Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-3995-2021
© Author(s) 2021. 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-14-3995-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
29 Jun 2021
Model evaluation paper |
| 29 Jun 2021
| 29 Jun 2021
Performance of the Adriatic Sea and Coast (AdriSC) climate component – a COAWST V3.3-based coupled atmosphere–ocean modelling suite: atmospheric dataset
Institute of Oceanography and Fisheries, Šetalište I. Meštrovića 63, 21000 Split, Croatia
Ruđer Bošković Institute, Division for Marine and Environmental Research, Bijenička cesta 54, 10000 Zagreb, Croatia
Petra Pranić
Institute of Oceanography and Fisheries, Šetalište I. Meštrovića 63, 21000 Split, Croatia
Damir Ivanković
Institute of Oceanography and Fisheries, Šetalište I. Meštrovića 63, 21000 Split, Croatia
Iva Tojčić
Institute of Oceanography and Fisheries, Šetalište I. Meštrovića 63, 21000 Split, Croatia
Ivica Vilibić
Institute of Oceanography and Fisheries, Šetalište I. Meštrovića 63, 21000 Split, Croatia
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H. Mihanović, I. Vilibić, S. Carniel, M. Tudor, A. Russo, A. Bergamasco, N. Bubić, Z. Ljubešić, D. Viličić, A. Boldrin, V. Malačič, M. Celio, C. Comici, and F. Raicich
Ocean Sci., 9, 561–572, https://doi.org/10.5194/os-9-561-2013, https://doi.org/10.5194/os-9-561-2013, 2013
S. Pasquet, I. Vilibić, and J. Šepić
Nat. Hazards Earth Syst. Sci., 13, 473–482, https://doi.org/10.5194/nhess-13-473-2013, https://doi.org/10.5194/nhess-13-473-2013, 2013
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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
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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
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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
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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
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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
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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
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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.
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
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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
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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
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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.
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
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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
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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
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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
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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
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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.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
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We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
Emily Black, John Ellis, and Ross I. Maidment
Geosci. Model Dev., 17, 8353–8372, https://doi.org/10.5194/gmd-17-8353-2024, https://doi.org/10.5194/gmd-17-8353-2024, 2024
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We present General TAMSAT-ALERT, a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and meteorological forecasting data into probabilistic hazard assessments. In this way, it complements existing systems and enhances their utility for actionable hazard assessment.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleussner
Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, https://doi.org/10.5194/gmd-17-8283-2024, 2024
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Precipitation and temperature are two of the most impact-relevant climatic variables. Yet, projecting future precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows us to generate monthly means of local precipitation and temperature at low computational costs. Our modelling framework is particularly useful for all downstream applications of climate model data.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3522, https://doi.org/10.5194/egusphere-2024-3522, 2024
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We analyzed carbon and nitrogen mass conservation in data from CMIP6 Earth System Models. Our findings reveal significant discrepancies between flux and pool size data, particularly in nitrogen, where cumulative imbalances can reach hundreds of gigatons. 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.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
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We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev., 17, 8173–8179, https://doi.org/10.5194/gmd-17-8173-2024, https://doi.org/10.5194/gmd-17-8173-2024, 2024
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When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
Philip J. Rasch, Haruki Hirasawa, Mingxuan Wu, Sarah J. Doherty, Robert Wood, Hailong Wang, Andy Jones, James Haywood, and Hansi Singh
Geosci. Model Dev., 17, 7963–7994, https://doi.org/10.5194/gmd-17-7963-2024, https://doi.org/10.5194/gmd-17-7963-2024, 2024
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We introduce a protocol to compare computer climate simulations to better understand a proposed strategy intended to counter warming and climate impacts from greenhouse gas increases. This slightly changes clouds in six ocean regions to reflect more sunlight and cool the Earth. Example changes in clouds and climate are shown for three climate models. Cloud changes differ between the models, but precipitation and surface temperature changes are similar when their cooling effects are made similar.
Trude Eidhammer, Andrew Gettelman, Katherine Thayer-Calder, Duncan Watson-Parris, Gregory Elsaesser, Hugh Morrison, Marcus van Lier-Walqui, Ci Song, and Daniel McCoy
Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, https://doi.org/10.5194/gmd-17-7835-2024, 2024
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We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
Geosci. Model Dev., 17, 7815–7834, https://doi.org/10.5194/gmd-17-7815-2024, https://doi.org/10.5194/gmd-17-7815-2024, 2024
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The regional Earth system model GCOAST-AHOI v2.0 that includes the regional climate model ICON-CLM coupled to the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in ICON-CLM makes it more flexible for coupling to an external ocean model and an external hydrological discharge model.
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, John A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, and Gabriel Chiodo
Geosci. Model Dev., 17, 7767–7793, https://doi.org/10.5194/gmd-17-7767-2024, https://doi.org/10.5194/gmd-17-7767-2024, 2024
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We quantified impacts and efficiency of stratospheric solar climate intervention via solid particle injection. Microphysical interactions of solid particles with the sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code of an aerosol–chemistry–climate model. Compared to injection of SO2 we only find a stronger cooling efficiency for solid particles when normalizing to the aerosol load but not when normalizing to the injection rate.
Ingo Richter, Ping Chang, Gokhan Danabasoglu, Dietmar Dommenget, Guillaume Gastineau, Aixue Hu, Takahito Kataoka, Noel Keenlyside, Fred Kucharski, Yuko Okumura, Wonsun Park, Malte Stuecker, Andrea Taschetto, Chunzai Wang, Stephen Yeager, and Sang-Wook Yeh
EGUsphere, https://doi.org/10.5194/egusphere-2024-3110, https://doi.org/10.5194/egusphere-2024-3110, 2024
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The tropical ocean basins influence each other through multiple pathways and mechanisms, here referred to 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. TBIMIP aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Samuel Rémy, Swen Metzger, Vincent Huijnen, Jason E. Williams, and Johannes Flemming
Geosci. Model Dev., 17, 7539–7567, https://doi.org/10.5194/gmd-17-7539-2024, https://doi.org/10.5194/gmd-17-7539-2024, 2024
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In this paper we describe the development of the future operational cycle 49R1 of the IFS-COMPO system, used for operational forecasts of atmospheric composition in the CAMS project, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12. The implementation of EQSAM4Clim significantly improves the simulated secondary inorganic aerosol surface concentration. The new aerosol and precipitation acidity diagnostics showed good agreement against observational datasets.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
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This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sergi Palomas, Mario C. Acosta, Gladys Utrera, and Etienne Tourigny
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-155, https://doi.org/10.5194/gmd-2024-155, 2024
Revised manuscript accepted for GMD
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This work presents an automatic tool to enhance the performance of climate models by optimizing how computer resources are allocated. Traditional methods are time-consuming and error-prone, often resulting in inefficient simulations. Our tool improves speed and reduces computational costs without needing expert knowledge. The tool has been tested on European climate models, making simulations up to 34 % faster while using fewer resources, helping to make climate simulations more efficient.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
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We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-183, https://doi.org/10.5194/gmd-2024-183, 2024
Revised manuscript accepted for GMD
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Improving climate predictions has significant socio-economic impacts. In this study, we developed and applied a 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. It also enhances the simulation of interannual precipitation and temperature variability over the southern US. This system is to support future predictability studies.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
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In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
Cited articles
Amante, C. and Eakins, B. W.:
ETOPO1 1 arc-minute global relief model: procedures, data sources and analysis, NOAA Technical Memorandum NESDIS NGDC-24, 2009.
Artegiani, A., Bregant, D., Paschini, E., Pinardi, N., Raicich, F., and Russo, A.:
The Adriatic Sea general circulation. Part I. Air–sea interactions and water mass structure,
J. Phys. Oceanogr.,
27, 1492–1514, https://doi.org/10.1175/1520-0485(1997)027<1492:TASGCP>2.0.CO;2, 1997.
Atlas, R., Hoffman, R. N., Ardizzone, J., Leidner, S. M., Jusem, J. C., Smith, D. K., and Gombos, D.:
A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications,
B. Am. Meteorol. Soc.,
92, 157–174, https://doi.org/10.1175/2010BAMS2946.1, 2011.
Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., Dee, D., Dutra, E., Muñoz-Sabater, J., Pappenberger, F., de Rosnay, P., Stockdale, T., and Vitart, F.: ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407, https://doi.org/10.5194/hess-19-389-2015, 2015.
Batistić, M., Garić, R., and Molinero, J. C.:
Interannual variations in Adriatic Sea zooplankton mirror shifts in circulation regimes in the Ionian Sea,
Clim. Res.,
61, 231–240, https://doi.org/10.3354/cr01248, 2014.
Bauer, P., Auligné, T., Bell, W., Geer, A., Guidard, V., Heilliette, S., Kazumori, M., Kim, M.-J., Liu, E. H.-C., McNally, A. P., Macpherson, B., Okamoto, K., Renshaw, R., and Riishøjgaard, L.-P.:
Satellite cloud and precipitation assimilation at operational NWP centres,
Q. J. Roy. Meteor. Soc.,
137, 1934–1951, https://doi.org/10.1002/qj.905, 2011.
Belušić, D. and Klaić, Z. B.:
Estimation of bora wind gusts using a limited area model,
Tellus A,
56, 296–307. https://doi.org/10.1111/j.1600-0870.2004.00068.x, 2004.
Belušić, D., Hrastinski, M., Večenaj, Ž., and Grisogono, B.:
Wind regimes associated with a mountain gap at the northeastern Adriatic coast,
J. Appl. Meteorol. Clim.,
52, 2089–2105, https://doi.org/10.1175/JAMC-D-12-0306.1, 2013.
Belušić Vozila, A., Güttler, I., Ahrens, B., Obermann-Hellhund, A., and Telišman Prtenjak, M.:
Wind over the Adriatic region in CORDEX climate change scenarios,
J. Geophys. Res.-Atmos.,
124, 110–130, https://doi.org/10.1029/2018JD028552, 2019.
Bensi, M., Cardin, V., Rubino, A., Notarstefano, G., and Poulain, P.-M.:
Effects of winter convection on the deep layer of the Southern Adriatic Sea in 2012,
J. Geophys. Res.-Oceans,
118, 6064–6075, https://doi.org/10.1002/2013JC009432, 2013.
Betts, A. K. and Harshvardhan:
Thermodynamic constraint on the cloud liquid water feedback in climate models,
J. Geophys. Res.,
92, 8483–8485, https://doi.org/10.1029/JD092iD07p08483, 1987.
Borzelli, G. L. E., Gačić, M., Cardin, V., and Civitarese, G.:
Eastern Mediterranean Transient and reversal of the Ionian Sea circulation,
Geophys. Res. Lett.,
36, L15108, https://doi.org/10.1029/2009GL039261, 2009.
Bretherton, C. S. and Park, S.:
A new moist turbulence parameterization in the Community Atmosphere Model,
J. Climate,
22, 3422–3448, https://doi.org/10.1175/2008JCLI2556.1, 2009.
Brzovíć, N. and Strelec Mahović, N.:
Cyclonic activity and severe Jugo in the Adriatic,
Phys. Chem. Earth Pt. B,
24, 653–657, https://doi.org/10.1016/S1464-1909(99)00061-1, 1999.
Cavaleri, L., Bertotti, L., Buizza, R., Buzzi, A., Masato, V., Umgiesser, G., and Zampieri, M.:
Predictability of extreme meteo-oceanographic events in the Adriatic Sea,
Q. J. Roy. Meteor. Soc.,
136, 400–413, https://doi.org/10.1002/qj.567, 2010.
Cavaleri, L., Abdalla, S., Benetazzo, A., Bertotti, L., Bidlot, J-R, Breivik, Ø., Carniel, S., Jensen, R. E., Portilla-Yandun, Rogers, W. E., Roland, A., Sanchez-Arcilla, A., Smith, J. M., Staneva, J., Toledo, Y., van Vledder, G. P., and van der Westhuysen, A. J.:
Wave modelling in coastal and inner seas,
Prog. Oceanogr.,
167, 164–233, https://doi.org/10.1016/j.pocean.2018.03.010, 2018.
Chan, S. C., Kahana, R., Kendon, E. J., and Fowler, H. J.:
Projected changes in extreme precipitation over Scotland and Northern England using a high-resolution regional climate model,
Clim. Dynam.,
51, 3559–3577, https://doi.org/10.1007/s00382-018-4096-4, 2018.
Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M., and Jones, P. D.:
An ensemble version of the E-OBS temperature and precipitation data sets,
J. Geophys. Res.-Atmos.,
123, 9391– 9409, https://doi.org/10.1029/2017JD028200, 2018.
da Rocha, R. P., Reboita, M. S., Dutra, L. M. M., Llopart, M. P., and Coppola, E.:
Interannual variability associated with ENSO: present and future climate projections of RegCM4 for South America-CORDEX domain,
Climatic Change,
125, 95–109, https://doi.org/10.1007/s10584-014-1119-y, 2014.
Davolio, S., Volonté, A., Manzato, A., Pucillo, A., Cicogna, A., and Ferrario, M. E.:
Mechanisms producing different precipitation patterns over North-Eastern Italy: insights from hymex-SOP1 and previous events,
Q. J. Roy. Meteor. Soc., 142, 188–205, https://doi.org/10.1002/qj.2731, 2016.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J. N., and Vitart, F.:
The ERA-Interim reanalysis: confguration and performance of the data assimilation system,
Q. J. Roy. Meteor. Soc.,
137, 553–597, https://doi.org/10.1002/qj.828, 2011.
Denamiel, C.: AdriSC Climate Model: evaluation run, https://doi.org/10.17605/OSF.IO/ZB3CM, 2021a.
Denamiel, C.: Evaluation of the AdriSC climate model: atmospheric part, https://doi.org/10.17605/OSF.IO/B2CKT, 2021b.
Denamiel, C., Šepić, J., Ivanković, D., and Vilibić, I.:
The Adriatic Sea and Coast modelling suite: Evaluation of the meteotsunami forecast component,
Ocean Model.,
135, 71–93, https://doi.org/10.1016/j.ocemod.2019.02.003, 2019.
Denamiel, C., Pranić, P., Quentin, F., Mihanović, H., and Vilibić, I.:
Pseudo-global warming projections of extreme wave storms in complex coastal regions: the case of the Adriatic Sea,
Clim. Dynam.,
55, 2483–2509, https://doi.org/10.1007/s00382-020-05397-x, 2020a.
Denamiel, C., Tojčić, I., and Vilibić, I.:
Far future climate (2060–2100) of the northern Adriatic air–sea heat transfers associated with extreme bora events,
Clim. Dynam.,
55, 3043–3066, https://doi.org/10.1007/s00382-020-05435-8, 2020b.
Denamiel, C., Tojčić, I., and Vilibić, I.:
Balancing accuracy and efficiency of atmospheric models in the northern Adriatic during severe bora events,
J. Geophys. Res.-Atmos.,
126, e2020JD033516, https://doi.org/10.1029/2020JD033516, 2021.
Di Virgilio, G., Evans, J. P., Di Luca, A., Olson, R., Argüeso, D., Kala, J., Andrys, J., Hoffmann, P., Katzfey, J. J., and Rockel, B.:
Evaluating reanalysis-driven CORDEX regional climate models over Australia: model performance and errors,
Clim. Dynam.,
53, 2985–3005, https://doi.org/10.1007/s00382-019-04672-w, 2019.
Drobinski, P., Silva, N. D., Panthou, G., Bastin, S., Muller, C., Ahrens, B., Borga, M., Conte, D., Fosser, G., Giorgi, F., Güttler, I., Kotroni, V., Li, L., Morin, E., Önol, B., Quintana-Segui, P., Romera R., and Zsolt Torma, C.:
Scaling precipitation extremes with temperature in the Mediterranean: past climate assessment and projection in anthropogenic scenarios,
Clim. Dynam.,
51, 1237–1257, https://doi.org/10.1007/s00382-016-3083-x, 2018.
Dudhia, J.:
Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model,
J. Atmos. Sci.,
46, 3077–3107, 1989.
Dudhia, J.: A Multi-Layer Soil Temperature Model for MM5, Sixth PSU/NCAR Mesoscale Model Users’ Workshop, Boulder, 22–24 July 1996, 49–50, 1996.
Gačić, M., Civitarese, G., Miserocchi, S., Cardin, V., Crise, A., and Mauri, E.:
The open-ocean convection in the Southern Adriatic: a controlling mechanism of the spring phytoplankton bloom,
Cont. Shelf Res.,
22, 1897–1908, https://doi.org/10.1016/S0278-4343(02)00050-X, 2002.
Gačić, M., Borzelli, G. L. E., Civitarese, G., Cardin, V., and Yari, S.:
Can internal processes sustain reversals of the ocean upper circulation? The Ionian Sea example,
Geophys. Res. Lett.,
37, L09608, https://doi.org/10.1029/2010GL043216, 2010.
García-Díez, M., Fernández, J., and Vautard, R.:
An RCM multi-physics ensemble over Europe: Multi-variable evaluation to avoid error compensation,
Clim. Dynam., 45, 3141–3156, https://doi.org/10.1007/s00382-015-2529-x, 2015.
Giorgi, F., Jones, C., and Asrar, G. R.:
Addressing climate information needs at the regional level: the CORDEX framework,
WMO Bulletin,
58, 175–183, 2009.
Grisogono, B. and Belušić, D.:
A review of recent advances in understanding the meso- and microscale properties of the severe Bora wind,
Tellus A,
61, 1–16, https://doi.org/10.1111/j.1600-0870.2008.00369.x, 2009.
Held, I. M. and Soden, B. J.:
Robust responses of the hydrological cycle to global warming,
J. Climate,
19, 5686–5699, https://doi.org/10.1175/JCLI3990.1, 2006.
Huang, B., Polanski, S., and Cubasch, U.:
Assessment of precipitation climatology in an ensemble of CORDEX-East Asia regional climate simulations,
Clim. Res.,
64, 141–158, https://doi.org/10.3354/CR01302, 2015.
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., Hong, Y., Bowman, K. P., and Stocker, E. F.:
The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales,
J. Hydrometeorol.,
8, 38–55, https://doi.org/10.1175/JHM560.1, 2007.
Ivanković, D., Denamiel, C., and Jelavić, D.:
Web visualization of data from numerical models and real-time stations network in frame of Adriatic Sea and Coast (AdriSC) Meteotsunami Forecast,
OCEANS 2019, Marseille, France, 17–20 June 2019, 1–5, https://doi.org/10.1109/OCEANSE.2019.8867225, 2019.
Janeković, I., Mihanović, H., Vilibić, I., and Tudor, M.:
Extreme cooling and dense water formation estimates in open and coastal regions of the Adriatic Sea during the winter of 2012,
J. Geophys. Res.-Oceans,
119, 3200–3218, https://doi.org/10.1002/2014JC009865, 2014.
Janjić, Z.:
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.
Kain, J. S.:
The Kain–Fritsch convective parameterization: an update,
J. Appl. Meteorol.,
43, 170–181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004.
Kehler-Poljak, G., Telišman Prtenjak, M., Kvakić, M., Šariri, K., and Večenaj, Ž.:
Interaction of sea breeze and deep convection over the northeastern Adriatic Coast: An analysis of sensitivity experiments using a high-resolution mesoscale model,
Pure Appl. Geophys.,
174, 4197–4224, https://doi.org/10.1007/s00024-017-1607-x, 2017.
Klaić, Z. B., Prodanov, A. D., and Belušić, D.:
Wind measurements in Senj – underestimation of true bora flows,
Geofizika,
26, 245–252, 2009.
Knist, S., Goergen, K., and Simmer, C.:
Evaluation and projected changes of precipitation statistics in convection-permitting WRF climate simulations over Central Europe,
Clim. Dynam.,
55, 325–341, https://doi.org/10.1007/s00382-018-4147-x, 2020.
Kolios, S. and Kalimeris, A.:
Evaluation of the TRMM rainfall product accuracy over the central Mediterranean during a 20-year period (1998–2017),
Theor. Appl. Climatol.,
139, 785–799, https://doi.org/10.1007/s00704-019-03015-3, 2020.
Kotlarski, S., Keuler, K., Christensen, O. B., Colette, A., Déqué, M., Gobiet, A., Goergen, K., Jacob, D., Lüthi, D., van Meijgaard, E., Nikulin, G., Schär, C., Teichmann, C., Vautard, R., Warrach-Sagi, K., and Wulfmeyer, V.: Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble, Geosci. Model Dev., 7, 1297–1333, https://doi.org/10.5194/gmd-7-1297-2014, 2014.
Kuzmić, M., Grisogono, B., Li, X., and Lehner, S.:
Examining deep and shallow Adriatic bora events,
Q. J. Roy. Meteor. Soc.,
141, 3434–3438. https://doi.org/10.1002/qj.2578, 2015.
Laprise, R.:
The Euler Equations of motion with hydrostatic pressure as independent variable,
Mon. Weather Rev.,
120, 197–207, https://doi.org/10.1175/1520-0493(1992)120<0197:TEEOMW>2.0.CO;2, 1992.
Larson, J., Jacob, R., and Ong, E.:
The Model Coupling Toolkit: A New Fortran90 Toolkit for Building Multiphysics Parallel Coupled Models,
Int. J. High Perform. C.,
19, 277–292, https://doi.org/10.1177/1094342005056115, 2005.
Lawrence, M. G.:
The relationship between relative humidity and the dewpoint temperature in moist air: A simple conversion and applications,
B. Am. Meteorol. Soc.,
86, 225–234, https://doi.org/10.1175/BAMS-86-2-225, 2005.
Li, Y., Li, Z., Zhang, Z., Chen, L., Kurkute, S., Scaff, L., and Pan, X.: High-resolution regional climate modeling and projection over western Canada using a weather research forecasting model with a pseudo-global warming approach, Hydrol. Earth Syst. Sci., 23, 4635–4659, https://doi.org/10.5194/hess-23-4635-2019, 2019.
Massonnet, F., Bellprat, O., Guemas, V., and Doblas-Reyes, F. J.:
Using climate models to estimate the quality of global observational data sets,
Science,
354, 452–455, https://doi.org/10.1126/science.aaf6369, 2016.
Mears, C. A., Scott, J., Wentz, F. J., Ricciardulli, L., Leidner, S. M., Hoffman, R., and Atlas, R.:
A near-real-time version of the Cross-Calibrated Multiplatform (CCMP) ocean surface wind velocity data set,
J. Geophys. Res.-Oceans,
124, 6997–7010, https://doi.org/10.1029/2019JC015367, 2019.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.:
Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave,
J. Geophys. Res.,
102, 16663, https://doi.org/10.1029/97JD00237, 1997.
Molcard, A., Pinardi, N., Iskandarani, M., and Haivogel, D. B.:
Wind driven general circulation of the Mediterranean Sea simulated with a Spectral Element Ocean Model,
Dynam. Atmos. Oceans,
35, 97–130, https://doi.org/10.1016/S0377-0265(01)00080-X, 2002.
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, 2013.
Morrison, H., Curry, J. A., and Khvorostyanov, V. I.:
A new double-moment microphysics parameterization for application in cloud and climate models. Part I: Description,
J. Atmos. Sci.,
62, 1665–1677, 2005.
Nikulin, G., Jones, C., Giorgi, F., Asrar, G., Büchner, M., Cerezo-Mota, R., Bøssing Christensen, O., Déqué, M., Fernandez, J., Hänsler, A., van Meijgaard, E., Samuelsson, P., Bamba Sylla, M., and Sushama, L.:
Precipitation climatology in an ensemble of CORDEX-Africa regional climate simulations,
J. Climate,
25, 6057–6078, https://doi.org/10.1175/JCLI-D-11-00375.1, 2012.
Orlić, M., Dadić, V., Grbec, B., Leder, N., Marki, A., Matić, F., Mihanović, H., Beg Paklar, G., Pasarić, M., Pasarić, Z., and Vilibić, I.:
Wintertime buoyancy forcing, changing seawater properties and two different circulation systems produced in the Adriatic,
J. Geophys. Res.,
111, C03S07, https://doi.org/10.1029/2005JC003271, 2007.
Pasarić, Z., Belušić, D., and Klaić, Z. B.: Orographic influences on the Adriatic sirocco wind, Ann. Geophys., 25, 1263–1267, https://doi.org/10.5194/angeo-25-1263-2007, 2007.
Pinardi, N., Zavatarelli, M., Adani, M., Coppini, G., Fratianni, C., Oddo, P., Simoncelli, S., Tonani, M., Lyubartsev, V., Dobricic, S., and Bonaduce, A.:
Mediterranean Sea large-scale low-frequency ocean variability and water mass formation rates from 1987 to 2007: A retrospective analysis,
Prog. Oceanogr.,
132, 318–332, https://doi.org/10.1016/j.pocean.2013.11.003, 2015.
Prein, A., Gobiet, A., Suklitsch, M., Truhetz, H., Awan, N., Keuler, K., and Georgievski, G.:
Added value of convection permitting seasonal simulations,
Clim. Dynam.,
41, 2655–2677, 2013.
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K., Keller, M., Tölle, M., Gutjahr, O., Feser, F., Brisson, E., Kollet, S., Schmidli, J., van Lipzig, N. P. M., and Leung, R.:
A review on regional convection-permitting climate modeling: Demonstrations, prospects and challenges,
Rev. Geophys.,
53, 323–361, https://doi.org/10.1002/2014RG000475, 2015.
Prtenjak, M. T., Viher, M., and Jurković, J.:
Sea-land breeze development during a summer bora event along the north-eastern Adriatic coast,
Q. J. Roy. Meteor. Soc.,
136, 1554–1571, https://doi.org/10.1002/qj.649, 2010.
Reale, M., Salon, S., Crise, A., Farneti, R., Mosetti, R., and Sannino, G.:
Unexpected covariant behavior of the Aegean and Ionian Seas in the period 1987–2008 by means of a nondimensional sea surface height index,
J. Geophys. Res.-Oceans,
122, 8020–8033, https://doi.org/10.1002/2017JC012983, 2017.
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.
Ricchi, A., Miglietta, M. M., Falco, P. P., Benetazzo, A., Bonaldo, D., Bergamasco, A., Sclavo, M., and Carniel, S.:
On the use of a coupled ocean–atmosphere–wave model during an extreme cold air outbreak over the Adriatic Sea,
Atmos. Res.,
172–173, 48–65, https://doi.org/10.1016/j.atmosres.2015.12.023, 2016.
Rinke, A. H., Matthes, J. H. Christensen, P., Kuhry, V., Romanovsky, and Dethloff, K.:
Arctic RCM simulations of temperature and precipitation derived indices relevant to future frozen ground conditions,
Global Planet. Change,
80–81,136-148, https://doi.org/10.1016/j.gloplacha.2011.10.011, 2011.
Roether, W. and Schlitzer, R.:
Eastern Mediterranean deep water renewal on the basis of chlorofluoromethane and tritium data,
Dynam. Atmos. Oceans,
15, 333–354, https://doi.org/10.1016/0377-0265(91)90025-B, 1991.
Rubino, A., Gačić, M., Bensi, M., Kovačević, V., Malačič, V., Menna, M., Negretti, M. E., Sommeria, J., Zanchettin, D., Barreto, R. V., Ursella, L., Cardin, V., Civitarese, G., Orlić, M., Petelin, B., and Siena, G.:
Experimental evidence of long-term oceanic circulation reversals without wind influence in the North Ionian Sea,
Sci. Rep.-UK,
10, 1905, https://doi.org/10.1038/s41598-020-57862-6, 2020.
Ruti, P., 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, A., Gaertner, M., Galan, P., Gallardo, C., Gualdi, S., Goncalves, M., Jorba, O., Jorda, G., Lheveder, B., Lebeaupin-Brossier, C., Li, L., Liguori, G., Lionello, P., Macias-Moy, D., Nabat, P., Onol, B., Rajkovic, 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.
Schär, C., Frei, C., Luthi, D., and Davies, H. C.:
Surrogate climate-change scenarios for regional climate models,
Geophys. Res. Lett.,
23, 669–672, https://doi.org/10.1029/96GL00265, 1996.
Sein, D. V., Gröger, M., Cabos, W., Alvarez-Garcia, F. J., Hagemann, S., Pinto, J. G., Izquierdo, A., de la Vara, A., Koldunov, N. V., Dvornikov, A. Y., Limareva, N., Alekseeva, E., Martinez-Lopez, B., and Jacob, D.:
Regionally coupled atmosphere–ocean-marine biogeochemistry model ROM: 2. Studying the climate change signal in the North Atlantic and Europe,
J. Adv. Model. Earth Sy.,
12, e2019MS001646, https://doi.org/10.1029/2019MS001646, 2020.
Sevault, F., Somot, S., Alias, A., Dubois, C., Lebeaupin-Brossier, C., Nabat, P., Adloff, F., Déqué, M., and Decharme, B.:
A fully coupled Mediterranean regional climate system model: design and evaluation of the ocean component for the 1980–2012 period,
Tellus A,
66, 23967, https://doi.org/10.3402/tellusa.v66.23967, 2014.
Shchepetkin, A. F. and McWilliams, J. C.:
Correction and commentary for “Ocean forecasting in terrain-following coordinates: Formulation and skill assessment of the regional ocean modeling system” by Haidvogel et al., J. Comput. Phys., 227, pp. 3595–3624,
J. Comput. Phys.,
228, 8985–9000, https://doi.org/10.1016/j.jcp.2009.09.002, 2009.
Simoncelli, S., Fratianni, C., Pinardi, N., Grandi, A., Drudi, M., Oddo, P., and Dobricic, S.:
Mediterranean Sea physical reanalysis (MEDREA 1987-2015) (Version 1),
Copernicus Monitoring Environment Marine Service (CMEMS), https://doi.org/10.25423/medsea_reanalysis_phys_006_004, 2014.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Wang, W., and Powers, J. G.:
A description of the Advanced Research WRF Version 2,. NCAR Technical Note NCAR/TN-468+STR, https://doi.org/10.5065/D6DZ069T, 2005.
Somot, S., Sevault, F., and Déqué, M.:
Transient climate change scenario simulation of the Mediterranean Sea for the twenty-first century using a high-resolution ocean circulation model,
Clim. Dynam.,
27, 851–879, https://doi.org/10.1007/s00382-006-0167-z, 2006.
Somot, S., Ruti, P., Ahrens, B., Coppola, E., Jordà, G., Sannino, G., and Solmon, F.:
Editorial for the Med-CORDEX special issue,
Clim. Dyn.,
51, 771–777, https://doi.org/10.1007/s00382-018-4325-x, 2018.
Taylor, K. E.:
Summarizing multiple aspects of model performance in a single diagram,
J. Geophys. Res.,
106, 7183–7192, https://doi.org/10.1029/2000JD900719, 2001.
Varga, Á. J. and Breuer, H.:
Sensitivity of simulated temperature, precipitation, and global radiation to different WRF configurations over the Carpathian Basin for regional climate applications,
Clim. Dynam.,
55, 2849–2866, https://doi.org/10.1007/s00382-020-05416-x, 2020.
Vilibić, I., Mihanović, H., Janeković, I., Denamiel, C., Poulain, P.-M., Orlić, M., Dunić, N., Dadić, V., Pasarić, M., Muslim, S., Gerin, R., Matić, F., Šepić, J., Mauri, E., Kokkini, Z., Tudor, M., Kovač, Ž., and Džoić, T.: Wintertime dynamics in the coastal northeastern Adriatic Sea: the NAdEx 2015 experiment, Ocean Sci., 14, 237–258, https://doi.org/10.5194/os-14-237-2018, 2018.
Warner, J. C., Armstrong, B., He, R., and Zambon, J. B.:
Development of a Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modeling system,
Ocean Model.,
35, 230–244, https://doi.org/10.1016/j.ocemod.2010.07.010, 2010.
Warrach-Sagi, K., Schwitalla, T., Wulfmeyer, V., and Bauer, H.-S.:
Evaluation of a climate simulation in Europe based on the WRF-NOAH model system: precipitation in Germany,
Clim. Dynam.,
41, 755–774, https://doi.org/10.1007/s00382-013-1727-7, 2013.
Weatherall, P., Marks, K. M., Jakobsson, M., Schmitt, T., Tani, S., Arndt, J. E., Rovere, M., Chayes, D., Ferrini, V., and Wigley, R.:
A new digital bathymetric model of the world's oceans,
Earth and Space Science,
2, 331–345, https://doi.org/10.1002/2015EA000107, 2015.
Westra, S., Fowler, H. J., Evans, J. P., Alexander, L. V., Berg, P., Johnson, F., Kendon, E. J., Lenderink, G., and Roberts, N. M.:
Future changes to the intensity and frequency of short-duration extreme rainfall,
Rev. Geophys., 52, 522–555, https://doi.org/10.1002/2014RG000464, 2014.
Zou, L. and Zhou, T.:
Dynamical downscaling of East Asian winter monsoon changes with a regional ocean–atmosphere coupled model,
Q. J. Roy. Meteor. Soc.,
143, 2245–2259, https://doi.org/10.1002/qj.3082, 2017.
Short summary
The atmospheric results of the Adriatic Sea and Coast (AdriSC) climate simulation (1987–2017) are evaluated against available observational datasets in the Adriatic region. Generally, the AdriSC model performs better than regional climate models that have resolutions that are 4 times more coarse, except concerning summer temperatures, which are systematically underestimated. High-resolution climate models may thus provide new insights about the local impacts of global warming in the Adriatic.
The atmospheric results of the Adriatic Sea and Coast (AdriSC) climate simulation (1987–2017)...
