Journal of Advances in Modeling Earth Systems最新文献

{"title":"Diagnosing Nonlocal Vertical Acceleration in Moist Convection Using a Large-Eddy Simulation","authors":"Fu-Sheng Kao,&nbsp;Yi-Hung Kuo,&nbsp;Chien-Ming Wu","doi":"10.1029/2024MS004636","DOIUrl":"https://doi.org/10.1029/2024MS004636","url":null,"abstract":"<p>The anelastic theory of effective buoyancy has been generalized to include effects of momentum flux convergence, and has suggested that the dynamics—mediated by the nonlocal perturbation pressure—tends to average over forcing details, yielding vertical acceleration robust to small-scale variations of the flow. Here we aim to substantiate this theoretical assertion through examining a large-eddy simulation (LES) with a 100-m horizontal grid spacing. Specifically, instances of convection in the LES are identified. For these, the buoyancy and dynamic contributions to the vertical momentum tendency are separately diagnosed, and their sensitivity resulting from averaging over sub-cloud-scale features quantified. In the absence of a background shear or vorticity, both buoyancy and vertical momentum flux convergence are the leading effect in the vertical acceleration while the influence of the horizontal momentum flux convergence on the vertical motion appears to be substantially weaker. For deep-convective cases, these contributions at the cloud scale (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>∼</mo>\u0000 <mn>8</mn>\u0000 </mrow>\u0000 <annotation> ${sim} 8$</annotation>\u0000 </semantics></math> km) exhibit a robustness, as measured in a root-mean-square sense, to horizontally smoothing out turbulent features of scales <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>≲</mo>\u0000 <mn>3</mn>\u0000 </mrow>\u0000 <annotation> $lesssim 3$</annotation>\u0000 </semantics></math> km. As expected, such scales depend on the size of the convective element of interest, while dynamic contributions tend to be more susceptible to horizontal smoothing than does the buoyancy contribution. We thus argue that including the anelastic nonlocal dynamics can help capture the evolution of convective-cloud-scale flows without fully resolving the finer-scale turbulent features embedded in the flow. Results here lend support to simplifying the subgrid-scale representation of moist convection for global climate models and storm-resolving simulations.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004636","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143490004","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 Transparency and Consistency: An Open-Source Optics Parameterization for Clouds and Precipitation","authors":"Jing Feng,&nbsp;Raymond Menzel,&nbsp;David Paynter","doi":"10.1029/2024MS004478","DOIUrl":"https://doi.org/10.1029/2024MS004478","url":null,"abstract":"<p>In this study, a new open-source package for cloud and precipitation modeling is introduced. Based on Mie theory and existing ice crystal data sets, the scheme generates optical properties for user-defined gas bands, particle size distribution, and crystal habits, ensuring continuity across wide spectral bands and from small particles (clouds) to large particles (precipitation). Compared with existing schemes in GFDL's AM4-MG2, it reduces shortwave reflection of liquid clouds at the top of the atmosphere (TOA) by 1.50 Wm⁻² and increases that of ice clouds by 1.62 Wm⁻², based on offline radiative calculations. Using the new scheme, we find that cloud radiative effects are sensitive to microphysics variables such as particle size and habit, which affect the effective radius. Systematic flux biases may arise if the effective radius is not fully predicted in microphysics due to predefined size and habit distributions. We show that assuming spherical ice crystals underestimates ice-cloud radiative effects by 3.20 Wm⁻² in the longwave TOA and 2.76 Wm⁻² in the shortwave TOA. These biases can be addressed by improving the effective radius approximation with a volume-to-radius ratio derived from in-situ measurements. Combining these findings, we propose that climate models use a set of optics parameterizations for each hydrometeor type while adequently accounting for radiation effects caused by size and habit distributions. Uncertainties due to this simplification are evaluated. This study offers a consistent and physically based representation of radiative processes of clouds and precipitation in weather and climate simulations.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004478","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143490003","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":"Characterizing How Meteorological Forcing Selection and Parameter Uncertainty Influence Community Land Model Version 5 Hydrological Applications in the United States","authors":"Hisham Eldardiry,&nbsp;Ning Sun,&nbsp;Hongxiang Yan,&nbsp;Patrick Reed,&nbsp;Travis Thurber,&nbsp;Jennie Rice","doi":"10.1029/2024MS004222","DOIUrl":"https://doi.org/10.1029/2024MS004222","url":null,"abstract":"<p>Despite the increasing use of large-scale Land Surface Models (LSMs) in predicting hydrological responses in extreme conditions, there's a critical gap in understanding the uncertainties in these predictions. This study addresses this gap through a detailed diagnostic evaluation of the uncertainties arising from meteorological forcing selection and model parametrization in hydrological simulations of the Community Land Model version 5 (CLM5). CLM5 is configured at a spatial scale of about 12-km to simulate runoff processes for 464 headwater watersheds, selected from the Catchment Attributes for Large-Sample Studies (CAMELS) data set to be representative of physiographic and climatic gradients across the conterminous United States. For each watershed, CLM5 is driven by five commonly used gridded forcing data sets in combination with a large ensemble (&gt;1,200) of key CLM5 hydrologic parameters. Our results suggest that uncertainty in CLM5 runoff simulations resulting from both forcing and parametric sources is markedly higher in arid regions, for example, Great Plains and Midwest regions. Uncertainty in low flow is dominated by parametric uncertainty, while the selection of meteorological forcing contributes more dominantly to high flow and seasonal flows during fall and spring. Our analysis also demonstrates that the selection of forcing data sets and the metrics used to calibrate CLM5 significantly impact the model's predictive accuracy in extreme event severity for both floods and droughts. Overall, the results from this study highlight the need to understand and account for forcing and parametric uncertainties in CLM5 simulations, particularly for hazard and risk assessments addressing hydrologic extremes.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004222","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143490005","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":"Insights on Tropical High-Cloud Radiative Effect From a New Conceptual Model","authors":"Jakob Deutloff,&nbsp;Stefan A. Buehler,&nbsp;Manfreth Brath,&nbsp;Ann Kristin Naumann","doi":"10.1029/2024MS004615","DOIUrl":"https://doi.org/10.1029/2024MS004615","url":null,"abstract":"&lt;p&gt;The new capabilities of global storm-resolving models to resolve individual clouds allow for a more physical perspective on the tropical high-cloud radiative effect and how it might change with warming. In this study, we develop a conceptual model of the high-cloud radiative effect as a function of cloud thickness measured by ice water path. We use atmospheric profiles from a global ICON simulation with &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;5&lt;/mn&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;mi&gt;k&lt;/mi&gt;\u0000 &lt;mi&gt;m&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $5hspace*{.5em}mathrm{k}mathrm{m}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; horizontal grid spacing to calculate the radiation offline with the ARTS line-by-line radiative transfer model. The conceptual model of the high-cloud radiative effect reveals that it is sufficient to approximate high clouds as a single layer characterized by an albedo, emissivity and temperature, which vary with ice water path. The increase of the short-wave high-cloud radiative effect with ice water path is solely explained by the high-cloud albedo. The increase of the long-wave high-cloud radiative effect with ice water path is governed by an increase of emissivity for ice water path below &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;msup&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;mi&gt;k&lt;/mi&gt;\u0000 &lt;mi&gt;g&lt;/mi&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;msup&gt;\u0000 &lt;mi&gt;m&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $1{0}^{-1}hspace*{.5em}mathrm{k}mathrm{g}hspace*{.5em}{mathrm{m}}^{-mathrm{2}}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, and by a decrease of high-cloud temperature with increasing ice water path above this threshold. The mean high-cloud radiative effect from the ARTS simulations for the chosen day of this ICON model run is &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1.25&lt;/mn&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;mi&gt;W&lt;/mi&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;msup&gt;\u0000 &lt;mi&gt;m&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $1.25hspace*{.5em}mathrm{W}hspace*{.5em","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 2","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004615","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489764","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 Novel Ensemble-Based Parameter Estimation for Improving Ocean Biogeochemistry in an Earth System Model","authors":"Tarkeshwar Singh,&nbsp;François Counillon,&nbsp;Jerry Tjiputra,&nbsp;Yiguo Wang","doi":"10.1029/2024MS004237","DOIUrl":"https://doi.org/10.1029/2024MS004237","url":null,"abstract":"<p>Estimating ocean biogeochemistry (BGC) parameters in Earth System Models is challenging due to multiple error sources and interlinked parameter sensitivities. Reducing the temperature and salinity bias in the ocean physical component of the Norwegian Earth System Model (NorESM) diminishes the BGC state bias at intermediate depth but leads to a greater bias increase near the surface. This suggests that BGC parameters are tuned to compensate for the physical ocean model biases. We successfully apply the iterative ensemble smoother data assimilation technique to estimate BGC parameters in NorESM with reduced bias in its physical ocean component. We estimate BGC parameters based on the monthly climatological error of nitrate, phosphate, and oxygen in a coupled reanalysis of NorESM that assimilates observed monthly climatology of temperature and salinity. First, we compare the performance of globally uniform and spatially varying parameter estimations. Both approaches reduce BGC bias obtained with default parameters, even for variables not assimilated in the parameter estimation (e.g., <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> fluxes and primary production). While spatial parameter estimation performs locally best, it also increases biases in areas with few observations, and overall performs poorer than global parameter estimation. A second iteration further reduces the bias in the near-surface BGC with global parameter estimation. Finally, we assess the performance of global estimated parameters in a 30-year coupled reanalysis produced by assimilating time-varying temperature and salinity observations. This reanalysis reduces error by 10%–20% for phosphate, nitrate, oxygen, and dissolved inorganic carbon compared to a reanalysis done with default parameters.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 2","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004237","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143481602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved Understanding of Multicentury Greenland Ice Sheet Response to Strong Warming in the Coupled CESM2-CISM2 With Regional Grid Refinement","authors":"Ziqi Yin,&nbsp;Adam R. Herrington,&nbsp;Rajashree Tri Datta,&nbsp;Aneesh C. Subramanian,&nbsp;Jan T. M. Lenaerts,&nbsp;Andrew Gettelman","doi":"10.1029/2024MS004310","DOIUrl":"https://doi.org/10.1029/2024MS004310","url":null,"abstract":"&lt;p&gt;The simulation of ice sheet-climate interactions, such as surface mass balance fluxes, is sensitive to model grid resolution. Here we simulate the multi-century evolution of the Greenland Ice Sheet (GrIS) and its interaction with the climate using the Community Earth System Model version 2.2 (CESM2.2) including an interactive GrIS component (the Community Ice Sheet Model v2.1 [CISM2.1]) under an idealized warming scenario (atmospheric &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mtext&gt;CO&lt;/mtext&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${text{CO}}_{2}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; increases by 1% &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mtext&gt;yr&lt;/mtext&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${text{yr}}^{-1}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; until quadrupling the pre-industrial level and then is held fixed). A variable-resolution (VR) grid with 1/&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;4&lt;/mn&gt;\u0000 &lt;mo&gt;°&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $4{}^{circ}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; regional refinement over the broader Arctic and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mo&gt;°&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $1{}^{circ}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; resolution elsewhere is applied to the atmosphere and land components, and the results are compared with conventional &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mo&gt;°&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $1{}^{circ}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; lat-lon grid simulations to investigate the impact of grid refinement. Compared with the &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mo&gt;°&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $1{}^{circ}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; runs, the VR run features a slower rate of surface melt, especially over the western and northern GrIS, where the ice surface slopes gently toward the periphery. This difference pattern originates primarily from higher snow albedo and, thus, weaker albedo feedback in the VR run. The VR grid better captures the CISM ice sheet topography by reducing elevation discrepancies between CAM and CISM and is, ","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 2","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004310","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143475724","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":"Coupled Lake-Atmosphere-Land Physics Uncertainties in a Great Lakes Regional Climate Model","authors":"William J. Pringle,&nbsp;Chenfu Huang,&nbsp;Pengfei Xue,&nbsp;Jiali Wang,&nbsp;Khachik Sargsyan,&nbsp;Miraj B. Kayastha,&nbsp;T. C. Chakraborty,&nbsp;Zhao Yang,&nbsp;Yun Qian,&nbsp;Robert D. Hetland","doi":"10.1029/2024MS004337","DOIUrl":"https://doi.org/10.1029/2024MS004337","url":null,"abstract":"<p>This study develops a surrogate-based method to assess the uncertainty within a convective permitting integrated modeling system of the Great Lakes region, arising from interacting physics parameterizations across the lake, atmosphere, and land surface. Perturbed physics ensembles of the model during the 2018 summer are used to train a neural network surrogate model to predict lake surface temperature (LST) and near-surface air temperature (T2m). Average physics uncertainties are determined to be 1.5<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>°</mo>\u0000 </mrow>\u0000 <annotation> ${}^{circ}$</annotation>\u0000 </semantics></math>C for LST and T2m over land, and 1.9<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>°</mo>\u0000 </mrow>\u0000 <annotation> ${}^{circ}$</annotation>\u0000 </semantics></math>C for T2m over lake, but these have significant spatiotemporal variations. We find that atmospheric physics parameterizations alone are the dominant sources of uncertainty (45%–53%), while lake and land parameterizations account for 33% and 38% of the uncertainty of LST and T2m over land respectively. Interactions of atmosphere physics parameterizations with those of the land and lake contribute to an additional 13%–17% of the total variance. LST and T2m over the lake are more uncertain in the deeper northern lakes, particularly during the rapid warming phase that occurs in late spring/early summer. The LST uncertainty increases with sensitivity to the lake model's surface wind stress scheme. T2m over land is more uncertain over forested areas in the north, where it is most sensitive to the land surface model, than the more agricultural land in the south, where it is most sensitive to the atmospheric planetary boundary and surface layer scheme. Uncertainty also increases in the southwest during multiday temperature declines with higher sensitivity to the land surface model.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 2","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004337","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455956","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":"Atmospheric Transport Modeling of CO2 With Neural Networks","authors":"Vitus Benson,&nbsp;Ana Bastos,&nbsp;Christian Reimers,&nbsp;Alexander J. Winkler,&nbsp;Fanny Yang,&nbsp;Markus Reichstein","doi":"10.1029/2024MS004655","DOIUrl":"https://doi.org/10.1029/2024MS004655","url":null,"abstract":"&lt;p&gt;Accurately describing the distribution of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mtext&gt;CO&lt;/mtext&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${text{CO}}_{2}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; in the atmosphere with atmospheric tracer transport models is essential for greenhouse gas monitoring and verification support systems to aid implementation of international climate agreements. Large deep neural networks are poised to revolutionize weather prediction, which requires 3D modeling of the atmosphere. While similar in this regard, atmospheric transport modeling is subject to new challenges. Both, stable predictions for longer time horizons and mass conservation throughout need to be achieved, while IO plays a larger role compared to computational costs. In this study we explore four different deep neural networks (UNet, GraphCast, Spherical Fourier Neural Operator and SwinTransformer) which have proven as state-of-the-art in weather prediction to assess their usefulness for atmospheric tracer transport modeling. For this, we assemble the CarbonBench data set, a systematic benchmark tailored for machine learning emulators of Eulerian atmospheric transport. Through architectural adjustments, we decouple the performance of our emulators from the distribution shift caused by a steady rise in atmospheric &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mtext&gt;CO&lt;/mtext&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${text{CO}}_{2}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;. More specifically, we center &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mtext&gt;CO&lt;/mtext&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${text{CO}}_{2}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; input fields to zero mean and then use an explicit flux scheme and a mass fixer to assure mass balance. This design enables stable and mass conserving transport for over 6 months with all four neural network architectures. In our study, the SwinTransformer displays particularly strong emulation skill: 90-day &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mi&gt;R&lt;/mi&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msup&gt;\u0000 &lt;mo&gt;&gt;&lt;/mo&gt;\u0000 &lt;mn&gt;0.99&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${R}^{2} &gt; 0.99$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; and physically plausible multi-year forward runs. This work paves the way toward high resolution forward and","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 2","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004655","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143404505","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 Ocean, Sea Ice or Atmosphere Initialization on Seasonal Prediction of Regional Antarctic Sea Ice","authors":"Yongwu Xiu,&nbsp;Yiguo Wang,&nbsp;Hao Luo,&nbsp;Lilian Garcia-Oliva,&nbsp;Qinghua Yang","doi":"10.1029/2024MS004382","DOIUrl":"https://doi.org/10.1029/2024MS004382","url":null,"abstract":"<p>Dynamical modeling is widely utilized for Antarctic sea ice prediction. However, the relative impact of initializing different model components remains unclear. We compare three sets of hindcasts of the Norwegian Climate Prediction Model (NorCPM), which are initialized by ocean, ocean/sea-ice, or atmosphere data and referred to as the OCN, OCNICE, and ATM hindcasts hereafter. The seasonal cycle of sea ice extent (SIE) in the ATM reanalysis shows a slightly better agreement with observations than the OCN and OCNICE reanalyzes. The trends of sea ice concentration (SIC) in the OCN and OCNICE reanalyzes compare well to observations, but the ATM reanalysis is poor over the western Antarctic. The OCNICE reanalysis yields the most accurate estimation of sea ice variability, while the OCN and ATM reanalyzes are comparable. Evaluation of the hindcasts reveals the predictive skill varies with region and season. Austral winter SIE of the western Antarctic can be skillfully predicted 12 months ahead, while the predictive skill in the eastern Antarctic is low. Austral winter SIE predictability can be largely attributed to high sea surface temperature predictability, thanks to skillful initialization of ocean heat content. The ATM hindcast from July or October performs best due to the effective initialization of sea-ice thickness, which enhances prediction skills until early austral summer via its long memory. Meanwhile, the stratosphere-troposphere coupling contributes to the prediction of springtime. The comparable skill between the OCN and OCNICE hindcasts implies limited benefits from SIC data on prediction when using ocean data.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 2","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004382","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143404599","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":"Integrating the Interconnections Between Groundwater and Land Surface Processes Through the Coupled NASA Land Information System and ParFlow Environment","authors":"Fadji Z. Maina,&nbsp;Dan Rosen,&nbsp;Peyman Abbaszadeh,&nbsp;Chen Yang,&nbsp;Sujay V. Kumar,&nbsp;Matthew Rodell,&nbsp;Reed Maxwell","doi":"10.1029/2024MS004415","DOIUrl":"https://doi.org/10.1029/2024MS004415","url":null,"abstract":"<p>Understanding the interactions between the atmosphere, the land, and the subsurface is fundamental to hydrology and is critical for a better assessment of the impacts of climate change and human management on hydrological systems. However, many land surface models simplify the subsurface hydrology and thereby these interactions. In this study, we couple the land surface model Noah-MP included in the NASA Land Information System (LIS) with the integrated hydrologic model ParFlow (ParFlow-LIS) using the Earth System Modeling Framework (ESMF) and the National United Operational Prediction Capability (NUOPC). This coupling improves the simulation of water and energy cycle processes by adding the three-dimensional variably saturated and heterogeneous flow in the subsurface using sophisticated and nonlinear physics-based equations as well as the advances in satellite remote sensing-based data assimilation of the land surface, thereby benefiting the integrated hydrologic modeling and data assimilation community. We use the High Plains aquifer, located in the central United States, as a testbed to evaluate the coupled ParFlow-LIS system. The new ParFlow-LIS system accounts for the effects of topographically driven flows on the land surface, producing more fine-scale patterns of land surface states and fluxes than standalone LIS. In addition, ParFlow-LIS enables the consideration of the effect of subsurface water storage on evapotranspiration. This is particularly important in areas and times with dry soils, such as during drought conditions or in the presence of a cone of depression due to pumping.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 2","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004415","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143404600","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}