Geoscientific Model Development最新文献

{"title":"New explicit formulae for the settling speed of prolate spheroids in the atmosphere: theoretical background and implementation in AerSett v2.0.2","authors":"S. Mailler, Sotiris Mallios, A. Cholakian, V. Amiridis, L. Menut, R. Pennel","doi":"10.5194/gmd-17-5641-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-5641-2024","url":null,"abstract":"Abstract. We propose two explicit expressions to calculate the settling speed of solid atmospheric particles with prolate spheroidal shapes under the hypothesis of horizontal and vertical orientation. The first formulation is based on theoretical arguments only. The second method, valid for particles with mass median diameter up to 1000 µm, is based on recent heuristic drag expressions based on numeric simulations. We show that these two formulations show equivalent results within 2 % for deq≤100 µm and within 10 % for particles with deq≤500 µm falling with a horizontal orientation, showing that the first, more simple, method is suitable for virtually all atmospheric aerosols, provided their shape can be adequately described as a prolate spheroid. Finally, in order to facilitate the use of our results in chemistry transport models, we provide an implementation of the first of these methods in AerSett v2.0.2, a module written in Fortran.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"39 42","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141800103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ZJU-AERO V0.5: an Accurate and Efficient Radar Operator designed for CMA-GFS/MESO with the capability to simulate non-spherical hydrometeors","authors":"Hejun Xie, Lei Bi, Wei Han","doi":"10.5194/gmd-17-5657-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-5657-2024","url":null,"abstract":"Abstract. In this study, we present a new forward polarimetric radar operator called the Accurate and Efficient Radar Operator designed by ZheJiang University (ZJU-AERO). This operator was designed to interface with the numerical weather prediction (NWP) model of the global forecast system/regional mesoscale model of the China Meteorological Administration (CMA-GFS/MESO). The main objective of developing this observation operator was to assimilate observations from the precipitation measurement radar (PMR). It is also capable of simulating the ground-based radar's polarimetric radar variables, excluding the Doppler variables such as radial velocity and spectrum width. To calculate the hydrometeor optical properties of ZJU-AERO, we utilize the invariant-imbedding T-matrix (IITM) method, which can handle non-spherical and inhomogeneous hydrometeor particles in the atmosphere. The optical database of ZJU-AERO was designed with a multi-layered architecture to ensure the flexibility in hydrometeor morphology and orientation specifications while maintaining operational efficiency. Specifically, three levels of databases are created that store the single-scattering properties for different shapes at discrete sizes for various fixed orientations, integrated single-scattering properties over shapes and orientations, and bulk-scattering properties incorporating the size average, respectively. In this work, we elaborate on the design concepts, physical basis, and hydrometeor specifications of ZJU-AERO. Additionally, we present a case study demonstrating the application of ZJU-AERO in simulating the radar observations of Typhoon Haishen.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"15 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141801876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters","authors":"Jorn Bruggeman, K. Bolding, Lars Nerger, A. Teruzzi, Simone Spada, J. Skákala, S. Ciavatta","doi":"10.5194/gmd-17-5619-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-5619-2024","url":null,"abstract":"Abstract. Data assimilation (DA) in marine and freshwater systems combines numerical models and observations to deliver the best possible characterization of a waterbody's physical and biogeochemical state. DA underpins the widely used 3D ocean state reanalyses and forecasts produced operationally by, e.g., the Copernicus Marine Service. The use of DA in natural waters is an active field of research, but testing new developments in realistic setting can be challenging as operational DA systems are demanding in terms of computational resources and technical skill. There is a need for test beds that are sufficiently realistic but also efficient to run and easy to operate. Here, we present the Ensemble and Assimilation Tool (EAT), a flexible and extensible software package that enables data assimilation of physical and biogeochemical variables in a one-dimensional water column. EAT builds on established open-source components for hydrodynamics (GOTM), biogeochemistry (FABM), and data assimilation (PDAF). It is easy to install and operate and is flexible through support for user-written plugins. EAT is well suited to explore and advance the state of the art in DA in natural waters thanks to its support for (1) strongly and weakly coupled data assimilation, (2) observations describing any prognostic and diagnostic element of the physical–biogeochemical model, and (3) the estimation of biogeochemical parameters. Its range of capabilities is demonstrated with three applications: ensemble-based coupled physical–biogeochemical assimilation, the use of variational methods (3D-Var) to assimilate sea surface chlorophyll, and the estimation of biogeochemical parameters.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"30 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GCAM–GLORY v1.0: representing global reservoir water storage in a multi-sector human–Earth system model","authors":"Mengqi Zhao, Thomas B. Wild, N. Graham, Son Kim, Matthew Binsted, A. K. Chowdhury, S. Msangi, Pralit L. Patel, Chris R. Vernon, Hassan Niazi, Hong-Yi Li, G. Abeshu","doi":"10.5194/gmd-17-5587-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-5587-2024","url":null,"abstract":"Abstract. Reservoirs play a significant role in modifying the spatiotemporal availability of surface water to meet multi-sector human demands, despite representing a relatively small fraction of the global water budget. Yet the integrated modeling frameworks that explore the interactions among climate, land, energy, water, and socioeconomic systems at a global scale often contain limited representations of water storage dynamics that incorporate feedbacks from other systems. In this study, we implement a representation of water storage in the Global Change Analysis Model (GCAM) to enable the exploration of the future role (e.g., expansion) of reservoir water storage globally in meeting demands for, and evolving in response to interactions with, the climate, land, and energy systems. GCAM represents 235 global water basins, operates at 5-year time steps, and uses supply curves to capture economic competition among renewable water (now including reservoirs), non-renewable groundwater, and desalination. Our approach consists of developing the GLObal Reservoir Yield (GLORY) model, which uses a linear programming (LP)-based optimization algorithm and dynamically linking GLORY with GCAM. The new coupled GCAM–GLORY approach improves the representation of reservoir water storage in GCAM in several ways. First, the GLORY model identifies the cost of supplying increasing levels of water supply from reservoir storage by considering regional physical and economic factors, such as evolving monthly reservoir inflows and demands, and the leveled cost of constructing additional reservoir storage capacity. Second, by passing those costs to GCAM, GLORY enables the exploration of future regional reservoir expansion pathways and their response to climate and socioeconomic drivers. To guide the model toward reasonable reservoir expansion pathways, GLORY applies a diverse array of feasibility constraints related to protected land, population, water sources, and cropland. Finally, the GLORY–GCAM feedback loop allows evolving water demands from GCAM to inform GLORY, resulting in an updated supply curve at each time step, thus enabling GCAM to establish a more meaningful economic value of water. This study improves our understanding of the sensitivity of reservoir water supply to multiple physical and economic dimensions, such as sub-annual variations in climate conditions and human water demands, especially for basins experiencing socioeconomic droughts.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"60 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141808923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reduced floating-point precision in regional climate simulations: an ensemble-based statistical verification","authors":"Hugo Banderier, Christian Zeman, D. Leutwyler, Stefan Rüdisühli, Christoph Schär","doi":"10.5194/gmd-17-5573-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-5573-2024","url":null,"abstract":"Abstract. The use of single precision in floating-point representation has become increasingly common in operational weather prediction. Meanwhile, climate simulations are still typically run in double precision. The reasons for this are likely manifold and range from concerns about compliance and conservation laws to the unknown effect of single precision on slow processes or simply the less frequent opportunity and higher computational costs of validation. Using an ensemble-based statistical methodology, Zeman and Schär (2022) could detect differences between double- and single-precision simulations from the regional weather and climate model COSMO. However, these differences are minimal and often only detectable during the first few hours or days of the simulation. To evaluate whether these differences are relevant for regional climate simulations, we have conducted 10-year-long ensemble simulations over the European domain of the Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX) in single and double precision with 100 ensemble members. By applying the statistical testing at a grid-cell level for 47 output variables every 12 or 24 h, we only detected a marginally increased rejection rate for the single-precision climate simulations compared to the double-precision reference based on the differences in distribution for all tested variables. This increase in the rejection rate is much smaller than that arising from minor variations of the horizontal diffusion coefficient in the model. Therefore, we deem it negligible as it is masked by model uncertainty. To our knowledge, this study represents the most comprehensive analysis so far on the effects of reduced precision in a climate simulation for a realistic setting, namely with a fully fledged regional climate model in a configuration that has already been used for climate change impact and adaptation studies. The ensemble-based verification of model output at a grid-cell level and high temporal resolution is very sensitive and suitable for verifying climate models. Furthermore, the verification methodology is model-agnostic, meaning it can be applied to any model. Our findings encourage exploiting the reduction of computational costs (∼30 % for COSMO) obtained from reduced precision for regional climate simulations.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"64 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141806738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Year of Polar Prediction site Model Intercomparison Project (YOPPsiteMIP) phase 1: project overview and Arctic winter forecast evaluation","authors":"Jonathan J. Day, Gunilla Svensson, Barbara Casati, T. Uttal, S. Khalsa, Eric Bazile, Elena Akish, Niramson Azouz, Lara Ferrighi, Helmut Frank, Michael Gallagher, Øystein Godøy, L. Hartten, Laura X. Huang, Jareth I. Holt, Massimo Di Stefano, I. Suomi, Z. Mariani, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Teresa Remes, R. Fadeev, Amy Solomon, Johanna Tjernström, M. Tolstykh","doi":"10.5194/gmd-17-5511-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-5511-2024","url":null,"abstract":"Abstract. Although the quality of weather forecasts in the polar regions is improving, forecast skill there still lags behind lower latitudes. So far there have been relatively few efforts to evaluate processes in numerical weather prediction systems using in situ and remote sensing datasets from meteorological observatories in the terrestrial Arctic and Antarctic compared to the mid-latitudes. Progress has been limited both by the heterogeneous nature of observatory and forecast data and by limited availability of the parameters needed to perform process-oriented evaluation in multi-model forecast archives. The Year of Polar Prediction (YOPP) site Model Inter-comparison Project (YOPPsiteMIP) is addressing this gap by producing merged observatory data files (MODFs) and merged model data files (MMDFs), bringing together observations and forecast data at polar meteorological observatories in a format designed to facilitate process-oriented evaluation. An evaluation of forecast performance was performed at seven Arctic sites, focussing on the first YOPP Special Observing Period in the Northern Hemisphere (NH-SOP1) in February and March 2018. It demonstrated that although the characteristics of forecast skill vary between the different sites and systems, an underestimation in boundary layer temperature variability across models, which goes hand in hand with an inability to capture cold extremes, is a common issue at several sites. It is found that many models tend to underestimate the sensitivity of the 2 m air temperature (T2m) and the surface skin temperature to variations in radiative forcing, and the reasons for this are discussed.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"97 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating CHASER V4.0 global formaldehyde (HCHO) simulations using satellite, aircraft, and ground-based remote-sensing observations","authors":"Hossain Mohammed Syedul Hoque, Kengo Sudo, Hitoshi Irie, Yanfeng He, Md Firoz Khan","doi":"10.5194/gmd-17-5545-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-5545-2024","url":null,"abstract":"Abstract. Formaldehyde (HCHO), a precursor to tropospheric ozone, is an important tracer of volatile organic compounds (VOCs) in the atmosphere. Two years (2019–2020) of HCHO simulations obtained from the global chemistry transport model CHASER at a horizontal resolution of 2.8° × 2.8° have been evaluated using the Tropospheric Monitoring Instrument (TROPOMI) and multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations. In situ measurements from the Atmospheric Tomography Mission (ATom) in 2018 were used to evaluate the HCHO simulations for 2018. CHASER reproduced the TROPOMI-observed global HCHO spatial distribution with a spatial correlation (r) of 0.93 and a negative bias of 7 %. The model showed a good capability to reproduce the observed magnitude of the HCHO seasonality in different regions, including the background conditions. The discrepancies between the model and satellite in the Asian regions were related mainly to the underestimated and missing anthropogenic emission inventories. The maximum difference between two HCHO simulations based on two different nitrogen oxide (NOx) emission inventories was 20 %. TROPOMI's finer spatial resolution than that of the Ozone Monitoring Instrument (OMI) sensor reduced the global model–satellite root-mean-square error (RMSE) by 20 %. The OMI- and TROPOMI-observed seasonal variations in HCHO abundances were consistent. The simulated seasonality showed better agreement with TROPOMI in most regions. The simulated HCHO and isoprene profiles correlated strongly (R=0.81) with the ATom observations. However, CHASER overestimated HCHO mixing ratios over dense vegetation areas in South America and the remote Pacific region (background condition), mainly within the planetary boundary layer (< 2 km). The simulated seasonal variations in the HCHO columns showed good agreement (R>0.70) with the MAX-DOAS observations and agreed within the 1σ standard deviation of the observed values. However, the temporal correlation (R∼0.40) was moderate on a daily scale. CHASER underestimated the HCHO levels at all sites, and the peak occurrences in the observed and simulated HCHO seasonality differed. The coarseness of the model's resolution could potentially lead to such discrepancies. Sensitivity studies showed that anthropogenic emissions were the highest contributor (up to ∼ 35 %) to the wintertime regional HCHO levels.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141809605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global variable-resolution simulations of extreme precipitation over Henan, China, in 2021 with MPAS-Atmosphere v7.3","authors":"Zijun Liu, Li Dong, Zongxu Qiu, Xingrong Li, Huiling Yuan, Dongmei Meng, Xiaobin Qiu, Dingyuan Liang, Yafei Wang","doi":"10.5194/gmd-17-5477-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-5477-2024","url":null,"abstract":"Abstract. A historic rainstorm occurred over Henan, China, in July 2021 (“7.20” extreme precipitation event), resulting in significant human casualties and socioeconomic losses. A global variable-resolution model (MPAS-Atmosphere v7.3) was employed to simulate this extreme precipitation event. A series of simulations have been done at both quasi-uniform (60 and 15 km) and variable-resolution (60–15 and 60–3 km) meshes from hydrostatic to nonhydrostatic scale with two parameterization scheme suites. For the 48 h peak precipitation duration (20–22 July), the 60–3 km variable-resolution simulation coupled with the scale-aware convection-permitting parameterization scheme suite stands out predominantly among other simulation experiments as it reproduces this extreme precipitation event most accurately. At 15 km resolution, the 60–15 km variable-resolution simulation achieves comparable forecasting skills to the 15 km quasi-uniform simulation but at a much reduced computing cost. In addition, we found that the default mesoscale suite generally outperforms the convection-permitting suite at 15 km resolution as simulations coupled with the convection-permitting suite missed the third peak of this extreme precipitation event, while the mesoscale suite did not. Furthermore, it is found that the large-scale circulation plays a critical role in the peak precipitation simulations at 15 km resolution, via influencing the simulated low-level wind. During the second peak precipitation period, simulations with the convection-permitting parameterization scheme suite at 15 km resolution generate a prominent low-level easterly wind component bias, which is largely attributed to the excessively evaporative cooling in the lower troposphere. This study further reveals that at 15 km resolution the diabatic heating from the grid-scale precipitation accounts more for the low-level wind bias than the convective-scale precipitation. Given that two different cloud microphysics schemes, namely Thompson and WSM6 schemes, are used in the convection-permitting and default mesoscale parameterization scheme suites, respectively, these microphysics schemes are found to be the primary contributor to the low-level wind simulation bias.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"19 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141813604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a novel storm surge inundation model framework for efficient prediction","authors":"Xuanxuan Gao, Shuiqing Li, Dongxue Mo, Yahao Liu, Po Hu","doi":"10.5194/gmd-17-5497-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-5497-2024","url":null,"abstract":"Abstract. Storm surge is a natural process that causes flood disasters in coastal zones and results in massive casualties and property losses. Therefore, storm surge inundation is of major concern in formulating appropriate strategies for disaster prevention and mitigation. However, traditional storm surge hydrodynamic models have large limits with respect to computational efficiency and stability in practical applications. In this study, a novel storm surge inundation model was developed based on a wetting and drying algorithm established from a simplified shallow-water momentum equation. The wetting and drying algorithm was applied to a rectangular grid that iterates through a cellular automata algorithm to improve computational efficiency. The model, referred to as the Hydrodynamical Cellular Automata Flood Model (HCA-FM), was evaluated by comparing the simulations to regional field observations and to a widely used hydrodynamic numerical model. The comparisons demonstrated that HCA-FM can reproduce the observed inundation distributions and predict results that are consistent with the numerical simulation in terms of the inundation extent and submerged depth with much improved computational efficiency (predicting inundation within a few minutes) and high stability. The results reflect significant advancement of HCA-FM toward efficient predictions of storm surge inundation and applications at large spatial scales.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"37 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141813334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TorchClim v1.0: a deep-learning plugin for climate model physics","authors":"D. Fuchs, S. Sherwood, A. Prasad, Kirill Trapeznikov, Jim Gimlett","doi":"10.5194/gmd-17-5459-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-5459-2024","url":null,"abstract":"Abstract. Climate models are hindered by the need to conceptualize and then parameterize complex physical processes that are not explicitly numerically resolved and for which no rigorous theory exists. Machine learning and artificial intelligence methods (ML and AI) offer a promising paradigm that can augment or replace the traditional parameterized approach with models trained on empirical process data. We offer a flexible and efficient plugin, TorchClim, that facilitates the insertion of ML and AI physics surrogates into the climate model to create hybrid models. A reference implementation is presented for the Community Earth System Model (CESM), where moist physics and radiation parameterizations of the Community Atmospheric Model (CAM) are replaced with such a surrogate. We present a set of best-practice principles for doing this with minimal changes to the general circulation model (GCM), exposing the surrogate model as any other parameterization module, and discuss how to accommodate the requirements of physics surrogates such as the need to avoid unphysical values and supply information needed by other GCM components. We show that a deep-neural-network surrogate trained on data from CAM itself can produce a model that reproduces the climate and variability in the original model, although with some biases. The efficiency and flexibility of this approach open up new possibilities for using physics surrogates trained on offline data to improve climate model performance, better understand model physical processes, and flexibly incorporate new processes into climate models.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"66 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141817596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}