Journal of Advances in Modeling Earth Systems最新文献

{"title":"VarDyn: Dynamical Joint-Reconstructions of Sea Surface Height and Temperature From Multi-Sensor Satellite Observations","authors":"Florian Le Guillou,&nbsp;Bertrand Chapron,&nbsp;Marie-Helene Rio","doi":"10.1029/2024MS004689","DOIUrl":"https://doi.org/10.1029/2024MS004689","url":null,"abstract":"<p>The VarDyn hybrid methodology, which combines minimal physically based constraints with a variational scheme, is demonstrated to enhance the mapping of sea surface height (SSH) and sea surface temperature (SST). By synthesizing multi-modal satellite observations, VarDyn produces SSH and SST maps with improved accuracy compared to operational products, achieving reductions in Root Mean Square Error and enhancements in effective spatial resolution. While most improvements are observed in highly energetic ocean regions, SSH map accuracy also improves slightly in low-energy regions—a significant advancement over other methods. VarDyn SSH fields and the associated geostrophic velocities show strong agreement with newly available high-resolution instantaneous SWOT estimates. Notably, the assimilation of SST proves particularly beneficial for SSH reconstruction when only two altimeters are available. The VarDyn methodology potentially offers a robust framework for refining climate SSH records by jointly assimilating SSH data from two altimeters and SST data from microwave sensors.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 4","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004689","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856696","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 Robust Constraint on the Response of Convective Mass Fluxes to Warming","authors":"Andrew I. L. Williams,&nbsp;Nadir Jeevanjee","doi":"10.1029/2024MS004695","DOIUrl":"https://doi.org/10.1029/2024MS004695","url":null,"abstract":"<p>A fundamental quantity in tropical dynamics is the “convective mass flux,” which measures the rate at which mass is transported upwards per unit area in convective updrafts. Convective mass flux encodes information about the frequency and intensity of thunderstorms, and has been linked to the strength of the large-scale tropical circulation. Changes in convective mass flux under warming are an important, but uncertain, aspect of climate change. Here we build off recent work linking changes in mass flux to the clear-sky energy budget to show that convective mass fluxes decrease along isotherms at around 3%–5% <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mi>K</mi>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation> ${mathrm{K}}^{-1}$</annotation>\u0000 </semantics></math> under warming. We show that this constraint holds throughout the free-troposphere and across a hierarchy of models; from idealized radiative-convective equilibrium simulations to CMIP6 models. This decrease in convective mass flux with warming is driven by a stabilization of the lapse rate and can be captured with a simple analytical model. We also revisit previous work by Held and Soden (2006), https://doi.org/10.1175/jcli3990.1, who proposed a scaling for changes in the convective mass flux with warming. We show that the Held and Soden scaling does not capture inter-model spread in cloud-base mass flux changes under warming, and that their original verification was likely coincidental. Our work provides a quantitative constraint on changes in convective mass flux throughout the troposphere which can be derived from first principles, and which is verified across a hierarchy of models.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 4","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004695","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856805","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":"Emergence of Self–Organization of Atmospheric Moist Convection, as Seen Through the Energy–Cycle in Wavelet Space","authors":"Jun-Ichi Yano,&nbsp;Robert S. Plant","doi":"10.1029/2024MS004517","DOIUrl":"https://doi.org/10.1029/2024MS004517","url":null,"abstract":"<p>The energy cycle of a convectively–organized system, as realized by a convective–scale idealized simulation, is analyzed in wavelet space. In the equilibrium state, most of the available potential energy that is generated by convective heating is immediately converted into kinetic energy by means of buoyancy forcing, consistent with the free–ride principle. In turn, most of the generated convective kinetic energy is manifest as gravity waves propagating away from convective centers. The kinetic energy of these small–scale gravity waves is transferred upscale by their own advective nonlinearities. Finally, a large–scale circulation generated by this “inverse cascade” drives the formation of an organized structure in the precipitation field.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 4","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004517","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850993","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 Simple Approach to Represent Irrigation Water Withdrawals in Earth System Models","authors":"Bertrand Decharme,&nbsp;Maya Costantini,&nbsp;Jeanne Colin","doi":"10.1029/2024MS004508","DOIUrl":"https://doi.org/10.1029/2024MS004508","url":null,"abstract":"<p>The increasing demand for water, driven by population growth and agricultural expansion, underscores the need for accurate representation of irrigation in Earth System Models (ESMs). While a few current-generation ESMs incorporate irrigation, their representation of water withdrawals remains overly simplistic. These models typically source water solely from rivers and, in some cases, the ocean. This oversimplification can lead to inaccuracies in projecting water resources under climate change scenarios. This study presents a simple approach to integrate irrigation water withdrawals within the ISBA-CTRIP global hydrological system, which is the land surface model integrated into the French National Center for Meteorological Research's ESM. The methodology encompasses the withdrawal of water from both groundwater and conceptual small dams. A global data set is employed to impose irrigation water demands on cropland areas. Irrigation water is distributed according to the three main irrigation techniques: flood, sprinkler, and drip. This approach ensures the closure of the global water budget that is essential in climate simulations. The model was evaluated against satellite and in situ observations over the period 1971–2010, demonstrating some improvements in simulating the continental water cycle. Our findings underscore the necessity of incorporating comprehensive irrigation processes in ESMs to account for the intricate interconnections between irrigation practices, water resources, and climate. By enhancing the representation of anthropogenic water withdrawals in ESMs, this study aims at contributing to the development of more robust climate projections which could help building more informed water management strategies in the future.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 4","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004508","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849193","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":"Scale-Aware Parameterization of Cloud Fraction and Condensate for a Global Atmospheric Model Machine-Learned From Coarse-Grained Kilometer-Scale Simulations","authors":"Cyril Morcrette,&nbsp;Tobias Cave,&nbsp;Helena Reid,&nbsp;Joana da Silva Rodrigues,&nbsp;Teo Deveney,&nbsp;Lisa Kreusser,&nbsp;Kwinten Van Weverberg,&nbsp;Chris Budd","doi":"10.1029/2024MS004651","DOIUrl":"https://doi.org/10.1029/2024MS004651","url":null,"abstract":"<p>Kilometer grid-length simulations over a variety of different locations worldwide are used as training data for a deep-learning model designed to predict clouds in a global climate model. The inputs to the neural network are profiles of temperature, humidity and pressure from the high-resolution model, averaged to the scale of the climate model. The outputs are profiles of cloud fraction and in-cloud liquid and ice water contents. The high-resolution data is coarse-grained to a range of sizes, allowing the model to learn how the cloud formation depends on the size of the area being considered. The machine-learned cloud fraction and cloud condensate scheme is coupled to a global climate model and used to run multi-year simulations where the clouds predicted by the neural-network are fully interacting with the rest of the model.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 4","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004651","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849052","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":"Exploring Precipitation Triple Oxygen Isotope Dynamics: Insights From GISS-E2.1 Simulations","authors":"Yilin Zhang,&nbsp;Allegra N. LeGrande,&nbsp;Nathalie Goodkin,&nbsp;Jesse Nusbaumer,&nbsp;Shaoneng He,&nbsp;Gavin A. Schmidt,&nbsp;Xianfeng Wang","doi":"10.1029/2024MS004509","DOIUrl":"https://doi.org/10.1029/2024MS004509","url":null,"abstract":"<p>Precipitation isotopes are valuable tracers for understanding the hydrologic cycle and climate variations. Distinct from d-excess, ¹⁷O-excess has recently emerged as a promising new tracer of precipitation processes because of its insensitivity to moisture source temperature. However, the control mechanisms on precipitation ¹⁷O-excess remain poorly understood. In this study, we evaluated the performance of the GISS-E2.1 climate model in simulating the precipitation isotopes, focusing on ¹⁷O-excess. Through comprehensive analysis, we explored how variations in seawater isotopes, rain evaporation, kinetic isotope fractionation parameters, and supersaturation factors influence the simulated precipitation d-excess and ¹⁷O-excess. Our findings reveal that GISS-E2.1 accurately captures the spatial distribution and temporal variations of precipitation <i>δ</i>¹⁸O. Moreover, it reasonably reproduces the spatial patterns of precipitation d-excess, though slightly underestimating the mean value in the low latitudes. Although most simulated ¹⁷O-excess values fall within the observed range, evaluating the accuracy of ¹⁷O-excess simulations is challenging due to the limited availability of observational data. Notably, in tropical regions, the spatiotemporal distributions of d-excess and ¹⁷O-excess are sensitive to convective processes, such as rain evaporation. The model's limitations in ¹⁷O-excess simulation suggest that current formulations are inadequate to fully capture the variability of ¹⁷O-excess. This underscores the complexity of the processes influencing ¹⁷O-excess and highlights the need for additional data and further research to comprehensively understand its controlling factors. Our findings contribute to our understanding of the mechanisms driving the observed variation in precipitation triple oxygen isotopes and to the validation and improvement of climate models.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 4","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004509","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143846161","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":"Sensitivity of the Shallow-To-Deep Convective Transition to Moisture and Wind Shear in the Amazon","authors":"Leandro Alex Moreira Viscardi,&nbsp;Giuseppe Torri,&nbsp;David K. Adams,&nbsp;Henrique de Melo Jorge Barbosa","doi":"10.1029/2024MS004238","DOIUrl":"https://doi.org/10.1029/2024MS004238","url":null,"abstract":"<p>Deep convection is the primary influence on weather and climate in tropical regions. However, understanding and simulating the shallow-to-deep (STD) convective transition has long been challenging. Here, we conduct high-resolution numerical simulations to assess the environmental controls on the evolution of isolated convection in the Amazon during the wet season. The large-scale forcing derived through a constrained variational analysis approach for the GoAmazon2014/5 Experiment is used in the simulations. Through sensitivity experiments, we examine the relative importance of moisture and wind shear in controlling the shallow-to-deep convective transition for isolated convective events. Convection exhibits the greatest sensitivity to humidity within the lowest 1.5 km, where a 4 mm reduction in column water vapor nearly suppresses ice water formation on deep convective days. In contrast, a reduction in column water vapor in the free troposphere by a factor of two or more is necessary to produce a comparable impact on convection. Increasing low-level wind speed from 6 to 9 m <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mi>s</mi>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation> ${mathrm{s}}^{-1}$</annotation>\u0000 </semantics></math> enhances afternoon deep convection, raising the cloud ice mixing ratio by approximately 25%. Conversely, upper-level wind shear reveals the weakest correlation with daytime convection in our simulations. Our results help characterize the role of moisture and wind shear on the STD transition and our understanding of the underlying mechanisms.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 4","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004238","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143846162","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":"Prediction Beyond the Medium Range With an Atmosphere-Ocean Model That Combines Physics-Based Modeling and Machine Learning","authors":"Dhruvit Patel,&nbsp;Troy Arcomano,&nbsp;Brian Hunt,&nbsp;Istvan Szunyogh,&nbsp;Edward Ott","doi":"10.1029/2024MS004480","DOIUrl":"https://doi.org/10.1029/2024MS004480","url":null,"abstract":"<p>This paper explores the potential of a hybrid modeling approach that combines machine learning (ML) with conventional physics-based modeling for weather prediction beyond the medium range. It extends the work of Arcomano et al. (2022, https://doi.org/10.1029/2021ms002712), which tested the approach for short- and medium-range weather prediction, and the work of Arcomano et al. (2023, https://doi.org/10.1029/2022gl102649), which investigated its potential for climate modeling. The hybrid model used for the forecast experiments of the paper is based on the low-resolution, simplified parameterization atmospheric general circulation model SPEEDY. In addition to the hybridized prognostic variables of SPEEDY, the model has three purely ML-based prognostic variables: the 6 hr cumulative precipitation, the sea surface temperature, and the heat content of the top 300 m deep layer of the ocean (a new addition compared to the model used in Arcomano et al., 2023, https://doi.org/10.1029/2022gl102649). The model has skill in predicting the El Niño cycle and its global teleconnections with precipitation for 3–7 months depending on the season. The model captures equatorial variability of the precipitation associated with Kelvin and Rossby waves and MJO. Predictions of the precipitation in the equatorial region have skill for 15 days in the East Pacific and 11.5 days in the West Pacific. Though the model has low spatial resolution, for these tasks it has prediction skill comparable to what has been published for high-resolution, purely physics-based, conventional, operational forecast models.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 4","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004480","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836186","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":"Modeling Antarctic Sea Ice Variability Using a Brittle Rheology","authors":"Rafael Santana,&nbsp;Guillaume Boutin,&nbsp;Christopher Horvat,&nbsp;Einar Ólason,&nbsp;Timothy Williams,&nbsp;Pierre Rampal","doi":"10.1029/2024MS004584","DOIUrl":"https://doi.org/10.1029/2024MS004584","url":null,"abstract":"<p>Sea ice is a composite solid material that sustains large fracture features at scales from meters to kilometres. These fractures can play an important role in coupled atmosphere-ocean processes. To model these features, brittle sea ice physics, via the Brittle-Bingham-Maxwell (BBM) rheology, has been implemented in the Lagrangian neXt generation Sea Ice Model (neXtSIM). In Arctic-only simulations, the BBM rheology has shown a capacity to represent observationally consistent sea ice fracture patterns and breakup across a wide range of time and length scales. Still, it has not been tested whether this approach is suitable for the modeling of Antarctic sea ice, which is thinner and more seasonal compared to Arctic sea ice, and whether the ability to reproduce sea ice fractures has an impact on simulating Antarctic sea ice properties. Here, we introduce a new 50-km grid-spacing Antarctic configuration of neXtSIM, neXtSIM-Ant, using the BBM rheology. We evaluate this simulation against observations of sea ice extent, drift, and thickness and compare it with identically-forced neXtSIM simulations that use the standard modified Elastic-Visco-Plastic (mEVP) rheology. In general, using BBM results in thicker sea ice and an improved correlation of sea ice drift with observations than mEVP. We suggest that this is related to short-duration breakup events caused by Antarctic storms that are not well-simulated in the viscous-plastic model.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 4","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004584","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836074","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":"Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties With Deep Learning Multi-Member and Stochastic Parameterizations","authors":"Gunnar Behrens,&nbsp;Tom Beucler,&nbsp;Fernando Iglesias-Suarez,&nbsp;Sungduk Yu,&nbsp;Pierre Gentine,&nbsp;Michael Pritchard,&nbsp;Mierk Schwabe,&nbsp;Veronika Eyring","doi":"10.1029/2024MS004272","DOIUrl":"https://doi.org/10.1029/2024MS004272","url":null,"abstract":"<p>Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here, we develop stochastic parameterizations with calibrated uncertainty quantification to learn subgrid convective and turbulent processes and surface radiative fluxes of a superparameterization embedded in an Earth System Model (ESM). We explore three methods to construct stochastic parameterizations: (a) a single Deep Neural Network (DNN) with Monte Carlo Dropout; (b) a multi-member parameterization; and (c) a Variational Encoder Decoder with latent space perturbation. We show that the multi-member parameterization improves the representation of convective processes, especially in the planetary boundary layer, compared to individual DNNs. The respective uncertainty quantification illustrates that methods (b) and (c) are advantageous compared to a dropout-based DNN parameterization regarding the spread of convective processes. Hybrid simulations with our best-performing multi-member parameterizations remained challenging and crash within the first days. Therefore, we develop a pragmatic partial coupling strategy relying on the superparameterization for condensate emulation. Partial coupling reduces the computational efficiency of hybrid Earth-like simulations but enables model stability over 5 months with our multi-member parameterizations. However, our hybrid simulations exhibit biases in thermodynamic fields and differences in precipitation patterns. Despite this, the multi-member parameterizations enable improvements in reproducing tropical extreme precipitation compared to a traditional convection parameterization. Despite these challenges, our results indicate the potential of a new generation of multi-member machine learning parameterizations leveraging uncertainty quantification to improve the representation of stochasticity of subgrid effects.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 4","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004272","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826732","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}