Journal of Advances in Modeling Earth Systems最新文献

{"title":"Improved Understanding of Multicentury Greenland Ice Sheet Response to Strong Warming in the Coupled CESM2-CISM2 With Regional Grid Refinement","authors":"Ziqi Yin, Adam R. Herrington, Rajashree Tri Datta, Aneesh C. Subramanian, Jan T. M. Lenaerts, Andrew Gettelman","doi":"10.1029/2024MS004310","DOIUrl":"https://doi.org/10.1029/2024MS004310","url":null,"abstract":"<p>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 <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> increases by 1% <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mtext>yr</mtext>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation> ${text{yr}}^{-1}$</annotation>\u0000 </semantics></math> until quadrupling the pre-industrial level and then is held fixed). A variable-resolution (VR) grid with 1/<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>4</mn>\u0000 <mo>°</mo>\u0000 </mrow>\u0000 <annotation> $4{}^{circ}$</annotation>\u0000 </semantics></math> regional refinement over the broader Arctic and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 <mo>°</mo>\u0000 </mrow>\u0000 <annotation> $1{}^{circ}$</annotation>\u0000 </semantics></math> resolution elsewhere is applied to the atmosphere and land components, and the results are compared with conventional <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 <mo>°</mo>\u0000 </mrow>\u0000 <annotation> $1{}^{circ}$</annotation>\u0000 </semantics></math> lat-lon grid simulations to investigate the impact of grid refinement. Compared with the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 <mo>°</mo>\u0000 </mrow>\u0000 <annotation> $1{}^{circ}$</annotation>\u0000 </semantics></math> 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}

{"title":"The Rapid Transition From Shallow to Precipitating Convection as a Predator–Prey Process","authors":"Cristian V. Vraciu,&nbsp;Julien Savre,&nbsp;Maxime Colin","doi":"10.1029/2024MS004630","DOIUrl":"https://doi.org/10.1029/2024MS004630","url":null,"abstract":"<p>Properly predicting the rapid transition from shallow to precipitating atmospheric convection within a diurnal cycle over land is of great importance for both weather prediction and climate projections. In this work, we consider that a cumulus cloud is formed due to the transport of water mass by multiple updrafts during its lifetime. Cumulus clouds then locally create favorable conditions for the subsequent convective updrafts to reach higher altitudes, leading to deeper precipitating convection. This mechanism is amplified by the cold pools formed by the evaporation of precipitation in the sub-cloud layer. Based on this conceptual view of cloud–cloud interactions which goes beyond the one cloud equals one–plume picture, it is argued that precipitating clouds may act as predators that prey on the total cloud population, such that the rapid shallow–to–deep transition can be modeled as a simple predator–prey system. This conceptual model is validated by comparing solutions of the Lotka-Volterra system of equations to results obtained using a high-resolution large-eddy simulation model. Moreover, we argue that the complete diurnal cycle of deep convection can be seen as a predator–prey system with varying food supply for the prey. Finally, we suggest that based on the present conceptual model, new unified cloud-convection parameterizations can be designed which may lead to improved representations of the transition from shallow to precipitating continental convection.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 2","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004630","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143397057","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 Influence of Parameterized Shallow Convection on Trade-Wind Clouds and Circulations in the HARMONIE-AROME Mesoscale Model","authors":"A. C. M. Savazzi,&nbsp;L. Nuijens,&nbsp;W. de Rooy,&nbsp;A. P. Siebesma","doi":"10.1029/2024MS004538","DOIUrl":"https://doi.org/10.1029/2024MS004538","url":null,"abstract":"<p>Mesoscale numerical weather prediction models currently operate at kilometer-scale and even sub-kilometer-scale resolutions. Although shallow cumulus convection is partly resolved at these resolutions, it is still common to use a shallow cumulus parameterization (SCP). Within the context of the EUREC4A model intercomparison project, we evaluate how the modeled mesoscale cloud field in the trades responds to parameterized or explicit shallow convection in the mesoscale model HARMONIE-AROME. We simulate a region of 3,200 × 2,025 <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mtext>km</mtext>\u0000 <mn>2</mn>\u0000 </msup>\u0000 </mrow>\u0000 <annotation> ${text{km}}^{2}$</annotation>\u0000 </semantics></math> east of Barbados using a grid spacing of 2.5 km for a 2 months period (1 January to 29 February 2020). We compare three configurations of HARMONIE-AROME: (a) one with an active SCP (control), (b) one without parameterized momentum transport by shallow convection, and (c) one with an inactive SCP. The experiments produce different responses in the cloud field that are not incremental. With the SCP inactive, the model produces a warmer lower troposphere with many smaller but deeper clouds that precipitate more. Along with stronger resolved eddy kinetic energy, wider and stronger shallow meridional overturning circulations develop. In the configuration without parameterized momentum transport by shallow convection, the eddy-diffusivity scheme effectively takes over the missing transport in the sub-cloud layer up to <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>∼</mo>\u0000 </mrow>\u0000 <annotation> ${sim} $</annotation>\u0000 </semantics></math> 800 m. Above that level, horizontal wind variance increases as the total momentum flux decreases, enhancing eddy kinetic energy at scales of 2.5 km and larger. In contrast to the configuration with an inactive SCP, cloud top heights hardly deepen, but stratiform cloudiness below the inversion and mean cloud size increase.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 2","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004538","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143389006","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 Terrestrial Ecosystem Carbon Sink Assessment Model Considering Forest Age Dynamics (CEVSA-AgeD)","authors":"Mengyu Zhang,&nbsp;Honglin He,&nbsp;Li Zhang,&nbsp;Guirui Yu,&nbsp;Xiaoli Ren,&nbsp;Yuanyuan Huang,&nbsp;Wenping Yuan,&nbsp;Zhong'en Niu","doi":"10.1029/2024MS004575","DOIUrl":"https://doi.org/10.1029/2024MS004575","url":null,"abstract":"<p>The large variation in net ecosystem productivity (NEP) with forest age was dominated by the dynamics of net primary productivity (NPP)–which in turn was determined by the different response slopes of gross primary productivity (GPP) and autotrophic respiration (Ra) with forest age. However, only few models can comprehensively represent the impacts of forest age and global changes including land-use change, climate change, nitrogen deposition, and atmospheric CO₂ from the perspective of ecological processes. Based on a process-based model (CEVSA-ES) that included these global changes, we developed an ecosystem carbon sink assessment model considering forest age dynamics (CEVSA-AgeD) using satellite-based relationships between GPP (or Ra) and forest age to constrain photosynthesis and autotrophic respiration processes. Subsequently, we used a model data-fusion framework combined with carbon flux observations to calibrate the model. The calibrated CEVSA-AgeD model performed well in simulating seasonal (R² values for GPP, ecosystem respiration, and NEP were 0.86, 0.79, and 0.66, respectively) and annual carbon flux changes (R² of GPP, ecosystem respiration, and NEP were 0.83, 0.77, and 0.67, respectively). The magnitude of average NEP in China estimated using this model was 0.35 ± 0.005 TgC/yr from 2001 to 2021, which was close to previous estimates, and the dynamics of forests age increased NEP by 87–92 TgC/yr. These results indicate that the CEVSA-AgeD model performed well in simulating carbon fluxes at the site and regional scales and that it was necessary to incorporate the effect of forest age dynamics on carbon cycling processes into process-based models.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 2","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004575","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143389007","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":"Predicting the Evolution of Shallow Cumulus Clouds With a Lotka-Volterra Like Model","authors":"Jingyi Chen,&nbsp;Samson Hagos,&nbsp;Jerome Fast,&nbsp;Zhe Feng","doi":"10.1029/2023MS003739","DOIUrl":"https://doi.org/10.1029/2023MS003739","url":null,"abstract":"<p>In numerical weather prediction and climate models, boundary-layer clouds are controlled by a wide range of subgrid-scale processes. However, understanding the nature of these processes and their role in the evolution of the cloud size distribution as a whole has been elusive. To address this issue, we adopt a novel empirical framework from the field of population dynamics to model the evolution of cloud size statistics by using the shallow cumulus properties obtained from a large-eddy simulation (LES). Our approach involves representing the cloud size distribution and the total cloud area using a revised Lotka-Volterra model and ridge linear model, respectively. The physical interpretation of the total cloud area and coefficients obtained from the optimization of the models reveals three stages probably interpreted by dominant processes: the formation of new clouds, the growth of single clouds, and a steady state with organized transitions involving the growth and decay of multiple clouds. Furthermore, we showcase the potential of this framework to serve as a component of scale-aware parameterizations of shallow-convective clouds in atmospheric models.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 2","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS003739","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143389008","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":"Ensemble-Based Spatially Distributed CLM5 Hydrological Parameter Estimation for the Continental United States","authors":"Hongxiang Yan,&nbsp;Ning Sun,&nbsp;Hisham Eldardiry,&nbsp;Travis Thurber,&nbsp;Patrick Reed,&nbsp;Daniel Kennedy,&nbsp;Sean Swenson,&nbsp;Jennie Rice","doi":"10.1029/2024MS004227","DOIUrl":"https://doi.org/10.1029/2024MS004227","url":null,"abstract":"<p>One of the major challenges in large-domain hydrological modeling efforts lies in the estimation of spatially distributed hydrological parameters while simultaneously accounting for their associated uncertainties. Addressing this challenge is particularly difficult in ungauged locations. With growing societal demands for large-scale streamflow projections to inform water resource management and long-term planning, evaluating and constraining hydrological parameter uncertainty is increasingly vital. This study introduces a hybrid regionalization approach to enhance hydrological predictions of the Community Land Model version 5 (CLM5) across the Continental United States (CONUS), with a total of 50,629 1/8° grid cells. This hybrid method combines the strengths of two existing techniques: parameter regionalization and streamflow signature regionalization. It identifies ensemble behavioral parameters for each 1/8° grid cell across the CONUS domain, tailored to three distinct streamflow signatures focused on low flows, high flows, and annual water balance. Evaluating this hybrid method for 464 CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) basins demonstrates a significant improvement in CLM5 hydrological predictions, even in challenging arid regions. In CONUS applications, the derived spatially distributed parameter sets capture both spatial continuity and variation of parameters, highlighting their heterogeneous nature within specific regions. Overall, this hybrid regionalization approach offers a promising solution to the complex task of improving hydrological modeling over large domains for important hydrological applications.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 2","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004227","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362517","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}