Journal of Advances in Modeling Earth Systems最新文献

{"title":"Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics","authors":"Yushu Xia,&nbsp;Jonathan Sanderman,&nbsp;Jennifer D. Watts,&nbsp;Megan B. Machmuller,&nbsp;Andrew L. Mullen,&nbsp;Charlotte Rivard,&nbsp;Arthur Endsley,&nbsp;Haydee Hernandez,&nbsp;John Kimball,&nbsp;Stephanie A. Ewing,&nbsp;Marcy Litvak,&nbsp;Tomer Duman,&nbsp;Praveena Krishnan,&nbsp;Tilden Meyers,&nbsp;Nathaniel A. Brunsell,&nbsp;Binayak Mohanty,&nbsp;Heping Liu,&nbsp;Zhongming Gao,&nbsp;Jiquan Chen,&nbsp;Michael Abraha,&nbsp;Russell L. Scott,&nbsp;Gerald N. Flerchinger,&nbsp;Patrick E. Clark,&nbsp;Paul C. Stoy,&nbsp;Anam M. Khan,&nbsp;E. N. Jack Brookshire,&nbsp;Quan Zhang,&nbsp;David R. Cook,&nbsp;Thomas Thienelt,&nbsp;Bhaskar Mitra,&nbsp;Marguerite Mauritz-Tozer,&nbsp;Craig E. Tweedie,&nbsp;Margaret S. Torn,&nbsp;Dave Billesbach","doi":"10.1029/2024MS004342","DOIUrl":"https://doi.org/10.1029/2024MS004342","url":null,"abstract":"<p>Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long-term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C-cycle processes. Bayesian calibration was conducted using quality-controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass-shrub mixture, and grass-tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (<i>R</i>² &gt; 0.6, RMSE &lt;390 g C m⁻²) relative to net ecosystem exchange of CO₂ (NEE) (<i>R</i>² &gt; 0.4, RMSE &lt;180 g C m⁻²). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with <i>R</i>² = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long-term network-based monitoring of vegetation biomass, C fluxes, and SOC stocks.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004342","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629891","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":"Benchmark Framework for Global River Models","authors":"Xudong Zhou,&nbsp;Dai Yamazaki,&nbsp;Menaka Revel,&nbsp;Gang Zhao,&nbsp;Prakat Modi","doi":"10.1029/2024MS004379","DOIUrl":"https://doi.org/10.1029/2024MS004379","url":null,"abstract":"<p>Global River Models (GRMs), which simulate river flow and flood processes, have rapidly developed in recent decades. However, these advancements necessitate meaningful and standardized quality assessments and comparisons against a suitable set of observational variables using appropriate metrics, a requirement currently lacking within GRM communities. This study proposes implementing a benchmark system designed to facilitate the assessment of river models and enable comparisons against established benchmarks. The benchmark system incorporates satellite remote sensing data complementing in situ data, including water surface elevation and inundation extent information, with necessary preprocessing. Consequently, this evaluation system encompasses a larger geographical area than traditional methods relying solely on in-situ river discharge measurements for GRMs. A set of evaluation and comparison metrics has been developed, including a quantile-based comparison metric that allows for a comprehensive analysis of multiple simulation outputs. The test application of this benchmark system to a global river model (CaMa-Flood), utilizing diverse runoff inputs, illustrates that incorporating bias-corrected runoff data leads to improved model performance across various observational variables and performance metrics. The current iteration of the benchmark system is suitable for global-scale assessments and can effectively evaluate the impact of model development and facilitate intercomparisons among different models. The source codes are accessible from https://doi.org/10.5281/zenodo.10903210.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004379","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622350","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":"Forest Carbon Modeling Improved Through Hierarchical Assimilation of Pool-Based Measurements","authors":"Yu Zhou,&nbsp;Christopher A. Williams","doi":"10.1029/2024MS004622","DOIUrl":"https://doi.org/10.1029/2024MS004622","url":null,"abstract":"<p>Accurate assessment of forest carbon dynamics is a critical element of appraising forest-based Natural Climate Solutions. National forest inventory and analysis (FIA) data provide valuable pool-based estimates of carbon stocks, but have been underutilized to inform carbon cycle modeling for forest carbon dynamics with stand development. This study introduces a hierarchical data assimilation (HDA) framework to optimize modeling parameters by incrementally assimilating measured carbon pool data into the model. We found that most carbon stocks could be reproduced by constrained parameters after each HDA step. Using aboveground live biomass (AGB) alone in HDA was able to reproduce the AGB trajectories but introduced biases in estimating the downstream dead biomass and soil carbon pools. Assimilating dead biomass measurements narrowed the posterior space of parameter solutions and improved consistency between measured and modeled carbon dynamics. The HDA framework also reduced uncertainties on modeled carbon fluxes. Young stands were found to release less carbon when the model was informed by dead biomass compared to simulations guided by aboveground biomass alone. The remaining mismatches between modeled and FIA pool estimates could be attributed to wide uncertainty in some FIA estimates, differing definitions of functional carbon pools, and structural rigidity in the model. Together, this study underscores the importance of pool-based measurements in forest carbon modeling, which improves the model-observation fit and reduces process-model uncertainty.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004622","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622337","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":"Using Deep Learning in Ensemble Streamflow Forecasting: Exploring the Predictive Value of Explicit Snowpack Information","authors":"Parthkumar Modi,&nbsp;Keith Jennings,&nbsp;Joseph Kasprzyk,&nbsp;Eric Small,&nbsp;Cameron Wobus,&nbsp;Ben Livneh","doi":"10.1029/2024MS004582","DOIUrl":"https://doi.org/10.1029/2024MS004582","url":null,"abstract":"<p>The Ensemble Streamflow Prediction (ESP) framework combines a probabilistic forecast structure with process-based models for water supply predictions. However, process-based models require computationally intensive parameter estimation, increasing uncertainties and limiting usability. Motivated by the strong performance of deep learning models, we seek to assess whether the Long Short-Term Memory (LSTM) model can provide skillful forecasts and replace process-based models within the ESP framework. Given challenges in <i>implicitly</i> capturing snowpack dynamics within LSTMs for streamflow prediction, we also evaluated the added skill of <i>explicitly</i> incorporating snowpack information to improve hydrologic memory representation. LSTM-ESPs were evaluated under four different scenarios: one excluding snow and three including snow with varied snowpack representations. The LSTM models were trained using information from 664 GAGES-II basins during WY1983–2000. During a testing period, WY2001–2010, 80% of basins exhibited Nash-Sutcliffe Efficiency (NSE) above 0.5 with a median NSE of around 0.70, indicating satisfactory utility in simulating seasonal water supply. LSTM-ESP forecasts were then tested during WY2011–2020 over 76 western US basins with operational Natural Resources Conservation Services (NRCS) forecasts. A key finding is that in high snow regions, LSTM-ESP forecasts using simplified ablation assumptions performed worse than those excluding snow, highlighting that snow data do not consistently improve LSTM-ESP performance. However, LSTM-ESP forecasts that explicitly incorporated past years' snow accumulation and ablation performed comparably to NRCS forecasts and better than forecasts excluding snow entirely. Overall, integrating deep learning within an ESP framework shows promise and highlights important considerations for including snowpack information in forecasting.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004582","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143595289","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":"Reducing Long-Standing Surface Ozone Overestimation in Earth System Modeling by High-Resolution Simulation and Dry Deposition Improvement","authors":"Yang Gao,&nbsp;Wenbin Kou,&nbsp;Wenxuan Cheng,&nbsp;Xiuwen Guo,&nbsp;Binglin Qu,&nbsp;Yubing Wu,&nbsp;Shaoqing Zhang,&nbsp;Hong Liao,&nbsp;Deliang Chen,&nbsp;L. Ruby Leung,&nbsp;Oliver Wild,&nbsp;Junxi Zhang,&nbsp;Guangxing Lin,&nbsp;Hang Su,&nbsp;Yafang Cheng,&nbsp;Ulrich Pöschl,&nbsp;Andrea Pozzer,&nbsp;Leiming Zhang,&nbsp;Jean-Francois Lamarque,&nbsp;Alex B. Guenther,&nbsp;Guy Brasseur,&nbsp;Zhao Liu,&nbsp;Haitian Lu,&nbsp;Chenlin Li,&nbsp;Bin Zhao,&nbsp;Shuxiao Wang,&nbsp;Xin Huang,&nbsp;Jingshan Pan,&nbsp;Guangliang Liu,&nbsp;Xin Liu,&nbsp;Haipeng Lin,&nbsp;Yuanhong Zhao,&nbsp;Chun Zhao,&nbsp;Junlei Meng,&nbsp;Xiaohong Yao,&nbsp;Huiwang Gao,&nbsp;Lixin Wu","doi":"10.1029/2023MS004192","DOIUrl":"https://doi.org/10.1029/2023MS004192","url":null,"abstract":"<p>The overestimation of surface ozone concentration in low-resolution global atmospheric chemistry and climate models has been a long-standing issue. We first update the ozone dry deposition scheme in both high- (0.25°) and low-resolution (1°) Community Earth System Model (CESM) version 1.3 runs, by adding the effects of leaf area index and correcting the sunlit and shaded fractions of stomatal resistances. With this update, 5-year-long summer simulations (2015–2019) using the low-resolution CESM still exhibit substantial ozone overestimation (by 6.0–16.2 ppbv) over the U.S., Europe, eastern China, and ozone pollution hotspots. The ozone dry deposition scheme is further improved by adjusting the leaf cuticle conductance, reducing the mean ozone bias by 19%, and increasing the model resolution further reduces the ozone overestimation by 43%. We elucidate the mechanism by which model grid spacing influences simulated ozone, revealing distinctive pathways in urban versus rural areas. In rural areas, grid spacing mainly affects daytime ozone levels, where additional NO_x emissions from nearby urban areas result in an ozone boost and overestimation in low-resolution simulations. In contrast, over urban areas, daytime ozone overestimation follows a similar mechanism due to the influence of volatile organic compounds from surrounding rural areas. However, nighttime ozone overestimation is closely linked to weakened NO titration owing to the redistribution of urban NO_x to rural areas. Additionally, stratosphere-troposphere exchange may also contribute to reducing ozone bias in high-resolution simulations, warranting further investigation. This optimized high-resolution CESM may enhance understanding of ozone formation mechanisms, sources, and changes in a warming climate.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023MS004192","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143571428","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":"Water Mass Transformation Budgets in Finite-Volume Generalized Vertical Coordinate Ocean Models","authors":"Henri F. Drake,&nbsp;Shanice Bailey,&nbsp;Raphael Dussin,&nbsp;Stephen M. Griffies,&nbsp;John Krasting,&nbsp;Graeme MacGilchrist,&nbsp;Geoffrey Stanley,&nbsp;Jan-Erik Tesdal,&nbsp;Jan D. Zika","doi":"10.1029/2024MS004383","DOIUrl":"https://doi.org/10.1029/2024MS004383","url":null,"abstract":"<p>Water Mass Transformation (WMT) theory provides conceptual tools that in principle enable innovative analyses of numerical ocean models; in practice, however, these methods can be challenging to implement and interpret, and therefore remain under-utilized. Our aim is to demonstrate the feasibility of diagnosing all terms in the water mass budget and to exemplify their usefulness for scientific inquiry and model development by quantitatively relating water mass changes, overturning circulations, boundary fluxes, and interior mixing. We begin with a pedagogical derivation of key results of classical WMT theory. We then describe best practices for diagnosing each of the water mass budget terms from the output of Finite-Volume Generalized Vertical Coordinate (FV-GVC) ocean models, including the identification of a non-negligible remainder term as the spurious numerical mixing due to advection scheme discretization errors. We illustrate key aspects of the methodology through the analysis of a polygonal region of the Greater Baltic Sea in a regional demonstration simulation using the Modular Ocean Model v6 (MOM6). We verify the convergence of our WMT diagnostics by brute-force, comparing time-averaged (“offline”) diagnostics on various vertical grids to timestep-averaged (“online”) diagnostics on the native model grid. Finally, we briefly describe a stack of xarray-enabled Python packages for evaluating WMT budgets in FV-GVC models (culminating in the new <span>xwmb</span> package), which is intended to be model-agnostic and available for community use and development.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004383","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143571427","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":"Postprocessing East African Rainfall Forecasts Using a Generative Machine Learning Model","authors":"Bobby Antonio,&nbsp;Andrew T. T. McRae,&nbsp;David MacLeod,&nbsp;Fenwick C. Cooper,&nbsp;John Marsham,&nbsp;Laurence Aitchison,&nbsp;Tim N. Palmer,&nbsp;Peter A. G. Watson","doi":"10.1029/2024MS004796","DOIUrl":"https://doi.org/10.1029/2024MS004796","url":null,"abstract":"<p>Existing weather models are known to have poor skill at forecasting rainfall over East Africa. Improved forecasts could reduce the effects of extreme weather events and provide significant socioeconomic benefits to the region. We present a novel machine learning (ML)-based method to improve precipitation forecasts in East Africa, using postprocessing based on a conditional generative adversarial network (cGAN). This addresses the challenge of realistically representing tropical rainfall, where convection dominates and is poorly simulated in conventional global forecast models. We postprocess hourly forecasts made by the European Centre for Medium-Range Weather Forecasts Integrated Forecast System at 6–18 hr lead times, at <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>0.1</mn>\u0000 <mo>°</mo>\u0000 </mrow>\u0000 <annotation> $0.1{}^{circ}$</annotation>\u0000 </semantics></math> resolution. We combine the cGAN predictions with a novel neighborhood version of quantile mapping, to integrate the strengths of ML and conventional postprocessing. Our results indicate that the cGAN substantially improves the diurnal cycle of rainfall, and improves predictions up to the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>99</mn>\u0000 <mo>.</mo>\u0000 <msup>\u0000 <mn>9</mn>\u0000 <mtext>th</mtext>\u0000 </msup>\u0000 </mrow>\u0000 <annotation> $99.{9}^{text{th}}$</annotation>\u0000 </semantics></math> percentile <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mrow>\u0000 <mo>∼</mo>\u0000 <mn>10</mn>\u0000 <mtext>mm</mtext>\u0000 <mo>/</mo>\u0000 <mtext>hr</mtext>\u0000 </mrow>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 <annotation> $(sim 10text{mm}/text{hr})$</annotation>\u0000 </semantics></math>. This improvement extends to the March–May 2018 season, which had extremely high rainfall, indicating that the approach has some ability to generalize to more extreme conditions. We explore the potential for the cGAN to produce probabilistic forecasts and find that the spread of this ensemble broadly reflects the predictability of the observations, but is also characterized by a mixture of under- and over-dispersion. Overall our results demonstrate how the strengths of ML and conventional postprocessing methods can be combined, and illuminate what benefits ML approaches can bring to this region.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004796","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564617","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":"Incorporating the Acclimation of Photosynthesis and Leaf Respiration in the Noah-MP Land Surface Model: Model Development and Evaluation","authors":"Yanghang Ren,&nbsp;Han Wang,&nbsp;Sandy P. Harrison,&nbsp;I. Colin Prentice,&nbsp;Giulia Mengoli,&nbsp;Long Zhao,&nbsp;Peter B. Reich,&nbsp;Kun Yang","doi":"10.1029/2024MS004599","DOIUrl":"https://doi.org/10.1029/2024MS004599","url":null,"abstract":"<p>Realistic simulation of leaf photosynthetic and respiratory processes is needed for accurate prediction of the global carbon cycle. These two processes systematically acclimate to long-term environmental changes by adjusting photosynthetic and respiratory traits (e.g., the maximum photosynthetic capacity at 25°C (<i>V</i>_cmax,25) and the leaf respiration rate at 25°C (<i>R</i>₂₅)) following increasingly well-understood principles. While some land surface models (LSMs) now account for thermal acclimation, they do so by assigning empirical parameterizations for individual plant functional types (PFTs). Here, we have implemented an Eco-Evolutionary Optimality (EEO)-based scheme to represent the universal acclimation of photosynthesis and leaf respiration to multiple environmental effects, and that therefore requires no PFT-specific parameterizations, in a standard version of the widely used LSM, Noah MP. We evaluated model performance with plant trait data from a 5-year experiment and extensive global field measurements, and carbon flux measurements from FLUXNET2015. We show that observed <i>R</i>₂₅ and <i>V</i>_cmax,25 vary substantially both temporally and spatially within the same PFT (<i>C.V.</i> &gt;20%). Our EEO-based scheme captures 62% of the temporal and 70% of the spatial variations in <i>V</i>_cmax,25 (73% and 54% of the variations in <i>R</i>₂₅). The standard scheme underestimates gross primary production by 10% versus 2% for the EEO-based scheme and generates a larger spread in <i>r</i> (correlation coefficient) across flux sites (0.79 ± 0.16 vs. 0.84 ± 0.1, mean ± S.D.). The standard scheme greatly overestimates canopy respiration (bias: ∼200% vs. 8% for the EEO scheme), resulting in less CO₂ uptake by terrestrial ecosystems. Our approach thus simulates climate-carbon coupling more realistically, with fewer parameters.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004599","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143565199","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":"Large Eddy Simulation of Shallow Cumulus Clouds in the Southern Great Plains With an Interactive Land Surface Model","authors":"Jake J. Gristey,&nbsp;Graham Feingold,&nbsp;Wayne M. Angevine,&nbsp;Yao-Sheng Chen","doi":"10.1029/2024MS004658","DOIUrl":"https://doi.org/10.1029/2024MS004658","url":null,"abstract":"<p>Shallow cumulus clouds are ubiquitous over continental land masses in summertime. They impart complex patterns of solar heating on the surface below. These patterns are dominated by cloud shadows, which drive spatial variability in the surface latent and sensible heat fluxes via the surface energy balance. This, in-turn, generates spatial variability in buoyancy that has been suggested to modulate cloud evolution. Despite the coupling between the land surface and clouds, it is commonplace to model continental shallow cumulus clouds with large eddy simulation (LES) using spatially-uniform prescribed surface heat fluxes. Here we present new LES of shallow cumulus clouds in the Southern Great Plains that are run with an interactive land surface model (LSM). The LSM is coupled to a 1D radiation scheme and therefore provides dynamic, heterogeneous surface heat fluxes that correspond to the evolving 1D surface solar heating pattern. We use this new simulation configuration to test whether spatially-variable fluxes impact cloud field evolution, finding limited impact for a typical case study. Furthermore, we find no evidence of systematic differences in radiatively-relevant cloud field properties when applying spatially-variable fluxes across 14 simulated cases. We therefore conclude that the heterogeneity of surface fluxes due to 1D cloud shading is insufficient to influence cloud evolution. This finding agrees with previously documented length scales of static surface heterogeneities required to develop secondary circulations that can influence cloud evolution, and provides a renewed focus for mechanistic understanding of recently reported large responses in cloud evolution when invoking 3D radiation.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004658","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling the Effect of Trees on Energy Demand for Indoor Cooling and Dehumidification Across Cities and Climates","authors":"Naika Meili,&nbsp;Xing Zheng,&nbsp;Yuya Takane,&nbsp;Ko Nakajima,&nbsp;Kazuki Yamaguchi,&nbsp;Dengkai Chi,&nbsp;Yue Zhu,&nbsp;Jing Wang,&nbsp;Yeshan Qiu,&nbsp;Athanasios Paschalis,&nbsp;Gabriele Manoli,&nbsp;Paolo Burlando,&nbsp;Puay Yok Tan,&nbsp;Simone Fatichi","doi":"10.1029/2024MS004590","DOIUrl":"https://doi.org/10.1029/2024MS004590","url":null,"abstract":"<p>Increasing urban tree cover is a common strategy to lower urban temperatures and indirectly the building energy demand for air-conditioning (AC). However, urban vegetation leads to increasing humidity with potential negative effects on the AC dehumidification loads in hot-humid climates, an effect that has so far been unexplored. Here, we included a building energy model into the urban ecohydrological model Urban Tethys-Chloris (UT&amp;C-BEM) to quantify the AC energy reduction effects of trees in seven hot cities with varying background humidity. A numerical experiment was performed simulating various urban densities and tree cover scenarios in the city-climates of Riyadh, Phoenix, Dubai, New Delhi, Singapore, Lagos, and Tokyo. The relative contribution of tree shade, air temperature reduction, and humidity increase on the AC energy reduction was further quantified. We found that well-watered trees provide the largest average summer AC energy reduction of −17% in the hot-dry climate (Riyadh, Phoenix). As tree shade is the dominant factor leading to the AC energy reduction in all city-climates, humid cities also show an average summer AC energy reduction ranging from −6% to −9%. However, increasing humidity is affecting AC dehumidification loads, especially under higher ventilation rates in humid climates and in these cities, AC energy reduction is most efficient with up to 40% tree cover. Additionally, we found that trees effectively reduce peak AC energy consumption due to higher shading effects in those hours. These results can inform urban planning strategies to maximize reduction in the AC energy demand using urban trees.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004590","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554792","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}