Journal of Advances in Modeling Earth Systems最新文献

{"title":"A Particle-in-Cell Wave Model for Efficient Sea-State Estimates in Earth System Models—PiCLES","authors":"Momme Hell,&nbsp;Baylor Fox-Kemper,&nbsp;Bertrand Chapron","doi":"10.1029/2025MS005221","DOIUrl":"10.1029/2025MS005221","url":null,"abstract":"<p>Ocean surface waves have been demonstrated to be an important component of coupled Earth System Models (ESMs), influencing atmosphere-ocean momentum transfer; ice floe breakage; CFC, carbon, and energy uptake; and mixed-layer depth. Modest errors in sea state properties do not strongly affect the impacts of these parameterizations. The modest data and accuracy needed contrast sharply with the high computational costs of spectral wave models in next-generation ESMs, which can very easily exceed the cost of the ocean model component. We establish an alternative, cost-efficient prototype wave modeling framework for air-sea and ice-ocean interactions, enabling the routine use of sea state-dependent air-sea coupling in future ESMs. In contrast to spectral models, the Particle-in-Cell for Efficient Swell (PiCLES) wave model is customized for coupled atmosphere-ocean-sea ice modeling. Combining Lagrangian wave growth solutions with the Particle-In-Cell method leads to a model that periodically projects wave information onto any convenient grid and scales in an embarrassingly parallel manner. The set of equations solves for the growth and propagation of a parametric wave spectrum's peak wavenumber vector and total wave energy, which reduces the state vector size by a factor of 50–200 compared to the standard resolution of spectral models. PiCLES's current computational costs in idealized wind-sea simulations are about one order of magnitude faster than established wave models used in ESMs, with sufficient accuracy in bulk sea-state variables relevant for coupling. PiCLES is compared to WAVEWATCH III in efficiency and accuracy in idealized cases.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 8","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025MS005221","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144905557","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":"Parameter Optimization for Global Soil Carbon Simulations: Not a Simple Problem","authors":"Charles B. Gauthier,&nbsp;Joe R. Melton,&nbsp;Gesa Meyer,&nbsp;S. N. Raj Deepak,&nbsp;Oliver Sonnentag","doi":"10.1029/2024MS004577","DOIUrl":"10.1029/2024MS004577","url":null,"abstract":"<p>Accurate simulation of soil organic carbon (SOC) dynamics by terrestrial biosphere models is hampered by poorly constrained parameters and parameter equifinality, amongst other issues. To address this, we use Bayesian optimization to constrain the 16 SOC-related parameters in the Canadian Land Surface Scheme Including biogeochemical Cycles (CLASSIC). We employed a global sensitivity analysis (Sobol’) to develop four parameter sets based upon different sensitivity criteria. We then optimized each set against observed SOC (World Soil Information Service; WoSIS) and soil respiration (Soil Respiration Database; SRDB). Using two different loss functions; one focused on reproducing the observational mean value, and the other explicitly accounting for an estimated observational uncertainty. The best optimized parameter sets for each loss function had an average relative difference of 61%. Thus, the choice of loss function impacts what parameter values are deemed optimal and should be considered carefully. The final set of selected optimal parameters saw a 12% improvement against WoSIS and SRDB, had global SOC totals in line with literature estimates, and better simulated high-latitude SOC stocks evaluated against the Northern Circumpolar Soil Carbon Database (RMSD: 16.39 vs. 17.61; bias: −5.57 vs. −10.78 kg C <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mrow>\u0000 <msup>\u0000 <mi>m</mi>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 </mrow>\u0000 <annotation> ${mathrm{m}}^{-2}$</annotation>\u0000 </semantics></math>) compared to the default CLASSIC parameters. However, some parameters were not well constrained, in particular those of needle-leaf deciduous trees that dominate the Siberian boreal forests, a region relatively poorly observed in WoSIS and SRDB. Future work should apply further constraints on the optimization framework and address observational gaps.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 8","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004577","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144869245","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":"Diapycnal Mixing and Tracer Dispersion in a Terrain-Following Coordinate Model","authors":"Noémie Schifano,&nbsp;Clément Vic,&nbsp;Jonathan Gula,&nbsp;M. Jeroen Molemaker,&nbsp;James C. McWilliams","doi":"10.1029/2024MS004768","DOIUrl":"10.1029/2024MS004768","url":null,"abstract":"<p>Diapycnal mixing, driven by small-scale turbulence, is crucial for the global ocean circulation, particularly for the upwelling of deep water masses. However, accurately representing diapycnal mixing in ocean models is challenging because numerical errors can introduce significant numerical mixing. In this study, we explore the diapycnal mixing in a high-resolution regional model of the North Atlantic subpolar gyre using the Coastal and Regional Ocean Community model (CROCO). CROCO uses terrain-following vertical coordinates that do not align with isopycnals. As such, tracer advection schemes produce spurious diapycnal mixing, which can nonetheless be reduced using rotated advection schemes. We focus on how different advection schemes and vertical resolutions affect numerical diapycnal mixing. Our approach includes online diagnostics of buoyancy fluxes and tracer release experiments to quantify the effective mixing, which combines parameterized and numerical diapycnal mixing. Our main results show that in flat-bottom regions, the effective diapycnal mixing is close to the parameterized mixing. However, in regions with steep topography, numerical mixing can locally significantly exceed parameterized mixing due to grid slope constraints imposed by the rotated mixing operator. While topography smoothing can mitigate this excessive mixing, it can also alter flow-topography interactions. In addition, while a higher vertical resolution reduces the numerical mixing induced by the vertical tracer advection, it can also increase numerical mixing in steep regions by introducing a stronger constraint on the grid slope. These results underscore that diapycnal mixing representation in a numerical model requires balancing high resolution and topographic smoothing with the control of numerical errors.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 8","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004768","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861868","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":"Development of a Global Representative Hillslope Data Set for Use in Earth System Models","authors":"Sean C. Swenson,&nbsp;David M. Lawrence","doi":"10.1029/2024MS004410","DOIUrl":"10.1029/2024MS004410","url":null,"abstract":"<p>The development of a global data set consisting of a distributed set of geomorphic parameters suitable for use in a representative hillslope parameterization within an Earth System model (ESM) is described. An element of a representative hillslope is defined by six geomorphic properties: height above the stream channel, distance to the stream channel, width, slope, aspect, and area. The methodology employs spectral analysis to identify an appropriate spatial scale at which to resolve the stream network and catchments in an ESM gridcell. This objective method is applied to an ESM grid using a global high-resolution digital elevation model (DEM) as input. The resulting spatially varying length scales then determine the stream order used to delineate catchments from the DEM. The geomorphic parameters describing the representative hillslopes are obtained by discretizing the catchments into elements based on elevation and aspect, and then averaging the geomorphic properties within each element, resulting in a statistical representation of the hillslopes within the domain. The method is applied to a DEM having a spatial resolution of 3 arcseconds (about 90 <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>m</mi>\u0000 </mrow>\u0000 <annotation> $mathrm{m}$</annotation>\u0000 </semantics></math> at the equator) on a grid having approximately <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 <mo>°</mo>\u0000 </mrow>\u0000 <annotation> $1{}^{circ}$</annotation>\u0000 </semantics></math> spatial resolution.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 8","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004410","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144833153","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 Microphysics and Convection Schemes on the Mean-State and Variability of Clouds and Precipitation in the E3SM Atmosphere Model","authors":"Christopher R. Terai,&nbsp;Shaocheng Xie,&nbsp;Xiaoliang Song,&nbsp;Chih-Chieh Chen,&nbsp;Jiwen Fan,&nbsp;Guang J. Zhang,&nbsp;Jadwiga H. Richter,&nbsp;Jacob Shpund,&nbsp;Wuyin Lin,&nbsp;Jean-Christophe Golaz,&nbsp;Vincent E. Larson,&nbsp;Mitchell W. Moncrieff,&nbsp;Yunpeng Shan,&nbsp;Chengzhu Zhang,&nbsp;Kai Zhang,&nbsp;Yuying Zhang","doi":"10.1029/2024MS004569","DOIUrl":"10.1029/2024MS004569","url":null,"abstract":"<p>Skillful representation of tropical variability and diurnal cycle of precipitation has remained a challenge in global atmosphere models, and often improvements in the variability lead to degradation in the mean-state. Here, we introduce a configuration of the E3SM Atmosphere Model with a new large-scale microphysics scheme and several enhancements to the deep convective scheme that improves the variability. The new configuration improves various modes of convectively-coupled equatorial waves, with increased strength of Kelvin waves and more coherent eastward propagation of the Madden-Julian Oscillation from the Indian Ocean to the central Pacific Ocean. The same configuration also improves the phase of the diurnal cycle of precipitation, particularly over the continental United States in the boreal summer and over Tropical land regions. Previous studies have shown that, individually taken, some of the deep convective enhancements can improve certain aspects of the variability, and here we show that combining their effects can lead to robust improvements in the variability. This model configuration can form the basis for future studies to examine the response of tropical and diurnal variability under various climate states and their relationships with other modes of variability.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 8","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004569","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144832551","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 New Prognostic Parameterization of Subgrid Ice Supersaturation and Cirrus Clouds in the ICOLMDZ AGCM","authors":"Audran Borella,&nbsp;Étienne Vignon,&nbsp;Olivier Boucher,&nbsp;Yann Meurdesoif,&nbsp;Laurent Fairhead","doi":"10.1029/2024MS004918","DOIUrl":"10.1029/2024MS004918","url":null,"abstract":"<p>A new cirrus cloud parameterization for the ICOLMDZ atmospheric general circulation model (AGCM) is presented. Prognostic equations for cloud fraction, ice water content and cloudy water vapor are introduced, and the processes that affect these quantities are parameterized. In particular, the tendency in ice crystal mass concentration due to changes in water phases is calculated, as is the macroscale mixing of cirrus clouds with their environment. The parameterization simulates ice supersaturation in clear sky, an ubiquitous metastable phenomenon in the upper troposphere that is necessary for the formation of in situ cirrus clouds. Our parameterization also allows for cirrus clouds to be sub- and supersaturated with respect to ice. It is evaluated on a case study of a warm conveyor belt cirrus cloud forming and dissipating above the Paris region. The representation of high cloud cover is improved with the new parameterization relative to the default version of the cloud scheme in comparison to observations, as is the representation of water vapor. In particular, the upper tropospheric dry bias from ICOLMDZ is corrected, although it is replaced by a wet bias in some cases. Global simulations show similar results. Contrarily to the default parameterization, ice water content and high cloud cover can be tuned separately, but the ice fall velocity parameter, that controls the intensity of ice autoconversion, remains the most determining parameter controlling the overall ice water content.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 8","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004918","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144832550","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":"One-at-a-Time Parameter Perturbation Ensemble of the Community Land Model, Version 5.1","authors":"D. Kennedy,&nbsp;K. Dagon,&nbsp;D. M. Lawrence,&nbsp;R. A. Fisher,&nbsp;B. M. Sanderson,&nbsp;N. Collier,&nbsp;F. M. Hoffman,&nbsp;C. D. Koven,&nbsp;E. Kluzek,&nbsp;S. Levis,&nbsp;X. Lu,&nbsp;K. W. Oleson,&nbsp;C. M. Zarakas,&nbsp;Y. Cheng,&nbsp;A. C. Foster,&nbsp;M. D. Fowler,&nbsp;L. R. Hawkins,&nbsp;T. Kavoo,&nbsp;S. Kumar,&nbsp;A. J. Newman,&nbsp;P. J. Lawrence,&nbsp;F. Li,&nbsp;D. L. Lombardozzi,&nbsp;Y. Luo,&nbsp;J. K. Shuman,&nbsp;A. L. S. Swann,&nbsp;S. C. Swenson,&nbsp;G. Tang,&nbsp;W. R. Wieder,&nbsp;A. W. Wood","doi":"10.1029/2024MS004715","DOIUrl":"10.1029/2024MS004715","url":null,"abstract":"<p>Comprehensive land models are subject to significant parametric uncertainty, which can be hard to quantify due to the large number of parameters and high model computational costs. We constructed a large parameter perturbation ensemble (PPE) for the Community Land Model version 5.1 with biogeochemistry configuration (CLM5.1-BGC). We performed more than 2,000 simulations perturbing 211 parameters across six forcing scenarios. This provides an expansive data set, which can be used to identify the most influential parameters on a wide range of output variables globally, by biome, or by plant functional type. We found that parameter effects can exceed scenario effects and that a small number of parameters explains a large fraction of variance across our ensemble. The most important parameters can differ regionally and also based on the forcing scenario. The software infrastructure developed for this experiment has greatly reduced the human and computer time needed for CLM PPEs, which can facilitate routine investigation of parameter sensitivity and uncertainty, as well as automated calibration.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 8","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004715","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145135367","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":"tmm4py: Global Ocean Biogeochemical Modeling in Python With the Transport Matrix Method","authors":"Samar Khatiwala","doi":"10.1029/2025MS005028","DOIUrl":"10.1029/2025MS005028","url":null,"abstract":"<p>Marine biogeochemical models are important tools in the quest to understand the cycling of chemical and biological tracers such as nutrients, carbon and oxygen, as well as key components of the Earth System Models used to project climate change. Historically, given the need for speed, global scale modeling has been performed in compiled languages like Fortran. However, as high level scripting languages such as Python and Julia gain popularity, the need for models and tools accessible from them has become imperative. This paper introduces <span>tmm4py</span>, a Python interface to a redesigned version of the Transport Matrix Method (TMM) software, a computationally efficient numerical scheme for “offline” simulation of marine geochemical and biogeochemical tracers. The TMM provides a convenient framework for developing and testing new biogeochemical parameterizations, as well as running existing complex models driven by circulations derived from state-of-the-art physical models. <span>tmm4py</span> exposes all of the TMM library's functionality in Python, including transparent parallelization, allowing users to not only interactively use models written in compiled languages, but also develop complex models in pure Python with performance similar to compiled code. <span>tmm4py</span> enables users to exploit the large Python-based scientific software ecosystem, including libraries for machine learning and deploying models on Graphics Processing Units. The various features of <span>tmm4py</span> are described and illustrated through practical examples, including a full-fledged biogeochemical model written entirely in Python.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 8","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025MS005028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145135365","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":"Generative Data Assimilation for Surface Ocean State Estimation From Multi-Modal Satellite Observations","authors":"Scott A. Martin,&nbsp;Georgy E. Manucharyan,&nbsp;Patrice Klein","doi":"10.1029/2025MS005063","DOIUrl":"10.1029/2025MS005063","url":null,"abstract":"<p>Estimating the surface ocean state at mesoscale eddy-resolving scales is essential for understanding the role of eddies in climate and marine ecosystems. Satellites provide multi-modal observations through sea surface height, temperature (SST), and salinity (SSS). However, each variable is observed with varying resolutions and sparsity, while some variables, such as surface currents, are not yet observed by satellites. All these variables must be accurately reconstructed across scales to study eddy dynamics. Dynamical data assimilation (DA) struggles to accurately reconstruct eddies since, to respect the equations of motion, it must reconstruct both the surface and interior ocean state, but the interior is sparsely observed. Relaxing this requirement and focusing only on the surface could improve surface state estimation, but a new method is required to ensure reconstructions remain physically realistic. Here, we introduce a score-based generative data assimilation (GenDA) framework for jointly reconstructing key surface ocean variables at eddy-resolving scales from multi-modal satellite observations. GenDA uses a two-stage approach: training a score-based diffusion model on a simulation to generate realistic ocean states before employing this as a Bayesian prior to assimilate sparse observations and generate state estimates. The learned diffusion prior leads to coherence between variables and realism across scales. By synergizing low-resolution SSS with high-resolution SST observations, GenDA improves the SSS resolution. Remarkably, GenDA can infer unobserved surface currents using only satellite observables, suggesting the learned prior encodes physical relationships between variables. Applied to real observations, GenDA demonstrates strong generalizability compared to regression-based deep learning and outperforms state-of-the-art dynamical DA.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 8","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025MS005063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145135366","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 Framework for Variational Inference and Data Assimilation of Soil Biogeochemical Models Using Normalizing Flows","authors":"H. W. Xie,&nbsp;D. Sujono,&nbsp;T. Ryder,&nbsp;E. B. Sudderth,&nbsp;S. D. Allison","doi":"10.1029/2024MS004547","DOIUrl":"10.1029/2024MS004547","url":null,"abstract":"<p>Soil biogeochemical models (SBMs) represent soil variables and their responses to global change. Data assimilation approaches help determine whether SBMs accurately represent soil processes consistent with soil pool and flux measurements. Bayesian inference is commonly used in data assimilation procedures that estimate posterior parameter distributions with Markov chain Monte Carlo (MCMC) methods. The ability to account for data and parameter uncertainty is a strength of MCMC inference, but the computational inefficiency of MCMC methods remains a barrier to their wider application, especially with large data sets. Given the limitations of MCMC approaches, we developed an alternative variational inference framework that uses a method called <i>normalizing flows</i> from the field of machine learning. Normalizing flows rely on deep learning to map probability distributions and approximate SBMs that have been discretized into state space models. As a test of our method, we fit approximated SBMs to synthetic data sourced from known data-generating processes to identify discrepancies between the inference results and true parameter values. Our approach compares favorably with established MCMC methods and could be a viable alternative for SBM data assimilation that reduces computational time and resource needs. However, our method has some limitations, including challenges assimilating data with irregular measurement intervals, underestimation of posterior parameter uncertainty, and limited goodness-of-fit metrics for comparison to MCMC inference methods. Many of these limitations could be overcome with additional algorithm development based on the approaches we report here.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 8","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004547","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145135364","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}