GMD - Methods for assessment of models

GMD - Methods for assessment of models
Methods for assessment of models
22 Apr 2026
Controls of the latitudinal migration of the Brazil-Malvinas confluence described in MOM6-SWA14
Nicole Cristine Laureanti, Enrique Curchitser, Katherine Hedstrom, Alistair Adcroft, Robert Hallberg, Matthew J. Harrison, Raphael Dussin, Sin Chan Chou, Paulo Nobre, Emanuel Giarolla, and Rosio Camayo
Geosci. Model Dev., 19, 3109–3128,
2026
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This study investigates the variability of currents in the Southwestern Atlantic Ocean using a high-resolution simulation. Particularly in the Brazil-Malvinas Confluence (BMC), it finds that the southward movement of the BMC, induced by the warming trends in the region, is balanced by northward flow from the Malvinas Current and Pacific Waves. The analysis also examines the intense northward displacement of the North Brazil Current, where inconsistencies in the simulation affect its evolution.
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22 Apr 2026
CMIP7 data request: land and land ice priorities and opportunities
Yue Li, Gang Tang, Eleanor O'Rourke, Samar Minallah, Martim Mas e Braga, Sophie Nowicki, Robin S. Smith, David M. Lawrence, George C. Hurtt, Daniele Peano, Gesa Meyer, Birgit Hassler, Jiafu Mao, Yongkang Xue, and Martin Juckes
Geosci. Model Dev., 19, 3129–3155,
2026
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Land and Land Ice Theme Opportunities describe a list that contains 25 variable groups with 716 variables, which are potentially available to the broad scientific audience for performing analysis in land–atmosphere coupling, hydrological processes and freshwater systems, glacier and ice sheet mass balance and their influence on the sea levels, land use, and plant phenology.
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15 Apr 2026
CMIP7 data request: Earth system priorities and opportunities
Mara Y. McPartland, Tomas Lovato, Charles Koven, Jamie D. Wilson, Briony Turner, Colleen M. Petrik, José Licón-Saláiz, Fang Li, Fanny Lhardy, Jaclyn Clement Kinney, Michio Kawamiya, Birgit Hassler, Nathan P. Gillett, Cheikh Modou Noreyni Fall, Christopher Danek, Chris M. Brierley, Ana Bastos, and Oliver Andrews
Geosci. Model Dev., 19, 2849–2880,
2026
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The Coupled Model Intercomparison Project (CMIP) is an international consortium of climate modeling groups that produce coordinated experiments in order to evaluate human influence on the climate and test knowledge of Earth systems. This paper describes the data requested for Earth systems research in CMIP7. We detail the request for model output of the carbon cycle, the flows of energy among the atmosphere, land and the oceans, and interactions between these and the global climate.
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07 Apr 2026
Validation strategies for deep learning-based groundwater level time series prediction using exogenous meteorological input features
Fabienne Doll, Tanja Liesch, Maria Wetzel, Stefan Kunz, and Stefan Broda
Geosci. Model Dev., 19, 2657–2675,
2026
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With the growing use of machine learning for groundwater level (GWL) prediction, proper performance estimation is crucial. This study compares three validation strategies—blocked cross-validation (bl-CV), repeated out-of-sample (repOOS), and out-of-sample (OOS)—for 1D-CNN and LSTM models using meteorological inputs. Results show that bl-CV offers the most reliable performance estimates, while OOS is the most uncertain, highlighting the need for careful method selection.
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01 Apr 2026
Introducing Volatile Organic Compound Model Intercomparison Project (VOCMIP)
Gunnar Myhre, Øivind Hodnebrog, Srinath Krishnan, Maria Sand, Marit Sandstad, Ragnhild B. Skeie, Lieven Clarisse, Bruno Franco, Dylan B. Millet, Kelley C. Wells, Alexander Archibald, Hannah N. Bryant, Alex T. Chaudhri, David S. Stevenson, Didier Hauglustaine, Michael Prather, J. Christopher Kaiser, Dirk J. L. Olivie, Michael Schulz, Oliver Wild, Ye Wang, Thérèse Salameh, Jason E. Williams, Philippe Le Sager, Fabien Paulot, Kostas Tsigaridis, and Haley E. Plaas
Geosci. Model Dev., 19, 2577–2591,
2026
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Volatile organic compounds (VOCs) affect air quality and climate, but their behavior in the atmosphere is still uncertain. We launched a global research effort to compare how different models represent these compounds and to improve their accuracy. By analyzing model results alongside observations and satellite data, we aim to better understand the atmospheric composition of these compounds.
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17 Mar 2026
CMIP6 data usage: Lessons learned from more than 200 million downloads
Juliette Lavoie, Aude Carreric, Alistair Duffey, Giovanni Chellini, and Elisa Ziegler
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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The Coupled Model Intercomparison Project (CMIP) is a large collaborative project to better understand the Earth’s climate system. The data produced through this project is downloaded by users around the world. In this paper, we analyze the patterns of downloads and the usage of this massive dataset. From this analysis, we make some recommendations for future data production and usage tracking.
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12 Mar 2026
Phenomena and Processes: A New MJO Diagnostic Framework using Moisture Mode Theory as the Testbed
Chun-Hao Chang, Kai-Chih Tseng, and Eric D. Maloney
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 3 comments)
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This study developed a unified Madden-Julian Oscillation (MJO) diagnostic framework that bridges the gap between two existed types of diagnostics (i.e. phenomenological diagnostics and process-oriented diagnostics). Utilizing this framework, we can attribute simulated MJO biases in general circulation models (GCMs) to specific physical processes.
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04 Mar 2026
Data-driven discovery and model reduction methods for the atmospheric effects of high altitude emissions
Jurriaan A. van 't Hoff, Tom S. van Cranenburgh, Urban Fasel, and Irene C. Dedoussi
Geosci. Model Dev., 19, 1867–1892,
2026
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Chemistry transport models (CTMs) are critical in environmental assessments, but their computational cost often limits direct use in decision-making. We evaluate data-driven model discovery and reduction methods as reduced-order models for CTM simulations, showing they can reconstruct and forecast changes in global ozone distribution from supersonic aircraft emissions for several years at a fraction of the CTM cost while also being more accessible.
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03 Mar 2026
Beyond behavioural models: equifinality and overparameterisation undermine confidence in predictions by soil organic matter models
Marijn Van de Broek and Johan Six
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 2 comments)
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Soil organic matter models are often characterised by equifinality, the phenomenon that multiple parameter sets yield similar results. This study shows that the number of identifiable parameters that can be optimised together is limited, even under data-rich conditions. As a result, overparameterised models showed a large variability when simulating future changes. Optimising only identifiable model parameters is therefore necessary to avoid this hidden uncertainty in soil organic matter models.
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27 Feb 2026
GHGPSE-Net: a method towards spaceborne automated extraction of greenhouse-gas point sources using point-object-detection deep neural network
Yiguo Pang, Denghui Hu, Longfei Tian, Shuang Gao, and Guohua Liu
Geosci. Model Dev., 19, 1683–1702,
2026
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Satellites can reveal greenhouse gas point sources, but current point source extraction methods rely on manual inspection. We developed a point-object-detection-based deep learning method for fast, automated detection and quantification of these sources. The model was trained on a large synthetic dataset and tested for generalization using two independent datasets, including simulations and satellite observations.
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23 Feb 2026
A Self-Supervised Precipitation Forecast Verification Based on Contrastive Learning
Yanwen Wang, Shuwen Huang, Qian Li, Xuan Peng, Haoming Chen, Kefeng Zhu, Liwen Wang, and Sheng Li
EGUsphere,
2026
Preprint under review for GMD
(discussion: final response, 2 comments)
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We developed a contrastive learning method (CLPFV) to improve the accuracy of precipitation forecast verification. The proposed method uses precipitation augmentation to simulate real-world forecast errors with gradients and then employs an improved loss function to reflect these errors in the contrastive learning. Experimental results show that the proposed method outperforms traditional and spatial verification methods across different error types and aligns better with expert judgment.
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20 Feb 2026
Quantifying the impact of input data-induced dataset shift on machine learning model applications: A case study of regional reactive nitrogen wet deposition
Yan Zhang, Jiani Tan, Qing Mu, Joshua S. Fu, and Li Li
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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Despite growing use of machine learning in environmental research, few have studied how input data affects findings. We examined the impact of input data characteristics on nitrogen deposition estimates in East and Southeast Asia. Insufficient sample size cuts accuracy by up to 12 %, while data-scarce and remote areas show up to 50 % bias due to poor training data representation. We created a transferable framework for uncertainty quantification, applicable to other data-scarce geospatial tasks.
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10 Feb 2026
EMMA-Tracker v1.0: A lifecycle-based algorithm for identifying and tracking mesoscale convective systems in observations and climate models
David Kneidinger, Armin Schaffer, and Douglas Maraun
External preprint server,
2026
Preprint under review for GMD
(discussion: final response, 9 comments)
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Mesoscale Convective Systems cause extreme weather and flash floods, yet they remain difficult to simulate in climate models. We developed the Evolution-based Mesoscale Convective System Model Assessment tool to identify these storms using standard model data. Our 27-year record for Europe shows these systems drive over 60 percent of heavy hourly rain. This benchmark allows us to evaluate climate model performance and investigate how these intense storms will change in a warming climate.
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04 Feb 2026
Evaluating Extreme Precipitation Forecasts: A Threshold-Weighted, Spatial Verification Approach for Comparing an AI Weather Prediction Model Against a High-Resolution NWP Model
Nicholas Loveday and Tracy Hertneky
External preprint server,
2026
Preprint under review for GMD
(discussion: final response, 6 comments)
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This study introduces a verification method that accounts for differences in grid resolution when evaluating extreme event forecasts. We apply it to an artificial intelligence-based weather prediction model and a high-resolution numerical weather prediction model. Results show that, when assessed on equivalent neighborhood scales, the high resolution numerical weather prediction model only outperforms the AI system for short lead times in predicting extreme precipitation.
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03 Feb 2026
Simulating the recent drought-induced mortality of European beech (
Fagus sylvatica
L.) and Norway spruce (
Picea abies
L.) in German forests
Gina Marano, Ulrike Hiltner, Nikolai Knapp, and Harald Bugmann
Geosci. Model Dev., 19, 1121–1141,
2026
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Drought is reshaping Europe's forests. Using an uncalibrated process-based model across 149 German sites, we identified key drivers of tree mortality in European beech and Norway spruce forests. Our model captured both the timing and extent of mortality. A new bark beetle module improved predictions for spruce. High soil water capacity and heterogeneous soils reduced drought impacts. These findings offer new insights to anticipate forest responses in a warming, drying climate.
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02 Feb 2026
Composite Sharpening by Vortex Symmetrization and Normalization of Tropical Cyclones
Andrina Caratsch, Sylvaine Ferrachat, and Ulrike Lohmann
EGUsphere,
2026
Preprint under review for GMD
(discussion: final response, 4 comments)
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Tropical cyclones come in various size and shape, which smoothes out key storm features in composite analyses. To address this, we developed a compositing method that symmetrizes storms and better aligns their eyewalls and horizontal extents prior to compositing. This approach preserves small-scale features in the composites, reduces within-group variance, and enhances the power of statistical testing. The method facilitates the investigation and understanding of tropical cyclone development.
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29 Jan 2026
TOAD v1.0: A Python Framework for Detecting Abrupt Shifts and Coherent Spatial Domains in Earth-System Data
Jakob Harteg, Lukas Röhrich, Kobe De Maeyer, Julius Garbe, Boris Sakschewski, Ann Kristin Klose, Jonathan F. Donges, Ricarda Winkelmann, and Sina Loriani
EGUsphere,
2026
Preprint under review for GMD
(discussion: final response, 2 comments)
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Climate systems can undergo abrupt, potentially irreversible changes with major impacts on ecosystems and societies, yet consistent tools to detect these transitions across different models are lacking. We present an open-source software package for systematically detecting where and when such changes occur in climate simulations and quantifying variation in transition timing. This enables robust comparison of abrupt changes across models and contributes to assessing climate-tipping risks.
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29 Jan 2026
Identifying sea breezes from atmospheric model output (sea_breeze v1.1)
Andrew Brown, Claire Vincent, and Ewan Short
Geosci. Model Dev., 19, 933–953,
2026
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We developed software to identify sea breezes from weather model output, using three different methods, and applied these to four models for a 6-month period over Australia. We tested each method using case studies and statistics of sea breeze occurrences, finding that a method that identifies atmospheric moisture fronts performs well. Some potential errors are demonstrated due to detection of other frontal systems, but this method could be useful for robustly analyzing sea breezes from models.
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26 Jan 2026
Local weather scenarios for soil and crop models: a simple generator based on historic data sampling
Stefan Anton Albert Gasser, Julius Ansorge, Ulrich Weller, Hans-Jörg Vogel, and Sara König
EGUsphere,
2026
Preprint under review for GMD
(discussion: final response, 5 comments)
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The LocalWeatherSampler generates 20–30 year weather scenarios at daily resolution using historical weather data. Wet/dry years can be defined by threshold or via the Standardized Precipitation Index and future weather sequences can be generated tailored to specific scenarios, like extremely dry or very wet sequences. This approach enables testing and analyzing precipitation patterns and temperature trends with models that rely on realistic, daily weather data, such as soil and crop models.
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23 Jan 2026
A novel ALE scheme with the internal boundary for true free surface simulation in geodynamic models
Neng Lu, Louis Moresi, and Julian Giordani
EGUsphere,
2026
Preprint under review for GMD
(discussion: final response, 2 comments)
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This study presents a novel scheme for simulating Earth's free surface. Traditional methods like 'Sticky Air' face limitations such as increased computational costs and marker fluctuation issues. Our approach integrates the 'Sticky Air' concept into an Arbitrary Lagrangian–Eulerian framework using an internal boundary enabling a true free surface simulation, which reduces marker noise, enhances numerical stability.
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13 Jan 2026
A new efficiency metric for the spatial evaluation and inter-comparison of climate and geoscientific model output
Andreas Karpasitis, Panos Hadjinicolaou, and George Zittis
Geosci. Model Dev., 19, 345–367,
2026
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This study introduces the Modified Spatial Efficiency metric to more rigorously evaluate how well climate models reproduce observed spatial patterns, addressing a long-standing challenge in model assessment. It demonstrates robust performance across a wide range of conditions, capturing spatial structures in an intuitive and physically meaningful way. This new metric offers researchers an improved tool for evaluating and inter-comparing climate models.
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12 Jan 2026
Optimisation of ICON-CLM for the EURO-CORDEX domain: developments, sensitivities, tuning
Beate Geyer, Angelo Campanale, Evgenii Churiulin, Hendrik Feldmann, Klaus Goergen, Stefan Hagemann, Ha Thi Minh Ho-Hagemann, Muhammed Muhshif Karadan, Klaus Keuler, Pavel Khain, Divyaja Lawand, Patrick Ludwig, Vera Maurer, Sergei Petrov, Stefan Poll, Christopher Purr, Emmanuele Russo, Martina Schubert-Frisius, Jan-Peter Schulz, Shweta Singh, Christian Steger, Heimo Truhetz, and Andreas Will
EGUsphere,
2026
Revised manuscript under review for GMD
(discussion: final response, 4 comments)
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Complex models in environmental science typically have a lot of tuning parameters, which has to be set by the users depending on the application. This study presents a new method of objective tuning of a huge number of parameters, by combining expert judgement with automated tuning (LiMMo). The method is successfully applied to the regional climate model ICON-CLM over Europe.
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08 Jan 2026
A Glass-Box Framework for Interpreting Source-Term–Related Functional Modules in a Global Deep Learning Wave Model
Ziliang Zhang, Huaming Yu, Xiaotian Dong, Jiaqi Dou, Danqin Ren, and Xin Qi
EGUsphere,
2026
Preprint under review for GMD
(discussion: open, 0 comments)
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Deep learning models for Earth system prediction are often criticized as "black boxes" that lack physical interpretability. This study introduces a "glass box" dissection framework to analyze the internal logic of these systems. Using the OceanCastNet wave model, we demonstrate that the AI autonomously organizes its computations into modules analogous to physical source terms (wind input, dissipation, and propagation), proving that data-driven models can spontaneously learn physical laws.
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05 Jan 2026
Integration of the Global Water and Lake Sectors within the ISIMIP framework through scaling of streamflow inputs to lakes
Ana I. Ayala, José L. Hinostroza, Daniel Mercado-Bettín, Rafael Marcé, Simon N. Gosling, Donald C. Pierson, and Sebastian Sobek
Geosci. Model Dev., 19, 41–56,
2026
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Climate change affect lakes by including not just the lakes themselves but also the land areas that drain into them. These surrounding areas influence how much water and nutrients flow into lakes which in turn impact water quality. Here, water fluxes from land, derived from a global hydrological model where water fluxes are modelled at the grid scale, were used to estimate streamflow inputs to lakes from their catchments. Using data from 70 Swedish lakes, we showed that our method works well.
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22 Dec 2025
Replicability in Earth System Models
Kai R. Keller, Marta Alerany Solé, and Mario Acosta
Geosci. Model Dev., 18, 10221–10243,
2025
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Can we be sure that different computing environments, that should not change the model climate, indeed leave the climate unaltered? In this article, we present a novel methodology that answers whether two model climates are statistically the same. Besides a new methodology, able to detect significant differences between two model climates 60 % more accurately than a similar recent state-of-the-art method, we also provide an analysis on what actually constitutes a different climate.
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19 Dec 2025
An objective dynamic multivariable weighting method for reducing uncertainty in WRF parameterization scheme selection
Tianyu Gou, Yaoyang Deng, Jun Niu, and Shaozhong Kang
EGUsphere,
2025
Preprint under review for GMD
(discussion: open, 1 comment)
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This study proposes a new method to improve climate simulation evaluation, tackling a key model error: selecting the best parameter combinations. Our "dynamic weighting" method automatically gives more importance to hard-to-predict variables, like precipitation and wind speed. When tested in two distinct climate regions, our approach identified model settings that produced more accurate and reliable forecasts than traditional equal-weighting methods, performing well in extreme weather years.
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19 Dec 2025
Estimation of local training data point densities to support the assessment of spatial prediction uncertainty
Fabian Lukas Schumacher, Christian Knoth, Marvin Ludwig, and Hanna Meyer
Geosci. Model Dev., 18, 10185–10202,
2025
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Machine learning is increasingly used in environmental sciences for spatial predictions, but its effectiveness is challenged when models are applied beyond the areas they were trained on. We propose a Local Training Data Point Density (LPD) approach that considers how well a model's environment is represented by training data. This method provides a valuable tool for evaluating model applicability and uncertainties, crucial for broader scientific and practical applications.
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10 Dec 2025
Validation of climate mitigation pathways
Pascal Weigmann, Rahel Mandaroux, Fabrice Lécuyer, Anne Merfort, Tabea Dorndorf, Johanna Hoppe, Jarusch Muessel, Robert Pietzcker, Oliver Richters, Lavinia Baumstark, Elmar Kriegler, Nico Bauer, Falk Benke, Chen Chris Gong, and Gunnar Luderer
Geosci. Model Dev., 18, 9897–9912,
2025
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We present the Potsdam Integrated Assessment Modeling validation tool, piamValidation, an open-source R package for validating climate scenarios. The tool enables comparison of model outputs with historical data, feasibility constraints and alternative scenarios. Designed as a community resource, validation configuration files can serve as a knowledge-sharing platform. The main objective is to improve the credibility of Integrated Assessment Models by promoting standardized validation practices.
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03 Dec 2025
Standardising the “Gregory method” for calculating equilibrium climate sensitivity
Anna Zehrung, Andrew D. King, Zebedee Nicholls, Mark D. Zelinka, and Malte Meinshausen
Geosci. Model Dev., 18, 9433–9450,
2025
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The Gregory method is a common approach for calculating the equilibrium climate sensitivity (ECS). However, studies which apply this method lack transparency in how model data is processed prior to calculating the ECS, inhibiting replicability. Different choices of global weighting, net radiative flux variable, anomaly calculation, and linear regression fit can affect the ECS estimates. We investigate the impact of these choices and propose a standardised method for future ECS calculations.
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27 Nov 2025
Quantifying coupling errors in atmosphere-ocean-sea ice models: A study of iterative and non-iterative approaches in the EC-Earth AOSCM
Valentina Schüller, Florian Lemarié, Philipp Birken, and Eric Blayo
Geosci. Model Dev., 18, 9167–9187,
2025
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Earth system models consist of many components, coupled in time and space. Standard coupling algorithms introduce a numerical error, which one can compute with iterative coupling methods. We use such a method for a coupled model of a single vertical column of the atmosphere, ocean, and sea ice. We find that coupling errors in the atmosphere and at the ice surface can be substantial and that discontinuous physics parameterizations lead to convergence issues of the iteration.
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20 Nov 2025
On the proper use of screen-level temperature measurements in weather forecasting models over mountains
Danaé Préaux, Ingrid Dombrowski-Etchevers, Isabelle Gouttevin, and Yann Seity
Geosci. Model Dev., 18, 8723–8749,
2025
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Air temperature is usually measured around 2 m above the ground following meteorological standards. However, in mountain regions, temperature sensors are often placed higher up to avoid being buried in snow in winter. We show that the measurement height is of high importance when quantifying the errors made by weather prediction models. Also, it should be accounted for when these observations are used to correct the models in real time, as doing otherwise degrades their forecasts at high altitudes.
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19 Nov 2025
Evaluation of annual trends in carbon cycle variables simulated by CMIP6 Earth system models in China
Ziyang Li, Lidong Zou, Anzhou Zhao, Haigang Zhang, Feng Yue, Zhe Luo, Rui Bian, and Ruihao Xu
Geosci. Model Dev., 18, 8703–8722,
2025
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To understand how well current Earth system models simulate the natural world, we compared the models' outputs against measurements from satellites. Our results show these models struggle to accurately capture trends in variables related to carbon cycle, because the models can’t respond to human and environmental influences. This evaluation is crucial because improving these models will lead to more reliable forecasts of how ecosystems and the climate will change in the future.
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13 Nov 2025
Meta-modelling of carbon fluxes from crop and grassland multi-model outputs
Roland Hollós, Nándor Zrinyi, Zoltán Barcza, Gianni Bellocchi, Renáta Sándor, János Ruff, and Nándor Fodor
EGUsphere,
2025
Revised manuscript under review for GMD
(discussion: final response, 8 comments)
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This work builds upon and extends previous multi-model ensemble studies by introducing four meta-modelling approaches to predict ecosystem-scale C fluxes. Our results show that meta-models consistently outperform both the multi-model median and the best individual process-based models, improving explained variance by up to 38.5 % and substantially reducing bias, even for challenging fluxes such as total ecosystem respiration and net ecosystem exchange.
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06 Nov 2025
Bias correcting regional scale Earth system model projections: novel approach using empirical mode decomposition
Arkaprabha Ganguli, Jeremy Feinstein, Ibraheem Raji, Akintomide Akinsanola, Connor Aghili, Chunyong Jung, Jordan Branham, Tom Wall, Whitney Huang, and Rao Kotamarthi
Geosci. Model Dev., 18, 8313–8332,
2025
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This study introduces a timescale-aware bias-correction framework to enhance Earth system model assessments, vital for the geoscience community. By decomposing model outputs into oscillatory components, we preserve critical information across various timescales, ensuring more reliable projections. This improved reliability supports strategic decisions in sectors such as agriculture, water resources, and disaster preparedness.
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29 Oct 2025
Intercomparison of bias correction methods for precipitation of multiple GCMs across six continents
Young Hoon Song and Eun-Sung Chung
Geosci. Model Dev., 18, 8017–8045,
2025
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This study assessed three methods for correcting daily precipitation data: Quantile Delta Mapping, Empirical Quantile Mapping (EQM), and Detrended Quantile Mapping (DQM) using 11 GCMs. EQM performed best overall, offering reliable corrections and lower uncertainty. The best bias correction method for each grid is selected differently depending on the weighting case. The best bias correction method can vary depending on factors such as climate and terrain.
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28 Oct 2025
A Python diagnostics package for evaluation of Madden–Julian Oscillation (MJO) teleconnections in subseasonal-to-seasonal (S2S) forecast systems
Cristiana Stan, Saisri Kollapaneni, Andrea M. Jenney, Jiabao Wang, Zheng Wu, Cheng Zheng, Hyemi Kim, Chaim I. Garfinkel, and Ayush Singh
Geosci. Model Dev., 18, 7969–7985,
2025
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The diagnostics package is an open-source Python software package used for evaluating the Madden–Julian Oscillation teleconnections to the extratropics, as predicted by subseasonal-to-seasonal (S2S) forecast systems.
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27 Oct 2025
Urban weather modeling using WRF: linking physical assumptions, code implementation, and observational needs
Parag Joshi, Tzu-Shun Lin, Cenlin He, and Katia Lamer
Geosci. Model Dev., 18, 7869–7890,
2025
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Present study revisits model that represent urban effects in the Weather Research & Forecasting model. We propose methods to identify evaluable parameters via field measurements and found inconsistencies between physics and its code implementation. Simulations reveal small errors can significantly impact outputs.
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