Journal of Advances in Modeling Earth Systems最新文献_第10页

Physically-Informed Super-Resolution Downscaling of Antarctic Surface Melt 南极地表融化的物理信息超分辨率降尺度研究

IF 4.4 2区地球科学

Journal of Advances in Modeling Earth Systems Pub Date : 2024-07-25 DOI: 10.1029/2023MS004212

Sophie de Roda Husman, Zhongyang Hu, Maurice van Tiggelen, Rebecca Dell, Jordi Bolibar, Stef Lhermitte, Bert Wouters, Peter Kuipers Munneke

{"title":"Physically-Informed Super-Resolution Downscaling of Antarctic Surface Melt","authors":"Sophie de Roda Husman, Zhongyang Hu, Maurice van Tiggelen, Rebecca Dell, Jordi Bolibar, Stef Lhermitte, Bert Wouters, Peter Kuipers Munneke","doi":"10.1029/2023MS004212","DOIUrl":"10.1029/2023MS004212","url":null,"abstract":"Because Antarctic surface melt is mostly driven by local processes, its simulation necessitates high-resolution regional climate models (RCMs). However, the current horizontal resolution of RCMs (≈25–30 km) is inadequate for capturing small-scale melt processes. To address this limitation, we present SUPREME (SUPer-REsolution-based Melt Estimation over Antarctica), a deep learning method to downscale surface melt to 5.5 km resolution using a physically-informed super-resolution model. The physical information integrated into the model originates from observations tied to surface melt, specifically remote sensing-derived albedo and elevation. These remote sensing data, in addition to a Regional Atmospheric Climate Model (RACMO) run at 27 km resolution, account for the diverse drivers of surface melt across Antarctica, facilitating effective generalization beyond the training region of the Antarctic Peninsula. A comparison of SUPREME with a dynamically downscaled RACMO run at 5.5 km over the Antarctic Peninsula shows high accuracy, with average yearly RMSE and bias of 5.5 mm w.e. yr−1 and 4.5 mm w.e. yr−1, respectively. Validation at five automatic weather stations reveals SUPREME's marked improvement with substantially lower average RMSE (81 mm w.e.) compared to RACMO 27 km (129 mm w.e.). Beyond the training region, SUPREME aligns more closely with remote sensing products associated with surface melt than super-resolution models lacking physical constraints. While further validation of SUPREME is needed, our study highlights the potential of super-resolution techniques with physical constraints for high-resolution surface melt monitoring in Antarctica, providing insights into the impacts of localized melting on processes affecting ice shelf integrity such as hydrofracturing.","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"16 7","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS004212","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141850916","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}

引用次数: 0

Regression Forest Approaches to Gravity Wave Parameterization for Climate Projection 用于气候预测的重力波参数化回归森林方法

IF 4.4 2区地球科学

Journal of Advances in Modeling Earth Systems Pub Date : 2024-07-25 DOI: 10.1029/2023MS004184

David S. Connelly, Edwin P. Gerber

{"title":"Regression Forest Approaches to Gravity Wave Parameterization for Climate Projection","authors":"David S. Connelly, Edwin P. Gerber","doi":"10.1029/2023MS004184","DOIUrl":"10.1029/2023MS004184","url":null,"abstract":"We train random and boosted forests, two machine learning architectures based on regression trees, to emulate a physics-based parameterization of atmospheric gravity wave momentum transport. We compare the forests to a neural network benchmark, evaluating both offline errors and online performance when coupled to an atmospheric model under the present day climate and in 800 and 1,200 ppm CO2 global warming scenarios. Offline, the boosted forest exhibits similar skill to the neural network, while the random forest scores significantly lower. Both forest models couple stably to the atmospheric model, and control climate integrations with the boosted forest exhibit lower biases than those with the neural network. Integrations with all three data-driven emulators successfully capture the Quasi-Biennial Oscillation (QBO) and sudden stratospheric warmings, key modes of stratospheric variability, with the boosted forest more accurate than the random forest in replicating their statistics across our range of carbon dioxide perturbations. The boosted forest and neural network capture the sign of the QBO period response to increased CO2, though both struggle with the magnitude of this response under the more extreme 1,200 ppm scenario. To investigate the connection between performance in the control climate and the ability to generalize, we use techniques from interpretable machine learning to understand how the data-driven methods use physical information. We leverage this understanding to develop a retraining procedure that improves the coupled performance of the boosted forest in the control climate and under the 800 ppm CO2 scenario.","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"16 7","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS004184","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141841171","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}

引用次数: 0

SUPHRE: A Reactive Transport Model With Unsaturated and Density-Dependent Flow SUPHRE：非饱和与密度相关流的反应迁移模型

IF 4.4 2区地球科学

Journal of Advances in Modeling Earth Systems Pub Date : 2024-07-23 DOI: 10.1029/2023MS003975

Zhaoyang Luo, Jun Kong, Chengji Shen, D. A. Barry

{"title":"SUPHRE: A Reactive Transport Model With Unsaturated and Density-Dependent Flow","authors":"Zhaoyang Luo, Jun Kong, Chengji Shen, D. A. Barry","doi":"10.1029/2023MS003975","DOIUrl":"10.1029/2023MS003975","url":null,"abstract":"Although unsaturated and density-dependent flow affect solute fate in groundwater, they are rarely both included in reactive transport models. Using the operator-splitting method, a new reactive transport model (SUPHRE) was developed by combining a variable-saturation and variable-density multiple-component solute transport model (SUTRA-MS) with a geochemical reaction module (PhreeqcRM). In contrast to existing reactive transport models, SUPHRE accounts for both unsaturated and density-dependent flow. Model setup for SUPHRE is convenient, as only one setup file is required in addition to the usual input files of SUTRA-MS and PhreeqcRM. By further implementing a time-variant boundary condition into SUTRA-MS, SUPHRE can simulate multi-component reactive transport in tidally influenced coastal unconfined aquifers where unsaturated and density-dependent flow prevail. Two examples were used to validate the new reactive transport model, including single-species decay and sorption within a one-dimensional soil column and a four-species decay chain in a two-dimensional aquifer. Following validation, SUPHRE was adopted to reveal unsaturated flow effects on oxygen consumption and nitrate reduction in tidally influenced coastal unconfined aquifers. Whether for simulating oxygen consumption or nitrate reduction, there were visible deviations between numerical results without and with unsaturated flow, highlighting that unsaturated flow can affect reactive solute transport and transformation.","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"16 7","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS003975","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141849207","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}

引用次数: 0

Solar Radiation Triggers the Bimodal Leaf Phenology of Central African Evergreen Broadleaved Forests 太阳辐射引发中非常绿阔叶林的双峰叶片物候期

IF 4.4 2区地球科学

Journal of Advances in Modeling Earth Systems Pub Date : 2024-07-23 DOI: 10.1029/2023MS004014

Liyang Liu, Philippe Ciais, Fabienne Maignan, Yuan Zhang, Nicolas Viovy, Marc Peaucelle, Elizabeth Kearsley, Koen Hufkens, Marijn Bauters, Colin A. Chapman, Zheng Fu, Shangrong Lin, Haibo Lu, Jiashun Ren, Xueqin Yang, Xianjin He, Xiuzhi Chen

{"title":"Solar Radiation Triggers the Bimodal Leaf Phenology of Central African Evergreen Broadleaved Forests","authors":"Liyang Liu, Philippe Ciais, Fabienne Maignan, Yuan Zhang, Nicolas Viovy, Marc Peaucelle, Elizabeth Kearsley, Koen Hufkens, Marijn Bauters, Colin A. Chapman, Zheng Fu, Shangrong Lin, Haibo Lu, Jiashun Ren, Xueqin Yang, Xianjin He, Xiuzhi Chen","doi":"10.1029/2023MS004014","DOIUrl":"10.1029/2023MS004014","url":null,"abstract":"Central African evergreen broadleaved forests around the equator exhibit a double annual cycle for canopy phenology and carbon uptake seasonality. The underlying drivers of this behavior are poorly understood and the double seasonality is not captured by land surface models (LSM). In this study, we developed a new leaf phenology module into the ORCHIDEE LSM (hereafter ORCHIDEE-AFP), which utilizes short-wave incoming radiation (SWd) as the main driver of leaf shedding and partial rejuvenation of the canopy, to simulate the double seasonality of central African forests. The ORCHIDEE-AFP model has been evaluated by using field data from two forest sites and satellite observations of the enhanced vegetation index (EVI), which is a proxy of young leaf area index (LAIYoung) with leafage less than 6 months, as well as six products of GPP or GPP proxies. Results demonstrate that ORCHIDEE-AFP successfully reproduces observed leaf turnover (R = 0.45) and young leaf abundance (R = 0.74), and greatly improve the representation of the bimodal leaf phenology. The proportion of grid cells with a significant positive correlation between the seasonality of modeled LAIYoung and observed EVI increased from 0.2% in the standard model to 27% in the new model. For photosynthesis, the proportions of grid cells with significant positive correlations between modeled and observed seasonality range from 26% to 65% across the six GPP evaluation products. The improved performance of the ORCHIDEE-AFP model in simulating leaf phenology and photosynthesis of central African forests will allow a more accurate assessment of the impacts of climate change in tropical forests.","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"16 7","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS004014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141849654","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}

引用次数: 0

Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions 机器学习驱动的 E3SM 土地模型参数对湿地甲烷排放的敏感性分析

IF 4.4 2区地球科学

Journal of Advances in Modeling Earth Systems Pub Date : 2024-07-21 DOI: 10.1029/2023MS004115

Sandeep Chinta, Xiang Gao, Qing Zhu

{"title":"Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions","authors":"Sandeep Chinta, Xiang Gao, Qing Zhu","doi":"10.1029/2023MS004115","DOIUrl":"https://doi.org/10.1029/2023MS004115","url":null,"abstract":"Methane (CH4) is globally the second most critical greenhouse gas after carbon dioxide, contributing to 16%–25% of the observed atmospheric warming. Wetlands are the primary natural source of methane emissions globally. However, wetland methane emission estimates from biogeochemistry models contain considerable uncertainty. One of the main sources of this uncertainty arises from the numerous uncertain model parameters within various physical, biological, and chemical processes that influence methane production, oxidation, and transport. Sensitivity Analysis (SA) can help identify critical parameters for methane emission and achieve reduced biases and uncertainties in future projections. This study performs SA for 19 selected parameters responsible for critical biogeochemical processes in the methane module of the Energy Exascale Earth System Model (E3SM) land model (ELM). The impact of these parameters on various CH4 fluxes is examined at 14 FLUXNET- CH4 sites with diverse vegetation types. Given the extensive number of model simulations needed for global variance-based SA, we employ a machine learning (ML) algorithm to emulate the complex behavior of ELM methane biogeochemistry. We found that parameters linked to CH4 production and diffusion generally present the highest sensitivities despite apparent seasonal variation. Comparing simulated emissions from perturbed parameter sets against FLUXNET-CH4 observations revealed that better performances can be achieved at each site compared to the default parameter values. This presents a scope for further improving simulated emissions using parameter calibration with advanced optimization techniques.","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"16 7","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS004115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968022","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}

引用次数: 0

A Machine Learning Framework to Evaluate Vegetation Modeling in Earth System Models 评估地球系统模型中植被建模的机器学习框架

IF 4.4 2区地球科学

Journal of Advances in Modeling Earth Systems Pub Date : 2024-07-19 DOI: 10.1029/2023MS004097

Ranjini Swaminathan, Tristan Quaife, Richard Allan

{"title":"A Machine Learning Framework to Evaluate Vegetation Modeling in Earth System Models","authors":"Ranjini Swaminathan, Tristan Quaife, Richard Allan","doi":"10.1029/2023MS004097","DOIUrl":"https://doi.org/10.1029/2023MS004097","url":null,"abstract":"Vegetation gross primary productivity (GPP) is the single largest carbon flux of the terrestrial biosphere which, in turn, is responsible for sequestering 25%–30% of anthropogenic carbon dioxide emissions. The ability to model GPP is therefore critical for calculating carbon budgets as well as understanding climate feedbacks. Earth system models (ESMs) have the capability to simulate GPP but vary greatly in their individual estimates, resulting in large uncertainties. We describe a machine learning (ML) approach to investigate two key factors responsible for differences in simulated GPP quantities from ESMs: the relative importance of different atmospheric drivers and differences in the representation of land surface processes. We describe the different steps in the development of our interpretable ML framework including the choice of algorithms, parameter tuning, training and evaluation. Our results show that ESMs largely agree on the physical climate drivers responsible for GPP as seen in the literature, for instance drought variables in the Mediterranean region or radiation and temperature in the Arctic region. However differences do exist since models don't necessarily agree on which individual variable is most relevant for GPP. We also explore a distance measure to attribute GPP differences to climate influences versus process differences and provide examples for where our methods work (South Asia, Mediterranean) and where they are inconclusive (Eastern North America).","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"16 7","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS004097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141732536","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}

引用次数: 0

Simultaneous Inference of Sea Ice State and Surface Emissivity Model Using Machine Learning and Data Assimilation 利用机器学习和数据同化同时推断海冰状态和地表发射率模型

IF 4.4 2区地球科学

Journal of Advances in Modeling Earth Systems Pub Date : 2024-07-18 DOI: 10.1029/2023MS004080

Alan J. Geer

{"title":"Simultaneous Inference of Sea Ice State and Surface Emissivity Model Using Machine Learning and Data Assimilation","authors":"Alan J. Geer","doi":"10.1029/2023MS004080","DOIUrl":"https://doi.org/10.1029/2023MS004080","url":null,"abstract":"Satellite microwave radiance observations are strongly sensitive to sea ice, but physical descriptions of the radiative transfer of sea ice and snow are incomplete. Further, the radiative transfer is controlled by poorly-known microstructural properties that vary strongly in time and space. A consequence is that surface-sensitive microwave observations are not assimilated over sea ice areas, and sea ice retrievals use heuristic rather than physical methods. An empirical model for sea ice radiative transfer would be helpful but it cannot be trained using standard machine learning techniques because the inputs are mostly unknown. The solution is to simultaneously train the empirical model and a set of empirical inputs: an “empirical state” method, which draws on both generative machine learning and physical data assimilation methodology. A hybrid physical-empirical network describes the known and unknown physics of sea ice and atmospheric radiative transfer. The network is then trained to fit a year of radiance observations from Advanced Microwave Scanning Radiometer 2, using the atmospheric profiles, skin temperature and ocean water emissivity taken from a weather forecasting system. This process estimates maps of the daily sea ice concentration while also learning an empirical model for the sea ice emissivity. The model learns to define its own empirical input space along with daily maps of these empirical inputs. These maps represent the otherwise unknown microstructural properties of the sea ice and snow that affect the radiative transfer. This “empirical state” approach could be used to solve many other problems of earth system data assimilation.","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"16 7","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS004080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141730212","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}

引用次数: 0

Uncertainty Quantification of a Machine Learning Subgrid-Scale Parameterization for Atmospheric Gravity Waves 大气重力波机器学习子网格尺度参数化的不确定性量化

IF 4.4 2区地球科学

Journal of Advances in Modeling Earth Systems Pub Date : 2024-07-17 DOI: 10.1029/2024MS004292

L. A. Mansfield, A. Sheshadri

{"title":"Uncertainty Quantification of a Machine Learning Subgrid-Scale Parameterization for Atmospheric Gravity Waves","authors":"L. A. Mansfield, A. Sheshadri","doi":"10.1029/2024MS004292","DOIUrl":"https://doi.org/10.1029/2024MS004292","url":null,"abstract":"Subgrid-scale processes, such as atmospheric gravity waves (GWs), play a pivotal role in shaping the Earth's climate but cannot be explicitly resolved in climate models due to limitations on resolution. Instead, subgrid-scale parameterizations are used to capture their effects. Recently, machine learning (ML) has emerged as a promising approach to learn parameterizations. In this study, we explore uncertainties associated with a ML parameterization for atmospheric GWs. Focusing on the uncertainties in the training process (parametric uncertainty), we use an ensemble of neural networks to emulate an existing GW parameterization. We estimate both offline uncertainties in raw NN output and online uncertainties in climate model output, after the neural networks are coupled. We find that online parametric uncertainty contributes a significant source of uncertainty in climate model output that must be considered when introducing NN parameterizations. This uncertainty quantification provides valuable insights into the reliability and robustness of ML-based GW parameterizations, thus advancing our understanding of their potential applications in climate modeling.","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"16 7","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004292","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141639532","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}

引用次数: 0

A New WENO-Based Momentum Advection Scheme for Simulations of Ocean Mesoscale Turbulence 用于模拟海洋中尺度湍流的基于 WENO 的新动量平流方案

IF 4.4 2区地球科学

Journal of Advances in Modeling Earth Systems Pub Date : 2024-07-15 DOI: 10.1029/2023MS004130

Simone Silvestri, Gregory L. Wagner, Jean-Michel Campin, Navid C. Constantinou, Christopher N. Hill, Andre Souza, Raffaele Ferrari

{"title":"A New WENO-Based Momentum Advection Scheme for Simulations of Ocean Mesoscale Turbulence","authors":"Simone Silvestri, Gregory L. Wagner, Jean-Michel Campin, Navid C. Constantinou, Christopher N. Hill, Andre Souza, Raffaele Ferrari","doi":"10.1029/2023MS004130","DOIUrl":"https://doi.org/10.1029/2023MS004130","url":null,"abstract":"Current eddy-permitting and eddy-resolving ocean models require dissipation to prevent a spurious accumulation of enstrophy at the grid scale. We introduce a new numerical scheme for momentum advection in large-scale ocean models that involves upwinding through a weighted essentially non-oscillatory (WENO) reconstruction. The new scheme provides implicit dissipation and thereby avoids the need for an additional explicit dissipation that may require calibration of unknown parameters. This approach uses the rotational, “vector invariant” formulation of the momentum advection operator that is widely employed by global general circulation models. A novel formulation of the WENO “smoothness indicators” is key for avoiding excessive numerical dissipation of kinetic energy and enstrophy at grid-resolved scales. We test the new advection scheme against a standard approach that combines explicit dissipation with a dispersive discretization of the rotational advection operator in two scenarios: (a) two-dimensional turbulence and (b) three-dimensional baroclinic equilibration. In both cases, the solutions are stable, free from dispersive artifacts, and achieve increased “effective” resolution compared to other approaches commonly used in ocean models.","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"16 7","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS004130","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141624275","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}

引用次数: 0

Resolving Weather Fronts Increases the Large-Scale Circulation Response to Gulf Stream SST Anomalies in Variable-Resolution CESM2 Simulations 在可变分辨率 CESM2 模拟中解决天气锋面问题可增强大尺度环流对湾流 SST 异常的响应

IF 4.4 2区地球科学

Journal of Advances in Modeling Earth Systems Pub Date : 2024-07-15 DOI: 10.1029/2023MS004123

Robert C. J. Wills, Adam R. Herrington, Isla R. Simpson, David S. Battisti

{"title":"Resolving Weather Fronts Increases the Large-Scale Circulation Response to Gulf Stream SST Anomalies in Variable-Resolution CESM2 Simulations","authors":"Robert C. J. Wills, Adam R. Herrington, Isla R. Simpson, David S. Battisti","doi":"10.1029/2023MS004123","DOIUrl":"https://doi.org/10.1029/2023MS004123","url":null,"abstract":"Canonical understanding based on general circulation models (GCMs) is that the atmospheric circulation response to midlatitude sea-surface temperature (SST) anomalies is weak compared to the larger influence of tropical SST anomalies. However, the ∼100-km horizontal resolution of modern GCMs is too coarse to resolve strong updrafts within weather fronts, which could provide a pathway for surface anomalies to be communicated aloft. Here, we investigate the large-scale atmospheric circulation response to idealized Gulf Stream SST anomalies in Community Atmosphere Model (CAM6) simulations with 14-km regional grid refinement over the North Atlantic, and compare it to the responses in simulations with 28-km regional refinement and uniform 111-km resolution. The highest resolution simulations show a large positive response of the wintertime North Atlantic Oscillation (NAO) to positive SST anomalies in the Gulf Stream, a 0.4-standard-deviation anomaly in the seasonal-mean NAO for 2°C SST anomalies. The lower-resolution simulations show a weaker response with a different spatial structure. The enhanced large-scale circulation response results from an increase in resolved vertical motions with resolution and an associated increase in the influence of SST anomalies on transient-eddy heat and momentum fluxes in the free troposphere. In response to positive SST anomalies, these processes lead to a stronger and less variable North Atlantic jet, as is characteristic of positive NAO anomalies. Our results suggest that the atmosphere responds differently to midlatitude SST anomalies in higher-resolution models and that regional refinement in key regions offers a potential pathway to improve multi-year regional climate predictions based on midlatitude SSTs.","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"16 7","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS004123","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141631186","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}

引用次数: 0