two heavy rainfall events over northern and northwestern Iran (23–24 October 2022). Three experiments are conducted: CTRL (no assimilation), TOMO3DVAR (3DVAR tomography assimilation), and TOMO4D (4DVAR tomography assimilation). Radiosonde (RS) validation at Tehran and Tabriz shows that TOMO4D improves the thermodynamic structure of the troposphere, reducing relative humidity RMSE by up to 38% and temperature RMSE by 9%–11% compared to CTRL, while decreasing temperature bias to within ±0.3K. Verification against 16 SYNOP stations further indicates that TOMO4D reduces accumulated precipitation RMSE by ∼17% and increases correlation by 35.3%. TOMO3DVAR results consistently fall between CTRL and TOMO4D, confirming that voxel-based tomography improves moisture initialization even in 3DVAR, while the additional TOMO4D gains arise from time-consistent assimilation within the 4DVAR window. Overall, GNSS tomography assimilation provides a promising pathway for improving humidity analyses and short-range precipitation forecasting in data-sparse regions such as Iran.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"18 4","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025MS005610","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708089","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":"Toward an Analytical Solution of the Liouville Equation via Data-Driven Methods: Applications to Ensemble Forecasting","authors":"Kai-Chih Tseng,&nbsp;Ray Kuo,&nbsp;Yi-An Feng","doi":"10.1029/2024MS004688","DOIUrl":"10.1029/2024MS004688","url":null,"abstract":"<p>Solving probabilistic weather forecasts is challenging due to computational constraints and the nonlinear nature of Earth atmosphere. This study proposes a proof-of-concept to address these challenges by solving the Liouville equation, that is, the analytical solution for probabilistic forecasts, with data-driven method. Using the sparse identification of nonlinear dynamics (SINDy) algorithm, our research demonstrates that data-driven models can achieve accuracy levels in probabilistic forecasts comparable to analytical solutions. Through various experiments, including Bernoulli differential equations, the Lorenz 84 model, and subseasonal forecasts of tropical intraseasonal variability, we show that the data-driven Liouville equations yield simple functional forms or smoothness across physical space when predictability is present. These findings suggest the potential of these advancements in tackling higher-dimensional weather forecasting problems. Additionally, we discuss potential applications and future challenges.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"18 4","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004688","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708144","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":"Deep Learning of Systematic Ocean Model Errors in a Coupled GCM From Data Assimilation Increments","authors":"Tarun Verma,&nbsp;F. Lu,&nbsp;A. Adcroft,&nbsp;L. Zanna,&nbsp;A. Gnanadesikan","doi":"10.1029/2025MS005155","DOIUrl":"https://doi.org/10.1029/2025MS005155","url":null,"abstract":"<p>We present a novel, data-driven approach to predict systematic model errors in the ocean component of a coupled general circulation model leveraging deep learning and data assimilation. We examine the skill of the proposed scheme in learning systematic model errors, including their spatial patterns, variance, scales, and test its sensitivity to different predictors and neural network architecture. The scheme utilizes local state variables such as ocean temperature, salinity, velocities, and surface fluxes to predict corrections to temperature tendency for the upper 1,000 m in the ocean on daily timescales. The performance is evaluated on the withheld test data set and compared against the empirical climatological temperature corrections that are geographically dependent. The performance is depth-dependent, with significant improvements over the benchmark in the upper 20 m in the ocean. It degrades rapidly with depth but remains comparable to the climatology benchmark. Neural networks can capture up to <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>40</mn>\u0000 <mo>−</mo>\u0000 <mn>50</mn>\u0000 <mi>%</mi>\u0000 </mrow>\u0000 <annotation> $40-50%$</annotation>\u0000 </semantics></math> of the daily variance in temperature increments in the upper 20 m relative to the benchmark's <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>20</mn>\u0000 <mi>%</mi>\u0000 </mrow>\u0000 <annotation> $20%$</annotation>\u0000 </semantics></math>. The improvements are associated with networks predicting finer spatiotemporal scales than the benchmark. They are expected to perform better in reducing surface ocean mixed layer bias than previously used techniques. Despite being column-local without geographical inputs, networks can sufficiently reproduce spatial patterns on daily and longer timescales. The patterns consist of corrections to regional dynamical features such as western boundary currents, equatorial undercurrents, bathymetry-related corrections in the Southern Ocean, and warm surface increments over subtropical and midlatitude belts.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"18 4","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025MS005155","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708147","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":"Deep Learning of Systematic Ocean Model Errors in a Coupled GCM From Data Assimilation Increments","authors":"Tarun Verma,&nbsp;F. Lu,&nbsp;A. Adcroft,&nbsp;L. Zanna,&nbsp;A. Gnanadesikan","doi":"10.1029/2025MS005155","DOIUrl":"10.1029/2025MS005155","url":null,"abstract":"<p>We present a novel, data-driven approach to predict systematic model errors in the ocean component of a coupled general circulation model leveraging deep learning and data assimilation. We examine the skill of the proposed scheme in learning systematic model errors, including their spatial patterns, variance, scales, and test its sensitivity to different predictors and neural network architecture. The scheme utilizes local state variables such as ocean temperature, salinity, velocities, and surface fluxes to predict corrections to temperature tendency for the upper 1,000 m in the ocean on daily timescales. The performance is evaluated on the withheld test data set and compared against the empirical climatological temperature corrections that are geographically dependent. The performance is depth-dependent, with significant improvements over the benchmark in the upper 20 m in the ocean. It degrades rapidly with depth but remains comparable to the climatology benchmark. Neural networks can capture up to <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>40</mn>\u0000 <mo>−</mo>\u0000 <mn>50</mn>\u0000 <mi>%</mi>\u0000 </mrow>\u0000 <annotation> $40-50%$</annotation>\u0000 </semantics></math> of the daily variance in temperature increments in the upper 20 m relative to the benchmark's <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>20</mn>\u0000 <mi>%</mi>\u0000 </mrow>\u0000 <annotation> $20%$</annotation>\u0000 </semantics></math>. The improvements are associated with networks predicting finer spatiotemporal scales than the benchmark. They are expected to perform better in reducing surface ocean mixed layer bias than previously used techniques. Despite being column-local without geographical inputs, networks can sufficiently reproduce spatial patterns on daily and longer timescales. The patterns consist of corrections to regional dynamical features such as western boundary currents, equatorial undercurrents, bathymetry-related corrections in the Southern Ocean, and warm surface increments over subtropical and midlatitude belts.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"18 4","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025MS005155","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708087","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":"Responses to Humidity and Temperature Perturbations in High-Resolution Simulations of Convection","authors":"Timothy H. Raupach,&nbsp;Chimene L. Daleu,&nbsp;Robert S. Plant,&nbsp;Steven C. Sherwood,&nbsp;Yi-Ling Hwong","doi":"10.1029/2025MS005340","DOIUrl":"10.1029/2025MS005340","url":null,"abstract":"<p>Convection parameterization is a leading source of uncertainty in global and regional climate models, and a lack of ground truth complicates the assessment of convection scheme performance. Here we test a linear framework for quantifying convective responses, using two models run at convection-permitting resolution, to examine model responses to temperature and humidity perturbations. For the models examined, a grid spacing finer than ∼1 km was required for consistent (hence potentially accurate) responses, implying good representation of convectively-coupled dynamics. The results from the tests at 100 and 250 m grid spacing could reasonably be used in idealized tests of convective schemes, as their spread is mostly small compared to the spread of previously reported scheme behavior.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"18 4","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025MS005340","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708125","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}