Journal of Advances in Modeling Earth Systems最新文献

{"title":"Coupled Climate Simulations With E3SM-MMF","authors":"W. M. Hannah,&nbsp;S. Mahajan,&nbsp;B. E. Harrop,&nbsp;N. Liu,&nbsp;L. Peng,&nbsp;M. S. Pritchard,&nbsp;B. R. Hillman,&nbsp;D. C. Bader,&nbsp;M. A. Taylor","doi":"10.1029/2025MS004935","DOIUrl":"10.1029/2025MS004935","url":null,"abstract":"<p>Simulations of the recent historical period from 1950 to 2014 are conducted with E3SM-MMF, which uses an embedded 2D cloud resolving model that runs efficiently on GPUs in place of traditional parameterizations for cloud and turbulence. Analysis of the climate and variability reveal several aspects where E3SM-MMF produces smaller biases compared to E3SMv2, including better agreement with the observed evolution of global mean surface temperature, although the representation of ENSO is too weak and fast. Three idealized abrupt CO₂ experiments were also conducted to assess climate sensitivity and feedbacks. These yield three estimates of effective climate sensitivity (4.38, 5.21, and 6.06 K), with a corresponding spread in the shortwave cloud feedbacks. These estimates are on the higher end of sensitivity estimates from CMIP ensembles, and the spread indicates substantial state-dependent feedbacks. These results demonstrate how multiscale modeling framework (MMF) models can be used for climate relevant experiments and projections by leveraging modern GPU enabled computational platforms. The unique qualities of E3SM-MMF shown in previous literature are largely still present, but various instances of reduced biases suggest that MMF models have utility in improving future projections.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 9","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025MS004935","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145101487","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":"Incremental Analysis Updates in a Convective-Scale Ensemble Kalman Filter Using Minute-by-Minute Phased Array Radar Observations","authors":"Zhaoyang Huo,&nbsp;Yubao Liu,&nbsp;James Taylor,&nbsp;Yongbo Zhou,&nbsp;Arata Amemiya,&nbsp;Hang Fan,&nbsp;Takemasa Miyoshi","doi":"10.1029/2024MS004802","DOIUrl":"10.1029/2024MS004802","url":null,"abstract":"<p>Rapid-update data assimilation (DA) cycles, particularly during the early stages of the assimilation process, often suffer from physical imbalances that degrade the quality of analyses and lead to a rapid decline in forecast skill. This study evaluates the impact of combining the incremental analysis update (IAU) method with the ensemble Kalman filter (EnKF) on the assimilation of observations from a Multi-Parameter Phased Array Weather Radar. A series of experiments were conducted for two convective precipitation cases using a numerical weather prediction model with a 500-m horizontal grid resolution and a 1-min DA interval. The results show that the IAU strategy effectively mitigates the imbalances introduced by intermittent EnKF assimilation. Moreover, IAU maintains a slightly higher ensemble spread while still effectively constraining the analysis toward observations, enhancing ensemble diversity without sacrificing accuracy. The time-continuous, four-dimensional assimilation provided by IAU enables the model to gradually develop and refine convective structures during the forward integration, resulting in a more pronounced surface cold pool and deeper updrafts, thereby slowing down the rapid decline of forecast skills, particularly in high-reflectivity regions. This study indicates that for convective-scale rapid cycling assimilation at minute intervals, combining IAU with EnKF is a superior approach for improving precipitation forecasts.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 9","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004802","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145101486","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 Ice Topography, Basal Channels and Subglacial Discharge on Basal Melting Under the Floating Ice Tongue of 79N Glacier, Northeast Greenland","authors":"Mahdi Mohammadi-Aragh,&nbsp;Ole Zeising,&nbsp;Markus Reinert,&nbsp;Knut Klingbeil,&nbsp;Angelika Humbert,&nbsp;Rebecca McPherson,&nbsp;Mathieu Morlighem,&nbsp;Ralph Timmermann,&nbsp;Claudia Wekerle,&nbsp;Hans Burchard","doi":"10.1029/2024MS004735","DOIUrl":"10.1029/2024MS004735","url":null,"abstract":"<p>The floating ice tongue of the 79N Glacier in Northeast Greenland has been thinning over the past two decades, with warning signs of a potential onset of disintegration. While previous studies primarily attribute the thinning of the ice shelf to oceanic heat flux, limited attention has been given to the significant role of ice shelf plume dynamics as a mechanism for distributing the heat beneath the ice shelf. Here, we develop a horizontal two-dimensional plume model to assess the effects of key factors influencing plume dynamics and, consequently, the estimation of a high-resolution basal melt rate. We examine the effect of ice basal topography roughness and the presence of basal channels, that is extreme roughness of the base in the hinge zone, as well as the impact and pathways of subglacial discharge on melt rates. Our model results show good agreement with observation-based melt rate estimates and indicate that basal channels in the hinge zone are the dominant control on the ice shelf's basal melt rates. In combination with subglacial discharge, the melt rate is increased to <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mrow>\u0000 <mn>150</mn>\u0000 <mspace></mspace>\u0000 <mi>m</mi>\u0000 <mspace></mspace>\u0000 <mi>y</mi>\u0000 <msup>\u0000 <mi>r</mi>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 </mrow>\u0000 <annotation> $150,mathrm{m},mathrm{y}{mathrm{r}}^{-mathrm{1}}$</annotation>\u0000 </semantics></math> at the grounding line, intensifying the channelized melt rate pattern created by basal channels and increasing spatial variability. Additionally, our results indicate that incorporating wet-dry algorithms and calculating a variable drag coefficient are crucial for accurately estimating melt rates during low subglacial discharge season, as well as for determining friction and turbulent exchange coefficients.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 9","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004735","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145101386","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":"Quantifying UAS Observation Error Variance Used in Data Assimilation Systems and Its Impact on Predictive Skill","authors":"J. Kay,&nbsp;J. O. Pinto,&nbsp;T. M. Weckwerth,&nbsp;G. de Boer","doi":"10.1029/2024MS004601","DOIUrl":"10.1029/2024MS004601","url":null,"abstract":"<p>Observation error determines the weights of the observations and background state used in data assimilation to generate analyses. Quantifying observation error is critical for the optimal assimilation of observational data sets. Uncrewed Aircraft System (UAS) observations have shown potential benefits in filling observational gaps in the lower atmosphere; however, characterization of their error characteristics has been limited. To optimize the use of UAS observations in numerical weather prediction, UAS observation error is estimated based on the 3-cornered hat diagnostic approach which uses three independent estimates of the atmospheric state. This approach is applied to data from the 2018 Lower Atmospheric Profiling Studies at Elevation-a Remotely-piloted Aircraft Team Experiment field campaign using collocated UAS and rawinsonde observations along with output from a set of convection-permitting model simulations. The estimated observation error values for UAS temperature, wind, and relative humidity measurements were found to be only weakly dependent on height AGL with mean values equal to 0.5°C, 0.8 m s⁻¹, and 3%, respectively. Only the newly estimated observation error for temperature differed from that previously used to assimilate commercial aircraft observations into global models (1.0°C). However, using this reduced temperature observation error produced more accurate mesoscale analyses and forecasts of both terrain-driven flows and convection initiation generated by colliding outflow boundaries within the San Luis Valley of Colorado.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 9","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004601","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145101145","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":"Countergradient Momentum Transport in Clear Convective Atmospheric Boundary Layers","authors":"Stephan R. de Roode,&nbsp;Fredrik Jansson,&nbsp;Lydia Mak,&nbsp;Louise Nuijens","doi":"10.1029/2024MS004579","DOIUrl":"10.1029/2024MS004579","url":null,"abstract":"<p>The vertical profiles of the wind speed and direction in atmospheric boundary layers are strongly controlled by turbulence. Most global weather forecast and climate models parameterize the vertical transport of horizontal momentum by turbulent eddies by means of a downgradient eddy diffusion approach, in which the same stability-dependent eddy viscosity profile is applied to both horizontal wind components. In this study we diagnose eddy viscosity profiles from large-eddy simulations of five convective boundary layers with wind shear. Each simulation was forced by the same geostrophic wind of 7.5 <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mtext>ms</mtext>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation> ${text{ms}}^{-1}$</annotation>\u0000 </semantics></math>, but with different surface heat fluxes in the range between 0.03 and 0.18 <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mtext>mKs</mtext>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation> ${text{mKs}}^{-1}$</annotation>\u0000 </semantics></math>. We find that the eddy viscosity profiles for the two horizontal wind components differ significantly, in particular, we diagnose negative eddy viscosities, indicating vertical turbulent transport that is counter the mean gradient. This suggests that a purely downgradient diffusion approach for turbulent momentum fluxes is inadequate. A modified solution of the Ekman spiral demonstrates that different eddy viscosity profiles for the two horizontal wind components lead to a different wind profile. To improve parameterizations that apply a downgradient diffusion approach for momentum, correction terms to allow for non-local, boundary-layer scale transport should be incorporated.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 9","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004579","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998703","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":"ADAF: An Artificial Intelligence Data Assimilation Framework for Weather Forecasting","authors":"Yanfei Xiang,&nbsp;Weixin Jin,&nbsp;Haiyu Dong,&nbsp;Jonathan Weyn,&nbsp;Mingliang Bai,&nbsp;Zuliang Fang,&nbsp;Pengcheng Zhao,&nbsp;Hongyu Sun,&nbsp;Kit Thambiratnam,&nbsp;Qi Zhang,&nbsp;Xiaomeng Huang","doi":"10.1029/2024MS004839","DOIUrl":"10.1029/2024MS004839","url":null,"abstract":"<p>The forecasting skill of numerical weather prediction (NWP) models critically depends on the accurate initial conditions, also known as analysis, provided by data assimilation (DA). Traditional DA methods often face a trade-off between computational cost and accuracy due to complex linear algebra computations and the high dimensionality of the model, especially in non-linear systems. Moreover, processing massive data in real-time requires substantial computational resources. To address this, we introduce an artificial intelligence-based data assimilation framework (ADAF) to generate high-quality kilometer-scale analysis. This study is the pioneering work using real-world observations from varied locations and multiple sources to verify the AI method's efficacy in DA, including sparse surface weather observations and satellite imagery. We implemented ADAF for four near-surface variables in the Contiguous United States (CONUS). The results demonstrate that ADAF consistently aligns closely with actual observations, providing high-quality analysis fields capable of reconstructing extreme events, such as tropical cyclone wind fields. Sensitivity experiments reveal that ADAF can generate high-quality analysis even with low-accuracy backgrounds and extremely sparse surface observations. ADAF can assimilate multi-source observations within a three-hour window at low computational cost, taking about two seconds on an AMD MI200 graphics processing unit (GPU). ADAF-generated analysis fields improved short-term (0–6 hr) forecasts of an AI-based weather prediction model, outperforming HRRRDAS-initialized forecasts. ADAF has been shown to be efficient and effective in real-world DA, underscoring its potential role in operational weather forecasting.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 9","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004839","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935165","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":"An Assessment of Representing Land-Ocean Heterogeneity via CAPE Relaxation Timescale in the Community Atmospheric Model 6 (CAM6)","authors":"Bidyut Bikash Goswami,&nbsp;Andrea Polesello,&nbsp;Caroline Muller","doi":"10.1029/2025MS005035","DOIUrl":"10.1029/2025MS005035","url":null,"abstract":"&lt;p&gt;The time needed by deep convection to bring the atmosphere back to equilibrium is called convective adjustment timescale or simply adjustment timescale, typically denoted by &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;τ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $tau $&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;. In the Community Atmospheric Model|Community Atmosphere Model (CAM), &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;τ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $tau $&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; is the convective available potential energy (CAPE) relaxation timescale and is 1 hr, worldwide. Observational evidence suggests that &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;τ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $tau $&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; is generally longer than 1 hr. Further, continental and oceanic convection are different in terms of the vigor of updrafts and can have different longevities. So using &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;τ&lt;/mi&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $tau =1$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; hour worldwide in CAM has two potential caveats. A longer &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;τ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $tau $&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; improves the simulation of the mean climate. However, it does not address the land-ocean heterogeneity of atmospheric deep convection. We investigate the prescription of two different CAPE relaxation timescales for land (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;τ&lt;/mi&gt;\u0000 &lt;mi&gt;L&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${tau }_{L}=1$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; hr) and ocean (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;τ&lt;/mi&gt;\u0000 &lt;mi&gt;O&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${tau }_{O}=1$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; to 4 hr). It is arguably an extremely crude parameterization of boundary layer control on atmospheric convection. We contrast a suite of 5-year-long simulations with two different &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 ","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 9","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025MS005035","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935166","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":"Stochastic Parameterization: The Importance of Nonlocality and Memory","authors":"Martin T. Brolly","doi":"10.1029/2025MS005223","DOIUrl":"10.1029/2025MS005223","url":null,"abstract":"<p>Stochastic parameterizations deployed in models of the Earth system frequently invoke locality assumptions such as Markovianity or spatial locality. This work highlights the impact of such assumptions on predictive performance. Both in terms of short-term forecasting and the representation of long-term statistics, we find locality assumptions to be detrimental in idealized experiments. We show, however, that judicious choice of Markovian parameterization can mitigate errors due to assuming Markovianity. We propose a simple modification to standard Markovian parameterizations, which yields significant improvements in predictive skill while reducing computational cost. We further note a divergence between configurations of a parameterization which perform best in short-term prediction and those which best represent time-invariant statistics.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 9","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025MS005223","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144923804","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":"The Path Toward Vertical Grid Options for the Community Atmosphere Model Version 7: The Impact of Vertical Resolution on the QBO and Tropical Waves","authors":"Isla R. Simpson,&nbsp;Rolando R. Garcia,&nbsp;Julio T. Bacmeister,&nbsp;Peter H. Lauritzen,&nbsp;Cecile Hannay,&nbsp;Brian Medeiros,&nbsp;Julie Caron,&nbsp;Gokhan Danabasoglu,&nbsp;Adam Herrington,&nbsp;Christiane Jablonowski,&nbsp;Dan Marsh,&nbsp;Richard B. Neale,&nbsp;Lorenzo M. Polvani,&nbsp;Jadwiga H. Richter,&nbsp;Nan Rosenbloom,&nbsp;Simone Tilmes","doi":"10.1029/2025MS004957","DOIUrl":"10.1029/2025MS004957","url":null,"abstract":"<p>The Community Earth System Model currently contains two primary atmospheric configurations: the Community Atmosphere Model 6 (CAM6, 32 levels, <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>∼</mo>\u0000 </mrow>\u0000 <annotation> ${sim} $</annotation>\u0000 </semantics></math>40-km top); and the Whole Atmosphere Community Climate Model 6 (WACCM6, 70 levels, <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>∼</mo>\u0000 </mrow>\u0000 <annotation> ${sim} $</annotation>\u0000 </semantics></math>140-km top). For CAM7, a number of factors motivate a raising of the model top and enhancement of the vertical resolution and this study documents the decision making process toward this next generation vertical grid. As vertical resolution in the troposphere/lower stratosphere is increased, the role of the resolved waves in driving the Quasi-Biennial Oscillation (QBO) is enhanced, becoming more similar in magnitude to ERA5 reanalysis. This can be traced to improved equatorial Kelvin waves and their vertical momentum fluxes. It is further shown that a model lid at <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>∼</mo>\u0000 </mrow>\u0000 <annotation> ${sim} $</annotation>\u0000 </semantics></math>80-km does not have detrimental impacts on the representation of the QBO compared to a 140-km top. Based on this analysis, the vertical grid for CAM7 will have an <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>∼</mo>\u0000 </mrow>\u0000 <annotation> ${sim} $</annotation>\u0000 </semantics></math>80-km top with 93 levels, 500-m grid spacing in the troposphere and lower stratosphere, and 10 additional levels in the boundary layer compared to CAM6. A 58-level/<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>∼</mo>\u0000 </mrow>\u0000 <annotation> ${sim} $</annotation>\u0000 </semantics></math>40-km low-top option will also be available. We further introduce new coupled simulations using CAM6 but with CAM7's vertical grid above the boundary layer and use these to demonstrate that basic features of the stratospheric circulation are similar to WACCM6, despite the lower model top. These simulations further show that despite the higher fidelity of the QBO, the observed connection between the QBO and the Madden-Julian Oscillation is absent.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 9","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025MS004957","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144915226","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":"The Impact of Internal Variability on Benchmarking Deep Learning Climate Emulators","authors":"Björn Lütjens,&nbsp;Raffaele Ferrari,&nbsp;Duncan Watson-Parris,&nbsp;Noelle E. Selin","doi":"10.1029/2024MS004619","DOIUrl":"10.1029/2024MS004619","url":null,"abstract":"<p>Full-complexity Earth system models (ESMs) are computationally very expensive, limiting their use in exploring the climate outcomes of multiple emission pathways. More efficient emulators that approximate ESMs can directly map emissions onto climate outcomes, and benchmarks are being used to evaluate their accuracy on standardized tasks and data sets. We investigate a popular benchmark in data-driven climate emulation, ClimateBench, on which deep learning-based emulators are currently achieving the best performance. We compare these deep learning emulators with a linear regression-based emulator, akin to pattern scaling, and show that it outperforms the incumbent 100M-parameter deep learning foundation model, ClimaX, on 3 out of 4 regionally resolved climate variables, notably surface temperature and precipitation. While emulating surface temperature is expected to be predominantly linear, this result is surprising for emulating precipitation. Precipitation is a much more noisy variable, and we show that deep learning emulators can overfit to internal variability noise at low frequencies, degrading their performance in comparison to a linear emulator. We address the issue of overfitting by increasing the number of climate simulations per emission pathway (from 3 to 50) and updating the benchmark targets with the respective ensemble averages from the MPI-ESM1.2-LR model. Using the new targets, we show that linear pattern scaling continues to be more accurate on temperature, but can be outperformed by a deep learning-based technique for emulating precipitation. We publish our code and data at https://github.com/blutjens/climate-emulator.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 8","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004619","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144897778","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}