Journal of Advances in Modeling Earth Systems最新文献

{"title":"Inclusion of Biomass Burning Plume Injection Height in GEOS-Chem-TOMAS: Global-Scale Implications for Atmospheric Aerosols and Radiative Forcing","authors":"Nicole A. June,&nbsp;Bonne Ford,&nbsp;Betty Croft,&nbsp;Rachel Y.-W. Chang,&nbsp;Jeffrey R. Pierce","doi":"10.1029/2024MS004849","DOIUrl":"https://doi.org/10.1029/2024MS004849","url":null,"abstract":"<p>Aerosols emitted from biomass burning affect human health and climate, both regionally and globally. The magnitude of these impacts is altered by the biomass burning plume injection height (BB-PIH). However, these alterations are not well-understood on a global scale. We present the novel implementation of BB-PIH in global simulations with an atmospheric chemistry model (GEOS-Chem) coupled with detailed TwO-Moment Aerosol Sectional (TOMAS) microphysics. We conduct BB-PIH simulations under three scenarios: (a) All smoke is well-mixed into the boundary layer, and (b) and (c) smoke injection height is based on Global Fire Assimilation System (GFAS) plume heights. Elevating BB-PIH increases the simulated global-mean aerosol optical depth (10%) despite a global-mean decrease (1%) in near-surface PM_2.5. Increasing the tropospheric column mass yields enhanced cooling by the global-mean clear-sky biomass burning direct radiative effect. However, increasing BB-PIH places more smoke above clouds in some regions; thus, the all-sky biomass burning direct radiative effect has weaker cooling in these regions as a result of increasing the BB-PIH. Elevating the BB-PIH increases the simulated global-mean cloud condensation nuclei concentrations at low-cloud altitudes, strengthening the global-mean cooling of the biomass burning aerosol indirect effect with a more than doubling over marine areas. Elevating BB-PIH also generally improves model agreement with the satellite-retrieved total and smoke extinction coefficient profiles. Our 2-year global simulations with new BB-PIH capability enable understanding of the global-scale impacts of BB-PIH modeling on simulated air-quality and radiative effects, going beyond the current understanding limited to specific biomass burning regions and seasons.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004849","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244091","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":"Photoacclimation and Photoadaptation Sensitivity in a Global Ocean Ecosystem Model","authors":"Charles A. Stock,&nbsp;John P. Dunne,&nbsp;Jessica Y. Luo,&nbsp;Andrew C. Ross,&nbsp;Nicolas Van Oostende,&nbsp;Niki Zadeh,&nbsp;Theresa J. Cordero,&nbsp;Xiao Liu,&nbsp;Yi-Cheng Teng","doi":"10.1029/2024MS004701","DOIUrl":"https://doi.org/10.1029/2024MS004701","url":null,"abstract":"<p>Chlorophyll underpins ocean productivity yet simulating chlorophyll across biomes, seasons and depths remains challenging for earth system models. Inconsistencies are often attributed to misrepresentation of the myriad nutrient supply, growth and loss processes that govern phytoplankton biomass. They may also arise, however, from unresolved or misspecified photoacclimation or photoadaptation responses. A series of global ocean ecosystem simulations were conducted to assess these latter sensitivities: alternative photoacclimation schemes implicitly modulated investments in light harvesting versus photodamage avoidance and other cellular functions. Photoadaptation experiments probed the impact of adding low- and high-light adapted phytoplankton ecotypes. Results showed that photoacclimation and photoadaptation alternatives generate chlorophyll differences exceeding a factor of 2 in some regions and seasons. In stratified waters, photoadaptation and acclimation to light levels over mixing depths consistent with the timescale of photoadaptation (days) benefitted model performance. In regions and seasons with deep mixed layers, surface-skewed photoacclimation yielded improved fidelity across satellite chlorophyll products. Large photoacclimation-driven differences in chlorophyll concentration had small impacts on primary productivity and carbon export, unlike those arising from changes in the nutrient supply. Improved photoacclimation and photoadaption constraints are thus needed to reduce ambiguities in the drivers of chlorophyll change and their biogeochemical implications.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004701","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244472","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":"Changes in Stratospheric Climate and Age-Of-Air in Recent GEOS Systems Since MERRA-2","authors":"Clara Orbe,&nbsp;Lawrence L. Takacs,&nbsp;Amal El Akkraoui,&nbsp;Krzysztof Wargan,&nbsp;Andrea Molod,&nbsp;Steven Pawson","doi":"10.1029/2024MS004442","DOIUrl":"https://doi.org/10.1029/2024MS004442","url":null,"abstract":"<p>Accurately modeling the large-scale transport of trace gases and aerosols is critical for interpreting past (and projecting future) changes in atmospheric composition. Simulations of the stratospheric mean age-of-air continue to show persistent biases in chemistry climate models, although the drivers of these biases are not well understood. Here we identify one driver of simulated stratospheric transport differences among various NASA Global Earth Observing System (GEOS) candidate model versions under consideration for the upcoming GEOS Retrospective analysis for the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>21</mn>\u0000 <mtext>st</mtext>\u0000 </mrow>\u0000 <annotation> $21text{st}$</annotation>\u0000 </semantics></math> Century (GEOS-R21C). In particular, we show that the simulated age-of-air values are sensitive to the so-called “remapping” algorithm used within the finite-volume dynamical core, which controls how individual material surfaces are vertically interpolated back to standard pressure levels after each horizontal advection time step. Differences in the age-of-air resulting from changes within the remapping algorithm approach <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>∼</mo>\u0000 </mrow>\u0000 <annotation> ${sim} $</annotation>\u0000 </semantics></math>1 year over the high latitude middle stratosphere—or about 30% climatological mean values—and imprint on several trace gases, including methane (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mtext>CH</mtext>\u0000 <mn>4</mn>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${text{CH}}_{4}$</annotation>\u0000 </semantics></math>) and nitrous oxide (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>N</mi>\u0000 <mn>2</mn>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${mathrm{N}}_{2}$</annotation>\u0000 </semantics></math>O). These transport sensitivities reflect, to first order, changes in the strength of tropical upwelling in the lower stratosphere (70–100 hPa) which are driven by changes in resolved wave convergence over northern midlatitudes as (critical lines of) wave propagation shift in latitude. Our results strongly support continued examination of the role of numerics in contributing to transport biases in composition modeling.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004442","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144220219","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 Online Paleoclimate Data Assimilation With a Deep Learning-Based Network","authors":"Haohao Sun,&nbsp;Lili Lei,&nbsp;Zhengyu Liu,&nbsp;Liang Ning,&nbsp;Zhe-Min Tan","doi":"10.1029/2024MS004675","DOIUrl":"https://doi.org/10.1029/2024MS004675","url":null,"abstract":"<p>An online paleoclimate data assimilation (PDA) that utilizes climate forecasts from a deep learning-based network (NET) along with assimilation of proxies to reconstruct surface air temperature, is investigated here. The NET is trained on ensemble simulations from the Community Earth System Model-Last Millennium Ensemble. Due to the nonlinear features with high-dimensional input, NET gains better predictive skills compared to the linear inverse model (LIM) in a reduced empirical orthogonal function (EOF) space. Thus, an alternative for online PDA is to couple the NET with the integrated hybrid ensemble Kalman filter (IHEnKF). Moreover, an analog blending strategy is proposed to increase ensemble spread and mitigate filter divergence, which blends the analog ensembles selected from climatological samples based on proxies and cycling ensembles advanced by NET. To account for the underestimated uncertainties of real proxy data, an observation error inflation method is applied, which inflates the proxy error variance based on the comparison between the estimated proxy error variance and its climatological innovation. Consistent results are obtained from the pseudoproxy experiments and the real proxy experiments. The more informative ensemble priors from the online PDA using NET enhance the reconstructions than the online PDA using LIM, and both outperform the offline PDA with randomly sampled climatological ensemble priors. The advantages of online PDA with NET over the online PDA with LIM and offline PDA become more pronounced, as the proxy data become sparser.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004675","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144197257","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 Four-Dimensional Variational Informed Generative Adversarial Network for Data Assimilation","authors":"Wuxin Wang,&nbsp;Boheng Duan,&nbsp;Weicheng Ni,&nbsp;Jingze Lu,&nbsp;Taikang Yuan,&nbsp;Dawei Li,&nbsp;Juan Zhao,&nbsp;Kaijun Ren","doi":"10.1029/2024MS004437","DOIUrl":"https://doi.org/10.1029/2024MS004437","url":null,"abstract":"<p>Data-driven weather prediction (DDWP) has made significant advancements in recent years. However, weather prediction using DDWPs still requires an accurate initial field as the input. To fulfill this requirement, the four-dimensional variational (4DVar) approach can offer initial fields. Recent studies have demonstrated the potential of deep learning (DL)-based methods in accelerating 4DVar. In this study, we propose a novel model called the 4DVar-informed generative adversarial network (4DVarGAN), which combines prior knowledge from 4DVar with the conditional generative network (CGAN). We employ a CGAN to non-iteratively solve the 4DVar cost function and utilize a cycle-consistent adversarial learning framework for data augmentation. Additionally, we incorporate a 4DVar-based adaptive adjustment to the output of the proposed model's analysis increment-generating component, which promotes reasonable stabilization. Experimental results using 500 hPa geopotential fields from the WeatherBench data set demonstrate that our approach achieves a 73-fold acceleration compared to the 4DVar implemented by the DDWP model. Furthermore, our model exhibits the lowest initial and forecast errors, outperforming state-of-the-art DL-based data assimilation (DA) methods. Moreover, our method demonstrates effective performance when starting from background fields of varying qualities, consistently achieving stable results. These findings highlight the potential of CGANs in enhancing the reliability of data-driven DA by incorporating the prior knowledge of the 4DVar method.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004437","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144179007","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":"SuperdropNet: A Stable and Accurate Machine Learning Proxy for Droplet-Based Cloud Microphysics","authors":"Shivani Sharma,&nbsp;David S. Greenberg","doi":"10.1029/2024MS004279","DOIUrl":"https://doi.org/10.1029/2024MS004279","url":null,"abstract":"<p>Cloud microphysics has important consequences for climate and weather phenomena, and inaccurate representations can limit forecast accuracy. While atmospheric models increasingly resolve storms and clouds, the accuracy of the underlying microphysics remains limited by computationally expedient bulk moment schemes based on simplifying assumptions. Droplet-based Lagrangian schemes are more accurate but are underutilized due to their large computational overhead. Machine learning (ML) based schemes can bridge this gap by learning from vast droplet-based simulation data sets, but have so far struggled to match the accuracy and stability of bulk moment schemes. To address this challenge, we developed SuperdropNet, an ML-based emulator of the Lagrangian superdroplet simulations. To improve accuracy and stability, we employ multi-step autoregressive prediction during training, impose physical constraints, and carefully control stochasticity in the training data. Superdropnet predicted hydrometeor states and cloud-to-rain transition times more accurately than previous ML emulators, and matched or outperformed bulk moment schemes in many cases. We further carried out detailed analyses to reveal how multistep autoregressive training improves performance, and how the performance of SuperdropNet and other microphysical schemes hydrometeors' mass, number and size distribution. Together our results suggest that ML models can effectively emulate cloud microphysics, in a manner consistent with droplet-based simulations.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004279","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144179413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved Cross-Scale Snow Cover Simulations by Developing a Scale-Aware Ground Snow Cover Fraction Parameterization in the Noah-MP Land Surface Model","authors":"Ronnie Abolafia-Rosenzweig,&nbsp;Cenlin He,&nbsp;Tzu-Shun Lin,&nbsp;Michael Barlage,&nbsp;Karl Rittger","doi":"10.1029/2024MS004704","DOIUrl":"https://doi.org/10.1029/2024MS004704","url":null,"abstract":"<p>Snow cover fraction (SCF) accuracy in land surface models (LSMs) impacts the accuracy of surface albedo and land-atmosphere interactions. However, SCF is a large source of uncertainty, partially because of the scale-dependent nature of snow depletion curves that is not parameterized by LSMs. Using the spatially and temporally complete observationally-informed STC-MODSCAG and Snow Data Assimilation System data sets, we develop a new scale-aware ground SCF parameterization and implement it into the Noah-MP LSM. The new scale-aware parameterization significantly reduces ground SCF errors and the scale-dependence of errors in the western U.S (WUS) compared with the baseline ground SCF formulation. Specifically, the baseline formulation overestimates ground SCF by 4%, 6%, 9%, and 12% at 1-km, 3-km, 13-km, and 25-km resolutions in the WUS, respectively, whereas biases from the enhanced scale-aware scheme are reduced to 0%–2% in box model simulations and do not exhibit a relationship with spatial scales. Noah-MP simulations using the scale-aware parameterization have smaller mean (peak) ground SCF biases than the baseline simulation by 1%–2% (3%–5%), with spatiotemporal variability depending on land cover, topography, and snow depth. Noah-MP simulations using the enhanced scale-aware parameterization remove the baseline WUS surface albedo overestimates of 0.01–0.03 in the 1-km to 25-km resolution simulations, relative to Moderate Resolution Imaging Spectroradiometer retrievals. The Noah-MP ground SCF and surface albedo improvements due to the scale-aware parameterization are found across most land cover classifications and elevations, indicating the enhanced ground SCF scheme can improve simulated snowpack and surface energy budget accuracy across a variety of WUS landscapes.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 6","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004704","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171930","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":"Non-Monotonic Convective Response to Vertical Wind Shear: A Closer Look From Cloud Resolving Model Simulations","authors":"Yang Tian,&nbsp;Rich Neale,&nbsp;Hugh Morrison","doi":"10.1029/2024MS004859","DOIUrl":"https://doi.org/10.1029/2024MS004859","url":null,"abstract":"<p>Using a three-dimensional cloud-resolving model, a systematic exploration is undertaken of the response of a radiative-convective equilibrium state to imposed vertical wind shear of varying magnitude. Domain-averaged surface precipitation exhibits a non-monotonic sensitivity to increasing shear magnitude, characterized by a decrease with increasing shear for weakly sheared conditions (&lt;1.5 × 10⁻³ s⁻¹) and an increase under stronger shear (&gt;1.5 × 10⁻³ s⁻¹), with a similar trend in surface heat fluxes. During the first 30–40 min after wind shear is imposed, convective activity and rainfall are suppressed, which is attributed to increased surface drag and reduced boundary layer eddy kinetic energy. As the shear persists over time, it eventually fosters the development of deep convection. An analysis of the condensed water budget shows that the overall response of the domain-mean surface precipitation rate to increasing shear magnitude is mainly explained by changes in condensation rate, which in turn is primarily controlled by the cloudy updraft mass flux. In the lower to middle troposphere where most condensation occurs, cloudy updraft fraction steadily increases with increasing shear magnitude, whereas mean updraft vertical velocity exhibits a general decreasing trend as the shear magnitude increases. The compensating responses of updraft fraction and mean vertical velocity explain the non-monotonic surface precipitation response to vertical wind shear. Vertical shear does not significantly impact the evaporation or precipitation efficiencies.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 5","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004859","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144118143","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":"Addressing Out-of-Sample Issues in Multi-Layer Convolutional Neural-Network Parameterization of Mesoscale Eddies Applied Near Coastlines","authors":"Cheng Zhang,&nbsp;Pavel Perezhogin,&nbsp;Alistair Adcroft,&nbsp;Laure Zanna","doi":"10.1029/2024MS004819","DOIUrl":"https://doi.org/10.1029/2024MS004819","url":null,"abstract":"<p>This study addresses the boundary artifacts in machine-learned (ML) parameterizations for ocean subgrid mesoscale momentum forcing, as identified in the online ML implementation from a previous study (Zhang et al., 2023, https://doi.org/10.1029/2023ms003697). We focus on the boundary condition (BC) treatment within the existing convolutional neural network (CNN) models and aim to mitigate the “out-of-sample” errors observed near complex coastal regions without developing new, complex network architectures. Our approach leverages two established strategies for placing BCs in CNN models, namely zero and replicate padding. Offline evaluations revealed that these padding strategies significantly reduce root mean squared error (RMSE) in coastal regions by limiting the dependence on random initialization of weights and restricting the range of out-of-sample predictions. Further online evaluations suggest that replicate padding consistently reduces boundary artifacts across various retrained CNN models. In contrast, zero padding sometimes intensifies artifacts in certain retrained models despite both strategies performing similarly in offline evaluations. This study underscores the need for BC treatments in CNN models trained on open water data when predicting near-coastal subgrid forces in ML parameterizations. The application of replicate padding, in particular, offers a robust strategy to minimize the propagation of extreme values that can contaminate computational models or cause simulations to fail. Our findings provide insights for enhancing the accuracy and stability of ML parameterizations in the online implementation of ocean circulation models with coastlines.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 5","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004819","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117868","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":"Data-Driven Dynamic Modal Bias Analysis and Correction for Earth System Models","authors":"S. P. McGowan,&nbsp;N. L. Jones,&nbsp;W. S. P. Robertson,&nbsp;S. Balasuriya","doi":"10.1029/2024MS004779","DOIUrl":"https://doi.org/10.1029/2024MS004779","url":null,"abstract":"<p>Predicting Earth systems weeks or months into the future is an important yet challenging problem due to the high dimensionality, chaotic behavior, and coupled dynamics of the ocean, atmosphere, and other subsystems of the Earth. Numerical models invariably contain model error due to incomplete domain knowledge, limited capabilities of representation, and unresolved processes due to finite spatial resolution. Hybrid modeling, the pairing of a physics-driven model with a data-driven component, has shown promise in outperforming both purely physics-driven and data-driven approaches in predicting complex systems. Here we demonstrate two new hybrid methods that combine uninitialized temporal or spatiotemporal models with a data-driven component that may be modally decomposed to give insight into model bias, or used to correct the bias of model projections. These techniques are demonstrated on a simulated chaotic system and two empirical ocean variables: coastal sea surface elevation and sea surface temperature, which highlight that the inclusion of the data-driven components increases the state accuracy of their short-term evolution. Our work demonstrates that these hybrid approaches may prove valuable for: improving models during model development, creating novel methods for data assimilation, and enhancing the predictive accuracy of forecasts when available models have significant structural error.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"17 5","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004779","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144118167","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}