Development and technical paper
16 Feb 2026
Development and validation of ARMS-gb v2.0: Extending fast radiative transfer modeling capability to all-sky conditions for ground-based microwave radiometer retrievalsZiyue Huang, Yi-Ning Shi, Fuzhong Weng, and Jun Yang
EGUsphere,https://doi.org/10.5194/egusphere-2025-5017, https://doi.org/10.5194/egusphere-2025-5017, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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We present an updated version of the Advanced Radiative Transfer Modeling System for ground-based sensors to better use microwave instruments in all weather. We added realistic cloud and rain effects and compared the results with six months of observations at two stations. The model accurately simulates observations in cloudy conditions. This advance can effectively improve the use of observational data and enhance weather forecasting capability.
13 Feb 2026
Volume of Fluid method applied to free surface boundaries in numerical geodynamic modelsTimothy Stephen Gray, Paul James Tackley, and Taras Gerya
EGUsphere,https://doi.org/10.5194/egusphere-2025-6547, https://doi.org/10.5194/egusphere-2025-6547, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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This study introduces a new way to track Earth’s surface and other boundaries in computer models of the planet’s interior. It replaces noisy, tracer-based methods with a technique that cleanly follows surfaces while conserving volume. The approach produces smoother, more accurate results in both 2D and 3D, reduces dependence on large numbers of tracers, and supports future links between deep Earth processes, oceans, and surface environments.
12 Feb 2026
A simple step heating approach for wall surface temperature estimation in the SOlar and LongWave Environmental Irradiance Geometry (SOLWEIG) modelNils Wallenberg, Björn Holmer, Fredrik Lindberg, Jessika Lönn, Erik Maesel, and David Rayner
Geosci. Model Dev., 19, 1321–1336,https://doi.org/10.5194/gmd-19-1321-2026, https://doi.org/10.5194/gmd-19-1321-2026, 2026
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This work presents a method to calculate wall surface temperatures in complex urban areas using a step heating equation based on air temperature and net radiation at the wall surface. Our results show that the step heating approach is fast and accurate, comparable to other more complex methods. This method can potentially be applied in different areas of interest where wall surface temperatures are important, e.g. modeling of outdoor thermal comfort, building energy and urban energy balance.
12 Feb 2026
Refining the Lagrangian approach for moisture source identification through sensitivity testing of assumptions using BTrIMS1.1Yinglin Mu, Jason P. Evans, Andréa S. Taschetto, and Chiara Holgate
Geosci. Model Dev., 19, 1367–1385,https://doi.org/10.5194/gmd-19-1367-2026, https://doi.org/10.5194/gmd-19-1367-2026, 2026
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Lagrangian approaches have been increasingly employed due to their suitability for extreme events and climatological studies in finding moisture sources of precipitation. However, as these approaches track independent air parcels carrying moisture – rather than simulate processes based on governing physical equations – they rely on several underlying assumptions. This study tests these assumptions and refines the approaches to enhance their broader applicability.
12 Feb 2026
Parameterization and Evaluation of Nonhydrostatic Effect in the Orographic Gravity Wave Drag in China Meteorological Administration Global Forecast System (CMA-GFS) v4.0 ModelRongrong Zhang, Zhenzhen Ai, Xin Xu, Haile Xue, and Qiying Chen
EGUsphere,https://doi.org/10.5194/egusphere-2025-6541, https://doi.org/10.5194/egusphere-2025-6541, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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In this study, the orographic gravity wave drag (OGWD) parameterization scheme in the CMA-GFS v4.0 model is revised to account for nonhydrostatic effects (NHE) on the surface momentum flux of subgrid-scale orographic gravity waves. Through a series of 10-day medium-range forecasts, the revised OGWD scheme is shown to significantly improve the simulation of large-scale circulation in the Northern Hemisphere (NH), especially in the high latitudes.
11 Feb 2026
Stratospheric aerosol forcing for CMIP7 (part 2): Volcanic sulfur dioxide emissionsThomas J. Aubry, Michael Sigl, Matthew Toohey, Man Mei Chim, Magali Verkerk, Anja Schmidt, and Simon A. Carn
EGUsphere,https://doi.org/10.5194/egusphere-2026-546, https://doi.org/10.5194/egusphere-2026-546, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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We document the historical (1750–2023) volcanic sulfur dioxide emission dataset created for phase 7 of the Coupled Model Intercomparison Project, which is a set of coordinated climate model experiments run by modelling center worldwide. Our dataset underpins the stratospheric aerosol optical property dataset which will be used as input by most climate models. However, models with interactive stratospheric aerosol capability can directly input our emission dataset to run CMIP7 experiments.
10 Feb 2026
Computation of fish larvae self-recruitment in using forward- and backward-in-time particle tracking in a Lagrangian model (SWIM-v2.0) of the simulated circulation of Lake Erie (AEM3D-v1.1.2)Wei Shi, Leon Boegman, Josef D. Ackerman, Shiliang Shan, and Yingming Zhao
Geosci. Model Dev., 19, 1213–1228,https://doi.org/10.5194/gmd-19-1213-2026, https://doi.org/10.5194/gmd-19-1213-2026, 2026
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Self-recruitment of a population at a given larval settlement location is dependent on larval production from each source location, independent of larval recruits at the settlement location. An arbitrary choice of the number of larvae released from each source location in forward tracking is found to cause ambiguous self-recruitment. In contrast, we found that an arbitrary choice of the number of larvae released from the settlement location in backtracking leads to unambiguous self-recruitment.
10 Feb 2026
A simple weather generator that converts statistical information from downscaled global climate models to 24-hr precipitation input for hydrological modelsRasmus Benestad
EGUsphere,https://doi.org/10.5194/egusphere-2026-351, https://doi.org/10.5194/egusphere-2026-351, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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The paper presents a weather generator that generates sequences of daily precipitation based on two key statistical parameters. It enables the use of downscaled projections of precipitation statistics for impact studies, such as hydrological modelling.
10 Feb 2026
ICON coupled to HAM-lite 1.0 in limited-area mode: an efficient framework for targeted kilometer-scale simulations with interactive aerosolsBernd Heinold, Philipp Weiss, Sadhitro De, Anne Kubin, Jason Müller, Fabian Senf, Philip Stier, and Ina Tegen
EGUsphere,https://doi.org/10.5194/egusphere-2026-328, https://doi.org/10.5194/egusphere-2026-328, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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A limited-area aerosol-climate model based on ICON coupled to HAM-lite is introduced for regional studies of natural and anthropogenic aerosols and interactions with clouds and radiation. Case studies over Central Europe, the Atlantic Arctic, and Australia exemplarily show the model’s capability to capture key aerosol patterns and variability, while remaining affected by simplified emissions and chemistry. The results guide future HAM-lite development.
10 Feb 2026
Inclusion of MyAMI-derived Mg/Ca corrections to the marine carbonate system in the cGENIE.cookie Earth system model (v.0.9.90)Markus Adloff, Terra M. Ganey, Mathis P. Hain, Michael J. Henehan, Sarah E. Greene, and Andy Ridgwell
EGUsphere,https://doi.org/10.5194/egusphere-2025-5564, https://doi.org/10.5194/egusphere-2025-5564, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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Seawater composition affects carbon cycling in the ocean and has changed over Earth history, requiring corrections when reconstructing past marine carbonate systems. We present a new correction scheme for the intermediate complexity Earth system model cGENIE based on the ion interaction model MyAMI. We validate the new scheme, find significant improvements over the default scheme, and discuss the relevance of accurate and consistent major ion correction in carbon cycle reconstructions.
09 Feb 2026
ClimateBenchPress (v1.0): A Benchmark for Lossy Compression of Climate DataTim Reichelt, Juniper Tyree, Milan Klöwer, Peter Dueben, Bryan N. Lawrence, Allison H. Baker, Sara Faghih-Naini, Torsten Hoefler, and Philip Stier
EGUsphere,https://doi.org/10.5194/egusphere-2026-60, https://doi.org/10.5194/egusphere-2026-60, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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The growing size of datasets used in climate science makes it difficult to store, analyze, and distribute dataset. Lossy compression algorithms can significantly reduce the disk space required to store datasets, but it can be difficult to understand and compare the behavior of different compression algorithms. ClimateBenchPress provides a benchmark to standardize comparisons between lossy compression algorithms and guide development of novel algorithms specifically targeted towards climate data.
09 Feb 2026
Scale-selective nudging with a diffusion-based filter in the variable-resolution Model for Prediction Across Scales version 8.2.2Yiyuan Cheng and Jianping Tang
EGUsphere,https://doi.org/10.5194/egusphere-2026-176, https://doi.org/10.5194/egusphere-2026-176, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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Global models can drift from observations, so we nudge them toward reanalysis. On variable-resolution unstructured meshes, standard nudging also damps small scales that shape rainfall. We introduce a diffusion filter that separates large and small spatial scales on the mesh and is fast in parallel. In a 1-year MPAS-Atmosphere run refined over East Asia, it keeps large-scale winds realistic while preserving rainfall differences between convection schemes, showing a clear trade-off.
06 Feb 2026
DReaMIT: A Dynamical Reanalysis Framework for Modelling Surface-Based Temperature Inversions in Cold EnvironmentsVictor Pozsgay, Nick C. Noad, Philip P. Bonnaventure, and Stephan Gruber
EGUsphere,https://doi.org/10.5194/egusphere-2025-5512, https://doi.org/10.5194/egusphere-2025-5512, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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Surface-based temperature inversions occur when cold air becomes trapped near the ground beneath a layer of warmer air. This study combines field data, analysis, and modelling to develop DReaMIT, a model that captures the timing and strength of inversions across northern mountain terrain. The model’s transferability beyond the valleys where it was developed makes it valuable globally to cold-region researchers for mapping and modelling permafrost and assessing climate change impacts.
05 Feb 2026
Interpolating station quantile biases for tropospheric ozone MDA8 bias correctionJan Peiker, Jan Karlický, and Peter Huszár
EGUsphere,https://doi.org/10.5194/egusphere-2025-6218, https://doi.org/10.5194/egusphere-2025-6218, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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We introduce a novel strategy for bias correction of tropospheric ozone maxima based on parametric interpolation of quantile biases (PIQB) from stations into the model grid. Its performance is evaluated and compared to other strategies found in literature. The results show that PIQB performs very well on simulations with a relatively high horizontal resolution, preserving model-resolved features yet mitigating model errors. We conclude that PIQB is suitable for correcting future projections.
05 Feb 2026
Confidence-Aware Framework for Mapping Satellite-Derived River Reaches to Gridded Routing NetworksKaushlendra Verma and Simon Munier
EGUsphere,https://doi.org/10.5194/egusphere-2026-509, https://doi.org/10.5194/egusphere-2026-509, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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Satellite provide river observations as vector reaches, while large-scale hydrological models represent rivers on gridded routing networks. This structural mismatch limits direct data assimilation. We present a global, confidence-aware framework that assigns vector river reaches to routing pixels using geometric and hydrological consistency criteria. Results show that most routing pixels can be assigned with high confidence while preserving basin-scale drainage topology into hydrological models.
04 Feb 2026
TRACE-Python: Tracer-based Rapid Anthropogenic Carbon Estimation Implemented in Python (version 1.0)Daniel E. Sandborn, Brendan R. Carter, Mark J. Warner, and Larissa M. Dias
EGUsphere,https://doi.org/10.5194/egusphere-2025-5793, https://doi.org/10.5194/egusphere-2025-5793, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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We present a new implementation of our method for estimation of human-created carbon dioxide in the ocean. "Tracer-based Rapid Anthropogenic Carbon Estimation" relies on transient tracer measurements to infer gas exchange and circulation. Our work implements practical and fundamental improvements increasing accessibility, flexibility, and skill of the method. We provide an updated data product of global ocean carbon inventories spanning the industrial era and a range of future projections.
03 Feb 2026
A revised temperature-dependent remineralization scheme for the Community Earth System Model (v1.2.2)Elizabeth K. Brabson, Loren F. Doyle, R. Paul Acosta, Alexey V. Fedorov, Pincelli M. Hull, and Natalie J. Burls
Geosci. Model Dev., 19, 1143–1156,https://doi.org/10.5194/gmd-19-1143-2026, https://doi.org/10.5194/gmd-19-1143-2026, 2026
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Earth System Models are an essential tool for climate studies, yet temperature-sensitive parameters are often absent, resulting in a gap in model predictive capabilities. Organic carbon breakdown, also known as remineralization, is one such process. Here, we add this parameter to the Community Earth System Model and find improved regional patterns of carbon export. The new code will serve as a useful tool to improve the examination of marine carbon cycle feedbacks to changing climate conditions.
02 Feb 2026
A hybrid physics–AI approach using universal differential equations with state-dependent neural networks for learnable, regionalizable, spatially distributed hydrological modelingNgo Nghi Truyen Huynh, Pierre-André Garambois, François Colleoni, and Jérôme Monnier
Geosci. Model Dev., 19, 1055–1074,https://doi.org/10.5194/gmd-19-1055-2026, https://doi.org/10.5194/gmd-19-1055-2026, 2026
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To better understand hydrological processes and improve flood simulation, combining artificial intelligence (AI) with process-based models is a promising direction. We introduce a hybrid physics–AI approach that seamlessly integrates neural networks into a distributed hydrological model to refine water flow dynamics within an implicit numerical scheme. The hybrid models demonstrate strong performance and interpretable results, leading to reliable streamflow simulations for flood modeling.
30 Jan 2026
Automated stratigraphic interpretation from drillhole lithological descriptions with uncertainty quantification: litho2strat 1.0Vitaliy Ogarko and Mark Jessell
Geosci. Model Dev., 19, 1007–1025,https://doi.org/10.5194/gmd-19-1007-2026, https://doi.org/10.5194/gmd-19-1007-2026, 2026
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Millions of historical drillholes contain rock descriptions but lack stratigraphic information needed for subsurface modeling. We developed an automated method converting rock descriptions into stratigraphic interpretations by testing plausible sequences using regional maps. The approach quantifies uncertainty and correlates multiple drillholes. Testing on fifty-two South Australian drillholes successfully predicted correct sequences, unlocking legacy data value for geological surveys.
28 Jan 2026
A novel ALE scheme with the internal boundary for coupling tectonic and surface processes in geodynamic modelsNeng Lu, Louis Moresi, Julian Giordani, and Ben Knight
EGUsphere,https://doi.org/10.5194/egusphere-2025-6324, https://doi.org/10.5194/egusphere-2025-6324, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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This study introduces a novel framework combining geodynamic and surface process models, enhancing our understanding of Earth's crust and upper mantle deformation. By integrating the codes Underworld 2 and Badlands within the Arbitrary Lagrangian-Eulerian with Internal Boundary (ALE-IB) scheme, our approach overcomes the limitations of previous methods. It maintains internal interface integrity and precise surface tracking, improving simulation fidelity.
27 Jan 2026
A microwave scattering database of oriented ice and snow particles: supporting habit-dependent growth models and radar applications (McRadar 1.0.0)Leonie von Terzi, Davide Ori, and Stefan Kneifel
Geosci. Model Dev., 19, 887–910,https://doi.org/10.5194/gmd-19-887-2026, https://doi.org/10.5194/gmd-19-887-2026, 2026
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We present a new database of radar-relevant optical properties for a wide range of ice particle shapes, computed using the Discrete Dipole Approximation (DDA) at 5.6, 9.6, 35.6, and 94 GHz. The database is designed to support habit-evolving microphysical schemes, which predict continuous changes in ice particle properties rather than the traditionally assumed fixed categories. It includes over 2600 individual crystals and 450 aggregates with varying riming degrees and morphology.
26 Jan 2026
Comparing the MEMS v1 model performance with MCMC and 4DEnVar calibration methods over a continental soil inventoryToni Viskari, Tristan Quaife, Fernando Fahl, Yao Zhang, and Emanuele Lugato
EGUsphere,https://doi.org/10.5194/egusphere-2025-4999, https://doi.org/10.5194/egusphere-2025-4999, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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In this work we examined how different assumptions regarding soil carbon model calibration affect the resulting model performance. We found that how the litter inputs are set have a meaningful impact on the calibrated model parameters. Furthermore, two calibration methods produced parameter sets that differed meaningfully from each other but fit the validation dataset equally well. These results raise meaningful questions how we evaluate soil carbon model performance.
26 Jan 2026
ITMSL: an improved ice thickness inversion model integrating basal sliding dynamics for High Mountain Asia (v1.0.0)Xiaoguang Pang, Liming Jiang, Yuxuan Wu, Xi Lu, Yi Liu, Xiaoen Li, and Tingting Yao
EGUsphere,https://doi.org/10.5194/egusphere-2025-5838, https://doi.org/10.5194/egusphere-2025-5838, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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Ice thickness models based on laminar flow theory often rely on conventional assumptions regarding basal sliding parameterization when studying alpine glaciers. This paper presents the Ice Thickness Model considering Sliding Law (ITMSL) model, which integrates a basal sliding law with laminar flow theory, with the objective of simulating basal sliding to enhance the accuracy of ice thickness inversion.
26 Jan 2026
Development of a next-generation general ocean circulation model for the Great LakesMeena Raju, David J. Cannon, Peter Alsip, He Wang, Jia Wang, Theresa Cordero, Robert W. Hallberg, Charles A. Stock, and Joseph A. Langan
EGUsphere,https://doi.org/10.5194/egusphere-2025-6556, https://doi.org/10.5194/egusphere-2025-6556, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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This study developed the Modular Ocean Model version 6.0 coupled with Sea Ice Simulator version 2.0 for the Great Lakes, validated against observations and an operational model. This study also tested two vertical coordinate systems, z* and hybrid. The model reproduced lake physics with good skill. The hybrid vertical coordinate improved thermocline representation and preserved deep cold-water during stratification, demonstrating the model’s suitability for large freshwater systems.
23 Jan 2026
Direct assimilation of ground-based microwave radiometer observations with machine learning bias correction based on developments of RTTOV-gb v1.0 and WRFDA v4.5Qing Zheng, Wei Sun, Zhiquan Liu, Jiajia Mao, Jieying He, Jian Li, and Xingwen Jiang
Geosci. Model Dev., 19, 731–754,https://doi.org/10.5194/gmd-19-731-2026, https://doi.org/10.5194/gmd-19-731-2026, 2026
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Ground-based microwave radiometers (GMWRs) offer high temporal resolution observations with strong sensitivity to the lower atmosphere, making them valuable for data assimilation. However, their assimilation has traditionally focused on retrieved profiles. This study implemented the direct assimilation of brightness temperatures from GMWRs with a machine learning-based bias correction scheme. The results show improvements in the low-level atmospheric structure and precipitation predictions.
23 Jan 2026
| Highlight paper
Operational numerical weather prediction with ICON on GPUs (version 2024.10)Xavier Lapillonne, Daniel Hupp, Fabian Gessler, André Walser, Andreas Pauling, Annika Lauber, Benjamin Cumming, Carlos Osuna, Christoph Müller, Claire Merker, Daniel Leuenberger, David Leutwyler, Dmitry Alexeev, Gabriel Vollenweider, Guillaume Van Parys, Jonas Jucker, Lukas Jansing, Marco Arpagaus, Marco Induni, Marek Jacob, Matthias Kraushaar, Michael Jähn, Mikael Stellio, Oliver Fuhrer, Petra Baumann, Philippe Steiner, Pirmin Kaufmann, Remo Dietlicher, Ralf Müller, Sergey Kosukhin, Thomas C. Schulthess, Ulrich Schättler, Victoria Cherkas, and William Sawyer
Geosci. Model Dev., 19, 755–772,https://doi.org/10.5194/gmd-19-755-2026, https://doi.org/10.5194/gmd-19-755-2026, 2026
Short summaryExecutive editorShort summary
The ICON climate and numerical weather prediction model was fully ported to Graphical Processing Units (GPUs) using OpenACC compiler directives, covering all components required for operational weather prediction. The GPU port together with several performance optimizations led to a speed-up of 5.6× when comparing to traditional Central Processing Units (CPUs) . Thanks to this adaptation effort, MeteoSwiss became the first national weather service to run the ICON model operationally on GPUs.
Executive editor
Increasingly, new supercomputers depend on GPUs for the vast bulk of their processing power. This makes the effective exploitation of GPUs an imperative across geoscientific modelling. This paper presents the port of a full numerical weather prediction system to GPU. It provides an excellent example of how such a port can be achieved in practice while delivering significant performance benefits. As such, this work offers particularly valuable guidance for the wider modelling community.
23 Jan 2026
A Preliminary Study on a Synergistic Assimilation Scheme for Multi-band Satellite Soil Moisture DataXuesong Bai, Zhaohui Lin, Zhengkun Qin, and Juan Li
EGUsphere,https://doi.org/10.5194/egusphere-2025-5721, https://doi.org/10.5194/egusphere-2025-5721, 2026
Preprint under review for GMD(discussion: open, 1 comment)
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Accurate soil moisture data is essential for predicting weather. This study examined how observations from three satellites can be combined to improve land-surface simulations. While each satellite helps, their value changes with vegetation type. Merging these data sources gives a more reliable estimate of soil wetness, especially in central and western China. This approach strengthens soil-water monitoring and supports more dependable climate forecasting.
23 Jan 2026
G&M3D 1.0: an Interactive Framework for 3D Model Construction and Forward Calculation of Potential FieldsDengkang Wang, Bo Chen, Kanggui Wei, Jiaxiang Peng, and Rongwen Guo
EGUsphere,https://doi.org/10.5194/egusphere-2025-5357, https://doi.org/10.5194/egusphere-2025-5357, 2026
Preprint under review for GMD(discussion: open, 1 comment)
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We developed G&M3D 1.0, a user-friendly software that allows anyone to build and explore 3D models of underground structures. We tested it on a real-world salt dome in Louisiana, demonstrating its practical use for interpreting geological data. Our research aimed to create an accessible platform for both learning and professional analysis, and we achieved this by building the software with widely-used programming tools, offering it as both an open-source project and a ready-to-use application.
23 Jan 2026
Lagrangian tracking methods applied to free surface boundaries in numerical geodynamic modelsTimothy Stephen Gray, Paul James Tackley, and Taras Gerya
EGUsphere,https://doi.org/10.5194/egusphere-2025-6546, https://doi.org/10.5194/egusphere-2025-6546, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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This study presents a new way to model how Earth’s surface changes over time as the deep interior moves. The method tracks the surface directly, allowing clearer and more detailed results worldwide while using less computing power. It improves accuracy compared to existing approaches and makes it easier to connect deep Earth processes with oceans, climate, landscapes, and life through time.
22 Jan 2026
Implementation of the ORACLE (v1.0) organic aerosol composition and evolution module into the EC-Earth3-AerChem modelStylianos Kakavas, Stelios Myriokefalitakis, Alexandra P. Tsimpidi, Vlassis A. Karydis, and Spyros N. Pandis
EGUsphere,https://doi.org/10.5194/egusphere-2026-37, https://doi.org/10.5194/egusphere-2026-37, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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The computationally efficient configuration of the ORACLE v1.0 module (ORACLE-lite) is implemented into the TM5-MP global chemical transport model, which represents the chemistry-transport component of the EC-Earth3-AerChem ESM. The models bias is reduced by approximately half in the standalone TM5-MP simulation and by a factor of three in EC-Earth3-AerChem when ORACLE-lite is implemented.
21 Jan 2026
Threshold atmospheric electric fields for initiating relativistic runaway electron avalanches: theoretical estimates and CORSIKA simulationsAshot Chilingarian, Liza Hovhannisyan, and Mary Zazyan
Geosci. Model Dev., 19, 621–626,https://doi.org/10.5194/gmd-19-621-2026, https://doi.org/10.5194/gmd-19-621-2026, 2026
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Thunderstorms can accelerate particles in the atmosphere, producing bursts of radiation at the ground. We investigated how strong the electric field inside a cloud must be to start such events. Using advanced computer simulations and comparing with measurements from mountain stations, we found that fields must be stronger than earlier theory suggested. Our results improve understanding of storm electricity and its role in natural radiation.
21 Jan 2026
Ocean–atmosphere turbulent flux algorithms in Earth system models do not always converge to unique and physical solutions: analysis and potential remedy in E3SMv2Justin Dong, Michael A. Brunke, Xubin Zeng, Carol S. Woodward, Hui Wan, and Christopher J. Vogl
EGUsphere,https://doi.org/10.5194/egusphere-2025-5430, https://doi.org/10.5194/egusphere-2025-5430, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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Accurately computing ocean–atmosphere turbulent fluxes, which measure the transfer of momentum, heat, and water between the Earth and its oceans, in Earth system models is important for overall model accuracy. Under certain meteorological conditions, the set of equations utilized in many Earth system models to parameterize these fluxes can have no solution or more than one solution. Modifying the equations to address these issues leads to substantial changes to the simulated turbulent fluxes.
21 Jan 2026
Including the triple isotopic composition of dissolved oxygen in the ocean into the iLOVECLIM model (version 1.1.7): development and evaluationEmeline Clermont, Ji-Woong Yang, Didier M. Roche, and Thomas Extier
EGUsphere,https://doi.org/10.5194/egusphere-2025-5230, https://doi.org/10.5194/egusphere-2025-5230, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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The triple isotopic composition of atmospheric oxygen (17Δ) is used to reconstruct past global biospheric productivity. We present the first implementation of the oceanic contribution (17Δocean) in the intermediate-complexity model iLOVECLIM. Photosynthesis, respiration, and air-sea gas exchange are represented under preindustrial conditions. Model results agree with observations, providing a future key tool to study marine biogeochemical processes and past ocean biospheric productivity.
20 Jan 2026
A neural network-based observation operator for weather radar data assimilationMarco Stefanelli, Žiga Zaplotnik, and Gregor Skok
External preprint server,https://doi.org/10.48550/arXiv.2512.18289, https://doi.org/10.48550/arXiv.2512.18289, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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Weather radars provide storm intensity and location, but weather forecasting systems do not readily use them. We trained a neural network on 5 years of reflectivity radar and model output data to map model fields into radar reflectivity space, allowing forecasts to be corrected with radar data. In a major flood case, this cut errors in storm position and strength. Broadly speaking, the methodology provides a simplified solution for assimilating observations with no direct model-equivalent field.
19 Jan 2026
Examining spin-up behaviour within WRF dynamical downscaling applicationsMegan S. Mallard, Tanya L. Spero, Jared H. Bowden, Jeff Willison, Christopher G. Nolte, and Anna M. Jalowska
Geosci. Model Dev., 19, 579–594,https://doi.org/10.5194/gmd-19-579-2026, https://doi.org/10.5194/gmd-19-579-2026, 2026
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“Spin-up” is time needed for a model’s result to become effectively free of influence from initial conditions, and it is usually excluded from analysis. Here, spin-up is examined by comparing one decadal simulation to another initialized 20 years prior, in order to determine when their solutions converge. Differences lessen over the first fall and winter, but re-emerge over the following spring and summer, suggesting that at least 1 annual cycle is needed to spin up regional climate simulations.
15 Jan 2026
HydroBlocks-MSSUBv0.1: a multiscale approach for simulating lateral subsurface flow dynamics in Land Surface ModelsDaniel Guyumus, Laura Torres-Rojas, Luiz Bacelar, Chengcheng Xu, and Nathaniel Chaney
Geosci. Model Dev., 19, 477–504,https://doi.org/10.5194/gmd-19-477-2026, https://doi.org/10.5194/gmd-19-477-2026, 2026
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This study explores a new tiling scheme within the HydroBlocks Land Surface Model to represent local, regional and intermediate subsurface flow. Using high-resolution environmental data, the scheme defines parameterized flow units, enabling water and energy flux simulations. Compared against a benchmark simulation, the multiscale scheme demonstrates strong agreement in spatial mean, standard deviation, and temporal variability, showcasing its potential for large-scale hydrological simulation.
15 Jan 2026
Implementation and evaluation of sea level operators in OceanVar2.0: an open-source oceanographic three-dimensional variational data assimilation systemPaolo Oddo, Mario Adani, Francesco Carere, Andrea Cipollone, Anna Chiara Goglio, Eric Jansen, Ali Aydogdu, Francesca Mele, Italo Epicoco, Jenny Pistoia, Emanuela Clementi, Nadia Pinardi, and Simona Masina
Geosci. Model Dev., 19, 423–445,https://doi.org/10.5194/gmd-19-423-2026, https://doi.org/10.5194/gmd-19-423-2026, 2026
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This study present a data assimilation system that combines ocean observational data with ocean model results to better understand the ocean and predict its future state. The method uses a three dimensional incremental variational approach focusing on the physical relationships between all the state vector variables errors. Testing in the Mediterranean Sea showed that a complex sea level operator based on a barotropic model works best.
15 Jan 2026
Stable Stream Temperature Prediction for Different Basins Using Time Series Encoding and Temporal Convolutional NetworksLichen Su and Wei Zhao
EGUsphere,https://doi.org/10.5194/egusphere-2025-4550, https://doi.org/10.5194/egusphere-2025-4550, 2026
Preprint under review for GMD(discussion: open, 6 comments)
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The establishment of a lateral lateral water temperature prediction model with strong generalization capabilities and stable prediction results presents a major challenge. To solve this problem, the coding of time series data incorporated in a temporary convolutional network (Fumenc-TCN) was modelled. The model effectively captured multimodal features of dynamic water temperature data from complex random time series, subsequently producing stable prediction results in different river basins.
15 Jan 2026
Implementation of a three-dimensional planetary boundary layer parameterization in a coupled modeling system and evaluation of "gray zone" simulations of a wind-wave event off the U.S. California Coast using observationsEric A. Hendricks, Timothy W. Juliano, Branko Kosović, Sue Haupt, Brian J. Gaudet, and Geng Xia
EGUsphere,https://doi.org/10.5194/egusphere-2025-4862, https://doi.org/10.5194/egusphere-2025-4862, 2026
Preprint under review for GMD(discussion: open, 5 comments)
Short summaryShort summary
A three-dimensional planetary boundary layer parameterization, suited for mesoscale model grid spacings of 100–1000 m with improved treatment of unresolved horizontal mixing, is added to a coupled atmosphere / wave modeling system and the first coupled simulations are executed using the parameterization. Simulations of a significant wind-wave event demonstrate that the new parameterization has similar behaviors as one-dimensional PBL parameterizations and compares well with observations.
14 Jan 2026
Overcoming the numerical challenges owing to rapid ductile localization with DEDLoc (version 1.0.0)Arne Spang, Marcel Thielmann, Casper Pranger, Albert de Montserrat, and Ludovic Räss
Geosci. Model Dev., 19, 369–388,https://doi.org/10.5194/gmd-19-369-2026, https://doi.org/10.5194/gmd-19-369-2026, 2026
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Concentration of deformation is difficult to capture accurately in computer simulations. We present a number of challenges associated with concentrated viscous deformation and demonstrate strategies to overcome them. The strategies include automatic selection of appropriate time steps to react to rapid changes in model behavior, automatic rescaling to avoid rounding errors, and three methods to prevent model instability. This way, we are able to accurately capture very fast viscous deformation.
14 Jan 2026
Application of flux footprint equations from Kljun et al. (2015) to field eddy-covariance systems for footprint characteristics into flux network datasetsXinhua Zhou, Zhi Chen, Ryan Campbell, Atefeh Hosseini, Tian Gao, Xiufen Li, Jianmin Chu, Sen Wu, Ning Zheng, and Jiaojun Zhu
EGUsphere,https://doi.org/10.5194/egusphere-2025-4576, https://doi.org/10.5194/egusphere-2025-4576, 2026
Preprint under review for GMD(discussion: open, 12 comments)
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To help environmental researchers better understand the sources of greenhouse gas measurements, we developed a practical method for field instruments to calculate the footprints. By using simplified math and efficient computing, our approach allows real-time analysis of measurement zones, which was previously too complex for on-site processing. This enables more accurate data collection worldwide, helping improve climate change monitoring and ecosystem studies.
13 Jan 2026
Comparison of two Euler equation sets in a Discontinuous Galerkin solver for atmospheric modelling (BRIDGE v0.9)Michael Baldauf and Florian Prill
EGUsphere,https://doi.org/10.5194/egusphere-2025-6441, https://doi.org/10.5194/egusphere-2025-6441, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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We present an implementation of the Discontinuous Galerkin approach, a numerical solver of the Euler equations (called BRIDGE), which is in particular well suited for numerical models for weather and climate prediction and atmospheric research. Two widespread formulations of the Euler equations with different thermodynamic variables are compared by the inspection of idealised benchmark test cases to assess the properties of the Discontinuous Galerkin method.
13 Jan 2026
Evaluating Modifications to Tiedtke Cumulus Parameterization for Improving Summer Precipitation Forecasts in the Nested Grid of Taiwan Global Forecast System (TGFS v1.1)Chang-Hung Lin, Guo-Yuan Lien, and Ling-Feng Hsiao
EGUsphere,https://doi.org/10.5194/egusphere-2025-5324, https://doi.org/10.5194/egusphere-2025-5324, 2026
Preprint under review for GMD(discussion: open, 7 comments)
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This study presents a series of modifications to the Tiedtke convection scheme, aiming to improve summer rainfall predictions in the 4.8-km-resolution nested grid of the Taiwan Global Forecast System (TGFS). The modifications improve the spatial distribution of rainfall and reduce the heavy rainfall bias in five-day forecast, as demonstrated by case studies and evaluations over a two-month period.
12 Jan 2026
A general physiologically driven representation of leaf turnover in grasslands in the QUINCY land surface model (revision: 974a6b7f)Josua Seitz, Midori Yajima, Yu Zhu, Lumnesh Swaroop Kumar Joseph, Jinyan Yang, Fabrice Lacroix, Yunpeng Luo, Andreas Schaumberger, Michael Bahn, Sönke Zaehle, and Silvia Caldararu
EGUsphere,https://doi.org/10.5194/egusphere-2025-5731, https://doi.org/10.5194/egusphere-2025-5731, 2026
Preprint under review for GMD(discussion: open, 4 comments)
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This study presents a new global leaf turnover model for grasslands in the QUINCY land surface model. Land surface models often struggle to simulate grassland carbon dynamics and phenology accurately. By allowing environmental conditions to directly control leaf senescence we improve its timing as well as the accuracy of whole-season carbon dynamics across a wide range of climates and grassland ecosystems.
12 Jan 2026
Evaluation of preCICE (version 3.3.0) in an Earth System Model Regridding BenchmarkAlex Hocks and Benjamin Uekermann
EGUsphere,https://doi.org/10.5194/egusphere-2025-5618, https://doi.org/10.5194/egusphere-2025-5618, 2026
Preprint under review for GMD(discussion: open, 1 comment)
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We tested the general coupling software preCICE for data mapping between atmosphere and ocean simulation meshes in Earth system modeling. In a recent benchmark, preCICE performed on par with specialized tools. Its general design and large user community make it broadly applicable across scientific domains, fostering knowledge transfer and collaboration beyond Earth system research.
11 Jan 2026
HyperGas 1.0: A Python package for analyzing hyperspectral data for greenhouse gases from retrieval to emission rate quantificationXin Zhang, Joannes D. Maasakkers, Tobias A. de Jong, Paul Tol, Frances Reuland, Adam R. Brandt, Eric A. Kort, Taylor J. Adams, and Ilse Aben
EGUsphere,https://doi.org/10.5194/egusphere-2025-6127, https://doi.org/10.5194/egusphere-2025-6127, 2026
Preprint under review for GMD(discussion: open, 1 comment)
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Reducing emissions of greenhouse gases such as methane and carbon dioxide is essential for addressing climate change. We developed HyperGas, an open tool that uses hyperspectral satellite images to retrieve and detect greenhouse gas plumes. It helps scientists locate emission sources, estimate their strength, and examine uncertainties through an easy workflow and visual app. Our goal is to make tracking human-made emissions more accurate and accessible, supporting better climate monitoring.
09 Jan 2026
ML-IAM v1.0: Emulating Integrated Assessment Models With Machine LearningYen Shin, Changyoon Lee, Eunsu Kim, Junho Myung, Kiwoong Park, Jiheun Ha, Min-Young Choi, Bomi Kim, Hyun W. Ka, Jung-Hun Woo, Alice Oh, and Haewon McJeon
EGUsphere,https://doi.org/10.5194/egusphere-2025-5305, https://doi.org/10.5194/egusphere-2025-5305, 2026
Preprint under review for GMD(discussion: open, 5 comments)
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Climate policy relies on computer models that predict future emissions and energy use under different scenarios. These models take up to hours to run, limiting their use. We developed a machine learning system that replicates these models accurately in seconds. Our system generates 2,000 scenarios in 50 seconds—thousands of times faster. This enables comprehensive analysis previously impossible and makes climate projections accessible to researchers studying other environmental impacts.
09 Jan 2026
A Systematic Atmospheric Parameter Optimization method to Improve ENSO Simulation in the ICON XPP Earth System ModelDakuan Yu, Dietmar Dommenget, Holger Pohlmann, and Wolfgang A. Müller
EGUsphere,https://doi.org/10.5194/egusphere-2025-5736, https://doi.org/10.5194/egusphere-2025-5736, 2026
Preprint under review for GMD(discussion: open, 4 comments)
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We developed a new method to improve how a leading climate model simulates El Niño, a major driver of global weather extremes. By testing how the model responds to small changes in key atmospheric settings, we identified which processes matter most and adjusted them systematically. This approach makes the model’s behavior closer to observations and shows a promising path for building more reliable climate predictions.
08 Jan 2026
Towards an integrated inventory of anthropogenic emissions for ChinaYijuan Zhang, Guy Brasseur, Maria Kanakidou, Claire Granier, Nikos Daskalakis, Alexandros Panagiotis Poulidis, Kun Qu, and Mihalis Vrekoussis
Geosci. Model Dev., 19, 217–237,https://doi.org/10.5194/gmd-19-217-2026, https://doi.org/10.5194/gmd-19-217-2026, 2026
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A new inventory of anthropogenic emissions, the China INtegrated Emission Inventory (CINEI), was developed in this study to better represent emission sectors, chemical speciation and spatiotemporal variations in China. Compared to simulations driven by global inventories, CINEI demonstrated better numerical modeling performance in ozone and its precursors (nitrogen dioxide and carbon monoxide). This study provides valuable insights for designing ozone mitigation strategies.
08 Jan 2026
Intermediate-complexity parameterisation of blowing snow in the ICOLMDZ AGCM: development and first applications in AntarcticaÉtienne Vignon, Nicolas Chiabrando, Cécile Agosta, Charles Amory, Valentin Wiener, Justine Charrel, Thomas Dubos, and Christophe Genthon
Geosci. Model Dev., 19, 239–259,https://doi.org/10.5194/gmd-19-239-2026, https://doi.org/10.5194/gmd-19-239-2026, 2026
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The erosion of surface snow by the wind is an important process for the Antarctic surface mass balance. This study presents the first development of a parameterisation of blowing snow for a global climate model. Simulations avec evaluated using measurements in Antarctica. Results show an overall decrease of the snow accumulation in the escarpment region of the ice sheet due to snow erosion and an increase at the coast due to blowing snow deposition and increase in precipitation.
06 Jan 2026
Attention-driven and multi-scale feature integrated approach for earth surface temperature data reconstructionMinghui Zhang, Yunjie Chen, Fan Yang, and Zhengkun Qin
Geosci. Model Dev., 19, 73–91,https://doi.org/10.5194/gmd-19-73-2026, https://doi.org/10.5194/gmd-19-73-2026, 2026
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Considering the crucial role of high-resolution surface observation temperature data in the study of surface atmospheric temperature in Marine areas, we propose a new two-stage deep learning model. This model is used to fill in the ocean surface temperature data that is missing in satellite observations due to the orbital gap of polar-orbiting satellites.
06 Jan 2026
Approximating the universal thermal climate index using sparse regression with orthogonal polynomialsSabin Roman, Gregor Skok, Ljupčo Todorovski, and Sašo Džeroski
External preprint server,https://doi.org/10.48550/arXiv.2508.11307, https://doi.org/10.48550/arXiv.2508.11307, 2026
Preprint under review for GMD(discussion: open, 1 comment)
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This study aimed to improve how the Universal Thermal Climate Index, a key measure of human thermal comfort, is calculated. Existing methods use a simplified polynomial approximation that is straightforward to apply but can introduce errors. We developed a new version using sparse regression with orthogonal polynomials, which keeps computational efficiency while improving accuracy and stability. The results enable more reliable assessments of outdoor thermal comfort and climate analyses.
06 Jan 2026
Exploring the applicability of Censored Shifted Gamma Distribution (CSGD) error model to radar based rainfall nowcasts: A UK case studyHung-Ming Lin, Li-Pen Wang, and Jen-Yu Han
EGUsphere,https://doi.org/10.5194/egusphere-2025-4590, https://doi.org/10.5194/egusphere-2025-4590, 2026
Preprint under review for GMD(discussion: open, 4 comments)
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We developed a framework to improve short-term rainfall forecasts by combining radar data with rain gauge observations. This approach reduces errors and uncertainty, giving more reliable predictions of when and where rain will fall. Such improvements are valuable for flood warnings, stormwater management, and other decisions that depend on timely and accurate rainfall information.
05 Jan 2026
Increasing resolution and accuracy in sub-seasonal forecasting through 3D U-Net: the western USJihun Ryu, Hisu Kim, Shih-Yu (Simon) Wang, and Jin-Ho Yoon
Geosci. Model Dev., 19, 27–39,https://doi.org/10.5194/gmd-19-27-2026, https://doi.org/10.5194/gmd-19-27-2026, 2026
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Using a neural network model, county-level weather forecasts was achieved in the Western US. By combining traditional forecasting data with actual weather observations, the AI system achieved better temperature predictions at local scales. While showed promise for temperature forecasting, it still had difficulty accurately predicting extreme rainfall events. The research advances weather forecasting capabilities, potentially helping communities prepare for severe weather conditions.
05 Jan 2026
Representing dynamic grassland density in the land surface model ORCHIDEE r9010Siqing Xu, Sebastiaan Luyssaert, Yves Balkanski, Philippe Ciais, Nicolas Viovy, Liang Wan, and Jean Sciare
Geosci. Model Dev., 19, 1–25,https://doi.org/10.5194/gmd-19-1-2026, https://doi.org/10.5194/gmd-19-1-2026, 2026
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Prescribing a fixed grassland density in the ORCHIDEE model limits its ability to capture grassland dynamics, leading to unrealistic mortality, especially in semi-arid grasslands. We proposed a dynamic density approach where a positive density-precipitation relationship emerges. This method improves spatial pattern, significantly reduces mortality, sustains productivity, and raises the aridity threshold above which frequent mortality events occur in grasslands.
05 Jan 2026
Parameter estimation for land-surface models using Neural PhysicsRuiyue Huang, Claire E. Heaney, and Maarten van Reeuwijk
External preprint server,https://doi.org/10.48550/arXiv.2505.02979, https://doi.org/10.48550/arXiv.2505.02979, 2026
Preprint under review for GMD(discussion: open, 2 comments)
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This paper uses the Neural Physics approach to determine parameters of a simple land-surface model. We show that we can only obtain a reliable parameter estimation using soil temperature measurements at more than one depth, and that latent and sensible heat fluxes cannot be differentiated. We then apply the inverse model to real urban flux tower data and show that parameters, as well as various heat fluxes, can be reliably estimated using an observed value for the effective surface albedo.
04 Jan 2026
Evaluation of HNO3, SO2, and NH3 in the Surface Tiled Aerosol and Gaseous Exchange (STAGE) option in the Community Multiscale Air Quality Model version 5.3.2 against field-scale,in situ and satellite observationsJesse O. Bash, John T. Walker, Zhiyong Wu, Ian C. Rumsey, Ben Murphy, Christian Hogrefe, Kathleen M. Fahey, Havala O. T. Pye, Matthew R. Jones, K. Wyat Appel, Mark Shephard, Najwa I. Alnsour, and Karen E. Cady-Periera
EGUsphere,https://doi.org/10.5194/egusphere-2025-3536, https://doi.org/10.5194/egusphere-2025-3536, 2026
Preprint under review for GMD(discussion: open, 0 comments)
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We applied a consistent modeling approach for both field and regional scales of multi-pollutants to evaluate the air-surface exchange processes contributing to regional air quality modeling biases when evaluated against observed network and satellite ammonia concentrations. This multi-resolution approach will serve the modeling and measurement community in their future development and generalization of air-surface exchange models utilizing flux, routine network and satellite observations.
28 Dec 2025
Variational Stokes method applied to free surface boundaries in numerical geodynamic modelsTimothy Stephen Gray, Paul James Tackley, and Taras Gerya
EGUsphere,https://doi.org/10.5194/egusphere-2025-6354, https://doi.org/10.5194/egusphere-2025-6354, 2025
Preprint under review for GMD(discussion: open, 6 comments)
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We developed a new way to model how planetary surfaces rise and sink as the deep interior slowly flows. Existing approaches are either costly or unstable. Our method represents the surface smoothly within a fixed grid, which avoids artificial air layers and numerical problems. Tests show it matches established results while running faster and working in more realistic settings, such as loaded surfaces and global models. This makes simulations of surface evolution more reliable and accessible.
22 Dec 2025
Towards standardising output datasets using the numerical obstacle-resolving model MITRAS as an exampleVivien Voss, K. Heinke Schlünzen, David Grawe, and Karolin S. Samsel
EGUsphere,https://doi.org/10.5194/egusphere-2025-5521, https://doi.org/10.5194/egusphere-2025-5521, 2025
Preprint under review for GMD(discussion: open, 1 comment)
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This work describes necessary adaptations and extensions to the post-processing of the obstacle-resolving microscale model MITRAS, with the aim of producing and publishing well-described model results that adhere to established meteorological data standards. The described process may help data producers facing similar difficulties to find ideas and solutions and addresses the need for standardisation within the urban microscale modelling community.
21 Dec 2025
Optimizing Gaussian Process Emulation and Generalized Additive Model Fitting for Rapid, Reproducible Earth System Model AnalysisKunal Ghosh and Leighton A. Regayre
EGUsphere,https://doi.org/10.5194/egusphere-2025-5533, https://doi.org/10.5194/egusphere-2025-5533, 2025
Preprint under review for GMD(discussion: open, 0 comments)
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Understanding which parts of climate models cause uncertainty requires many large computer experiments. We developed a new workflow that greatly improves the speed and efficiency of these studies. It can analyse millions of model variations up to 25 times faster without losing accuracy, allowing scientists to explore uncertainty in more detail and make climate predictions more reliable.
19 Dec 2025
Numerical modelling of diffusion-limited mineral growth for geospeedometry applicationsAnnalena Stroh, Pascal S. Aellig, and Evangelos Moulas
Geosci. Model Dev., 18, 10203–10220,https://doi.org/10.5194/gmd-18-10203-2025, https://doi.org/10.5194/gmd-18-10203-2025, 2025
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Crystal growth and diffusion are common processes in geology. Our softwareMovingBoundaryMinerals.jl calculates compositional profiles in diffusion couples by simulating diffusion-growth processes for geometries with planar/cylindrical/spherical symmetries. Our software has been tested versus various benchmark cases and is provided as an open access software package. This package allows the further use of diffusion/growth phenomena in the calculation of the thermal histories of rocks.
19 Dec 2025
Wave effect mechanisms enhancing sea–air CO2 exchange and modulating seawater carbonate–pH adaptation in the POP2–waves coupled modelYung-Yao Lan, Huang-Hsiung Hsu, Wei-Liang Lee, and Simon Chou
EGUsphere,https://doi.org/10.5194/egusphere-2025-4773, https://doi.org/10.5194/egusphere-2025-4773, 2025
Preprint under review for GMD(discussion: open, 0 comments)
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Waves and bubbles enhance CO₂ exchange between ocean and air, especially under strong winds, but most models ignore these effects. We added a wave module to CESM1.2.2, capturing impacts on solubility and diffusivity, and compared results with NOAA’s CarbonTracker (CT2022). The new model better matches global CO₂ flux patterns, reduces pH changes anddpCO₂ differences, and shows how wave effects reveal the ocean’s buffering capacity through the carbonate–pH system.
19 Dec 2025
MESMER v1.0.0: Consolidating the Modular Earth System Model Emulator into a Sustainable Research Software PackageVictoria M. Bauer, Mathias Hauser, Yann Quilcaille, Sarah Schöngart, Lukas Gudmundsson, and Sonia I. Seneviratne
EGUsphere,https://doi.org/10.5194/egusphere-2025-4917, https://doi.org/10.5194/egusphere-2025-4917, 2025
Preprint under review for GMD(discussion: final response, 2 comments)
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MESMER is a Python-based climate emulator that provides spatially resolved realizations of multiple climate variables. Version 1.0.0 of MESMER consolidates previous emulation methods into one numerically stable, well-documented, and user-friendly software package. It can generate large ensembles of annual and monthly mean temperatures, as well as several climate extreme indicators, within minutes. The software is shared together with pre-calibrated parameters to enable broad community adoption.
18 Dec 2025
GUST1.0: a GPU-accelerated 3D urban surface temperature modelShuo-Jun Mei, Guanwen Chen, Jian Hang, and Ting Sun
Geosci. Model Dev., 18, 10143–10167,https://doi.org/10.5194/gmd-18-10143-2025, https://doi.org/10.5194/gmd-18-10143-2025, 2025
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Cities face growing heat challenges due to dense buildings, but predicting surface temperatures is complex because sunlight, airflow, and heat radiation interact. By simulating how sunlight bounces between structures and how heat transfers through materials, we accurately predicted temperatures on roofs, roads, and walls. The model successfully handled intricate city layouts thanks to GPU speed. By revealing which heat matters most, we aim to guide smarter city designs for a warming climate.
18 Dec 2025
An emulator-based modelling framework for studying astronomical controls on ocean anoxia with an application to the DevonianLoïc Sablon, Pierre Maffre, Yves Goddéris, Paul J. Valdes, Justin Gérard, Jarno J. C. Huygh, Anne-Christine Da Silva, and Michel Crucifix
Geosci. Model Dev., 18, 10095–10117,https://doi.org/10.5194/gmd-18-10095-2025, https://doi.org/10.5194/gmd-18-10095-2025, 2025
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We propose an innovative climate modelling framework that combines statistical methods with climate simulations to study Earth's environmental systems. The model captures how orbital changes and carbon dioxide levels influence climate atmospheric dynamics, offering a detailed and efficient way to explore long-term processes. This tool provides new opportunities to investigate Earth's climate history and its implications for future changes.
18 Dec 2025
Incorporation of lumped IVOC emissions into the ORACLE model (V1.1): a multi-product framework for assessing global SOA formation from internal combustion enginesSusanne M. C. Scholz, Vlassis A. Karydis, Georgios I. Gkatzelis, Hendrik Fuchs, Spyros N. Pandis, and Alexandra P. Tsimpidi
Geosci. Model Dev., 18, 10119–10142,https://doi.org/10.5194/gmd-18-10119-2025, https://doi.org/10.5194/gmd-18-10119-2025, 2025
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We studied how pollution from cars and trucks contributes to tiny airborne particles that affect air quality and climate. These particles, called secondary organic aerosols, were often underestimated in global models. By improving how certain overlooked emissions from fuel use are represented in our model, we found that their impact is much larger than previously thought. Our results suggest that road traffic plays a far greater role in global air pollution than earlier estimates showed.
18 Dec 2025
A Lagrangian Particle Tracking Framework for the Super-Droplet Method: Development, Implementation, and Application of Backward and Forward Algorithms in SCALE-SDM 5.2.6-2.3.1Chongzhi Yin, Shin-Ichiro Shima, and Chunsong Lu
EGUsphere,https://doi.org/10.5194/egusphere-2025-6221, https://doi.org/10.5194/egusphere-2025-6221, 2025
Preprint under review for GMD(discussion: open, 0 comments)
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We developed a tracking tool for cloud simulations that works in two directions. It allows researchers to follow droplets forward to observe their future evolution or trace droplets backward to identify their origins. Crucially, the system records every coalescence event between droplets. This preserves the complete growth history of rain, serving as a diagnostic tool to help scientists verify the detailed physics within cloud models.
17 Dec 2025
Transfer learning-based hybrid machine learning in single-column model of AFES v4Yuya Baba
EGUsphere,https://doi.org/10.5194/egusphere-2025-4612, https://doi.org/10.5194/egusphere-2025-4612, 2025
Preprint under review for GMD(discussion: final response, 3 comments)
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Machine learning is becoming a useful tool for weather and climate prediction, but it has deficiencies in long-term prediction. Hybrid machine learning incorporated in dynamical models is expected to overcome the problem. To enhance the prediction using the hybrid model, this study adopted transfer learning to the model. The transfer learning reduces model’s mean state bias, thereby enhancing its potential for improving long-term prediction.
16 Dec 2025
Improvement of near-surface wind speed modeling through refined aerodynamic roughness length in high-roughness surface regions: implementation and validation in the Weather Research and Forecasting (WRF) model version 4.0Jiamin Wang, Kun Yang, Jiarui Liu, Xu Zhou, Xiaogang Ma, Wenjun Tang, Ling Yuan, and Zuhuan Ren
Geosci. Model Dev., 18, 10077–10094,https://doi.org/10.5194/gmd-18-10077-2025, https://doi.org/10.5194/gmd-18-10077-2025, 2025
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We set out to improve the accuracy of near-surface wind simulations in areas where buildings and tall vegetation have made the ground surface very rough. Through a clever use of differences between weather station measurements and reanalysis data, we estimated more realistic surface roughness values and created a new high-resolution map for China. This map greatly improves wind speed simulations and supports better decisions in wind-related fields.
16 Dec 2025
A hybrid framework for the spin-up and initialization of distributed coupled ecohydrological-biogeochemical modelsTaiqi Lian, Ziyan Zhang, Athanasios Paschalis, and Sara Bonetti
EGUsphere,https://doi.org/10.5194/egusphere-2025-4796, https://doi.org/10.5194/egusphere-2025-4796, 2025
Preprint under review for GMD(discussion: open, 6 comments)
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We introduce a new method to define initial conditions for spatially-distributed ecohydrological models with soil biogeochemistry. By combining a simplified simulation setup with a random forest technique, we reduced the computation time for model initialization by up to 90 % while adequately reconstructing soil carbon/nutrient spatial patterns. This efficient framework is broadly applicable to other models, enhancing the reliability of large-scale simulations of carbon and nutrient cycles.
11 Dec 2025
Traffic impact modelling in SURFEX-TEB V9.0 model for improved road surface temperature predictionGabriel Colas, Valéry Masson, François Bouttier, and Ludovic Bouilloud
Geosci. Model Dev., 18, 9945–9966,https://doi.org/10.5194/gmd-18-9945-2025, https://doi.org/10.5194/gmd-18-9945-2025, 2025
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Each vehicle from road traffic is a source of heat and an obstacle that induce wind when it passes. It directly impacts the local atmospheric conditions and the road surface temperature. These impacts are included in the numerical model of the Town Energy Balance, used to simulate local conditions in urbanised environments. Simulations show that road traffic has a significant impact on the road surface temperature up to a few degrees, and on local variables.
10 Dec 2025
LISFLOOD-FP 8.2: GPU-accelerated multiwavelet discontinuous Galerkin solver with dynamic resolution adaptivity for rapid, multiscale flood simulationAlovya Ahmed Chowdhury and Georges Kesserwani
Geosci. Model Dev., 18, 9827–9854,https://doi.org/10.5194/gmd-18-9827-2025, https://doi.org/10.5194/gmd-18-9827-2025, 2025
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LISFLOOD-FP 8.2 is a framework for running real-world simulations of rapid, multiscale floods driven by impact events like tsunamis. It builds on the LISFLOOD-FP 8.0 and 8.1 papers published in GMD: whereas LISFLOOD-FP 8.0 focussed on GPU-parallelisation, and LISFLOOD-FP 8.1 focussed on static mesh adaptivity of (multi)wavelets, LISFLOOD-FP 8.2 combines GPU (graphics processing unit)-parallelisation with multiwavelet dynamic mesh adaptivity to drastically reduce simulation runtimes, achieving up to a 4.5-fold speedup.
10 Dec 2025
rsofun v5.1: a model-data integration framework for simulating ecosystem processesJosefa Arán Paredes, Fabian Bernhard, Koen Hufkens, Mayeul Marcadella, and Benjamin D. Stocker
Geosci. Model Dev., 18, 9855–9878,https://doi.org/10.5194/gmd-18-9855-2025, https://doi.org/10.5194/gmd-18-9855-2025, 2025
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Mechanistic vegetation models serve to estimate terrestrial carbon fluxes and climate impacts on ecosystems across diverse conditions. Here, we demonstrate and evaluate thersofun R package, which provides a computationally efficient implementation of the P-model for site-scale simulations of ecosystem photosynthesis. Bayesian model fitting to observed fluxes and traits and evaluation on an independent test data set indicated robust calibration and unbiased prediction capabilities.
09 Dec 2025
Enhancing volcanic eruption simulations with the WRF-Chem v4.8Alexander Ukhov, Georgiy Stenchikov, Jordan Schnell, Ravan Ahmadov, Umberto Rizza, Georg Grell, and Ibrahim Hoteit
Geosci. Model Dev., 18, 9805–9825,https://doi.org/10.5194/gmd-18-9805-2025, https://doi.org/10.5194/gmd-18-9805-2025, 2025
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Volcanic eruptions are natural hazards impacting aviation, the environment, and climate. Here, we improve the simulation of volcanic material transport using the Weather Research and Forecasting (WRF-Chem) version 4.8. Analysis of ash, sulfate, and SO2 mass budgets was performed. The direct radiative effect of volcanic aerosols was implemented. A preprocessor, PrepEmisSources, was developed to streamline the preparation of volcanic emissions.
09 Dec 2025
From Reanalysis to Climatology: Deep Learning Reconstruction of Tropical Cyclogenesis in the Western North PacificDuc-Trong Le, Tran-Binh Dang, Anh-Duc Hoang Gia, Duc-Hai Nguyen, Minh-Hoa Tien, Xuan-Truong Ngo, Quang-Trung Luu, Quang-Lap Luu, Tai-Hung Nguyen, Thanh T. N. Nguyen, and Chanh Kieu
EGUsphere,https://doi.org/10.5194/egusphere-2025-4333, https://doi.org/10.5194/egusphere-2025-4333, 2025
Preprint under review for GMD(discussion: final response, 2 comments)
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We study how and where tropical storms begin in the western North Pacific. Using many years of global weather data and a modern pattern-recognition method, we built a model that learns signals that come before storm formation and maps when and where formation is likely. It reproduces known seasonal and regional patterns and identifies key environmental cues. These results can support better risk planning and help refine climate projections.
09 Dec 2025
Automatic tuning of iterative pseudo-transient solvers for modelling the deformation of heterogeneous mediaThibault Duretz, Albert de Monserrat, Rubén Sevilla, Ludovic Räss, Ivan Utkin, and Arne Spang
EGUsphere,https://doi.org/10.5194/egusphere-2025-5641, https://doi.org/10.5194/egusphere-2025-5641, 2025
Preprint under review for GMD(discussion: final response, 3 comments)
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Geodynamic modeling helps scientists understand how the Earth deforms. New computer methods make these simulations faster and more efficient, especially on powerful computers. They automatically adjust settings for better performance and can handle complex materials and flow types. This approach makes it easier to study large, detailed models of Earth processes.
09 Dec 2025
SWEET – Shallow Water Equation Environment for Tests v1.0Keerthi Gaddameedi, François Hamon, Dominik Huber, Thibaut Lunet, Pedro S. Peixoto, João Guilherme Caldas Steinstraesser, Martin Schreiber, and Valentina Schüller
EGUsphere,https://doi.org/10.5194/egusphere-2025-5156, https://doi.org/10.5194/egusphere-2025-5156, 2025
Preprint under review for GMD(discussion: open, 1 comment)
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We present the open-source software SWEET, with core written in C++, dedicated to the numerical simulation of global spectral methods for the rotating shallow water equations on the biperiodic plane and on the sphere. SWEET is designed to provide a fast and efficient environment for research around time integration methods relevant to atmospheric circulation models. The software offers a versatile implementation that allows users to easily set up and run custom time-stepping schemes.
08 Dec 2025
Improving the fine structure of intense rainfall forecast by a designed generative adversarial networkZuliang Fang, Qi Zhong, Haoming Chen, Xiuming Wang, Zhicha Zhang, and Hongli Liang
Geosci. Model Dev., 18, 9723–9749,https://doi.org/10.5194/gmd-18-9723-2025, https://doi.org/10.5194/gmd-18-9723-2025, 2025
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We developed a deep learning model based on Generative Adversarial Networks (GANs) to improve rainfall forecasts in northern China. Traditional models struggle with accuracy, especially for heavy rain. Our model merges data from multiple forecasts, capturing detailed rainfall patterns and offering more reliable short-term predictions.
08 Dec 2025
MET-AICE v1.0: an operational data-driven sea ice prediction system for the European ArcticCyril Palerme, Johannes Röhrs, Thomas Lavergne, Jozef Rusin, Are Frode Kvanum, Atle Macdonald Sørensen, Arne Melsom, Julien Brajard, Martina Idžanović, Marina Durán Moro, and Malte Müller
Geosci. Model Dev., 18, 9751–9766,https://doi.org/10.5194/gmd-18-9751-2025, https://doi.org/10.5194/gmd-18-9751-2025, 2025
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We present MET-AICE, a sea ice prediction system based on artificial intelligence techniques that has been running operationally since March 2024. The forecasts are produced daily and provide sea ice concentration predictions for the next 10 days. We evaluate the MET-AICE forecasts from the first year of operation, and we compare them to forecasts produced by three physically-based models. We show that MET-AICE is skillful and provides more accurate forecasts than the physically-based models.
05 Dec 2025
Evaluating the impact of task aggregation in workflows with shared resource environments: use case for the MONARCH applicationManuel G. Marciani, Miguel Castrillo, Gladys Utrera, Mario C. Acosta, Bruno P. Kinoshita, and Francisco Doblas-Reyes
Geosci. Model Dev., 18, 9709–9721,https://doi.org/10.5194/gmd-18-9709-2025, https://doi.org/10.5194/gmd-18-9709-2025, 2025
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Earth System Model simulations are typically run on large, highly congested flagship computers using workflows. These workflows can consist of thousands of tasks. If these tasks are queued individually, the wait time can add up, resulting in a long response time. In this paper, we explore a technique for aggregating tasks into a single submission. We found that this simple technique reduced the time spent in the queue by up to 7 %.
04 Dec 2025
Modelling herbivory impacts on vegetation structure and productivityJens Krause, Peter Anthoni, Mike Harfoot, Moritz Kupisch, and Almut Arneth
Geosci. Model Dev., 18, 9633–9651,https://doi.org/10.5194/gmd-18-9633-2025, https://doi.org/10.5194/gmd-18-9633-2025, 2025
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While animal biodiversity is facing a global crisis as more and more species are becoming endangered or extinct, the role of animals for the functioning of ecosystems is still not fully understood. We contribute to bridging this gap by coupling a animal population model with a vegetation and thus enable future research in this topic.
04 Dec 2025
Development of a global 5 arcmin groundwater model (H08-GMv1.0): model setup and steady-state simulationQing He, Naota Hanasaki, Akiko Matsumura, Edwin H. Sutanudjaja, and Taikan Oki
Geosci. Model Dev., 18, 9653–9686,https://doi.org/10.5194/gmd-18-9653-2025, https://doi.org/10.5194/gmd-18-9653-2025, 2025
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This work presents a global groundwater modeling framework at 5 arcmin resolution, developed through an offline coupling of the H08 water resource model and MODFLOW6. The model includes a single-layer aquifer and is designed to capture long-term mean groundwater dynamics under varying climate types. The manuscript describes the model structure, input datasets, and evaluation against available observations.
04 Dec 2025
CMIP7 data request: impacts and adaptation priorities and opportunitiesAlex C. Ruane, Charlotte L. Pascoe, Claas Teichmann, David J. Brayshaw, Carlo Buontempo, Ibrahima Diouf, Jesus Fernandez, Paula L. M. Gonzalez, Birgit Hassler, Vanessa Hernaman, Ulas Im, Doroteaciro Iovino, Martin Juckes, Iréne L. Lake, Timothy Lam, Xiaomao Lin, Jiafu Mao, Negin Nazarian, Sylvie Parey, Indrani Roy, Wan-Ling Tseng, Briony Turner, Andrew Wiebe, Lei Zhao, and Damaris Zurell
Geosci. Model Dev., 18, 9497–9540,https://doi.org/10.5194/gmd-18-9497-2025, https://doi.org/10.5194/gmd-18-9497-2025, 2025
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This paper describes how the Coupled Model Intercomparison Project organized its 7th phase (CMIP7) to encourage the production of Earth system model outputs relevant for impacts and adaptation. Community engagement identified 13 opportunities for application across human and natural systems, 60 variable groups and 539 unique variables. We also show how simulations can more efficiently meet applications needs by targeting appropriate resolution, time slices, experiments and variable groups.
03 Dec 2025
Adjoint-based simultaneous state and parameter estimation in an Arctic Sea Ice-Ocean Model using MITgcm (c63m)Guokun Lyu, Longjiang Mu, Armin Koehl, Ruibo Lei, Xi Liang, and Chuanyu Liu
Geosci. Model Dev., 18, 9451–9468,https://doi.org/10.5194/gmd-18-9451-2025, https://doi.org/10.5194/gmd-18-9451-2025, 2025
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In the sea ice-ocean models, errors in the parameters and missing spatiotemporal variations contribute to the deviations between the simulations and the observations. We extended an adjoint method to optimize spatiotemporally varying parameters together with the atmosphere forcing and the initial conditions using satellite and in-situ observations. Seasonally, this scheme demonstrates a more prominent advantage in mid-autumn and show great potential for accurately reproducing the Arctic changes.
03 Dec 2025
Data clustering to optimise the representativity of observational data in air quality data assimilation: a case study with EURAD-IM (version 5.9.1 DA)Alexander Hermanns, Anne Caroline Lange, Julia Kowalski, Hendrik Fuchs, and Philipp Franke
Geosci. Model Dev., 18, 9417–9432,https://doi.org/10.5194/gmd-18-9417-2025, https://doi.org/10.5194/gmd-18-9417-2025, 2025
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For air quality analyses, data assimilation models split available data into assimilation and validation data sets. The former is used to generate the analysis, the latter to verify the simulations. A preprocessor classifying the observations by the data characteristics is developed based on clustering algorithms. The assimilation and validation data sets are compiled by equally allocating data of each cluster. The resulting improvement of the analysis is evaluated with an air quality model.
02 Dec 2025
Calibrating the GAMIL3-1° climate model using a derivative-free optimization methodWenjun Liang, Simon Frederick Barnard Tett, Lijuan Li, Coralia Cartis, Danya Xu, Wenjie Dong, and Junjie Huang
Geosci. Model Dev., 18, 9293–9318,https://doi.org/10.5194/gmd-18-9293-2025, https://doi.org/10.5194/gmd-18-9293-2025, 2025
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Predicting climate accurately is challenging due to uncertainties in model parameters. This study introduced an automated approach to refine key parameters, focusing on processes like cloud formation and atmospheric circulation. Testing adjustments to 10 and 20 parameters improved the model’s accuracy and stability, reducing errors in long-term simulations. This faster, more reliable method enhances climate models, supporting better future predictions and aiding global decision-making.
02 Dec 2025
A process-based modeling of soil organic matter physical properties for land surface models – Part 1: Soil mixture theoryBertrand Decharme
Geosci. Model Dev., 18, 9349–9384,https://doi.org/10.5194/gmd-18-9349-2025, https://doi.org/10.5194/gmd-18-9349-2025, 2025
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This study resolves a key inconsistency in how Earth system models represent the physical properties of soil organic matter in land surface models. It introduces a new method to compute its volumetric fraction and physical effects using standard input data and soil mixture theory. Validated with experimental mixtures and field observations, the proposed framework improves the physical realism of soil property estimates.
01 Dec 2025
Predicting and correcting the influence of boundary conditions in regional inverse analysesHannah Nesser, Kevin W. Bowman, Matthew D. Thill, Daniel J. Varon, Cynthia A. Randles, Ashutosh Tewari, Felipe J. Cardoso-Saldaña, Emily Reidy, Joannes D. Maasakkers, and Daniel J. Jacob
Geosci. Model Dev., 18, 9279–9291,https://doi.org/10.5194/gmd-18-9279-2025, https://doi.org/10.5194/gmd-18-9279-2025, 2025
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Regional analyses of atmospheric trace gases can improve knowledge of fluxes at high resolution but rely on specified boundary conditions (BCs) at the domain edges. Biases in the often-uncertain BCs propagate to the inferred fluxes. We develop a framework to explain how errors in the BCs influence the optimized fluxes, derive two metrics to estimate this influence, and compare two methods to correct for the biases. We demonstrate correcting BCs directly is more effective at reducing bias.
28 Nov 2025
Exploiting physics-based machine learning to quantify geodynamic effects – insights from the Alpine regionDenise Degen, Ajay Kumar, Magdalena Scheck-Wenderoth, and Mauro Cacace
Geosci. Model Dev., 18, 9219–9236,https://doi.org/10.5194/gmd-18-9219-2025, https://doi.org/10.5194/gmd-18-9219-2025, 2025
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Geodynamical simulations cover a wide spatial and temporal range and are crucial to understand and assess the evolution of the Earth system. To enable computationally efficient modeling approaches that can account for potentially unknown subsurface properties, we present a surrogate modeling technique. This technique combines physics-based and machine-learning techniques to enable reliable predictions of geodynamical applications, as we illustrate for the case study of the Alpine Region.
26 Nov 2025
Development of UI-WRF-Chem (v1.0) for the MAIA satellite mission: case demonstrationHuanxin Zhang, Jun Wang, Nathan Janechek, Cui Ge, Meng Zhou, Lorena Castro García, Tong Sha, Yanyu Wang, Weizhi Deng, Zhixin Xue, Chengzhe Li, Lakhima Chutia, Yi Wang, Sebastian Val, James L. McDuffie, Sina Hasheminassab, Scott E. Gluck, David J. Diner, Peter R. Colarco, Arlindo M. da Silva, and Jhoon Kim
Geosci. Model Dev., 18, 9061–9099,https://doi.org/10.5194/gmd-18-9061-2025, https://doi.org/10.5194/gmd-18-9061-2025, 2025
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We present the development of the Unified Inputs (of initial and boundary conditions) for WRF (Weather Research and Forecasting)-Chem (UI-WRF-Chem) framework to support the Multi-Angle Imager for Aerosols (MAIA) satellite mission. Major updates include improving dust size distribution in the chemical boundary conditions, updating land surface properties using recent available satellite data and enhancing the representation of soil NOx emissions. We demonstrate subsequent model improvements over several of the MAIA target areas.
25 Nov 2025
ISARD (v1.0) : A Reproducible Geostatistical Framework for Daily Precipitation Ensemble in Mountainous TerrainValentin Dura, Guillaume Evin, Anne-Catherine Favre, and David Penot
EGUsphere,https://doi.org/10.5194/egusphere-2025-5679, https://doi.org/10.5194/egusphere-2025-5679, 2025
Preprint under review for GMD(discussion: open, 4 comments)
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Traditional precipitation analysis often misrepresent seasonal totals and spatial variability of intense rainfall in mountains. This study introduces a reproducible workflow to generate a daily precipitation ensembles, conditioned on rain gauges. It outperforms standard products by better capturing seasonal totals. It also quantifies interpolation uncertainty, improving flood modeling. The open-source workflow is transferable to regions with sparse rain-gauge networks or limited radar coverage.
24 Nov 2025
Development of the global maize yield model MATCRO-Maize version 1.0Marin Nagata, Astrid Yusara, Tomomichi Kato, and Yuji Masutomi
Geosci. Model Dev., 18, 8927–8948,https://doi.org/10.5194/gmd-18-8927-2025, https://doi.org/10.5194/gmd-18-8927-2025, 2025
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We developed a maize version of a process-based crop model coupled to a land-surface model by incorporating photosynthesis for C4 plants and maize-specific parameters. The model was calibrated with field data and literature, and it was extensively validated with global reference yields. The model effectively captured interannual yield variability in global and county-level yield data, demonstrating its potential for assessing the climate impacts on maize production.
24 Nov 2025
Development and Testing of Ensemble-Variational Data Assimilation Capabilities for Radar Data within JEDI coupled with FV3-LAM ModelJun Park, Chengsi Liu, and Ming Xue
EGUsphere,https://doi.org/10.5194/egusphere-2025-5411, https://doi.org/10.5194/egusphere-2025-5411, 2025
Preprint under review for GMD(discussion: final response, 9 comments)
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This study develops and tests new methods to improve weather forecasts by using radar observations within a modern data assimilation system called the Joint Effort for Data Assimilation Integration. The approach combines information from radar measurements and computer models to better describe storms. Tests with a major U.S. storm show improved prediction of rainfall and storm structure.
21 Nov 2025
Description and evaluation of airborne microplastics in the United Kingdom Earth System Model (UKESM1.1) using GLOMAP-modeCameron McErlich, Felix Goddard, Alex Aves, Catherine Hardacre, Nikolaos Evangeliou, Alan J. Hewitt, and Laura E. Revell
Geosci. Model Dev., 18, 8827–8854,https://doi.org/10.5194/gmd-18-8827-2025, https://doi.org/10.5194/gmd-18-8827-2025, 2025
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Airborne microplastics are a new air pollutant but are not yet included in most global models. We add them to the UK Earth System Model to show how they move, change, and are removed from air. Smaller microplastics persist for longer and can travel further, even to Antarctica. While their current role in air pollution is small, their presence is expected to grow in future. This work offers a framework to assess future impacts of microplastics on air quality and climate.
20 Nov 2025
Autoencoder-based feature extraction for the automatic detection of snow avalanches in seismic dataAndri Simeon, Cristina Pérez-Guillén, Michele Volpi, Christine Seupel, and Alec van Herwijnen
Geosci. Model Dev., 18, 8751–8776,https://doi.org/10.5194/gmd-18-8751-2025, https://doi.org/10.5194/gmd-18-8751-2025, 2025
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Avalanche detection systems are crucial for forecasting, but distinguishing avalanches from other seismic sources remains a challenge. We propose novel autoencoder models to automatically extract features and compare them with engineered seismic features. These features are then used to classify avalanches and noise events. The autoencoder feature classifiers exhibit the highest sensitivity in detecting avalanches, while the engineered seismic classifier performs better overall.
20 Nov 2025
Developing an eco-physiological process-based model of soybean growth and yield (MATCRO-Soy v.1): model calibration and evaluationAstrid Yusara, Tomomichi Kato, Elizabeth A. Ainsworth, Rafael Battisti, Etsushi Kumagai, Satoshi Nakano, Yushan Wu, Yutaka Tsutsumi-Morita, Kazuhiko Kobayashi, and Yuji Masutomi
Geosci. Model Dev., 18, 8801–8826,https://doi.org/10.5194/gmd-18-8801-2025, https://doi.org/10.5194/gmd-18-8801-2025, 2025
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We developed a soybean model, an ecosystem model for crop yield (namely MATCRO-Soy), integrating crop response toward climate variables. It offers a detailed yield estimation. Parameter tuning in the model used literature and field experiments. The model shows a moderate correlation with observed yields at the global, national, and grid-cell levels. Development of this model enhances crop modeling diversity approaches, particularly in climate change impact studies.
20 Nov 2025
HAMSOM-VICE v0.9: Comparison of two variable ice-ocean drag coefficient parameterizations on annual simulations of Bohai Sea iceLibang Xu, Bin Jia, Yu Liu, Xue'en Chen, and Donglin Guo
EGUsphere,https://doi.org/10.5194/egusphere-2025-4290, https://doi.org/10.5194/egusphere-2025-4290, 2025
Preprint under review for GMD(discussion: open, 3 comments)
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We compared two methods to calculate ice-ocean drag coefficient in Bohai Sea. Results demonstrate that in the thin ice environment, the ice-bottom surface skin drag and the ice floe edge form drag are the main components. One method better predicts ice extent, the other better predicts ice season duration. Higher ice-ocean drag melts ice from below and cools water to form new ice. Our findings improve regional ice forecasts, enhancing safety for shipping and coastal industries.
18 Nov 2025
A computationally efficient method to model similar and alternate stratospheric aerosol injection experiments using prescribed aerosols in a lower-complexity version of the same model: a case study using CESM(CAM) and CESM(WACCM)Jasper de Jong, Daniel Pflüger, Simone Lingbeek, Claudia E. Wieners, Michiel L. J. Baatsen, and René R. Wijngaard
Geosci. Model Dev., 18, 8679–8702,https://doi.org/10.5194/gmd-18-8679-2025, https://doi.org/10.5194/gmd-18-8679-2025, 2025
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Injection of reflective sulfate aerosols high in the atmosphere is a proposed method to mitigate global warming. Climate simulations with injection are more expensive than standard future projections. We propose a method that dynamically scales the forcing fields based on pre-existing full-complexity data. This opens up possibilities for ensemble generation, new scenarios and higher resolution runs. We show that our method works for multiple model versions, injection scenarios and resolutions.
17 Nov 2025
The tracer nudging method for correcting and preventing uneven tracer distributions in geodynamical modelsPaul James Tackley
Geosci. Model Dev., 18, 8651–8662,https://doi.org/10.5194/gmd-18-8651-2025, https://doi.org/10.5194/gmd-18-8651-2025, 2025
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Tracers are commonly used in geodynamical models to track various quantities as material moves around. However, methods used to advect them typically do not respect the mass conservation equation, resulting in gaps and bunches in the tracer distribution. Here a method to correct this, based on nudging tracer positions in order to respect mass conservation, is presented. Tests show that it is effective and has a low computational cost.
17 Nov 2025
The Speciated isoprene emission model with the MEGAN algorithm for China (SieMAC)Shengjun Xi, Yuhang Wang, Xiangyang Yuan, Zhaozhong Feng, Fanghe Zhao, Yanli Zhang, and Xinming Wang
Geosci. Model Dev., 18, 8627–8649,https://doi.org/10.5194/gmd-18-8627-2025, https://doi.org/10.5194/gmd-18-8627-2025, 2025
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We developed the Speciated Isoprene Emission Model with Model of Emissions of Gases and Aerosols from Nature Algorithm for China to improve biogenic emission estimates using updated vegetation data and local measurements. The model predicts summer 2013 emissions of 10.92–11.37 teragrams of carbon. Validation shows our model performs better than the existing models, revealing underestimated isoprene impacts on ozone pollution in eastern China.
14 Nov 2025
Automatic optical depth parametrization in radiative transfer model RTTOV v13 via LASSO-induced sparsityFranklin Vargas Jiménez and Juan Carlos De los Reyes
Geosci. Model Dev., 18, 8511–8534,https://doi.org/10.5194/gmd-18-8511-2025, https://doi.org/10.5194/gmd-18-8511-2025, 2025
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This study proposes an automatic method to parameterize atmospheric optical depths in the Radiative Transfer for TIROS Operational Vertical Sounder (RTTOV) version 13 model. The approach combines statistical inference and Least Absolute Shrinkage and Selection Operator (LASSO) regression to reduce parameters and select relevant gases. Tests with Visible Infrared Imaging Radiometer Suite (VIIRS) channels show reduced computation while preserving accuracy.
14 Nov 2025
All-sky AMSU-A radiance data assimilation using the gain-form of Local Ensemble Transform Kalman filter within MPAS-JEDI-2.1.0: implementation, tuning, and evaluationTao Sun, Jonathan J. Guerrette, Zhiquan Liu, Junmei Ban, Byoung-Joo Jung, Ivette Hernandez Banos, and Chris Snyder
Geosci. Model Dev., 18, 8569–8587,https://doi.org/10.5194/gmd-18-8569-2025, https://doi.org/10.5194/gmd-18-8569-2025, 2025
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We evaluated a new ensemble data assimilation system that uses satellite observations in all weather conditions for global weather forecasts. The results show that including cloud- and precipitation-affected satellite data improves forecasts of moisture, wind, and clouds, especially in the tropics. This work highlights the potential of this new ensemble data assimilation system to enhance global weather forecasts.
14 Nov 2025
Improvement of the Computational Efficiency in SVD-3DEnVar Data Assimilation Scheme and Its Preliminary Application to the TRAMS 3.0 ModelKun Liu, Daosheng Xu, Fei Zheng, Juanxiong He, Chun Li, Jeremy Cheuk-Hin Leung, Mingyang Zhang, Dingchi Zhao, Quanjun He, Yuewei Zhang, Yi Li, and Banglin Zhang
EGUsphere,https://doi.org/10.5194/egusphere-2025-4632, https://doi.org/10.5194/egusphere-2025-4632, 2025
Revised manuscript under review for GMD(discussion: final response, 6 comments)
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The Singular Value Decomposition-three Dimensional Ensemble Variational data assimilation scheme is applied for the first time in the Tropical Regional Atmospheric Model System. With optimized three-dimensional perturbation generation and parallel strategies, computational costs were greatly reduced. Results indicate that the optimized scheme maintains reasonable accuracy while achieving much higher efficiency, suggesting good potential for practical forecasting use.
13 Nov 2025
Curlew 1.0: Spatio-temporal implicit geological modelling with neural fields in pythonAkshay V. Kamath, Samuel T. Thiele, Marie Moulard, Lachlan Grose, Raimon Tolosana-Delgado, Michael J. Hillier, Florian Wellmann, and Richard Gloaguen
External preprint server,https://doi.org/10.31223/X5KX81, https://doi.org/10.31223/X5KX81, 2025
Preprint under review for GMD(discussion: final response, 5 comments)
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We presentcurlew, an open-source Python tool for constructing 3D geological models using machine learning. It integrates diverse spatial data and structural observations into a flexible, event-based framework.Curlew captures complex features like folds and faults, handles uncertainty, and supports learning from sparse or unlabelled data. We demonstrate its capabilities on synthetic and real-world examples.
12 Nov 2025
Grounding-line dynamics in a Stokes ice-flow model (Elmer/Ice v9.0): Improved numerical stability allows larger time stepsA. Clara J. Henry, Thomas Zwinger, and Josefin Ahlkrona
EGUsphere,https://doi.org/10.5194/egusphere-2025-4192, https://doi.org/10.5194/egusphere-2025-4192, 2025
Preprint under review for GMD(discussion: open, 1 comment)
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To overcome time-step restrictions, we implement the Free-Surface Stabilisation Algorithm (FSSA) at the ice-ocean interface in Stokes ice-sheet simulations. In 2D experiments, a time step of 10 years is generally numerically stable and accurate, whereas a time step of 50 years is stable, but cannot fully capture grounding-line dynamics. Implementation at the ice-ocean interface increases the applicability of Stokes models and motivates future coupling with adaptive time-stepping schemes.
11 Nov 2025
Enhancing particle number concentration modelling accuracy in China by incorporating various nucleation parameterization schemes into the CMAQ version 5.3.2 modelJianjiong Mao, Lei Jiang, Zhicheng Feng, Jingyi Li, Yanhong Zhu, Momei Qin, Song Guo, Min Hu, and Jianlin Hu
Geosci. Model Dev., 18, 8423–8438,https://doi.org/10.5194/gmd-18-8423-2025, https://doi.org/10.5194/gmd-18-8423-2025, 2025
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Tiny air particles impact air quality and climate change. Our study improved their prediction in eastern cities by modeling two key formation processes: ions + sulfuric acid + ammonia (daytime) and sulfuric acid + dimethylamine (morning/evening). This improved model increases predictions by 36–84 % in Beijing and Nanjing. These advancements enable better demonstrate how these chemical processes significantly influence China eastern cities' particulate pollution.
10 Nov 2025
Modeling wheat development under extreme weather with WOFOST-EW v1Jinhui Zheng, Le Yu, Zhenrong Du, Liujun Xiao, and Xiaomeng Huang
Geosci. Model Dev., 18, 8379–8400,https://doi.org/10.5194/gmd-18-8379-2025, https://doi.org/10.5194/gmd-18-8379-2025, 2025
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This study integrates the extreme weather index and deep learning algorithms with the World Food Studies Simulation Model (WOFOST), proposing the WOFOST-EW v1. WOFOST-EW significantly improves the simulation of winter wheat growth under extreme weather conditions, providing more accurate predictions of phenology and yield. As extreme weather events become more frequent, WOFOST-EW provides a key tool for agricultural development.
07 Nov 2025
Datasets and protocols for including anomalous freshwater from melting ice sheets in climate simulationsGavin A. Schmidt, Kenneth D. Mankoff, Jonathan L. Bamber, Clara Burgard, Dustin Carroll, David M. Chandler, Violaine Coulon, Benjamin J. Davison, Matthew H. England, Paul R. Holland, Nicolas C. Jourdain, Qian Li, Juliana M. Marson, Pierre Mathiot, Clive R. McMahon, Twila A. Moon, Ruth Mottram, Sophie Nowicki, Anna Olivé Abelló, Andrew G. Pauling, Thomas Rackow, and Damien Ringeisen
Geosci. Model Dev., 18, 8333–8361,https://doi.org/10.5194/gmd-18-8333-2025, https://doi.org/10.5194/gmd-18-8333-2025, 2025
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The impact of increasing mass loss from the Greenland and Antarctic ice sheets has not so far been included in historical climate model simulations. This paper describes the protocols and data available for modeling groups to add this anomalous freshwater to their ocean modules to better represent the impacts of these fluxes on ocean circulation, sea ice, salinity and sea level.
07 Nov 2025
A highly-efficient automated optimization approach for kilometer-level resolution Earth system models on heterogeneous many-core supercomputersXiaojing Lv, Zhao Liu, Yuxuan Li, Shaoqing Zhang, Haohuan Fu, Xiaohui Duan, Shiming Xu, Yang Gao, Yujing Fan, Lifeng Yan, Haopeng Huang, Haitian Lu, Lingfeng Wan, Haoran Lin, Qixin Chang, Chenlin Li, Quanjie He, Yangyang Yu, Qinghui Lin, Sheng Jia, Tengda Zhao, Weiguo Liu, and Guangwen Yang
EGUsphere,https://doi.org/10.5194/egusphere-2025-5297, https://doi.org/10.5194/egusphere-2025-5297, 2025
Preprint under review for GMD(discussion: final response, 9 comments)
Short summaryShort summary
This study introduces a highly-efficient optimization approach that integrates automated and fine-grained optimizations for kilometer-level Earth System Models on heterogeneous many-core supercomputers. Our optimization achieves full parallel coverage for code segments exceeding 1 % of runtime. The optimized 5-km/3-km coupled model reaches 222 Simulated Days Per Day. This work signifies a pivotal advancement in ESMs, providing a robust platform for HR climate simulations.
06 Nov 2025
Stripe patterns in wind forecasts induced by physics-dynamics coupling on a staggered grid in CMA-GFS 3.0Jiong Chen, Yong Su, Zhe Li, Zhanshan Ma, and Xueshun Shen
Geosci. Model Dev., 18, 8253–8267,https://doi.org/10.5194/gmd-18-8253-2025, https://doi.org/10.5194/gmd-18-8253-2025, 2025
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Weather forecasts sometimes show high-frequency noise degrading predictions. Our study reveals stripe patterns arise from mismatches between dynamic and physical calculations in models. Simplified experiments demonstrate that adjusting their connection eliminates stripes. This advances numerical weather prediction understanding, aiding forecasters and the public. Our diagnostic methods provide a framework for solving this global meteorological modeling challenge.
05 Nov 2025
A Python interface to the Fortran-based Parallel Data Assimilation Framework: pyPDAF v1.0.2Yumeng Chen, Lars Nerger, and Amos S. Lawless
Geosci. Model Dev., 18, 8235–8252,https://doi.org/10.5194/gmd-18-8235-2025, https://doi.org/10.5194/gmd-18-8235-2025, 2025
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In this paper, we present pyPDAF, a Python interface to the parallel data assimilation framework (PDAF) allowing for coupling with Python-based models. We demonstrate the capability and efficiency of pyPDAF under a coupled data assimilation setup.
05 Nov 2025
Sunburned plankton: ultraviolet radiation inhibition of phytoplankton photosynthesis in the Community Earth System Model version 2Joshua Coupe, Nicole S. Lovenduski, Luise S. Gleason, Michael N. Levy, Kristen Krumhardt, Keith Lindsay, Charles Bardeen, Clay Tabor, Cheryl Harrison, Kenneth G. MacLeod, Siddhartha Mitra, and Julio Sepúlveda
Geosci. Model Dev., 18, 8217–8234,https://doi.org/10.5194/gmd-18-8217-2025, https://doi.org/10.5194/gmd-18-8217-2025, 2025
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We have developed a new feature in the atmosphere and ocean components of the Community Earth System Model version 2 by implementing ultraviolet (UV) radiation inhibition of photosynthesis of four marine phytoplankton functional groups represented in the Marine Biogeochemistry Library. The new feature is tested with varying levels of UV radiation, and it will enable an analysis of an asteroid impact’s effect on the ozone layer and how that affects the base of the marine food web.
05 Nov 2025
Development of a model framework for terrestrial carbon flux prediction: the Regional Carbon and Climate Analytics Tool (RCCAT) applied to non-tidal wetlandsAshley Brereton, Zelalem A. Mekonnen, Bhavna Arora, William J. Riley, Kunxiaojia Yuan, Yi Xu, Yu Zhang, Qing Zhu, Tyler L. Anthony, and Adina Paytan
Geosci. Model Dev., 18, 8157–8173,https://doi.org/10.5194/gmd-18-8157-2025, https://doi.org/10.5194/gmd-18-8157-2025, 2025
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Wetlands absorb carbon dioxide (CO2), helping slow climate change, but they also release methane, a potent warming gas. We developed a collection of AI-based models to estimate magnitudes of CO2 and methane exchanged between the land and the atmosphere, for wetlands on a regional scale. This approach helps to inform land-use planning, restoration, and greenhouse gas accounting, while also creating a foundation for future advancements in prediction accuracy.
05 Nov 2025
Iterative run-time bias corrections in an atmospheric GCM (LMDZ v6.3)Gerhard Krinner, Aude Champouillon, Juliette Blanchet, and Frédérique Chéruy
EGUsphere,https://doi.org/10.5194/egusphere-2025-3553, https://doi.org/10.5194/egusphere-2025-3553, 2025
Preprint under review for GMD(discussion: open, 1 comment)
Short summaryShort summary
Although the scientific community has made much progress over the last decades, climate models still do not perfectly simulate the present climate. Therefore, the model outputs are usually corrected for these errors. This article presents a method to apply successive stages of repeated error correction that lead to a better simulation of the present climate than in previous studies, in which the same correction method had been applied only once.
05 Nov 2025
Assimilating Geostationary Satellite Visible Reflectance Data: developing and testing the GSI-EnKF-CRTM-Vis techniqueChong Luo, Yongbo Zhou, Yubao Liu, Wei Han, Bin Yao, and Chao Liu
EGUsphere,https://doi.org/10.5194/egusphere-2025-4553, https://doi.org/10.5194/egusphere-2025-4553, 2025
Preprint under review for GMD(discussion: final response, 2 comments)
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We developed a new technique to assimilate satellite visible reflectance. By testing our technique on a heavy rainfall event, we found that it significantly reduces errors in cloud water estimates and enhances light precipitation forecasts. This data assimilation also better improved thin clouds. This advancement helps increase the accuracy of weather predictions in situations where clouds and rain play a major role.
03 Nov 2025
UFS-RAQMS global atmospheric composition model: TROPOMI CO column assimilationMaggie Bruckner, R. Bradley Pierce, Allen Lenzen, Glenn Diskin, Joshua P. DiGangi, Martine De Maziere, Nicholas Jones, and Maria Makarova
Geosci. Model Dev., 18, 8109–8127,https://doi.org/10.5194/gmd-18-8109-2025, https://doi.org/10.5194/gmd-18-8109-2025, 2025
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UFS-RAQMS incorporates the Real-time Air Quality Modeling System (RAQMS) stratosphere/troposphere chemistry into the existing NOAA Global Ensemble Forecast System (GEFS-Aerosols) version of NOAA's Unified Forecast System (UFS). Chemical data assimilation using TROPOMI CO column observations is conducted during the July–August–September 2019 period. Comparison of the CO column with independent measurements shows a systematic low bias in biomass burning CO emissions without assimilation.
03 Nov 2025
Implementation of water tracers in the Met Office Unified ModelAlison J. McLaren, Louise C. Sime, Simon Wilson, Jeff Ridley, Qinggang Gao, Merve Gorguner, Giorgia Line, Martin Werner, and Paul Valdes
Geosci. Model Dev., 18, 8129–8142,https://doi.org/10.5194/gmd-18-8129-2025, https://doi.org/10.5194/gmd-18-8129-2025, 2025
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We describe a new development in a state-of-the-art computer atmosphere model, which follows the movement of the model’s water. This provides an efficient way to track all the model's rain and snow back to the average location of the evaporative source, as shown in a present-day simulation. The new scheme can be used in simulations of the future to predict how sources of regional rain or snowfall might change owing to human actions, providing useful information for water management purposes.
03 Nov 2025
Actionable reporting of CPU-GPU performance comparisons: Insights from a CLUBB case studyGunther Huebler, Vincent E. Larson, John Dennis, and Sheri Voelz
EGUsphere,https://doi.org/10.5194/egusphere-2025-4435, https://doi.org/10.5194/egusphere-2025-4435, 2025
Preprint under review for GMD(discussion: final response, 2 comments)
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Central processing units (CPUs) and graphics processing units (GPUs) are different devices that suit different kinds of work. Using a climate modeling component, we provide a clearer way to tell which device type is faster for a given task. This matters because runs usually use only one device type. Our results are actionable: they guide device choice, report performance gains fairly, highlight code areas to improve, and show how code structure and optimization can change conclusions.
03 Nov 2025
Benchmarking the reactive transport code SCEPTER v1.0.2Yoshiki Kanzaki and Christopher T. Reinhard
EGUsphere,https://doi.org/10.5194/egusphere-2025-4035, https://doi.org/10.5194/egusphere-2025-4035, 2025
Preprint under review for GMD(discussion: final response, 2 comments)
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The SCEPTER model has been recently developed for simulating elemental cycles in managed lands, especially soil acidity management and carbon sequestration via enhanced weathering. This paper demonstrates that the performance of SCEPTER is essentially identical to other soil hydrological and reactive transport codes through benchmark experiments. We also discussed the emerging need for a benchmarking protocol fit for the purpose of predictive modeling of soil pH management in agricultural lands.
30 Oct 2025
Combining empirical and mechanistic understanding of spruce bark beetle outbreak dynamics in the LPJ-GUESS (v4.1, r13130) vegetation modelFredrik Lagergren, Anna Maria Jönsson, Mats Lindeskog, and Thomas A. M. Pugh
Geosci. Model Dev., 18, 8071–8090,https://doi.org/10.5194/gmd-18-8071-2025, https://doi.org/10.5194/gmd-18-8071-2025, 2025
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The European spruce bark beetle (SBB) has, in recent years, been the most important disturbance agent in many European forests. We implemented a SBB module in a dynamic vegetation model and calibrated it against observations from Sweden, Switzerland, Austria and France. The start and duration of outbreaks triggered by storm damage and the increased damage driven by recent warm and dry periods were reasonably well simulated, although the spread was reflected in uncertain parameter estimates.
30 Oct 2025
DINO: a diabatic model of pole-to-pole ocean dynamics to assess subgrid parameterizations across horizontal scalesDavid Kamm, Julie Deshayes, and Gurvan Madec
Geosci. Model Dev., 18, 8091–8107,https://doi.org/10.5194/gmd-18-8091-2025, https://doi.org/10.5194/gmd-18-8091-2025, 2025
Short summaryShort summary
We propose an idealized model of pole-to-pole ocean dynamics designed as a testbed for eddy parameterizations across a range of horizontal scales. While computationally affordable, it is able to capture key metrics of the climate system. By comparing simulations at low, intermediate, and high horizontal resolution, we demonstrate its utility for evaluating eddy parameterizations, in terms of both their effect on the mean state and diagnosis of the unresolved eddy fluxes they aim to represent.
30 Oct 2025
T-REX: The tile-based representation of lateral exchange processes in ICON-LandPhilipp de Vrese, Tobias Stacke, Veronika Gayler, Helena Bergstedt, Clemens von Baeckmann, Melanie Thurner, Christian Beer, and Victor Brovkin
EGUsphere,https://doi.org/10.5194/egusphere-2025-4031, https://doi.org/10.5194/egusphere-2025-4031, 2025
Preprint under review for GMD(discussion: final response, 2 comments)
Short summaryShort summary
The spatial variability in the land surface properties is often not captured by the resolution of land surface models. To overcome this limitation, most models subdivide the grid cells into fractions with homogeneous characteristics, for which the land processes are calculated separately. In reality, the fractions interact via the lateral exchange of water and heat, and the present manuscript details an approach to include these fluxes in the land component of the ICON modeling framework.
29 Oct 2025
Development of the global hydro-economic model (ECHO-Global version 1.0) for assessing the performance of water management optionsTaher Kahil, Safa Baccour, Julian Joseph, Reetik Sahu, Peter Burek, Jia Yi Ng, Samar Asad, Dor Fridman, Jose Albiac, Frank A. Ward, and Yoshihide Wada
Geosci. Model Dev., 18, 7987–8015,https://doi.org/10.5194/gmd-18-7987-2025, https://doi.org/10.5194/gmd-18-7987-2025, 2025
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This study presents the development of the global version of the ECHO hydro-economic model for assessing the economic and environmental performance of water management options. This improved version covers a large number of basins worldwide, includes a detailed representation of irrigated agriculture, and accounts for economic benefits and costs of water use. Results of this study demonstrates the capacity of ECHO-Global to address emerging water-related research and practical questions.
28 Oct 2025
Tensorweave 1.0: interpolating geophysical tensor fields with spatial neural networksAkshay V. Kamath, Samuel T. Thiele, Hernan Ugalde, Bill Morris, Raimon Tolosana-Delgado, Moritz Kirsch, and Richard Gloaguen
Geosci. Model Dev., 18, 7951–7968,https://doi.org/10.5194/gmd-18-7951-2025, https://doi.org/10.5194/gmd-18-7951-2025, 2025
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We present a new machine learning approach to reconstruct gravity and magnetic tensor data from sparse airborne surveys. By treating the data as derivatives of a hidden potential field and enforcing physical laws, our method improves accuracy and captures geological features more clearly. This enables better subsurface imaging in regions where traditional interpolation methods fall short.
27 Oct 2025
Coupling the TKE-ACM2 Planetary Boundary Layer Scheme with the Building Effect Parameterization ModelWanliang Zhang, Chao Ren, Edward Yan Yung Ng, Michael Mau Fung Wong, and Jimmy Chi Hung Fung
Geosci. Model Dev., 18, 7781–7813,https://doi.org/10.5194/gmd-18-7781-2025, https://doi.org/10.5194/gmd-18-7781-2025, 2025
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This study focuses on improving the accuracy of numerical weather prediction (NWP) model particularly in urbanized areas. We coupled a recently validated boundary layer model with a building effect model within an NWP. Validation has been performed under idealized atmospheric conditions by benchmarking the coupled model with a fine-scale numerical model. Subsequently, the improvements and limitations are investigated aided by observations in real case simulations.
27 Oct 2025
Interactive coupling of a Greenland ice sheet model in NorESM2Heiko Goelzer, Petra M. Langebroek, Andreas Born, Stefan Hofer, Konstanze Haubner, Michele Petrini, Gunter Leguy, William H. Lipscomb, and Katherine Thayer-Calder
Geosci. Model Dev., 18, 7853–7867,https://doi.org/10.5194/gmd-18-7853-2025, https://doi.org/10.5194/gmd-18-7853-2025, 2025
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On the backdrop of observed accelerating ice sheet mass loss over the last few decades, there is growing interest in the role of ice sheet changes in global climate projections. In this regard, we have coupled an Earth system model with an ice sheet model and have produced an initial set of climate projections including an interactive coupling with a dynamic Greenland ice sheet.
27 Oct 2025
Implementation of solar UV and energetic particle precipitation within the LINOZ scheme in ICON-ARTMaryam Ramezani Ziarani, Miriam Sinnhuber, Thomas Reddmann, Bernd Funke, Stefan Bender, and Michael Prather
Geosci. Model Dev., 18, 7891–7905,https://doi.org/10.5194/gmd-18-7891-2025, https://doi.org/10.5194/gmd-18-7891-2025, 2025
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Our study aims to present a new method for incorporating top-down solar forcing into stratospheric ozone relying on linearized ozone scheme. The addition of geomagnetic forcing led to significant ozone losses in the polar upper stratosphere of both hemispheres due to the catalytic cycles involving NOy. In addition to the particle precipitation effect, accounting for solar UV variability in the ICON-ART model leads to the changes in ozone in the tropical stratosphere.
27 Oct 2025
Multigrid beta filter for faster computation of ensemble covariance localizationSho Yokota, Miodrag Rancic, Ting Lei, R. James Purser, and Manuel S. F. V. De Pondeca
Geosci. Model Dev., 18, 7815–7829,https://doi.org/10.5194/gmd-18-7815-2025, https://doi.org/10.5194/gmd-18-7815-2025, 2025
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Covariance localization to mitigate sampling error of ensemble-based forecast error covariances is one of the main parts of the calculation in ensemble-variational data assimilation for the atmosphere. This study clarifies that the multigrid beta filter-based localization makes it several times faster than the conventional recursive filter-based one without significantly changing the analysis if a coarser filter grid is applied and filters except for the coarsest resolution are omitted.
27 Oct 2025
Handling discontinuities in numerical ODE methods for Lagrangian oceanographyJenny M. Mørk, Tor Nordam, and Siren Rühs
Geosci. Model Dev., 18, 7831–7851,https://doi.org/10.5194/gmd-18-7831-2025, https://doi.org/10.5194/gmd-18-7831-2025, 2025
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A common task in applied oceanography is to calculate the trajectories of floating objects in the ocean. We propose an alteration to some common numerical methods to improve their performance in such computations, and compare results with and without this alteration. This will help researchers to ensure they obtain a higher accuracy in their results without compromising on computer resources.
24 Oct 2025
A new hybrid particle-puff approach to atmospheric dispersion modeling, implemented in the Danish Emergency Response Model of the Atmosphere (DERMA)Kasper Skjold Tølløse and Jens Havskov Sørensen
Geosci. Model Dev., 18, 7763–7779,https://doi.org/10.5194/gmd-18-7763-2025, https://doi.org/10.5194/gmd-18-7763-2025, 2025
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In this study, we improve the short-scale dispersion modeling capabilities of the Danish Emergency Response Model of the Atmosphere (DERMA) by developing and implementing a new hybrid particle-puff description of turbulent diffusion and by updating a few other parameterizations in the model. The new model is evaluated against data from three different tracer gas experiments, and the promising results are an important first step towards also using DERMA for short-range dispersion modeling.
23 Oct 2025
| Highlight paper
nextGEMS: entering the era of kilometer-scale Earth system modelingHans Segura, Xabier Pedruzo-Bagazgoitia, Philipp Weiss, Sebastian K. Müller, Thomas Rackow, Junhong Lee, Edgar Dolores-Tesillos, Imme Benedict, Matthias Aengenheyster, Razvan Aguridan, Gabriele Arduini, Alexander J. Baker, Jiawei Bao, Swantje Bastin, Eulàlia Baulenas, Tobias Becker, Sebastian Beyer, Hendryk Bockelmann, Nils Brüggemann, Lukas Brunner, Suvarchal K. Cheedela, Sushant Das, Jasper Denissen, Ian Dragaud, Piotr Dziekan, Madeleine Ekblom, Jan Frederik Engels, Monika Esch, Richard Forbes, Claudia Frauen, Lilli Freischem, Diego García-Maroto, Philipp Geier, Paul Gierz, Álvaro González-Cervera, Katherine Grayson, Matthew Griffith, Oliver Gutjahr, Helmuth Haak, Ioan Hadade, Kerstin Haslehner, Shabeh ul Hasson, Jan Hegewald, Lukas Kluft, Aleksei Koldunov, Nikolay Koldunov, Tobias Kölling, Shunya Koseki, Sergey Kosukhin, Josh Kousal, Peter Kuma, Arjun U. Kumar, Rumeng Li, Nicolas Maury, Maximilian Meindl, Sebastian Milinski, Kristian Mogensen, Bimochan Niraula, Jakub Nowak, Divya Sri Praturi, Ulrike Proske, Dian Putrasahan, René Redler, David Santuy, Domokos Sármány, Reiner Schnur, Patrick Scholz, Dmitry Sidorenko, Dorian Spät, Birgit Sützl, Daisuke Takasuka, Adrian Tompkins, Alejandro Uribe, Mirco Valentini, Menno Veerman, Aiko Voigt, Sarah Warnau, Fabian Wachsmann, Marta Wacławczyk, Nils Wedi, Karl-Hermann Wieners, Jonathan Wille, Marius Winkler, Yuting Wu, Florian Ziemen, Janos Zimmermann, Frida A.-M. Bender, Dragana Bojovic, Sandrine Bony, Simona Bordoni, Patrice Brehmer, Marcus Dengler, Emanuel Dutra, Saliou Faye, Erich Fischer, Chiel van Heerwaarden, Cathy Hohenegger, Heikki Järvinen, Markus Jochum, Thomas Jung, Johann H. Jungclaus, Noel S. Keenlyside, Daniel Klocke, Heike Konow, Martina Klose, Szymon Malinowski, Olivia Martius, Thorsten Mauritsen, Juan Pedro Mellado, Theresa Mieslinger, Elsa Mohino, Hanna Pawłowska, Karsten Peters-von Gehlen, Abdoulaye Sarré, Pajam Sobhani, Philip Stier, Lauri Tuppi, Pier Luigi Vidale, Irina Sandu, and Bjorn Stevens
Geosci. Model Dev., 18, 7735–7761,https://doi.org/10.5194/gmd-18-7735-2025, https://doi.org/10.5194/gmd-18-7735-2025, 2025
Short summaryExecutive editorShort summary
The Next Generation of Earth Modeling Systems project (nextGEMS) developed two Earth system models that use horizontal grid spacing of 10 km and finer, giving more fidelity to the representation of local phenomena, globally. In its fourth cycle, nextGEMS simulated the Earth System climate over the 2020–2049 period under the SSP3-7.0 scenario. Here, we provide an overview of nextGEMS, insights into the model development, and the realism of multi-decadal, kilometer-scale simulations.
Executive editor
Kilometre-scale global climate models are pivotal for delivering nuanced regional climate insights and informing climate action, though they face the formidable challenge of balancing computational demands with the precision required to simulate complex subgrid processes. This paper is one of the landmarks in climate modelling, demonstrating the potential of kilometre-scale models to enhance regional climate understanding. It overcomes computational hurdles to achieve high-resolution simulations, crucial for capturing mesoscale phenomena. The authors' transparent exploration of challenges and successes makes this a vital read for climate scientists, offering insights into the future of climate modelling and its applications in climate action.
22 Oct 2025
Data-Informed Inversion Model (DIIM): a framework to retrieve marine optical constituents using a three-stream irradiance modelCarlos Enmanuel Soto López, Mirna Gharbi Dit Kacem, Fabio Anselmi, and Paolo Lazzari
Geosci. Model Dev., 18, 7575–7602,https://doi.org/10.5194/gmd-18-7575-2025, https://doi.org/10.5194/gmd-18-7575-2025, 2025
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We used a semi-analytical expression to estimate the concentration of optically active constituents, allowing us to have an interpretable formulation consistent with the laws of physics. We focused on a probabilistic approach, inverting the model with its respective uncertainty. Considering future applications to big data, we explored a neural-network-based method, retrieving computationally efficient estimates with an accuracy comparable to existing state-of-the-art algorithms.
21 Oct 2025
Towards the assimilation of atmospheric CO2 concentration data in a land surface model using adjoint-free variational methodsSimon Beylat, Nina Raoult, Cédric Bacour, Natalie Douglas, Tristan Quaife, Vladislav Bastrikov, Peter J. Rayner, and Philippe Peylin
Geosci. Model Dev., 18, 7501–7527,https://doi.org/10.5194/gmd-18-7501-2025, https://doi.org/10.5194/gmd-18-7501-2025, 2025
Short summaryShort summary
Land surface models are important tools for understanding and predicting the land components of the carbon cycle. Atmospheric CO2 concentration data are a valuable source of information that can be used to improve the accuracy of these models. In this study, we present a statistical ensemble-variational data assimilation method named EnVarDA to calibrate parameters of a land surface model using these data. We show that this method is easy to implement and more efficient and accurate than traditional methods.
21 Oct 2025
HAPI2LIBIS (v1.0): a new tool for flexible high-resolution radiative transfer computations with libRadtran (version 2.0.5)Antti Kukkurainen, Antti Mikkonen, Antti Arola, Antti Lipponen, Ville Kolehmainen, and Neus Sabater
Geosci. Model Dev., 18, 7529–7544,https://doi.org/10.5194/gmd-18-7529-2025, https://doi.org/10.5194/gmd-18-7529-2025, 2025
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HAPI2LIBIS is a new software tool that enhances the capabilities of the radiative transfer model libRadtran. It simplifies high-wavelength-resolution simulations by using up-to-date molecular data from the HITRAN (High-Resolution Transmission Molecular Absorption) database and streamlining computations. This tool helps researchers analyze how gases interact with radiation in the Earth's atmosphere and at the surface, improving atmospheric studies and satellite observations and making detailed modeling more accurate and accessible.
21 Oct 2025
Improving Thermodynamic Nudging in the E3SM Atmosphere Model Version 2 (EAMv2): Strategy and Hindcast Skills on Weather SystemsShixuan Zhang, L. Ruby Leung, Bryce E. Harrop, Aniruddha Bora, George Karniadakis, Khemraj Shukla, and Kai Zhang
EGUsphere,https://doi.org/10.5194/egusphere-2025-4277, https://doi.org/10.5194/egusphere-2025-4277, 2025
Revised manuscript under review for GMD(discussion: final response, 4 comments)
Short summaryShort summary
We developed a new method to guide the simulated atmosphere in an Earth system model so it better reflects real-world weather. By adjusting temperature and humidity, it reduces unwanted side effects and improves the realism of rainfall, energy flows, land–surface conditions, and extreme storms such as cyclones and atmospheric rivers. This makes the model more useful for testing its performance, understanding high-impact weather events, and creating reliable training data for machine learning.
20 Oct 2025
A Hybrid Method for Winter Road Surface Temperature Prediction Using Improved LSTMs and Stacking-Based Ensemble LearningWanting Li, Linyi Zhou, Xianghua Wu, Miaomiao Ren, Yuanhao Guo, Kun Chen, and Huiwen Lin
EGUsphere,https://doi.org/10.5194/egusphere-2025-3638, https://doi.org/10.5194/egusphere-2025-3638, 2025
Preprint under review for GMD(discussion: open, 6 comments)
Short summaryShort summary
To improve road safety during winter, we developed a model that predicts road surface temperatures using advanced deep learning and ensemble methods. By combining local pattern recognition with attention-based time modeling, our hybrid system outperformed individual models. Tested on real meteorological data, it achieved high accuracy even under sub-zero and extreme weather conditions, offering robust support for winter road management.
17 Oct 2025
On stabilisation of compositional density jumps in compressible mantle convection simulationsPaul James Tackley
Geosci. Model Dev., 18, 7389–7397,https://doi.org/10.5194/gmd-18-7389-2025, https://doi.org/10.5194/gmd-18-7389-2025, 2025
Short summaryShort summary
Large density jumps in numerical simulations of solid Earth dynamics can cause numerical oscillations. An effective method to prevent these at a free surface already exists. Here this is tested for compositional layers deeper in the mantle. The stabilisation method works effectively if density gradients due purely to compositional gradients are used but produces severe artefacts if total density is used.
16 Oct 2025
Simple Eulerian–Lagrangian approach to solving equations for sinking particulate organic matter in the oceanVladimir Maderich, Igor Brovchenko, Kateryna Kovalets, Seongbong Seo, and Kyeong Ok Kim
Geosci. Model Dev., 18, 7373–7387,https://doi.org/10.5194/gmd-18-7373-2025, https://doi.org/10.5194/gmd-18-7373-2025, 2025
Short summaryShort summary
We have developed a new, simple Eulerian–Lagrangian approach to solve equations for sinking particulate organic matter in the ocean. We rely on the known parameterizations, but our approach to solving the problem differs, allowing the algorithm to be incorporated into biogeochemical global ocean models with relative ease. New analytical and numerical solutions have confirmed that feedback between the degradation rate and sinking velocity significantly alters particulate matter fluxes.
16 Oct 2025
Representing Subgrid-Scale Cloud Effects in a Radiation Parameterization using Machine Learning: MLe-radiation v1.0Katharina Hafner, Sara Shamekh, Guillaume Bertoli, Axel Lauer, Robert Pincus, Julien Savre, and Veronika Eyring
External preprint server,https://doi.org/10.48550/arXiv.2510.05963, https://doi.org/10.48550/arXiv.2510.05963, 2025
Preprint under review for GMD(discussion: final response, 3 comments)
Short summaryShort summary
Most climate models cannot resolve clouds and cloud-radiation interactions at coarse horizontal resolutions of about 100 km, which introduces uncertainties. High-resolution models resolve clouds better but are expensive to run. We use short high-resolution simulations and artificial intelligence to learn the cloud-radiation interactions without making any assumptions about the small scales. We propose a new method that significantly reduces cloud related errors.
16 Oct 2025
Stratospheric aerosol forcing for CMIP7 (part 1): Optical properties for pre-industrial, historical, and scenario simulations (version 2.2.1)Thomas Jacques Aubry, Matthew Toohey, Sujan Khanal, Man Mei Chim, Magali Verkerk, Ben Johnson, Anja Schmidt, Mahesh Kovilakam, Michael Sigl, Zebedee Nicholls, Larry Thomason, Vaishali Naik, Landon Rieger, Dominik Stiller, Elisa Ziegler, and Isabel Smith
EGUsphere,https://doi.org/10.5194/egusphere-2025-4990, https://doi.org/10.5194/egusphere-2025-4990, 2025
Preprint under review for GMD(discussion: final response, 4 comments)
Short summaryShort summary
Climate forcings, such as solar radiation or anthropogenic greenhouse gases, are required to run global climate model simulations. Stratospheric aerosols, which mostly originate from large volcanic eruptions, are a key natural forcing. In this paper, we document the stratospheric aerosol forcing dataset that will feed the next generation (CMIP7) of climate models. Our dataset is very different from its predecessor (CMIP6), which might affect simulations of the 1850–2021 climate.
15 Oct 2025
PyESPERv1.0.0: a Python implementation of empirical seawater property estimation routines (ESPERs)Larissa M. Dias and Brendan R. Carter
Geosci. Model Dev., 18, 7275–7295,https://doi.org/10.5194/gmd-18-7275-2025, https://doi.org/10.5194/gmd-18-7275-2025, 2025
Short summaryShort summary
The increasing availability of oceanographic physical and chemical data necessitates accompanying methods for optimizing use of these data. This project produced algorithms (PyESPERs) for estimating biogeochemical seawater properties in Python, a freely available coding language. These algorithms were based on empirical seawater property estimation routines (ESPERs), which were originally written in the proprietary MATLAB coding language and can be used in studies of marine carbonate chemistry.
15 Oct 2025
Evaluation of plume rise parameterizations in GEM-MACHv2 with analysis of image data using a deep convolutional neural networkKevin M. Axelrod, Mark Gordon, Mohammad Koushafar, Jingliang Hao, Paul A. Makar, Sepehr Fathi, and Gunho Sohn
EGUsphere,https://doi.org/10.5194/egusphere-2025-4582, https://doi.org/10.5194/egusphere-2025-4582, 2025
Preprint under review for GMD(discussion: open, 5 comments)
Short summaryShort summary
The is a study of the plumes that rise from smokestacks. Knowing how these plume behave helps predict downwind pollutant concentrations. We use photos over a 2-year period to investigate how these plumes rise under different conditions and compare this to a commonly used model parameterization. It is found that the equations used to model plume rise in current models do well for some condition, but these equations can over-predict the plume rise, typically during the day when it is hot.
15 Oct 2025
Recognizing geochemical spatial patterns using deformable convolutional networks guided with geological knowledgeXinyu Zhang, Yihui Xiong, and Zhiyi Chen
EGUsphere,https://doi.org/10.5194/egusphere-2025-4877, https://doi.org/10.5194/egusphere-2025-4877, 2025
Preprint under review for GMD(discussion: final response, 6 comments)
Short summaryShort summary
Geochemical anomalies associated with mineralization represent one of the most significant types of geo-anomalies for mineral exploration.This study develops a AI method that combines geological knowledge with a flexible deep learning model. It helps identify geochemical anomaly patterns more accurately and reliably by focusing on key features like ore-controlling faults. The model's decisions are easier to understand through visual explanations, increasing transparency and trust in the results.
14 Oct 2025
The discontinuous Galerkin coastal and estuarine modelling system (DGCEMS v1.0.0): a three-dimensional, nonsplit-mode, implicit–explicit Runge–Kutta hydrostatic modelZereng Chen, Qinghe Zhang, Guoquan Ran, and Yang Nie
Geosci. Model Dev., 18, 7199–7214,https://doi.org/10.5194/gmd-18-7199-2025, https://doi.org/10.5194/gmd-18-7199-2025, 2025
Short summaryShort summary
This study presents the development of a novel three-dimensional discontinuous Galerkin coastal and estuarine modelling system named DGCEMS. The model has low spurious mixing and second-order convergence of surface water elevation, horizontal velocity, and the tracer field. It has the capability to simulate salt–freshwater interactions in the presence of wetting and drying boundaries.
13 Oct 2025
FZStats v1.0: a raster statistics toolbox for simultaneous management of spatial stratified heterogeneity and positional dependence in PythonNa Ren, Daojun Zhang, and Qiuming Cheng
Geosci. Model Dev., 18, 7165–7184,https://doi.org/10.5194/gmd-18-7165-2025, https://doi.org/10.5194/gmd-18-7165-2025, 2025
Short summaryShort summary
While focal statistics and zonal statistics deal with spatial position dependence (SPD) and spatial stratified heterogeneity (SSH) separately, the developed foca–zonal mixed statistics can handle both simultaneously. This new tool has the potential to become a general statistics tool. The integrated FZStats v1.0 toolbox in this study includes all three models mentioned above, providing new methodological support for understanding and addressing spatial statistical issues.
13 Oct 2025
Short-Lived Halogen Sources and Chemistry in the Community Earth System Model v2 (CESM2-SLH)Rafael Pedro Fernandez, Carlos Alberto Cuevas, Julián Villamayor, Aryeh Feinberg, Douglas E. Kinnison, Francis Vitt, Adriana Bossolasco, Javier A. Barrera, Amelia Reynoso, Orlando G. Tomazzeli, Qinyi Li, and Alfonso Saiz-Lopez
EGUsphere,https://doi.org/10.5194/egusphere-2025-3250, https://doi.org/10.5194/egusphere-2025-3250, 2025
Preprint under review for GMD(discussion: final response, 2 comments)
Short summaryShort summary
In this work we summarize 15 years of research and developments of short-lived halogens (SLH) using the Community Earth System Model (CESM) and present a complete description of the implementation and capabilities achieved with the new released version CESM2-SLH, including specific namelist options, input files and technical notes detailing the most important SLH updates that must be considered for the different model configurations and resolutions.
13 Oct 2025
CarboKitten.jl – an open source toolkit for carbonate stratigraphic modelingJohan Hidding, Emilia Jarochowska, Niklas Hohmann, Xianyi Liu, Peter Burgess, and Hanno Spreeuw
EGUsphere,https://doi.org/10.5194/egusphere-2025-4561, https://doi.org/10.5194/egusphere-2025-4561, 2025
Preprint under review for GMD(discussion: final response, 2 comments)
Short summaryShort summary
Coral reefs and limestones hold crucial records of Earth's climate history, but scientists have lacked accessible tools to simulate how these systems form over thousands to millions of years. We developed CarboKitten, free software that models how tropical sediments and associated organisms grow under changing sea levels and environmental conditions. The program runs fast on standard computers and can test scientific theories about how these geological features preserve the Earth’s history.
10 Oct 2025
A hybrid-grid global model for the estimation of atmospheric weighted mean temperature considering time-varying vertical adjustment rate in GNSS precipitable water vapour retrievalShaofeng Xie, Jihong Zhang, Liangke Huang, Fade Chen, Yongfeng Wu, Yijie Wang, and Lilong Liu
Geosci. Model Dev., 18, 6987–7002,https://doi.org/10.5194/gmd-18-6987-2025, https://doi.org/10.5194/gmd-18-6987-2025, 2025
Short summaryShort summary
We developed a new global atmospheric weighted mean temperature (Tm) model considering time-varying vertical adjustment rate. Firstly, a globalTm vertical adjustment rate model (NGGTm-H) was developed using the sliding-window algorithm. Secondly, the daily variation characteristics of Tm and its relationships with geographical situations were investigated. Finally, a hybrid-grid globalTm model considering the time-varying vertical adjustment rate (NGGTm) was developed.
10 Oct 2025
MLUCM BEP + BEM: an offline one-dimensional multi-layer urban canopy model based on the BEP + BEM schemeGianluca Pappaccogli, Andrea Zonato, Alberto Martilli, Riccardo Buccolieri, and Piero Lionello
Geosci. Model Dev., 18, 7129–7145,https://doi.org/10.5194/gmd-18-7129-2025, https://doi.org/10.5194/gmd-18-7129-2025, 2025
Short summaryShort summary
We present a multilayer urban model, named MLUCM BEP+BEM, able to represent detailed urban geometry and vegetation, while simulating their interactions and feedback with the atmosphere. Its accuracy and low computational cost make it ideal for offline climate projections assessing urban impacts under various emission scenarios. Its features enable analysis of urban overheating, energy demand, thermal comfort, and evaluation of strategies like green/cool roofs and photovoltaic panels.
10 Oct 2025
| Highlight paper
A dilatant visco-elasto-viscoplasticity model with globally continuous tensile cap: stable two-field mixed formulationAnton A. Popov, Nicolas Berlie, and Boris J. P. Kaus
Geosci. Model Dev., 18, 7035–7058,https://doi.org/10.5194/gmd-18-7035-2025, https://doi.org/10.5194/gmd-18-7035-2025, 2025
Short summaryExecutive editorShort summary
We present a simple plasticity model that can be used for robust modeling of strain localization in both shear and tensile failure regimes. The new model overcomes the difficulty related to combining these regimes and enables for particularly simple and reliable numerical implementation, which delivers regularized solutions that are insensitive to mesh resolution. We describe algorithmic details and demonstrate the applications to a number of relevant strain localization problems.
Executive editor
The paper by Popov et al. provides a comprehensive insight into overcoming challenges related to modelling the brittle-ductile transition in materials. This study offers a detailed description and bridges to similar concepts used beyond the geosciences, such as in engineering.
10 Oct 2025
Flood Volume Allocation Method for Flood Hazard Mapping Using River Model with Levee SchemeMuhammad Hasnain Aslam, Yukiko Hirabayashi, Dai Yamazaki, Gang Zhao, Yuki Kita, and Do Ngoc Khanh
EGUsphere,https://doi.org/10.5194/egusphere-2025-4358, https://doi.org/10.5194/egusphere-2025-4358, 2025
Revised manuscript accepted for GMD(discussion: final response, 6 comments)
Short summaryShort summary
We present a simple method that turns coarse flood volume estimates into local flood depth maps by using the shape of the land and mapped levee zones. Used with a large-scale river model, it keeps total water volume consistent while spreading water realistically inside and outside levees. Tests show levees often confine water and cut flood volume by about 10–15 % for many event sizes. The method reveals place-to-place differences in protection and yields clearer hazard maps for planning.
09 Oct 2025
A bound-constrained formulation for complex solution phase minimizationNicolas Riel, Boris J. P. Kaus, Albert de Montserrat, Evangelos Moulas, Eleanor C. R. Green, and Hugo Dominguez
Geosci. Model Dev., 18, 6951–6962,https://doi.org/10.5194/gmd-18-6951-2025, https://doi.org/10.5194/gmd-18-6951-2025, 2025
Short summaryShort summary
Our research focuses on improving the way we predict mineral assemblage. Current methods, while accurate, are slowed by complex calculations. We developed a new approach that simplifies these calculations and speeds them up significantly using a technique called the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. This breakthrough reduces computation time by more than five times, potentially unlocking new horizons in modeling reactive magmatic systems.
08 Oct 2025
Enhanced land subsidence interpolation through a hybrid deep convolutional neural network and InSAR time seriesZahra Azarm, Hamid Mehrabi, and Saeed Nadi
Geosci. Model Dev., 18, 6903–6919,https://doi.org/10.5194/gmd-18-6903-2025, https://doi.org/10.5194/gmd-18-6903-2025, 2025
Short summaryShort summary
The article introduces a new method to estimate land subsidence using deep convolutional neural networks (CNNs) and persistent scatterer interferometric synthetic aperture radar (PSInSAR), addressing the limitations of traditional methods. It focuses on Isfahan Province, Iran, and demonstrates substantial improvement over conventional techniques. The deep CNN method showed a 70 % enhancement in subsidence prediction, with the study area experiencing over 38 cm of subsidence between 2014 and 2020.
07 Oct 2025
Implementation of an intermediate-complexity snow-physics scheme (ISBA-Explicit Snow) into a sea ice model (SI3): 1D thermodynamic coupling and validationThéo Brivoal, Virginie Guemas, Martin Vancoppenolle, Clément Rousset, and Bertrand Decharme
Geosci. Model Dev., 18, 6885–6902,https://doi.org/10.5194/gmd-18-6885-2025, https://doi.org/10.5194/gmd-18-6885-2025, 2025
Short summaryShort summary
Snow in polar regions is key to sea ice formation and the Earth's climate, but current climate models simplify snow cover on sea ice. This study integrates an intermediate-complexity snow-physics scheme into a sea ice model designed for climate applications. We show that modeling the temporal changes in properties such as the density and thermal conductivity of the snow layers leads to a more accurate representation of heat transfer between the underlying sea ice and the atmosphere.
06 Oct 2025
Implementation of a sigma coordinate system in PALM-Sigma v1.0 (based on PALM v21.10) for LES study of the marine atmospheric boundary layerXu Ning and Mostafa Bakhoday-Paskyabi
EGUsphere,https://doi.org/10.5194/egusphere-2025-4390, https://doi.org/10.5194/egusphere-2025-4390, 2025
Revised manuscript accepted for GMD(discussion: final response, 6 comments)
Short summaryShort summary
Ocean waves shape winds close to the surface and extend their impact throughout the atmospheric boundary layer. In this study, we built a new modeling tool that allows simulations to follow the moving wave surface itself. By testing different wave and wind conditions, we show how waves change air motion, turbulence, and energy exchange above the ocean. This approach improves our ability to represent air–sea interactions, with implications for weather studies and offshore wind energy.
05 Oct 2025
AIRTRAC v2.0: a Lagrangian aerosol tagging submodel for the analysis of aviation SO4 transport patternsJin Maruhashi, Mattia Righi, Monica Sharma, Johannes Hendricks, Patrick Jöckel, Volker Grewe, and Irene C. Dedoussi
EGUsphere,https://doi.org/10.5194/egusphere-2025-4204, https://doi.org/10.5194/egusphere-2025-4204, 2025
Preprint under review for GMD(discussion: final response, 4 comments)
Short summaryShort summary
Aerosol-cloud interactions remain a large source of uncertainty in assessing aviation’s climate impact. We develop, evaluate and present AIRTRAC v2.0 within the EMAC modeling framework, which enables tracking of aviation-emitted SO2 and H2SO4as they are chemically transformed into sulfate aerosols and transported in the atmosphere. The development allows the identification of atmospheric regions with elevated potential for aerosol–cloud interactions due to sulfur emissions from aircraft.
02 Oct 2025
Improving annual fine mineral dust representation from the surface to the column in GEOS-Chem 14.4.1Dandan Zhang, Randall V. Martin, Xuan Liu, Aaron van Donkelaar, Christopher R. Oxford, Yanshun Li, Jun Meng, Danny M. Leung, Jasper F. Kok, Longlei Li, Haihui Zhu, Jay R. Turner, Yu Yan, Michael Brauer, Yinon Rudich, and Eli Windwer
Geosci. Model Dev., 18, 6767–6803,https://doi.org/10.5194/gmd-18-6767-2025, https://doi.org/10.5194/gmd-18-6767-2025, 2025
Short summaryShort summary
This study develops the fine mineral dust simulation in GEOS-Chem by: 1) implementing a new dust emission scheme with further refinements; 2) revisiting the size distribution of emitted dust; 3) explicitly tracking fine dust for emission, transport and deposition in 4 size bins; 4) updating the parametrization for below-cloud scavenging. All revisions significantly reduce the overestimation of surface fine dust from 94 % to 35 % while retaining comparable skill in representing columnar abundance.
01 Oct 2025
Implementation of a dry deposition module (DEPAC v3.11_ext) in a large eddy simulation code (DALES v4.4)Leon Geers, Ruud Janssen, Gudrun Thorkelsdottir, Jordi Vilà-Guerau de Arellano, and Martijn Schaap
Geosci. Model Dev., 18, 6647–6669,https://doi.org/10.5194/gmd-18-6647-2025, https://doi.org/10.5194/gmd-18-6647-2025, 2025
Short summaryShort summary
High-resolution data on reactive nitrogen deposition are needed to inform cost-effective policies. Here, we describe the implementation of a dry deposition module in a large eddy simulation code. With this model, we are able to represent the turbulent exchange of tracers at the hectometer resolution. The model calculates the dispersion and deposition of NOx and NH3 in great spatial detail, clearly showing the influence of local land use patterns.
01 Oct 2025
A Fully Implicit Second Order Method for Viscous Free Surface Stokes Flow – Application to Glacier SimulationsJosefin Ahlkrona, A. Clara J. Henry, and André Löfgren
EGUsphere,https://doi.org/10.5194/egusphere-2025-4359, https://doi.org/10.5194/egusphere-2025-4359, 2025
Revised manuscript under review for GMD(discussion: final response, 6 comments)
Short summaryShort summary
This paper leverages the Free Surface Stabilization Algorithm of Kaus et al. (2010) to construct the first fully implicit discretization of viscous free surface flows. It also presents the first second order accurate time-stepping scheme applicable to ice sheet models. We test the new method on an idealized problem and on a 2D glacier simulation. The results indicates that the method has great potential to speedup ice sheet models.
30 Sep 2025
Implementing a process-based representation of soil water movement in a second-generation dynamic vegetation model: application to dryland ecosystems (LPJ-GUESS-RE v1.0)Wim Verbruggen, David Wårlind, Stéphanie Horion, Félicien Meunier, Hans Verbeeck, Aleksander Wieckowski, Torbern Tagesson, and Guy Schurgers
Geosci. Model Dev., 18, 6623–6645,https://doi.org/10.5194/gmd-18-6623-2025, https://doi.org/10.5194/gmd-18-6623-2025, 2025
Short summaryShort summary
We improved the representation of soil water movement in a state-of-the-art dynamic vegetation model. This is important for dry ecosystems, as they are often driven by changes in soil water availability. We showed that this update resulted in a better match with observations and that the updated model is more sensitive to soil texture. The new model can also simulate a groundwater table. This updated model can help us to better understand the future of dry ecosystems under climate change.
29 Sep 2025
PM2.5 Assimilation within JEDI for NOAA's Regional Air Quality Model (AQMv7): Application to the September 2020 Western U.S. WildfiresHongli Wang, Cory Martin, Jérôme Barré, Ruifang Li, Steve Weygandt, Jianping Huang, Youhua Tang, Hyundeok Choi, Andrew Tangborn, Kai Wang, Haixia Liu, and Jeffrey Lee
EGUsphere,https://doi.org/10.5194/egusphere-2025-4098, https://doi.org/10.5194/egusphere-2025-4098, 2025
Preprint under review for GMD(discussion: final response, 6 comments)
Short summaryShort summary
This paper describes efforts to establish aerosol data assimilation capabilities for a NOAA's regional air quality modeling system by assimilating fine particulate matter PM2.5 observation within the Joint Effort for Data assimilation Integration framework. Results from the Western U.S. wildfires in September 2020 show that assimilating either AirNow or PurpleAir PM2.5 data reduces 1–24 h forecast errors over the continental United States.
29 Sep 2025
Twenty Years of Trials and Insights: Bridging Legacy and Next Generation in ParFlow and Land Surface Model CouplingChen Yang, Aoqi Sun, Shupeng Zhang, Yongjiu Dai, Stefan Kollet, and Reed Maxwell
EGUsphere,https://doi.org/10.5194/egusphere-2025-3935, https://doi.org/10.5194/egusphere-2025-3935, 2025
Revised manuscript under review for GMD(discussion: final response, 4 comments)
Short summaryShort summary
Groundwater strongly influences how water and energy move between land and air, yet most large-scale climate and Earth system models treat it too simply. We reviewed 20 years of work combining a detailed groundwater model, ParFlow, with land surface models, showing ways groundwater shapes energy and water cycles. We also updated this model link, improving its performance, and proposed a flexible framework to support future advances.
26 Sep 2025
Development of a high-resolution coupled SHiELD-MOM6 model – Part 1: Model overview, coupling technique, and validation in a regional setupJoseph Mouallem, Kun Gao, Brandon G. Reichl, Lauren Chilutti, Lucas Harris, Rusty Benson, Niki Zadeh, Jing Chen, Jan-Huey Chen, and Cheng Zhang
Geosci. Model Dev., 18, 6461–6478,https://doi.org/10.5194/gmd-18-6461-2025, https://doi.org/10.5194/gmd-18-6461-2025, 2025
Short summaryShort summary
We introduce a new high-resolution model that couples the atmosphere and ocean to better simulate extreme weather events. It combines the Geophysical Fluid Dynamics Laboratory (GFDL) advanced atmospheric and ocean models with a powerful coupling system that enables robust and efficient two-way interactions. Simulations show that the model accurately captures hurricane behavior and its impact on the ocean. It also runs efficiently on supercomputers. This model represents a key step toward improving extreme weather forecasts.
25 Sep 2025
FastCTM (v1.0): Atmospheric chemical transport modelling with a principle-informed neural network for air quality simulationsBaolei Lyu, Ran Huang, Xinlu Wang, Weiguo Wang, and Yongtao Hu
Geosci. Model Dev., 18, 6295–6312,https://doi.org/10.5194/gmd-18-6295-2025, https://doi.org/10.5194/gmd-18-6295-2025, 2025
Short summaryShort summary
FastCTM is a neural network model to simulate key criteria air pollution levels, offering an efficient alternative to traditional chemical transport models. Its structure is informed by the physical and chemical principles of the atmosphere, allowing it to learn and replicate complex atmospheric processes. FastCTM demonstrated matching accuracy to traditional models with less computational demand. It also provides analysis of how different atmospheric processes contribute to air quality changes.
25 Sep 2025
High-resolution mapping of urban NO2 concentrations using Retina v2: a case study on data assimilation of surface and satellite observations in MadridBas Mijling, Henk Eskes, Sascha Hofmann, Pau Moreno, David García Falin, and María Encarnación de Vega Pastor
Geosci. Model Dev., 18, 6439–6460,https://doi.org/10.5194/gmd-18-6439-2025, https://doi.org/10.5194/gmd-18-6439-2025, 2025
Short summaryShort summary
Given the serious health risks of urban air pollution, monitoring local pollution levels is crucial. The Retina v2 algorithm creates high-resolution pollution maps by integrating satellite and local measurements with an air quality model. Easily portable to other cities, it balances accuracy with low computational demands, matching or outperforming complex dispersion models and data-heavy machine learning. Satellite data proves especially valuable in cities with sparse or no monitoring networks.
24 Sep 2025
ROMSOC: a regional atmosphere–ocean coupled model for CPU–GPU hybrid system architecturesGesa K. Eirund, Matthieu Leclair, Matthias Muennich, and Nicolas Gruber
Geosci. Model Dev., 18, 6255–6274,https://doi.org/10.5194/gmd-18-6255-2025, https://doi.org/10.5194/gmd-18-6255-2025, 2025
Short summaryShort summary
To realistically simulate small-scale processes in the atmosphere and ocean, such as clouds or mixing, high-resolution numerical models are needed. However, these models are computationally very demanding. Here, we present a recently developed atmosphere–ocean model which is able to resolve most of these processes and is less expensive to run due to its computational design. Our model can be used for a wide range of applications like the investigation of marine heatwaves or future projections.
23 Sep 2025
PyGLDA: a fine-scale python-based global land data assimilation system for integrating satellite gravity data into hydrological modelsFan Yang, Maike Schumacher, Leire Retegui-Schiettekatte, Albert I. J. M. van Dijk, and Ehsan Forootan
Geosci. Model Dev., 18, 6195–6217,https://doi.org/10.5194/gmd-18-6195-2025, https://doi.org/10.5194/gmd-18-6195-2025, 2025
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Satellite gravimetry enables direct measurement of total water storage (TWS), a capability that was previously unattainable. In this study, we present an open-source land data assimilation system with global hydrological model, which temporally, vertically, and laterally dis-aggregates satellite-based TWS. This study provides a practical framework establishing operational water management with current and future satellite gravity missions.
23 Sep 2025
Improvement of the Rnnmm type climate index approach with a spatio-temporal model based on the Hawkes processFidel Ernesto Castro Morales, Antonio Marcos Batista do Nascimento, Marina Silva Paez, Daniele Torres Rodrigues, and Carla de Moraes Apolinário
EGUsphere,https://doi.org/10.5194/egusphere-2025-2542, https://doi.org/10.5194/egusphere-2025-2542, 2025
Preprint under review for GMD(discussion: final response, 2 comments)
Short summaryShort summary
This paper introduces a new spatio-temporal model for analyzing the Rnnmm index, based on Hawkes processes. It improves the understanding of extreme rainfall dynamics in Brazil’s Northeast region. The model is implemented in R and estimated via MCMC under a Bayesian framework.
23 Sep 2025
A Local Terrain Smoothing Approach for Stabilizing Microscale and High-Resolution Mesoscale Simulations: a Case Study Using FastEddy® (v3.0) and WRF (v4.6.0)Eloisa Raluy-López, Domingo Muñoz-Esparza, and Juan Pedro Montávez
EGUsphere,https://doi.org/10.5194/egusphere-2025-3744, https://doi.org/10.5194/egusphere-2025-3744, 2025
Preprint under review for GMD(discussion: open, 3 comments)
Short summaryShort summary
Steep terrain can cause numerical problems in weather and climate simulations. We present a new local method that smooths only the steepest areas, preserving important terrain details elsewhere. This improves numerical stability without reducing resolution across the entire map, as was common in previous global approaches. The technique is simple, fast, and effective across models and scales, helping researchers run more accurate and reliable high-resolution simulations over complex landscapes.
22 Sep 2025
Enhancing the advection module performance in the EPICC-Model V1.0 via GPU-HADVPPM4HIP V1.0 coupling and GPU-optimized strategiesKai Cao, Qizhong Wu, Xiao Tang, Jinxi Li, Xueshun Chen, Huansheng Chen, Wending Wang, Huangjian Wu, Lei Kong, Jie Li, Jiang Zhu, and Zifa Wang
EGUsphere,https://doi.org/10.5194/egusphere-2025-2918, https://doi.org/10.5194/egusphere-2025-2918, 2025
Revised manuscript under review for GMD(discussion: final response, 14 comments)
Short summaryShort summary
This study achieves significant acceleration by developing an optimized advection module for Emission and atmospheric Processes Integrated and Coupled Community Model on GPU-like accelerators. Through implementing thread-block coordinated indexing, minimizing CPU-GPU communication, and an hybrid parallelization framework, we demonstrate prominent speedups: 556.5× faster offline performance for the Heterogeneous Interface PPM solver and 20.5× acceleration in coupled simulations.
22 Sep 2025
A Fast and Physically Grounded Ocean Model for GCMs: The Dynamical Slab Ocean Model of the Generic-PCM (rev. 3423)Siddharth Bhatnagar, Francis Codron, Ehouarn Millour, Emeline Bolmont, Maura Brunetti, Jérôme Kasparian, Martin Turbet, and Guillaume Chaverot
EGUsphere,https://doi.org/10.5194/egusphere-2025-3786, https://doi.org/10.5194/egusphere-2025-3786, 2025
Preprint under review for GMD(discussion: final response, 2 comments)
Short summaryShort summary
We present an efficient ocean model coupled to a 3-D climate model (the Generic-PCM) that captures realistic ocean heat transport, closely matching the global heat flows of more complex models. It closely reproduces Earth's sea surface temperatures and sea ice, while also influencing atmospheric patterns realistically. Balancing speed and accuracy, the model is ideal for studying exoplanet climates and paleoclimates, where observations are limited, thus motivating a broad parameter space search.
19 Sep 2025
A new vertical reduction model for enhancing the interpolation accuracy of VMF1/VMF3 tropospheric delay productsPeng Sun, Kefei Zhang, Dantong Zhu, Shuangshuang Shi, Xuexi Liu, Dongsheng Zhao, Minghao Zhang, and Suqin Wu
Geosci. Model Dev., 18, 6167–6176,https://doi.org/10.5194/gmd-18-6167-2025, https://doi.org/10.5194/gmd-18-6167-2025, 2025
Short summaryShort summary
A new method has been developed to more accurately adjust atmospheric delay data for use in satellite positioning, especially in areas with large height differences. By using long-term weather data and testing with global observation stations, the new method significantly improves accuracy compared to traditional approaches. This can benefit applications such as precise positioning and weather monitoring using navigation satellite signals.
18 Sep 2025
A new parameterisation for homogeneous ice nucleation driven by highly variable dynamical forcingsAlena Kosareva, Stamen Dolaptchiev, Peter Spichtinger, and Ulrich Achatz
Geosci. Model Dev., 18, 6117–6133,https://doi.org/10.5194/gmd-18-6117-2025, https://doi.org/10.5194/gmd-18-6117-2025, 2025
Short summaryShort summary
This study improves how we predict ice formation in clouds by accounting for variable ice sizes and different weather conditions. Using simulations, we developed a more accurate method that works efficiently, making it suitable for application in weather and climate prediction models. The new approach is numerically verified and provides precise predictions of ice formation events and reliable estimates of key parameters.
18 Sep 2025
Pre-training for Deep Statistical Climate Downscaling: A case study within the Spanish National Adaptation Plan (PNACC)Jose González-Abad, Maialen Iturbide, Alfonso Hernanz, and José Manuel Gutiérrez
EGUsphere,https://doi.org/10.5194/egusphere-2025-3754, https://doi.org/10.5194/egusphere-2025-3754, 2025
Revised manuscript under review for GMD(discussion: final response, 7 comments)
Short summaryShort summary
We explore how deep learning can improve local climate projections by adapting a national model to regional data. By relying on a paradigm called pre-training, we showed that models can learn faster, generalize better, and produce more consistent results, even when data is limited. This helps make future climate projections more reliable and supports better planning at both national and local levels.
17 Sep 2025
SanDyPALM v1.0: static and dynamic drivers for the PALM model to facilitate urban microclimate simulationsJulian Vogel, Sebastian Stadler, Ganesh Chockalingam, Afshin Afshari, Johanna Henning, and Matthias Winkler
Geosci. Model Dev., 18, 6063–6094,https://doi.org/10.5194/gmd-18-6063-2025, https://doi.org/10.5194/gmd-18-6063-2025, 2025
Short summaryShort summary
This study presents a toolkit to simplify input data creation for an urban microclimate model. It introduces novel methods to automate the use of open data sources. Our analysis of four test cases created from different geographic data sources shows variations in temperature, humidity, and wind speed, influenced by data quality. Our validation indicates that the automated methods can yield results comparable to expert-driven approaches, facilitating user-friendly urban climate modeling.
17 Sep 2025
Applying Corrective Machine Learning in the E3SM Atmosphere Model in C++ (EAMxx)Aaron S. Donahue, Elynn Wu, W. Andre Perkins, Peter M. Caldwell, Christopher S. Bretherton, Finn O. Rebassoo, and Jean-Christophe Golaz
EGUsphere,https://doi.org/10.5194/egusphere-2025-3883, https://doi.org/10.5194/egusphere-2025-3883, 2025
Preprint under review for GMD(discussion: final response, 6 comments)
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This study tested using machine learning to speed up detailed simulations in the SCREAM model. By training ML models to correct a simpler version of SCREAM, some results improved, but others did not. Technical challenges were addressed, and new tools were developed. The work shows promise for making simulations more efficient, though further improvements are needed.
17 Sep 2025
Revisiting the parameterization of dense water plume dynamics in geopotential coordinates in NEMO v4.2.2Robinson Hordoir, Jarle Berntsen, Magnus Hieronymus, Per Pemberton, and Hjalmar Hatun
EGUsphere,https://doi.org/10.5194/egusphere-2025-4288, https://doi.org/10.5194/egusphere-2025-4288, 2025
Preprint under review for GMD(discussion: final response, 2 comments)
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Dense water created in high latitude regions flows at the bottom of the ocean, from one basin to the next, and contributes to the global ocean circulation. The flow between shallow and deeper basins occurs at straits such as the Faeroe Bank Channel as underwater streams of dense water. Their representation in ocean models is problematic. In the present article, we use a mathematical formulation of dense water plumes to show that the representation of these dense overflows can be improved.
16 Sep 2025
Representing high-latitude deep carbon in the pre-industrial state of the ORCHIDEE-MICT land surface model (r8704)Yi Xi, Philippe Ciais, Dan Zhu, Chunjing Qiu, Yuan Zhang, Shushi Peng, Gustaf Hugelius, Simon P. K. Bowring, Daniel S. Goll, and Ying-Ping Wang
Geosci. Model Dev., 18, 6043–6062,https://doi.org/10.5194/gmd-18-6043-2025, https://doi.org/10.5194/gmd-18-6043-2025, 2025
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Including high-latitude deep carbon is critical for projecting future soil carbon emissions, yet it is absent in most land surface models. Here we propose a new carbon accumulation protocol by integrating deep carbon from Yedoma deposits and representing the observed history of peat carbon formation in ORCHIDEE-MICT. Our results show an additional 157 Pg C in present-day Yedoma deposits and a 1–5 m shallower peat depth and 43 % less passive soil carbon in peatlands compared to the conventional protocol.
16 Sep 2025
REtrieval Method for optical and physical Aerosol Properties in the stratosphere (REMAPv1)Andrin Jörimann, Timofei Sukhodolov, Beiping Luo, Gabriel Chiodo, Graham Mann, and Thomas Peter
Geosci. Model Dev., 18, 6023–6041,https://doi.org/10.5194/gmd-18-6023-2025, https://doi.org/10.5194/gmd-18-6023-2025, 2025
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Aerosol particles in the stratosphere affect our climate. Climate models therefore need an accurate description of their properties and evolution. Satellites measure how strongly aerosol particles extinguish light passing through the stratosphere. We describe a method to use such aerosol extinction data to retrieve the number and sizes of the aerosol particles and calculate their optical effects. The resulting data sets for models are validated against ground-based and balloon observations.
16 Sep 2025
MCSeg (v1.0): A Deep Learning Framework for Long-Term Large-Scale Mesoscale Convective Systems Identification and Precipitation Event AnalysisPeng Li, Zhanao Huang, Yongqiang Yu, Xi Wu, Xiaomeng Huang, and Xiaojie Li
EGUsphere,https://doi.org/10.5194/egusphere-2025-3622, https://doi.org/10.5194/egusphere-2025-3622, 2025
Preprint under review for GMD(discussion: final response, 8 comments)
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Mesoscale convective systems (MCSs) are a major cause of severe weather events. Traditional MCS identification methods rely on threshold-based approaches, which are computationally inefficient. To address this limitation, we propose a novel deep learning model for automated MCS detection. Our model achieves comparable accuracy to threshold-based methods while delivering a 200× speedup in processing efficiency.
15 Sep 2025
On moist ocean-atmosphere coupling mechanismsOksana Guba, Arjun Sharma, Mark A. Taylor, Peter A. Bosler, and Erika L. Roesler
EGUsphere,https://doi.org/10.5194/egusphere-2025-3966, https://doi.org/10.5194/egusphere-2025-3966, 2025
Preprint under review for GMD(discussion: final response, 2 comments)
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It is important for computational Earth system models to capture interactions between the ocean and the atmosphere accurately. Because of incredible complexity of these interactions, computational models contain simplifications, which may hinder the models' capabilities. Here we focus on detailed analysis of thermodynamic interactions between the ocean and the atmosphere in computational Earth system models. We also provide a framework to show how modeling these interactions can be improved.
15 Sep 2025
Automated forward and adjoint modelling of viscoelastic deformation of the solid EarthWilliam Scott, Mark Hoggard, Thomas Duvernay, Sia Ghelichkhan, Angus Gibson, Dale Roberts, Stephan C. Kramer, and D. Rhodri Davies
EGUsphere,https://doi.org/10.5194/egusphere-2025-4168, https://doi.org/10.5194/egusphere-2025-4168, 2025
Preprint under review for GMD(discussion: final response, 4 comments)
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Melting ice sheets drive solid Earth deformation and sea-level change on timescales of decades to thousands of years. Here, we present G-ADOPT, which models movement of the solid Earth in response to surface loads. It has flexibility in domain geometry, deformation mechanism parameterisation, and is scalable on high performance computers. Automatic derivation of adjoint sensitivity kernels also provides a means to assimilate historical and modern observations into future sea-level forecasts.
15 Sep 2025
SPREADS: From Research to Operational Open-Source Data Assimilation SystemCarla Cardinali, Giovanni Conti, Marcelo Guatura, Sami Saarinen, Luis Gustavo Gonçalves De Gonçalves, Jeffrey Anderson, and Kevin Raeder
EGUsphere,https://doi.org/10.5194/egusphere-2025-4294, https://doi.org/10.5194/egusphere-2025-4294, 2025
Preprint under review for GMD(discussion: final response, 2 comments)
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Scientists have developed research systems to test new ideas in data assimilation, but these often lack the efficiency and robustness needed for operational use. We addressed this gap with key innovations: a flexible observation database, first guess at the appropriate time, and modular, parallelised software enabling the assimilation of millions of observations.
11 Sep 2025
OIRF-LEnKF v1.0: A Self-evolving Data Assimilation System by Integrating Incremental Machine Learning with a Localized EnKF for Enhanced PM2.5 Chemical Component Forecasting and AnalysisHongyi Li, Ting Yang, Lei Kong, Di Zhang, Guigang Tang, and Zifa Wang
EGUsphere,https://doi.org/10.5194/egusphere-2025-3960, https://doi.org/10.5194/egusphere-2025-3960, 2025
Revised manuscript under review for GMD(discussion: final response, 9 comments)
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Chemical transport model-based data assimilation is computationally inefficient for large ensemble sizes and offers limited improvements in forecasting PM2.5chemical components. This paper introduces a machine learning-based data assimilation system that facilitates rapid iterations for forecasting, assimilation, and incremental learning. Results show that our system achieves superior efficiency and accuracy in forecasting and assimilation compared to traditional data assimilation.
11 Sep 2025
PALM-meteo 2.6: Processor of PALM meteorological input dataPavel Krč, Michal Belda, Martin Bureš, Kryštof Eben, Jan Geletič, Jelena Radović, Hynek Řezníček, and Jaroslav Resler
EGUsphere,https://doi.org/10.5194/egusphere-2025-4120, https://doi.org/10.5194/egusphere-2025-4120, 2025
Preprint under review for GMD(discussion: final response, 2 comments)
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PALM is a highly versatile open-source microscale atmospheric modelling system. One of its most useful applications is modelling detailed street-level urban climate, e.g. for evaluation of climate change adaptation and mitigation measures in cities. However, to produce real-case microscale simulations, they need to be forced by real or realistic weather conditions. The presented tool enables PALM to use meteorological inputs from a large selection of meteorological models and other sources.
10 Sep 2025
Statistical summaries for streamed data from climate simulations: one-pass algorithmsKatherine Grayson, Stephan Thober, Aleksander Lacima-Nadolnik, Ivan Alsina-Ferrer, Llorenç Lledó, Ehsan Sharifi, and Francisco Doblas-Reyes
Geosci. Model Dev., 18, 5873–5890,https://doi.org/10.5194/gmd-18-5873-2025, https://doi.org/10.5194/gmd-18-5873-2025, 2025
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We present One_Pass (v0.8.0), a Python package enabling computation of statistics from streamed global climate model output using one-pass algorithms. Users often need statistics covering periods longer than the stream duration, requiring algorithms that do not store full time series. One-pass methods address this need while avoiding full data archiving, offering memory-efficient, accurate results for high-performance computing (HPC) workflows and downstream applications like bias adjustment.
10 Sep 2025
A new sub-chunking strategy for fast netCDF-4 access in local, remote and cloud infrastructures, chunkindex V1.1.0Cédric Penard, Flavien Gouillon, Xavier Delaunay, and Sylvain Herlédan
EGUsphere,https://doi.org/10.5194/egusphere-2025-2983, https://doi.org/10.5194/egusphere-2025-2983, 2025
Revised manuscript accepted for GMD(discussion: final response, 4 comments)
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In this work, we propose a novel approach, called chunkindex, that was designed to improve the access to time series from native NetCDF (Network Common Data Form) files in the cloud. The advantage of our approach is that it keeps existing data as they are without requiring any reformatting. The idea is to reduce the amount of data read from the NetCDF file by creating sub-chunks that allow extracting smaller portions of compressed data without reading the entire chunk.
08 Sep 2025
Numerical simulations of ocean surface waves along the Australian coast with a focus on the Great Barrier ReefXianghui Dong, Qingxiang Liu, Stefan Zieger, Alberto Alberello, Ali Abdolali, Jian Sun, Kejian Wu, and Alexander V. Babanin
Geosci. Model Dev., 18, 5801–5823,https://doi.org/10.5194/gmd-18-5801-2025, https://doi.org/10.5194/gmd-18-5801-2025, 2025
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Ocean surface wave research is vital for coastal management, marine ecology, and ocean engineering. This study simulates waves along the Australian coast using advanced physical and numerical schemes. Model verification with altimeter and buoy data shows good performance. A two-step parameterization improves accuracy in the complex Great Barrier Reef. This study will help us better understand coastal wave climates and assess sea states, enabling us to better develop, protect, and use the sea.
05 Sep 2025
Features of mid- and high-latitude low-level clouds and their relation to strong aerosol effects in the Energy Exascale Earth System Model version 2 (E3SMv2)Hui Wan, Abhishek Yenpure, Berk Geveci, Richard C. Easter, Philip J. Rasch, Kai Zhang, and Xubin Zeng
Geosci. Model Dev., 18, 5655–5680,https://doi.org/10.5194/gmd-18-5655-2025, https://doi.org/10.5194/gmd-18-5655-2025, 2025
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In the Energy Exascale Earth System Model version 2 (E3SMv2) and many other global models, the simulated anthropogenic aerosol effective radiative forcing is sensitive to the presence of clouds with very low droplet number concentrations. Numerical experiments presented in this paper indicate that mid- and high-latitude low-level stratus occurring under weak turbulence is a key cloud regime for investigating the causes of these very low cloud droplet number concentrations in E3SMv2.
04 Sep 2025
The process and value of reprogramming a legacy global hydrological modelEmmanuel Nyenah, Petra Döll, Martina Flörke, Leon Mühlenbruch, Lasse Nissen, and Robert Reinecke
Geosci. Model Dev., 18, 5635–5653,https://doi.org/10.5194/gmd-18-5635-2025, https://doi.org/10.5194/gmd-18-5635-2025, 2025
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We reprogrammed the latest WaterGAP model (2.2e) to create a sustainable global hydrological model. By utilizing best software practices like modular design, version control, and clear documentation, the new WaterGAP supports collaboration across teams. It can be easily understood, applied, and enhanced by both novice and experienced modellers. Additionally, we share the reprogramming process to assist in the reprogramming of other large geoscientific research software.
02 Sep 2025
Accurate and fast prediction of radioactive pollution by kriging coupled with auto-associative modelsRaphaël Périllat, Sylvain Girard, and Irène Korsakissok
Geosci. Model Dev., 18, 5513–5525,https://doi.org/10.5194/gmd-18-5513-2025, https://doi.org/10.5194/gmd-18-5513-2025, 2025
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We developed a method to improve decision-making during nuclear crises by predicting the spread of radiation more efficiently. Existing approaches are often too slow, especially when analyzing complex data like radiation maps. Our method combines techniques to simplify these maps and predict them quickly using statistical tools. This approach could help authorities respond faster and more accurately in emergencies, reducing risks to the population and the environment.
01 Sep 2025
Implementation and validation of a supermodeling framework into Community Earth System Model version 2.1.5William E. Chapman, Francine Schevenhoven, Judith Berner, Noel Keenlyside, Ingo Bethke, Ping-Gin Chiu, Alok Gupta, and Jesse Nusbaumer
Geosci. Model Dev., 18, 5451–5465,https://doi.org/10.5194/gmd-18-5451-2025, https://doi.org/10.5194/gmd-18-5451-2025, 2025
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We introduce the first state-of-the-art atmosphere-connected supermodel, where two advanced atmospheric models share information in real time to form a new dynamical system. By synchronizing the models, particularly in storm track regions, we achieve better predictions without losing variability. This approach maintains key climate patterns and reduces bias in some variables compared to traditional models, demonstrating a useful technique for improving atmospheric simulations.
01 Sep 2025
A framework for three-dimensional dynamic modeling of mountain glaciers in the Community Ice Sheet Model (CISM v2.2)Samar Minallah, William H. Lipscomb, Gunter Leguy, and Harry Zekollari
Geosci. Model Dev., 18, 5467–5486,https://doi.org/10.5194/gmd-18-5467-2025, https://doi.org/10.5194/gmd-18-5467-2025, 2025
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We developed a new modeling framework within an Earth system model to study mountain glacier evolution under different climate scenarios, applied here to the European Alps. Substantial Alpine glacier mass loss is projected under current climate conditions, with near-total loss under further warming. This is the first use of a 3D, higher-order ice-flow model for regional glacier simulations, enabling assessment of coupled land ice–Earth system processes.
29 Aug 2025
Spatialize v1.0: A Python/C++ Library for Ensemble Spatial InterpolationFelipe Navarro, Alvaro F. Egaña, Alejandro Ehrenfeld, Felipe Garrido, María Jesús Valenzuela, and Juan F. Sánchez-Pérez
EGUsphere,https://doi.org/10.5194/egusphere-2025-3272, https://doi.org/10.5194/egusphere-2025-3272, 2025
Revised manuscript under review for GMD(discussion: final response, 4 comments)
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Spatialize is an open-source Python/C++ library for Ensemble Spatial Interpolation (ESI), combining simple interpolation with geostatistics like Kriging. It uses random space partitions (Mondrian and Voronoi forests) and ensemble learning for robust, scalable spatial interpolation and uncertainty quantification. Designed for non-experts, Spatialize supports gridded and non-gridded data, automates hyperparameter search, and delivers competitive accuracy in geoscientific applications.
29 Aug 2025
Simultaneous versus sequential estimation of biogeochemical and physical parameters in coupled marine ecosystem modelsSkyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
EGUsphere,https://doi.org/10.5194/egusphere-2025-3795, https://doi.org/10.5194/egusphere-2025-3795, 2025
Preprint under review for GMD(discussion: final response, 4 comments)
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The parameters that control a model's behavior determine its ability to represent a system. In this work, multiple cases test how to estimate the parameters of a model with components corresponding to both the physics and the chemical and biological processes (i.e. the biogeochemistry) of the ocean. While demonstrating how to approach this problem type, the results show estimating both sets of parameters simultaneously is better than estimating the physics then the biogeochemistry separately.
28 Aug 2025
Modelling diffusion, decay and ingrowth of U–Pb isotopes in zirconBen Steven Knight and Chris Clark
EGUsphere,https://doi.org/10.5194/egusphere-2025-2278, https://doi.org/10.5194/egusphere-2025-2278, 2025
Revised manuscript under review for GMD(discussion: final response, 7 comments)
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This study examines how high temperatures can alter the chemical record in zircon crystals used to date rock events. Using computer simulations, we model how movement of atoms, radioactive decay, and the formation of new elements interact in a zircon under changing heat conditions. Our simulation is compared with measurements from rocks in southern India to estimate temperature history of the region. The work aims to improve insights from rock dating and our understanding of Earth’s past.
27 Aug 2025
Generalized local fractions – a method for the calculation of sensitivities to emissions from multiple sources for chemically active species, illustrated using the EMEP MSC-W model (rv5.5)Peter Wind and Willem van Caspel
Geosci. Model Dev., 18, 5397–5411,https://doi.org/10.5194/gmd-18-5397-2025, https://doi.org/10.5194/gmd-18-5397-2025, 2025
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This paper presents a numerical method to assess the origin of air pollution. Combined with a numerical air pollution transport and chemistry model, it can follow the contributions from a large number of emission sources. The result is a series of maps that give the relative contributions from, for example, all European countries at any point on the map.
27 Aug 2025
swLICOM: the multi-core version of an ocean general circulation model on the new generation Sunway supercomputer and its kilometer-scale applicationKai Xu, Maoxue Yu, Jiangfeng Yu, Jingwei Xie, Xiang Han, Jiaying Song, Mingyao Geng, Jinrong Jiang, Hailong Liu, Pengfei Wang, and Pengfei Lin
EGUsphere,https://doi.org/10.5194/egusphere-2025-2231, https://doi.org/10.5194/egusphere-2025-2231, 2025
Revised manuscript accepted for GMD(discussion: final response, 4 comments)
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swLICOM represents a significant advancement in kilometer-scale resolution ocean general circulation models on heterogeneous computing architectures. Our optimization efforts addressed a series of challenges that are particularly crucial for high-resolution modeling. We use swLICOM with a horizontal resolution of 2 km to conduct a short-term simulation test. The 2-km resolution global simulation shows the high capacity of swLICOM to capture the oceanic meso- to submesoscale processes.