GMD - Recent

GMD - Recent
Recent
The following lists the recent preprints posted on EGUsphere with GMD-related topics, the recent preprints posted in GMD’s discussion forum, as well as final revised papers published recently in GMD.
24 Apr 2026
G6-1.5K-MCB: Marine Cloud Brightening scenario design for the Geoengineering Model Intercomparison Project (GeoMIP) in CESM2.1, E3SMv2.0, and UKESM1.1
Haruki Hirasawa, Matthew Henry, Philip J. Rasch, Robert Wood, Sarah J. Doherty, James Haywood, Alex Wong, Jean-Francois Lamarque, Ezra Brody, and Hailong Wang
Geosci. Model Dev., 19, 3257–3283,
2026
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Marine cloud brightening (MCB) is a proposal to emit sea salt aerosols to make clouds more reflective and cool the climate. Here, we use three climate models to study a hypothetical future where MCB is used to maintain temperatures near 2020–2039 conditions. The simulation results indicate that using MCB in midlatitude ocean regions can keep the climate close to present day conditions. This reduces many of the negative impacts shown in previous studies, informing future modeling efforts.
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24 Apr 2026
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
Geosci. Model Dev., 19, 3285–3316,
2026
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We present an efficient ocean model coupled to a 3-D climate model (the Generic-PCM) that captures key features of ocean heat transport, matching well the global heat flows of more complex models. It closely reproduces Earth’s sea surface temperatures and sea ice, while influencing atmospheric circulation consistently. Balancing speed and accuracy, the model is ideal for exoplanet and paleoclimate studies, where observations are limited and broad parameter exploration is necessary.
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24 Apr 2026
swLICOM: the multi-core version of an ocean general circulation model on the new generation Sunway supercomputer and its kilometer-scale application
Kai Xu, Maoxue Yu, Jiangfeng Yu, Jingwei Xie, Xiang Han, Jiaying Song, Mingyao Geng, Jinrong Jiang, Hailong Liu, Pengfei Wang, and Pengfei Lin
Geosci. Model Dev., 19, 3317–3333,
2026
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We develop an ocean general circulation model based on heterogeneous computing architectures. The model is optimized to address a series of challenges that are particularly crucial for kilometer-scale resolution ocean modeling. We conduct a short-term global simulation test with a horizontal resolution of 2 km. The simulation demonstrates the high capacity of the model to capture the oceanic meso- to submesoscale processes.
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23 Apr 2026
The one-Layer Antarctic model for Dynamical Downscaling of Ice–ocean Exchanges (LADDIE) version 2.0
Erwin Lambert, Franka Jesse, and Constantijn J. Berends
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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The future contribution of Antarctica to sea-level rise is strongly dependent on the melting of floating ice shelves by the underlying ocean. Here, we present version 2 of the two-dimensional ocean model LADDIE. We evaluate the model by comparing it to three-dimensional ocean models and satellite observations, showing good performance at low computational cost. With this open-source model, we hope to contribute to the evolution toward more realistic melting in ice sheet model simulations.
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23 Apr 2026
MeteoSaver v1.0: a machine-learning based software for the transcription of historical weather data
Derrick Muheki, Bas Vercruysse, Krishna Kumar Thirukokaranam Chandrasekar, Christophe Verbruggen, Julie M. Birkholz, Koen Hufkens, Hans Verbeeck, Pascal Boeckx, Seppe Lampe, Ed Hawkins, Peter Thorne, Dominique Kankonde Ntumba, Olivier Kapalay Moulasa, and Wim Thiery
Geosci. Model Dev., 19, 3213–3255,
2026
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Archives worldwide host vast records of observed weather data crucial for understanding climate variability. However, most of these records are still in paper form, limiting their use. To address this, we developed MeteoSaver, an open-source tool, to transcribe these records to machine-readable format. Applied to ten handwritten temperature sheets, it achieved a median accuracy of 74 %. This tool offers a promising solution to preserve records from archives and unlock historical weather insights.
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23 Apr 2026
A Generalized Framework for Multi-Parameter Optimization of Numerical Wind–Wave Model: Application to Typhoon Waves near Taiwan Island
Zongyu Li, Shuiqing Li, Jinrui Chen, Yuan Kong, Yong Fang, Jiageng Han, Pei Zhu, and Po Hu
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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Accurate prediction of extreme ocean waves is crucial for coastal safety, yet numerical models often struggle during severe storms. We developed an automated method to adjust key model settings together, rather than one by one, using observations from typhoon events. Tests show that this approach clearly improves wave height predictions and reduces systematic errors. The method is transparent, efficient, and can be applied to many Earth system models to better simulate hazardous conditions.
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23 Apr 2026
Numerical strategies for representing Richards' equation and its couplings in snowpack models
Kévin Fourteau, Julien Brondex, Clément Cancès, and Marie Dumont
Geosci. Model Dev., 19, 3193–3212,
2026
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The percolation of liquid water down snowpacks is a complex phenomenon, and its representation can sometimes be complicated for snowpack models. The goal of this article is to transpose some state-of-the-art strategies used for modeling liquid percolation in other media (such as rocks or soil) into snowpack models. With this, snowpack models can be made more efficient, requiring less time and power to perform their computation.
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23 Apr 2026
Evolving beyond collapse: An adaptive particle batch smoother for cryospheric data assimilation
Kristoffer Aalstad, Esteban Alonso-González, Norbert Pirk, Sebastian Westermann, Clarissa Willmes, and Ruitang Yang
External preprint server,
2026
Preprint under review for GMD
(discussion: open, 1 comment)
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AdaPBS is a new algorithm to combine observations with cryospheric numerical models. AdaPBS is an iterative algorithm that automatically adjusts computing effort to the task, allowing the implementation of early stopping strategies. We tested AdaPBS at multiple sites with different models, matching or outperforming standard methods, when compared against more complex (computationally expensive) algorithms.
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23 Apr 2026
Improving the CLASSIC (v1.8) Snow Model to Better Simulate Arctic Snowpacks
Mickaël Lalande, Alexandre Roy, Libo Wang, Diana Verseghy, Vincent Vionnet, Florent Dominé, and Christophe Kinnard
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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This study enhances a snow model for Arctic environments by improving the heat exchanges within the snowpack and at its interfaces, revising the compaction scheme, and adding consideration of blowing snow sublimation losses. Simulations at ten Arctic, mid-latitude, and Alpine sites show significant reductions in simulated soil and snow temperature biases and improved simulated snow depth and density, which are key features to improve simulated energy, water, and carbon budgets in the Arctic.
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22 Apr 2026
DCU-accelerated 3DVAR data assimilation with automatic differentiation for WRF-Chem
Hancheng Ye, Zengliang Zang, Wei You, Yiwen Hu, Ning Liu, and Yi Li
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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Our study pioneers a PyTorch-based atmospheric assimilation system that leverages automatic differentiation and Deep Computing Unit acceleration to achieve order-of-magnitude speedups while establishing a direct pathway for future integration with deep learning.
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22 Apr 2026
A wind-farm wake-turbulence parameterization for the WRF model (EWP v2.0)
Oscar García-Santiago, Jake Badger, Andrea N. Hahmann, Patrick J. H. Volker, Søren Ott, M. Paul van der Laan, and Mark Kelly
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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We improve how weather models represent the turbulence generated by wind turbines within and behind wind farms. Rather than adding this turbulence only at grid squares with turbine locations, the new method transports it through the wake as it moves downwind. Tests against high-resolution simulations of an idealised wind farm showed better agreement in wake turbulence and more accurate reductions in wind speed, providing a more realistic picture of wake effects across the wind farm.
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22 Apr 2026
EnsAI: An Emulator for Atmospheric Chemical Ensembles
Michael Sitwell
External preprint server,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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EnsAI is a newly developed artificial intelligence based program for efficiently generating ensembles of atmospheric chemical concentrations that can be used in assimilation and emissions inversions systems. Ensemble-based data assimilation methods are widely used for assimilation and emissions inversions, but are usually very computationally demanding. Once trained, EnsAI can run thousands of times faster than the physics-based models when run on graphics processing units.
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22 Apr 2026
Controls of the latitudinal migration of the Brazil-Malvinas confluence described in MOM6-SWA14
Nicole Cristine Laureanti, Enrique Curchitser, Katherine Hedstrom, Alistair Adcroft, Robert Hallberg, Matthew J. Harrison, Raphael Dussin, Sin Chan Chou, Paulo Nobre, Emanuel Giarolla, and Rosio Camayo
Geosci. Model Dev., 19, 3109–3128,
2026
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This study investigates the variability of currents in the Southwestern Atlantic Ocean using a high-resolution simulation. Particularly in the Brazil-Malvinas Confluence (BMC), it finds that the southward movement of the BMC, induced by the warming trends in the region, is balanced by northward flow from the Malvinas Current and Pacific Waves. The analysis also examines the intense northward displacement of the North Brazil Current, where inconsistencies in the simulation affect its evolution.
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22 Apr 2026
CMIP7 data request: land and land ice priorities and opportunities
Yue Li, Gang Tang, Eleanor O'Rourke, Samar Minallah, Martim Mas e Braga, Sophie Nowicki, Robin S. Smith, David M. Lawrence, George C. Hurtt, Daniele Peano, Gesa Meyer, Birgit Hassler, Jiafu Mao, Yongkang Xue, and Martin Juckes
Geosci. Model Dev., 19, 3129–3155,
2026
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Land and Land Ice Theme Opportunities describe a list that contains 25 variable groups with 716 variables, which are potentially available to the broad scientific audience for performing analysis in land–atmosphere coupling, hydrological processes and freshwater systems, glacier and ice sheet mass balance and their influence on the sea levels, land use, and plant phenology.
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22 Apr 2026
DSCALE v0.1 – an open-source algorithm for downscaling regional and global mitigation pathways to the country level
Fabio Sferra, Bas van Ruijven, Keywan Riahi, Philip Hackstock, Florian Maczek, Jarmo S. Kikstra, and Reinhard Haas
Geosci. Model Dev., 19, 3157–3191,
2026
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Integrated Assessment Models are widely used by researchers to assess future emissions and the performance of climate policies. Bringing together insights from these models with information at the country level has remained difficult, as they usually provide results for a limited number of highly aggregated regions. We address this issue by presenting a novel algorithm designed to downscale regional outcomes to the country level and show the results for a current policy and a 1.5C scenario.
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21 Apr 2026
Machine learning significantly improves the simulation of hourly-to-yearly scale cloud nuclei concentration and radiative forcing in polluted atmosphere
Jingye Ren, Songjian Zou, Honghao Xu, Guiquan Liu, Zhe Wang, Anran Zhang, Chuanfeng Zhao, Min Hu, Dongjie Shang, Lizi Tang, Ru-Jin Huang, Yele Sun, and Fang Zhang
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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In this study, a new framework of cloud condensation nuclei (CCN) prediction in polluted region has been developed and it achieves well prediction of hourly-to-yearly scale across North China Plain. The study reveals the machine learning model can largely reduce the uncertainty in simulating cloud radiative forcing, illustrating the high sensitivity of climate forcing to changes in CCN. This improvement of our new model would be helpful to aerosols climate effect assessment in models.
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21 Apr 2026
Predicting Forecast Errors with Diffusion Model for Uncertainty Quantification in Wind Speed Nowcasting
Yanwei Zhu, Aitor Atencia, Markus Dabernig, Yong Wang, and Shuyan Zhou
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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The study proposes a diffusion-based framework for uncertainty quantification in wind speed nowcasting by learning forecast error distributions. By randomly generating errors and adding them to a physics-based wind nowcast, multiple forecast scenarios can be produced. The results improve forecast accuracy and provide reliable estimates of forecast uncertainty.
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21 Apr 2026
Transferable Hourly Ozone Forecasting with Transformers
Sindhu Vasireddy, Michael Langguth, and Martin Schultz
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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This study evaluates a transformer model for hourly air quality forecasting using past pollution, weather, and anthropogenic metadata (emissions, land use). It outperforms Copernicus Atmosphere Monitoring Service forecasts, especially in urban regions, with lower bias and improved stability. Trained in Germany, it transfers to South Korea with minimal adaptation, preserving geochemical relationships and showing strong cross-regional generalization.
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21 Apr 2026
LFD (v1.0): Latent-Compression-Free Generative Diffusion with Geological Priors and Geophysical Regularization for Implicit Structural Modeling
Zhixiang Guo, Xinming Wu, Yimin Dou, Hui Gao, and Guillaume Caumon
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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We present a fast way to generate subsurface structure models from seismic surveys while honoring known horizons and faults. Instead of compressing the data into a hidden representation, our method works directly with the original model values and applies geological constraints during generation. Tests on synthetic and real surveys show more realistic structures and efficient prediction, producing a 512 by 512 model in 1.56 seconds on an NVIDIA H20 graphics processing unit.
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21 Apr 2026
Task aggregation as a strategy to optimize Earth System Model workflows in HPC: assessing real scenarios with EC-Earth
Pablo Goitia, Manuel G. Marciani, Miguel Castrillo, and Mario C. Acosta
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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Earth System Model workflows commonly run on highly congested high-performance computing platforms, meaning that each individual workflow task potentially faces lengthy waiting times in the queues of the schedulers. In this work, we evaluate the task aggregation approach in EC-Earth3 workflows to reduce the queue times and, consequently, the total execution time. The results show an increase of up to 23.04 % in the actual simulated years per day, with queuing times reduced by up to 12.33 times.
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21 Apr 2026
Impact of vertical coordinate systems on simulations of barotropic and baroclinic tides in the Yellow Sea using a regional MOM6 configuration for the Northwest Pacific
Inseong Chang, Young Ho Kim, Young-Gyu Park, Hyunkeun Jin, Gyundo Pak, Andrew C. Ross, and Robert Hallberg
Geosci. Model Dev., 19, 3053–3074,
2026
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This study assesses how vertical coordinate choice shapes barotropic and baroclinic tide simulations in a high-resolution, MOM6 (Modular Ocean Model version 6) regional model. Focusing on the Yellow Sea under realistic forcing and seasonal stratification, we compare
and
-isopycnal hybrid to quantify coordinate-dependent impacts on tidal energetics and vertical structure. The results underscore that vertical representation is critical for accurately reproducing coastal stratification and tide–stratification interactions.
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21 Apr 2026
Landslide-Tsurrogate v1.0: a computationally efficient framework for probabilistic tsunami hazard assessment applied to Mayotte (France)
Cléa Denamiel, Alexis Marboeuf, Anne Mangeney, Anne Le Friant, Marc Peruzzetto, Antoine Lucas, Manuel J. Castro Díaz, and Enrique Fernández-Nieto
Geosci. Model Dev., 19, 3075–3107,
2026
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Landslide-Tsurrogate v1.0 is an open-source Python/MATLAB tool that create surrogate models that replace costly numerical simulations. These models estimate tsunami hazards from submarine landslides in a few seconds. Based on polynomial chaos expansions, they also enable sensitivity analyses, fast probabilistic results, and user-friendly visualization. Tested in Mayotte, Landslide-Tsurrogate v1.0 can be applied to any coastal region.
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20 Apr 2026
Hybrid implicit–explicit XFEM simulation of injection-induced seismicity: resolving multi-scale rupture nucleation and dynamics
Mohammad Sabah, Mauro Cacace, Inga Berre, Iman R. Kivi, and Hannes Hofmann
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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We developed a new way to simulate earthquakes triggered by fluid injection deep underground. These events involve both slow pressure build-up and rapid fault movement, which are difficult to capture together. Our method combines two calculation approaches and switches between them when needed. It reproduces earthquake behavior accurately while reducing computing time by up to seventy percent, making it more practical for assessing risks in energy and storage projects.
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20 Apr 2026
Accelerating 3D Magnetotelluric Forward Modelling with Domain Decomposition and Order-Reduction Methods
Luis Tao, Alba Muixí, Sergio Zlotnik, Fabio Ivan Zyserman, Juan Carlos Afonso, and Pedro Diez
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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We present a new approach for performing 3D magnetotelluric forward simulations more efficiently. Conventional methods become increasingly demanding as model resolution increases. Our approach combines numerical techniques that reduce problem size and computational cost. Tests on benchmark examples and a real-world case demonstrate speed-ups of over 90% with acceptable loss of accuracy, enabling high-resolution simulations within practical time frames.
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20 Apr 2026
The Community Fire Behavior model for coupled fire–atmosphere modeling: implementation in the Unified Forecast System
Pedro A. Jiménez y Muñoz, Maria Frediani, Masih Eghdami, Daniel Rosen, Michael Kavulich, and Timothy W. Juliano
Geosci. Model Dev., 19, 3035–3052,
2026
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We present the Community Fire Behavior model (CFBM) a fire behavior model designed to facilitate coupling to atmospheric models. We describe its implementation in the Unified Forecast System (UFS). Simulations of the Cameron Peak fire allowed us to verify our implementation. Our vision is to foster collaborative development in fire behavior modeling with the ultimate goal of increasing our fundamental understanding of fire science and minimizing the adverse impacts of wildland fires.
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20 Apr 2026
Development of the CCPP-Based GEFS-Aerosols Component in the Unified Forecast System for Subseasonal Prediction (UFS-Chem v1.0)
Li Zhang, Haiqin Li, Georg A. Grell, Partha S. Bhattacharjee, Gonzalo A. Ferrada, Benjamin W. Green, Shan Sun, Ligia Bernardet, Anders Jensen, Barry Baker, Li Pan, Jian He, Jordan Schnell, Ravan Ahmadov, Samuel Trahan, Dustin Swales, Anning Cheng, Fanglin Yang, Rebecca H. Schwantes, Brian C. McDonald, Dominikus Heinzeller, and Shobha Kondragunta
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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Based on the operational Global Ensemble Forecast System-Aerosols at the National Centers for Environmental Prediction, we developed an upgraded system using the Common Community Physics Package framework that allows aerosol particles to directly influence radiation and cloud formation, including how precipitation removes particles from the atmosphere. Evaluation against observations and reanalysis data demonstrates improved forecast skill for weather and subseasonal prediction.
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17 Apr 2026
The coupled Southern Ocean–Sea ice–Ice shelf Model (SOSIM v1.0): configuration and evaluation
Chengyan Liu, Zhaomin Wang, Dake Chen, Xianxian Han, Hengling Leng, Xi Liang, Liangjun Yan, Xiang Li, Craig Stevens, Andrew McC. Hogg, Kazuya Kusahara, Kaihe Yamazaki, Kay I. Ohshima, Meng Zhou, Xiao Cheng, Dongxiao Wang, Changming Dong, Jiping Liu, Qinghua Yang, Xichen Li, Ruibo Lei, Minghu Ding, Zhaoru Zhang, Dujuan Kang, Di Qi, Tongya Liu, Jihai Dong, Lu An, Ru Chen, Tong Zhang, Xiaoming Hu, Bo Han, Haibo Bi, Qi Shu, Longjiang Mu, Shiming Xu, Hu Yang, Hailong Liu, Tingfeng Dou, Zhixuan Feng, Lei Zheng, Xueyuan Tang, Guitao Shi, Yongqing Cai, Bingrui Li, Yang Wu, Xia Lin, Wenjin Sun, Yu Liu, Kai Yu, Yu Zhang, Weizeng Shao, Xiaoyu Wang, Shaojun Zheng, Chengyi Yuan, Chunxia Zhou, Jian Liu, Yang Liu, Yue Xia, Xiaoyu Pan, Jiabao Zeng, Kechen Liu, Jiahao Fan, Chen Cheng, and Qi Li
Geosci. Model Dev., 19, 2985–3033,
2026
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We developed a high-resolution computer model to simulate how the ocean, sea ice, and ice shelves interact around Antarctica. This helps us understand their critical role in global climate and sea-level rise. Our model successfully captures essential features like major currents and seasonal ice changes. Despite some remaining biases, it provides a useful tool for predicting future changes in this vital and rapidly evolving region.
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17 Apr 2026
GLIDE-SOL: A GPU-accelerated Global Lightweight Infrastructure for Diagnostic Environmental Modeling with SOLWEIG
Andrea Zonato, Harsh G. Kamath, Naveen Sudharsan, Luca Monaco, Jonas Kittner, Luise Wolf, Matthias Andreas Demuzere, Ariane Middel, Benjamin Bechtel, and Massimo Milelli
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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Cities need fast, reliable heat-stress maps to plan cooling measures and protect people. We built an automated workflow that gathers global public data, runs an outdoor comfort model much faster on graphics processing units, and adds simple corrections for wind and night-time warming. Tested in Dortmund against many sensors, errors fell from about ten to under three degrees Celsius.
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17 Apr 2026
CMIP7 Data Request: atmosphere priorities and opportunities
Beth Dingley, James A. Anstey, Marta Abalos, Carsten Abraham, Tommi Bergman, Lisa Bock, Sonya Fiddes, Birgit Hassler, Ryan J. Kramer, Fei Luo, Fiona M. O'Connor, Petr Šácha, Isla R. Simpson, Laura J. Wilcox, and Mark D. Zelinka
Geosci. Model Dev., 19, 2945–2984,
2026
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This manuscript defines as a list of variables and scientific opportunities which are requested from the Coupled Model Intercomparison Project Phase 7 (CMIP7) Assessment Fast Track to address open atmospheric science questions. The list reflects the output of a large public community engagement effort, coordinated across autumn 2025 through to summer 2025.
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16 Apr 2026
The Ice Sheet State and Parameter Estimator (ICESEE) Library (v1.0.0): Ensemble Kalman Filtering for Ice Sheet Models
Brian Kyanjo, Talea L. Mayo, and Alexander A. Robel
External preprint server,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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We developed ICESEE, an open-source tool that helps scientists combine observations with physics-based models to better understand how ice sheets change over time. It improves estimates of current conditions and also helps identify hard-to-measure factors such as friction beneath the ice. Our tests indicate that it works efficiently on large computing systems and can be used with multiple models, making it useful for more reliable long-term studies of ice-sheet change and sea-level rise.
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16 Apr 2026
Towards improved Euro-Mediterranean discharge simulations in regional coupled climate models: a comparative assessment of hydrologic performance
Mohamed Hamitouche, Giorgia Fosser, Arezoo RafieeiNasab, and Alessandro Anav
Geosci. Model Dev., 19, 2881–2901,
2026
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Predicting how much water flows from rivers into the Mediterranean is challenging due to climate change and human impacts. We compared two hydrological models – a global river routing model and a fully coupled land surface–hydrology model – to assess their performance. The coupled model, especially after calibration, better reproduces river discharge and seasonal flow, helping improve flood and drought planning.
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16 Apr 2026
RTSEvo v1.0: a retrogressive thaw slump evolution model
Jiwei Xu, Shuping Zhao, Zhuotong Nan, Fujun Niu, and Yaonan Zhang
Geosci. Model Dev., 19, 2919–2943,
2026
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Permafrost is warming, causing more ground collapses known as retrogressive thaw slumps that damage ecosystems and infrastructure. We created a new computer model to predict how these slumps grow and spread over time. By combining satellite data, statistics, and rules that mimic natural erosion, the model can reproduce changes with high accuracy. This helps scientists and planners better forecast future permafrost hazards.
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16 Apr 2026
MinSIA v1: a lightweight and efficient implementation of the shallow ice approximation
Stefan Hergarten
Geosci. Model Dev., 19, 2903–2917,
2026
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Numerical glacier and ice-sheet models have been widely used in the context of climate change and landform evolution. While simulations of ice flow were numerically expensive for a long time, their performance has recently been boosted to an unprecedented level by machine learning techniques. This paper aims at keeping classical numerics competitive by introducing a novel numerical scheme, which allows for simulations at spatial resolutions of 25 m or even finer on standard desktop PCs.
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15 Apr 2026
CMIP7 data request: Earth system priorities and opportunities
Mara Y. McPartland, Tomas Lovato, Charles Koven, Jamie D. Wilson, Briony Turner, Colleen M. Petrik, José Licón-Saláiz, Fang Li, Fanny Lhardy, Jaclyn Clement Kinney, Michio Kawamiya, Birgit Hassler, Nathan P. Gillett, Cheikh Modou Noreyni Fall, Christopher Danek, Chris M. Brierley, Ana Bastos, and Oliver Andrews
Geosci. Model Dev., 19, 2849–2880,
2026
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The Coupled Model Intercomparison Project (CMIP) is an international consortium of climate modeling groups that produce coordinated experiments in order to evaluate human influence on the climate and test knowledge of Earth systems. This paper describes the data requested for Earth systems research in CMIP7. We detail the request for model output of the carbon cycle, the flows of energy among the atmosphere, land and the oceans, and interactions between these and the global climate.
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14 Apr 2026
Enhancing Data-Driven Weather Forecasting via Gated Relative Position Encoding and Spatial-Aware Feed-Forward Network
Leyi Wang, Duo Zhang, Jerry Zhijian Yang, Baoxiang Pan, Dazhi Xi, and Xiaoyu Huang
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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We built a new artificial intelligence model to forecast the weather, designed to better understand air movement and how landscapes shape atmospheric motions. We trained this model on historical data to predict future conditions. Our tool proved highly accurate at predicting weather up to three days in advance. It also outperforms top models over land area. Our method requires significantly less resources. It paves the way for more efficient and more accurate daily weather forecasts worldwide.
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14 Apr 2026
CMIP7 Data Request: co-created guidance for the production of CMIP7 data [v1.2.2.3]
Chloe Mackallah, Martin Juckes, James Anstey, Beth Dingley, Charlotte Pascoe, Gaëlle Rigoudy, Marie-Pierre Moine, Tomas Lovato, Alison Pamment, Martin Schupfner, Michio Kawamiya, Tommi Bergman, Charles Koven, Eleanor O'Rourke, Briony Turner, Daniel Ellis, and Matthew Mizielinski
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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This paper describes the creation of a new set of output data requirements for upcoming global climate model experiments performed for CMIP7, an international climate modelling activity. Experts from the community helped to co-create a database that describes which data should be produced, and the scientific justifications behind these choices. It supports growing climate research and policy needs by linking experiments and variables to scientific objectives and real‑world applications.
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14 Apr 2026
| Highlight paper
The Destination Earth digital twin for climate change adaptation
Francisco J. Doblas-Reyes, Jenni Kontkanen, Irina Sandu, Mario Acosta, Mohammed Hussam Al Turjmam, Ivan Alsina-Ferrer, Miguel Andrés-Martínez, Costanza Anerdi, Leo Arriola, Marvin Axness, Marc Batlle Martín, Peter Bauer, Tobias Becker, Daniel Beltrán, Sebastian Beyer, Hendryk Bockelmann, Pierre-Antoine Bretonnière, Sebastien Cabaniols, Silvia Caprioli, Miguel Castrillo, Aparna Chandrasekar, Suvarchal Cheedela, Victor Correal, Emanuele Danovaro, Paolo Davini, Jussi Enkovaara, Claudia Frauen, Barbara Früh, Aina Gaya Àvila, Paolo Ghinassi, Rohit Ghosh, Supriyo Ghosh, Iker González, Katherine Grayson, Matthew Griffith, Ioan Hadade, Christopher Haine, Carl Hartick, Utz-Uwe Haus, Shane Hearne, Heikki Järvinen, Bernat Jiménez, Amal John, Marlin Juchem, Thomas Jung, Jessica Kegel, Matthias Kelbling, Kai Keller, Bruno Kinoshita, Theresa Kiszler, Daniel Klocke, Lukas Kluft, Nikolay Koldunov, Tobias Kölling, Joonas Kolstela, Luis Kornblueh, Sergey Kosukhin, Aleksander Lacima-Nadolnik, Jeisson Javier Leal Rojas, Jonni Lehtiranta, Tuomas Lunttila, Anna Luoma, Pekka Manninen, Alexey Medvedev, Sebastian Milinski, Ali Mohammed, Sebastian Müller, Devaraju Naryanappa, Natalia Nazarova, Sami Niemelä, Bimochan Niraula, Henrik Nortamo, Aleksi Nummelin, Matteo Nurisso, Pablo Ortega, Stella Paronuzzi, Xabier Pedruzo-Bagazgoitia, Charles Pelletier, Carlos Peña, Suraj Polade, Himansu Kesari Pradhan, Rommel Quintanilla, Tiago Quintino, Thomas Rackow, Jouni Räisänen, Maqsood Mubarak Rajput, René Redler, Balthasar Reuter, Nuno Rocha Monteiro, Francesc Roura-Adserias, Silva Ruppert, Susan Sayed, Reiner Schnur, Tanvi Sharma, Dmitry Sidorenko, Outi Sievi-Korte, Albert Soret, Christian Steger, Bjorn Stevens, Jan Streffing, Jaleena Sunny, Luiggi Tenorio, Stephan Thober, Ulf Tigerstedt, Oriol Tinto, Juha Tonttila, Heikki Tuomenvirta, Lauri Tuppi, Ginka Van Thielen, Emanuele Vitali, Jost von Hardenberg, Ingo Wagner, Nils Wedi, Jan Wehner, Sven Willner, Xavier Yepes-Arbós, Florian Ziemen, and Janos Zimmermann
Geosci. Model Dev., 19, 2821–2848,
2026
Short summary
Editorial statement
Short summary
The Climate Change Adaptation Digital Twin (Climate DT) pioneers the operationalisation of global climate projections. It produces global simulations with local granularity for adaptation decision-making. Applications are embedded to generate tailored indicators. A unified workflow orchestrates all components in several supercomputers. Data management ensures consistency and streaming enables real-time use. It is a complementary innovation to initiatives like CMIP, CORDEX, and climate services.
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Editorial statement
Destination Earth is a breakthrough in the fidelity and scale of climate simulation. The simulation data this makes available, and the recurrent simulation approach to working with the vast quantity of data generated by very high resolution climate simulations, will change this field for ever. The result will be more accurate and fine-grained climate science far better able to inform policy and decision-making than has hitherto been possible.
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14 Apr 2026
Modeling thermodynamically consistent phase transitions in multi-component assemblages: An entropy method for geodynamic models
Ranpeng Li, Juliane Dannberg, Rene Gassmöller, and Robert Myhill
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
Short summary
Short summary
Deep inside Earth, the minerals that make up rocks transform into different phases under high temperature and pressure. These transformations change rock density, affecting how material moves and how Earth’s interior evolves. We developed a new method to better model these effects in computer simulations. Our results show that even small density differences can lead to large changes in rising plumes and sinking slabs, which are key processes linked to volcanoes and earthquakes.
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14 Apr 2026
UKCM2-LL: a new low-resolution GC5 configuration with constrained climate sensitivity – methodology and development
John W. Rostron, Alejandro Bodas-Salcedo, David M. H. Sexton, Colin G. Jones, Edward W. Blockley, Till Kuhlbrodt, Jane P. Mulcahy, Tamzin E. Palmer, Saloua Peatier, Mark A. Ringer, Steven T. Rumbold, Benjamin M. Sanderson, Yongming Tang, and Martin R. Willet
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
Short summary
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The Met Office’s latest weather and climate model warms very strongly in response to increases in carbon dioxide. We created a modified version of the model with a more moderate warming response by adjusting key model parameters, using both automated methods and expert judgement. The new model matches historical temperatures more closely and is better suited for studies of long‑term climate, but has reduced overall accuracy when simulating the baseline climate.
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13 Apr 2026
High-performance coupled surface-subsurface flow simulation with SERGHEI-SWE-RE
Na Zheng, Zhi Li, Gregor Rickert, Mario Morales-Hernández, Ilhan Özgen-Xian, and Daniel Caviedes-Voullième
Geosci. Model Dev., 19, 2799–2819,
2026
Short summary
Short summary
This study introduces a computer model that simulates water flow both on the land surface and underground, and their interaction. The model can run efficiently on many kinds of computers, and its design lets each part update at its own pace to save time. In the tests performed, the model's results matched those from well-known tools in the field. Overall, the model offers a fast, flexible, and scalable way to study combined surface and groundwater behavior.
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13 Apr 2026
“Norkyst” version 3: the coastal ocean forecasting system for Norway
Kai Håkon Christensen, Jon Albretsen, Lars Asplin, Håvard Guldbrandsen Frøysa, Yvonne Gusdal, Silje Christine Iversen, Mari Fjalstad Jensen, Ingrid Askeland Johnsen, Nils Melsom Kristensen, Pål Næverlid Sævik, Anne Dagrun Sandvik, Magne Simonsen, Jofrid Skarðhamar, Ann Kristin Sperrevik, and Marta Trodahl
Geosci. Model Dev., 19, 2785–2798,
2026
Short summary
Short summary
This paper describes
Norkyst
, the operational coastal ocean forecasting system for mainland Norway, which is now in version 3. The system produces five day forecasts of ocean currents, temperature, salinity, and sea surface height every day, and we also maintain an archive of historical data going back to 2012. We show that the outputs of Norkyst have sufficient quality so that it's intended use as a free public service supporting scientists, ocean managers, and industry is justified.
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13 Apr 2026
ShyBFM v1.0: unstructured grid advection-diffusion-reaction modelling for coastal biogeochemical processes
Jacopo Alessandri, Giulia Bonino, Tomas Lovato, Momme Butenschön, Lorenzo Mentaschi, Giorgia Verri, Ivan Federico, and Nadia Pinardi
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
Short summary
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Coastal seas are strongly shaped by complex coastlines, shallow depths, and inputs from rivers, which influence marine life and water quality. This study introduces a new high-resolution modelling system that combines ocean circulation and marine ecosystem processes on unstructured grids. Applied to the northern Adriatic Sea, the model realistically captures seasonal changes in key biogeochemical variables, offering improved tools to support coastal environmental management.
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10 Apr 2026
Automated forward and adjoint modelling of viscoelastic deformation of the solid Earth
William Scott, Mark Hoggard, Thomas Duvernay, Sia Ghelichkhan, Angus Gibson, Dale Roberts, Stephan C. Kramer, and D. Rhodri Davies
Geosci. Model Dev., 19, 2717–2745,
2026
Short summary
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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 (Geoscientific Adjoint Optimisation Platform), 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.
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10 Apr 2026
AIRTRAC v2.0: a Lagrangian aerosol tagging submodel for the analysis of aviation SO
transport patterns
Jin Maruhashi, Mattia Righi, Monica Sharma, Johannes Hendricks, Patrick Jöckel, Volker Grewe, and Irene C. Dedoussi
Geosci. Model Dev., 19, 2747–2784,
2026
Short summary
Short summary
Aerosol-cloud interactions remain a major source of uncertainty in assessing aviation's net climate impact. We develop and evaluate a new Lagrangian tagging model that tracks aviation-emitted SO
and H
SO
as they are chemically transformed into SO
aerosols and transported throughout the atmosphere. This development allows the identification of atmospheric regions with elevated potential for aerosol–cloud interactions driven by SO
from aircraft.
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10 Apr 2026
Machine learning-driven characterization and prescription of aerosol optical properties for atmospheric models
Nilton Évora do Rosário, Karla M. Longo, Pedro H. Toso, Saulo R. Freitas, Marcia A. Yamasoe, Luiz Flávio Rodrigues, Otavio Medeiros, Haroldo Campos Velho, Isilda da Cunha Menezes, and Ana Isabel Miranda
Geosci. Model Dev., 19, 2691–2716,
2026
Short summary
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This study maps aerosol regimes over the Iberian Peninsula using AERONET data and machine learning. Five types were identified, from Saharan dust to smoke, highlighting differences in particle size and absorption. Combining observations with model data improves aerosol representation in climate simulations, reducing uncertainties and enhancing understanding of regional air quality and climate impacts.
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10 Apr 2026
Representation of the nitrogen cycle and its coupling with the carbon cycle in ISBA (SURFEX v9) the land surface model: evaluation using two Free-Air CO
Enrichment experiment sites
Jeanne Decayeux, Bertrand Decharme, Romain Darnajoux, and Christine Delire
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
Short summary
Short summary
The article describes the implementation of the nitrogen cycle in the land surface model ISBA. The model is evaluated using Free Air CO
Enrichment experiments. A comparison with a multi-model analysis shows that the nitrogen model version performs better than the carbon-only version, notably a reduced sensitivity to elevated CO
, and smaller C stocks. We also present a detailed analysis of the simulated N dynamics in the soil.
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09 Apr 2026
GOFS16: an operational global ocean analysis and forecasting system at eddy-resolving resolution
Simona Masina, Andrea Cipollone, Doroteaciro Iovino, Stefania Ciliberti, Rita Lecci, Sergio Cretí, Vladyslav Lyubartsev, Giovanni Coppini, and Emanuela Clementi
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
Short summary
Short summary
The paper presents GOFS16, an eddy-resolving global operational ocean and sea ice forecasting system which provides 6-day forecasts of three-dimensional temperature, salinity, currents, sea level, and sea ice properties. The system assimilates satellite and in situ observations using a 3D variational data assimilation scheme. Validation is conducted routinely using global and regional metrics. Results indicate that GOFS16 performs within the expected range of skill for current global systems.
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09 Apr 2026
Revisiting the parameterization of dense water plume dynamics in geopotential coordinates in NEMO v4.2.2
Robinson Hordoir, Jarle Berntsen, Magnus Hieronymus, Per Pemberton, and Hjálmar Hátún
Geosci. Model Dev., 19, 2677–2690,
2026
Short summary
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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.
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09 Apr 2026
Meso-NH-ISO v1.0: a water stable isotopes scheme in the non-hydrostatic mesoscale atmospheric model Meso-NH. Application to a 2D West African squall line
Christelle Barthe, Françoise Vimeux, Camille Risi, and Sören François
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
Short summary
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We implemented water stable isotopes in the non-hydrostatic mesoscale atmospheric model Meso-NH. We validated this isotopic version (Meso-NH-ISO) with a simulation of a well-documented squall line in the Sahel region. In future works, simulations with Meso-NH-ISO will be done on real cases of cyclones or squall lines. The goal is to better interpret and quantify isotopic observations on the field in terms of atmospheric processes that drive development and intensity of those thunderstorms.
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08 Apr 2026
TIPMIP-OCEAN experimental protocol phase 1: Tipping dynamics of the AMOC
Didier Swingedouw, Laura Jackson, Aixue Hu, Anastasia Romanou, Nicole C. Laureanti, Wilbert Weijer, Sina Loriani, Bette Otto-Bliesner, Ayako Abe-Ouchi, Lucas Almeida, Alessio Bellucci, Reyk Börner, Gokhan Danabasoglu, Donovan P. Dennis, Marion Devilliers, Sybren Drijfhout, Jonathan Donges, Friederike Fröb, Thomas L. Frölicher, Guillaume Gastineau, Heiko Goelzer, Chuncheng Guo, Urs Hofmann, Anna Höse, Colin Jones, Torben Koenigk, Ann Kristin Klose, Valerio Lembo, Jose Licon-Salaiz, Ken Mankoff, Virna Meccia, Irina Melnikova, Oliver Mehling, Laurie Menviel, Juliette Mignot, Jon I. Robson, Gavin A. Schmidt, Robin Smith, Yuchen Sun, Irene Trombini, Matteo Willeit, Richard Wood, Fanghua Wu, Lin Zhaohui, and Ricarda Winkelmann
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
Short summary
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This study presents a plan for climate model experiments to better understand how changes in freshwater in the North Atlantic affect major ocean currents. We designed coordinated simulations to test their response to warming, added freshwater, and possible recovery after weakening. Comparing results across models and past climate evidence helps improve confidence in projections and assess risks of large ocean circulation changes.
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08 Apr 2026
ORACLE-lite (v3.0): A reduced-complexity module for simulating organic aerosol formation and evolution in long term chemistry-climate simulations
Alexandra P. Tsimpidi and Vlassis A. Karydis
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 1 comment)
Short summary
Short summary
We developed a simplified representation of organic aerosol formation and evolution for long-term climate simulations. Organic aerosol affects air quality, human health, and climate but is difficult to model due to its complexity. Our approach preserves the key physical and chemical processes while reducing computational cost by about 14%. The model reproduces observed global patterns reasonably well, enabling more efficient and reliable studies of long-term changes in air pollution and climate.
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08 Apr 2026
A new Earth Observation–based WRF configuration for urban regional climate simulations over Paris
Iraklis Kyriakidis, Vasileios Pavlidis, Maria Gkolemi, Zina Mitraka, Nektarios Chrysoulakis, Josipa Milovac, Jesus Fernandez, and Eleni Katragkou
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
Short summary
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This study introduces a novel approach to incorporating city-specific Earth Observation (EO) data into an urban canopy model named BEP-BEM integrated in WRF. We highlight the added value given by BEP-BEM in the representation of the urban processes compared to a slab urban bulk approach. The implementation of the EO data improved specific aspects of the model performance.
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07 Apr 2026
Validation strategies for deep learning-based groundwater level time series prediction using exogenous meteorological input features
Fabienne Doll, Tanja Liesch, Maria Wetzel, Stefan Kunz, and Stefan Broda
Geosci. Model Dev., 19, 2657–2675,
2026
Short summary
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With the growing use of machine learning for groundwater level (GWL) prediction, proper performance estimation is crucial. This study compares three validation strategies—blocked cross-validation (bl-CV), repeated out-of-sample (repOOS), and out-of-sample (OOS)—for 1D-CNN and LSTM models using meteorological inputs. Results show that bl-CV offers the most reliable performance estimates, while OOS is the most uncertain, highlighting the need for careful method selection.
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07 Apr 2026
DEEP-SEAM: an explainable semi-supervised deep learning framework for mineral prospectivity mapping
Zijing Luo, Ehsan Farahbakhsh, Stephen Hore, and R. Dietmar Müller
Geosci. Model Dev., 19, 2593–2625,
2026
Short summary
Short summary
By combining multi-source data with advanced processing techniques, our deep learning model effectively identifies mineralisation patterns despite extremely limited deposit samples, analyses data and validates the geological relevance of its decisions through explainability analysis, providing a universally reliable solution for artificial intelligence-assisted mineral prospectivity mapping.
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07 Apr 2026
| Highlight paper
The Scenario Model Intercomparison Project for CMIP7 (ScenarioMIP-CMIP7)
Detlef P. Van Vuuren, Brian C. O'Neill, Claudia Tebaldi, Benjamin M. Sanderson, Louise P. Chini, Pierre Friedlingstein, Tomoko Hasegawa, Keywan Riahi, Bala Govindasamy, Nico Bauer, Veronika Eyring, Cheikh M. N. Fall, Katja Frieler, Matthew J. Gidden, Laila K. Gohar, Annika Högner, Andrew D. Jones, Jarmo Kikstra, Andrew King, Reto Knutti, Elmar Kriegler, Peter Lawrence, Chris Lennard, Jason Lowe, Camilla Mathison, Shahbaz Mehmood, Zebedee Nicholls, Luciana F. Prado, Qiang Zhang, Steven K. Rose, Alex C. Ruane, Marit Sandstad, Carl-Friedrich Schleussner, Roland Seferian, Jana Sillmann, Chris Smith, Anna A. Sörensson, Swapna Panickal, Kaoru Tachiiri, Naomi Vaughan, Saritha S. Vishwanathan, Tokuta Yokohata, Marco Zecchetto, and Tilo Ziehn
Geosci. Model Dev., 19, 2627–2656,
2026
Short summary
Editorial statement
Short summary
We propose a set of seven plausible 21st century emission scenarios, and their multi-century extensions, that will be used by the international community of climate modeling centers to produce the next generation of climate projections. These projections will support climate, impact and mitigation researchers, provide information to practitioners to address future risks from climate change, and contribute to policymakers’ considerations of the trade-offs among various levels of mitigation.
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Editorial statement
This article describes the design of the next version of emission scenarios that will be used for the 7th phase of the Coupled Model Intercomparison Project, which in turn will be used for the 7th Assessment Report of the Intergovernmental Panel on Climate Change. It provides the story lines for the creation of the emission scenarios and therefore it envisions future trajectories of policies and energy use. Models in CMIP 7 will use these scenarios to run simulations of future climate change using the scenarios as the main forcing. The authors carefully considered all community comments and maintained an open approach to develop these scenarios.
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07 Apr 2026
Simulating the impacts of utility-scale photovoltaic installations with a physically based coupled WRF-PV model
Yiran Chen, Jiming Jin, Yimin Liu, Jannik Heusinger, Jesús Carrera, and Zeyu Zhou
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
Short summary
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Solar power plants are expanding rapidly worldwide, but their impacts on local climate remain uncertain. In this study, we developed a coupled model that explicitly represents interactions between solar panels, land surface, and the atmosphere. Simulations show that large solar farms can cool the land surface while warming the air near the ground, reduce incoming shortwave radiation, and shift rainfall toward more extreme events.
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07 Apr 2026
Optimization of the Fast Layer Transmittance Algorithm in RTTOV v13.1 for Strong Water Vapor Absorption Channels of the FY-3F HIRAS-II Instrument Using LBLRTM v12.11
Panxiang Zhang, Peng Zhang, Gang Ma, Rui Li, Lu Lee, Wenguang Bai, and Chengli Qi
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 1 comment)
Short summary
Short summary
To enhance atmospheric transmittance accuracy in strong water vapor absorption bands, this study proposes an optimized scheme for the fast transmittance algorithm applied to the Hyperspectral Infrared Atmospheric Sounder-II. It introduces a transmittance threshold for sample selection and a weighted least squares regression with transmittance weighting. Validation against line-by-line models and observations shows significant improvements in forward model accuracy and stability at 6.7 μm.
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02 Apr 2026
Convolution Based Techniques for Computing Self Attraction and Loading in MOM6
Anthony Chen, He Wang, Brian Arbic, and Robert Krasny
External preprint server,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
Short summary
Short summary
Self Attraction and Loading (SAL) is an important force that affects many oceanic motions, including tides. Computing SAL is challenging and ocean models neglected to include the impacts of SAL for a long time. Recent work has proposed a method for incorporating the effects of SAL, but the method has several limitations that limit the accuracy. This work proposes an alternative method. Tests of this new method in an ocean model indicate that it reduces the amount of error in the modeled tides.
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01 Apr 2026
Introducing Volatile Organic Compound Model Intercomparison Project (VOCMIP)
Gunnar Myhre, Øivind Hodnebrog, Srinath Krishnan, Maria Sand, Marit Sandstad, Ragnhild B. Skeie, Lieven Clarisse, Bruno Franco, Dylan B. Millet, Kelley C. Wells, Alexander Archibald, Hannah N. Bryant, Alex T. Chaudhri, David S. Stevenson, Didier Hauglustaine, Michael Prather, J. Christopher Kaiser, Dirk J. L. Olivie, Michael Schulz, Oliver Wild, Ye Wang, Thérèse Salameh, Jason E. Williams, Philippe Le Sager, Fabien Paulot, Kostas Tsigaridis, and Haley E. Plaas
Geosci. Model Dev., 19, 2577–2591,
2026
Short summary
Short summary
Volatile organic compounds (VOCs) affect air quality and climate, but their behavior in the atmosphere is still uncertain. We launched a global research effort to compare how different models represent these compounds and to improve their accuracy. By analyzing model results alongside observations and satellite data, we aim to better understand the atmospheric composition of these compounds.
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01 Apr 2026
SLUCM+BEM (v2.0): implementing a prognostic indoor temperature scheme for application to global cities
Yuya Takane, Yukihiro Kikegawa, Zhiwen Luo, Hiroyuki Kusaka, and Sue Grimmond
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
Short summary
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We developed and released the single-layer urban canopy model coupled with building energy model v2. This incorporates a new scheme enabling dynamic changes in indoor temperature. This allows the model to be applied not only to air-conditioned conditions but also to non-air-conditioned scenarios, making it applicable to all seasons and cities worldwide. This upgrade facilitates the assessment of climate change adaptation measures for both outdoor and indoor environments.
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01 Apr 2026
SeapoPym v0.1: Implementation of the SEAPODYM low and mid trophic levels in Python with a flexible optimisation framework
Jules Victor Lehodey, Alexandre Mignot, Alexandre Ganachaud, Sarah Albernhe, and Simon Nicol
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
Short summary
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Marine zooplankton transfer energy from microscopic algae to fish and larger predators. Understanding their distribution helps predict how oceans respond to climate change. We developed SeapoPym, a freely available model that simulates zooplankton using ocean temperature and plant productivity. This tool lets scientists test biological hypotheses and estimate parameters from observations.
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31 Mar 2026
Application and evaluation of CRACMM v1.0 mechanism in PM
2.5
simulation over China
Qingfang Su, Yifei Chen, Yangjun Wang, David C. Wong, Havala O. T. Pye, Ling Huang, Golam Sarwar, Benjamin Murphy, Bryan Place, and Li Li
Geosci. Model Dev., 19, 2531–2550,
2026
Short summary
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This study evaluated the PM
2.5
simulation by the latest Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM) mechanism coupled with the Community Multiscale Air Quality (CMAQ) model, covering different seasons and specific regions over China. Modelling results derived by CRACMM are compared with two well-established chemical mechanisms. The research findings provide a solid foundation for the further application of CRACMM in understanding and regulating air pollution globally.
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31 Mar 2026
Evaluating the radiative fidelity of PALM (v25.04) in high-resolution: impact of diverse urban morphology and vegetation on short-wave radiation
Jelena Radović, Michal Belda, Martin Bureš, Kryštof Eben, Jan Geletič, Jakub Jura, Pavel Krč, Hynek Řezníček, and Jaroslav Resler
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
Short summary
Short summary
In this experiment, the Parallelized Large-Eddy Simulation Model (PALM)’s performance in simulating incoming and outgoing short-wave radiation in a densely built, highly heterogeneous urban environment was validated. In particular, we assessed whether the micro-scale model realistically resolves the effects of three-dimensional urban morphology and vegetation on short-wave radiation, including its propagation, shading, reflection, and attenuation within the simulated domain.
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31 Mar 2026
Design and Implementation of a Newtonian Relaxation Scheme in the NOAA GFDL Sea Ice Model (SIS2)
Dmitry S. Dukhovskoy, Theresa Cordero, Katherine Hedstrom, Michael Alexander, Michael Jacox, Robert Hallberg, Matthew Harrison, and Jessie Liu
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
Short summary
Short summary
A method for improving sea ice simulations by adjusting ice cover and thickness using observations or analysis data has been implemented in a regional sea ice model. Tests show improved representation of ice along the edges and within the ice-covered area. This suggests the method can provide more accurate initial conditions for forecasts, which is important for predicting ocean, sea ice, and ecosystem conditions in polar regions.
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31 Mar 2026
Precipitation Nowcasting Based on Convolutional LSTM with Spatio-Temporal Information Transformation Using Multi-Meteorological Factors
Dufu Liu, Feihu Huang, Peng Zheng, Xiaomeng Huang, Xi Wu, Xia Yuan, Jiafeng Zheng, Xiaojie Li, and Jing Hu
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 2 comments)
Short summary
Short summary
Due to the limitations of past data-based models and the high cost of numerical weather prediction computing, accurately forecasting precipitation proximity remains challenging. A dual encoder-decoder framework is proposed to enhance short-term forecasting and reduce underestimation in extreme precipitation by using spatio-temporal information conversion equations and adaptive weighted gradient loss. Demonstrates better accuracy than existing deep learning methods in precipitation datasets.
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31 Mar 2026
Optimizing WRF physics for multi-decadal simulation of near-surface climate over arid Xinjiang, China
Yang Xu, Liang Zhang, Mengxin Bai, Shenzhen Tian, and Zhixin Hao
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 8 comments)
Short summary
Short summary
We compared many model configurations to identify a reliable setup for long-term climate simulation in arid Xinjiang, China. Dozens of options were tested over six decades for temperature, rainfall, wind, humidity, radiation, and pressure. Performance depends strongly on how atmospheric and land processes are combined. We recommend a balanced configuration to support climate studies and high-quality data products in dry, complex terrain.
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31 Mar 2026
Advancing crop modeling and data assimilation using AquaCrop v7.2 in NASA's Land Information System Framework v7.5
Gabriëlle J. M. De Lannoy, Louise Busschaert, Michel Bechtold, Niccolò Lanfranco, Shannon de Roos, Zdenko Heyvaert, Martynas Bielinis, Jonas Mortelmans, Samuel A. Scherrer, Maxime Van den Bossche, Sujay Kumar, David M. Mocko, Eric Kemp, Lee Heng, Pasquale Steduto, and Dirk Raes
Geosci. Model Dev., 19, 2551–2575,
2026
Short summary
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The AquaCrop model has been incorporated into the NASA Land Information System, to advance regional crop growth simulations at any spatial resolution, with a range of different input sources for meteorology, soil and crop parameters. This system also facilitates the assimilation of satellite data to update the crop and water conditions during model simulations. We present three exploratory applications to highlight pathways for future research on regional-scale crop estimation.
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30 Mar 2026
Evaluation and improvement of CAMS-derived CCN number concentrations using in-situ measurements
Yannick Emanuel Anders, Karoline Block, Mira Pöhlker, and Johannes Quaas
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 1 comment)
Short summary
Short summary
Particles in the atmosphere can trigger the formation of cloud droplets, affecting cloud properties and climate. This study evaluates a new global dataset of these particles with measurements from 25 sites around the world. The variability in time and space and their conditional formation behaviour is analysed. The authors identify systematic biases and introduce a simple correction based on observations that greatly improves the dataset’s accuracy.
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30 Mar 2026
Modular wind profile retrieval software for heterogeneous Doppler lidar measurements (AtmoProKIT v1.1)
Anselm Erdmann and Philipp Gasch
Geosci. Model Dev., 19, 2497–2529,
2026
Short summary
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A new software for the calculation of quality controlled wind profiles from heterogeneous Doppler lidar measurements is presented. The processing is designed modularly. A provided standard processing chain is validated using radiosondes for three common Doppler lidar types at different locations.
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27 Mar 2026
A Raster–Vector Framework for Multi-Scale Hydrological–Hydraulic Modeling Across Large Domains
Mohamed Amine Berkaoui, Mohamed Saadi, François Colleoni, Ngo Nghi Truyen Huynh, Ahmad Akhtari, Kevin Larnier, Hélène Roux, and Pierre-André Garambois
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 1 comment)
Short summary
Short summary
We present an integrated hydrological–hydraulic (H&H) modeling framework that combines grid-based hydrology with vector-based river routing, leveraging sub-grid information derived from high-resolution topography. This approach improves the representation of river networks and drainage areas across spatial resolutions, reducing errors and spatial distortions associated with regular grid discretization, and leading to more stable streamflow simulations across scales.
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27 Mar 2026
GEE-DisALEXI: Cloud-Based Implementation of the DisALEXI Model for Evapotranspiration Monitoring Using Google Earth Engine
Yun Yang, Martha Anderson, Charles Morton, Yanghui Kang, Feng Gao, Weina Duan, Hui Liu, John Volk, and Christopher Hain
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 1 comment)
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Evapotranspiration (ET) describes how water moves from land to the atmosphere and is key to understanding crops, ecosystems, and drought. We developed a cloud-based system that uses satellite data to map ET at high resolution over large areas. This approach makes it easier to monitor water use, support farmers, and improve drought detection, helping better manage water resources.
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27 Mar 2026
PM
2.5
assimilation within JEDI for NOAA's regional Air Quality Model (AQMv7): application to the September 2020 Western US wildfires
Hongli Wang, Cory Martin, Jérôme Barré, Ruifang Li, Steve Weygandt, Jianping Huang, Youhua Tang, Hyundeok Choi, Andrew Tangborn, Kai Wang, Haixia Liu, and Jeffrey Lee
Geosci. Model Dev., 19, 2479–2495,
2026
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This paper describes efforts to develop aerosol data assimilation capabilities for NOAA’s regional air quality modeling system by assimilating PM
2.5
observations within the Joint Effort for Data Assimilation Integration framework. Results from the September 2020 Western U.S. wildfires show that assimilating AirNow and/or PurpleAir PM
2.5
data reduces mean absolute errors of 1–24 h PM
2.5
forecasts over the continental United States on average relative to a control without data assimilation.
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26 Mar 2026
Deep learning representation of the aerosol size distribution
Donifan Barahona, Katherine H. Breen, Karoline Block, and Anton Darmenov
Geosci. Model Dev., 19, 2437–2459,
2026
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Particulate matter impacts Earth's radiation, clouds, and human health, but modeling their size is challenging due to computational and observational limits. We developed a machine learning model to predict aerosol size distributions, which accurately replicates advanced models and field measurements.
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26 Mar 2026
The microbial community model MCoM 1.0: a scalable framework for modelling phototroph–heterotrophic interactions in diverse microbial communities
Leonhard Lücken, Michael J. Follows, Jason G. Bragg, and Sinikka T. Lennartz
Geosci. Model Dev., 19, 2461–2477,
2026
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The Microbial Community Model (MCoM) is a flexible biogeochemical modeling framework which resolves a rich set of interactions between photosynthetic and heterotrophic microbes, including cross-feeding, metabolite exchange, and nutrient recycling. As such, it allows to assess community-level effects on elemental turnover emerging from microbial interactions. Its scalability allows to represent both simple pairwise interactions and large, diverse communities.
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25 Mar 2026
| Review and perspective paper
Best practices in software development for robust and reproducible geoscientific models based on insights from the Global Carbon Budget's dynamic vegetation models
Konstantin Gregor, Benjamin F. Meyer, Tillmann Gaida, Victor Justo Vasquez, Karina Bett-Williams, Matthew Forrest, João P. Darela-Filho, Sam Rabin, Marcos Longo, Joe R. Melton, Johan Nord, Peter Anthoni, Vladislav Bastrikov, Thomas Colligan, Christine Delire, Michael C. Dietze, George Hurtt, Akihiko Ito, Lasse T. Keetz, Jürgen Knauer, Johannes Köster, Tzu-Shun Lin, Lei Ma, Marie Minvielle, Stefan Olin, Sebastian Ostberg, Hao Shi, Reiner Schnur, Qing Sun, Peter E. Thornton, and Anja Rammig
Geosci. Model Dev., 19, 2407–2436,
2026
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Editorial statement
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Geoscientific models are crucial for understanding Earth’s processes. However, they sometimes do not adhere to highest software quality standards, and scientific results are often hard to reproduce due to the complexity of the workflows. Here we gather the expertise of 20 modeling groups and software engineers to define best practices for making geoscientific models maintainable, usable, and reproducible. We conclude with an open-source example serving as a reference for modeling communities.
This article is included in the
Encyclopedia of Geosciences
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Editorial statement
The manuscript presents a perspective on the reproducibility of geoscientific models, which fits in the type of manuscript.
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25 Mar 2026
The 4-mode Modal Aerosol Module in C++ (MAM4xx) v1.0: Representing Prognostic Aerosols in a Global Cloud-System Resolving Atmosphere Model for GPU Exascale Computing
Jerome D. Fast, Balwinder Singh, Oscar Diaz-Ibarra, Jeff Johnson, Chandru Dhandapani, Brian Gaudet, Taufiq Hassan, Meng Huang, Jaelyn Litzinger, James Overfelt, Kyle Pressel, Michael Schmidt, Shuaiqi Tang, Adam C. Varble, Hui Wan, Mingxuan Wu, Kai Zhang, and Po-Lun Ma
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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We ported a prognostic representation of aerosols to C++ and integrated it into an Earth system model that runs on powerful GPU supercomputers. The code conversion approach keeps the same detailed physics as the Fortran version, was carefully tested, and results show that new code produces aerosol simulations consistent with real‑world data over the central U.S. in spring 2016. Future work will optimize the code for GPUs so to reduce the overall computational time.
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25 Mar 2026
New classes of climate model emulators to improve paleoclimate reconstructions
Auguste Gaudin and Myriam Khodri
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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Reconstructing past climate variability requires computationally efficient models able to capture the behaviour of complex climate systems. We develop a suite of climate model emulators that improve the representation, reconstruction, and prediction of spatial climate variability compared to traditional approaches. Results highlight the importance of predictability-oriented representations and nonlinear dynamical memory for scalable emulators suited to paleoclimate data assimilation frameworks.
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25 Mar 2026
The spatio-temporal visualization tool HMMLVis in renewable energy applications
Rainer Wöß, Kateřina Hlaváčková-Schindler, Irene Schicker, Petrina Papazek, and Claudia Plant
Geosci. Model Dev., 19, 2385–2405,
2026
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Our tool is an easy-to-use, interpretable causal inference software. It can be applied in any scientific discipline exploring time series. The tool uses heterogeneous Granger causality. It can be used on time-series data to infer causal relationships between multiple variables and a target time-series. The tool is demonstrated on different types of applications related to meteorological events in a renewable energy, air pollution, and postprocessing benchmark data.
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