GMD - Development and technical paper

GMD - Development and technical paper
Development and technical paper
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
Short summary
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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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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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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
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)
Short summary
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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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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
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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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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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)
Short summary
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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
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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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
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)
Short summary
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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
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
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)
Short summary
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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)
Short summary
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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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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)
Short summary
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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
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
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)
Short summary
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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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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
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)
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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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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)
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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
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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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
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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
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
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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
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)
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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
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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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
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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)
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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
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
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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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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)
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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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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
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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
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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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
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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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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
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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
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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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
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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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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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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)
Short summary
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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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24 Mar 2026
Further evaluating the generalized Itô correction for accelerating convergence of stochastic parameterizations with colored noise
William Johns, Lidong Fang, Huan Lei, and Panos Stinis
Geosci. Model Dev., 19, 2373–2383,
2026
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Colored noise processes can be used to imitate processes that are two small to include fully in a model. The naïve introduction of a colored noise process to a numerical algorithm can lead to unrealistic outputs. This is remedied by the introduction of the recently introduced the Generalized Ito Correction (GIC). We demonstrate the effectiveness of GIC to improve results at a low cost on two models from the atmosphere modeling literature for a range of colored noise processes.
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24 Mar 2026
A Bayesian statistical method to estimate the climatology of extreme temperature under multiple scenarios: the ANKIALE package
Yoann Robin, Mathieu Vrac, Aurélien Ribes, Occitane Barbaux, and Philippe Naveau
Geosci. Model Dev., 19, 2349–2372,
2026
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We describe an improved method and the associated free licensed package ANKIALE (ANalysis of Klimate with bayesian Inference: AppLication to extreme Events) for estimating the statistics of temperature extremes. This method uses climate model simulations (including multiple scenarios simultaneously) to provide a prior of the real-world changes, constrained by the observations. The method and the tool are illustrated via an application to temperature over Europe until 2100, for four scenarios.
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24 Mar 2026
TRAILS – A novel framework for time-height-resolved attribution of long-range transported wildfire smoke
Johanna Roschke, Benedikt Gast, Martin Radenz, Albert Ansmann, Patric Seifert, George McCosh, and Heike Kalesse-Los
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 1 comment)
Short summary
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This research introduces a new method that combines model simulations with satellite observations to attribute the influence of wildfire smoke on an airmass. By dynamically determining the height of smoke plumes, we overcome a key limitation of earlier fixed reception-height approaches. This advancement is crucial for improving our understanding of how wildfire emissions influence cloud formation and the broader Earth's climate system.
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24 Mar 2026
Advancing the Capabilities for Efficient Hurricane-Centric Simulations with the Atmospheric Model ICON
Fabian Senf and Roxana Cremer
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 1 comment)
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Computer models for hurricane prediction are becoming increasingly detailed but require substantial computing resources. We developed a flexible approach that follows hurricanes as they move, applying high-resolution simulations only where needed. This method reduces computing costs by factors of 13–175 while achieving resolutions down to 300 meters. The approach enables more efficient hurricane research and improved understanding of tropical dynamics.
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23 Mar 2026
A fully implicit second order method for viscous free surface Stokes flow – application to glacier simulations
Josefin Ahlkrona, A. Clara J. Henry, and André Löfgren
Geosci. Model Dev., 19, 2333–2348,
2026
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This paper leverages the Free Surface Stabilization Algorithm of Kaus et al. (2010) to construct a 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.
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23 Mar 2026
Regularisation of the 4DEnVar Data Assimilation method for Calibration of Land Surface Models
Natalie Douglas, Simon Beylat, Tristan Quaife, Philippe Peylin, Nina Raoult, and Ross Bannister
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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This study investigates a statistical method, called 4DEnVar, to calibrate uncertain land surface model parameters against observations. This method is easy to use but can lead to unphysical parameter values. The study explores the causes of this while proposing a simple way to overcome the problem. We show that the 4DEnVar method exhibits considerable versatility by applying the method to two different land surface models, under different settings, to calibrate photosynthetic parameters.
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20 Mar 2026
Stochastic perturbation of inputs to parametrisation schemes machine-learnt from high-resolution model variability
Helena Reid and Cyril Julien Morcrette
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 3 comments)
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Atmospheric models used for weather and climate benefit from representing the random effects of processes that are too small to be resolved by the model. Here, very detailed simulations are used to learn about the amount of variability that would be expected in a coarser model. We then use machine learning techniques to predict that fine-scale variability and show that including these predictions improve some idealised simulations over the tropical ocean.
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20 Mar 2026
Benchmarking a new urban scheme in the ORCHIDEE v2.2 land surface model
Morgane Lalonde, Sophie Bastin, Ludovic Oudin, Pedro Felipe Arboleda-Obando, and Agnès Ducharne
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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Some climate models still represent cities as if they were natural ground. For one of these models, we built a new way to represent cities. The update includes how reflective surfaces are, building height, stored heat, and how much ground is sealed. The novelty is to treat sealed ground not only at the surface, but also below it. Tested at twenty urban sites, the new version better represents exchanges of energy between the ground and the air, supporting more reliable urban climate studies.
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19 Mar 2026
EcoTWIN 1.0: a fully distributed tracer-aided ecohydrological model tracking water, isotopes, and nutrients
Songjun Wu, Doerthe Tetzlaff, Yi Zheng, and Chris Soulsby
Geosci. Model Dev., 19, 2257–2278,
2026
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We developed EcoTWIN v1.0, a new fully distributed tracer-aided ecohydrological model that tracks water, isotopes, and nutrients fluxes. The model was successfully tested in 17 large European catchments across diverse geological and climatic backgrounds. As a tracer-aided model, EcoTWIN not only captures flow paths but also estimates water ages/travel times, thus bridging hydrology with water quality. This opens new possibilities for understanding the synergy between water and nitrogen cycles.
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19 Mar 2026
Improvement of the computational efficiency in SVD-3DEnVar data assimilation scheme and its preliminary application to the TRAMS 3.0 model
Kun 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
Geosci. Model Dev., 19, 2279–2297,
2026
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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.
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18 Mar 2026
Integrating Ozone–vegetation Damage Schemes into SSiB4/TRIFFID: Evaluation of Six Parameterizations and Refinement of Ozone Decay Process Across Plant Functional Types
Lingfeng Li, Bo Qiu, Siwen Zhao, Xin Miao, Chaorong Chen, Jiuyi Chen, Yueyang Ni, Xin Huang, Haishan Chen, and Weidong Guo
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 1 comment)
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Ground-level ozone can harm plant photosynthesis, but models describe these effects in different ways. In this study, we implemented six ozone damage schemes in a land surface model and compared their behaviour within a unified framework. We also improved one scheme by using observations of leaf lifespan to better represent how ozone stress accumulates and recovers in plants. This work helps identify key differences among schemes and supports the development of more realistic models.
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17 Mar 2026
HIDRA-D: deep-learning model for dense sea level forecasting using sparse altimetry and tide gauge data
Marko Rus, Matjaž Ličer, and Matej Kristan
Geosci. Model Dev., 19, 2177–2195,
2026
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This paper introduces HIDRA-D, a novel deep-learning model for dense, gridded sea level forecasting from sparse satellite altimetry and tide gauge data. By forecasting low-frequency spatial components, HIDRA-D offers a faster alternative to traditional numerical models. Evaluated on the satellite altimetry data in the Adriatic Sea, it outperforms the NEMO general circulation model, reducing the mean absolute error by 28.0 %. The model is robust but shows limitations in complex coastal areas.
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17 Mar 2026
Recognizing spatial geochemical anomaly patterns using deformable convolutional networks guided with geological knowledge
Xinyu Zhang, Yihui Xiong, and Zhiyi Chen
Geosci. Model Dev., 19, 2219–2238,
2026
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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.
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13 Mar 2026
Implementation and Evaluation of an Observation-Constrained Secondary Organic Aerosol Parameterization in MOZART–GOCART Chemistry in WRF-Chem
Rajmal Jat, Akash Sagar Vispute, Sachin D. Ghude, Rajesh Kumar, Vinayak Sinha, Baerbel Sinha, Gaurav Govardhan, Zhining Tao, Prafull P. Yadav, Sandeep Wagh, Sreyashi Debnath, Aditi Rathore, and Madhavan Rajeevan
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 2 comments)
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This study developed a simplified and computationally efficient secondary organic aerosol parameterization in the MOZART–GOCART scheme within WRF-Chem using volatile organic compound observations in Delhi. This parameterization was evaluated for a period with severe pollution influenced by crop residue burning. Results show that the approach improves the model’s ability to reproduce organic aerosols and fine particulate matter while remaining much faster than more complex chemical schemes.
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12 Mar 2026
Development of the TCWA2 Bulk Cloud Microphysics Scheme and Its Integration with a Dual-Polarization Radar Operator for Forecasting Applications
Tzu-Chin Tsai, Jen-Ping Chen, Zhiquan Liu, Siou-Ying Jiang, Rong Kong, Ying-Jhang Wu, Junmei Ban, Ling-Feng Hsiao, Yu-Shuang Tang, Pao-Liang Chang, and Jing-Shan Hong
External preprint server,
2026
Preprint under review for GMD
(discussion: open, 2 comments)
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Weather radars observe rain and ice inside clouds, but models often cannot fully use this information because their cloud physics schemes do not describe the particle properties needed to simulate radar signals. This study develops a new cloud microphysics scheme directly linked to a radar operator that uses the same particle information. Tests using an idealized and a real case show that TCWA2 can reproduce observed radar features, supporting improved radar-based weather forecasting.
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11 Mar 2026
Validation of the ALARO1-SFX (CY43T2) regional climate model over Belgium across different resolutions
Wout Dewettinck, Hans Van de Vyver, Daan Degrauwe, Rafiq Hamdi, Michiel Van Ginderachter, Bert Van Schaeybroeck, Kwinten Van Weverberg, Kobe Vandelanotte, Steven Caluwaerts, and Piet Termonia
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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This study assesses an updated version of the ALARO regional climate model over Belgium at multiple resolutions, by using long-term climate simulations. Incorporating the land surface model SURFEX and simulating at higher resolutions led to improved simulation of temperature, precipitation, and extreme rainfall events. These findings support the value of high-resolution modelling for better representing local climate extremes and informing adaptation measures.
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11 Mar 2026
Assimilation of ground based lidar and ceilometer observations of aerosols from the European E-Profile network into ECMWF's Integrated Forecasting System (IFS-COMPO, CY49R1)
Michael Kahnert, Melanie Ades, Mickaël Bacles, Johannes Flemming, Vincent Guidard, Alexander Haefele, Robin J. Hogan, Samuel Rémy, and Eric Sauvageat
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 1 comment)
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The Copernicus Atmosphere Monitoring Service (CAMS) provides quality-controlled information related to air quality and health. We explore the possibility to constrain the CAMS global forecasting model by use of ground-based observations of laser light backscattered by particulate matter. We find that the vertical distribution of particulate matter can be predicted more faithfully with this approach, which can have implications for air quality forecasts provided by CAMS to end users.
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11 Mar 2026
Deploying Machine Learning components coupled to Earth System Models with OASIS3-MCT (v6) and Eophis (v1.1)
Alexis Barge, Julien Le Sommer, Andrea Storto, and Sophie Valcke
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 1 comment)
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Scientists use programs, called Earth System Models, to study and predict climate. These models are based on physical theories but can be completed with AI tools. However, combining these tools with traditional models is difficult due to their different nature. Our research introduces a new method that connects these AI tools with existing climate models. We tested this method by integrating it with an ocean model. This work should help scientists explore new ways of making climate predictions.
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10 Mar 2026
GeoSIRR 1.0: Conversational Geological Cross-Section Modeling Using Large Language Models
Denis Anikiev, Juan Esteban Mosquera, Korhan Ayranci, Judith Bott, and Umair Bin Waheed
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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Geological cross sections are essential for understanding the Earth’s subsurface, but they are usually slow to create manually. We developed GeoSIRR 1.0, a new framework that converts plain geological descriptions into consistent cross section models using generative artificial intelligence. The approach allows interactive refinement through conversation and helps bridge expert geological reasoning with digital modeling for faster exploration, education, and scenario testing.
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10 Mar 2026
Observation operator and detection limits for MODIS and VIIRS Fire Radiative Power products
Mikhail Sofiev and Rostislav Kouznetsov
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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Satellites can observe fires globally, but sensitivity of instruments in space is limited: they miss small fires and cannot see through clouds. We analyzed the MODIS and VIIRS fire observations and obtained their sensitivity to small fires, which depends on the resolution of the instrument, timing of the observation (day or night), and details of the data processing. We developed a procedure for comparing fire model predictions with satellite observations accounting for their limited sensitivity
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10 Mar 2026
Process-based upgrades to the WRF multi-layer green-roof scheme (WRF-MLGR v2.0) and evaluation against field observations
Alireza Saeedi, Maria Martinez Mendoza, Eric Scott Krayenhoff, James Voogt, Andrea Zonato, Sylvie Leroyer, and Claudia Wagner-Riddle
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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Green roofs can help cool cities, but models must represent how heat and water move through soil and plants correctly. We improved the green roof part of the WRF multi-layer weather model by adding more realistic descriptions of evaporation, heat storage, and plant water uptake. When tested against real measurements from a roof in London, Ontario, Canada, the updated model more accurately matched observed ground heat and latent heat fluxes.
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09 Mar 2026
RADIv2: an adaptable and versatile diagenetic model for coastal and open-ocean sediments
Hinne F. van der Zant, Olivier Sulpis, Jack J. Middelburg, Matthew P. Humphreys, Raphaël Savelli, Dustin Carroll, Dimitris Menemenlis, Kay Sušelj, and Vincent Le Fouest
Geosci. Model Dev., 19, 1965–1989,
2026
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We developed a model to simulate seafloor biogeochemical processes across a wide range of marine environments, from shallow coastal zones to deep-sea sediments. From this model, we derived a set of simple equations that predict how carbon, oxygen, and alkalinity are exchanged between sediments and overlying waters. These equations provide an efficient way to improve how ocean models represent seafloor interactions, which are often missing or overly simplified.
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09 Mar 2026
Improving thermodynamic nudging in the E3SM Atmosphere Model version 2 (EAMv2): strategy and hindcast skills on weather systems
Shixuan Zhang, L. Ruby Leung, Bryce E. Harrop, Aniruddha Bora, George Karniadakis, Khemraj Shukla, and Kai Zhang
Geosci. Model Dev., 19, 1937–1964,
2026
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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.
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09 Mar 2026
Python-Fortran Hybrid Programming for Deep Incorporation of AI and Physics Modeling and Data Assimilation (Hf2pMDA_1.0)
Xianrui Zhu, Zikuan Lin, Shaoqing Zhang, Zebin Lu, Songhua Wu, Xiangyun Hou, Zhisheng Xiao, Zhicheng Ren, Jiangyu Li, Jing Xu, Yang Gao, Rixu Hao, Xiaolin Yu, and Mingkui Li
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 5 comments)
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Deep integration of Artificial intelligence (AI) algorithms and traditional scientific models is crucial for progress, but Fortran-based scientific codes and Python-based AI are difficult to combine. We develop a Python–Fortran hybrid procedure that enables mutual invocation of AI and scientific modules. Applied to climate and weather models, it supports strongly coupled data assimilation and high-precision prediction, promoting future advances in both AI and scientific modeling.
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06 Mar 2026
Implementation of the reduced complexity model InMAP at urban scale using a high-resolution WRF-Chem simulation
Diego Roberto Rojas Neisa, Alejandro Piracoca-Mayorga, Sebastián Espitia-Cano, and Ricardo Morales Betancourt
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 7 comments)
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In this work, we explored the ability of simpler atmospheric models to analyze the effectiveness of reducing air pollutant emissions to improve air quality. We showed that, despite its simplicity, these models correctly estimate the areas where impacts will be felt the most, and therefore, can be used by decision makers to maximize the positive impacts of planned air quality improvements.
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06 Mar 2026
A process-based modeling of soil organic matter physical properties for land surface models – Part 2 : Global land surface simulations and mineral soil compaction adjustment
Bertrand Decharme, Diane Tzanos, Lucas Hardouin, Aaron Boone, Marie Minvielle, Patrick Le Moigne, and Rémi Gaillard
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 1 comment)
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We developed a new method to represent how organic matter in soils, together with a mineral soil compaction adjustment, influences the movement of water and heat in land models. We implemented this approach in a global model and performed long-term simulations driven by weather data and global soil maps. Compared with an older empirical method, it produces more consistent soil moisture, runoff, evaporation, and ground temperature and shows closer agreement with observations.
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06 Mar 2026
All-sky ATMS radiance data assimilation with MPAS-JEDI
Junmei Ban, Zhiquan Liu, Byoung-Joo Jung, Ivette Hernandez Banos, Benjamin Ruston, and Andrew Collard
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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The assimilation of all-sky radiances in MPAS–JEDI, the data assimilation system for the Model for Prediction Across Scales–Atmosphere (MPAS-A) based on the Joint Effort for Data Assimilation Integration (JEDI), has been extended in this study to incorporate ATMS observations. Month-long cycling experiments demonstrate consistent and encouraging improvements in dynamical, thermodynamic, and moisture-related fields resulting from the assimilation of all-sky ATMS radiances.
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05 Mar 2026
Enabling fast greenhouse gas emissions inference from satellites with GATES: a Graph-Neural-Network Atmospheric Transport Emulation System
Elena Fillola, Raul Santos-Rodriguez, Rachel Tunnicliffe, Jeffrey N. Clark, Nawid Keshtmand, Anita Ganesan, and Matthew Rigby
Geosci. Model Dev., 19, 1893–1915,
2026
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Satellite-based greenhouse gas measurements can be used in “inverse models” to improve emissions reporting, but one of the key components, the simulations of atmospheric transport, struggle to scale to large datasets. We introduce the model GATES, an AI-driven emulator that outputs transport plumes 1000× faster than traditional models. Applied to Brazil’s methane emissions, GATES produces estimates consistent with physics-based methods, offering a scalable path for timely emissions monitoring.
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05 Mar 2026
An ADCP-Based Data-Driven Framework for Proxy Sediment Transport Monitoring: From Controlled Flumes to Natural Rivers
Mohammd Tanvir Haque Tuhin, Reinhard Hinkelmann, and Christoph Mudersbach
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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This study tests how Acoustic Doppler Current Profiler (ADCP) data can support proxy sediment-transport monitoring without labour-intensive sediment sampling. Using data from a flume and a natural river, we train and compare several machine-learning models to predict a near-bed velocity signal. The results show which ADCP features and model types work best as practical indicators of bed activity.
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05 Mar 2026
GPU-accelerated Finite-Element Method for the Three-dimensional Unstructured Mesh Atmospheric Dynamic Framework
Leisheng Li, Ximeng Fu, Xiyu Zheng, Huiyuan Li, and Jinxi Li
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 2 comments)
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Scientists use irregular grid models for accurate weather simulation, which help capture details but also make the calculations slow on traditional computers. We redesigned this model for GPUs by reorganizing data and calculations. This makes the slowest parts hundreds of times faster and the whole simulation over ten times faster. This allows for higher-resolution simulations.
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05 Mar 2026
Integrating reservoirs and lakes in the CoSWAT global hydrological model
Jose P. Teran, Celray J. Chawanda, Albert Nkwasa, Inne Vanderkelen, Jeffrey G. Arnold, and Ann Van Griensven
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 2 comments)
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Global water models help us understand how human activities and climate change affect water resources. One of them is the CoSWAT Global Model. In this study we improved this model by adding a better representations of lakes, reservoirs, and irrigation demand. Evaluation shows these changes improve river flow simulation and enable explicit assessment of lake and reservoir water balances, producing a more robust tool for global freshwater studies.
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04 Mar 2026
A Climate Intervention Dynamical Emulator (CIDER) for scenario space exploration
Jared Farley, Douglas G. MacMartin, Daniele Visioni, Ben Kravitz, Ewa M. Bednarz, Alistair Duffey, Matthew Henry, and Ali Akherati
Geosci. Model Dev., 19, 1809–1831,
2026
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As the climate changes, many are studying sunlight reflection as a potential method of cooling. Such climate intervention could be deployed in many possible ways, including in scenarios where not every actor agrees on the strategy of cooling. These scenarios are so diverse that to explore all of them using earth system models proves to be too costly. In this paper, we develop a simplified climate model that allows users to easily explore climate intervention scenarios of their choice.
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04 Mar 2026
Deposition velocity concept does not apply to fluxes of ambient aerosol
Rostislav Kouznetsov, Mikhail Sofiev, Andreas Uppstu, and Risto Hänninen
Geosci. Model Dev., 19, 1833–1847,
2026
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The paper addresses a two-order-of-magnitude discrepancy in deposition velocities of accumulation-mode aerosols measured with different methods. This uncertainty affects current atmospheric deposition models. By explicitly accounting for gas-particle transition and explicitly evaluating the observed quantities we could reproduce the observations . Resolving the discrepancy, reduces uncertainties in simulated concentrations and fallout.
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04 Mar 2026
20 years of trials and insights: bridging legacy and next generation in ParFlow and Land Surface Model Coupling
Chen Yang, Aoqi Sun, Shupeng Zhang, Yongjiu Dai, Stefan Kollet, and Reed Maxwell
Geosci. Model Dev., 19, 1849–1866,
2026
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Groundwater plays a key role in land–atmosphere water and energy exchange, yet it is often simplified in large-scale Earth system models. We review 20 years of efforts to couple the groundwater model ParFlow with land surface and atmospheric models, showing how groundwater dynamics shape terrestrial fluxes. We also present an updated coupling framework that enhances model performance and flexibility, and outline a modular strategy to guide future development.
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04 Mar 2026
Effects of assimilating phytoplankton carbon in marine ecosystem modelling in NEMO4.0.4-MEDUSA2.0-PDAF2.0
Yumeng Chen, Dale Partridge, and Lars Nerger
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 1 comment)
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Operational marine ecosystem forecasts traditionally rely on combining phytoplankton chlorophyll observations with model forecasts. However, using our newly developed ensemble data assimilation system, we demonstrate that assimilating phytoplankton carbon data leads to more accurate phytoplankton estimates and improves estimates of global ocean carbon.
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04 Mar 2026
A deep learning framework for gridding daily climate variables from a sparse station network
Alexandru Dumitrescu
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 1 comment)
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High-quality climate maps are key for flood-risk assessment. We present a deep-learning framework that maps daily temperature and precipitation from sparse weather-station data. By learning orographic effects, it delivers more accurate rainfall fields with well-calibrated uncertainty, enabling reliable monitoring of environmental change in complex terrain.
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03 Mar 2026
Conditional diffusion models for downscaling and bias correction of Earth system model precipitation
Michael Aich, Philipp Hess, Baoxiang Pan, Sebastian Bathiany, Yu Huang, and Niklas Boers
Geosci. Model Dev., 19, 1791–1808,
2026
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Accurately simulating rainfall is essential to understand the impacts of climate change, especially extreme events such as floods and droughts. Climate models simulate the atmosphere at a coarse resolution and often misrepresent precipitation, leading to biased and overly smooth fields. We improve the precipitation using a machine learning model that is data-efficient, preserves key climate signals such as trends and variability, and significantly improves the representation of extreme events.
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03 Mar 2026
Implementation of a sigma coordinate system in PALM-Sigma v1.0 (based on PALM v21.10) for LES study of the marine atmospheric boundary layer
Xu Ning and Mostafa Bakhoday-Paskyabi
Geosci. Model Dev., 19, 1769–1789,
2026
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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.
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03 Mar 2026
An Online Spectral Nudging-Based Correction System: Improving Physical Model Forecasts by Incorporating Large-Scale Circulations Derived from Machine Learning Models
Yong Su, Jincheng Wang, Xueshun Shen, Couhua Liu, Xingliang Li, Hao Jing, Jin Zhang, and Yingying Hu
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 8 comments)
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The traditional weather prediction models improve slowly, while machine learning models struggle with extreme weather and fine details. To address these gaps, we developed an online correction system that leverages a machine learning model's skillful large-scale circulation to guide a physical model. This hybrid model enhances large-scale skill while preserving small-scale features, providing a viable pathway for improving operational weather forecasting.
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02 Mar 2026
Effectively Assimilate Satellite Land Surface Temperature into Offline Land Surface Models within Ensemble-based Assimilation Frameworks
Yunhao Fu, Yongjun Zheng, and Jingjia Luo
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 6 comments)
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It is challenging to assimilate land surface temperature (LST) owing to its fast temporally varying nature. This study proposes a scheme by jointly updating the soil temperature and soil moisture. Results show marginal enhancement in LST, yet soil temperature bias over Northeast Asia (NA) drops sharply. Snow temperature and snow depth over NA, and soil moisture in the humid tropics also improve significantly. These consistent improvements demonstrate the effectiveness of the proposed scheme.
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26 Feb 2026
Weather and air pollution influences on solar energy performance in West Africa: A Bayesian nonlinear mixed-effects approach
Konin Pierre-Claver Kakou, Dungall Laouali, Boko Aka, and Georg Frey
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 1 comment)
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We explored how weather conditions and air pollution shape the sunlight available for solar power. Using a statistical approach that learns from prior knowledge and follows changes across different places and times, we found that these factors affect sunlight in complex ways. Our method predicted solar energy more accurately than common models, suggesting it can support better planning and smoother expansion of solar power.
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24 Feb 2026
Development of an under-ice river discharge forecasting system in Delft-Flood Early Warning System (Delft-FEWS) for the Chaudière River based on a coupled hydrological-hydrodynamic modelling approach
Kh Rahat Usman, Rodolfo Alvarado Montero, Tadros Ghobrial, François Anctil, and Arnejan van Loenen
Geosci. Model Dev., 19, 1559–1580,
2026
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Rivers in cold climate regions such as Canada undergo freeze up during winters which makes the estimation forecasting of under-ice discharge very challenging and uncertain since there is no reliable method other than direct measurements. The current study explored the potential of deploying a coupled modelling framework for the estimation and forecasting of this parameter. The framework showed promising potential in addressing the challenge of estimating and forecasting the under-ice discharge.
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24 Feb 2026
A Deep Learning Approach for Lake Ice Cover Forecasting
Samuel J. Johnston, Justin Murfitt, and Claude Duguay
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 2 comments)
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The Lake Ice Forecasting using Deep Learning model produces spatially explicit forecasts, which consistently outperform the popular Freshwater Lake model. Freeze-up and break-up timing was improved to within 3–9 days with greatly enhanced spatial accuracy of forecasted ice cover patterns. This establishes the potential of data-driven methods to advance lake ice models, with implications for enhancing weather prediction, northern transportation planning, and climate change adaptation.
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24 Feb 2026
A Continuous Implicit Neural Representation Framework with Gradient Regularization for Sea Surface Height Reconstruction From Satellite Altimetry
Dongshuang Li, Liming Pan, Zhaoyuan Yu, and Linwang Yuan
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 1 comment)
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Satellite measurements of sea level are uneven and incomplete, which limits our ability to map the ocean surface. This study introduces a new approach that represents sea level as a smooth surface in space and time. Experiments with satellite data and simulations show that the method produces stable and detailed reconstructions, particularly in regions with strong ocean activity, and enables improved analysis of ocean dynamics.
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23 Feb 2026
Development of fully interactive hydrogen with methane in UKESM1.0
Megan A. J. Brown, Nicola J. Warwick, Nathan Luke Abraham, Paul T. Griffiths, Steve T. Rumbold, Gerd A. Folberth, Fiona M. O'Connor, Hannah Bryant, and Alex T. Archibald
Geosci. Model Dev., 19, 1537–1557,
2026
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Hydrogen (H
) is an indirect greenhouse gas by increasing methane (CH
) lifetime. Interaction between H
and CH
is important for hydrogen’s global warming potential (GWP). Global models do not represent this interaction well; H
or CH
are prescribed at the surface. We implement an interactive H
scheme into a global model coupled with interactive CH
. We simulate scenarios demonstrating its capability, improving model performance and more accurately representing H
-CH
interaction.
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23 Feb 2026
Assessment of an updated polar stratospheric cloud parameterisation for the UK Earth System Model (UKESM1.1) within the UK Met Office Unified Model (v13.9) using CALIOP and MLS observations
Isabelle Sangha, Nathan Luke Abraham, Andrew Orr, Hua Lu, Michael C. Pitts, Lamont R. Poole, and Michael Weimer
EGUsphere,
2026
Preprint under review for GMD
(discussion: final response, 3 comments)
Short summary
Short summary
The UK Earth System Model is updated with an improved polar stratospheric cloud scheme. The performance of the scheme is evaluated against satellite data. While the observed wave ice still fails to form in the model, the scheme improves its ability to represent different polar stratospheric cloud types and their variations. This brings the model closer to satellite observations and highlights the need for further development to capture the polar stratospheric cloud formation in mountain waves.
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20 Feb 2026
A new sub-chunking strategy for fast netCDF-4 access in local, remote and cloud infrastructures, chunkindex V1.1.0
Cédric Penard, Flavien Gouillon, Xavier Delaunay, and Sylvain Herlédan
Geosci. Model Dev., 19, 1519–1535,
2026
Short summary
Short summary
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.
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20 Feb 2026
Implementation of the Generalized Double-Moment Scaling Normalization Method for Raindrop Size Distribution in a WRF 4.3.1 Bulk-Type Cloud Microphysics Scheme: A Case Study over the Korean Peninsula
Joonghyun Jo, Kyo-Sun Sunny Lim, Sun-Young Park, Juhee Kwon, Wonbae Bang, Hyang Suk Park, Jae-Young Byon, Hyun-Suk Kang, and Gyuwon Lee
EGUsphere,
2026
Preprint under review for GMD
(discussion: final response, 2 comments)
Short summary
Short summary
This study implements the generalized double-moment scaling normalization method for raindrop size distribution in the WDM6 microphysics scheme. Numerical experiments for a convective summer rainfall event show that the modified scheme better captures precipitation cell propagation, spatial rainfall distribution, and vertical reflectivity structures compared to the original WDM6 and other bulk/bin schemes.
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19 Feb 2026
Development and improvement of a nonhydrostatic spectral model using non-constant coefficient semi-implicit and vertically conservative semi-Lagrangian schemes
Hiromasa Yoshimura
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
Short summary
Short summary
We have developed a two-dimensional x–z nonhydrostatic spectral model that achieves high computational efficiency by allowing long timesteps. The model incorporates several improvements that enhance numerical stability and accuracy. The model was tested with various cases, and good results were obtained. The model ran stably even in the case of an extremely steep mountain with an average slope of 63.4°. These improvements will be applied to a global atmospheric model in future work.
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19 Feb 2026
mLDNDCv1.0: A Machine Learning-based Surrogate of LandscapeDNDC for Optimising Cropping Systems in Denmark
Meshach Ojo Aderele, Edwin Haas, Licheng Liu, João Serra, David Kraus, Klaus Butterbach-Bahl, and Jaber Rahimi
EGUsphere,
2026
Preprint under review for GMD
(discussion: final response, 5 comments)
Short summary
Short summary
This study develops a fast, data‑driven tool to virtually test millions of ways to manage winter wheat fields in Denmark, without running slow process-based crop models each time. It finds fertilizer, residue, manure, catch crop and irrigation strategies that cut nitrogen pollution and greenhouse gases while increasing yields and soil carbon, all without using more fertilizer overall.
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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 retrievals
Ziyue Huang, Yi-Ning Shi, Fuzhong Weng, and Jun Yang
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 1 comment)
Short summary
Short summary
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.
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13 Feb 2026
Volume of Fluid method applied to free surface boundaries in numerical geodynamic models
Timothy Stephen Gray, Paul James Tackley, and Taras Gerya
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 1 comment)
Short summary
Short summary
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.
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12 Feb 2026
A simple step heating approach for wall surface temperature estimation in the SOlar and LongWave Environmental Irradiance Geometry (SOLWEIG) model
Nils Wallenberg, Björn Holmer, Fredrik Lindberg, Jessika Lönn, Erik Maesel, and David Rayner
Geosci. Model Dev., 19, 1321–1336,
2026
Short summary
Short summary
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.
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12 Feb 2026
Refining the Lagrangian approach for moisture source identification through sensitivity testing of assumptions using BTrIMS1.1
Yinglin Mu, Jason P. Evans, Andréa S. Taschetto, and Chiara Holgate
Geosci. Model Dev., 19, 1367–1385,
2026
Short summary
Short summary
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.
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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 Model
Rongrong Zhang, Zhenzhen Ai, Xin Xu, Haile Xue, and Qiying Chen
EGUsphere,
2026
Preprint under review for GMD
(discussion: final response, 4 comments)
Short summary
Short summary
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.
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11 Feb 2026
Stratospheric aerosol forcing for CMIP7 (part 2): Volcanic sulfur dioxide emissions
Thomas J. Aubry, Michael Sigl, Matthew Toohey, Man Mei Chim, Magali Verkerk, Anja Schmidt, and Simon A. Carn
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 1 comment)
Short summary
Short summary
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.
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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,
2026
Short summary
Short summary
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.
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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,
2026
Preprint under review for GMD
(discussion: final response, 2 comments)
Short summary
Short summary
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.
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10 Feb 2026
ICON coupled to HAM-lite 1.0 in limited-area mode: an efficient framework for targeted kilometer-scale simulations with interactive aerosols
Bernd Heinold, Philipp Weiss, Sadhitro De, Anne Kubin, Jason Müller, Fabian Senf, Philip Stier, and Ina Tegen
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 1 comment)
Short summary
Short summary
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.
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10 Feb 2026
A simple weather generator that converts statistical information from downscaled global climate models to 24-hr precipitation input for hydrological models
Rasmus Benestad
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 6 comments)
Short summary
Short summary
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.
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09 Feb 2026
ClimateBenchPress (v1.0): A Benchmark for Lossy Compression of Climate Data
Tim Reichelt, Juniper Tyree, Milan Klöwer, Peter Dueben, Bryan N. Lawrence, Allison H. Baker, Sara Faghih-Naini, Torsten Hoefler, and Philip Stier
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 2 comments)
Short summary
Short summary
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.
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09 Feb 2026
Scale-selective nudging with a diffusion-based filter in the variable-resolution Model for Prediction Across Scales version 8.2.2
Yiyuan Cheng and Jianping Tang
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 1 comment)
Short summary
Short summary
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.
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06 Feb 2026
DReaMIT: A Dynamical Reanalysis Framework for Modelling Surface-Based Temperature Inversions in Cold Environments
Victor Pozsgay, Nick C. Noad, Philip P. Bonnaventure, and Stephan Gruber
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
Short summary
Short summary
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.
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05 Feb 2026
Interpolating station quantile biases for tropospheric ozone MDA8 bias correction
Jan Peiker, Jan Karlický, and Peter Huszár
EGUsphere,
2026
Revised manuscript under review for GMD
(discussion: final response, 4 comments)
Short summary
Short summary
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.
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05 Feb 2026
Confidence-Aware Framework for Mapping Satellite-Derived River Reaches to Gridded Routing Networks
Kaushlendra Verma and Simon Munier
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 2 comments)
Short summary
Short summary
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.
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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,
2026
Preprint under review for GMD
(discussion: open, 1 comment)
Short summary
Short summary
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.
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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,
2026
Short summary
Short summary
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.
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02 Feb 2026
A hybrid physics–AI approach using universal differential equations with state-dependent neural networks for learnable, regionalizable, spatially distributed hydrological modeling
Ngo Nghi Truyen Huynh, Pierre-André Garambois, François Colleoni, and Jérôme Monnier
Geosci. Model Dev., 19, 1055–1074,
2026
Short summary
Short summary
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.
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30 Jan 2026
Automated stratigraphic interpretation from drillhole lithological descriptions with uncertainty quantification: litho2strat 1.0
Vitaliy Ogarko and Mark Jessell
Geosci. Model Dev., 19, 1007–1025,
2026
Short summary
Short summary
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.
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28 Jan 2026
A novel ALE scheme with the internal boundary for coupling tectonic and surface processes in geodynamic models
Neng Lu, Louis Moresi, Julian Giordani, and Ben Knight
EGUsphere,
2026
Revised manuscript accepted for GMD
(discussion: final response, 4 comments)
Short summary
Short summary
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.
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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,
2026
Short summary
Short summary
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.
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26 Jan 2026
Comparing the MEMS v1 model performance with MCMC and 4DEnVar calibration methods over a continental soil inventory
Toni Viskari, Tristan Quaife, Fernando Fahl, Yao Zhang, and Emanuele Lugato
EGUsphere,
2026
Preprint under review for GMD
(discussion: final response, 3 comments)
Short summary
Short summary
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.
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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,
2026
Preprint under review for GMD
(discussion: final response, 2 comments)
Short summary
Short summary
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.
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26 Jan 2026
Development of a next-generation general ocean circulation model for the Great Lakes
Meena Raju, David J. Cannon, Peter Alsip, He Wang, Jia Wang, Theresa Cordero, Robert W. Hallberg, Charles A. Stock, and Joseph A. Langan
EGUsphere,
2026
Revised manuscript under review for GMD
(discussion: final response, 4 comments)
Short summary
Short summary
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.
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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 v4.5
Qing Zheng, Wei Sun, Zhiquan Liu, Jiajia Mao, Jieying He, Jian Li, and Xingwen Jiang
Geosci. Model Dev., 19, 731–754,
2026
Short summary
Short summary
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.
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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,
2026
Short summary
Editorial statement
Short 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.
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Editorial statement
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.
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23 Jan 2026
G&M3D 1.0: an Interactive Framework for 3D Model Construction and Forward Calculation of Potential Fields
Dengkang Wang, Bo Chen, Kanggui Wei, Jiaxiang Peng, and Rongwen Guo
EGUsphere,
2026
Preprint under review for GMD
(discussion: final response, 2 comments)
Short summary
Short summary
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.
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23 Jan 2026
A Preliminary Study on a Synergistic Assimilation Scheme for Multi-band Satellite Soil Moisture Data
Xuesong Bai, Zhaohui Lin, Zhengkun Qin, and Juan Li
EGUsphere,
2026
Preprint under review for GMD
(discussion: final response, 7 comments)
Short summary
Short summary
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.
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23 Jan 2026
Lagrangian tracking methods applied to free surface boundaries in numerical geodynamic models
Timothy Stephen Gray, Paul James Tackley, and Taras Gerya
EGUsphere,
2026
Preprint under review for GMD
(discussion: final response, 2 comments)
Short summary
Short summary
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.
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22 Jan 2026
Implementation of the ORACLE (v1.0) organic aerosol composition and evolution module into the EC-Earth3-AerChem model
Stylianos Kakavas, Stelios Myriokefalitakis, Alexandra P. Tsimpidi, Vlassis A. Karydis, and Spyros N. Pandis
EGUsphere,
2026
Revised manuscript under review for GMD
(discussion: final response, 6 comments)
Short summary
Short summary
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.
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21 Jan 2026
Threshold atmospheric electric fields for initiating relativistic runaway electron avalanches: theoretical estimates and CORSIKA simulations
Ashot Chilingarian, Liza Hovhannisyan, and Mary Zazyan
Geosci. Model Dev., 19, 621–626,
2026
Short summary
Short summary
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.
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21 Jan 2026
Including the triple isotopic composition of dissolved oxygen in the ocean into the iLOVECLIM model (version 1.1.7): development and evaluation
Emeline Clermont, Ji-Woong Yang, Didier M. Roche, and Thomas Extier
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
Short summary
Short summary
The triple isotopic composition of atmospheric oxygen (
17
Δ) is used to reconstruct past global biospheric productivity. We present the first implementation of the oceanic contribution (
17
ocean
) 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.
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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 E3SMv2
Justin Dong, Michael A. Brunke, Xubin Zeng, Carol S. Woodward, Hui Wan, and Christopher J. Vogl
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 1 comment)
Short summary
Short summary
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.
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20 Jan 2026
A neural network-based observation operator for weather radar data assimilation
Marco Stefanelli, Žiga Zaplotnik, and Gregor Skok
External preprint server,
2026
Revised manuscript under review for GMD
(discussion: final response, 4 comments)
Short summary
Short summary
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.
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19 Jan 2026
Examining spin-up behaviour within WRF dynamical downscaling applications
Megan S. Mallard, Tanya L. Spero, Jared H. Bowden, Jeff Willison, Christopher G. Nolte, and Anna M. Jalowska
Geosci. Model Dev., 19, 579–594,
2026
Short summary
Short summary
“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.
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15 Jan 2026
HydroBlocks-MSSUBv0.1: a multiscale approach for simulating lateral subsurface flow dynamics in Land Surface Models
Daniel Guyumus, Laura Torres-Rojas, Luiz Bacelar, Chengcheng Xu, and Nathaniel Chaney
Geosci. Model Dev., 19, 477–504,
2026
Short summary
Short summary
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.
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15 Jan 2026
Implementation and evaluation of sea level operators in OceanVar2.0: an open-source oceanographic three-dimensional variational data assimilation system
Paolo 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,
2026
Short summary
Short summary
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.
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15 Jan 2026
Stable Stream Temperature Prediction for Different Basins Using Time Series Encoding and Temporal Convolutional Networks
Lichen Su and Wei Zhao
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 6 comments)
Short summary
Short summary
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.
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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 observations
Eric A. Hendricks, Timothy W. Juliano, Branko Kosović, Sue Haupt, Brian J. Gaudet, and Geng Xia
EGUsphere,
2026
Preprint under review for GMD
(discussion: final response, 7 comments)
Short summary
Short 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.
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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,
2026
Short summary
Short summary
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.
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14 Jan 2026
Application of flux footprint equations from Kljun et al. (2015) to field eddy-covariance systems for footprint characteristics into flux network datasets
Xinhua Zhou, Zhi Chen, Ryan Campbell, Atefeh Hosseini, Tian Gao, Xiufen Li, Jianmin Chu, Sen Wu, Ning Zheng, and Jiaojun Zhu
EGUsphere,
2026
Preprint under review for GMD
(discussion: final response, 25 comments)
Short summary
Short summary
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.
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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,
2026
Preprint under review for GMD
(discussion: final response, 12 comments)
Short summary
Short summary
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.
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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,
2026
Revised manuscript under review for GMD
(discussion: final response, 4 comments)
Short summary
Short summary
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.
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12 Jan 2026
Evaluation of preCICE (version 3.3.0) in an Earth System Model Regridding Benchmark
Alex Hocks and Benjamin Uekermann
EGUsphere,
2026
Preprint under review for GMD
(discussion: final response, 4 comments)
Short summary
Short summary
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.
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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,
2026
Preprint under review for GMD
(discussion: final response, 6 comments)
Short summary
Short summary
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.
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11 Jan 2026
HyperGas
1.0: A Python package for analyzing hyperspectral data for greenhouse gases from retrieval to emission rate quantification
Xin 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,
2026
Preprint under review for GMD
(discussion: final response, 2 comments)
Short summary
Short summary
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.
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09 Jan 2026
ML-IAM v1.0: Emulating Integrated Assessment Models With Machine Learning
Yen 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,
2026
Preprint under review for GMD
(discussion: final response, 6 comments)
Short summary
Short summary
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.
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09 Jan 2026
A Systematic Atmospheric Parameter Optimization method to Improve ENSO Simulation in the ICON XPP Earth System Model
Dakuan Yu, Dietmar Dommenget, Holger Pohlmann, and Wolfgang A. Müller
EGUsphere,
2026
Revised manuscript under review for GMD
(discussion: final response, 8 comments)
Short summary
Short summary
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.
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08 Jan 2026
Towards an integrated inventory of anthropogenic emissions for China
Yijuan Zhang, Guy Brasseur, Maria Kanakidou, Claire Granier, Nikos Daskalakis, Alexandros Panagiotis Poulidis, Kun Qu, and Mihalis Vrekoussis
Geosci. Model Dev., 19, 217–237,
2026
Short summary
Short summary
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.
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08 Jan 2026
Intermediate-complexity parameterisation of blowing snow in the ICOLMDZ AGCM: development and first applications in Antarctica
Étienne Vignon, Nicolas Chiabrando, Cécile Agosta, Charles Amory, Valentin Wiener, Justine Charrel, Thomas Dubos, and Christophe Genthon
Geosci. Model Dev., 19, 239–259,
2026
Short summary
Short summary
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.
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06 Jan 2026
Attention-driven and multi-scale feature integrated approach for earth surface temperature data reconstruction
Minghui Zhang, Yunjie Chen, Fan Yang, and Zhengkun Qin
Geosci. Model Dev., 19, 73–91,
2026
Short summary
Short summary
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.
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06 Jan 2026
Exploring the applicability of Censored Shifted Gamma Distribution (CSGD) error model to radar based rainfall nowcasts: A UK case study
Hung-Ming Lin, Li-Pen Wang, and Jen-Yu Han
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 5 comments)
Short summary
Short summary
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.
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06 Jan 2026
Approximating the universal thermal climate index using sparse regression with orthogonal polynomials
Sabin Roman, Gregor Skok, Ljupčo Todorovski, and Sašo Džeroski
External preprint server,
2026
Preprint under review for GMD
(discussion: final response, 4 comments)
Short summary
Short summary
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.
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05 Jan 2026
Increasing resolution and accuracy in sub-seasonal forecasting through 3D U-Net: the western US
Jihun Ryu, Hisu Kim, Shih-Yu (Simon) Wang, and Jin-Ho Yoon
Geosci. Model Dev., 19, 27–39,
2026
Short summary
Short summary
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.
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05 Jan 2026
Representing dynamic grassland density in the land surface model ORCHIDEE r9010
Siqing Xu, Sebastiaan Luyssaert, Yves Balkanski, Philippe Ciais, Nicolas Viovy, Liang Wan, and Jean Sciare
Geosci. Model Dev., 19, 1–25,
2026
Short summary
Short summary
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.
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05 Jan 2026
Parameter estimation for land-surface models using Neural Physics
Ruiyue Huang, Claire E. Heaney, and Maarten van Reeuwijk
External preprint server,
2026
Revised manuscript under review for GMD
(discussion: final response, 8 comments)
Short summary
Short summary
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.
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04 Jan 2026
Evaluation of HNO
, SO
, and NH
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 observations
Jesse 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,
2026
Preprint under review for GMD
(discussion: final response, 2 comments)
Short summary
Short summary
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.
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28 Dec 2025
Variational Stokes method applied to free surface boundaries in numerical geodynamic models
Timothy Stephen Gray, Paul James Tackley, and Taras Gerya
EGUsphere,
2025
Revised manuscript under review for GMD
(discussion: final response, 8 comments)
Short summary
Short summary
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.
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22 Dec 2025
Towards standardising output datasets using the numerical obstacle-resolving model MITRAS as an example
Vivien Voss, K. Heinke Schlünzen, David Grawe, and Karolin S. Samsel
EGUsphere,
2025
Preprint under review for GMD
(discussion: open, 1 comment)
Short summary
Short summary
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.
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21 Dec 2025
Optimizing Gaussian Process Emulation and Generalized Additive Model Fitting for Rapid, Reproducible Earth System Model Analysis
Kunal Ghosh and Leighton A. Regayre
EGUsphere,
2025
Revised manuscript under review for GMD
(discussion: final response, 4 comments)
Short summary
Short summary
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.
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19 Dec 2025
Numerical modelling of diffusion-limited mineral growth for geospeedometry applications
Annalena Stroh, Pascal S. Aellig, and Evangelos Moulas
Geosci. Model Dev., 18, 10203–10220,
2025
Short summary
Short summary
Crystal growth and diffusion are common processes in geology. Our software
MovingBoundaryMinerals.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.
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19 Dec 2025
Wave effect mechanisms enhancing sea–air CO
exchange and modulating seawater carbonate–pH adaptation in the POP2–waves coupled model
Yung-Yao Lan, Huang-Hsiung Hsu, Wei-Liang Lee, and Simon Chou
EGUsphere,
2025
Preprint under review for GMD
(discussion: final response, 2 comments)
Short summary
Short summary
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 and
dp
CO₂ differences, and shows how wave effects reveal the ocean’s buffering capacity through the carbonate–pH system.
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19 Dec 2025
MESMER v1.0.0: Consolidating the Modular Earth System Model Emulator into a Sustainable Research Software Package
Victoria M. Bauer, Mathias Hauser, Yann Quilcaille, Sarah Schöngart, Lukas Gudmundsson, and Sonia I. Seneviratne
EGUsphere,
2025
Revised manuscript under review for GMD
(discussion: final response, 4 comments)
Short summary
Short summary
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.
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18 Dec 2025
GUST1.0: a GPU-accelerated 3D urban surface temperature model
Shuo-Jun Mei, Guanwen Chen, Jian Hang, and Ting Sun
Geosci. Model Dev., 18, 10143–10167,
2025
Short summary
Short summary
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.
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18 Dec 2025
An emulator-based modelling framework for studying astronomical controls on ocean anoxia with an application to the Devonian
Loïc 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,
2025
Short summary
Short summary
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.
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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 engines
Susanne M. C. Scholz, Vlassis A. Karydis, Georgios I. Gkatzelis, Hendrik Fuchs, Spyros N. Pandis, and Alexandra P. Tsimpidi
Geosci. Model Dev., 18, 10119–10142,
2025
Short summary
Short summary
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.
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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 5.2.6-2.3.1
Chongzhi Yin, Shin-Ichiro Shima, and Chunsong Lu
EGUsphere,
2025
Preprint under review for GMD
(discussion: final response, 2 comments)
Short summary
Short summary
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.
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17 Dec 2025
Transfer learning-based hybrid machine learning in single-column model of AFES v4
Yuya Baba
EGUsphere,
2025
Revised manuscript under review for GMD
(discussion: final response, 4 comments)
Short summary
Short summary
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.
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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 4.0
Jiamin Wang, Kun Yang, Jiarui Liu, Xu Zhou, Xiaogang Ma, Wenjun Tang, Ling Yuan, and Zuhuan Ren
Geosci. Model Dev., 18, 10077–10094,
2025
Short summary
Short summary
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.
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16 Dec 2025
A hybrid framework for the spin-up and initialization of distributed coupled ecohydrological-biogeochemical models
Taiqi Lian, Ziyan Zhang, Athanasios Paschalis, and Sara Bonetti
EGUsphere,
2025
Preprint under review for GMD
(discussion: final response, 9 comments)
Short summary
Short summary
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.
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11 Dec 2025
Traffic impact modelling in SURFEX-TEB V9.0 model for improved road surface temperature prediction
Gabriel Colas, Valéry Masson, François Bouttier, and Ludovic Bouilloud
Geosci. Model Dev., 18, 9945–9966,
2025
Short summary
Short summary
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.
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10 Dec 2025
LISFLOOD-FP 8.2: GPU-accelerated multiwavelet discontinuous Galerkin solver with dynamic resolution adaptivity for rapid, multiscale flood simulation
Alovya Ahmed Chowdhury and Georges Kesserwani
Geosci. Model Dev., 18, 9827–9854,
2025
Short summary
Short summary
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.
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10 Dec 2025
rsofun v5.1: a model-data integration framework for simulating ecosystem processes
Josefa Arán Paredes, Fabian Bernhard, Koen Hufkens, Mayeul Marcadella, and Benjamin D. Stocker
Geosci. Model Dev., 18, 9855–9878,
2025
Short summary
Short summary
Mechanistic vegetation models serve to estimate terrestrial carbon fluxes and climate impacts on ecosystems across diverse conditions. Here, we demonstrate and evaluate the
rsofun
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.
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09 Dec 2025
Enhancing volcanic eruption simulations with the WRF-Chem v4.8
Alexander Ukhov, Georgiy Stenchikov, Jordan Schnell, Ravan Ahmadov, Umberto Rizza, Georg Grell, and Ibrahim Hoteit
Geosci. Model Dev., 18, 9805–9825,
2025
Short summary
Short summary
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 SO
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.
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09 Dec 2025
From Reanalysis to Climatology: Deep Learning Reconstruction of Tropical Cyclogenesis in the Western North Pacific
Duc-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,
2025
Revised manuscript accepted for GMD
(discussion: final response, 4 comments)
Short summary
Short summary
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.
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09 Dec 2025
SWEET – Shallow Water Equation Environment for Tests v1.0
Keerthi Gaddameedi, François Hamon, Dominik Huber, Thibaut Lunet, Pedro S. Peixoto, João Guilherme Caldas Steinstraesser, Martin Schreiber, and Valentina Schüller
EGUsphere,
2025
Preprint under review for GMD
(discussion: open, 1 comment)
Short summary
Short summary
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.
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09 Dec 2025
Automatic tuning of iterative pseudo-transient solvers for modelling the deformation of heterogeneous media
Thibault Duretz, Albert de Monserrat, Rubén Sevilla, Ludovic Räss, Ivan Utkin, and Arne Spang
EGUsphere,
2025
Revised manuscript under review for GMD
(discussion: final response, 6 comments)
Short summary
Short summary
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.
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08 Dec 2025
Improving the fine structure of intense rainfall forecast by a designed generative adversarial network
Zuliang Fang, Qi Zhong, Haoming Chen, Xiuming Wang, Zhicha Zhang, and Hongli Liang
Geosci. Model Dev., 18, 9723–9749,
2025
Short summary
Short summary
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.
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08 Dec 2025
MET-AICE v1.0: an operational data-driven sea ice prediction system for the European Arctic
Cyril 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,
2025
Short summary
Short summary
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.
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05 Dec 2025
Evaluating the impact of task aggregation in workflows with shared resource environments: use case for the MONARCH application
Manuel G. Marciani, Miguel Castrillo, Gladys Utrera, Mario C. Acosta, Bruno P. Kinoshita, and Francisco Doblas-Reyes
Geosci. Model Dev., 18, 9709–9721,
2025
Short summary
Short summary
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 %.
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04 Dec 2025
Modelling herbivory impacts on vegetation structure and productivity
Jens Krause, Peter Anthoni, Mike Harfoot, Moritz Kupisch, and Almut Arneth
Geosci. Model Dev., 18, 9633–9651,
2025
Short summary
Short summary
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.
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04 Dec 2025
Development of a global 5 arcmin groundwater model (H08-GMv1.0): model setup and steady-state simulation
Qing He, Naota Hanasaki, Akiko Matsumura, Edwin H. Sutanudjaja, and Taikan Oki
Geosci. Model Dev., 18, 9653–9686,
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.
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04 Dec 2025
CMIP7 data request: impacts and adaptation priorities and opportunities
Alex 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,
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.
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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,
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.
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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,
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.
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02 Dec 2025
Calibrating the GAMIL3-1° climate model using a derivative-free optimization method
Wenjun Liang, Simon Frederick Barnard Tett, Lijuan Li, Coralia Cartis, Danya Xu, Wenjie Dong, and Junjie Huang
Geosci. Model Dev., 18, 9293–9318,
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.
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02 Dec 2025
A process-based modeling of soil organic matter physical properties for land surface models – Part 1: Soil mixture theory
Bertrand Decharme
Geosci. Model Dev., 18, 9349–9384,
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.
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01 Dec 2025
Predicting and correcting the influence of boundary conditions in regional inverse analyses
Hannah 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,
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.
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28 Nov 2025
Exploiting physics-based machine learning to quantify geodynamic effects – insights from the Alpine region
Denise Degen, Ajay Kumar, Magdalena Scheck-Wenderoth, and Mauro Cacace
Geosci. Model Dev., 18, 9219–9236,
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.
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26 Nov 2025
Development of UI-WRF-Chem (v1.0) for the MAIA satellite mission: case demonstration
Huanxin 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,
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 NO
emissions. We demonstrate subsequent model improvements over several of the MAIA target areas.
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25 Nov 2025
ISARD (v1.0) : A Reproducible Geostatistical Framework for Daily Precipitation Ensemble in Mountainous Terrain
Valentin Dura, Guillaume Evin, Anne-Catherine Favre, and David Penot
EGUsphere,
2025
Preprint under review for GMD
(discussion: final response, 12 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.
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24 Nov 2025
Development of the global maize yield model MATCRO-Maize version 1.0
Marin Nagata, Astrid Yusara, Tomomichi Kato, and Yuji Masutomi
Geosci. Model Dev., 18, 8927–8948,
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.
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24 Nov 2025
Development and Testing of Ensemble-Variational Data Assimilation Capabilities for Radar Data within JEDI coupled with FV3-LAM Model
Jun Park, Chengsi Liu, and Ming Xue
EGUsphere,
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.
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21 Nov 2025
Description and evaluation of airborne microplastics in the United Kingdom Earth System Model (UKESM1.1) using GLOMAP-mode
Cameron McErlich, Felix Goddard, Alex Aves, Catherine Hardacre, Nikolaos Evangeliou, Alan J. Hewitt, and Laura E. Revell
Geosci. Model Dev., 18, 8827–8854,
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.
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20 Nov 2025
Autoencoder-based feature extraction for the automatic detection of snow avalanches in seismic data
Andri Simeon, Cristina Pérez-Guillén, Michele Volpi, Christine Seupel, and Alec van Herwijnen
Geosci. Model Dev., 18, 8751–8776,
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.
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20 Nov 2025
Developing an eco-physiological process-based model of soybean growth and yield (MATCRO-Soy v.1): model calibration and evaluation
Astrid 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,
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.
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20 Nov 2025
HAMSOM-VICE v0.9: Comparison of two variable ice-ocean drag coefficient parameterizations on annual simulations of Bohai Sea ice
Libang Xu, Bin Jia, Yu Liu, Xue'en Chen, and Donglin Guo
EGUsphere,
2025
Preprint under review for GMD
(discussion: final response, 5 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.
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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,
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.
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17 Nov 2025
The tracer nudging method for correcting and preventing uneven tracer distributions in geodynamical models
Paul James Tackley
Geosci. Model Dev., 18, 8651–8662,
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.
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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,
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.
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14 Nov 2025
Automatic optical depth parametrization in radiative transfer model RTTOV v13 via LASSO-induced sparsity
Franklin Vargas Jiménez and Juan Carlos De los Reyes
Geosci. Model Dev., 18, 8511–8534,
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.
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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 evaluation
Tao Sun, Jonathan J. Guerrette, Zhiquan Liu, Junmei Ban, Byoung-Joo Jung, Ivette Hernandez Banos, and Chris Snyder
Geosci. Model Dev., 18, 8569–8587,
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.
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13 Nov 2025
Curlew
1.0: Spatio-temporal implicit geological modelling with neural fields in python
Akshay V. Kamath, Samuel T. Thiele, Marie Moulard, Lachlan Grose, Raimon Tolosana-Delgado, Michael J. Hillier, Florian Wellmann, and Richard Gloaguen
External preprint server,
2025
Revised manuscript accepted for GMD
(discussion: final response, 8 comments)
Short summary
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We present
curlew
, 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.
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12 Nov 2025
Grounding-line dynamics in a Stokes ice-flow model (Elmer/Ice v9.0): Improved numerical stability allows larger time steps
A. Clara J. Henry, Thomas Zwinger, and Josefin Ahlkrona
EGUsphere,
2025
Preprint under review for GMD
(discussion: final response, 4 comments)
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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.
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11 Nov 2025
Enhancing particle number concentration modelling accuracy in China by incorporating various nucleation parameterization schemes into the CMAQ version 5.3.2 model
Jianjiong Mao, Lei Jiang, Zhicheng Feng, Jingyi Li, Yanhong Zhu, Momei Qin, Song Guo, Min Hu, and Jianlin Hu
Geosci. Model Dev., 18, 8423–8438,
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.
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10 Nov 2025
Modeling wheat development under extreme weather with WOFOST-EW v1
Jinhui Zheng, Le Yu, Zhenrong Du, Liujun Xiao, and Xiaomeng Huang
Geosci. Model Dev., 18, 8379–8400,
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.
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07 Nov 2025
Datasets and protocols for including anomalous freshwater from melting ice sheets in climate simulations
Gavin 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,
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.
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07 Nov 2025
A highly-efficient automated optimization approach for kilometer-level resolution Earth system models on heterogeneous many-core supercomputers
Xiaojing 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,
2025
Preprint under review for GMD
(discussion: final response, 9 comments)
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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.
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06 Nov 2025
Stripe patterns in wind forecasts induced by physics-dynamics coupling on a staggered grid in CMA-GFS 3.0
Jiong Chen, Yong Su, Zhe Li, Zhanshan Ma, and Xueshun Shen
Geosci. Model Dev., 18, 8253–8267,
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.
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05 Nov 2025
A Python interface to the Fortran-based Parallel Data Assimilation Framework: pyPDAF v1.0.2
Yumeng Chen, Lars Nerger, and Amos S. Lawless
Geosci. Model Dev., 18, 8235–8252,
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.
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05 Nov 2025
Sunburned plankton: ultraviolet radiation inhibition of phytoplankton photosynthesis in the Community Earth System Model version 2
Joshua 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,
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.
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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 wetlands
Ashley 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,
2025
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Wetlands absorb carbon dioxide (CO
), 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 CO
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.
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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,
2025
Revised manuscript under review for GMD
(discussion: final response, 7 comments)
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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.
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05 Nov 2025
Assimilating Geostationary Satellite Visible Reflectance Data: developing and testing the GSI-EnKF-CRTM-Vis technique
Chong Luo, Yongbo Zhou, Yubao Liu, Wei Han, Bin Yao, and Chao Liu
EGUsphere,
2025
Revised manuscript under review for GMD
(discussion: final response, 4 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.
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03 Nov 2025
UFS-RAQMS global atmospheric composition model: TROPOMI CO column assimilation
Maggie 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,
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.
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03 Nov 2025
Implementation of water tracers in the Met Office Unified Model
Alison 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,
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.
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03 Nov 2025
Benchmarking the reactive transport code SCEPTER v1.0.2
Yoshiki Kanzaki and Christopher T. Reinhard
EGUsphere,
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.
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03 Nov 2025
Actionable reporting of CPU-GPU performance comparisons: Insights from a CLUBB case study
Gunther Huebler, Vincent E. Larson, John Dennis, and Sheri Voelz
EGUsphere,
2025
Revised manuscript accepted for GMD
(discussion: final response, 5 comments)
Short summary
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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.
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30 Oct 2025
Combining empirical and mechanistic understanding of spruce bark beetle outbreak dynamics in the LPJ-GUESS (v4.1, r13130) vegetation model
Fredrik Lagergren, Anna Maria Jönsson, Mats Lindeskog, and Thomas A. M. Pugh
Geosci. Model Dev., 18, 8071–8090,
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.
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30 Oct 2025
DINO: a diabatic model of pole-to-pole ocean dynamics to assess subgrid parameterizations across horizontal scales
David Kamm, Julie Deshayes, and Gurvan Madec
Geosci. Model Dev., 18, 8091–8107,
2025
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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.
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30 Oct 2025
T-REX: The tile-based representation of lateral exchange processes in ICON-Land
Philipp de Vrese, Tobias Stacke, Veronika Gayler, Helena Bergstedt, Clemens von Baeckmann, Melanie Thurner, Christian Beer, and Victor Brovkin
EGUsphere,
2025
Revised manuscript under review for GMD
(discussion: final response, 4 comments)
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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.
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29 Oct 2025
Development of the global hydro-economic model (ECHO-Global version 1.0) for assessing the performance of water management options
Taher 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,
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.
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28 Oct 2025
Tensorweave 1.0: interpolating geophysical tensor fields with spatial neural networks
Akshay V. Kamath, Samuel T. Thiele, Hernan Ugalde, Bill Morris, Raimon Tolosana-Delgado, Moritz Kirsch, and Richard Gloaguen
Geosci. Model Dev., 18, 7951–7968,
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.
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27 Oct 2025
Interactive coupling of a Greenland ice sheet model in NorESM2
Heiko 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,
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.
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27 Oct 2025
Coupling the TKE-ACM2 Planetary Boundary Layer Scheme with the Building Effect Parameterization Model
Wanliang Zhang, Chao Ren, Edward Yan Yung Ng, Michael Mau Fung Wong, and Jimmy Chi Hung Fung
Geosci. Model Dev., 18, 7781–7813,
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.
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27 Oct 2025
Implementation of solar UV and energetic particle precipitation within the LINOZ scheme in ICON-ART
Maryam Ramezani Ziarani, Miriam Sinnhuber, Thomas Reddmann, Bernd Funke, Stefan Bender, and Michael Prather
Geosci. Model Dev., 18, 7891–7905,
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 NO
. 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.
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27 Oct 2025
Multigrid beta filter for faster computation of ensemble covariance localization
Sho Yokota, Miodrag Rancic, Ting Lei, R. James Purser, and Manuel S. F. V. De Pondeca
Geosci. Model Dev., 18, 7815–7829,
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.
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27 Oct 2025
Handling discontinuities in numerical ODE methods for Lagrangian oceanography
Jenny M. Mørk, Tor Nordam, and Siren Rühs
Geosci. Model Dev., 18, 7831–7851,
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.
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