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
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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
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W. J. McKiver, G. Sannino, F. Braga, and D. Bellafiore
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L. Visinelli, S. Masina, M. Vichi, and A. Storto
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For the first time, we coupled a regional climate chemistry model, RegCM-Chem, with a dynamic vegetation model, YIBs, to create a regional climate–chemistry–ecology model, RegCM-Chem–YIBs. We applied it to simulate climatic, chemical, and ecological parameters in East Asia and fully validated it on a variety of observational data. Results show that RegCM-Chem–YIBs model is a valuable tool for studying the terrestrial carbon cycle, atmospheric chemistry, and climate change on a regional scale.
Bryce E. Harrop, Jian Lu, L. Ruby Leung, William K. M. Lau, Kyu-Myong Kim, Brian Medeiros, Brian J. Soden, Gabriel A. Vecchi, Bosong Zhang, and Balwinder Singh
Geosci. Model Dev., 17, 3111–3135, https://doi.org/10.5194/gmd-17-3111-2024, https://doi.org/10.5194/gmd-17-3111-2024, 2024
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Seven new experimental setups designed to interfere with cloud radiative heating have been added to the Energy Exascale Earth System Model (E3SM). These experiments include both those that test the mean impact of cloud radiative heating and those examining its covariance with circulations. This paper documents the code changes and steps needed to run these experiments. Results corroborate prior findings for how cloud radiative heating impacts circulations and rainfall patterns.
Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Gladys Utrera, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Sylvie Joussaume, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, https://doi.org/10.5194/gmd-17-3081-2024, 2024
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We present a collection of performance metrics gathered during the Coupled Model Intercomparison Project Phase 6 (CMIP6), a worldwide initiative to study climate change. We analyse the metrics that resulted from collaboration efforts among many partners and models and describe our findings to demonstrate the utility of our study for the scientific community. The research contributes to understanding climate modelling performance on the current high-performance computing (HPC) architectures.
Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval
Geosci. Model Dev., 17, 3099–3110, https://doi.org/10.5194/gmd-17-3099-2024, https://doi.org/10.5194/gmd-17-3099-2024, 2024
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Especially over the midlatitudes, precipitation is mainly formed via the ice phase. In this study we focus on the initial snow formation process in the ICON-AES, the aggregation process. We use a stochastical approach for the aggregation parameterization and investigate the influence in the ICON-AES. Therefore, a distribution function of cloud ice is created, which is evaluated with satellite data. The new approach leads to cloud ice loss and an improvement in the process rate bias.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
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Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024, https://doi.org/10.5194/gmd-17-3025-2024, 2024
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Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
Geosci. Model Dev., 17, 2755–2781, https://doi.org/10.5194/gmd-17-2755-2024, https://doi.org/10.5194/gmd-17-2755-2024, 2024
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We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
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This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
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The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.
Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 17, 2525–2545, https://doi.org/10.5194/gmd-17-2525-2024, https://doi.org/10.5194/gmd-17-2525-2024, 2024
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This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based
mass-fluxterm. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
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Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
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Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, https://doi.org/10.5194/gmd-17-2287-2024, 2024
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Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.
Tian Gan, Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Irina Overeem, Albert J. Kettner, Benjamin Campforts, Julia M. Moriarty, Brianna Undzis, Ethan Pierce, and Lynn McCready
Geosci. Model Dev., 17, 2165–2185, https://doi.org/10.5194/gmd-17-2165-2024, https://doi.org/10.5194/gmd-17-2165-2024, 2024
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This study presents the design, implementation, and application of the CSDMS Data Components. The case studies demonstrate that the Data Components provide a consistent way to access heterogeneous datasets from multiple sources, and to seamlessly integrate them with various models for Earth surface process modeling. The Data Components support the creation of open data–model integration workflows to improve the research transparency and reproducibility.
Jérémy Bernard, Erwan Bocher, Matthieu Gousseff, François Leconte, and Elisabeth Le Saux Wiederhold
Geosci. Model Dev., 17, 2077–2116, https://doi.org/10.5194/gmd-17-2077-2024, https://doi.org/10.5194/gmd-17-2077-2024, 2024
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Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is seen as a standard approach for classifying any zone according to a set of geographic indicators. While many methods already exist to map the LCZ, only a few tools are openly and freely available. We present the algorithm implemented in GeoClimate software to identify the LCZ of any place in the world using OpenStreetMap data.
Thomas Extier, Thibaut Caley, and Didier M. Roche
Geosci. Model Dev., 17, 2117–2139, https://doi.org/10.5194/gmd-17-2117-2024, https://doi.org/10.5194/gmd-17-2117-2024, 2024
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Stable water isotopes are used to infer changes in the hydrological cycle for different time periods in climatic archive and climate models. We present the implementation of the δ2H and δ17O water isotopes in the coupled climate model iLOVECLIM and calculate the d- and 17O-excess. Results of a simulation under preindustrial conditions show that the model correctly reproduces the water isotope distribution in the atmosphere and ocean in comparison to data and other global circulation models.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024, https://doi.org/10.5194/gmd-17-1869-2024, 2024
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We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant timescales, enable demonstration of inter-model spread in land–atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
Marlene Klockmann, Udo von Toussaint, and Eduardo Zorita
Geosci. Model Dev., 17, 1765–1787, https://doi.org/10.5194/gmd-17-1765-2024, https://doi.org/10.5194/gmd-17-1765-2024, 2024
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Reconstructions of climate variability before the observational period rely on climate proxies and sophisticated statistical models to link the proxy information and climate variability. Existing models tend to underestimate the true magnitude of variability, especially if the proxies contain non-climatic noise. We present and test a promising new framework for climate-index reconstructions, based on Gaussian processes, which reconstructs robust variability estimates from noisy and sparse data.
Aaron A. Naidoo-Bagwell, Fanny M. Monteiro, Katharine R. Hendry, Scott Burgan, Jamie D. Wilson, Ben A. Ward, Andy Ridgwell, and Daniel J. Conley
Geosci. Model Dev., 17, 1729–1748, https://doi.org/10.5194/gmd-17-1729-2024, https://doi.org/10.5194/gmd-17-1729-2024, 2024
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As an extension to the EcoGEnIE 1.0 Earth system model that features a diverse plankton community, EcoGEnIE 1.1 includes siliceous plankton diatoms and also considers their impact on biogeochemical cycles. With updates to existing nutrient cycles and the introduction of the silicon cycle, we see improved model performance relative to observational data. Through a more functionally diverse plankton community, the new model enables more comprehensive future study of ocean ecology.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
Geosci. Model Dev., 17, 1709–1727, https://doi.org/10.5194/gmd-17-1709-2024, https://doi.org/10.5194/gmd-17-1709-2024, 2024
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In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period, but also exhibit some discrepancies.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024, https://doi.org/10.5194/gmd-17-1689-2024, 2024
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This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean–atmosphere interactions.
Skyler Graap and Colin M. Zarzycki
Geosci. Model Dev., 17, 1627–1650, https://doi.org/10.5194/gmd-17-1627-2024, https://doi.org/10.5194/gmd-17-1627-2024, 2024
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A key target for improving climate models is how low, bright clouds are predicted over tropical oceans, since they have important consequences for the Earth's energy budget. A climate model has been updated to improve the physical realism of the treatment of how momentum is moved up and down in the atmosphere. By comparing this updated model to real-world observations from balloon launches, it can be shown to more accurately depict atmospheric structure in trade-wind areas close to the Equator.
Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld
EGUsphere, https://doi.org/10.5194/egusphere-2024-45, https://doi.org/10.5194/egusphere-2024-45, 2024
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This study focused on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies were applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method of Random Forest for increasing the accuracy of climate models, concerning the projection of the number of wet days.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024, https://doi.org/10.5194/gmd-17-1585-2024, 2024
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Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
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Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024, https://doi.org/10.5194/gmd-17-1443-2024, 2024
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The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
Oksana Guba, Mark A. Taylor, Peter A. Bosler, Christopher Eldred, and Peter H. Lauritzen
Geosci. Model Dev., 17, 1429–1442, https://doi.org/10.5194/gmd-17-1429-2024, https://doi.org/10.5194/gmd-17-1429-2024, 2024
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We want to reduce errors in the moist energy budget in numerical atmospheric models. We study a few common assumptions and mechanisms that are used for the moist physics. Some mechanisms are more consistent with the underlying equations. Separately, we study how assumptions about models' thermodynamics affect the modeled energy of precipitation. We also explain how to conserve energy in the moist physics for nonhydrostatic models.
Konstantin Aiteew, Jarno Rouhiainen, Claas Nendel, and René Dechow
Geosci. Model Dev., 17, 1349–1385, https://doi.org/10.5194/gmd-17-1349-2024, https://doi.org/10.5194/gmd-17-1349-2024, 2024
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This study evaluated the biogeochemical model MONICA and its performance in simulating soil organic carbon changes. MONICA can reproduce plant growth, carbon and nitrogen dynamics, soil water and temperature. The model results were compared with five established carbon turnover models. With the exception of certain sites, adequate reproduction of soil organic carbon stock change rates was achieved. The MONICA model was capable of performing similar to or even better than the other models.
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, https://doi.org/10.5194/gmd-17-1327-2024, 2024
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By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
Bernd Funke, Thierry Dudok de Wit, Ilaria Ermolli, Margit Haberreiter, Doug Kinnison, Daniel Marsh, Hilde Nesse, Annika Seppälä, Miriam Sinnhuber, and Ilya Usoskin
Geosci. Model Dev., 17, 1217–1227, https://doi.org/10.5194/gmd-17-1217-2024, https://doi.org/10.5194/gmd-17-1217-2024, 2024
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We outline a road map for the preparation of a solar forcing dataset for the upcoming Phase 7 of the Coupled Model Intercomparison Project (CMIP7), considering the latest scientific advances made in the reconstruction of solar forcing and in the understanding of climate response while also addressing the issues that were raised during CMIP6.
Fiona Raphaela Spuler, Jakob Benjamin Wessel, Edward Comyn-Platt, James Varndell, and Chiara Cagnazzo
Geosci. Model Dev., 17, 1249–1269, https://doi.org/10.5194/gmd-17-1249-2024, https://doi.org/10.5194/gmd-17-1249-2024, 2024
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Before using climate models to study the impacts of climate change, bias adjustment is commonly applied to the models to ensure that they correspond with observations at a local scale. However, this can introduce undesirable distortions into the climate model. In this paper, we present an open-source python package called ibicus to enable the comparison and detailed evaluation of bias adjustment methods, facilitating their transparent and rigorous application.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024, https://doi.org/10.5194/gmd-17-1197-2024, 2024
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We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled earth system model. We found that calibrating only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of the water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
Douglas McNeall, Eddy Robertson, and Andy Wiltshire
Geosci. Model Dev., 17, 1059–1089, https://doi.org/10.5194/gmd-17-1059-2024, https://doi.org/10.5194/gmd-17-1059-2024, 2024
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We can run simulations of the land surface and carbon cycle, using computer models to help us understand and predict climate change and its impacts. These simulations are not perfect reproductions of the real land surface, and that can make them less effective tools. We use new statistical and computational techniques to help us understand how different our models are from the real land surface, how to make them more realistic, and how well we can simulate past and future climate.
Genevieve L. Clow, Nicole S. Lovenduski, Michael N. Levy, Keith Lindsay, and Jennifer E. Kay
Geosci. Model Dev., 17, 975–995, https://doi.org/10.5194/gmd-17-975-2024, https://doi.org/10.5194/gmd-17-975-2024, 2024
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Satellite observations of chlorophyll allow us to study marine phytoplankton on a global scale; yet some of these observations are missing due to clouds and other issues. To investigate the impact of missing data, we developed a satellite simulator for chlorophyll in an Earth system model. We found that missing data can impact the global mean chlorophyll by nearly 20 %. The simulated observations provide a more direct comparison to real-world data and can be used to improve model validation.
Jiateng Guo, Xuechuang Xu, Luyuan Wang, Xulei Wang, Lixin Wu, Mark Jessell, Vitaliy Ogarko, Zhibin Liu, and Yufei Zheng
Geosci. Model Dev., 17, 957–973, https://doi.org/10.5194/gmd-17-957-2024, https://doi.org/10.5194/gmd-17-957-2024, 2024
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This study proposes a semi-supervised learning algorithm using pseudo-labels for 3D geological modelling. We establish a 3D geological model using borehole data from a complex real urban local survey area in Shenyang and make an uncertainty analysis of this model. The method effectively expands the sample space, which is suitable for geomodelling and uncertainty analysis from boreholes. The modelling results perform well in terms of spatial morphology and geological semantics.
Shih-Wei Wei, Mariusz Pagowski, Arlindo da Silva, Cheng-Hsuan Lu, and Bo Huang
Geosci. Model Dev., 17, 795–813, https://doi.org/10.5194/gmd-17-795-2024, https://doi.org/10.5194/gmd-17-795-2024, 2024
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This study describes the modeling system and the evaluation results for the first prototype version of a global aerosol reanalysis product at NOAA, prototype NOAA Aerosol ReAnalysis version 1.0 (pNARA v1.0). We evaluated pNARA v1.0 against independent datasets and compared it with other reanalyses. We identified deficiencies in the system (both in the forecast model and in the data assimilation system) and the uncertainties that exist in our reanalysis.
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, https://doi.org/10.5194/gmd-17-731-2024, 2024
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The BARPA-R modelling configuration has been developed to produce high-resolution climate hazard projections within the Australian region. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
Deepeshkumar Jain, Suryachandra A. Rao, Ramu A. Dandi, Prasanth A. Pillai, Ankur Srivastava, Maheswar Pradhan, and Kiran V. Gangadharan
Geosci. Model Dev., 17, 709–729, https://doi.org/10.5194/gmd-17-709-2024, https://doi.org/10.5194/gmd-17-709-2024, 2024
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The present paper discusses and evaluates the new Monsoon Mission Coupled Forecast System model (MMCFS) version 2.0 which upgrades the currently operational MMCFS v1.0 at the Indian Meteorological Department, India. The individual model components have been substantially upgraded independently by their respective scientific groups. MMCFS v2.0 includes these upgrades in the operational coupled model. The new model shows significant skill improvement in simulating the Indian monsoon.
Nathan Beech, Thomas Rackow, Tido Semmler, and Thomas Jung
Geosci. Model Dev., 17, 529–543, https://doi.org/10.5194/gmd-17-529-2024, https://doi.org/10.5194/gmd-17-529-2024, 2024
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Cost-reducing modeling strategies are applied to high-resolution simulations of the Southern Ocean in a changing climate. They are evaluated with respect to observations and traditional, lower-resolution modeling methods. The simulations effectively reproduce small-scale ocean flows seen in satellite data and are largely consistent with traditional model simulations after 4 °C of warming. Small-scale flows are found to intensify near bathymetric features and to become more variable.
Carl Svenhag, Moa Kristina Sporre, Tinja Olenius, Daniel Yazgi, Sara Marie Blichner, Lars Peter Nieradzik, and Pontus Roldin
EGUsphere, https://doi.org/10.5194/egusphere-2023-2665, https://doi.org/10.5194/egusphere-2023-2665, 2024
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Our research shows the importance of modeling new particle formation (NPF) and growth of particles in the atmosphere on a global scale, as they influence the outcomes of clouds and our climate. With the global model EC-Earth3 we showed that using a new method for NPF modeling, which includes new detailed processes with NH3 and H2SO4, significantly impacted the number of particles in the air and clouds. It also changed the radiation balance in the same magnitude as man-made greenhouse emissions.
Karl E. Taylor
Geosci. Model Dev., 17, 415–430, https://doi.org/10.5194/gmd-17-415-2024, https://doi.org/10.5194/gmd-17-415-2024, 2024
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Remapping gridded data in a way that preserves the conservative properties of the climate system can be essential in coupling model components and for accurate assessment of the system’s energy and mass constituents. Remapping packages capable of handling a wide variety of grids can, for some common grids, calculate remapping weights that are somewhat inaccurate. Correcting for these errors, guidelines are provided to ensure conservation when the weights are used in practice.
Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, and Angelina Bushenkova
Geosci. Model Dev., 17, 229–259, https://doi.org/10.5194/gmd-17-229-2024, https://doi.org/10.5194/gmd-17-229-2024, 2024
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This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
Geosci. Model Dev., 17, 261–273, https://doi.org/10.5194/gmd-17-261-2024, https://doi.org/10.5194/gmd-17-261-2024, 2024
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We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere–ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 45 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Diana R. Gergel, Steven B. Malevich, Kelly E. McCusker, Emile Tenezakis, Michael T. Delgado, Meredith A. Fish, and Robert E. Kopp
Geosci. Model Dev., 17, 191–227, https://doi.org/10.5194/gmd-17-191-2024, https://doi.org/10.5194/gmd-17-191-2024, 2024
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The freely available Global Downscaled Projections for Climate Impacts Research (GDPCIR) dataset gives researchers a new tool for studying how future climate will evolve at a local or regional level, corresponding to the latest global climate model simulations prepared as part of the UN Intergovernmental Panel on Climate Change’s Sixth Assessment Report. Those simulations represent an enormous advance in quality, detail, and scope that GDPCIR translates to the local level.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
Geosci. Model Dev., 17, 169–189, https://doi.org/10.5194/gmd-17-169-2024, https://doi.org/10.5194/gmd-17-169-2024, 2024
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We performed systematic evaluation of clouds simulated in the Energy
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev., 17, 91–116, https://doi.org/10.5194/gmd-17-91-2024, https://doi.org/10.5194/gmd-17-91-2024, 2024
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For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
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...
