Articles | Volume 16, issue 16
https://doi.org/10.5194/gmd-16-4811-2023
© Author(s) 2023. 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-16-4811-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
24 Aug 2023
Model description paper |
| 24 Aug 2023
| 24 Aug 2023
MESMAR v1: a new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region
Institute of Marine Sciences (ISMAR), National Research Council
(CNR), Rome, Italy
National Research Center for High Performance Computing,
Big Data and Quantum Computing, ICSC, Italy
Yassmin Hesham Essa
Institute of Marine Sciences (ISMAR), National Research Council
(CNR), Rome, Italy
Central Laboratory for Agricultural Climate (CLAC), Agricultural
Research Center (ARC), Cairo, Egypt
Vincenzo de Toma
Institute of Marine Sciences (ISMAR), National Research Council
(CNR), Rome, Italy
Alessandro Anav
National Research Center for High Performance Computing,
Big Data and Quantum Computing, ICSC, Italy
Italian National Agency for New Technologies, Energy and
Sustainable Economic Development (ENEA), Rome, Italy
Gianmaria Sannino
National Research Center for High Performance Computing,
Big Data and Quantum Computing, ICSC, Italy
Italian National Agency for New Technologies, Energy and
Sustainable Economic Development (ENEA), Rome, Italy
Rosalia Santoleri
Institute of Marine Sciences (ISMAR), National Research Council
(CNR), Rome, Italy
Chunxue Yang
Institute of Marine Sciences (ISMAR), National Research Council
(CNR), Rome, Italy
National Research Center for High Performance Computing,
Big Data and Quantum Computing, ICSC, Italy
Related authors
Marie Bouih, Anne Barnoud, Chunxue Yang, Andrea Storto, Alejandro Blazquez, William Llovel, Robin Fraudeau, and Anny Cazenave
EGUsphere, https://doi.org/10.5194/egusphere-2024-3945, https://doi.org/10.5194/egusphere-2024-3945, 2025
Short summary
Short summary
Present-day sea level rise is not uniform regionally. For better understanding of regional sea level variations, a classical approach is to compare the observed sea level trend patterns with those of the sum of the contributions. If the regional sea level budget is not closed, this allows to detect errors in the observing systems. Our study based on this approach shows the the budget is not closed in the North Atlantic Ocean and identifies as main suspect, errors in Argo-based salinity data.
Andrea Storto, Sergey Frolov, Laura Slivinski, and Chunxue Yang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-185, https://doi.org/10.5194/gmd-2024-185, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Inaccuracies in air-sea heat fluxes severely downgrade the accuracy of ocean numerical simulations. Here, we use artificial neural networks to correct the air-sea heat fluxes as a function of oceanic and atmospheric state predictors. The correction successfully improves surface and subsurface ocean temperatures beyond the training period and in prediction experiments.
Andrea Storto, Giulia Chierici, Julia Pfeffer, Anne Barnoud, Romain Bourdalle-Badie, Alejandro Blazquez, Davide Cavaliere, Noémie Lalau, Benjamin Coupry, Marie Drevillon, Sebastien Fourest, Gilles Larnicol, and Chunxue Yang
State Planet, 4-osr8, 12, https://doi.org/10.5194/sp-4-osr8-12-2024, https://doi.org/10.5194/sp-4-osr8-12-2024, 2024
Short summary
Short summary
The variability in the manometric sea level (i.e. the sea level mass component) in three ocean basins is investigated in this study using three different methods (reanalyses, gravimetry, and altimetry in combination with in situ observations). We identify the emerging long-term signals, the consistency of the datasets, and the influence of large-scale climate modes on the regional manometric sea level variations at both seasonal and interannual timescales.
Vincenzo de Toma, Daniele Ciani, Yassmin Hesham Essa, Chunxue Yang, Vincenzo Artale, Andrea Pisano, Davide Cavaliere, Rosalia Santoleri, and Andrea Storto
Geosci. Model Dev., 17, 5145–5165, https://doi.org/10.5194/gmd-17-5145-2024, https://doi.org/10.5194/gmd-17-5145-2024, 2024
Short summary
Short summary
This study explores methods to reconstruct diurnal variations in skin sea surface temperature in a model of the Mediterranean Sea. Our new approach, considering chlorophyll concentration, enhances spatial and temporal variations in the warm layer. Comparative analysis shows context-dependent improvements. The proposed "chlorophyll-interactive" method brings the surface net total heat flux closer to zero annually, despite a net heat loss from the ocean to the atmosphere.
Eric Jansen, Sam Pimentel, Wang-Hung Tse, Dimitra Denaxa, Gerasimos Korres, Isabelle Mirouze, and Andrea Storto
Ocean Sci., 15, 1023–1032, https://doi.org/10.5194/os-15-1023-2019, https://doi.org/10.5194/os-15-1023-2019, 2019
Short summary
Short summary
The assimilation of satellite SST data into ocean models is complex. The temperature of the thin uppermost layer that is measured by satellites may differ from the much thicker upper layer used in numerical models, leading to biased results. This paper shows how canonical correlation analysis can be used to generate observation operators from existing datasets of model states and corresponding observation values. This type of operator can correct for near-surface effects when assimilating SST.
Gerasimos Korres, Dimitra Denaxa, Eric Jansen, Isabelle Mirouze, Sam Pimentel, Wang-Hung Tse, and Andrea Storto
Ocean Sci. Discuss., https://doi.org/10.5194/os-2018-158, https://doi.org/10.5194/os-2018-158, 2019
Preprint withdrawn
Short summary
Short summary
A statistical-dynamical observation operator (SOSSTA) for satellite SST data assimilation able to account for SST diurnal variability, is formulated and implemented into the POSEIDON forecasting system (Aegean Sea). Model experiments where daytime SST retrievals from the SEVIRI infrared radiometer are introduced into the data assimilation procedure through the application of the observation operator, showed an improvement of the POSEIDON modelling system performance.
Marianne Pietschnig, Michael Mayer, Takamasa Tsubouchi, Andrea Storto, Sebastian Stichelberger, and Leopold Haimberger
Ocean Sci. Discuss., https://doi.org/10.5194/os-2017-98, https://doi.org/10.5194/os-2017-98, 2017
Revised manuscript not accepted
Short summary
Short summary
New estimates of volume and temperature transports into the Arctic Ocean through the four major gateways (Davis, Fram and Bering Strait and the Barents Sea Opening) have recently become available. These estimates are derived from moored observations. In this study, the same transports derived from a recent ocean reanalysis are compared to the observation-based estimates in the straits. In addition, cross-section plots of velocity, temperature and temperature flux density are investigated.
Zhaoyi Wang, Andrea Storto, Nadia Pinardi, Guimei Liu, and Hui Wang
Nat. Hazards Earth Syst. Sci., 17, 17–30, https://doi.org/10.5194/nhess-17-17-2017, https://doi.org/10.5194/nhess-17-17-2017, 2017
Andrea Storto and Simona Masina
Earth Syst. Sci. Data, 8, 679–696, https://doi.org/10.5194/essd-8-679-2016, https://doi.org/10.5194/essd-8-679-2016, 2016
Short summary
Short summary
A large number of applications related to the study of ocean climate require reliable datasets of the main physical variables of the ocean. Ocean reanalyses are a methodology based on the synthesis of information from ocean observations and models, and near-surface atmospheric observations into a dataset in a way as consistent in time as possible. In this paper, we describe and validate an upgraded version of the CMCC global ocean physical reanalysis (1980–present) at 1 / 4° resolution.
Paolo Oddo, Andrea Storto, Srdjan Dobricic, Aniello Russo, Craig Lewis, Reiner Onken, and Emanuel Coelho
Ocean Sci., 12, 1137–1153, https://doi.org/10.5194/os-12-1137-2016, https://doi.org/10.5194/os-12-1137-2016, 2016
Doroteaciro Iovino, Simona Masina, Andrea Storto, Andrea Cipollone, and Vladimir N. Stepanov
Geosci. Model Dev., 9, 2665–2684, https://doi.org/10.5194/gmd-9-2665-2016, https://doi.org/10.5194/gmd-9-2665-2016, 2016
Short summary
Short summary
An 11-year simulation of a global eddying ocean (1/16) configuration is presented. Model performance is evaluated against observations and a twin 1/4 configuration. The model realistically represents the variability at upper and intermediate depths, the position and strength of the surface circulation, and exchanges of mass through key passages. Sea ice properties are close to satellite observations. This simulation constitutes the groundwork for future applications to short range ocean forecasting.
L. Visinelli, S. Masina, M. Vichi, and A. Storto
Biogeosciences Discuss., https://doi.org/10.5194/bgd-11-5399-2014, https://doi.org/10.5194/bgd-11-5399-2014, 2014
Revised manuscript not accepted
Mohamed Hamitouche, Giorgia Fosser, Alessandro Anav, Cenlin He, and Tzu-Shun Lin
Hydrol. Earth Syst. Sci., 29, 1221–1240, https://doi.org/10.5194/hess-29-1221-2025, https://doi.org/10.5194/hess-29-1221-2025, 2025
Short summary
Short summary
This study evaluates how different methods of simulating runoff impact river flow predictions globally. By comparing seven approaches within the Noah-Multi-parameterisation (Noah-MP) land surface model, we found significant differences in accuracy, with some methods underestimating or overestimating runoff. The results are crucial for improving water resource management and flood prediction. Our work highlights the need for precise modelling to better prepare for climate-related challenges.
Maria Vittoria Struglia, Alessandro Anav, Marta Antonelli, Sandro Calmanti, Franco Catalano, Alessandro Dell'Aquila, Emanuela Pichelli, and Giovanna Pisacane
EGUsphere, https://doi.org/10.5194/egusphere-2025-387, https://doi.org/10.5194/egusphere-2025-387, 2025
Short summary
Short summary
We present the results of downscaling global climate projections for the Mediterranean and Italian regions aiming to produce high-resolution climate information for the assessment of climate change signals, focusing on extreme events. A general warming is foreseen by the end of century with a mean precipitation reduction accompanied, over Italian Peninsula, by a strong increase in the intensity of extreme precipitation events, particularly relevant for the high emissions scenario during autumn
Marie Bouih, Anne Barnoud, Chunxue Yang, Andrea Storto, Alejandro Blazquez, William Llovel, Robin Fraudeau, and Anny Cazenave
EGUsphere, https://doi.org/10.5194/egusphere-2024-3945, https://doi.org/10.5194/egusphere-2024-3945, 2025
Short summary
Short summary
Present-day sea level rise is not uniform regionally. For better understanding of regional sea level variations, a classical approach is to compare the observed sea level trend patterns with those of the sum of the contributions. If the regional sea level budget is not closed, this allows to detect errors in the observing systems. Our study based on this approach shows the the budget is not closed in the North Atlantic Ocean and identifies as main suspect, errors in Argo-based salinity data.
Andrea Storto, Sergey Frolov, Laura Slivinski, and Chunxue Yang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-185, https://doi.org/10.5194/gmd-2024-185, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Inaccuracies in air-sea heat fluxes severely downgrade the accuracy of ocean numerical simulations. Here, we use artificial neural networks to correct the air-sea heat fluxes as a function of oceanic and atmospheric state predictors. The correction successfully improves surface and subsurface ocean temperatures beyond the training period and in prediction experiments.
Andrea Storto, Giulia Chierici, Julia Pfeffer, Anne Barnoud, Romain Bourdalle-Badie, Alejandro Blazquez, Davide Cavaliere, Noémie Lalau, Benjamin Coupry, Marie Drevillon, Sebastien Fourest, Gilles Larnicol, and Chunxue Yang
State Planet, 4-osr8, 12, https://doi.org/10.5194/sp-4-osr8-12-2024, https://doi.org/10.5194/sp-4-osr8-12-2024, 2024
Short summary
Short summary
The variability in the manometric sea level (i.e. the sea level mass component) in three ocean basins is investigated in this study using three different methods (reanalyses, gravimetry, and altimetry in combination with in situ observations). We identify the emerging long-term signals, the consistency of the datasets, and the influence of large-scale climate modes on the regional manometric sea level variations at both seasonal and interannual timescales.
Karina von Schuckmann, Lorena Moreira, Mathilde Cancet, Flora Gues, Emmanuelle Autret, Jonathan Baker, Clément Bricaud, Romain Bourdalle-Badie, Lluis Castrillo, Lijing Cheng, Frederic Chevallier, Daniele Ciani, Alvaro de Pascual-Collar, Vincenzo De Toma, Marie Drevillon, Claudia Fanelli, Gilles Garric, Marion Gehlen, Rianne Giesen, Kevin Hodges, Doroteaciro Iovino, Simon Jandt-Scheelke, Eric Jansen, Melanie Juza, Ioanna Karagali, Thomas Lavergne, Simona Masina, Ronan McAdam, Audrey Minière, Helen Morrison, Tabea Rebekka Panteleit, Andrea Pisano, Marie-Isabelle Pujol, Ad Stoffelen, Sulian Thual, Simon Van Gennip, Pierre Veillard, Chunxue Yang, and Hao Zuo
State Planet, 4-osr8, 1, https://doi.org/10.5194/sp-4-osr8-1-2024, https://doi.org/10.5194/sp-4-osr8-1-2024, 2024
Karina von Schuckmann, Lorena Moreira, Mathilde Cancet, Flora Gues, Emmanuelle Autret, Ali Aydogdu, Lluis Castrillo, Daniele Ciani, Andrea Cipollone, Emanuela Clementi, Gianpiero Cossarini, Alvaro de Pascual-Collar, Vincenzo De Toma, Marion Gehlen, Rianne Giesen, Marie Drevillon, Claudia Fanelli, Kevin Hodges, Simon Jandt-Scheelke, Eric Jansen, Melanie Juza, Ioanna Karagali, Priidik Lagemaa, Vidar Lien, Leonardo Lima, Vladyslav Lyubartsev, Ilja Maljutenko, Simona Masina, Ronan McAdam, Pietro Miraglio, Helen Morrison, Tabea Rebekka Panteleit, Andrea Pisano, Marie-Isabelle Pujol, Urmas Raudsepp, Roshin Raj, Ad Stoffelen, Simon Van Gennip, Pierre Veillard, and Chunxue Yang
State Planet, 4-osr8, 2, https://doi.org/10.5194/sp-4-osr8-2-2024, https://doi.org/10.5194/sp-4-osr8-2-2024, 2024
Marco Chericoni, Giorgia Fosser, Emmanouil Flaounas, Gianmaria Sannino, and Alessandro Anav
EGUsphere, https://doi.org/10.5194/egusphere-2024-2829, https://doi.org/10.5194/egusphere-2024-2829, 2024
Short summary
Short summary
This study explores how sea surface energy influences both the atmosphere and ocean at various vertical levels during extreme Mediterranean cyclones. It focuses on cyclones' precipitation and wind speed response, as well as on ocean temperature variation. The analysis shows the effectiveness of the Regional Coupled Model in coherently representing the thermodynamic processes associated with extreme cyclones across both the atmosphere and the ocean.
Vincenzo de Toma, Daniele Ciani, Yassmin Hesham Essa, Chunxue Yang, Vincenzo Artale, Andrea Pisano, Davide Cavaliere, Rosalia Santoleri, and Andrea Storto
Geosci. Model Dev., 17, 5145–5165, https://doi.org/10.5194/gmd-17-5145-2024, https://doi.org/10.5194/gmd-17-5145-2024, 2024
Short summary
Short summary
This study explores methods to reconstruct diurnal variations in skin sea surface temperature in a model of the Mediterranean Sea. Our new approach, considering chlorophyll concentration, enhances spatial and temporal variations in the warm layer. Comparative analysis shows context-dependent improvements. The proposed "chlorophyll-interactive" method brings the surface net total heat flux closer to zero annually, despite a net heat loss from the ocean to the atmosphere.
Davide Zanchettin, Sara Bruni, Fabio Raicich, Piero Lionello, Fanny Adloff, Alexey Androsov, Fabrizio Antonioli, Vincenzo Artale, Eugenio Carminati, Christian Ferrarin, Vera Fofonova, Robert J. Nicholls, Sara Rubinetti, Angelo Rubino, Gianmaria Sannino, Giorgio Spada, Rémi Thiéblemont, Michael Tsimplis, Georg Umgiesser, Stefano Vignudelli, Guy Wöppelmann, and Susanna Zerbini
Nat. Hazards Earth Syst. Sci., 21, 2643–2678, https://doi.org/10.5194/nhess-21-2643-2021, https://doi.org/10.5194/nhess-21-2643-2021, 2021
Short summary
Short summary
Relative sea level in Venice rose by about 2.5 mm/year in the past 150 years due to the combined effect of subsidence and mean sea-level rise. We estimate the likely range of mean sea-level rise in Venice by 2100 due to climate changes to be between about 10 and 110 cm, with an improbable yet possible high-end scenario of about 170 cm. Projections of subsidence are not available, but historical evidence demonstrates that they can increase the hazard posed by climatically induced sea-level rise.
Alessandro Anav, Adriana Carillo, Massimiliano Palma, Maria Vittoria Struglia, Ufuk Utku Turuncoglu, and Gianmaria Sannino
Geosci. Model Dev., 14, 4159–4185, https://doi.org/10.5194/gmd-14-4159-2021, https://doi.org/10.5194/gmd-14-4159-2021, 2021
Short summary
Short summary
The Mediterranean Basin is a complex region, characterized by the presence of pronounced topography and a complex land–sea distribution including a considerable number of islands and straits; these features generate strong local atmosphere–sea interactions.
Regional Earth system models have been developed and used to study both present and future Mediterranean climate systems. The main aims of this paper are to present and evaluate the newly developed regional Earth system model ENEA-REG.
Jasdeep Singh Anand, Alessandro Anav, Marcello Vitale, Daniele Peano, Nadine Unger, Xu Yue, Robert J. Parker, and Hartmut Boesch
Biogeosciences Discuss., https://doi.org/10.5194/bg-2021-125, https://doi.org/10.5194/bg-2021-125, 2021
Publication in BG not foreseen
Short summary
Short summary
Ozone damages plants, which prevents them from absorbing CO2 from the atmosphere. This poses a potential threat to preventing dangerous climate change. In this work, satellite observations of forest cover, ozone, climate, and growing season are combined with an empirical model to estimate the carbon lost due to ozone exposure over Europe. The estimated carbon losses agree well with prior modelled estimates, showing for the first time that satellites can be used to better understand this effect.
Eric Jansen, Sam Pimentel, Wang-Hung Tse, Dimitra Denaxa, Gerasimos Korres, Isabelle Mirouze, and Andrea Storto
Ocean Sci., 15, 1023–1032, https://doi.org/10.5194/os-15-1023-2019, https://doi.org/10.5194/os-15-1023-2019, 2019
Short summary
Short summary
The assimilation of satellite SST data into ocean models is complex. The temperature of the thin uppermost layer that is measured by satellites may differ from the much thicker upper layer used in numerical models, leading to biased results. This paper shows how canonical correlation analysis can be used to generate observation operators from existing datasets of model states and corresponding observation values. This type of operator can correct for near-surface effects when assimilating SST.
Winfried Hoke, Tina Swierczynski, Peter Braesicke, Karin Lochte, Len Shaffrey, Martin Drews, Hilppa Gregow, Ralf Ludwig, Jan Even Øie Nilsen, Elisa Palazzi, Gianmaria Sannino, Lars Henrik Smedsrud, and ECRA network
Adv. Geosci., 46, 1–10, https://doi.org/10.5194/adgeo-46-1-2019, https://doi.org/10.5194/adgeo-46-1-2019, 2019
Short summary
Short summary
The European Climate Research Alliance is a bottom-up association of European research institutions helping to facilitate the development of climate change research, combining the capacities of national research institutions and inducing closer ties between existing national research initiatives, projects and infrastructures. This article briefly introduces the network's structure and organisation, as well as project management issues and prospects.
Gerasimos Korres, Dimitra Denaxa, Eric Jansen, Isabelle Mirouze, Sam Pimentel, Wang-Hung Tse, and Andrea Storto
Ocean Sci. Discuss., https://doi.org/10.5194/os-2018-158, https://doi.org/10.5194/os-2018-158, 2019
Preprint withdrawn
Short summary
Short summary
A statistical-dynamical observation operator (SOSSTA) for satellite SST data assimilation able to account for SST diurnal variability, is formulated and implemented into the POSEIDON forecasting system (Aegean Sea). Model experiments where daytime SST retrievals from the SEVIRI infrared radiometer are introduced into the data assimilation procedure through the application of the observation operator, showed an improvement of the POSEIDON modelling system performance.
Antonio Sanchez-Roman, Gabriel Jorda, Gianmaria Sannino, and Damia Gomis
Ocean Sci., 14, 1547–1566, https://doi.org/10.5194/os-14-1547-2018, https://doi.org/10.5194/os-14-1547-2018, 2018
Short summary
Short summary
We explore the vertical transfers of heat, salt and mass between the inflowing and outflowing layers at the Strait of Gibraltar by using a 3-D model with very high spatial resolution that allows for a realistic representation of the exchange. Results show a significant transformation of the water mass properties along their path through the strait, mainly induced by the recirculation of water between layers, while mixing seems to have little influence on the heat and salt exchanged.
Alessandro Anav, Chiara Proietti, Laurent Menut, Stefano Carnicelli, Alessandra De Marco, and Elena Paoletti
Atmos. Chem. Phys., 18, 5747–5763, https://doi.org/10.5194/acp-18-5747-2018, https://doi.org/10.5194/acp-18-5747-2018, 2018
Short summary
Short summary
Soil moisture and water stress play a pivotal role in regulating stomatal behaviour of plants; however, the role of water availability is often neglected in atmospheric chemistry modelling studies.
We show how dry deposition significantly declines when soil moisture is used to regulate the stomatal opening, mainly in semi-arid environments. Despite the fact that dry deposition occurs from the top of canopy to ground level, it affects the concentration of gases remaining in the lower atmosphere.
Marianne Pietschnig, Michael Mayer, Takamasa Tsubouchi, Andrea Storto, Sebastian Stichelberger, and Leopold Haimberger
Ocean Sci. Discuss., https://doi.org/10.5194/os-2017-98, https://doi.org/10.5194/os-2017-98, 2017
Revised manuscript not accepted
Short summary
Short summary
New estimates of volume and temperature transports into the Arctic Ocean through the four major gateways (Davis, Fram and Bering Strait and the Barents Sea Opening) have recently become available. These estimates are derived from moored observations. In this study, the same transports derived from a recent ocean reanalysis are compared to the observation-based estimates in the straits. In addition, cross-section plots of velocity, temperature and temperature flux density are investigated.
Pierre Sicard, Alessandro Anav, Alessandra De Marco, and Elena Paoletti
Atmos. Chem. Phys., 17, 12177–12196, https://doi.org/10.5194/acp-17-12177-2017, https://doi.org/10.5194/acp-17-12177-2017, 2017
Short summary
Short summary
A few issues about surface ozone, e.g. a better understanding of spatial changes and a better assessment of ozone impacts worldwide, are still challenging. To overcome these issues, this study assessed, for the first time, the spatial and temporal changes in the projected potential ozone impacts on carbon assimilation of vegetation at global scale, by comparing the ozone potential injury at present with that expected at the end of the 21st century from different global chemistry models.
Gianpiero Cossarini, Stefano Querin, Cosimo Solidoro, Gianmaria Sannino, Paolo Lazzari, Valeria Di Biagio, and Giorgio Bolzon
Geosci. Model Dev., 10, 1423–1445, https://doi.org/10.5194/gmd-10-1423-2017, https://doi.org/10.5194/gmd-10-1423-2017, 2017
Short summary
Short summary
The BFMCOUPLER (v1.0) is a coupling scheme that links the MITgcm and BFM models for ocean biogeochemistry simulations. The online coupling is based on an open-source code characterizd by a modular structure. Modularity preserves the potentials of the two models, allowing for a sustainable programming effort to handle future evolutions in the two codes. The BFMCOUPLER code is released along with an idealized problem (a cyclonic gyre in a mid-latitude closed basin).
Zhaoyi Wang, Andrea Storto, Nadia Pinardi, Guimei Liu, and Hui Wang
Nat. Hazards Earth Syst. Sci., 17, 17–30, https://doi.org/10.5194/nhess-17-17-2017, https://doi.org/10.5194/nhess-17-17-2017, 2017
Andrea Storto and Simona Masina
Earth Syst. Sci. Data, 8, 679–696, https://doi.org/10.5194/essd-8-679-2016, https://doi.org/10.5194/essd-8-679-2016, 2016
Short summary
Short summary
A large number of applications related to the study of ocean climate require reliable datasets of the main physical variables of the ocean. Ocean reanalyses are a methodology based on the synthesis of information from ocean observations and models, and near-surface atmospheric observations into a dataset in a way as consistent in time as possible. In this paper, we describe and validate an upgraded version of the CMCC global ocean physical reanalysis (1980–present) at 1 / 4° resolution.
Paolo Oddo, Andrea Storto, Srdjan Dobricic, Aniello Russo, Craig Lewis, Reiner Onken, and Emanuel Coelho
Ocean Sci., 12, 1137–1153, https://doi.org/10.5194/os-12-1137-2016, https://doi.org/10.5194/os-12-1137-2016, 2016
Doroteaciro Iovino, Simona Masina, Andrea Storto, Andrea Cipollone, and Vladimir N. Stepanov
Geosci. Model Dev., 9, 2665–2684, https://doi.org/10.5194/gmd-9-2665-2016, https://doi.org/10.5194/gmd-9-2665-2016, 2016
Short summary
Short summary
An 11-year simulation of a global eddying ocean (1/16) configuration is presented. Model performance is evaluated against observations and a twin 1/4 configuration. The model realistically represents the variability at upper and intermediate depths, the position and strength of the surface circulation, and exchanges of mass through key passages. Sea ice properties are close to satellite observations. This simulation constitutes the groundwork for future applications to short range ocean forecasting.
W. J. McKiver, G. Sannino, F. Braga, and D. Bellafiore
Ocean Sci., 12, 51–69, https://doi.org/10.5194/os-12-51-2016, https://doi.org/10.5194/os-12-51-2016, 2016
Short summary
Short summary
First modeling work comparing SHYFEM and MITgcm performance in the north Adriatic Sea; the treatment of heat/mass fluxes at the surface affects the models skill to reproduce coastal processes; high resolution is needed close to the coast, while lower resolution in the offshore is adequate to capture the dense water event; correct river discharges and temperature are vital for the reproduction of estuarine dynamics; non-hydrostatic processes do not influence the dense water formation.
L. Visinelli, S. Masina, M. Vichi, and A. Storto
Biogeosciences Discuss., https://doi.org/10.5194/bgd-11-5399-2014, https://doi.org/10.5194/bgd-11-5399-2014, 2014
Revised manuscript not accepted
Related subject area
Climate and Earth system modeling
A method for quantifying uncertainty in spatially interpolated meteorological data with application to daily maximum air temperature
Baseline Climate Variables for Earth System Modelling
PaleoSTeHM v1.0: a modern, scalable spatiotemporal hierarchical modeling framework for paleo-environmental data
The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)
ZEMBA v1.0: an energy and moisture balance climate model to investigate Quaternary climate
Development and evaluation of a new 4DEnVar-based weakly coupled ocean data assimilation system in E3SMv2
TemDeep: a self-supervised framework for temporal downscaling of atmospheric fields at arbitrary time resolutions
The ensemble consistency test: from CESM to MPAS and beyond
Presentation, calibration and testing of the DCESS II Earth system model of intermediate complexity (version 1.0)
Synthesizing global carbon–nitrogen coupling effects – the MAGICC coupled carbon–nitrogen cycle model v1.0
Historical trends and controlling factors of isoprene emissions in CMIP6 Earth system models
Investigating carbon and nitrogen conservation in reported CMIP6 Earth system model data
From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting
A Fortran–Python interface for integrating machine learning parameterization into earth system models
A rapid-application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)
The DOE E3SM version 2.1: overview and assessment of the impacts of parameterized ocean submesoscales
WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions
Modeling commercial-scale CO2 storage in the gas hydrate stability zone with PFLOTRAN v6.0
DiuSST: a conceptual model of diurnal warm layers for idealized atmospheric simulations with interactive sea surface temperature
High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7
T&C-CROP: representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5) – model formulation and validation
An updated non-intrusive, multi-scale, and flexible coupling interface in WRF 4.6.0
Monitoring and benchmarking Earth system model simulations with ESMValTool v2.12.0
The Earth Science Box Modeling Toolkit (ESBMTK 0.14.0.11): a Python library for research and teaching
CropSuite v1.0 – a comprehensive open-source crop suitability model considering climate variability for climate impact assessment
ICON ComIn – the ICON Community Interface (ComIn version 0.1.0, with ICON version 2024.01-01)
Using feature importance as an exploratory data analysis tool on Earth system models
A new metrics framework for quantifying and intercomparing atmospheric rivers in observations, reanalyses, and climate models
The real challenges for climate and weather modelling on its way to sustained exascale performance: a case study using ICON (v2.6.6)
Improving the representation of major Indian crops in the Community Land Model version 5.0 (CLM5) using site-scale crop data
Evaluation of CORDEX ERA5-forced NARCliM2.0 regional climate models over Australia using the Weather Research and Forecasting (WRF) model version 4.1.2
Design, evaluation, and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble
The Detection and Attribution Model Intercomparison Project (DAMIP v2.0) contribution to CMIP7
Amending the algorithm of aerosol–radiation interactions in WRF-Chem (v4.4)
The very-high-resolution configuration of the EC-Earth global model for HighResMIP
GOSI9: UK Global Ocean and Sea Ice configurations
Decomposition of skill scores for conditional verification: impact of Atlantic Multidecadal Oscillation phases on the predictability of decadal temperature forecasts
Virtual Integration of Satellite and In-situ Observation Networks (VISION) v1.0: In-Situ Observations Simulator (ISO_simulator)
Climate model downscaling in central Asia: a dynamical and a neural network approach
Advanced climate model evaluation with ESMValTool v2.11.0 using parallel, out-of-core, and distributed computing
Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5 and NEMOv3.4
Subsurface hydrological controls on the short-term effects of hurricanes on nitrate–nitrogen runoff loading: a case study of Hurricane Ida using the Energy Exascale Earth System Model (E3SM) Land Model (v2.1)
CARIB12: a regional Community Earth System Model/Modular Ocean Model 6 configuration of the Caribbean Sea
Process-based modeling framework for sustainable irrigation management at the regional scale: Integrating rice production, water use, and greenhouse gas emissions
A regional physical-biogeochemical ocean model for marine resource applications in the Northeast Pacific (MOM6-COBALT-NEP10k v1.0)
Architectural insights into and training methodology optimization of Pangu-Weather
Evaluation of global fire simulations in CMIP6 Earth system models
Evaluating downscaled products with expected hydroclimatic co-variances
Software sustainability of global impact models
fair-calibrate v1.4.1: calibration, constraining, and validation of the FaIR simple climate model for reliable future climate projections
Conor T. Doherty, Weile Wang, Hirofumi Hashimoto, and Ian G. Brosnan
Geosci. Model Dev., 18, 3003–3016, https://doi.org/10.5194/gmd-18-3003-2025, https://doi.org/10.5194/gmd-18-3003-2025, 2025
Short summary
Short summary
We present, analyze, and validate a methodology for quantifying uncertainty in gridded meteorological data products produced by spatial interpolation. In a validation case study using daily maximum near-surface air temperature (Tmax), the method works well and produces predictive distributions with closely matching theoretical versus actual coverage levels. Application of the method reveals that the magnitude of uncertainty in interpolated Tmax varies significantly in both space and time.
Martin Juckes, Karl E. Taylor, Fabrizio Antonio, David Brayshaw, Carlo Buontempo, Jian Cao, Paul J. Durack, Michio Kawamiya, Hyungjun Kim, Tomas Lovato, Chloe Mackallah, Matthew Mizielinski, Alessandra Nuzzo, Martina Stockhause, Daniele Visioni, Jeremy Walton, Briony Turner, Eleanor O'Rourke, and Beth Dingley
Geosci. Model Dev., 18, 2639–2663, https://doi.org/10.5194/gmd-18-2639-2025, https://doi.org/10.5194/gmd-18-2639-2025, 2025
Short summary
Short summary
The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 135 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most frequently used variables from Earth system models based on an assessment of data publication and download records from the largest archive of global climate projects.
Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
Geosci. Model Dev., 18, 2609–2637, https://doi.org/10.5194/gmd-18-2609-2025, https://doi.org/10.5194/gmd-18-2609-2025, 2025
Short summary
Short summary
PaleoSTeHM v1.0 is a state-of-the-art framework designed to reconstruct past environmental conditions using geological data. Built on modern machine learning techniques, it efficiently handles the sparse and noisy nature of paleo-records, allowing scientists to make accurate and scalable inferences about past environmental change. By using flexible statistical models, PaleoSTeHM separates different sources of uncertainty, improving the precision of historical climate reconstructions.
Ingo Richter, Ping Chang, Ping-Gin Chiu, Gokhan Danabasoglu, Takeshi Doi, Dietmar Dommenget, Guillaume Gastineau, Zoe E. Gillett, Aixue Hu, Takahito Kataoka, Noel S. Keenlyside, Fred Kucharski, Yuko M. Okumura, Wonsun Park, Malte F. Stuecker, Andréa S. Taschetto, Chunzai Wang, Stephen G. Yeager, and Sang-Wook Yeh
Geosci. Model Dev., 18, 2587–2608, https://doi.org/10.5194/gmd-18-2587-2025, https://doi.org/10.5194/gmd-18-2587-2025, 2025
Short summary
Short summary
Tropical ocean basins influence each other through multiple pathways and mechanisms, referred to here as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models but have obtained conflicting results. This may be partly due to differences in experiment protocols and partly due to systematic model errors. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Daniel F. J. Gunning, Kerim H. Nisancioglu, Emilie Capron, and Roderik S. W. van de Wal
Geosci. Model Dev., 18, 2479–2508, https://doi.org/10.5194/gmd-18-2479-2025, https://doi.org/10.5194/gmd-18-2479-2025, 2025
Short summary
Short summary
This work documents the first results from ZEMBA: an energy balance model of the climate system. The model is a computationally efficient tool designed to study the response of climate to changes in the Earth's orbit. We demonstrate that ZEMBA reproduces many features of the Earth's climate for both the pre-industrial period and the Earth's most recent cold extreme – the Last Glacial Maximum. We intend to develop ZEMBA further and investigate the glacial cycles of the last 2.5 million years.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev., 18, 2443–2460, https://doi.org/10.5194/gmd-18-2443-2025, https://doi.org/10.5194/gmd-18-2443-2025, 2025
Short summary
Short summary
Improving climate predictions has significant socio-economic impacts. In this study, we develop and apply a new weakly coupled ocean data assimilation (WCODA) system to a coupled climate model. The WCODA system improves simulations of ocean temperature and salinity across many global regions. This system is meant to advance our understanding of the ocean's role in climate predictability.
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025, https://doi.org/10.5194/gmd-18-2427-2025, 2025
Short summary
Short summary
Our research presents a novel deep learning approach called "TemDeep" for downscaling atmospheric variables at arbitrary time resolutions based on temporal coherence. Results show that our method can accurately recover evolution details superior to other methods, reaching 53.7 % in the restoration rate. Our findings are important for advancing weather forecasting models and enabling more precise and reliable predictions to support disaster preparedness, agriculture, and sustainable development.
Teo Price-Broncucia, Allison Baker, Dorit Hammerling, Michael Duda, and Rebecca Morrison
Geosci. Model Dev., 18, 2349–2372, https://doi.org/10.5194/gmd-18-2349-2025, https://doi.org/10.5194/gmd-18-2349-2025, 2025
Short summary
Short summary
The ensemble consistency test (ECT) and its ultrafast variant (UF-ECT) have become powerful tools in the development community for the identification of unwanted changes in the Community Earth System Model (CESM). We develop a generalized setup framework to enable easy adoption of the ECT approach for other model developers and communities. This framework specifies test parameters to accurately characterize model variability and balance test sensitivity and computational cost.
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025, https://doi.org/10.5194/gmd-18-2161-2025, 2025
Short summary
Short summary
We describe, calibrate and test the Danish Center for Earth System Science (DCESS) II model, a new, broad, adaptable and fast Earth system model. DCESS II is designed for global simulations over timescales of years to millions of years using limited computer resources like a personal computer. With its flexibility and comprehensive treatment of the global carbon cycle, DCESS II is a useful, computationally friendly tool for simulations of past climates as well as for future Earth system projections.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025, https://doi.org/10.5194/gmd-18-2193-2025, 2025
Short summary
Short summary
We studied carbon–nitrogen coupling in Earth system models by developing a global carbon–nitrogen cycle model (CNit v1.0) within the widely used emulator MAGICC. CNit effectively reproduced the global carbon–nitrogen cycle dynamics observed in complex models. Our results show persistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100, suggesting that nitrogen deficiency may constrain future land carbon sequestration.
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025, https://doi.org/10.5194/gmd-18-2079-2025, 2025
Short summary
Short summary
Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth system models mainly due to partially incorporating CO2 effects and land cover changes rather than to climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant–climate interactions.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
Geosci. Model Dev., 18, 2111–2136, https://doi.org/10.5194/gmd-18-2111-2025, https://doi.org/10.5194/gmd-18-2111-2025, 2025
Short summary
Short summary
We analyzed carbon and nitrogen mass conservation in data from various Earth system models. Our findings reveal significant discrepancies between flux and pool size data, where cumulative imbalances can reach hundreds of gigatons of carbon or nitrogen. These imbalances appear primarily due to missing or inconsistently reported fluxes – especially for land-use and fire emissions. To enhance data quality, we recommend that future climate data protocols address this issue at the reporting stage.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
Geosci. Model Dev., 18, 2005–2019, https://doi.org/10.5194/gmd-18-2005-2025, https://doi.org/10.5194/gmd-18-2005-2025, 2025
Short summary
Short summary
Forecasting river runoff, which is crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using convolutional long short-term memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
Short summary
Short summary
Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025, https://doi.org/10.5194/gmd-18-1785-2025, 2025
Short summary
Short summary
We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is designed to take new emissions scenarios and rapidly provide regional impact information. PRIME allows large ensembles to be run on multi-centennial timescales, including the analysis of many important variables for impact assessments. Our evaluation shows that PRIME reproduces the climate response for known scenarios, providing confidence in using PRIME for novel scenarios.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
Short summary
Short summary
Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Short summary
We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev., 18, 1413–1425, https://doi.org/10.5194/gmd-18-1413-2025, https://doi.org/10.5194/gmd-18-1413-2025, 2025
Short summary
Short summary
Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most severe effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor, where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a subsea CO2 injection.
Reyk Börner, Jan O. Haerter, and Romain Fiévet
Geosci. Model Dev., 18, 1333–1356, https://doi.org/10.5194/gmd-18-1333-2025, https://doi.org/10.5194/gmd-18-1333-2025, 2025
Short summary
Short summary
The daily cycle of sea surface temperature (SST) impacts clouds above the ocean and could influence the clustering of thunderstorms linked to extreme rainfall and hurricanes. However, daily SST variability is often poorly represented in modeling studies of how clouds cluster. We present a simple, wind-responsive model of upper-ocean temperature for use in atmospheric simulations. Evaluating the model against observations, we show that it performs significantly better than common slab models.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
Short summary
Short summary
HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
Geosci. Model Dev., 18, 1287–1305, https://doi.org/10.5194/gmd-18-1287-2025, https://doi.org/10.5194/gmd-18-1287-2025, 2025
Short summary
Short summary
We present and validate enhancements to the process-based T&C model aimed at improving its representation of crop growth and management practices. The updated model, T&C-CROP, enables applications such as analysing the hydrological and carbon storage impacts of land use transitions (e.g. conversions between crops, forests, and pastures) and optimizing irrigation and fertilization strategies in response to climate change.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev., 18, 1241–1263, https://doi.org/10.5194/gmd-18-1241-2025, https://doi.org/10.5194/gmd-18-1241-2025, 2025
Short summary
Short summary
This article details a new feature we implemented in the popular regional atmospheric model WRF. This feature allows for data exchange between WRF and any other model (e.g. an ocean model) using the coupling library Ocean–Atmosphere–Sea–Ice–Soil Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Axel Lauer, Lisa Bock, Birgit Hassler, Patrick Jöckel, Lukas Ruhe, and Manuel Schlund
Geosci. Model Dev., 18, 1169–1188, https://doi.org/10.5194/gmd-18-1169-2025, https://doi.org/10.5194/gmd-18-1169-2025, 2025
Short summary
Short summary
Earth system models are important tools to improve our understanding of current climate and to project climate change. Thus, it is crucial to understand possible shortcomings in the models. New features of the ESMValTool software package allow one to compare and visualize a model's performance with respect to reproducing observations in the context of other climate models in an easy and user-friendly way. We aim to help model developers assess and monitor climate simulations more efficiently.
Ulrich G. Wortmann, Tina Tsan, Mahrukh Niazi, Irene A. Ma, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
Geosci. Model Dev., 18, 1155–1167, https://doi.org/10.5194/gmd-18-1155-2025, https://doi.org/10.5194/gmd-18-1155-2025, 2025
Short summary
Short summary
The Earth Science Box Modeling Toolkit (ESBMTK) is a user-friendly Python library that simplifies the creation of models to study earth system processes, such as the carbon cycle and ocean chemistry. It enhances learning by emphasizing concepts over programming and is accessible to students and researchers alike. By automating complex calculations and promoting code clarity, ESBMTK accelerates model development while improving reproducibility and the usability of scientific research.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
Geosci. Model Dev., 18, 1067–1087, https://doi.org/10.5194/gmd-18-1067-2025, https://doi.org/10.5194/gmd-18-1067-2025, 2025
Short summary
Short summary
CropSuite is a new open-source crop suitability model. It provides a GUI and a wide range of options, including a spatial downscaling of climate data. We apply CropSuite to 48 staple and opportunity crops at a 1 km spatial resolution in Africa. We find that climate variability significantly impacts suitable areas but also affects optimal sowing dates and multiple cropping potential. The results provide valuable information for climate impact assessments, adaptation, and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev., 18, 1001–1015, https://doi.org/10.5194/gmd-18-1001-2025, https://doi.org/10.5194/gmd-18-1001-2025, 2025
Short summary
Short summary
The ICOsahedral Non-hydrostatic (ICON) model system Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++, and Python), and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev., 18, 1041–1065, https://doi.org/10.5194/gmd-18-1041-2025, https://doi.org/10.5194/gmd-18-1041-2025, 2025
Short summary
Short summary
Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis A. O'Brien
Geosci. Model Dev., 18, 961–976, https://doi.org/10.5194/gmd-18-961-2025, https://doi.org/10.5194/gmd-18-961-2025, 2025
Short summary
Short summary
A metrics package designed for easy analysis of atmospheric river (AR) characteristics and statistics is presented. The tool is efficient for diagnosing systematic AR bias in climate models and useful for evaluating new AR characteristics in model simulations. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the North and South Atlantic (South Pacific and Indian Ocean).
Panagiotis Adamidis, Erik Pfister, Hendryk Bockelmann, Dominik Zobel, Jens-Olaf Beismann, and Marek Jacob
Geosci. Model Dev., 18, 905–919, https://doi.org/10.5194/gmd-18-905-2025, https://doi.org/10.5194/gmd-18-905-2025, 2025
Short summary
Short summary
In this paper, we investigated performance indicators of the climate model ICON (ICOsahedral Nonhydrostatic) on different compute architectures to answer the question of how to generate high-resolution climate simulations. Evidently, it is not enough to use more computing units of the conventionally used architectures; higher memory throughput is the most promising approach. More potential can be gained from single-node optimization rather than simply increasing the number of compute nodes.
Kangari Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
Geosci. Model Dev., 18, 763–785, https://doi.org/10.5194/gmd-18-763-2025, https://doi.org/10.5194/gmd-18-763-2025, 2025
Short summary
Short summary
The study aimed to improve the representation of wheat and rice in a land model for the Indian region. The modified model performed significantly better than the default model in simulating crop phenology, yield, and carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific crop parameters for accurately simulating vegetation processes and land surface processes.
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason P. Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew L. Riley
Geosci. Model Dev., 18, 703–724, https://doi.org/10.5194/gmd-18-703-2025, https://doi.org/10.5194/gmd-18-703-2025, 2025
Short summary
Short summary
We evaluate the skill in simulating the Australian climate of some of the latest generation of regional climate models. We show when and where the models simulate this climate with high skill versus model limitations. We show how new models perform relative to the previous-generation models, assessing how model design features may underlie key performance improvements. This work is of national and international relevance as it can help guide the use and interpretation of climate projections.
Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
Geosci. Model Dev., 18, 671–702, https://doi.org/10.5194/gmd-18-671-2025, https://doi.org/10.5194/gmd-18-671-2025, 2025
Short summary
Short summary
We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
EGUsphere, https://doi.org/10.5194/egusphere-2024-4086, https://doi.org/10.5194/egusphere-2024-4086, 2025
Short summary
Short summary
Climate model simulations of the response to human and natural influences together, natural climate influences alone, and greenhouse gases alone, among others, are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies, and to underpin the next IPCC report.
Jiawang Feng, Chun Zhao, Qiuyan Du, Zining Yang, and Chen Jin
Geosci. Model Dev., 18, 585–603, https://doi.org/10.5194/gmd-18-585-2025, https://doi.org/10.5194/gmd-18-585-2025, 2025
Short summary
Short summary
In this study, we improved the calculation of how aerosols in the air interact with radiation in WRF-Chem. The original model used a simplified method, but we developed a more accurate approach. We found that this method significantly changes the properties of the estimated aerosols and their effects on radiation, especially for dust aerosols. It also impacts the simulated weather conditions. Our work highlights the importance of correctly representing aerosol–radiation interactions in models.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev., 18, 461–482, https://doi.org/10.5194/gmd-18-461-2025, https://doi.org/10.5194/gmd-18-461-2025, 2025
Short summary
Short summary
We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10–15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100 km and a 25 km grid. The three models are compared with observations to study the improvements thanks to the increased resolution.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
Short summary
Short summary
The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
Short summary
Short summary
The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
Short summary
Short summary
Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
Short summary
Short summary
We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-236, https://doi.org/10.5194/gmd-2024-236, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system models. Here, we describe recent significant improvements of ESMValTool’s computational efficiency including parallel, out-of-core, and distributed computing. Evaluations with the enhanced version of ESMValTool are faster, use less computational resources, and can handle input data larger than the available memory.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
Short summary
Short summary
Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
Short summary
Short summary
Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
Short summary
Short summary
A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-212, https://doi.org/10.5194/gmd-2024-212, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This study proposed an advancing framework for modeling regional rice production, water use, and greenhouse gas emissions. The framework integrated a process-based soil-crop model with key physiological effects, a novel model upscaling method, and the NSGA-II multi-objective optimization algorithm at a parallel computing platform. The framework provides a valuable tool for irrigation optimization to deliver co-benefits of ensuring food production, reducing water use and greenhouse gas emissions.
Elizabeth J. Drenkard, Charles A. Stock, Andrew C. Ross, Yi-Cheng Teng, Theresa Morrison, Wei Cheng, Alistair Adcroft, Enrique Curchitser, Raphael Dussin, Robert Hallberg, Claudine Hauri, Katherine Hedstrom, Albert Hermann, Michael G. Jacox, Kelly A. Kearney, Remi Pages, Darren J. Pilcher, Mercedes Pozo Buil, Vivek Seelanki, and Niki Zadeh
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-195, https://doi.org/10.5194/gmd-2024-195, 2024
Preprint under review for GMD
Short summary
Short summary
We made a new regional ocean model to assist fisheries and ecosystem managers make decisions in the Northeast Pacific Ocean (NEP). We found that the model did well simulating past ocean conditions like temperature, and nutrient and oxygen levels, and can even reproduce metrics used by and important to ecosystem managers.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
Short summary
Short summary
Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
Short summary
Short summary
This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
Geosci. Model Dev., 17, 8665–8681, https://doi.org/10.5194/gmd-17-8665-2024, https://doi.org/10.5194/gmd-17-8665-2024, 2024
Short summary
Short summary
We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev., 17, 8593–8611, https://doi.org/10.5194/gmd-17-8593-2024, https://doi.org/10.5194/gmd-17-8593-2024, 2024
Short summary
Short summary
Research software is vital for scientific progress but is often developed by scientists with limited skills, time, and funding, leading to challenges in usability and maintenance. Our study across 10 sectors shows strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. We recommend workshops; code quality metrics; funding; and following the findable, accessible, interoperable, and reusable (FAIR) standards.
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
Short summary
Short summary
Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Cited articles
Akhtar, N., Brauch, J., Dobler, A., Béranger, K., and Ahrens, B.: Medicanes in an ocean–atmosphere coupled regional climate model, Nat. Hazards Earth Syst. Sci., 14, 2189–2201, https://doi.org/10.5194/nhess-14-2189-2014, 2014.
Akhtar, N., Brauch, J., and Ahrens, B.: Climate modeling over the Mediterranean Sea:
impact of resolution and ocean coupling, Clim. Dynam., 51, 933–948, 2018.
Anav, A., Carillo, A., Palma, M., Struglia, M. V., Turuncoglu, U. U., and Sannino, G.: The ENEA-REG system (v1.0), a multi-component regional Earth system model: sensitivity to different atmospheric components over the Med-CORDEX (Coordinated Regional Climate Downscaling Experiment) region, Geosci. Model Dev., 14, 4159–4185, https://doi.org/10.5194/gmd-14-4159-2021, 2021.
Artale, V., Calmanti, S., Carillo, A., Dell'Aquila, A., Herrmann, M., Pisacane, G., Ruti, P. M., Sannino, G., Struglia, M. V., Giorgi, F., Bi, X., Pal, J. S., Rauscher, S., and The PROTHEUS Group: An atmosphere–ocean regional
climate model for the Mediterranean area: assessment of a present climate
simulation, Clim. Dynam., 35, 721–740,
https://doi.org/10.1007/s00382-009-0691-8, 2010.
Astraldi, M., Balopoulos, S., Candela, J., Font, J., Gacic, M., Gasparini, G. P., Manca, B.,
Theocharis, A., and Tintore, J.: The role of straits and channels in understanding
the characteristics of Mediterranean circulation, Prog. Oceanogr.,
44, 65–108, 1999.
Bourdalle-Badie, R. and Treguier, A. M.: A climatology of run-off for the global
ocean-ice model ORCA025, report MOO-RP-425-365-MER, Mercator-Ocean:
Toulouse, France, 8 pp., https://www.drakkar-ocean.eu/publications/reports/runoff-mercator-06.pdf (last access: 14 August 2023), 2006.
Brewin, R. J. W., Sathyendranath, S., Müller, D., Brockmann, C.,
Deschamps, P.-Y., Devred, E., Doerffer, R., Fomferra, N., Franz, B., Grant, M.,
Groom, S., Horseman, A., Hu, C., Krasemann, H, Lee, Z. P., Maritorena, S.,
Mélin, F., Peters, M., Platt, T., Regner, P., Smyth, T., Steinmetz, F.,
Swinton, J., Werdell, J., and White, G. N.: The Ocean Colour Climate Change
Initiative: III. A round-robin comparison on in-water bio-optical
algorithms, Remote Sens. Environ., 162, 271–294,
https://doi.org/10.1016/j.rse.2013.09.016, 2015.
Canuto, V. M., Howard, A., Cheng, Y., and Dubovikov, M. S.: Ocean turbulence.
part I: One-point closure model-momentum and heat vertical diffusivities,
J. Phys. Oceanogr., 31, 1413–1426, 2001.
Cassola, F., Ferrari, F., Mazzino, A., and Miglietta, M. M.: The role of the sea on the flash floods events over Liguria,
Geoph. Res. Lett., 43, 3534–3542, 2016.
Cavicchia, L. and von Storch, H.: The simulation of medicanes in a
high-resolution regional climate model, Clim. Dynam., 39, 2273–2290,
https://doi.org/10.1007/s00382-011-1220-0, 2012.
Cavicchia, L., von Storch, H., and Gualdi, S.: Mediterranean Tropical-Like
Cyclones in Present and Future Climate, J. Climate, 27, 7493–7501,
https://doi.org/10.1175/JCLI-D-14-00339.1, 2014.
Choi, S.-J. and Lee, D.-K.: Impact of spectral nudging on the downscaling
of tropical cyclones in regional climate simulations, Adv. Atmos. Sci., 33,
730–742, https://doi.org/10.1007/s00376-016-5061-y, 2016.
Cornes, R., van der Schrier, G., van den Besselaar, E. J. M., and Jones, P. D.: An
Ensemble Version of the E-OBS Temperature and Precipitation Datasets, J.
Geophys. Res.-Atmos., 123, 9391–9409, https://doi.org/10.1029/2017JD028200, 2018.
Cos, J., Doblas-Reyes, F., Jury, M., Marcos, R., Bretonnière, P.-A., and Samsó, M.: The Mediterranean climate change hotspot in the CMIP5 and CMIP6 projections, Earth Syst. Dynam., 13, 321–340, https://doi.org/10.5194/esd-13-321-2022, 2022.
Craig, A., Valcke, S., and Coquart, L.: Development and performance of a new version of the OASIS coupler, OASIS3-MCT_3.0, Geosci. Model Dev., 10, 3297–3308, https://doi.org/10.5194/gmd-10-3297-2017, 2017.
Dai, A. and Trenberth, K. E.: Estimates of freshwater discharge from
continents: Latitudinal and seasonal variations, J. Hydrometeorol.,
3, 660–687, 2002.
Davies, H. C. and Turner, R. E.: Updating prediction models by dynamical
relaxation: An examination of the technique, Q. J. Roy. Meteor. Soc.,
103, 225–245, 1977.
Davin, E. L., Maisonnave, E., and Seneviratne, S. I.: Is land surface processes
representation a possible weak link in current Regional Climate Models?,
Environ. Res. Lett., 11, 1–8, https://doi.org/10.1088/1748-9326/11/7/074027,
2016.
Duffourg, F. and Ducrocq, V.: Origin of the moisture feeding the Heavy Precipitating Systems over Southeastern France, Nat. Hazards Earth Syst. Sci., 11, 1163–1178, https://doi.org/10.5194/nhess-11-1163-2011, 2011.
Escudier, R., Clementi, E., Cipollone, A., Pistoia, J., Drudi, M., Grandi, A.,
Lyubartsev, V., Lecci, R., Aydogdu, A., Delrosso, D., Omar, M., Masina, S., Coppini, G., and Pinardi, N.: A High Resolution Reanalysis for the Mediterranean Sea,
Front. Earth Sci., 9, 702285, https://doi.org/10.3389/feart.2021.702285, 2021.
Feser, F., Rockel, B., von Storch, H., Winterfeldt, J., and Zahn, M.: Regional
Climate Models Add Value to Global Model Data: A Review and Selected
Examples, B. Am. Meteorol. Soc., 92, 1181–1192,
https://doi.org/10.1175/2011BAMS3061.1, 2011.
Fita, L., Polcher, J., Giannaros, T. M., Lorenz, T., Milovac, J., Sofiadis, G., Katragkou, E., and Bastin, S.: CORDEX-WRF v1.3: development of a module for the Weather Research and Forecasting (WRF) model to support the CORDEX community, Geosci. Model Dev., 12, 1029–1066, https://doi.org/10.5194/gmd-12-1029-2019, 2019.
Flaounas, E., Davolio, S., Raveh-Rubin, S., Pantillon, F., Miglietta, M. M.,
Gaertner, M. A., Hatzaki, M., Homar, V., Khodayar, S., Korres, G., Kotroni,
V., Kushta, J., Reale, M., and Ricard, D.: Mediterranean cyclones: current
knowledge and open questions on dynamics, prediction, climatology and
impacts, Weather Clim. Dynam., 3, 173–208,
https://doi.org/10.5194/wcd-3-173-2022, 2022.
Flather, R. A.: A Storm Surge Prediction Model for the Northern Bay of
Bengal with Application to the Cyclone Disaster in April 1991, J. Phys.
Oceanogr., 24, 172–190, https://doi.org/10.1175/1520-0485(1994)024<0172:ASSPMF>2.0.CO;2, 1994.
Foley, A. M.: Uncertainty in regional climate modelling: A review, Prog.
Phys. Geogr.-Earth and Environment, 34, 647–670,
https://doi.org/10.1177/0309133310375654, 2010.
Giorgi, F.: Regional Dynamical Downscaling. Oxford Research Encyclopedia of
Climate Science,
https://oxfordre.com/climatescience/view/10.1093/acrefore/9780190228620.001.0001/acrefore-9780190228620-e-784 (last access: 22 June 2023),
2020.
Giorgi, F.: Thirty years of regional climate modeling: Where are we and
where are we going next?, J. Geophys. Res.-Atmos., 124,
5696–5723, https://doi.org/10.1029/2018JD030094, 2019.
Good, S. A., Martin, M. J., and Rayner, N. A.: EN4: quality controlled ocean
temperature and salinity profiles and monthly objective analyses with
uncertainty estimates, J. Geophys. Res.-Oceans, 118,
6704–6716, https://doi.org/10.1002/2013JC009067, 2013.
Grell, G. A. and Freitas, S. R.: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling, Atmos. Chem. Phys., 14, 5233–5250, https://doi.org/10.5194/acp-14-5233-2014, 2014.
Hagemann, S. and Ho-Hagemann, H. T. M.: The Hydrological Discharge Model – a river runoff component for offline and coupled model applications (5.1.0), Zenodo [code], https://doi.org/10.5281/zenodo.5707587, 2021.
Hagemann, S. and Dümenil Gates, L.: Validation of the hydrological cycle of
ECMWF and NCEP reanalyses using the MPI hydrological discharge model, J.
Geophys. Res., 106, 1503–1510, 2001.
Hagemann, S., Stacke, T., and Ho-Hagemann, H.: High resolution discharge
simulations over Europe and the Baltic Sea catchment, Front. Earth Sci.,
8, 12, https://doi.org/10.3389/feart.2020.00012, 2020.
Harzallah, A., Jordà, G., Dubois, C., Sannino, G., Carillo, A., Li, L., Arsouze, T., Cavicchia, L., Beuvier, J., and Akhtar, N.: Long term evolution of heat
budget in the Mediterranean Sea from Med-CORDEX forced and coupled
simulations, Clim. Dynam., 51, 1145–1165,
https://doi.org/10.1007/s00382-016-3363-5, 2018.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. R. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hirons, L. C., Klingaman, N. P., and Woolnough, S. J.: The impact of air
sea interactions on the representation of tropical precipitation extremes.
J. Adv. Model. Earth Sy., 10, 550559,
https://doi.org/10.1002/2017MS001252, 2018.
Ho-Hagemann, H. T. M., Hagemann, S., Grayek, S., Petrik, R., Rockel, B.,
Staneva, J., Feser, F., and Schrum, C.: Internal variability in the regional
coupled system model GCOAST-AHOI, Atmos., 11, 227,
https://doi.org/10.3390/atmos11030227, 2020.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S.
A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys. Res., 113,
D13103, https://doi.org/10.1029/2008JD009944, 2008.
Iona, A., Theodorou, A., Sofianos, S., Watelet, S., Troupin, C., and Beckers, J.-M.: Mediterranean Sea climatic indices: monitoring long-term variability and climate changes, Earth Syst. Sci. Data, 10, 1829–1842, https://doi.org/10.5194/essd-10-1829-2018, 2018.
Janjić, Z. I.: The Step-Mountain Eta Coordinate Model: Further
Developments of the Convection, Viscous Sublayer, and Turbulence Closure
Schemes, Mon. Weather Rev., 122, 927–945, https://doi.org/10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2, 1994.
Jordà, G., Von Schuckmann, K., Josey, S. A., Caniaux, G., García-Lafuente, J., Sammartino, S., Özsoy, E., Polcher, J., Notarstefano, G., Poulain, P.-M., Adloff, F., Salat, J., Naranjo, C., Schroeder, K., Chiggiato, J., Sannino, G., and Macías, D.: The Mediterranean Sea Heat
and Mass Budgets: Estimates, Uncertainties and Perspectives, Prog. Oceanogr.,
156, 174–208, https://doi.org/10.1016/j.pocean.2017.07.001, 2017.
Kourafalou, V. H. and Barbopoulos, K.: High resolution simulations on the North Aegean Sea seasonal circulation, Ann. Geophys., 21, 251–265, https://doi.org/10.5194/angeo-21-251-2003, 2003.
Lebeaupin Brossier, C. and Drobinski, P.: Numerical high-resolution air-sea
coupling over the Gulf of Lions during two Tramontane/Mistral events, J.
Geophys. Res., 114, D10110, https://doi.org/10.1029/2008JD011601, 2009.
Lebeaupin Brossier, C., Drobinski, P., Béranger, K., Bastin, S., and
Orain, F.: Ocean memory effect on the dynamics of coastal heavy
precipitation preceded by a mistral event in the northwestern Mediterranean,
Q. J. Roy. Meteor. Soc., 139, 1583–1597, https://doi.org/10.1002/qj.2049,
2013.
Lebeaupin Brossier, C., Bastin, S., Béranger, K., and Dobrinski, P.:
Regional mesoscale air sea coupling impacts and extreme meteorological
events role on the Mediterranean Sea water budget, Clim. Dynam., 44, 1029,
https://doi.org/10.1007/s00382-014-2252-z, 2015.
Lellouche, J.-M., Greiner, E., Bourdallé-Badie, R., Garric, G., Melet, A., Drévillon, M., Bricaud, C., Hamon, M., Le Galloudec, O., Regnier, C., Candela, T., Testut, C.-E., Gasparin, F., Ruggiero, G., Benkiran, M., Drillet, Y., and Le Traon, P.-Y.: The Copernicus Global 1/12∘ Oceanic
and Sea Ice GLORYS12 Reanalysis, Front. Earth Sci., 9, 698876,
https://doi.org/10.3389/feart.2021.698876, 2021.
Li, M., Zhang, S., Wu, L., Lin, X., Chang, P., Danabasoglu, G., Wei, Z., Yu,
X., Hu, H., Ma, X., Ma, W., Jia, D., Liu, X., Zhao, H., Mao, K., Ma, Y.,
Jiang, Y., Wang, X., Liu, G., and Chen, Y.: A high-resolution Asia-Pacific
regional coupled prediction system with dynamically downscaling coupled data
assimilation, Sci. Bull., 65, 1849–1858,
https://doi.org/10.1016/j.scib.2020.07.022, 2020.
Li, Y. and Toumi, R.: Improved tropical cyclone intensity forecasts by
assimilating coastal surface currents in an idealized study, Geophys.
Res. Lett., 45, 10019–10026, https://doi.org/10.1029/2018GL079677,
2018.
Lionello, P. and Scarascia, L.: The relation between climate change in the
Mediterranean region and global warming, Reg. Environ. Change, 18, 1481–1493,
https://doi.org/10.1007/s10113-018-1290-1, 2018.
Lionello, P., Martucci, G., and Zampieri, M.: Implementation of a Coupled
Atmosphere-Wave-Ocean Model in the Mediterranean Sea: Sensitivity of the
Short Time Scale Evolution to the Air-Sea Coupling Mechanisms, J.
Atmos. Ocean Sci., 9, 65–95,
https://doi.org/10.1080/1023673031000151421, 2003.
MacDonald, A. M., Candela, J., and Bryden, H. L.: An estimate of the net heat transport
through the Strait of Gibraltar, in: Seasonal and
interannual variability of the Western Mediterranean Sea, edited by: LaViolette, P. E., Coastal Estuarine
Stud. AGU, Washington DC, 13–32, https://doi.org/10.1029/CE046p0013, 1994.
Madec, G. and The NEMO System Team: NEMO Ocean Engine. Note Du Pole De
Modélisation. Paris, France: Institut Pierre-Simon Laplace, Zenodo,
https://doi.org/10.5281/zenodo.3248739, 2017.
Mellor, G. L. and Yamada, T.: Development of a turbulence closure model for
geophysical fluid problems, Rev. Geophys., 20, 851–875, 1982.
Mooney, P. A., Mulligan, F. J., and Fealy, R.: Evaluation of the Sensitivity
of the Weather Research and Forecasting Model to Parameterization Schemes
for Regional Climates of Europe over the Period 1990–95, J. Climate, 26,
1002–1017, https://doi.org/10.1175/JCLI-D-11-00676.1, 2013.
Morel, A. and Maritorena, S.: Bio-optical properties of oceanic waters: A
reappraisal, J. Geophys. Res.-Oceans, 106, 7163–7180,
https://doi.org/10.1029/2000JC000319, 2001.
Nabat, P., Somot, S., Cassou, C., Mallet, M., Michou, M., Bouniol, D., Decharme, B., Drugé, T., Roehrig, R., and Saint-Martin, D.: Modulation of radiative aerosols effects by atmospheric circulation over the Euro-Mediterranean region, Atmos. Chem. Phys., 20, 8315–8349, https://doi.org/10.5194/acp-20-8315-2020, 2020.
Nakanishi, M. and Niino, H.: An Improved Mellor–Yamada Level-3 Model: Its
Numerical Stability and Application to a Regional Prediction of Advection
Fog, Bound.-Lay. Meteorol., 119, 397–407,
https://doi.org/10.1007/s10546-005-9030-8, 2006.
NEMO: https://forge.ipsl.jussieu.fr/nemo/wiki, last access: 14 August 2023.
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with
multiparameterization options (Noah-MP): 1. Model description and evaluation
with local-scale measurements, J. Geophys. Res., 116, D12109,
https://doi.org/10.1029/2010JD015139, 2011.
Omrani, H., Drobinski, P., and Dubos, T.: Using nudging to improve
global-regional dynamic consistency in limited-area climate modeling: What
should we nudge?, Clim. Dynam., 44, 1627–1644, 2015.
Penny, S. G., Akella, S., Balmaseda, M. A., Browne, P., Carton, J. A., Chevallier, M.,
Counillon, F., Domingues, C., Frolov, S., Heimbach, P., Hogan, P., Hoteit, I., Iovino, D.,
Laloyaux, P., Martin, M. J., Masina, S., Moore, A. M., de Rosnay, P., Schepers, D., Sloyan,
B. M., Storto, A., Subramanian, A., Nam, S., Vitart, F., Yang, C., Fujii, Y., Zuo, H., O'Kane,
T., Sandery, P., Moore, T., and Chapman, C. C.: Observational Needs for Improving
Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction, Front.
Mar. Sci., 6, 391, https://doi.org/10.3389/fmars.2019.00391, 2019.
Pisano, A., Marullo, S., Artale, V., Falcini, F., Yang, C., Leonelli, F. E.,
Santoleri, R., and Buongiorno Nardelli, B.: New evidence of mediterranean
climate change and variability from sea surface temperature observations,
Remote Sens., 12, 132, https://doi.org/10.3390/rs12010132, 2020.
Reale, M., Giorgi, F., Solidoro, C., Di Biagio, V., Di Sante, F., Mariotti, L., Farneti, R., and Sannino, G.: The regional Earth system Model RegCM-ES: Evaluation
of the Mediterranean climate and marine biogeochemistry, J. Adv.
Model. Earth Sy., 12, e2019MS001812,
https://doi.org/10.1029/2019MS001812, 2020.
Reale, M., Cabos Narvaez, W. D., Cavicchia, L., Conte, D., Coppola, E., Flaounas, E., Giorgi, F., Gualdi, S., Hochman, A., Li, L., Lionello, P., Podrascanin, Z., Salon, S., Sanchez-Gomez, E., Scoccimarro, E., Sein, D. V., an Somot, S.: Future projections of
Mediterranean cyclone characteristics using the Med-CORDEX ensemble of
coupled regional climate system models, Clim. Dynam., 58, 2501–2524,
https://doi.org/10.1007/s00382-021-06018-x, 2022a.
Reale, M., Cossarini, G., Lazzari, P., Lovato, T., Bolzon, G., Masina, S., Solidoro, C., and Salon, S.: Acidification, deoxygenation, and nutrient and biomass declines in a warming Mediterranean Sea, Biogeosciences, 19, 4035–4065, https://doi.org/10.5194/bg-19-4035-2022, 2022b.
Ricchi, A., Miglietta, M. M., Barbariol, F., Benetazzo, A., Bergamasco, A.,
Bonaldo, D., Cassardo, C., Falcieri, F. M., Modugno, G., Russo, A., Sclavo,
M., and Carniel, S.: Sensitivity of a Mediterranean Tropical-Like Cyclone to
Different Model Configurations and Coupling Strategies, Atmosphere, 8, 92,
https://doi.org/10.3390/atmos8050092, 2017.
Rockel, B.: The Regional Downscaling Approach: a Brief History and Recent
Advances, Curr. Clim. Change Rep., 1, 22–29,
https://doi.org/10.1007/s40641-014-0001-3, 2015.
Rockel, B., Castro, C. L., Pielke Sr., R. A., von Storch, H., and Lencini, G.:
Dynamical downscaling: Assessment of model system dependent retained and
added variability for two different regional climate models, J. Geophys.
Res., 113, D21107, https://doi.org/10.1029/2007JD009461, 2008.
Rummukainen, M.: Added value in regional climate modeling, Wires Clim. Change,
7, 145–159, https://doi.org/10.1002/wcc.378, 2016.
Rummukainen, M., Rockel, B., Bärring, L., Christensen, J. H., and
Reckermann, M.: Twenty-First-Century Challenges in Regional Climate Modeling,
B. Am. Meteorol. Soc., 96, ES135–ES138,
https://doi.org/10.1175/BAMS-D-14-00214.1, 2015.
Ruti, P. M., Somot, S., Giorgi, F., Dubois, C., Flaounas, E., Obermann, A., Dell’Aquila, A., Pisacane, G., Harzallah, A.,
Lombardi, E., Ahrens, B., Akhtar, N., Alias, A., Arsouze, T., Aznar, R., Bastin, S., Bartholy, J., Béranger, K., Beuvier, J., Bouffies-Cloché, S., Brauch, J., Cabos, W., Calmanti, S., Calvet, J.-C., Carillo, A., Conte, D., Coppola, E., Djurdjevic, V., Drobinski, P., Elizalde-Arellano, A., Gaertner, M., Galàn, P., Gallardo, C., Gualdi, S., Goncalves, M., Jorba, O., Jordà, G., L’Heveder, B., Lebeaupin-Brossier, C., Li, L., Liguori, G., Lionello, P., Maciàs, D., Nabat, P., Önol, B., Raikovic, B., Ramage, K., Sevault, F., Sannino, G., Struglia, M. V., Sanna, A., Torma, C., and Vervatis, V.: Med-CORDEX Initiative for Mediterranean Climate
Studies, B. Am. Meteorol. Soc., 97, 1187–1208,
https://doi.org/10.1175/BAMS-D-14-00176.1, 2016.
Scoccimarro, E., Bellucci, A., Storto, A., Gualdi, S., Masina, S., and
Navarra, A.: Remote sub-surface ocean temperature as a predictor of Atlantic
hurricane activity, P. Natl. Acad. Sci. USA, 115, 11460–11464,
https://doi.org/10.1073/pnas.1810755115, 2018.
Skamarock, W. C., Klemp, J., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M., Barker, D., and Huang, X.-Y.: A Description of the Advanced Research WRF
Model Version 4.3 (No. NCAR/TN-556+STR),
https://doi.org/10.5065/1dfh-6p97, 2021.
Shchepetkin, A. F. and McWilliams, J. C.: The regional oceanic modeling
system (ROMS): a splitexplicit, free-surface,
topography-following-coordinate oceanic model, Ocean Modell., 9,
347–404, 2005.
Soto-Navarro, J., Jordá, G., Amores, A., Cabos, W., Somot, S., Sevault, F., Macías, D., Djurdjevic, V., Sannino, G., Li, L., and Sein, D.: Evolution of Mediterranean Sea water properties under climate
change scenarios in the Med-CORDEX ensemble, Clim. Dynam., 54, 2135–2165,
https://doi.org/10.1007/s00382-019-05105-4, 2020.
Storkey, D., Blaker, A. T., Mathiot, P., Megann, A., Aksenov, Y., Blockley, E. W., Calvert, D., Graham, T., Hewitt, H. T., Hyder, P., Kuhlbrodt, T., Rae, J. G. L., and Sinha, B.: UK Global Ocean GO6 and GO7: a traceable hierarchy of model resolutions, Geosci. Model Dev., 11, 3187–3213, https://doi.org/10.5194/gmd-11-3187-2018, 2018.
Storto, A.: Variational quality control of hydrographic profile data with
non-Gaussian errors for global ocean variational data assimilation systems,
Ocean Model., 104, 226–241, https://doi.org/10.1016/j.ocemod.2016.06.011,
2016.
Storto, A.: MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region, Article data (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.7899115, 2023.
Storto, A. and Randriamampianina, R.: Ensemble variational assimilation for
the representation of background error covariances in a high-latitude
regional model, J. Geophys. Res., 115, D17204,
https://doi.org/10.1029/2009JD013111, 2010.
Storto, A., Masina, S., and Dobricic, S.: Estimation and Impact of
Nonuniform Horizontal Correlation Length Scales for Global Ocean Physical
Analyses, J. Atmos. Ocean. Tech., 31, 2330–2349,
https://doi.org/10.1175/JTECH-D-14-00042.1, 2014.
Storto, A., Masina, S., and Navarra, A.: Evaluation of the CMCC
eddy-permitting global ocean physical reanalysis system (C-GLORS,
1982–2012) and its assimilation components, Q. J. Roy. Meteor. Soc., 142,
738–758, https://doi.org/10.1002/qj.2673, 2016.
Storto, A., Oddo, P., Cipollone, A., Mirouze, I., and Lemieux-Dudon, B.:
Extending an oceanographic variational scheme to allow for affordable hybrid
and four-dimensional data assimilation, Ocean Model., 128, 67–86,
https://doi.org/10.1016/j.ocemod.2018.06.005, 2018a.
Storto, A., Martin, M. J., Deremble, B., and Masina, S.: Strongly Coupled Data
Assimilation Experiments with Linearized Ocean–Atmosphere Balance
Relationships, Mon. Weather Rev., 146, 1233–1257,
https://doi.org/10.1175/MWR-D-17-0222.1, 2018b.
Storto, A., Masina, S., Simoncelli, S., Iovino, D., Cipollone, A., Drevillon, M., Drillet, Y., von Schuckman, K., Parent, L., Garric, G., Greiner, E., Desportes, C., Zuo, H., Balmaseda, M. A., and Peterson, K. A.: The added value of the
multi-system spread information for ocean heat content and steric sea level
investigations in the CMEMS GREP ensemble reanalysis product, Clim. Dynam., 53,
287–312, https://doi.org/10.1007/s00382-018-4585-5, 2019.
Storto, A., Hesham Essa, Y., de Toma, V., Anav, A., Sannino, G., Santoleri, R., and Yang, C.: MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region, Coupled model code (1.0), Zenodo [code], https://doi.org/10.5281/zenodo.7898938, 2023.
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit
Forecasts of Winter Precipitation Using an Improved Bulk Microphysics
Scheme. Part II: Implementation of a New Snow Parameterization, Mon.
Weather Rev., 136, 5095–5115, https://doi.org/10.1175/2008MWR2387.1,
2008.
Tsujino, H., Urakawa, S.,
Nakano, H., Small, R. J., Kim, W. M.,
Yeager, S. G., Danabasoglu, G.,
Suzuki, T., Bamber, J. L., Bentsen, M., Böning, C. W.,
Bozec, A., Chassignet, E. P., Curchitser, E., Boeira Dias, F., Durack, P. J.,
Griffies, S. M., Harada, Y., Ilicak, M.,
Josey, S. A., Kobayashi, C., Kobayashi, S., Komuro, Y.,
Large, W. G., Le Sommer, J., Marsland, S. J., Masina, S.,
Scheinert, M., Tomita, H., Valdivieso, M., and Yamazaki, D.: JRA-55 based surface dataset for driving ocean-sea-ice
models (JRA55-do)., Ocean Model., 130, 79–139,
https://doi.org/10.1016/j.ocemod.2018.07.002, 2018.
Umlauf, L. and Burchard, H.: A generic length-scale equation for geophysical
turbulence models, J. Mar. Res., 61, 235–265, 2003.
Vannucchi, V., Taddei, S., Capecchi, V., Bendoni, M., and Brandini, C.: Dynamical
Downscaling of ERA5 Data on the North-Western Mediterranean Sea: From
Atmosphere to High-Resolution Coastal Wave Climate, J. Mar.
Sci. Eng., 9, 208, https://doi.org/10.3390/jmse9020208,
2021.
Wang, M., Du, Y., Qiu, B., Xie, S., and Feng, M.: Dynamics on Seasonal
Variability of EKE Associated with TIWs in the Eastern Equatorial Pacific
Ocean, J. Phys. Oceanogr., 49, 1503–1519,
https://doi.org/10.1175/JPO-D-18-0163.1, 2019.
wrf-model: WRF, GitHub [code], https://github.com/wrf-model/WRF (last access: 14 August 2023.
Wunsch, C. and Stammer, D.: Atmospheric loading and the oceanic “inverted
barometer” effect, Rev. Geophys., 35, 79–107, https://doi.org/10.1029/96RG03037,
1997.
Zeng, X. and Beljaars, A.: A prognostic scheme of sea surface skin
temperature for modeling and data assimilation, Geophys. Res. Lett., 32,
L14605, https://doi.org/10.1029/2005gl023030, 2005.
Zhang, F. and Emanuel, K.: Promises in air-sea fully coupled data
assimilation for future hurricane prediction, Geophys. Res. Lett.,
45, 13173–13177, https://doi.org/10.1029/2018GL080970, 2018.
Zuo, H., Balmaseda, M. A., Tietsche, S., Mogensen, K., and Mayer, M.: The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment, Ocean Sci., 15, 779–808, https://doi.org/10.5194/os-15-779-2019, 2019.
Short summary
Regional climate models are a fundamental tool for a very large number of applications and are being increasingly used within climate services, together with other complementary approaches. Here, we introduce a new regional coupled model, intended to be later extended to a full Earth system model, for climate investigations within the Mediterranean region, coupled data assimilation experiments, and several downscaling exercises (reanalyses and long-range predictions).
Regional climate models are a fundamental tool for a very large number of applications and are...
