Articles | Volume 19, issue 1
https://doi.org/10.5194/gmd-19-345-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-345-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A new efficiency metric for the spatial evaluation and inter-comparison of climate and geoscientific model output
Andreas Karpasitis
CORRESPONDING AUTHOR
Climate and Atmosphere Research Center (CARE-C), The Cyprus Institute, Nicosia, Cyprus
Panos Hadjinicolaou
Climate and Atmosphere Research Center (CARE-C), The Cyprus Institute, Nicosia, Cyprus
George Zittis
Climate and Atmosphere Research Center (CARE-C), The Cyprus Institute, Nicosia, Cyprus
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Ioannis Sofokleous, George Zittis, Gerald Dörflinger, and Adriana Bruggeman
EGUsphere, https://doi.org/10.5194/egusphere-2025-2478, https://doi.org/10.5194/egusphere-2025-2478, 2025
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We developed a new method to improve numerical models that predict future water availability under climate change. The method works across different regions and climate scenarios. Applying the method to mountain river basins in the Eastern Mediterranean showed that streamflow could drop by 39 % on average between 2030 and 2060, and by up to 70 % during the driest years. These findings help support better water planning in a changing climate.
Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld
Geosci. Model Dev., 17, 4689–4703, https://doi.org/10.5194/gmd-17-4689-2024, https://doi.org/10.5194/gmd-17-4689-2024, 2024
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This study focuses on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies are applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method random forest to increase the accuracy of climate models concerning the projection of the number of wet days.
Assaf Hochman, Francesco Marra, Gabriele Messori, Joaquim G. Pinto, Shira Raveh-Rubin, Yizhak Yosef, and Georgios Zittis
Earth Syst. Dynam., 13, 749–777, https://doi.org/10.5194/esd-13-749-2022, https://doi.org/10.5194/esd-13-749-2022, 2022
Short summary
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Gaining a complete understanding of extreme weather, from its physical drivers to its impacts on society, is important in supporting future risk reduction and adaptation measures. Here, we provide a review of the available scientific literature, knowledge gaps and key open questions in the study of extreme weather events over the vulnerable eastern Mediterranean region.
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Short summary
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.
This study introduces the Modified Spatial Efficiency metric to more rigorously evaluate how...