Journal of Advances in Modeling Earth Systems最新文献

{"title":"Spatial Attribution of Temporal Variability in Global Land-Atmosphere CO2 Exchange Using a Model-Data Integration Framework","authors":"H. Lee,&nbsp;M. Jung,&nbsp;N. Carvalhais,&nbsp;M. Reichstein,&nbsp;M. Forkel,&nbsp;A. A. Bloom,&nbsp;J. Pacheco-Labrador,&nbsp;S. Koirala","doi":"10.1029/2024MS004479","DOIUrl":"https://doi.org/10.1029/2024MS004479","url":null,"abstract":"<p>The spatial contribution to the global land-atmosphere carbon dioxide (CO₂) exchange is crucial in understanding and projecting the global carbon cycle, yet different studies diverge on the dominant regions. Informing land models with observational data is a promising way to reduce the parameter and structural uncertainties and advance our understanding. Here, we develop a parsimonious diagnostic process-based model of land carbon cycles, constraining parameters with observation-based products. We compare CO₂ flux estimates from our model with observational constraints and Trends in Net Land-Atmosphere Carbon Exchange (TRENDY) model ensemble to show that our model reasonably reproduces the seasonality of net ecosystem exchange (NEE) and gross primary productivity (GPP) and interannual variability (IAV) of NEE. Finally, we use the developed model, TRENDY models, and observational constraints to attribute variability in global NEE and GPP to regional variability. The attribution analysis confirms the dominance of Northern temperate and boreal regions in the seasonality of CO₂ fluxes. Regarding NEE IAV, we identify a significant contribution from tropical savanna regions as previously perceived. Furthermore, we highlight that tropical humid regions are also identified as at least equally relevant contributors as semi-arid regions. At the same time, the largest uncertainty among ensemble members of NEE constraint and TRENDY models in the tropical humid regions underscore the necessity of better process understanding and more observations in these regions. Overall, our study identifies tropical humid regions as key regions for global land-atmosphere CO₂ exchanges and the inter-model spread of its modeling.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004479","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689795","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 Atmospheric Instability Perturbation Approach for Ensemble Forecasts and Its Application in Heavy Rain Cases","authors":"S. Wang,&nbsp;J. Min,&nbsp;X. Li,&nbsp;X. Qiao","doi":"10.1029/2024MS004556","DOIUrl":"https://doi.org/10.1029/2024MS004556","url":null,"abstract":"<p>An ensemble perturbation approach focusing on Atmospheric Instability Perturbation was proposed. This approach perturbs diagnostics quantifying atmospheric instability and calculates corresponding model state perturbations through a data assimilation-like procedure, with flexibility enhanced through the numerical estimation of derivatives of diagnostic equations. The amplitude perturbation of moist potential vorticity (MPV) measuring convective (MPV1) and baroclinic instability (MPV2) is investigated. Flow-dependent characteristics of MPV amplitude perturbations are observed through single-point tests, with the MPV2 perturbation enhancing the temperature gradient in the baroclinic instability area. For 10 heavy rain cases in Eastern China during the summer of 2019, the ensemble using the combination of a positive MPV2 amplitude perturbation and a negative MPV1 amplitude perturbation outperforms the ensemble with the downscaled Global Ensemble Forecast System (GEFS) perturbations. This superiority of MPV perturbations is attributed to their ability to capture more precipitation events through enhancing the instability environment, which is conducive to both convection initialization and precipitation intensity. However, the MPV perturbations contribute less to the heavy rain probability forecast skill and reliability, because more false alarms are produced. The experimental results also indicate the necessity of cycle perturbation of MPV during forecasting, as the forecast model may underestimate instability after the initial condition perturbation impact diminishes. Considering that all types of model state perturbations adjust atmospheric instability, with most instability adjustments being secondary outcomes, the results of MPV amplitude perturbations highlight the effectiveness of directly perturbing atmospheric instability in ensemble precipitation forecasting.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004556","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689513","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":"Advancing Organized Convection Representation in the Unified Model: Implementing and Enhancing Multiscale Coherent Structure Parameterization","authors":"Zhixiao Zhang,&nbsp;Hannah M. Christensen,&nbsp;Mark R. Muetzelfeldt,&nbsp;Tim Woollings,&nbsp;Robert S. Plant,&nbsp;Alison J. Stirling,&nbsp;Michael A. Whitall,&nbsp;Mitchell W. Moncrieff,&nbsp;Chih-Chieh Chen,&nbsp;Zhe Feng","doi":"10.1029/2024MS004370","DOIUrl":"https://doi.org/10.1029/2024MS004370","url":null,"abstract":"<p>To address the effect of stratiform latent heating on meso- to large-scale circulations, an enhanced implementation of the Multiscale Coherent Structure Parameterization (MCSP) is developed for the Met Office Unified Model. MCSP represents the top-heavy stratiform latent heating from under-resolved organized convection in general circulation models. We couple the MCSP with a mass-flux convection scheme (CoMorph-A) to improve storm lifecycle continuity. The improved MCSP trigger is specifically designed for mixed-phase deep convective cloud, combined with a background vertical wind shear, both known to be crucial for stratiform development. We also test a cloud top temperature dependent convective-stratiform heating partitioning, in contrast to the earlier fixed partitioning. Assessments from ensemble weather forecasts and decadal simulations demonstrate that MCSP directly reduces cloud deepening and precipitation areas by moderating mesoscale circulations. Indirectly, it amends tropical precipitation biases, notably correcting dry and wet biases over India and the Indian Ocean, respectively. Remarkably, the scheme outperforms a climate model ensemble by improving seasonal precipitation cycle predictions in these regions. The scheme also improves Madden-Julian Oscillation (MJO) spectra, achieving better alignment with observational and reanalysis data by intensifying the simulated MJO over the Indian Ocean during phases 4 to 5. However, the scheme increases precipitation overestimation over the Western Pacific. Shifting from fixed to temperature-dependent convective-stratiform partitioning reduces the Pacific precipitation overestimation and further improves the seasonal cycle in India. Spatially correlated biases highlight the necessity for advances beyond deterministic approaches to align MCSP with environmental conditions.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004370","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689512","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":"Tipping to an Aggregated State by Mesoscale Convective Systems","authors":"I. L. Kruse,&nbsp;R. Fiévet,&nbsp;J. O. Haerter","doi":"10.1029/2024MS004369","DOIUrl":"https://doi.org/10.1029/2024MS004369","url":null,"abstract":"<p>Radiative-convective equilibrium simulations were suggested to resist self-aggregation within a linearly stable regime at low surface temperatures. Recent numerical work shows that this linearly stable regime can rapidly transition to an aggregated state when exposed to realistic diurnal surface temperature variations. The resultant aggregated state is then stable, even when the surface temperature is set constant. Here we argue, by constructing a reaction-diffusion model, that this tipping process can be explained by the formation of mesoscale convective systems under the diurnal forcing. The model implies that strong cold pool interactions, invoked by the diurnal cycle, drive the self-organization of long-term buoyancy memory. Thus, whereas previous conceptual work disregarded the boundary layer dynamics, we here attribute key organizing mechanisms to them: namely the ability to cause rapid self-aggregation over continents and its advection over the ocean—with potential implications for hurricane formation.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004369","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638985","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":"Evaluating Near-Surface Wind Speeds Simulated by the CRCM6-GEM5 Model Using AmeriFlux Data Over North America","authors":"Tim Whittaker,&nbsp;Alejandro Di Luca,&nbsp;Francois Roberge,&nbsp;Katja Winger","doi":"10.1029/2024MS004666","DOIUrl":"https://doi.org/10.1029/2024MS004666","url":null,"abstract":"<p>We evaluate the performance of various configurations of the Canadian Regional Climate Model (CRCM6-GEM5) in simulating 10-m wind speeds using data from 27 AmeriFlux stations across North America. The assessment employs a hierarchy of error metrics, ranging from simple mean bias to advanced metrics that account for the dependence of wind speeds on variables such as friction velocity and stability. The results reveal that (a) the value of roughness length (z0) has a large effect on the simulation of wind speeds, (b) using a lower limit for the Obhukov length instead of a lower limit for the lowest level wind speed seems to deteriorate the simulation of wind speeds under very stable conditions, (c) the choice of stability function has a small but noticeable impact on the wind speeds, (d) the turbulent orographic form drag scheme shows improvement over effective roughness length approach.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004666","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143646109","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":"Sensitivity of Self-Aggregation and the Key Role of the Free Convection Distance","authors":"A. Casallas,&nbsp;A. M. Tompkins,&nbsp;C. Muller,&nbsp;G. Thompson","doi":"10.1029/2024MS004791","DOIUrl":"https://doi.org/10.1029/2024MS004791","url":null,"abstract":"<p>Recently, Biagioli and Tompkins (2023, https://doi.org/10.1029/2022ms003231) used a simple stochastic model to derive a dimensionless parameter to predict convective self aggregation (SA) development, which was based on the derivation of the maximum free convective distance <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mfenced>\u0000 <msub>\u0000 <mi>d</mi>\u0000 <mi>clr</mi>\u0000 </msub>\u0000 </mfenced>\u0000 </mrow>\u0000 <annotation> $left({d}_{mathit{clr}}right)$</annotation>\u0000 </semantics></math> expected in the pre-aggregated, random state. Our goal is to test and further investigate this hypothesis, namely that <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>d</mi>\u0000 <mi>clr</mi>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${d}_{mathit{clr}}$</annotation>\u0000 </semantics></math> can predict SA occurrence, using an ensemble of 24 distinct combinations of horizontal mixing, planetary boundary layer (PBL), and microphysical parameterizations. We conclude that the key impact of parameterization schemes on SA is through their control of the number of convective cores and their relative spacing, <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>d</mi>\u0000 <mi>clr</mi>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${d}_{mathit{clr}}$</annotation>\u0000 </semantics></math>, which itself is impacted by cold-pool (CP) properties and mean updraft core size. SA is more likely when the convective core count is small, while CPs modify convective spacing via suppression in their interiors and triggering by gust-front convergence and collisions. Each parameterization scheme emphasizes a different mechanism. Subgrid-scale horizontal turbulent mixing mainly affects SA through the determination of convective core size and thus spacing. The sensitivity to the microphysics is mainly through rain evaporation and the subsequent impact on CPs, while perturbations to the ice cloud microphysics have a limited effect. Non-local PBL mixing schemes promote SA primarily by increasing convective inhibition through inversion entrainment and altering low cloud amounts, leading to fewer convective cores and larger <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>d</mi>\u0000 <mi>clr</mi>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${d}_{mathit{clr}}$</annotation>\u0000 </semantics></math>.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004791","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143646110","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":"Downward Convective Moisture Transport Dominated by a Few Overshooting Clouds in Marine and Continental Shallow Convection","authors":"Heng Xiao,&nbsp;Adam Varble,&nbsp;Colleen Kaul,&nbsp;Johannes Mülmenstädt","doi":"10.1029/2024MS004489","DOIUrl":"https://doi.org/10.1029/2024MS004489","url":null,"abstract":"<p>In a previous study (Xiao et al., 2023, https://doi.org/10.1029/2022ms003526), we found that ignoring the moist convective downdrafts associated with overshooting clouds in parameterizations can lead to significant biases in the simulated depth and liquid water content of a shallow cloud layer. In this study, we seek to better quantify the properties of the clouds responsible for these moist downdrafts to help improve shallow convection parameterizations. We apply a 3-D cloud-tracking algorithm to large-eddy simulations (LESs) of marine and continental shallow convection. We find that top 1% and 2% of the tracked cloud population ranked by lifetime-mean cloud-base mass flux can explain 90%–95% of the total downward moisture transport in the upper cloud layer whereas top 10%–20% is required to explain 90%–95% of the total upward moisture transport near mean cloud base. The vertical structure of the clouds in the top 1% and 2% (the overshooting “deep mode”) is also distinctively different from that of the rest of the cloud population (the “shallow mode”). Shallow convection parameterizations need to capture accurately the properties and convective transports of the clouds in both the deep and shallow modes. To do that, our results suggest that mass-flux parameterizations need to (a) accurately predict the size and number of the deep-mode clouds and (b) explicitly represent overshooting cloud updrafts and associated moist downdrafts.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004489","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143646111","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":"Thank You to Our 2024 Peer Reviewers","authors":"Stephen M. Griffies,&nbsp;Jiwen Fan,&nbsp;Yuanyuan Huang,&nbsp;Natasha MacBean,&nbsp;Tapio Schneider","doi":"10.1029/2025MS005079","DOIUrl":"https://doi.org/10.1029/2025MS005079","url":null,"abstract":"<p>The editors of <i>Journal of Advances in Modeling Earth Systems</i> thank the 1,001 reviewers who provided 1,593 reviews during 2024. Their hard work and insights, typically done anonymously, benefits authors, readers, and the broader science community.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025MS005079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638955","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":"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}