Articles | Volume 15, issue 5
https://doi.org/10.5194/gmd-15-2035-2022
© Author(s) 2022. 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-15-2035-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
10 Mar 2022
Model evaluation paper |
| 10 Mar 2022
| 10 Mar 2022
IBI-CCS: a regional high-resolution model to simulate sea level in western Europe
Alisée A. Chaigneau
CORRESPONDING AUTHOR
CNRM UMR 3589, Météo-France/CNRS, Toulouse, France
Mercator Ocean International, Research and development department, Toulouse, France
Guillaume Reffray
Mercator Ocean International, Research and development department, Toulouse, France
Aurore Voldoire
CNRM UMR 3589, Météo-France/CNRS, Toulouse, France
Angélique Melet
Mercator Ocean International, Research and development department, Toulouse, France
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Climate-change-induced sea level rise increases the frequency of extreme sea levels. We analyze projected changes in extreme sea levels for western European coasts produced with high-resolution models (∼ 6 km). Unlike commonly used coarse-scale global climate models, this approach allows us to simulate key processes driving coastal sea level variations, such as long-term sea level rise, tides, storm surges induced by low atmospheric surface pressure and winds, waves, and their interactions.
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Phytoplankton, tiny photosynthetic organisms, produce nearly half of Earth's oxygen. To analyze their physiology, diversity, and evolution in the ocean, we developed a model that treats phytoplankton as individual particles. Moreover, our model considers phytoplankton size, temperature, and light traits and allows for mutations in phytoplankton cells. Thus, our model provides a valuable tool for advancing the study of phytoplankton physiology, diversity, and evolution.
Renbo Pang, Fujiang Yu, Yuanyong Gao, Ye Yuan, Liang Yuan, and Zhiyi Gao
Geosci. Model Dev., 18, 4119–4136, https://doi.org/10.5194/gmd-18-4119-2025, https://doi.org/10.5194/gmd-18-4119-2025, 2025
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The application of distributed graph communication topology in Earth models has rarely been studied. We tested and compared this topology with the traditional point-to-point communication method using a wave model. We found that this topology with the Intel MPI (message-passing interface) library is more efficient. Additionally, using this topology can greatly improve the performance of the wave model and could help improve the performance of other Earth models.
Usama Kadri, Ali Abdolali, and Maxim Filimonov
Geosci. Model Dev., 18, 3487–3507, https://doi.org/10.5194/gmd-18-3487-2025, https://doi.org/10.5194/gmd-18-3487-2025, 2025
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The GREAT v1.0 software introduces a novel tsunami warning technology for global real-time analysis. It leverages acoustic signals generated by tsunamis, which propagate faster than the tsunami itself, enabling real-time detection and assessment. Integrating various models, the software provides reliable and rapid assessment, maps risk areas, and estimates tsunami amplitude. This advancement reduces false alarms and enhances global tsunami warning systems' accuracy and efficiency.
Marina Amadori, Abolfazl Irani Rahaghi, Damien Bouffard, and Marco Toffolon
Geosci. Model Dev., 18, 3473–3486, https://doi.org/10.5194/gmd-18-3473-2025, https://doi.org/10.5194/gmd-18-3473-2025, 2025
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Models simplify reality using assumptions, which can sometimes introduce flaws and affect their accuracy. Properly calibrating model parameters is essential, and although automated tools can speed up this process, they may occasionally produce incorrect values due to inconsistencies in the model. We demonstrate that by carefully applying automated tools, we were able to identify and correct a flaw in a widely used model for lake environments.
Davi Mignac, Jennifer Waters, Daniel J. Lea, Matthew J. Martin, James While, Anthony T. Weaver, Arthur Vidard, Catherine Guiavarc'h, Dave Storkey, David Ford, Edward W. Blockley, Jonathan Baker, Keith Haines, Martin R. Price, Michael J. Bell, and Richard Renshaw
Geosci. Model Dev., 18, 3405–3425, https://doi.org/10.5194/gmd-18-3405-2025, https://doi.org/10.5194/gmd-18-3405-2025, 2025
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We describe major improvements of the Met Office's global ocean–sea ice forecasting system. The models and the way observations are used to improve the forecasts were changed, which led to a significant error reduction of 1 d forecasts. The new system performance in past conditions, where subsurface observations are scarce, was improved with more consistent ocean heat content estimates. The new system will be of better use for climate studies and will provide improved forecasts for end users.
David Storkey, Pierre Mathiot, Michael J. Bell, Dan Copsey, Catherine Guiavarc'h, Helene T. Hewitt, Jeff Ridley, and Malcolm J. Roberts
Geosci. Model Dev., 18, 2725–2745, https://doi.org/10.5194/gmd-18-2725-2025, https://doi.org/10.5194/gmd-18-2725-2025, 2025
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The Southern Ocean is a key region of the world ocean in the context of climate change studies. We show that the Met Office Hadley Centre coupled model with intermediate ocean resolution struggles to accurately simulate the Southern Ocean. Increasing the frictional drag that the seafloor exerts on ocean currents and introducing a representation of unresolved ocean eddies both appear to reduce the large-scale biases in this model.
Larissa Marie Dias and Brendan Rae Carter
EGUsphere, https://doi.org/10.5194/egusphere-2025-458, https://doi.org/10.5194/egusphere-2025-458, 2025
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The increasing availability of oceanographic physical and chemical data necessitates accompanying methods for optimizing use of this data. This project produced algorithms (PyESPERs) for estimating biogeochemical seawater properties in Python, a freely available coding language. These algorithms were based on Empirical Seawater Property Estimation Routines (ESPERs), which were originally written in the proprietary MATLAB coding language and can be used in studies of marine carbonate chemistry.
Betty John Kaimathuruthy, Isabel Jalón-Rojas, and Damien Sous
EGUsphere, https://doi.org/10.5194/egusphere-2025-529, https://doi.org/10.5194/egusphere-2025-529, 2025
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Studies on plastic pollution have emerged as a rapidly growing field of research. Modelling microplastic transport in estuaries stems from their complex hydrodynamics and diverse particle behaviours affecting the dispersion and retention of microplastics. Our paper reviews key modelling approaches applied in estuaries analyzing their setups and parameterizations. We provide recommendations and future directions to improve the accuracy and modelling strategies for estuarine microplastic research.
Axelle Gaffet, Xavier Bertin, Damien Sous, Héloïse Michaud, Aron Roland, and Emmanuel Cordier
Geosci. Model Dev., 18, 1929–1946, https://doi.org/10.5194/gmd-18-1929-2025, https://doi.org/10.5194/gmd-18-1929-2025, 2025
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This study presents a new global wave model that improves predictions of sea states in tropical areas by using a high-resolution grid and corrected wind fields. The model is validated globally with satellite data and nearshore using in situ data. The model allows for the first time direct comparisons with in situ data collected at 10–30 m water depth, which is very close to shore due to the steep slope usually surrounding volcanic islands.
David Kamm, Julie Deshayes, and Gurvan Madec
EGUsphere, https://doi.org/10.5194/egusphere-2025-1100, https://doi.org/10.5194/egusphere-2025-1100, 2025
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We propose an idealized model of pole-to-pole ocean dynamics designed as a testbed for eddy parameterizations across a range of horizontal scales. While computationally affordable, it is able to capture key metrics of the climate system. By comparing simulations at low, intermediate and high horizontal resolution, we demonstrate its utility for evaluating eddy parameterizations, both in terms of their effect on the mean-state and by diagnosing the unresolved eddy fluxes they aim to represent.
Antoine Mathieu, Yeulwoo Kim, Tian-Jian Hsu, Cyrille Bonamy, and Julien Chauchat
Geosci. Model Dev., 18, 1561–1573, https://doi.org/10.5194/gmd-18-1561-2025, https://doi.org/10.5194/gmd-18-1561-2025, 2025
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Most of the tools available to model sediment transport do not account for complex physical mechanisms such as surface-wave-driven processes. In this study, a new model, sedInterFoam, allows us to reproduce numerically complex configurations in order to investigate coastal sediment transport applications dominated by surface waves and to gain insight into the complex physical processes associated with breaking waves and morphodynamics.
Júlia Crespin, Jordi Solé, and Miquel Canals
EGUsphere, https://doi.org/10.5194/egusphere-2025-865, https://doi.org/10.5194/egusphere-2025-865, 2025
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This study presents the Offline Fennel model, a tool designed to simulate ocean biogeochemical processes efficiently. By using existing hydrodynamic data, the model significantly reduces computation time from 6 hours to just 30 minutes. We tested its accuracy in the Northern Gulf of Mexico and found it closely matches physical-biogeochemical coupled simulations. This model allows researchers to conduct more tests and simulations without the need for extensive computational resources.
Zhaoru Zhang, Chuan Xie, Chuning Wang, Yuanjie Chen, Heng Hu, and Xiaoqiao Wang
Geosci. Model Dev., 18, 1375–1393, https://doi.org/10.5194/gmd-18-1375-2025, https://doi.org/10.5194/gmd-18-1375-2025, 2025
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A coupled fine-resolution ocean–ice model is developed for the Ross Sea and adjacent regions in Antarctica, a key area for the formation of global ocean bottom water, the Antarctic Bottom Water (AABW), which affects global ocean circulation. The model has a high skill level in simulating sea ice production driving the AABW source water formation and AABW properties when assessed against observations. A model experiment shows a significant impact of ice shelf melting on the AABW characteristics.
Swantje Bastin, Aleksei Koldunov, Florian Schütte, Oliver Gutjahr, Marta Agnieszka Mrozowska, Tim Fischer, Radomyra Shevchenko, Arjun Kumar, Nikolay Koldunov, Helmuth Haak, Nils Brüggemann, Rebecca Hummels, Mia Sophie Specht, Johann Jungclaus, Sergey Danilov, Marcus Dengler, and Markus Jochum
Geosci. Model Dev., 18, 1189–1220, https://doi.org/10.5194/gmd-18-1189-2025, https://doi.org/10.5194/gmd-18-1189-2025, 2025
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Vertical mixing is an important process, for example, for tropical sea surface temperature, but cannot be resolved by ocean models. Comparisons of mixing schemes and settings have usually been done with a single model, sometimes yielding conflicting results. We systematically compare two widely used schemes with different parameter settings in two different ocean models and show that most effects from mixing scheme parameter changes are model-dependent.
Matjaž Zupančič Muc, Vitjan Zavrtanik, Alexander Barth, Aida Alvera-Azcarate, Matjaž Ličer, and Matej Kristan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-208, https://doi.org/10.5194/gmd-2024-208, 2025
Revised manuscript accepted for GMD
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Accurate sea surface temperature data (SST) is crucial for weather forecasting and climate modeling, but satellite observations are often incomplete. We developed a new method called CRITER, which uses machine learning to fill in the gaps in SST data. Our two-stage approach reconstructs large-scale patterns and refines details. Tested on Mediterranean, Adriatic, and Atlantic seas data, CRITER outperforms current methods, reducing errors by up to 44 %.
Marko Rus, Hrvoje Mihanović, Matjaž Ličer, and Matej Kristan
Geosci. Model Dev., 18, 605–620, https://doi.org/10.5194/gmd-18-605-2025, https://doi.org/10.5194/gmd-18-605-2025, 2025
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HIDRA3 is a deep-learning model for predicting sea levels and storm surges, offering significant improvements over previous models and numerical simulations. It utilizes data from multiple tide gauges, enhancing predictions even with limited historical data and during sensor outages. With its advanced architecture, HIDRA3 outperforms current state-of-the-art models by achieving a mean absolute error of up to 15 % lower, proving effective for coastal flood forecasting under diverse conditions.
Isabel Jalón-Rojas, Damien Sous, and Vincent Marieu
Geosci. Model Dev., 18, 319–336, https://doi.org/10.5194/gmd-18-319-2025, https://doi.org/10.5194/gmd-18-319-2025, 2025
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This study presents a novel modeling approach for understanding microplastic transport in coastal waters. The model accurately replicates experimental data and reveals key transport mechanisms. The findings enhance our knowledge of how microplastics move in nearshore environments, aiding in coastal management and efforts to combat plastic pollution globally.
Greig Oldford, Tereza Jarníková, Villy Christensen, and Michael Dunphy
Geosci. Model Dev., 18, 211–237, https://doi.org/10.5194/gmd-18-211-2025, https://doi.org/10.5194/gmd-18-211-2025, 2025
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We developed a 3D ocean model called the Hindcast of the Salish Sea (HOTSSea v1) that recreates physical conditions throughout the Salish Sea from 1980 to 2018. It was not clear that acceptable accuracy could be achieved because of computational and data limitations, but the model's predictions agreed well with observations. When we used the model to examine ocean temperature trends in areas that lack observations, it indicated that some seasons and areas are warming faster than others.
Sofia Flora, Laura Ursella, and Achim Wirth
EGUsphere, https://doi.org/10.5194/egusphere-2024-3391, https://doi.org/10.5194/egusphere-2024-3391, 2025
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We developed a hierarchy of idealized deterministic-stochastic models to simulate the sea surface currents in the Gulf of Trieste. They include tide-and-wind driven sea surface current components, resolving the slowly varying part of the flow and a stochastic signal, representing the fast-varying small-scale dynamics. The comparison with High Frequency Radar observations shows that the non-Gaussian stochastic model captures the essential dynamics and permits to mimic the observed fat-tailed PDF.
Yankun Gong, Xueen Chen, Jiexin Xu, Zhiwu Chen, Qingyou He, Ruixiang Zhao, Xiao-Hua Zhu, and Shuqun Cai
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-165, https://doi.org/10.5194/gmd-2024-165, 2024
Revised manuscript accepted for GMD
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A new internal solitary wave forecasting (ISW) model in the northern South China Sea (ISWFM-NSCS v2.0) improves ISW predictions by incorporating background currents and inhomogeneous stratifications. Additionally, viscosity and diffusivity coefficients are optimized to maintain stable stratifications, extending the forecasting period. Sensitivity experiments illustrate that ISWFM-NSCS v2.0 significantly enhances predictions of various wave properties.
Cecilia Äijälä, Yafei Nie, Lucía Gutiérrez-Loza, Chiara De Falco, Siv Kari Lauvset, Bin Cheng, David A. Bailey, and Petteri Uotila
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-213, https://doi.org/10.5194/gmd-2024-213, 2024
Revised manuscript accepted for GMD
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The sea ice around Antarctica has experienced record lows in recent years. To understand these changes, models are needed. MetROMS-UHel is a new version of an ocean–sea ice model with updated sea ice code and the atmospheric data. We investigate the effect of our updates on different variables with a focus on sea ice and show an improved sea ice representation as compared with observations.
Kyoko Ohashi, Arnaud Laurent, Christoph Renkl, Jinyu Sheng, Katja Fennel, and Eric Oliver
Geosci. Model Dev., 17, 8697–8733, https://doi.org/10.5194/gmd-17-8697-2024, https://doi.org/10.5194/gmd-17-8697-2024, 2024
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We developed a modelling system of the northwest Atlantic Ocean that simulates the currents, temperature, salinity, and parts of the biochemical cycle of the ocean, as well as sea ice. The system combines advanced, open-source models and can be used to study, for example, the ocean capture of atmospheric carbon dioxide, which is a key process in the global climate. The system produces realistic results, and we use it to investigate the roles of tides and sea ice in the northwest Atlantic Ocean.
Eui-Jong Kang, Byung-Ju Sohn, Sang-Woo Kim, Wonho Kim, Young-Cheol Kwon, Seung-Bum Kim, Hyoung-Wook Chun, and Chao Liu
Geosci. Model Dev., 17, 8553–8568, https://doi.org/10.5194/gmd-17-8553-2024, https://doi.org/10.5194/gmd-17-8553-2024, 2024
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Sea surface temperature (SST) is vital in climate, weather, and ocean sciences because it influences air–sea interactions. Errors in the ECMWF model's scheme for predicting ocean skin temperature prompted a revision of the ocean mixed layer model. Validation against infrared measurements and buoys showed a good correlation with minimal deviations. The revised model accurately simulates SST variations and aligns with solar radiation distributions, showing promise for weather and climate models.
Qin Zhou, Chen Zhao, Rupert Gladstone, Tore Hattermann, David Gwyther, and Benjamin Galton-Fenzi
Geosci. Model Dev., 17, 8243–8265, https://doi.org/10.5194/gmd-17-8243-2024, https://doi.org/10.5194/gmd-17-8243-2024, 2024
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We introduce an accelerated forcing approach to address timescale discrepancies between the ice sheets and ocean components in coupled modelling by reducing the ocean simulation duration. The approach is evaluated using idealized coupled models, and its limitations in real-world applications are discussed. Our results suggest it can be a valuable tool for process-oriented coupled ice sheet–ocean modelling and downscaling climate simulations with such models.
Jan D. Zika and Taimoor Sohail
Geosci. Model Dev., 17, 8049–8068, https://doi.org/10.5194/gmd-17-8049-2024, https://doi.org/10.5194/gmd-17-8049-2024, 2024
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We describe a method to relate fluxes of heat and freshwater at the sea surface to the resulting distribution of seawater among categories such as warm and salty or cold and salty. The method exploits the laws that govern how heat and salt change when water mixes. The method will allow the climate community to improve estimates of how much heat the ocean is absorbing and how rainfall and evaporation are changing across the globe.
Xinxin Wang, Jiuke Wang, Wenfang Lu, Changming Dong, Hao Qin, and Haoyu Jiang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-181, https://doi.org/10.5194/gmd-2024-181, 2024
Revised manuscript accepted for GMD
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Large-scale wave modeling is essential for science and society, typically relying on resource-intensive numerical methods to simulate wave dynamics. In this study, we introduce a rolling AI-based method for modeling global significant wave height. Our model achieves accuracy comparable to traditional numerical methods while significantly improving speed, making it operable on standard laptops. This work demonstrates AI's potential to enhance the accuracy and efficiency of global wave modeling.
Gloria Pietropolli, Luca Manzoni, and Gianpiero Cossarini
Geosci. Model Dev., 17, 7347–7364, https://doi.org/10.5194/gmd-17-7347-2024, https://doi.org/10.5194/gmd-17-7347-2024, 2024
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Monitoring the ocean is essential for studying marine life and human impact. Our new software, PPCon, uses ocean data to predict key factors like nitrate and chlorophyll levels, which are hard to measure directly. By leveraging machine learning, PPCon offers more accurate and efficient predictions.
Rafael Santana, Richard Gorman, Emily Lane, Stuart Moore, Cyprien Bosserelle, Glen Reeve, and Christo Rautenbach
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-110, https://doi.org/10.5194/gmd-2024-110, 2024
Revised manuscript accepted for GMD
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This research explores improving wave forecasts in New Zealand, particularly at Banks Peninsula and Baring Head. We used detailed models, finding that forecasts at Baring Head improved significantly due to its strong tidal currents, but changes at Banks Peninsula were minimal. The study demonstrates that local conditions greatly influence the effectiveness of wave prediction models, highlighting the need for tailored approaches in coastal forecasting to enhance accuracy in the predictions.
Jan De Rydt, Nicolas C. Jourdain, Yoshihiro Nakayama, Mathias van Caspel, Ralph Timmermann, Pierre Mathiot, Xylar S. Asay-Davis, Hélène Seroussi, Pierre Dutrieux, Ben Galton-Fenzi, David Holland, and Ronja Reese
Geosci. Model Dev., 17, 7105–7139, https://doi.org/10.5194/gmd-17-7105-2024, https://doi.org/10.5194/gmd-17-7105-2024, 2024
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Global climate models do not reliably simulate sea-level change due to ice-sheet–ocean interactions. We propose a community modelling effort to conduct a series of well-defined experiments to compare models with observations and study how models respond to a range of perturbations in climate and ice-sheet geometry. The second Marine Ice Sheet–Ocean Model Intercomparison Project will continue to lay the groundwork for including ice-sheet–ocean interactions in global-scale IPCC-class models.
Qian Wang, Yang Zhang, Fei Chai, Y. Joseph Zhang, and Lorenzo Zampieri
Geosci. Model Dev., 17, 7067–7081, https://doi.org/10.5194/gmd-17-7067-2024, https://doi.org/10.5194/gmd-17-7067-2024, 2024
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We coupled an unstructured hydro-model with an advanced column sea ice model to meet the growing demand for increased resolution and complexity in unstructured sea ice models. Additionally, we present a novel tracer transport scheme for the sea ice coupled model and demonstrate that this scheme fulfills the requirements for conservation, accuracy, efficiency, and monotonicity in an idealized test. Our new coupled model also has good performance in realistic tests.
Adrien Garinet, Marine Herrmann, Patrick Marsaleix, and Juliette Pénicaud
Geosci. Model Dev., 17, 6967–6986, https://doi.org/10.5194/gmd-17-6967-2024, https://doi.org/10.5194/gmd-17-6967-2024, 2024
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Mixing is a crucial aspect of the ocean, but its accurate representation in computer simulations is made challenging by errors that result in unwanted mixing, compromising simulation realism. Here we illustrate the spurious effect that tides can have on simulations of south-east Asia. Although they play an important role in determining the state of the ocean, they can increase numerical errors and make simulation outputs less realistic. We also provide insights into how to reduce these errors.
Mohamed Ayache, Jean-Claude Dutay, Anne Mouchet, Kazuyo Tachikawa, Camille Risi, and Gilles Ramstein
Geosci. Model Dev., 17, 6627–6655, https://doi.org/10.5194/gmd-17-6627-2024, https://doi.org/10.5194/gmd-17-6627-2024, 2024
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Water isotopes (δ18O, δD) are one of the most widely used proxies in ocean climate research. Previous studies using water isotope observations and modelling have highlighted the importance of understanding spatial and temporal isotopic variability for a quantitative interpretation of these tracers. Here we present the first results of a high-resolution regional dynamical model (at 1/12° horizontal resolution) developed for the Mediterranean Sea, one of the hotspots of ongoing climate change.
Cara Nissen, Nicole S. Lovenduski, Mathew Maltrud, Alison R. Gray, Yohei Takano, Kristen Falcinelli, Jade Sauvé, and Katherine Smith
Geosci. Model Dev., 17, 6415–6435, https://doi.org/10.5194/gmd-17-6415-2024, https://doi.org/10.5194/gmd-17-6415-2024, 2024
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Autonomous profiling floats have provided unprecedented observational coverage of the global ocean, but uncertainties remain about whether their sampling frequency and density capture the true spatiotemporal variability of physical, biogeochemical, and biological properties. Here, we present the novel synthetic biogeochemical float capabilities of the Energy Exascale Earth System Model version 2 and demonstrate their utility as a test bed to address these uncertainties.
Ye Yuan, Fujiang Yu, Zhi Chen, Xueding Li, Fang Hou, Yuanyong Gao, Zhiyi Gao, and Renbo Pang
Geosci. Model Dev., 17, 6123–6136, https://doi.org/10.5194/gmd-17-6123-2024, https://doi.org/10.5194/gmd-17-6123-2024, 2024
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Accurate and timely forecasting of ocean waves is of great importance to the safety of marine transportation and offshore engineering. In this study, GPU-accelerated computing is introduced in WAve Modeling Cycle 6 (WAM6). With this effort, global high-resolution wave simulations can now run on GPUs up to tens of times faster than the currently available models can on a CPU node with results that are just as accurate.
Krysten Rutherford, Laura Bianucci, and William Floyd
Geosci. Model Dev., 17, 6083–6104, https://doi.org/10.5194/gmd-17-6083-2024, https://doi.org/10.5194/gmd-17-6083-2024, 2024
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Nearshore ocean models often lack complete information about freshwater fluxes due to numerous ungauged rivers and streams. We tested a simple rain-based hydrological model as inputs into an ocean model of Quatsino Sound, Canada, with the aim of improving the representation of the land–ocean connection in the nearshore model. Through multiple tests, we found that the performance of the ocean model improved when providing 60 % or more of the freshwater inputs from the simple runoff model.
Neill Mackay, Taimoor Sohail, Jan David Zika, Richard G. Williams, Oliver Andrews, and Andrew James Watson
Geosci. Model Dev., 17, 5987–6005, https://doi.org/10.5194/gmd-17-5987-2024, https://doi.org/10.5194/gmd-17-5987-2024, 2024
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The ocean absorbs carbon dioxide from the atmosphere, mitigating climate change, but estimates of the uptake do not always agree. There is a need to reconcile these differing estimates and to improve our understanding of ocean carbon uptake. We present a new method for estimating ocean carbon uptake and test it with model data. The method effectively diagnoses the ocean carbon uptake from limited data and therefore shows promise for reconciling different observational estimates.
Lucille Barré, Frédéric Diaz, Thibaut Wagener, Camille Mazoyer, Christophe Yohia, and Christel Pinazo
Geosci. Model Dev., 17, 5851–5882, https://doi.org/10.5194/gmd-17-5851-2024, https://doi.org/10.5194/gmd-17-5851-2024, 2024
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The carbonate system is typically studied using measurements, but modeling can contribute valuable insights. Using a biogeochemical model, we propose a new representation of total alkalinity, dissolved inorganic carbon, pCO2, and pH in a highly dynamic Mediterranean coastal area, the Bay of Marseille, a useful addition to measurements. Through a detailed analysis of pCO2 and air–sea CO2 fluxes, we show that variations are strongly impacted by the hydrodynamic processes that affect the bay.
Xuanxuan Gao, Shuiqing Li, Dongxue Mo, Yahao Liu, and Po Hu
Geosci. Model Dev., 17, 5497–5509, https://doi.org/10.5194/gmd-17-5497-2024, https://doi.org/10.5194/gmd-17-5497-2024, 2024
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Storm surges generate coastal inundation and expose populations and properties to danger. We developed a novel storm surge inundation model for efficient prediction. Estimates compare well with in situ measurements and results from a numerical model. The new model is a significant improvement on existing numerical models, with much higher computational efficiency and stability, which allows timely disaster prevention and mitigation.
Vincenzo de Toma, Daniele Ciani, Yassmin Hesham Essa, Chunxue Yang, Vincenzo Artale, Andrea Pisano, Davide Cavaliere, Rosalia Santoleri, and Andrea Storto
Geosci. Model Dev., 17, 5145–5165, https://doi.org/10.5194/gmd-17-5145-2024, https://doi.org/10.5194/gmd-17-5145-2024, 2024
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This study explores methods to reconstruct diurnal variations in skin sea surface temperature in a model of the Mediterranean Sea. Our new approach, considering chlorophyll concentration, enhances spatial and temporal variations in the warm layer. Comparative analysis shows context-dependent improvements. The proposed "chlorophyll-interactive" method brings the surface net total heat flux closer to zero annually, despite a net heat loss from the ocean to the atmosphere.
Peter Mlakar, Antonio Ricchi, Sandro Carniel, Davide Bonaldo, and Matjaž Ličer
Geosci. Model Dev., 17, 4705–4725, https://doi.org/10.5194/gmd-17-4705-2024, https://doi.org/10.5194/gmd-17-4705-2024, 2024
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We propose a new point-prediction model, the DEep Learning WAVe Emulating model (DELWAVE), which successfully emulates the Simulating WAves Nearshore model (SWAN) over synoptic to climate timescales. Compared to control climatology over all wind directions, the mismatch between DELWAVE and SWAN is generally small compared to the difference between scenario and control conditions, suggesting that the noise introduced by surrogate modelling is substantially weaker than the climate change signal.
Susanna Winkelbauer, Michael Mayer, and Leopold Haimberger
Geosci. Model Dev., 17, 4603–4620, https://doi.org/10.5194/gmd-17-4603-2024, https://doi.org/10.5194/gmd-17-4603-2024, 2024
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Oceanic transports shape the global climate, but the evaluation and validation of this key quantity based on reanalysis and model data are complicated by the distortion of the used modelling grids and the large number of different grid types. We present two new methods that allow the calculation of oceanic fluxes of volume, heat, salinity, and ice through almost arbitrary sections for various models and reanalyses that are independent of the used modelling grids.
Xiaoyu Fan, Baylor Fox-Kemper, Nobuhiro Suzuki, Qing Li, Patrick Marchesiello, Peter P. Sullivan, and Paul S. Hall
Geosci. Model Dev., 17, 4095–4113, https://doi.org/10.5194/gmd-17-4095-2024, https://doi.org/10.5194/gmd-17-4095-2024, 2024
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Simulations of the oceanic turbulent boundary layer using the nonhydrostatic CROCO ROMS and NCAR-LES models are compared. CROCO and the NCAR-LES are accurate in a similar manner, but CROCO’s additional features (e.g., nesting and realism) and its compressible turbulence formulation carry additional costs.
Jilian Xiong and Parker MacCready
Geosci. Model Dev., 17, 3341–3356, https://doi.org/10.5194/gmd-17-3341-2024, https://doi.org/10.5194/gmd-17-3341-2024, 2024
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The new offline particle tracking package, Tracker v1.1, is introduced to the Regional Ocean Modeling System, featuring an efficient nearest-neighbor algorithm to enhance particle-tracking speed. Its performance was evaluated against four other tracking packages and passive dye. Despite unique features, all packages yield comparable results. Running multiple packages within the same circulation model allows comparison of their performance and ease of use.
Sylvain Cailleau, Laurent Bessières, Léonel Chiendje, Flavie Dubost, Guillaume Reffray, Jean-Michel Lellouche, Simon van Gennip, Charly Régnier, Marie Drevillon, Marc Tressol, Matthieu Clavier, Julien Temple-Boyer, and Léo Berline
Geosci. Model Dev., 17, 3157–3173, https://doi.org/10.5194/gmd-17-3157-2024, https://doi.org/10.5194/gmd-17-3157-2024, 2024
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In order to improve Sargassum drift forecasting in the Caribbean area, drift models can be forced by higher-resolution ocean currents. To this goal a 3 km resolution regional ocean model has been developed. Its assessment is presented with a particular focus on the reproduction of fine structures representing key features of the Caribbean region dynamics and Sargassum transport. The simulated propagation of a North Brazil Current eddy and its dissipation was found to be quite realistic.
Gaetano Porcile, Anne-Claire Bennis, Martial Boutet, Sophie Le Bot, Franck Dumas, and Swen Jullien
Geosci. Model Dev., 17, 2829–2853, https://doi.org/10.5194/gmd-17-2829-2024, https://doi.org/10.5194/gmd-17-2829-2024, 2024
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Here a new method of modelling the interaction between ocean currents and waves is presented. We developed an advanced coupling of two models, one for ocean currents and one for waves. In previous couplings, some wave-related calculations were based on simplified assumptions. Our method uses more complex calculations to better represent wave–current interactions. We tested it in a macro-tidal coastal area and found that it significantly improves the model accuracy, especially during storms.
Colette Gabrielle Kerry, Moninya Roughan, Shane Keating, David Gwyther, Gary Brassington, Adil Siripatana, and Joao Marcos A. C. Souza
Geosci. Model Dev., 17, 2359–2386, https://doi.org/10.5194/gmd-17-2359-2024, https://doi.org/10.5194/gmd-17-2359-2024, 2024
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Ocean forecasting relies on the combination of numerical models and ocean observations through data assimilation (DA). Here we assess the performance of two DA systems in a dynamic western boundary current, the East Australian Current, across a common modelling and observational framework. We show that the more advanced, time-dependent method outperforms the time-independent method for forecast horizons of 5 d. This advocates the use of advanced methods for highly variable oceanic regions.
Ivan Hernandez, Leidy M. Castro-Rosero, Manuel Espino, and Jose M. Alsina Torrent
Geosci. Model Dev., 17, 2221–2245, https://doi.org/10.5194/gmd-17-2221-2024, https://doi.org/10.5194/gmd-17-2221-2024, 2024
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The LOCATE numerical model was developed to conduct Lagrangian simulations of the transport and dispersion of marine debris at coastal scales. High-resolution hydrodynamic data and a beaching module that used particle distance to the shore for land–water boundary detection were used on a realistic debris discharge scenario comparing hydrodynamic data at various resolutions. Coastal processes and complex geometric structures were resolved when using nested grids and distance-to-shore beaching.
Ngoc B. Trinh, Marine Herrmann, Caroline Ulses, Patrick Marsaleix, Thomas Duhaut, Thai To Duy, Claude Estournel, and R. Kipp Shearman
Geosci. Model Dev., 17, 1831–1867, https://doi.org/10.5194/gmd-17-1831-2024, https://doi.org/10.5194/gmd-17-1831-2024, 2024
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A high-resolution model was built to study the South China Sea (SCS) water, heat, and salt budgets. Model performance is demonstrated by comparison with observations and simulations. Important discards are observed if calculating offline, instead of online, lateral inflows and outflows of water, heat, and salt. The SCS mainly receives water from the Luzon Strait and releases it through the Mindoro, Taiwan, and Karimata straits. SCS surface interocean water exchanges are driven by monsoon winds.
Louis Thiry, Long Li, Guillaume Roullet, and Etienne Mémin
Geosci. Model Dev., 17, 1749–1764, https://doi.org/10.5194/gmd-17-1749-2024, https://doi.org/10.5194/gmd-17-1749-2024, 2024
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We present a new way of solving the quasi-geostrophic (QG) equations, a simple set of equations describing ocean dynamics. Our method is solely based on the numerical methods used to solve the equations and requires no parameter tuning. Moreover, it can handle non-rectangular geometries, opening the way to study QG equations on realistic domains. We release a PyTorch implementation to ease future machine-learning developments on top of the presented method.
Zheqi Shen, Yihao Chen, Xiaojing Li, and Xunshu Song
Geosci. Model Dev., 17, 1651–1665, https://doi.org/10.5194/gmd-17-1651-2024, https://doi.org/10.5194/gmd-17-1651-2024, 2024
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Parameter estimation is the process that optimizes model parameters using observations, which could reduce model errors and improve forecasting. In this study, we conducted parameter estimation experiments using the CESM and the ensemble adjustment Kalman filter. The obtained initial conditions and parameters are used to perform ensemble forecast experiments for ENSO forecasting. The results revealed that parameter estimation could reduce analysis errors and improve ENSO forecast skills.
Cited articles
Adloff, F., Somot, S., Sevault, F., Jordà, G., Aznar, R., Déqué,
M., Herrmann, M., Marcos, M., Dubois, C., Padorno, E., Alvarez-Fanjul, E.,
and Gomis, D.: Mediterranean Sea response to climate change in an ensemble
of twenty first century scenarios, Clim. Dynam., 45, 2775–2802,
https://doi.org/10.1007/s00382-015-2507-3, 2015.
Adloff, F., Jordà, G., Somot, S., Sevault, F., Arsouze, T., Meyssignac,
B., Li, L., and Planton, S.: Improving sea level simulation in Mediterranean
regional climate models, Clim. Dynam., 51, 1167–1178,
https://doi.org/10.1007/s00382-017-3842-3, 2018.
Aviso+: MDT data, https://www.aviso.altimetry.fr/en/data/products/auxiliary-products/mdt/mdt-global-cnes-cls18.html, last access: 8 March 2022.
Baladrón, A. A., Levier, B., and Sotillo, M. G.: Product User Manual (CMEMS-IBI-PUM-005-002), https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-IBI-PUM-005-002.pdf (last access: 7 March 2022), 2020.
Barnier, B., Madec, G., Penduff, T., Molines, J.-M., Treguier, A.-M., Le
Sommer, J., Beckmann, A., Biastoch, A., Böning, C., Dengg, J., Derval,
C., Durand, E., Gulev, S., Remy, E., Talandier, C., Theetten, S., Maltrud,
M., McClean, J., and De Cuevas, B.: Impact of partial steps and momentum
advection schemes in a global ocean circulation model at eddy-permitting
resolution, Ocean Dynam., 56, 543–567,
https://doi.org/10.1007/s10236-006-0082-1, 2006.
Berrisford, P., Dee, D., Fielding, K., Fuentes, M., Kallberg, P., Kobayashi,
S., and Uppala, S.: The ERA-Interim Archive, ERA report series, European
Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading, 16 pp., https://www.ecmwf.int/node/8173 (last access: 7 March 2022), 2009.
Blanke, B. and Delecluse, P.: Variability of the Tropical Atlantic Ocean
Simulated by a General Circulation Model with Two Different Mixed-Layer
Physics, J. Phys. Oceanogr., 23, 1363–1388,
https://doi.org/10.1175/1520-0485(1993)023<1363:VOTTAO>2.0.CO;2, 1993.
Bozec, A., Lozier, M. S., Chassignet, E. P., and Halliwell, G. R.: On the
variability of the Mediterranean Outflow Water in the North Atlantic from
1948 to 2006, J. Geophys. Res.-Oceans, 116,
https://doi.org/10.1029/2011JC007191, 2011.
Brodeau, L., Barnier, B., Gulev, S. K., and Woods, C.: Climatologically
Significant Effects of Some Approximations in the Bulk Parameterizations of
Turbulent Air–Sea Fluxes, J. Phys. Oceanogr., 47, 5–28,
https://doi.org/10.1175/JPO-D-16-0169.1, 2017.
Chen, X., Zhou, T., Wu, P., Guo, Z., and Wang, M.: Emergent constraints on
future projections of the western North Pacific Subtropical High, Nat.
Commun., 11, 2802, https://doi.org/10.1038/s41467-020-16631-9, 2020.
Church, J. A., Clark, P. U., Cazenave, A., Gregory, J. M., Jevrejeva, S.,
Levermann, A., Merrifield, M. A., Milne, G. A., Nerem, R. S., Nunn, P. D.,
Payne, A. J., Pfeffer, W. T., Stammer, D., and Unnikrishnan, A. S.: Sea level
change, in: Climate
change 2013: The physical science basis, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., et al., Contribution of Working Group I to
the Fifth Assessment Report of the Intergovernmental Panel on Climate
Change, Cambridge University Press, Cambridge, New York, 1137–1216,
2013.
Copernicus: Reanalysis data, https://doi.org/10.48670/moi-00029, last access: 8 March 2022a.
Copernicus: Observational data, https://doi.org/10.48670/moi-00148, last access: 8 March 2022b.
Cordeiro, N. G. F., Dubert, J., Nolasco, R., and Barton, E. D.: Transient
response of the Northwestern Iberian upwelling regime, PLOS ONE, 13,
e0197627, https://doi.org/10.1371/journal.pone.0197627, 2018.
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.
Dosio, A.: Projections of climate change indices of temperature and
precipitation from an ensemble of bias-adjusted high-resolution EURO-CORDEX
regional climate models, J. Geophys. Res.-Atmos., 121, 5488–5511,
https://doi.org/10.1002/2015JD024411, 2016.
ECMWF: IFS documentation – Cy40r1. Operational implementation 22 November 2013, Part IV: Physical processes, https://www.ecmwf.int/en/elibrary/9204-ifs-documentation-cy40r1-part-iv-physical-processes (last access: 7 March 2022), 2014.
ESGF: Historical data, http://esgf-data.dkrz.de/search/cmip6-dkrz/?mip_era=CMIP6&activity_id=CMIP&institution_id=CNRM-CERFACS&source_id=CNRM-CM6-1-HR&experiment_id=historical, last access: 8 March 2022a.
ESGF: piControl data, http://esgf-data.dkrz.de/search/cmip6-dkrz/?mip_era=CMIP6&activity_id=CMIP&institution_id=CNRM-CERFACS&source_id=CNRM-CM6-1-HR&experiment_id=piControl, last access: 8 March 2022b.
ESGF: ssp126 data, http://esgf-data.dkrz.de/search/cmip6-dkrz/?mip_era=CMIP6&activity_id=ScenarioMIP&institution_id=CNRM-CERFACS&source_id=CNRM-CM6-1-HR&experiment_id=ssp126, last access: 8 March 2022c.
ESGF: ssp585 data, http://esgf-data.dkrz.de/search/cmip6-dkrz/?mip_era=CMIP6&activity_id=ScenarioMIP&institution_id=CNRM-CERFACS&source_id=CNRM-CM6-1-HR&experiment_id=ssp585, last access: 8 March 2022d.
Egbert, G. D. and Erofeeva, S. Y.: Efficient Inverse Modeling of Barotropic
Ocean Tides, J. Atmos. Ocean. Technol., 19, 183–204,
https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2, 2002.
Fasullo, J. T.: Evaluating simulated climate patterns from the CMIP archives
using satellite and reanalysis datasets using the Climate Model Assessment
Tool (CMATv1), Geosci. Model Dev., 13, 3627–3642,
https://doi.org/10.5194/gmd-13-3627-2020, 2020.
Flather, R. A. and Davies, A. M.: Note on a preliminary scheme for storm
surge prediction using numerical models, Q. J. Roy. Meteor. Soc., 102,
123–132, https://doi.org/10.1002/qj.49710243110, 1976.
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins,
W., Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P.,
Guilyardi, E., Jakob, C., Kattsov, V., Reason, C., and Rummukainen, M.:
Evaluation of Climate Models, in: Climate Change 2013: The Physical Science
Basis. Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner,
G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V.,
and Midgley, P. M., Cambridge University Press, Cambridge, United
Kingdom and New York, NY, USA, 2013
Fofonoff, N. P. and Millard Jr., R. C.: Algorithms for the computation of
fundamental properties of seawater, Unesco techn. pap. mar. sci., 44, 1–53, https://doi.org/10.25607/OBP-1450,
1983.
Forget, G. and Ponte, R. M.: The partition of regional sea level
variability, Prog. Oceanogr., 137, 173–195,
https://doi.org/10.1016/j.pocean.2015.06.002, 2015.
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame,
D., Lunt, D. J., Mauritsen, T., Palmer, M. D., Watanabe, M., Wild, M., and
Zhang, H.: The Earth's Energy Budget, Climate Feedbacks, and Climate
Sensitivity, in: Climate Change 2021: The Physical Science Basis.
Contribution of Working Group I to the Sixth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P.,
Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y.,
Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews,
J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, in press, 2021.
Fox-Kemper, B., Hewitt, H. T., Xiao, C., Aðalgeirsdóttir, G.,
Drijfhout, S. S., Edwards, T. L., Golledge, N. R., Hemer, M., Kopp, R. E.,
Krinner, G., Mix, A., Notz, D., Nowicki, S., Nurhati, I. S., Ruiz, L.,
Sallée, J.-B., Slangen, A. B. A., and Yu, Y.: Ocean, Cryosphere and Sea
Level Change, in: Climate Change 2021: The Physical Science Basis,
Contribution of Working Group I to the Sixth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P.,
Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y.,
Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews,
J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, in press, 2021.
Garric G. and Parent L.,: Quality Information Document (CMEMS-GLO-QUID-001-025-011-017), https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-025-011-017.pdf (last access: 7 March 2022), 2017.
Gaspar, P., Grégoris, Y., and Lefevre, J.-M.: A simple eddy kinetic
energy model for simulations of the oceanic vertical mixing: Tests at
station Papa and long-term upper ocean study site, J. Geophys. Res.-Oceans,
95, 16179–16193, https://doi.org/10.1029/JC095iC09p16179, 1990.
GESLA Version 2: High frequency sea level dataset, https://www.bodc.ac.uk/data/published_data_library/catalogue/10.5285/3b602f74-8374-1e90-e053-6c86abc08d39/, last access: 8 March 2022.
Gomis, D., Álvarez-Fanjul, E., Jordà, G., Marcos, M., Aznar, R.,
Rodríguez-Camino, E., Sánchez-Perrino, J. C.,
Rodríguez-González, J. M., Martínez-Asensio, A., Llasses, J.,
Pérez, B., and Sotillo, M. G.: Regional marine climate scenarios in the
NE Atlantic sector close to the Spanish shores, Sci. Mar., 80, 215–234,
https://doi.org/10.3989/scimar.04328.07A, 2016.
Greatbatch, R. J.: A note on the representation of steric sea level in
models that conserve volume rather than mass, J. Geophys. Res.-Oceans, 99,
12767–12771, https://doi.org/10.1029/94JC00847, 1994.
Gregory, J. M., Griffies, S. M., Hughes, C. W., Lowe, J. A., Church, J. A.,
Fukimori, I., Gomez, N., Kopp, R. E., Landerer, F., Cozannet, G. L., Ponte,
R. M., Stammer, D., Tamisiea, M. E., and van de Wal, R. S. W.: Concepts and
Terminology for Sea Level: Mean, Variability and Change, Both Local and
Global, Surv. Geophys., 40, 1251–1289,
https://doi.org/10.1007/s10712-019-09525-z, 2019.
Griffies, S. M. and Greatbatch, R. J.: Physical processes that impact the
evolution of global mean sea level in ocean climate models, Ocean Model.,
51, 37–72, https://doi.org/10.1016/j.ocemod.2012.04.003, 2012.
Grinsted, A. and Christensen, J. H.: The transient sensitivity of sea level rise, Ocean Sci., 17, 181–186, https://doi.org/10.5194/os-17-181-2021, 2021.
Grinsted, A., Jevrejeva, S., Riva, R. E. M., and Dahl-Jensen, D.: Sea level
rise projections for northern Europe under RCP8.5, Clim. Res., 64, 15–23,
https://doi.org/10.3354/cr01309, 2015.
Gupta, A. S., Jourdain, N. C., Brown, J. N., and Monselesan, D.: Climate
Drift in the CMIP5 Models, J. Climate, 26, 8597–8615,
https://doi.org/10.1175/JCLI-D-12-00521.1, 2013.
Hallberg, R.: Using a resolution function to regulate parameterizations of
oceanic mesoscale eddy effects, Ocean Model., 72, 92–103,
https://doi.org/10.1016/j.ocemod.2013.08.007, 2013.
Hermans, T. H. J., Le Bars, D., Katsman, C. A., Camargo, C. M. L., Gerkema,
T., Calafat, F. M., Tinker, J., and Slangen, A. B. A.: Drivers of
Interannual Sea Level Variability on the Northwestern European Shelf, J.
Geophys. Res.-Oceans, 125, e2020JC016325,
https://doi.org/10.1029/2020JC016325, 2020a.
Hermans, T. H. J., Tinker, J., Palmer, M. D., Katsman, C. A., Vermeersen, B.
L. A., and Slangen, A. B. A.: Improving sea-level projections on the
Northwestern European shelf using dynamical downscaling, Clim. Dynam., 54,
1987–2011, https://doi.org/10.1007/s00382-019-05104-5, 2020b.
Hermans, T. H. J., Gregory, J. M., Palmer, M. D., Ringer, M. A., Katsman, C.
A., and Slangen, A. B. A.: Projecting Global Mean Sea-Level Change Using
CMIP6 Models, Geophys. Res. Lett., 48, e2020GL092064,
https://doi.org/10.1029/2020GL092064, 2021.
Hernández-Díaz, L., Nikiéma, O., Laprise, R., Winger, K., and Dandoy, S.: Effect of empirical correction of sea-surface temperature biases on the CRCM5-simulated climate and projected climate changes over North America, Clim. Dyn., 53, 453–476, https://doi.org/10.1007/s00382-018-4596-2, 2019.
Hock, R., Bliss, A., Marzeion, B., Giesen, R. H., Hirabayashi, Y., Huss, M.,
Radić, V., and Slangen, A. B. A.: GlacierMIP – A model intercomparison
of global-scale glacier mass-balance models and projections, J. Glaciol.,
65, 453–467, https://doi.org/10.1017/jog.2019.22, 2019.
Hui, Y., Xu, Y., Chen, J., Xu, C.-Y., and Chen, H.: Impacts of bias
nonstationarity of climate model outputs on hydrological simulations,
Hydrol. Res., 51, 925–941, https://doi.org/10.2166/nh.2020.254, 2020.
Idier, D., Paris, F., Cozannet, G. L., Boulahya, F., and Dumas, F.:
Sea-level rise impacts on the tides of the European Shelf, Cont. Shelf Res.,
137, 56–71, https://doi.org/10.1016/j.csr.2017.01.007, 2017.
Irving, D., Hobbs, W., Church, J., and Zika, J.: A Mass and Energy
Conservation Analysis of Drift in the CMIP6 Ensemble, J. Climate, 34,
3157–3170, https://doi.org/10.1175/JCLI-D-20-0281.1, 2021.
Jin, Y., Zhang, X., Church, J. A., and Bao, X.: Projected Sea Level Changes
in the Marginal Seas near China Based on Dynamical Downscaling, J. Climate,
34, 7037–7055, https://doi.org/10.1175/JCLI-D-20-0796.1, 2021.
Kirezci, E., Young, I. R., Ranasinghe, R., Muis, S., Nicholls, R. J.,
Lincke, D., and Hinkel, J.: Projections of global-scale extreme sea levels
and resulting episodic coastal flooding over the 21st Century, Sci. Rep.,
10, 11629, https://doi.org/10.1038/s41598-020-67736-6, 2020.
Kopp, R. E., Horton, R. M., Little, C. M., Mitrovica, J. X., Oppenheimer,
M., Rasmussen, D. J., Strauss, B. H., and Tebaldi, C.: Probabilistic 21st
and 22nd century sea-level projections at a global network of tide-gauge
sites, Earths Future, 2, 383–406, https://doi.org/10.1002/2014EF000239,
2014.
Krinner, G. and Flanner, M. G.: Striking stationarity of large-scale climate
model bias patterns under strong climate change, P. Natl. Acad. Sci. USA, 115, 9462–9466, https://doi.org/10.1073/pnas.1807912115, 2018.
Lavergne, C. de, Vic, C., Madec, G., Roquet, F., Waterhouse, A. F., Whalen,
C. B., Cuypers, Y., Bouruet-Aubertot, P., Ferron, B., and Hibiya, T.: A
Parameterization of Local and Remote Tidal Mixing, J. Adv. Model. Earth
Syst., 12, e2020MS002065, https://doi.org/10.1029/2020MS002065, 2020.
Lee, J.-Y., Marotzke, J., Bala, G., Cao, L., Corti, S., Dunne, J. P.,
Engelbrecht, F., Fischer, E., Fyfe, J. C., Jones, C., Maycock, A., Mutemi,
J., Ndiaye, O., Panickal, S., and Zhou, T.: Future Global Climate:
Scenario-Based Projections and Near-Term Information, in: Climate Change
2021: The Physical Science Basis, Contribution of Working Group I to the
Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C.,
Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M.,
Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T.,
Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press,
in press, 2021.
Leonard, B. P.: A stable and accurate convective modelling procedure based
on quadratic upstream interpolation, Comput. Methods Appl. Mech. Eng., 19,
59–98, https://doi.org/10.1016/0045-7825(79)90034-3, 1979.
Levier, B., Lorente, P., and Reffray, G., Sotillo M.: Quality Information Document (CMEMS-IBI-QUID-005-002), https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-IBI-QUID-005-002.pdf (last access: 7 March 2022), 2020.
Liu, Z.-J., Minobe, S., Sasaki, Y. N., and Terada, M.: Dynamical downscaling
of future sea level change in the western North Pacific using ROMS, J.
Oceanogr., 72, 905–922, https://doi.org/10.1007/s10872-016-0390-0, 2016.
Lyard, F., Lefevre, F., Letellier, T., and Francis, O.: Modelling the global
ocean tides: modern insights from FES2004, Ocean. Dynam., 56, 394–415,
https://doi.org/10.1007/s10236-006-0086-x, 2006.
Macias, D., Garcia-Gorriz, E., Dosio, A., Stips, A., and Keuler, K.:
Obtaining the correct sea surface temperature: bias correction of regional
climate model data for the Mediterranean Sea, Clim. Dynam., 51, 1095–1117,
https://doi.org/10.1007/s00382-016-3049-z, 2018.
Madec, G., Bourdallé-Badie, R., Bouttier, P.-A., Bricaud, C.,
Bruciaferri, D., Calvert, D., Chanut, J., Clementi, E., Coward, A.,
Delrosso, D., Ethé, C., Flavoni, S., Graham, T., Harle, J., Iovino, D.,
Lea, D., Lévy, C., Lovato, T., Martin, N., Masson, S., Mocavero, S.,
Paul, J., Rousset, C., Storkey, D., Storto, A., and Vancoppenolle, M.: NEMO
ocean engine, Zenodo [code], https://doi.org/10.5281/zenodo.3248739, 2017.
Maraldi, C., Chanut, J., Levier, B., Ayoub, N., De Mey, P., Reffray, G., Lyard, F., Cailleau, S., Drévillon, M., Fanjul, E. A., Sotillo, M. G., Marsaleix, P., and the Mercator Research and Development Team: NEMO on the shelf: assessment of the Iberia–Biscay–Ireland configuration, Ocean Sci., 9, 745–771, https://doi.org/10.5194/os-9-745-2013, 2013.
Maraun, D.: Nonstationarities of regional climate model biases in European
seasonal mean temperature and precipitation sums, Geophys. Res. Lett., 39, L06706, https://doi.org/10.1029/2012GL051210, 2012.
Mathis, M., Mayer, B., and Pohlmann, T.: An uncoupled dynamical downscaling
for the North Sea: Method and evaluation, Ocean Model., 72, 153–166,
https://doi.org/10.1016/j.ocemod.2013.09.004, 2013.
McGranahan, G., Balk, D., and Anderson, B.: The rising tide: assessing the
risks of climate change and human settlements in low elevation coastal
zones, Environ. Urban., 19, 17–37,
https://doi.org/10.1177/0956247807076960, 2007.
Melet, A., Meyssignac, B., Almar, R., and Le Cozannet, G.: Under-estimated
wave contribution to coastal sea-level rise, Nat. Clim. Change, 8, 234–239,
https://doi.org/10.1038/s41558-018-0088-y, 2018.
Melet, A., Almar, R., Hemer, M., Cozannet, G. L., Meyssignac, B., and
Ruggiero, P.: Contribution of Wave Setup to Projected Coastal Sea Level
Changes, J. Geophys. Res.-Oceans, 125, e2020JC016078,
https://doi.org/10.1029/2020JC016078, 2020.
Menéndez, M. and Woodworth, P. L.: Changes in extreme high water levels
based on a quasi-global tide-gauge data set, J. Geophys. Res., 115, C10011, https://doi.org/10.1029/2009JC005997, 2010.
Meyssignac, B., Piecuch, C. G., Merchant, C. J., Racault, M.-F., Palanisamy,
H., MacIntosh, C., Sathyendranath, S., and Brewin, R.: Causes of the
Regional Variability in Observed Sea Level, Sea Surface Temperature and
Ocean Colour Over the Period 1993–2011, Surv. Geophys., 38, 187–215,
https://doi.org/10.1007/s10712-016-9383-1, 2017a.
Meyssignac, B., Slangen, A. B. A., Melet, A., Church, J. A., Fettweis, X.,
Marzeion, B., Agosta, C., Ligtenberg, S. R. M., Spada, G., Richter, K.,
Palmer, M. D., Roberts, C. D., and Champollion, N.: Evaluating Model
Simulations of Twentieth-Century Sea-Level Rise. Part II: Regional Sea-Level
Changes, J. Climate, 30, 8565–8593, https://doi.org/10.1175/JCLI-D-17-0112.1,
2017b.
Muis, S., Apecechea, M. I., Dullaart, J., de Lima Rego, J., Madsen, K. S.,
Su, J., Yan, K., and Verlaan, M.: A High-Resolution Global Dataset of
Extreme Sea Levels, Tides, and Storm Surges, Including Future Projections,
Front. Mar. Sci., 7, 263, https://doi.org/10.3389/fmars.2020.00263, 2020.
Mulet, S., Rio, M.-H., Etienne, H., Artana, C., Cancet, M., Dibarboure, G., Feng, H., Husson, R., Picot, N., Provost, C., and Strub, P. T.: The new CNES-CLS18 global mean dynamic topography, Ocean Sci., 17, 789–808, https://doi.org/10.5194/os-17-789-2021, 2021.
Nahar, J., Johnson, F., and Sharma, A.: Assessing the extent of
non-stationary biases in GCMs, J. Hydrol., 549, 148–162,
https://doi.org/10.1016/j.jhydrol.2017.03.045, 2017.
NEMO: code, https://www.nemo-ocean.eu/, last access: 8 March 2022.
Neumann, B., Vafeidis, A. T., Zimmermann, J., and Nicholls, R. J.: Future
Coastal Population Growth and Exposure to Sea-Level Rise and Coastal
Flooding – A Global Assessment, PLOS ONE, 10, e0118571,
https://doi.org/10.1371/journal.pone.0118571, 2015.
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016.
Oppenheimer, M., Glavovic, B. C., Hinkel, J., van de Wal, R., Magnan, A. K.,
Abd-Elgawad, A., Cai, R., Cifuentes-Jara, M., DeConto, R. M., Ghosh, T., Hay,
J., Isla, F., Marzeion, B., Meyssignac, B., and Sebesvari, Z.: Sea Level
Rise and Implications for Low-Lying Islands, Coasts and Communities, in: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, edited by: Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M.,
Poloczanska, E., Mintenbeck, K., Alegría, A., Nicolai, M., Okem, A.,
Petzold, J., Rama, B., and Weyer, N. M., in press, 2019
Pujol, M. and Mertz, F.: Product User Manual, available at: https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-SL-PUM-008-032-062.pdf (last access: 7 March 2022), 2020.
Reffray, G., Bourdalle-Badie, R., and Calone, C.: Modelling turbulent vertical mixing sensitivity using a 1-D version of NEMO, Geosci. Model Dev., 8, 69–86, https://doi.org/10.5194/gmd-8-69-2015, 2015.
Richter, K., Riva, R. E. M., and Drange, H.: Impact of self-attraction and
loading effects induced by shelf mass loading on projected regional sea
level rise, Geophys. Res. Lett., 40, 1144–1148,
https://doi.org/10.1002/grl.50265, 2013.
Roehrig, R., Beau, I., Saint-Martin, D., Alias, A., Decharme, B.,
Guérémy, J.-F., Voldoire, A., Abdel-Lathif, A. Y., Bazile, E.,
Belamari, S., Blein, S., Bouniol, D., Bouteloup, Y., Cattiaux, J., Chauvin,
F., Chevallier, M., Colin, J., Douville, H., Marquet, P., Michou, M., Nabat,
P., Oudar, T., Peyrillé, P., Piriou, J.-M., Salas y Mélia, D.,
Séférian, R., and Sénési, S.: The CNRM Global Atmosphere
Model ARPEGE-Climat 6.3: Description and Evaluation, J. Adv. Model. Earth
Syst., 12, e2020MS002075, https://doi.org/10.1029/2020MS002075, 2020.
Roquet, F., Madec, G., Brodeau, L., and Nycander, J.: Defining a Simplified
Yet “Realistic” Equation of State for Seawater, J. Phys. Oceanogr., 45,
2564–2579, https://doi.org/10.1175/JPO-D-15-0080.1, 2015.
Shin, S.-I. and Alexander, M. A.: Dynamical Downscaling of Future
Hydrographic Changes over the Northwest Atlantic Ocean, J. Climate, 33,
2871–2890, https://doi.org/10.1175/JCLI-D-19-0483.1, 2020.
Slangen, A. B. A., Carson, M., Katsman, C. A., van de Wal, R. S. W.,
Köhl, A., Vermeersen, L. L. A., and Stammer, D.: Projecting twenty-first
century regional sea-level changes, Clim. Change, 124, 317–332,
https://doi.org/10.1007/s10584-014-1080-9, 2014.
Slangen, A. B. A., Meyssignac, B., Agosta, C., Champollion, N., Church, J.
A., Fettweis, X., Ligtenberg, S. R. M., Marzeion, B., Melet, A., Palmer, M.
D., Richter, K., Roberts, C. D., and Spada, G.: Evaluating Model Simulations
of Twentieth-Century Sea Level Rise. Part I: Global Mean Sea Level Change,
J. Climate, 30, 8539–8563, https://doi.org/10.1175/JCLI-D-17-0110.1, 2017.
Sotillo, M. G., Cailleau, S., Lorente, P., Levier, B., Aznar, R., Reffray,
G., Amo-Baladrón, A., Chanut, J., Benkiran, M., and Alvarez-Fanjul, E.:
The MyOcean IBI Ocean Forecast and Reanalysis Systems: operational products
and roadmap to the future Copernicus Service, J. Oper. Oceanogr., 8, 63–79,
https://doi.org/10.1080/1755876X.2015.1014663, 2015.
Soto-Navarro, J., Criado-Aldeanueva, F., García-Lafuente, J., and
Sánchez-Román, A.: Estimation of the Atlantic inflow through the
Strait of Gibraltar from climatological and in situ data, J. Geophys. Res., 115, C10023, https://doi.org/10.1029/2010JC006302, 2010.
Soto-Navarro, J., Somot, S., Sevault, F., Beuvier, J., Criado-Aldeanueva,
F., García-Lafuente, J., and Béranger, K.: Evaluation of regional
ocean circulation models for the Mediterranean Sea at the Strait of
Gibraltar: volume transport and thermohaline properties of the outflow,
Clim. Dynam., 44, 1277–1292, https://doi.org/10.1007/s00382-014-2179-4, 2015.
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.
Stammer, D. and Hüttemann, S.: Response of Regional Sea Level to
Atmospheric Pressure Loading in a Climate Change Scenario, J. Climate, 21,
2093–2101, https://doi.org/10.1175/2007JCLI1803.1, 2008.
Taburet, G. and Pujol, M.: Quality Information Document (CMEMS-SL-QUID-008-032-062), https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-SL-QUID-008-032-062.pdf (last access: 7 March 2022), 2021.
Takayabu, I., Kanamaru, H., Dairaku, K., Benestad, R., Storch, H. von, and
Christensen, J. H.: Reconsidering the Quality and Utility of Downscaling, J.
Meteorol. Soc. Jpn. Ser II, 94A, 31–45,
https://doi.org/10.2151/jmsj.2015-042, 2016.
Umlauf, L. and Burchard, H.: A generic length-scale equation for geophysical
turbulence models, J. Mar. Res., 61, 235–265,
https://doi.org/10.1357/002224003322005087, 2003.
Voldoire, A.: CMIP6 simulations of the CNRM-CERFACS based on CNRM-CM6-1 model for CMIP experiment historical, Version YYYYMMDD[1], Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4066, 2018.
Voldoire, A.: CNRM-CERFACS CNRM-CM6-1-HR model output prepared for CMIP6 CMIP historical, Version YYYYMMDD[1], Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4067, 2019a.
Voldoire, A.: CNRM-CERFACS CNRM-CM6-1-HR model output prepared for CMIP6 CMIP piControl, Version YYYYMMDD[1], Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4164, 2019b.
Voldoire, A.: CNRM-CERFACS CNRM-CM6-1-HR model output prepared for CMIP6 ScenarioMIP ssp585, Version YYYYMMDD[1], Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4225, 2019c.
Voldoire, A.: CNRM-CERFACS CNRM-CM6-1-HR model output prepared for CMIP6 ScenarioMIP ssp126, Version YYYYMMDD[1], Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4185, 2020a.
Voldoire, A.: River to ocean models interpolation, report, CNRM,
Université de Toulouse, Météo-France, CNRS, https://hal-meteofrance.archives-ouvertes.fr/meteo-02986574/document, (last access: 7 March 2022), 2020.
Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A.,
Chevallier, M., Colin, J., Guérémy, J.-F., Michou, M., Moine, M.-P.,
Nabat, P., Roehrig, R., Salas y Mélia, D., Séférian, R., Valcke,
S., Beau, I., Belamari, S., Berthet, S., Cassou, C., Cattiaux, J., Deshayes,
J., Douville, H., Ethé, C., Franchistéguy, L., Geoffroy, O.,
Lévy, C., Madec, G., Meurdesoif, Y., Msadek, R., Ribes, A.,
Sanchez-Gomez, E., Terray, L., and Waldman, R.: Evaluation of CMIP6 DECK
Experiments With CNRM-CM6-1, J. Adv. Model. Earth Syst., 11, 2177–2213,
https://doi.org/10.1029/2019MS001683, 2019.
Vousdoukas, M. I., Mentaschi, L., Voukouvalas, E., Bianchi, A., Dottori, F.,
and Feyen, L.: Climatic and socioeconomic controls of future coastal flood
risk in Europe, Nat. Clim. Change, 8, 776–780,
https://doi.org/10.1038/s41558-018-0260-4, 2018a.
Vousdoukas, M. I., Mentaschi, L., Voukouvalas, E., Verlaan, M., Jevrejeva,
S., Jackson, L. P., and Feyen, L.: Global probabilistic projections of
extreme sea levels show intensification of coastal flood hazard, Nat.
Commun., 9, 2360, https://doi.org/10.1038/s41467-018-04692-w, 2018b.
van Westen, R. M., Dijkstra, H. A., van der Boog, C. G., Katsman, C. A.,
James, R. K., Bouma, T. J., Kleptsova, O., Klees, R., Riva, R. E. M.,
Slobbe, D. C., Zijlema, M., and Pietrzak, J. D.: Ocean model resolution
dependence of Caribbean sea-level projections, Sci. Rep., 10, 14599,
https://doi.org/10.1038/s41598-020-71563-0, 2020.
Woodworth, P. L., Hunter, J. R., Marcos, M., Caldwell, P., Menéndez, M.,
and Haigh, I.: Towards a global higher-frequency sea level dataset, Geosci.
Data J. [data set], 3, 50–59, https://doi.org/10.1002/gdj3.42, 2017.
Woodworth, P. L., Melet, A., Marcos, M., Ray, R. D., Wöppelmann, G.,
Sasaki, Y. N., Cirano, M., Hibbert, A., Huthnance, J. M., Monserrat, S., and
Merrifield, M. A.: Forcing Factors Affecting Sea Level Changes at the Coast,
Surv. Geophys., 40, 1351–1397, https://doi.org/10.1007/s10712-019-09531-1,
2019.
Xu, Z., Han, Y., and Yang, Z.: Dynamical downscaling of regional climate: A
review of methods and limitations, Sci. China Earth Sci., 62, 365–375,
https://doi.org/10.1007/s11430-018-9261-5, 2019.
Zalesak, S. T.: Fully multidimensional flux-corrected transport algorithms
for fluids, J. Comput. Phys., 31, 335–362,
https://doi.org/10.1016/0021-9991(79)90051-2, 1979.
Zhang, X., Church, J. A., Monselesan, D., and McInnes, K. L.: Sea level
projections for the Australian region in the 21st century, Geophys. Res.
Lett., 44, 8481–8491, https://doi.org/10.1002/2017GL074176, 2017.
Short summary
Climate-change-induced sea level rise is a major threat for coastal and low-lying regions. Projections of coastal sea level changes are thus of great interest for coastal risk assessment and have significantly developed in recent years. In this paper, the objective is to provide high-resolution (6 km) projections of sea level changes in the northeastern Atlantic region bordering western Europe. For that purpose, a regional model is used to refine existing coarse global projections.
Climate-change-induced sea level rise is a major threat for coastal and low-lying regions....
