Geoscientific Model Development最新文献

{"title":"HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model","authors":"Qi Tang, H. Delottier, Wolfgang Kurtz, Lars Nerger, O. Schilling, Philip Brunner","doi":"10.5194/gmd-17-3559-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-3559-2024","url":null,"abstract":"Abstract. This article describes a modular ensemble-based data assimilation (DA) system which is developed for an integrated surface–subsurface hydrological model. The software environment for DA is the Parallel Data Assimilation Framework (PDAF), which provides various assimilation algorithms like the ensemble Kalman filters, non-linear filters, 3D-Var and combinations among them. The integrated surface–subsurface hydrological model is HydroGeoSphere (HGS), a physically based modelling software for the simulation of surface and variably saturated subsurface flow, as well as heat and mass transport. The coupling and capabilities of the modular DA system are described and demonstrated using an idealised model of a geologically heterogeneous alluvial river–aquifer system with drinking water production via riverbank filtration. To demonstrate its modularity and adaptability, both single and multivariate assimilations of hydraulic head and soil moisture observations are demonstrated in combination with individual and joint updating of multiple simulated states (i.e. hydraulic heads and water saturation) and model parameters (i.e. hydraulic conductivity). With the integrated model and this modular DA framework, we have essentially developed the hydrologically and DA-wise robust toolbox for developing the basic model for operational management of coupled surface water–groundwater resources.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"21 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141020165","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":"Assessing acetone for the GISS ModelE2.1 Earth system model","authors":"Alexandra Rivera, K. Tsigaridis, G. Faluvegi, D. Shindell","doi":"10.5194/gmd-17-3487-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-3487-2024","url":null,"abstract":"Abstract. Acetone is an abundant volatile organic compound (VOC) in the atmosphere, with important influences on ozone and oxidation capacity. Direct sources include chemical production from other VOCs and anthropogenic emissions, terrestrial vegetation, biomass-burning emissions, and ocean production. Sinks include chemical loss, deposition onto the land surface, and ocean uptake. Acetone also has a lifetime that is long enough to allow transport and reactions with other compounds remote from its sources. The NASA Goddard Institute for Space Studies (GISS) Earth system model ModelE2.1 simulates a variety of Earth system interactions. Previously, acetone had a very simplistic representation in the ModelE chemical scheme. This study assesses a more sophisticated acetone scheme in which acetone is a full 3-dimensional tracer with explicit sources, sinks, and atmospheric transport. We first evaluate the new global acetone budget in the context of past literature. Estimated source and sink fluxes fall within the range of previous models, although total atmospheric burden and lifetime are at the lower end of the published literature. Acetone's new representation in ModelE2.1 also results in more realistic spatial and vertical distributions, which we compare against previous models and field observations. The seasonality of acetone-related processes was also studied in conjunction with field measurements, and these comparisons show promising agreement but also shortcomings at high-emission urban locations, where the model's resolution is too coarse to capture the true behavior. Finally, we conduct a variety of sensitivity studies that explore the influence of key parameters on the acetone budget and its global distribution. An impactful finding is that the production of acetone from precursor hydrocarbon oxidation has strong leverage on the overall chemical source, indicating the importance of accurate molar yields. Overall, our implementation is one that corroborates with previous studies and marks a significant improvement in the development of the acetone tracer in GISS ModelE2.1.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141019507","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":"Identifying atmospheric rivers and their poleward latent heat transport with generalizable neural networks: ARCNNv1","authors":"A. Mahesh, T. O’Brien, B. Loring, Abdelrahman A. Elbashandy, W. Boos, W. Collins","doi":"10.5194/gmd-17-3533-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-3533-2024","url":null,"abstract":"Abstract. Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. By transporting significant amounts of latent energy towards the poles, they are crucial to maintaining the climate system's energy balance. Since there is no first-principle definition of an AR grounded in geophysical fluid mechanics, AR identification is currently performed by a multitude of expert-defined, threshold-based algorithms. The variety of AR detection algorithms has introduced uncertainty into the study of ARs, and the thresholds of the algorithms may not generalize to new climate datasets and resolutions. We train convolutional neural networks (CNNs) to detect ARs while representing this uncertainty; we name these models ARCNNs. To detect ARs without requiring new labeled data and labor-intensive AR detection campaigns, we present a semi-supervised learning framework based on image style transfer. This framework generalizes ARCNNs across climate datasets and input fields. Using idealized and realistic numerical models, together with observations, we assess the performance of the ARCNNs. We test the ARCNNs in an idealized simulation of a shallow-water fluid in which nearly all the tracer transport can be attributed to AR-like filamentary structures. In reanalysis and a high-resolution climate model, we use ARCNNs to calculate the contribution of ARs to meridional latent heat transport, and we demonstrate that this quantity varies considerably due to AR detection uncertainty.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"15 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141020932","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":"biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)","authors":"Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, D. Gerten, J. Heinke, Sarah Matej, S. Ostberg, S. Schaphoff, Wolfgang Lucht","doi":"10.5194/gmd-17-3235-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-3235-2024","url":null,"abstract":"Abstract. Ecosystems are under multiple stressors, and impacts can be measured with multiple variables. Humans have altered mass and energy flows of basically all ecosystems on Earth towards dangerous levels. However, integrating the data and synthesizing conclusions is becoming more and more complicated. Here we present an automated and easy-to-apply R package to assess terrestrial biosphere integrity that combines two complementary metrics. (i) The BioCol metric that quantifies the human colonization pressure exerted on the biosphere through alteration and extraction (appropriation) of net primary productivity.(ii) The EcoRisk metric that quantifies biogeochemical and vegetation structural changes as a proxy for the risk of ecosystem destabilization. Applied to simulations with the dynamic global vegetation model LPJmL5 for 1500–2016, we find that large regions presently (period 2007–2016) show modification and extraction of >20 % of the preindustrial potential net primary production. The modification (degradation) of net primary production (NPP) as a result of land use change and extraction in terms of biomass removal (e.g., from harvest) leads to drastic alterations in key ecosystem properties, which suggests a high risk of ecosystem destabilization. As a consequence of these dynamics, EcoRisk shows particularly high values in regions with intense land use and deforestation and in regions prone to impacts of climate change, such as the Arctic and boreal zone. The metrics presented here enable spatially explicit global-scale evaluation of historical and future states of the biosphere and are designed for use by the wider scientific community, being applicable not only to assessing biosphere integrity but also to benchmarking model performance. The package will be maintained on GitHub and through that we encourage its future application to other models and data sets.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"43 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140657152","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":"Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications","authors":"W. V. van Verseveld, A. Weerts, Martijn Visser, J. Buitink, R. Imhoff, Hélène Boisgontier, L. Bouaziz, D. Eilander, M. Hegnauer, Corine ten Velden, Bobby Russell","doi":"10.5194/gmd-17-3199-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-3199-2024","url":null,"abstract":"Abstract. The wflow_sbm hydrological model, recently released by Deltares, as part of the Wflow.jl (v0.7.3) modelling framework, is being used to better understand and potentially address multiple operational and water resource planning challenges from a catchment scale to national scale to continental and global scale. Wflow.jl is a free and open-source distributed hydrological modelling framework written in the Julia programming language. The development of wflow_sbm, the model structure, equations and functionalities are described in detail, including example applications of wflow_sbm. The wflow_sbm model aims to strike a balance between low-resolution, low-complexity and high-resolution, high-complexity hydrological models. Most wflow_sbm parameters are based on physical characteristics or processes, and at the same time wflow_sbm has a runtime performance well suited for large-scale high-resolution model applications. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets and through the use of point-scale (pedo)transfer functions and suitable upscaling rules and generally result in a satisfactory (0.4 ≥ Kling–Gupta efficiency (KGE) < 0.7) to good (KGE ≥ 0.7) performance for discharge a priori (without further tuning). Wflow_sbm includes relevant hydrological processes such as glacier and snow processes, evapotranspiration processes, unsaturated zone dynamics, (shallow) groundwater, and surface flow routing including lakes and reservoirs. Further planned developments include improvements on the computational efficiency and flexibility of the routing scheme, implementation of a water demand and allocation module for water resource modelling, the addition of a deep groundwater concept, and computational efficiency improvements through for example distributed computing and graphics processing unit (GPU) acceleration.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"31 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140657615","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":"CAR36, a regional high-resolution ocean forecasting system for improving drift and beaching of Sargassum in the Caribbean archipelago","authors":"S. Cailleau, Laurent Bessières, Léonel Chiendje, Flavie Dubost, G. Reffray, J. Lellouche, Simon van Gennip, C. Régnier, M. Drévillon, Marc Tressol, Matthieu Clavier, Julien Temple-Boyer, Léo Berline","doi":"10.5194/gmd-17-3157-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-3157-2024","url":null,"abstract":"Abstract. The stranding of Sargassum seaweed on the Caribbean archipelago beaches constitutes real socio-economic, ecological and health problems. Météo-France currently operates a model of Sargassum drift forecast (called MOTHY) forced by ocean currents from the global analysis and forecasting system GLO12 at 1/12° (∼9 km over the Caribbean) operated by Mercator Ocean International (MOi). In order to improve the Météo-France drift forecast, MOi has developed a regional high-resolution ocean forecasting system CAR36 at 1/36° (∼3 km) centred in the Caribbean archipelago region. In addition to a finer spatial resolution, this system was designed to resolve higher-frequency signals such as the tidal forcing and to use hourly atmospheric forcing including the inverse barometer effect. Here the added value of the CAR36 system relative to GLO12 is evaluated with particular focus on the reproduction of meso- and sub-mesoscale structures representing key features of the Caribbean region dynamics and therefore Sargassum transport. The realism of the local dynamics was examined with standard statistical validation diagnostics using satellite data (sea surface height, sea surface temperature, Sargassum detection) and drifting buoys, together with more process-oriented diagnostics such as eddy detection and tracking across the domain. GLO12 and CAR36 hindcast simulations were compared over the year 2019. CAR36 showed marginally better scores using OceanPredict diagnostics (https://oceanpredict.org/, last access: 10 April 2024). The dynamics of a westward-propagating North Brazil Current (NBC) eddy from its entry into the domain to its dissipation were found to be more realistic in CAR36, especially at the end of its lifetime when it collides with the Caribbean archipelago. The transfer of kinetic energy from the eddy dissipating westward into the Caribbean Sea was diagnosed as more pronounced for CAR36, corresponding to filamentary structures crossing the Caribbean archipelago and resulting in part from the friction of the NBC eddy along the islands to the east. Using detection from satellites, aggregation of Sargassum around eddies or along filaments suggests that CAR36 should improve the algae drift forecasts.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"17 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140666239","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":"An overview of cloud–radiation denial experiments for the Energy Exascale Earth System Model version 1","authors":"B. Harrop, Jian Lu, L. R. Leung, William K. M. Lau, Kyu-Myong Kim, Brian Medeiros, B. Soden, G. Vecchi, Bosong Zhang, Balwinder Singh","doi":"10.5194/gmd-17-3111-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-3111-2024","url":null,"abstract":"Abstract. The interaction between clouds and radiation is a key process within the climate system, and assessing the impacts of that interaction provides valuable insights into both the present-day climate and future projections. Many modeling experiments have been designed over the years to probe the impact of the cloud radiative effect (CRE) on the climate, including those that seek to disrupt the mean CRE effect and those that only disrupt the covariance of the CRE with the circulation. Seven such experimental designs have been added to the Energy Exascale Earth System Model version 1 (E3SMv1) of the US Department of Energy. These experiments include both the first and second iterations of the Clouds On/Off Klimate Intercomparison Experiment (COOKIE) experimental design, as well as the cloud-locking method. This paper documents the code changes necessary to implement such experiments and also provides detailed instructions for how to run them. Analyses across experiment types provide valuable insights and confirm the findings of prior studies, including the role of cloud radiative heating toward intensifying the monsoon, intensifying rain rates, and poleward expansion of the general circulation owing to cloud feedbacks.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"123 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140669654","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":"Reservoir Assessment Tool version 3.0: a scalable and user-friendly software platform to mobilize the global water management community","authors":"Sanchit Minocha, F. Hossain, Pritam Das, Sarath Suresh, Shahzaib Khan, George K. Darkwah, H. Lee, S. Galelli, K. Andreadis, P. Oddo","doi":"10.5194/gmd-17-3137-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-3137-2024","url":null,"abstract":"Abstract. In the modern world, dams and the artificial reservoirs behind them serve the increasing demand for water across diverse needs such as agriculture, energy production, and drinking water. As dams continue to proliferate, monitoring water availability influenced by reservoir operations is now of paramount importance. The Reservoir Assessment Tool (RAT) is a data-driven software platform that integrates satellite remote sensing with hydrological models, enabling the estimation of key reservoir parameters such as inflow, outflow, surface area, evaporation, and storage changes. The earliest version of RAT (version 1.0) was set up for 1598 reservoirs around the world with limitations in functional robustness, updating frequency, and scalability. Some of these limitations on updating frequency and functional robustness were addressed in version 2.0 that was later made operational for the intergovernmental agency of the Mekong River Commission. Recognizing the need for scalability to mobilize the global water management community to benefit from satellite remote sensing, we hereby introduce RAT version 3.0. This version is optimized for accelerating open collaboration among users for continuous improvement and customization of RAT to enable reservoir management breakthroughs. RAT 3.0 represents a wholesale overhaul from the previous versions to empower the global community of users and developers in the spirit of the open-source movement. RAT 3.0 allows reservoir monitoring advancements and new functional developments that can be freely exchanged and seamlessly integrated for continuous evolution of the software. A centralized web application has also been established to facilitate the storage and dissemination of global reservoir monitoring information along with comprehensive training resources. RAT 3.0 aspires to bridge the traditional practices of water management community with the capabilities of satellite remote sensing. The global impact of the software can be expected to increase as uptake spreads, enabling a more sustainable and equitable utilization of our planet's water resources.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"126 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140668928","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":"NorSand4AI: a comprehensive triaxial test simulation database for NorSand constitutive model materials","authors":"L. Ozelim, Michéle Dal Toé Casagrande, André Luís Brasil Cavalcante","doi":"10.5194/gmd-17-3175-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-3175-2024","url":null,"abstract":"Abstract. In soil sciences, parametric models known as constitutive models (e.g., the Modified Cam Clay and the NorSand) are used to represent the behavior of natural and artificial materials. In contexts where liquefaction may occur, the NorSand constitutive model has been extensively applied by both industry and academia due to its relatively simple critical state formulation and low number of input parameters. Despite its suitability as a good modeling framework to assess static liquefaction, the NorSand model still is based on premises which may not perfectly represent the behavior of all soil types. In this context, the creation of data-driven and physically informed metamodels emerges. The literature suggests that data-driven models should initially be developed using synthetic datasets to establish a general framework, which can later be applied to experimental datasets to enhance the model's robustness and aid in discovering potential mechanisms of soil behavior. Therefore, creating large and reliable synthetic datasets is a crucial step in constructing data-driven constitutive models. In this context, the NorSand model comes in handy: by using NorSand simulations as the training dataset, data-driven constitutive metamodels can then be fine-tuned using real test results. The models created that way will combine the power of NorSand with the flexibility provided by data-driven approaches, enhancing the modeling capabilities for liquefaction. Therefore, for a material following the NorSand model, the present paper presents a first-of-its-kind database that addresses the size and complexity issues of creating synthetic datasets for nonlinear constitutive modeling of soils by simulating both drained and undrained triaxial tests. Two datasets are provided: the first one considers a nested Latin hypercube sampling of input parameters encompassing 2000 soil types, each subjected to 40 initial test configurations, resulting in a total of 160 000 triaxial test results. The second one considers nested quasi-Monte Carlo sampling techniques (Sobol and Halton) of input parameters encompassing 2048 soil types, each subjected to 42 initial test configurations, resulting in a total of 172 032 triaxial test results. By using the quasi-Monte Carlo dataset and 49 of its subsamples, it is shown that the dataset of 2000 soil types and 40 initial test configurations is sufficient to represent the general behavior of the NorSand model. In this process, four machine learning algorithms (Ridge Regressor, KNeighbors Regressor and two variants of the Ridge Regressor which incorporate nonlinear Nystroem kernel mappings of the input and output values) were trained to predict the constitutive and test parameters based solely on the triaxial test results. These algorithms achieved 13.91 % and 16.18 % mean absolute percentage errors among all 14 predicted parameters for undrained and drained triaxial test inputs, respectively. As a secondary outcome, this work introduce","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":"77 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140670577","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":"Subgrid-scale variability of cloud ice in the ICON-AES 1.3.00","authors":"Sabine Doktorowski, J. Kretzschmar, Johannes Quaas, M. Salzmann, O. Sourdeval","doi":"10.5194/gmd-17-3099-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-3099-2024","url":null,"abstract":"Abstract. This paper presents a stochastic approach for the aggregation process rate in the ICOsahedral Nonhydrostatic general circulation model (ICON-AES), which takes subgrid-scale variability into account. This method creates a stochastic parameterization of the process rate by choosing a new specific cloud ice mass at random from a uniform distribution function. This distribution, which is consistent with the model's cloud cover scheme, is evaluated in terms of cloud ice mass variance with a combined satellite retrieval product (DARDAR) from the satellite cloud radar CloudSat and the Cloud–Aerosol Lidar and Infrared Pathfinder Observations (CALIPSO). The global patterns of simulated and observed cloud ice mixing ratio variance are in a good agreement, despite an underestimation in the tropical regions, especially at lower altitudes, and an overestimation in higher latitudes from the modeled variance. Due to this stochastic approach the yearly mean of cloud ice shows an overall decrease. As a result of the nonlinear nature of the aggregation process, the yearly mean of the process rates increases when taking subgrid-scale variability into account. An increased process rate leads to a stronger transformation of cloud ice into snow and therefore to a cloud ice loss. The yearly averaged global mean aggregation rate is more than 20 % higher at selected pressure levels due to the stochastic approach. A strong interaction of aggregation and accretion, however, lowers the effect of cloud ice loss due to a higher aggregation rate. The new stochastic method presented lowers the bias of the aggregation rate.\u0000","PeriodicalId":509138,"journal":{"name":"Geoscientific Model Development","volume":" 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140683756","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}