Articles | Volume 12, issue 3
https://doi.org/10.5194/esd-12-939-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/esd-12-939-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review
| 
16 Sep 2021
Review |
| 16 Sep 2021
| 16 Sep 2021
Coupled regional Earth system modeling in the Baltic Sea region
Matthias Gröger
CORRESPONDING AUTHOR
Department of Physical Oceanography and Instrumentation, Leibniz Institute for Baltic Sea Research Warnemünde, Rostock, Germany
Christian Dieterich
Research and Development Department, Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
deceased
Jari Haapala
Finnish Meteorological Institute, Helsinki, Finland
Ha Thi Minh Ho-Hagemann
Institute of Coastal Systems – Analysis and Modeling, Helmholtz-Zentrum Hereon, Geesthacht, Germany
Stefan Hagemann
Institute of Coastal Systems – Analysis and Modeling, Helmholtz-Zentrum Hereon, Geesthacht, Germany
Jaromir Jakacki
Institute of Oceanology, Polish Academy of Sciences, Sopot, Poland
Wilhelm May
Centre for Environmental and Climate Science, Lund University, Lund, Sweden
H. E. Markus Meier
Department of Physical Oceanography and Instrumentation, Leibniz Institute for Baltic Sea Research Warnemünde, Rostock, Germany
Research and Development Department, Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
Paul A. Miller
Centre for Environmental and Climate Science, Lund University, Lund, Sweden
Department of Physical Geography and Ecosystem Science, Lund University, Sweden
Anna Rutgersson
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Lichuan Wu
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
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Jenny Hieronymus, Magnus Hieronymus, Matthias Gröger, Jörg Schwinger, Raffaele Bernadello, Etienne Tourigny, Valentina Sicardi, Itzel Ruvalcaba Baroni, and Klaus Wyser
Biogeosciences, 21, 2189–2206, https://doi.org/10.5194/bg-21-2189-2024, https://doi.org/10.5194/bg-21-2189-2024, 2024
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The timing of the net primary production annual maxima in the North Atlantic in the period 1750–2100 is investigated using two Earth system models and the high-emissions scenario SSP5-8.5. It is found that, for most of the region, the annual maxima occur progressively earlier, with the most change occurring after the year 2000. Shifts in the seasonality of the primary production may impact the entire ecosystem, which highlights the need for long-term monitoring campaigns in this area.
Itzel Ruvalcaba Baroni, Elin Almroth-Rosell, Lars Axell, Sam T. Fredriksson, Jenny Hieronymus, Magnus Hieronymus, Sandra-Esther Brunnabend, Matthias Gröger, Ivan Kuznetsov, Filippa Fransner, Robinson Hordoir, Saeed Falahat, and Lars Arneborg
Biogeosciences, 21, 2087–2132, https://doi.org/10.5194/bg-21-2087-2024, https://doi.org/10.5194/bg-21-2087-2024, 2024
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The health of the Baltic and North seas is threatened due to high anthropogenic pressure; thus, different methods to assess the status of these regions are urgently needed. Here, we validated a novel model simulating the ocean dynamics and biogeochemistry of the Baltic and North seas that can be used to create future climate and nutrient scenarios, contribute to European initiatives on de-eutrophication, and provide water quality advice and support on nutrient load reductions for both seas.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024, https://doi.org/10.5194/gmd-17-1689-2024, 2024
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This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean–atmosphere interactions.
Matthias Gröger, Manja Placke, H. E. Markus Meier, Florian Börgel, Sandra-Esther Brunnabend, Cyril Dutheil, Ulf Gräwe, Magnus Hieronymus, Thomas Neumann, Hagen Radtke, Semjon Schimanke, Jian Su, and Germo Väli
Geosci. Model Dev., 15, 8613–8638, https://doi.org/10.5194/gmd-15-8613-2022, https://doi.org/10.5194/gmd-15-8613-2022, 2022
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Comparisons of oceanographic climate data from different models often suffer from different model setups, forcing fields, and output of variables. This paper provides a protocol to harmonize these elements to set up multidecadal simulations for the Baltic Sea, a marginal sea in Europe. First results are shown from six different model simulations from four different model platforms. Topical studies for upwelling, marine heat waves, and stratification are also assessed.
Dmitry V. Sein, Anton Y. Dvornikov, Stanislav D. Martyanov, William Cabos, Vladimir A. Ryabchenko, Matthias Gröger, Daniela Jacob, Alok Kumar Mishra, and Pankaj Kumar
Earth Syst. Dynam., 13, 809–831, https://doi.org/10.5194/esd-13-809-2022, https://doi.org/10.5194/esd-13-809-2022, 2022
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The effect of the marine biogeochemical variability upon the South Asian regional climate has been investigated. In the experiment where its full impact is activated, the average sea surface temperature is lower over most of the ocean. When the biogeochemical coupling is included, the main impacts include the enhanced phytoplankton primary production, a shallower thermocline, decreased SST and water temperature in subsurface layers.
Ralf Döscher, Mario Acosta, Andrea Alessandri, Peter Anthoni, Thomas Arsouze, Tommi Bergman, Raffaele Bernardello, Souhail Boussetta, Louis-Philippe Caron, Glenn Carver, Miguel Castrillo, Franco Catalano, Ivana Cvijanovic, Paolo Davini, Evelien Dekker, Francisco J. Doblas-Reyes, David Docquier, Pablo Echevarria, Uwe Fladrich, Ramon Fuentes-Franco, Matthias Gröger, Jost v. Hardenberg, Jenny Hieronymus, M. Pasha Karami, Jukka-Pekka Keskinen, Torben Koenigk, Risto Makkonen, François Massonnet, Martin Ménégoz, Paul A. Miller, Eduardo Moreno-Chamarro, Lars Nieradzik, Twan van Noije, Paul Nolan, Declan O'Donnell, Pirkka Ollinaho, Gijs van den Oord, Pablo Ortega, Oriol Tintó Prims, Arthur Ramos, Thomas Reerink, Clement Rousset, Yohan Ruprich-Robert, Philippe Le Sager, Torben Schmith, Roland Schrödner, Federico Serva, Valentina Sicardi, Marianne Sloth Madsen, Benjamin Smith, Tian Tian, Etienne Tourigny, Petteri Uotila, Martin Vancoppenolle, Shiyu Wang, David Wårlind, Ulrika Willén, Klaus Wyser, Shuting Yang, Xavier Yepes-Arbós, and Qiong Zhang
Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, https://doi.org/10.5194/gmd-15-2973-2022, 2022
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The Earth system model EC-Earth3 is documented here. Key performance metrics show physical behavior and biases well within the frame known from recent models. With improved physical and dynamic features, new ESM components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.
Matthias Gröger, Christian Dieterich, Cyril Dutheil, H. E. Markus Meier, and Dmitry V. Sein
Earth Syst. Dynam., 13, 613–631, https://doi.org/10.5194/esd-13-613-2022, https://doi.org/10.5194/esd-13-613-2022, 2022
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Atmospheric rivers transport high amounts of water from subtropical regions to Europe. They are an important driver of heavy precipitation and flooding. Their response to a warmer future climate in Europe has so far been assessed only by global climate models. In this study, we apply for the first time a high-resolution regional climate model that allow to better resolve and understand the fate of atmospheric rivers over Europe.
H. E. Markus Meier, Madline Kniebusch, Christian Dieterich, Matthias Gröger, Eduardo Zorita, Ragnar Elmgren, Kai Myrberg, Markus P. Ahola, Alena Bartosova, Erik Bonsdorff, Florian Börgel, Rene Capell, Ida Carlén, Thomas Carlund, Jacob Carstensen, Ole B. Christensen, Volker Dierschke, Claudia Frauen, Morten Frederiksen, Elie Gaget, Anders Galatius, Jari J. Haapala, Antti Halkka, Gustaf Hugelius, Birgit Hünicke, Jaak Jaagus, Mart Jüssi, Jukka Käyhkö, Nina Kirchner, Erik Kjellström, Karol Kulinski, Andreas Lehmann, Göran Lindström, Wilhelm May, Paul A. Miller, Volker Mohrholz, Bärbel Müller-Karulis, Diego Pavón-Jordán, Markus Quante, Marcus Reckermann, Anna Rutgersson, Oleg P. Savchuk, Martin Stendel, Laura Tuomi, Markku Viitasalo, Ralf Weisse, and Wenyan Zhang
Earth Syst. Dynam., 13, 457–593, https://doi.org/10.5194/esd-13-457-2022, https://doi.org/10.5194/esd-13-457-2022, 2022
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Based on the Baltic Earth Assessment Reports of this thematic issue in Earth System Dynamics and recent peer-reviewed literature, current knowledge about the effects of global warming on past and future changes in the climate of the Baltic Sea region is summarised and assessed. The study is an update of the Second Assessment of Climate Change (BACC II) published in 2015 and focuses on the atmosphere, land, cryosphere, ocean, sediments, and the terrestrial and marine biosphere.
H. E. Markus Meier, Christian Dieterich, Matthias Gröger, Cyril Dutheil, Florian Börgel, Kseniia Safonova, Ole B. Christensen, and Erik Kjellström
Earth Syst. Dynam., 13, 159–199, https://doi.org/10.5194/esd-13-159-2022, https://doi.org/10.5194/esd-13-159-2022, 2022
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In addition to environmental pressures such as eutrophication, overfishing and contaminants, climate change is believed to have an important impact on the marine environment in the future, and marine management should consider the related risks. Hence, we have compared and assessed available scenario simulations for the Baltic Sea and found considerable uncertainties of the projections caused by the underlying assumptions and model biases, in particular for the water and biogeochemical cycles.
Ole Bøssing Christensen, Erik Kjellström, Christian Dieterich, Matthias Gröger, and Hans Eberhard Markus Meier
Earth Syst. Dynam., 13, 133–157, https://doi.org/10.5194/esd-13-133-2022, https://doi.org/10.5194/esd-13-133-2022, 2022
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The Baltic Sea Region is very sensitive to climate change, whose impacts could easily exacerbate biodiversity stress from society and eutrophication of the Baltic Sea. Therefore, there has been a focus on estimations of future climate change and its impacts in recent research. Models show a strong warming, in particular in the north in winter. Precipitation is projected to increase in the whole region apart from the south during summer. New results improve estimates of future climate change.
Stelios Myriokefalitakis, Matthias Gröger, Jenny Hieronymus, and Ralf Döscher
Ocean Sci., 16, 1183–1205, https://doi.org/10.5194/os-16-1183-2020, https://doi.org/10.5194/os-16-1183-2020, 2020
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Global inorganic and organic nutrient deposition fields are coupled to PISCES to investigate their effect on ocean biogeochemistry. Pre-industrial deposition fluxes are lower compared to the present day, resulting in lower oceanic productivity. Future changes result in a modest decrease in the nutrients put into the global ocean. This work provides a first assessment of the atmospheric organic nutrients' contribution, highlighting the importance of their representation in biogeochemistry models.
Jalisha Theanutti Kallingal, Marko Scholze, Paul Anthony Miller, Johan Lindström, Janne Rinne, Mika Aurela, Patrik Vestin, and Per Weslien
Biogeosciences, 22, 4061–4086, https://doi.org/10.5194/bg-22-4061-2025, https://doi.org/10.5194/bg-22-4061-2025, 2025
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We explored the possibilities of a Bayesian-based data assimilation algorithm to improve the wetland CH4 flux estimates by a dynamic vegetation model. By assimilating CH4 observations from 14 wetland sites, we calibrated model parameters and estimated large-scale annual emissions from northern wetlands. Our findings indicate that this approach leads to more reliable estimates of CH4 dynamics, which will improve our understanding of the climate change feedback from wetland CH4 emissions.
Wolfgang A. Müller, Stephan Lorenz, Trang V. Pham, Andrea Schneidereit, Renate Brokopf, Victor Brovkin, Nils Brüggemann, Fatemeh Chegini, Dietmar Dommenget, Kristina Fröhlich, Barbara Früh, Veronika Gayler, Helmuth Haak, Stefan Hagemann, Moritz Hanke, Tatiana Ilyina, Johann Jungclaus, Martin Köhler, Peter Korn, Luis Kornblüh, Clarissa Kroll, Julian Krüger, Karel Castro-Morales, Ulrike Niemeier, Holger Pohlmann, Iuliia Polkova, Roland Potthast, Thomas Riddick, Manuel Schlund, Tobias Stacke, Roland Wirth, Dakuan Yu, and Jochem Marotzke
EGUsphere, https://doi.org/10.5194/egusphere-2025-2473, https://doi.org/10.5194/egusphere-2025-2473, 2025
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ICON XPP is a newly developed Earth System model configuration based on the ICON modeling framework. It merges accomplishments from the recent operational numerical weather prediction model with well-established climate components for the ocean, land and ocean-biogeochemistry. ICON XPP reaches typical targets of a coupled climate simulation, and is able to run long integrations and large-ensemble experiments, making it suitable for climate predictions and projections, and for climate research.
Amali A. Amali, Clemens Schwingshackl, Akihiko Ito, Alina Barbu, Christine Delire, Daniele Peano, David M. Lawrence, David Wårlind, Eddy Robertson, Edouard L. Davin, Elena Shevliakova, Ian N. Harman, Nicolas Vuichard, Paul A. Miller, Peter J. Lawrence, Tilo Ziehn, Tomohiro Hajima, Victor Brovkin, Yanwu Zhang, Vivek K. Arora, and Julia Pongratz
Earth Syst. Dynam., 16, 803–840, https://doi.org/10.5194/esd-16-803-2025, https://doi.org/10.5194/esd-16-803-2025, 2025
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Our study explored the impact of anthropogenic land-use change (LUC) on climate dynamics, focusing on biogeophysical (BGP) and biogeochemical (BGC) effects using data from the Land Use Model Intercomparison Project (LUMIP) and the Coupled Model Intercomparison Project Phase 6 (CMIP6). We found that LUC-induced carbon emissions contribute to a BGC warming of 0.21 °C, with BGC effects dominating globally over BGP effects, which show regional variability. Our findings highlight discrepancies in model simulations and emphasize the need for improved representations of LUC processes.
Igor V. Polyakov, Andrey V. Pnyushkov, Eddy C. Carmack, Matthew Charette, Kyoung-Ho Cho, Steven Dykstra, Jari Haapala, Jinyoung Jung, Lauren Kipp, and Eun Jin Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2316, https://doi.org/10.5194/egusphere-2025-2316, 2025
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The Siberian Arctic Ocean greatly influences the Arctic climate system. Moreover, the region is experiencing some of the most notable Arctic climate change. In the summer, strong near-inertial currents in the upper (<30m) ocean account for more than half of the current speed and shear. In the winter, upper ocean ventilation due to atlantification distributes wind energy to far deeper (>100m) layers. Understanding the implications for mixing and halocline weakening depends on these findings.
Nikolina Mileva, Julia Pongratz, Vivek K. Arora, Akihiko Ito, Sebastiaan Luyssaert, Sonali S. McDermid, Paul A. Miller, Daniele Peano, Roland Séférian, Yanwu Zhang, and Wolfgang Buermann
EGUsphere, https://doi.org/10.5194/egusphere-2025-979, https://doi.org/10.5194/egusphere-2025-979, 2025
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Despite forests being so important for mitigating climate change, there are still uncertainties about how much the changes in forest cover contribute to the cooling/warming of the climate. Climate models and real-world observations often disagree about the magnitude and even the direction of these changes. We constrain climate models scenarios of widespread deforestation with satellite and in-situ data and show that models still have difficulties representing the movement of heat and water.
Matias Uusinoka, Jari Haapala, Jan Åström, Mikko Lensu, and Arttu Polojärvi
EGUsphere, https://doi.org/10.5194/egusphere-2025-311, https://doi.org/10.5194/egusphere-2025-311, 2025
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We tracked sea ice deformation over a nine-month period using high-resolution ship radar data and a state-of-the-art deep learning technique. We observe that the typically consistent scale-invariant pattern in sea ice deformation has a lower limit of about 102 meters in winter, but this behavior disappears during summer. Our findings provide critical insights for considering current modeling assumptions and for connecting the scales of interest in sea ice dynamics.
Ferran Lopez-Marti, Mireia Ginesta, Davide Faranda, Anna Rutgersson, Pascal Yiou, Lichuan Wu, and Gabriele Messori
Earth Syst. Dynam., 16, 169–187, https://doi.org/10.5194/esd-16-169-2025, https://doi.org/10.5194/esd-16-169-2025, 2025
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Explosive cyclones and atmospheric rivers are two main drivers of extreme weather in Europe. In this study, we investigate their joint changes in future climates over the North Atlantic. Our results show that both the concurrence of these events and the intensity of atmospheric rivers increase by the end of the century across different future scenarios. Furthermore, explosive cyclones associated with atmospheric rivers last longer and are deeper than those without atmospheric rivers.
Tuula Aalto, Aki Tsuruta, Jarmo Mäkelä, Jurek Müller, Maria Tenkanen, Eleanor Burke, Sarah Chadburn, Yao Gao, Vilma Mannisenaho, Thomas Kleinen, Hanna Lee, Antti Leppänen, Tiina Markkanen, Stefano Materia, Paul A. Miller, Daniele Peano, Olli Peltola, Benjamin Poulter, Maarit Raivonen, Marielle Saunois, David Wårlind, and Sönke Zaehle
Biogeosciences, 22, 323–340, https://doi.org/10.5194/bg-22-323-2025, https://doi.org/10.5194/bg-22-323-2025, 2025
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Wetland methane responses to temperature and precipitation were studied in a boreal wetland-rich region in northern Europe using ecosystem models, atmospheric inversions, and upscaled flux observations. The ecosystem models differed in their responses to temperature and precipitation and in their seasonality. However, multi-model means, inversions, and upscaled fluxes had similar seasonality, and they suggested co-limitation by temperature and precipitation.
Zhen Zhang, Benjamin Poulter, Joe R. Melton, William J. Riley, George H. Allen, David J. Beerling, Philippe Bousquet, Josep G. Canadell, Etienne Fluet-Chouinard, Philippe Ciais, Nicola Gedney, Peter O. Hopcroft, Akihiko Ito, Robert B. Jackson, Atul K. Jain, Katherine Jensen, Fortunat Joos, Thomas Kleinen, Sara H. Knox, Tingting Li, Xin Li, Xiangyu Liu, Kyle McDonald, Gavin McNicol, Paul A. Miller, Jurek Müller, Prabir K. Patra, Changhui Peng, Shushi Peng, Zhangcai Qin, Ryan M. Riggs, Marielle Saunois, Qing Sun, Hanqin Tian, Xiaoming Xu, Yuanzhi Yao, Yi Xi, Wenxin Zhang, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Biogeosciences, 22, 305–321, https://doi.org/10.5194/bg-22-305-2025, https://doi.org/10.5194/bg-22-305-2025, 2025
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This study assesses global methane emissions from wetlands between 2000 and 2020 using multiple models. We found that wetland emissions increased by 6–7 Tg CH4 yr-1 in the 2010s compared to the 2000s. Rising temperatures primarily drove this increase, while changes in precipitation and CO2 levels also played roles. Our findings highlight the importance of wetlands in the global methane budget and the need for continuous monitoring to understand their impact on climate change.
Alberto Elizalde, Gibran Romero-Mujalli, Tobias Stacke, and Stefan Hagemann
EGUsphere, https://doi.org/10.5194/egusphere-2024-3645, https://doi.org/10.5194/egusphere-2024-3645, 2025
Preprint archived
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This study examines phosphorus land-to-sea transport in Europe, exploring changes over time and predicting future trends under various scenarios. It integrates human and environmental factors, offering a comprehensive analysis. Our findings show how global warming-induced rainfall patterns affect phosphorus levels. While pollution reduction policies are helpful, population growth, land-use changes, and increased rainfall could lead to higher phosphorus levels in the future.
Stefan Hagemann, Thao Thi Nguyen, and Ha Thi Minh Ho-Hagemann
Ocean Sci., 20, 1457–1478, https://doi.org/10.5194/os-20-1457-2024, https://doi.org/10.5194/os-20-1457-2024, 2024
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We have developed a methodology for the bias correction of simulated river runoff to force ocean models in which low, medium, and high discharges are corrected once separated at the coast. We show that the bias correction generally leads to an improved representation of river runoff in Europe. The methodology is suitable for model regions with a sufficiently high coverage of discharge observations, and it can be applied to river runoff based on climate hindcasts or climate change simulations.
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.
Pascal Simon, Martin Otto Paul Ramacher, Stefan Hagemann, Volker Matthias, Hanna Joerss, and Johannes Bieser
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-236, https://doi.org/10.5194/essd-2024-236, 2024
Revised manuscript accepted for ESSD
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Per- and Polyfluorinated Alkyl Substances (PFAS) constitute a group of often toxic, persistent, and bioaccumulative substances. We constructed a global Emissions model and inventory based on multiple datasets for 23 widely used PFAS. The model computes temporally and spatially resolved model ready emissions distinguishing between emissions to air and emissions to water covering the time span from 1950 up until 2020 on an annual basis to be used for chemistry transport modelling.
José A. Jiménez, Gundula Winter, Antonio Bonaduce, Michael Depuydt, Giulia Galluccio, Bart van den Hurk, H. E. Markus Meier, Nadia Pinardi, Lavinia G. Pomarico, and Natalia Vazquez Riveiros
State Planet, 3-slre1, 3, https://doi.org/10.5194/sp-3-slre1-3-2024, https://doi.org/10.5194/sp-3-slre1-3-2024, 2024
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The Knowledge Hub on Sea Level Rise (SLR) has done a scoping study involving stakeholders from government and academia to identify gaps and needs in SLR information, impacts, and policies across Europe. Gaps in regional SLR projections and uncertainties were found, while concerns were raised about shoreline erosion and emerging problems like saltwater intrusion and ineffective adaptation plans. The need for improved communication to make better decisions on SLR adaptation was highlighted.
Angélique Melet, Roderik van de Wal, Angel Amores, Arne Arns, Alisée A. Chaigneau, Irina Dinu, Ivan D. Haigh, Tim H. J. Hermans, Piero Lionello, Marta Marcos, H. E. Markus Meier, Benoit Meyssignac, Matthew D. Palmer, Ronja Reese, Matthew J. R. Simpson, and Aimée B. A. Slangen
State Planet, 3-slre1, 4, https://doi.org/10.5194/sp-3-slre1-4-2024, https://doi.org/10.5194/sp-3-slre1-4-2024, 2024
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The EU Knowledge Hub on Sea Level Rise’s Assessment Report strives to synthesize the current scientific knowledge on sea level rise and its impacts across local, national, and EU scales to support evidence-based policy and decision-making, primarily targeting coastal areas. This paper complements IPCC reports by documenting the state of knowledge of observed and 21st century projected changes in mean and extreme sea levels with more regional information for EU seas as scoped with stakeholders.
Kévin Dubois, Morten Andreas Dahl Larsen, Martin Drews, Erik Nilsson, and Anna Rutgersson
Nat. Hazards Earth Syst. Sci., 24, 3245–3265, https://doi.org/10.5194/nhess-24-3245-2024, https://doi.org/10.5194/nhess-24-3245-2024, 2024
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Both extreme river discharge and storm surges can interact at the coast and lead to flooding. However, it is difficult to predict flood levels during such compound events because they are rare and complex. Here, we focus on the quantification of uncertainties and investigate the sources of limitations while carrying out such analyses at Halmstad, Sweden. Based on a sensitivity analysis, we emphasize that both the choice of data source and statistical methodology influence the results.
Jan Åström, Fredrik Robertsen, Jari Haapala, Arttu Polojärvi, Rivo Uiboupin, and Ilja Maljutenko
The Cryosphere, 18, 2429–2442, https://doi.org/10.5194/tc-18-2429-2024, https://doi.org/10.5194/tc-18-2429-2024, 2024
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The HiDEM code has been developed for analyzing the fracture and fragmentation of brittle materials and has been extensively applied to glacier calving. Here, we report on the adaptation of the code to sea-ice dynamics and breakup. The code demonstrates the capability to simulate sea-ice dynamics on a 100 km scale with an unprecedented resolution. We argue that codes of this type may become useful for improving forecasts of sea-ice dynamics.
Jenny Hieronymus, Magnus Hieronymus, Matthias Gröger, Jörg Schwinger, Raffaele Bernadello, Etienne Tourigny, Valentina Sicardi, Itzel Ruvalcaba Baroni, and Klaus Wyser
Biogeosciences, 21, 2189–2206, https://doi.org/10.5194/bg-21-2189-2024, https://doi.org/10.5194/bg-21-2189-2024, 2024
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The timing of the net primary production annual maxima in the North Atlantic in the period 1750–2100 is investigated using two Earth system models and the high-emissions scenario SSP5-8.5. It is found that, for most of the region, the annual maxima occur progressively earlier, with the most change occurring after the year 2000. Shifts in the seasonality of the primary production may impact the entire ecosystem, which highlights the need for long-term monitoring campaigns in this area.
Félix García-Pereira, Jesús Fidel González-Rouco, Camilo Melo-Aguilar, Norman Julius Steinert, Elena García-Bustamante, Philip de Vrese, Johann Jungclaus, Stephan Lorenz, Stefan Hagemann, Francisco José Cuesta-Valero, Almudena García-García, and Hugo Beltrami
Earth Syst. Dynam., 15, 547–564, https://doi.org/10.5194/esd-15-547-2024, https://doi.org/10.5194/esd-15-547-2024, 2024
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According to climate model estimates, the land stored 2 % of the system's heat excess in the last decades, while observational studies show it was around 6 %. This difference stems from these models using land components that are too shallow to constrain land heat uptake. Deepening the land component does not affect the surface temperature. This result can be used to derive land heat uptake estimates from different sources, which are much closer to previous observational reports.
Itzel Ruvalcaba Baroni, Elin Almroth-Rosell, Lars Axell, Sam T. Fredriksson, Jenny Hieronymus, Magnus Hieronymus, Sandra-Esther Brunnabend, Matthias Gröger, Ivan Kuznetsov, Filippa Fransner, Robinson Hordoir, Saeed Falahat, and Lars Arneborg
Biogeosciences, 21, 2087–2132, https://doi.org/10.5194/bg-21-2087-2024, https://doi.org/10.5194/bg-21-2087-2024, 2024
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The health of the Baltic and North seas is threatened due to high anthropogenic pressure; thus, different methods to assess the status of these regions are urgently needed. Here, we validated a novel model simulating the ocean dynamics and biogeochemistry of the Baltic and North seas that can be used to create future climate and nutrient scenarios, contribute to European initiatives on de-eutrophication, and provide water quality advice and support on nutrient load reductions for both seas.
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
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By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Fredrik Lagergren, Robert G. Björk, Camilla Andersson, Danijel Belušić, Mats P. Björkman, Erik Kjellström, Petter Lind, David Lindstedt, Tinja Olenius, Håkan Pleijel, Gunhild Rosqvist, and Paul A. Miller
Biogeosciences, 21, 1093–1116, https://doi.org/10.5194/bg-21-1093-2024, https://doi.org/10.5194/bg-21-1093-2024, 2024
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The Fennoscandian boreal and mountain regions harbour a wide range of ecosystems sensitive to climate change. A new, highly resolved high-emission climate scenario enabled modelling of the vegetation development in this region at high resolution for the 21st century. The results show dramatic south to north and low- to high-altitude shifts of vegetation zones, especially for the open tundra environments, which will have large implications for nature conservation, reindeer husbandry and forestry.
Jalisha Theanutti Kallingal, Marko Scholze, Paul Anthony Miller, Johan Lindström, Janne Rinne, Mika Aurela, Patrik Vestin, and Per Weslien
EGUsphere, https://doi.org/10.5194/egusphere-2024-373, https://doi.org/10.5194/egusphere-2024-373, 2024
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Our study employs an Adaptive MCMC algorithm (GRaB-AM) to constrain process parameters in the wetlands emission module of the LPJ-GUESS model, using CH4 EC flux observations from 14 diverse wetlands. We aim to derive a single set of parameters capable of representing the diversity of northern wetlands. By reducing uncertainties in model parameters and improving simulation accuracy, our research contributes to more reliable projections of future wetland CH4 emissions and their climate impact.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024, https://doi.org/10.5194/gmd-17-1689-2024, 2024
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This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean–atmosphere interactions.
Julika Zinke, Ernst Douglas Nilsson, Piotr Markuszewski, Paul Zieger, Eva Monica Mårtensson, Anna Rutgersson, Erik Nilsson, and Matthew Edward Salter
Atmos. Chem. Phys., 24, 1895–1918, https://doi.org/10.5194/acp-24-1895-2024, https://doi.org/10.5194/acp-24-1895-2024, 2024
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We conducted two research campaigns in the Baltic Sea, during which we combined laboratory sea spray simulation experiments with flux measurements on a nearby island. To combine these two methods, we scaled the laboratory measurements to the flux measurements using three different approaches. As a result, we derived a parameterization that is dependent on wind speed and wave state for particles with diameters 0.015–10 μm. This parameterization is applicable to low-salinity waters.
Kévin Dubois, Morten Andreas Dahl Larsen, Martin Drews, Erik Nilsson, and Anna Rutgersson
Ocean Sci., 20, 21–30, https://doi.org/10.5194/os-20-21-2024, https://doi.org/10.5194/os-20-21-2024, 2024
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Coastal floods occur due to extreme sea levels (ESLs) which are difficult to predict because of their rarity. Long records of accurate sea levels at the local scale increase ESL predictability. Here, we apply a machine learning technique to extend sea level observation data in the past based on a neighbouring tide gauge. We compared the results with a linear model. We conclude that both models give reasonable results with a better accuracy towards the extremes for the machine learning model.
Wilhelm May
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2023-13, https://doi.org/10.5194/esd-2023-13, 2023
Revised manuscript not accepted
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Land-surface conditions have prominent effects on local and regional climate through the exchanges of energy, moisture and carbon dioxide with the atmosphere. Therefore, it is important that the relevant processes are simulated realistically. The land-surface component of the EC-Earth3 ESM is characterized by marked regional biases in various aspects of surface climate. The coupling with the atmosphere enhances the biases in surface climate, in particular for land-surface temperature.
Philipp Heinrich, Stefan Hagemann, Ralf Weisse, Corinna Schrum, Ute Daewel, and Lidia Gaslikova
Nat. Hazards Earth Syst. Sci., 23, 1967–1985, https://doi.org/10.5194/nhess-23-1967-2023, https://doi.org/10.5194/nhess-23-1967-2023, 2023
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High seawater levels co-occurring with high river discharges have the potential to cause destructive flooding. For the past decades, the number of such compound events was larger than expected by pure chance for most of the west-facing coasts in Europe. Additionally rivers with smaller catchments showed higher numbers. In most cases, such events were associated with a large-scale weather pattern characterized by westerly winds and strong rainfall.
H. E. Markus Meier, Marcus Reckermann, Joakim Langner, Ben Smith, and Ira Didenkulova
Earth Syst. Dynam., 14, 519–531, https://doi.org/10.5194/esd-14-519-2023, https://doi.org/10.5194/esd-14-519-2023, 2023
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The Baltic Earth Assessment Reports summarise the current state of knowledge on Earth system science in the Baltic Sea region. The 10 review articles focus on the regional water, biogeochemical and carbon cycles; extremes and natural hazards; sea-level dynamics and coastal erosion; marine ecosystems; coupled Earth system models; scenario simulations for the regional atmosphere and the Baltic Sea; and climate change and impacts of human use. Some highlights of the results are presented here.
Lucía Gutiérrez-Loza, Erik Nilsson, Marcus B. Wallin, Erik Sahlée, and Anna Rutgersson
Biogeosciences, 19, 5645–5665, https://doi.org/10.5194/bg-19-5645-2022, https://doi.org/10.5194/bg-19-5645-2022, 2022
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The exchange of CO2 between the ocean and the atmosphere is an essential aspect of the global carbon cycle and is highly relevant for the Earth's climate. In this study, we used 9 years of in situ measurements to evaluate the temporal variability in the air–sea CO2 fluxes in the Baltic Sea. Furthermore, using this long record, we assessed the effect of atmospheric and water-side mechanisms controlling the efficiency of the air–sea CO2 exchange under different wind-speed conditions.
Matthias Gröger, Manja Placke, H. E. Markus Meier, Florian Börgel, Sandra-Esther Brunnabend, Cyril Dutheil, Ulf Gräwe, Magnus Hieronymus, Thomas Neumann, Hagen Radtke, Semjon Schimanke, Jian Su, and Germo Väli
Geosci. Model Dev., 15, 8613–8638, https://doi.org/10.5194/gmd-15-8613-2022, https://doi.org/10.5194/gmd-15-8613-2022, 2022
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Comparisons of oceanographic climate data from different models often suffer from different model setups, forcing fields, and output of variables. This paper provides a protocol to harmonize these elements to set up multidecadal simulations for the Baltic Sea, a marginal sea in Europe. First results are shown from six different model simulations from four different model platforms. Topical studies for upwelling, marine heat waves, and stratification are also assessed.
Kathrin Wehrli, Fei Luo, Mathias Hauser, Hideo Shiogama, Daisuke Tokuda, Hyungjun Kim, Dim Coumou, Wilhelm May, Philippe Le Sager, Frank Selten, Olivia Martius, Robert Vautard, and Sonia I. Seneviratne
Earth Syst. Dynam., 13, 1167–1196, https://doi.org/10.5194/esd-13-1167-2022, https://doi.org/10.5194/esd-13-1167-2022, 2022
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The ExtremeX experiment was designed to unravel the contribution of processes leading to the occurrence of recent weather and climate extremes. Global climate simulations are carried out with three models. The results show that in constrained experiments, temperature anomalies during heatwaves are well represented, although climatological model biases remain. Further, a substantial contribution of both atmospheric circulation and soil moisture to heat extremes is identified.
Fei Luo, Frank Selten, Kathrin Wehrli, Kai Kornhuber, Philippe Le Sager, Wilhelm May, Thomas Reerink, Sonia I. Seneviratne, Hideo Shiogama, Daisuke Tokuda, Hyungjun Kim, and Dim Coumou
Weather Clim. Dynam., 3, 905–935, https://doi.org/10.5194/wcd-3-905-2022, https://doi.org/10.5194/wcd-3-905-2022, 2022
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Recent studies have identified the weather systems in observational data, where wave patterns with high-magnitude values that circle around the whole globe in either wavenumber 5 or wavenumber 7 are responsible for the extreme events. In conclusion, we find that the climate models are able to reproduce the large-scale atmospheric circulation patterns as well as their associated surface variables such as temperature, precipitation, and sea level pressure.
Dmitry V. Sein, Anton Y. Dvornikov, Stanislav D. Martyanov, William Cabos, Vladimir A. Ryabchenko, Matthias Gröger, Daniela Jacob, Alok Kumar Mishra, and Pankaj Kumar
Earth Syst. Dynam., 13, 809–831, https://doi.org/10.5194/esd-13-809-2022, https://doi.org/10.5194/esd-13-809-2022, 2022
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The effect of the marine biogeochemical variability upon the South Asian regional climate has been investigated. In the experiment where its full impact is activated, the average sea surface temperature is lower over most of the ocean. When the biogeochemical coupling is included, the main impacts include the enhanced phytoplankton primary production, a shallower thermocline, decreased SST and water temperature in subsurface layers.
Ralf Döscher, Mario Acosta, Andrea Alessandri, Peter Anthoni, Thomas Arsouze, Tommi Bergman, Raffaele Bernardello, Souhail Boussetta, Louis-Philippe Caron, Glenn Carver, Miguel Castrillo, Franco Catalano, Ivana Cvijanovic, Paolo Davini, Evelien Dekker, Francisco J. Doblas-Reyes, David Docquier, Pablo Echevarria, Uwe Fladrich, Ramon Fuentes-Franco, Matthias Gröger, Jost v. Hardenberg, Jenny Hieronymus, M. Pasha Karami, Jukka-Pekka Keskinen, Torben Koenigk, Risto Makkonen, François Massonnet, Martin Ménégoz, Paul A. Miller, Eduardo Moreno-Chamarro, Lars Nieradzik, Twan van Noije, Paul Nolan, Declan O'Donnell, Pirkka Ollinaho, Gijs van den Oord, Pablo Ortega, Oriol Tintó Prims, Arthur Ramos, Thomas Reerink, Clement Rousset, Yohan Ruprich-Robert, Philippe Le Sager, Torben Schmith, Roland Schrödner, Federico Serva, Valentina Sicardi, Marianne Sloth Madsen, Benjamin Smith, Tian Tian, Etienne Tourigny, Petteri Uotila, Martin Vancoppenolle, Shiyu Wang, David Wårlind, Ulrika Willén, Klaus Wyser, Shuting Yang, Xavier Yepes-Arbós, and Qiong Zhang
Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, https://doi.org/10.5194/gmd-15-2973-2022, 2022
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The Earth system model EC-Earth3 is documented here. Key performance metrics show physical behavior and biases well within the frame known from recent models. With improved physical and dynamic features, new ESM components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.
Karol Kuliński, Gregor Rehder, Eero Asmala, Alena Bartosova, Jacob Carstensen, Bo Gustafsson, Per O. J. Hall, Christoph Humborg, Tom Jilbert, Klaus Jürgens, H. E. Markus Meier, Bärbel Müller-Karulis, Michael Naumann, Jørgen E. Olesen, Oleg Savchuk, Andreas Schramm, Caroline P. Slomp, Mikhail Sofiev, Anna Sobek, Beata Szymczycha, and Emma Undeman
Earth Syst. Dynam., 13, 633–685, https://doi.org/10.5194/esd-13-633-2022, https://doi.org/10.5194/esd-13-633-2022, 2022
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The paper covers the aspects related to changes in carbon, nitrogen, and phosphorus (C, N, P) external loads; their transformations in the coastal zone; changes in organic matter production (eutrophication) and remineralization (oxygen availability); and the role of sediments in burial and turnover of C, N, and P. Furthermore, this paper also focuses on changes in the marine CO2 system, the structure of the microbial community, and the role of contaminants for biogeochemical processes.
Matthias Gröger, Christian Dieterich, Cyril Dutheil, H. E. Markus Meier, and Dmitry V. Sein
Earth Syst. Dynam., 13, 613–631, https://doi.org/10.5194/esd-13-613-2022, https://doi.org/10.5194/esd-13-613-2022, 2022
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Atmospheric rivers transport high amounts of water from subtropical regions to Europe. They are an important driver of heavy precipitation and flooding. Their response to a warmer future climate in Europe has so far been assessed only by global climate models. In this study, we apply for the first time a high-resolution regional climate model that allow to better resolve and understand the fate of atmospheric rivers over Europe.
H. E. Markus Meier, Madline Kniebusch, Christian Dieterich, Matthias Gröger, Eduardo Zorita, Ragnar Elmgren, Kai Myrberg, Markus P. Ahola, Alena Bartosova, Erik Bonsdorff, Florian Börgel, Rene Capell, Ida Carlén, Thomas Carlund, Jacob Carstensen, Ole B. Christensen, Volker Dierschke, Claudia Frauen, Morten Frederiksen, Elie Gaget, Anders Galatius, Jari J. Haapala, Antti Halkka, Gustaf Hugelius, Birgit Hünicke, Jaak Jaagus, Mart Jüssi, Jukka Käyhkö, Nina Kirchner, Erik Kjellström, Karol Kulinski, Andreas Lehmann, Göran Lindström, Wilhelm May, Paul A. Miller, Volker Mohrholz, Bärbel Müller-Karulis, Diego Pavón-Jordán, Markus Quante, Marcus Reckermann, Anna Rutgersson, Oleg P. Savchuk, Martin Stendel, Laura Tuomi, Markku Viitasalo, Ralf Weisse, and Wenyan Zhang
Earth Syst. Dynam., 13, 457–593, https://doi.org/10.5194/esd-13-457-2022, https://doi.org/10.5194/esd-13-457-2022, 2022
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Based on the Baltic Earth Assessment Reports of this thematic issue in Earth System Dynamics and recent peer-reviewed literature, current knowledge about the effects of global warming on past and future changes in the climate of the Baltic Sea region is summarised and assessed. The study is an update of the Second Assessment of Climate Change (BACC II) published in 2015 and focuses on the atmosphere, land, cryosphere, ocean, sediments, and the terrestrial and marine biosphere.
Andreas Lehmann, Kai Myrberg, Piia Post, Irina Chubarenko, Inga Dailidiene, Hans-Harald Hinrichsen, Karin Hüssy, Taavi Liblik, H. E. Markus Meier, Urmas Lips, and Tatiana Bukanova
Earth Syst. Dynam., 13, 373–392, https://doi.org/10.5194/esd-13-373-2022, https://doi.org/10.5194/esd-13-373-2022, 2022
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The salinity in the Baltic Sea is not only an important topic for physical oceanography as such, but it also integrates the complete water and energy cycle. It is a primary external driver controlling ecosystem dynamics of the Baltic Sea. The long-term dynamics are controlled by river runoff, net precipitation, and the water mass exchange between the North Sea and Baltic Sea. On shorter timescales, the ephemeral atmospheric conditions drive a very complex and highly variable salinity regime.
Anna Rutgersson, Erik Kjellström, Jari Haapala, Martin Stendel, Irina Danilovich, Martin Drews, Kirsti Jylhä, Pentti Kujala, Xiaoli Guo Larsén, Kirsten Halsnæs, Ilari Lehtonen, Anna Luomaranta, Erik Nilsson, Taru Olsson, Jani Särkkä, Laura Tuomi, and Norbert Wasmund
Earth Syst. Dynam., 13, 251–301, https://doi.org/10.5194/esd-13-251-2022, https://doi.org/10.5194/esd-13-251-2022, 2022
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A natural hazard is a naturally occurring extreme event with a negative effect on people, society, or the environment; major events in the study area include wind storms, extreme waves, high and low sea level, ice ridging, heavy precipitation, sea-effect snowfall, river floods, heat waves, ice seasons, and drought. In the future, an increase in sea level, extreme precipitation, heat waves, and phytoplankton blooms is expected, and a decrease in cold spells and severe ice winters is anticipated.
H. E. Markus Meier, Christian Dieterich, Matthias Gröger, Cyril Dutheil, Florian Börgel, Kseniia Safonova, Ole B. Christensen, and Erik Kjellström
Earth Syst. Dynam., 13, 159–199, https://doi.org/10.5194/esd-13-159-2022, https://doi.org/10.5194/esd-13-159-2022, 2022
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In addition to environmental pressures such as eutrophication, overfishing and contaminants, climate change is believed to have an important impact on the marine environment in the future, and marine management should consider the related risks. Hence, we have compared and assessed available scenario simulations for the Baltic Sea and found considerable uncertainties of the projections caused by the underlying assumptions and model biases, in particular for the water and biogeochemical cycles.
Ole Bøssing Christensen, Erik Kjellström, Christian Dieterich, Matthias Gröger, and Hans Eberhard Markus Meier
Earth Syst. Dynam., 13, 133–157, https://doi.org/10.5194/esd-13-133-2022, https://doi.org/10.5194/esd-13-133-2022, 2022
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The Baltic Sea Region is very sensitive to climate change, whose impacts could easily exacerbate biodiversity stress from society and eutrophication of the Baltic Sea. Therefore, there has been a focus on estimations of future climate change and its impacts in recent research. Models show a strong warming, in particular in the north in winter. Precipitation is projected to increase in the whole region apart from the south during summer. New results improve estimates of future climate change.
Marcus Reckermann, Anders Omstedt, Tarmo Soomere, Juris Aigars, Naveed Akhtar, Magdalena Bełdowska, Jacek Bełdowski, Tom Cronin, Michał Czub, Margit Eero, Kari Petri Hyytiäinen, Jukka-Pekka Jalkanen, Anders Kiessling, Erik Kjellström, Karol Kuliński, Xiaoli Guo Larsén, Michelle McCrackin, H. E. Markus Meier, Sonja Oberbeckmann, Kevin Parnell, Cristian Pons-Seres de Brauwer, Anneli Poska, Jarkko Saarinen, Beata Szymczycha, Emma Undeman, Anders Wörman, and Eduardo Zorita
Earth Syst. Dynam., 13, 1–80, https://doi.org/10.5194/esd-13-1-2022, https://doi.org/10.5194/esd-13-1-2022, 2022
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As part of the Baltic Earth Assessment Reports (BEAR), we present an inventory and discussion of different human-induced factors and processes affecting the environment of the Baltic Sea region and their interrelations. Some are naturally occurring and modified by human activities, others are completely human-induced, and they are all interrelated to different degrees. The findings from this study can largely be transferred to other comparable marginal and coastal seas in the world.
Tobias Stacke and Stefan Hagemann
Geosci. Model Dev., 14, 7795–7816, https://doi.org/10.5194/gmd-14-7795-2021, https://doi.org/10.5194/gmd-14-7795-2021, 2021
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HydroPy is a new version of an established global hydrology model. It was rewritten from scratch and adapted to a modern object-oriented infrastructure to facilitate its future development and application. With this study, we provide a thorough documentation and evaluation of our new model. At the same time, we open our code base and publish the model's source code in a public software repository. In this way, we aim to contribute to increasing transparency and reproducibility in science.
Adrian Gustafson, Paul A. Miller, Robert G. Björk, Stefan Olin, and Benjamin Smith
Biogeosciences, 18, 6329–6347, https://doi.org/10.5194/bg-18-6329-2021, https://doi.org/10.5194/bg-18-6329-2021, 2021
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We performed model simulations of vegetation change for a historic period and a range of climate change scenarios at a high spatial resolution. Projected treeline advance continued at the same or increased rates compared to our historic simulation. Temperature isotherms advanced faster than treelines, revealing a lag in potential vegetation shifts that was modulated by nitrogen availability. At the year 2100 projected treelines had advanced by 45–195 elevational metres depending on the scenario.
Jenny Hieronymus, Kari Eilola, Malin Olofsson, Inga Hense, H. E. Markus Meier, and Elin Almroth-Rosell
Biogeosciences, 18, 6213–6227, https://doi.org/10.5194/bg-18-6213-2021, https://doi.org/10.5194/bg-18-6213-2021, 2021
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Dense blooms of cyanobacteria occur every summer in the Baltic Proper and can add to eutrophication by their ability to turn nitrogen gas into dissolved inorganic nitrogen. Being able to correctly estimate the size of this nitrogen fixation is important for management purposes. In this work, we find that the life cycle of cyanobacteria plays an important role in capturing the seasonality of the blooms as well as the size of nitrogen fixation in our ocean model.
Alexandra Pongracz, David Wårlind, Paul A. Miller, and Frans-Jan W. Parmentier
Biogeosciences, 18, 5767–5787, https://doi.org/10.5194/bg-18-5767-2021, https://doi.org/10.5194/bg-18-5767-2021, 2021
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This study shows that the introduction of a multi-layer snow scheme in the LPJ-GUESS DGVM improved simulations of high-latitude soil temperature dynamics and permafrost extent compared to observations. In addition, these improvements led to shifts in carbon fluxes that contrasted within and outside of the permafrost region. Our results show that a realistic snow scheme is essential to accurately simulate snow–soil–vegetation relationships and carbon–climate feedbacks.
Jens Daniel Müller, Bernd Schneider, Ulf Gräwe, Peer Fietzek, Marcus Bo Wallin, Anna Rutgersson, Norbert Wasmund, Siegfried Krüger, and Gregor Rehder
Biogeosciences, 18, 4889–4917, https://doi.org/10.5194/bg-18-4889-2021, https://doi.org/10.5194/bg-18-4889-2021, 2021
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Based on profiling pCO2 measurements from a field campaign, we quantify the biomass production of a cyanobacteria bloom in the Baltic Sea, the export of which would foster deep water deoxygenation. We further demonstrate how this biomass production can be accurately reconstructed from long-term surface measurements made on cargo vessels in combination with modelled temperature profiles. This approach enables a better understanding of a severe concern for the Baltic’s good environmental status.
Richard Essery, Hyungjun Kim, Libo Wang, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Bertrand Decharme, Emanuel Dutra, Xing Fang, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Anna Kontu, Gerhard Krinner, Matthieu Lafaysse, Yves Lejeune, Thomas Marke, Danny Marks, Christoph Marty, Cecile B. Menard, Olga Nasonova, Tomoko Nitta, John Pomeroy, Gerd Schädler, Vladimir Semenov, Tatiana Smirnova, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan
The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020, https://doi.org/10.5194/tc-14-4687-2020, 2020
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Climate models are uncertain in predicting how warming changes snow cover. This paper compares 22 snow models with the same meteorological inputs. Predicted trends agree with observations at four snow research sites: winter snow cover does not start later, but snow now melts earlier in spring than in the 1980s at two of the sites. Cold regions where snow can last until late summer are predicted to be particularly sensitive to warming because the snow then melts faster at warmer times of year.
Stelios Myriokefalitakis, Matthias Gröger, Jenny Hieronymus, and Ralf Döscher
Ocean Sci., 16, 1183–1205, https://doi.org/10.5194/os-16-1183-2020, https://doi.org/10.5194/os-16-1183-2020, 2020
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Global inorganic and organic nutrient deposition fields are coupled to PISCES to investigate their effect on ocean biogeochemistry. Pre-industrial deposition fluxes are lower compared to the present day, resulting in lower oceanic productivity. Future changes result in a modest decrease in the nutrients put into the global ocean. This work provides a first assessment of the atmospheric organic nutrients' contribution, highlighting the importance of their representation in biogeochemistry models.
Cited articles
Akhtar, N., Brauch, J., Dobler, A., Béranger, K., and Ahrens, B.: Medicanes in an ocean–atmosphere coupled regional climate model, Nat. Hazards Earth Syst. Sci., 14, 2189–2201, https://doi.org/10.5194/nhess-14-2189-2014, 2014.
Akhtar, N., Krug, A., Brauch, J., A., T., D., and Christian, A., B.: European Marginal Seas in a regional atmosphere–ocean coupled model and their impact on Vb-cyclones and associated precipitation, Clim. Dynam., 53, 5967–5984, https://doi.org/10.1007/s00382-019-04906-x, 2019.
Alari, V., Staneva, J., Breivik, Ø., Bidlot, J. R., Mogensen, K., and Janssen, P.,: Surface wave effects on water temperature in the baltic sea: simulations with the coupled nemo–wam model, Ocean Dynam., 66, 917–930, 2016.
Ali, A., Christensen, K. H., Breivik, Ø., Malila, M., Raj, R. P., Bertino, L., Chassignet, E. P., and Bakhoday-Paskyabi, M.: A comparison of langmuir turbulence parameterizations and key wave effects in a numerical model of the north atlantic and arctic oceans, Ocean Model., 137, 76–97, 2019.
Anderson, R. G., Canadell, J. G., Randerson, J. T., Jackson, R. B., Hungate, B. A., Baldocchi, D. D., Ban-Weiss, G. A., Bonan, G. B., Caldeira, K., Cao, L., Diffenbaugh, N. S., Gurney, K. R., Kueppers, L. M., Law, B. E., Luyssaert, S., and O'Halloran, T. L.: Biophysical considerations in forestry for climate protection, Front. Ecol. Environ., 9, 174–182, 2011.
Andreas, E. L., Mahrt, L., and Vickers, D.: An improved bulk air–sea surface flux algorithm, including spray-mediated transfer, Q. J. Roy. Meteor. Soc., 141, 642–654, 2015.
Argles, A. P. K., Moore, J. R., Huntingford, C., Wiltshire, A. J., Harper, A. B., Jones, C. D., and Cox, P. M.: Robust Ecosystem Demography (RED version 1.0): a parsimonious approach to modelling vegetation dynamics in Earth system models, Geosci. Model Dev., 13, 4067–4089, https://doi.org/10.5194/gmd-13-4067-2020, 2020.
Arheimer, B., Dahné, J., and Donnelly, C.: Climate change impact on riverine nutrient load and land-based remedial measures of the Baltic Sea Action Plan, Ambio, 41, 600–612, https://doi.org/10.1007/s13280-012-0323-0, 2012.
Axell, L. B.: Wind-driven internal waves and Langmuir circulations in a numerical ocean model of the southern Baltic Sea, J. Geophys. Res., 107, 3204, https://doi.org/10.1029/2001JC000922, 2002.
Babanin, A. V. and Haus, B. K.: On the existence of water turbulence induced by nonbreaking surface waves, J. Phys. Oceanogr., 39, 2675–2679, 2009.
Balaji, V., Anderson, J., Held, I., Winton, M., Durachta, J., Malyshev, S., and Stouffer, R. J.: The Exchange Grid: A mechanism for data exchange between Earth System components on independent grids, in: Parallel Computational Fluid Dynamics 2005, Elsevier, 179–186, https://doi.org/10.1016/B978-044452206-1/50021-5, 2006.
Balmaseda, M. A., Mogensen, K., and Weaver, A. T.: Evaluation of the ECMWF ocean reanalysis system ORAS4, Q. J. Roy. Meteor. Soc., 139, 1132–1161, https://doi.org/10.1002/qj.2063, 2013.
Bartosova A, Capell R., Olesen J. E., Jabloun M., Refsgaard J. C., Donnelly C., Hyytiäinen K., Pihlainen S., Zandersen M., and Arheimer B.: Future socioeconomic conditions may have larger impact on nutrient loads to the Baltic Sea than climate change, Ambio, 48, 1325, https://doi.org/10.1007/s13280-019-01243-5, 2019.
Bauer, T. P., Holtermann, P., Heinold, B., Radtke, H., Knoth, O., and Klingbeil, K.: ICONGETM v1.0 – flexible NUOPC-driven two-way coupling via ESMF exchange grids between the unstructured-grid atmosphere model ICON and the structured-grid coastal ocean model GETM, Geosci. Model Dev., 14, 4843–4863, https://doi.org/10.5194/gmd-14-4843-2021, 2021.
Beck, H., Zimmermann, N., McVicar, T., Hylke, E., Beck, H. E., Zimmermann, N., McVicar, T. R., Vergopolan, N.,
Berg, A., and Wood, E. F.: Present and future Köppen-Geiger climate classification maps at 1 km resolution, Sci. Data, 5, 180214, https://doi.org/10.1038/sdata.2018.214, 2018.
Belcher, S. E., Grant, A. L., Hanley, K. E., Fox-Kemper, B., Van Roekel, L., Sullivan, P. P., Large, W. G., Brown, A., Hines, A., Calvert, D., Rutgersson, A., Petersson, H., Bidlot, J.-R.,Janssen, P. A. E. M., and Polton, J. A.: A global perspective on langmuir turbulence in the ocean surface boundary layer, Geophys. Res. Lett., 39, https://doi.org/10.1029/2012GL052932, 2012.
Belušić, D., Fuentes-Franco, R., Strandberg, G., and Jukimenko, A.: Afforestation reduces cyclone intensity and precipitation extremes over Europé, Environ. Res. Lett., 14 074009, 2019.
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011.
Bitz, C. M., Holland, M. M., Weaver, A. J., and Eby, M.: Simulating the ice-thickness distribution in a coupled climate model, J. Geophys. Res., 106, 2441–2463, https://doi.org/10.1029/1999JC000113, 2001.
Christensen, O. B., Kjellström, E., and Zorita, E.: Projected Change – Atmosphere, in: Second Assessment of Climate Change for the Baltic Sea Basin. Regional Climate Studies, edited by: The BACC II Author Team, Springer, Cham, https://doi.org/10.1007/978-3-319-16006-1_11, 2015.
Christensen, O. B., Kjellström, E., Dieterich, C., Gröger, M., and Meier, H. E. M.: Atmospheric regional climate projections for the Baltic Sea Region until 2100, Earth Syst. Dynam. Discuss. [preprint], https://doi.org/10.5194/esd-2021-51, in review, 2021.
Boé, J., Somot, S., Corre, L., and Nabat, P.:. Large discrepancies in summer climate change over Europe as projected by global and regional climate models: causes and consequences, Clim. Dynam., 54, 2981–3002, https://doi.org/10.1007/s00382-020-05153-1, 2020.
Bonan, G. B.: Forests and climate change: Forcings, feedbacks, and the climate benefits of forests, Science, 320, 1444–1449, https://doi.org/10.1126/science.1155121, 2008.
Bouwman, A. F., Bierkens, M. F. P., Griffioen, J., Hefting, M. M., Middelburg, J. J., Middelkoop, H., and Slomp, C. P.: Nutrient dynamics, transfer and retention along the aquatic continuum from land to ocean: towards integration of ecological and biogeochemical models, Biogeosciences, 10, 1–22, https://doi.org/10.5194/bg-10-1-2013, 2013.
Bremicker, M.: Das Wasserhaushaltsmodell LARSIM – Modellgrundlagen und Anwendungsbeispiele, Freiburger Schriften zur Hydrologie, 11, Universität Freiburg im Breisgau, Freiburg im Breisgau, available at: https://www.hydrology.uni-freiburg.de/publika/FSH-Bd11-Bremicker.pdf (last access: 10 September 2021), 2000.
Cabos, W., de la Vara, A., Álvarez-García, F. J. Sanchez, E., Siek, K., Perez-Sanz, J. I., Limareva, N., and Sein, D. V.: Impact of ocean-atmosphere coupling on regional climate: the Iberian Peninsula case, Clim. Dynam., 54, 4441–4467, https://doi.org/10.1007/s00382-020-05238-x, 2020.
Champeaux, J. L., Masson, V., and Chauvin, F.: ECOCLIMAP: A global database of land surface parameters at 1 km resolution. Meteorol. Appl., 12, 29–32, https://doi.org/10.1017/S1350482705001519, 2005.
Chen, J., Brissette, F. P., Chaumont, D., and Braun, M.: Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America, Water Resour. Res., 49, 4187–4205, https://doi.org/10.1002/wrcr.20331, 2013.
Claussen, M.: Earth System Models, in: Understanding the Earth System, edited by: Ehlers, E. and Krafft, T., Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-56843-5_10, 2001.
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.
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.
Craig, P. D. and Banner, M. L.: Modeling wave-enhanced turbulence in the ocean surface layer, J. Phys. Oceanogr., 24, 2546–2559, 1994.
Daewel, U. and Schrum, C.: Low-frequency variability in North Sea and Baltic Sea identified through simulations with the 3-D coupled physical–biogeochemical model ECOSMO, Earth Syst. Dynam., 8, 801–815, https://doi.org/10.5194/esd-8-801-2017, 2017.
D'Asaro, E. A.: Turbulence in the upper-ocean mixed layer, Annu. Rev. Mar. Sci., 6, 101–115, 2014.
Davies, A. M. and Lawrence, J.: Modeling the effect of wave–current interaction on the three-dimensional wind-driven circulation of the eastern irish sea, J. Phys. Oceanogr., 25, 29–45, 1995.
Dell'Aquila, A., Calmanti, S., Ruti, P., Struglia, M. V., Pisacane, G., Carillo, A., and Sannino, G.: Effects of seasonal cycle fluctuations in an A1B scenario over the Euro-Mediterranean region. Clim. Res., 52, 135–157, https://doi.org/10.3354/cr01037, 2012.
Dieterich, C., Gröger, M., Arneborg, L., and Andersson, H. C.: Extreme sea levels in the Baltic Sea under climate change scenarios – Part 1: Model validation and sensitivity, Ocean Sci., 15, 1399–1418, https://doi.org/10.5194/os-15-1399-2019, 2019a.
Dieterich, C., Wang, S., Schimanke, S., Gröger, M., Klein, B., Hordoir, R., Samuelsson, P., Liu, Y., Axell, L., Höglund, A., and Meier, H. E. M.: Surface heat budget over the North Sea in climate change simulations, Atmosphere, 10, 272, https://doi.org/10.3390/atmos10050272, 2019b.
Donnelly, C., Andersson, J. C. M., and Arheimer, B.: Using flow signatures and catchment similarities to evaluate the e-hype multi-basin model across europe, Hydrol. Sci. J., 61, 255–273, https://doi.org/10.1080/02626667.2015.1027710, 2016.
Döscher, R. and Meier, H. E. M.: Simulated sea surface temperature and heat fluxes in different climates of the Baltic Sea, Ambio, 33, 242–248, 2004.
Döscher, R., Willen, U., Jones, C., Rutgersson, A., Meier, H. E. M., Hansson, U., and Graham, P.: The development of the regional coupled ocean-atmosphere model RCAO, Boreal Environ. Res., 7, 183–192, 2002.
ECMWF: IFS Documentation CY43r1, Part VII: ECMWF Wave Model, ECMWF Model Documentation, European Centre for Medium-Range Weather Forecasts, https://doi.org/10.21957/18mel2ooj, 2017.
Elizalde, A.: The water cycle in the Mediterranean region and the impacts of climate change, PhD thesis, Berichte zur Erdsystemforschung, 103, MPI for Meteorology, Hamburg, Germany, https://doi.org/10.17617/2.1216556, 2011.
Ellison, D., Morris, C. E., Locatelli, B., Sheil, D., Cohen, J., Murdiyarso, D., Gutierrez, V., van Noordwijk, M., Creed, I. F., Pokorny, J., Gaveau, D., Spracklen, D. V., Bargués Tobella, A., Ilstedt, U., Teuling, A. J., Gebreyohannis Gebrehiwot, S., Sands, D. C., Muys, B., Verbist, B., Springgay, E., Sugandi, Y., and Sullivan, C. A.: Trees, forests and water: cool insights for a hot world, Glob Environ. Chang., 43, 51–61, https://doi.org/10.1016/j.gloenvcha.2017.01.002, 2017.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Fang, Y., Zhang, Y., Tang, J., and Ren, X.: A regional air–sea coupled model and its application over East Asia in the summer of 2000, Adv. Atmos. Sci., 27, 583–593, https://doi.org/10.1007/s00376-009-8203-7, 2009.
Fiedler, S., Stevens, B., Gidden, M., Smith, S. J., Riahi, K., and van Vuuren, D.: First forcing estimates from the future CMIP6 scenarios of anthropogenic aerosol optical properties and an associated Twomey effect, Geosci. Model Dev., 12, 989–1007, https://doi.org/10.5194/gmd-12-989-2019, 2019.
Fiorino, M.: A multi-decadal daily sea-surface temperature and sea-ice con- centration data set for the ERA-40 reanalysis, ECMWF ERA-40 Project Report Series, 12, European Centre for Medium-Range Weather Forecasts, Shinfield, Reading, UK, available at: https://www.ecmwf.int/node/9396 (last access: 10 September 1021), 2004.
Fischer, E. and Schär, C.: Consistent geographical patterns of changes in high-impact European heatwaves, Nat. Geosci., 3, 398–403, https://doi.org/10.1038/ngeo866, 2010.
Fisher, R. A., Muszala, S., Verteinstein, M., Lawrence, P., Xu, C., McDowell, N. G., Knox, R. G., Koven, C., Holm, J., Rogers, B. M., Spessa, A., Lawrence, D., and Bonan, G.: Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes, CLM4.5(ED), Geosci. Model Dev., 8, 3593–3619, https://doi.org/10.5194/gmd-8-3593-2015, 2015.
Fisher, R. A., Koven, C. D., Anderegg, W. R. L., Christoffersen, B. O., Dietze, M. C., Farrior, C. E., Holm, J. A., Hurtt, G. C., Knox, R. G., Lawrence, P. J., Lichstein, J. W., Longo, M., Matheny, A. M., Medvigy, D., Muller-Landau, H. C., Powell, T. L., Serbin, S. P., Sato, H., Shuman, J. K., Smith, B., Trugman, A. T., Viskari, T., Verbeeck, H., Weng, E., Xu, C., Xu, X., Zhang, T., and Moorcroft, P. R.: Vegetation demographics in Earth System Models: A review of progress and priorities, Glob. Change Biol., 24, 35–54, https://doi.org/10.1111/gcb.13910, 2018.
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., Qin, D., Plattner, G.-K., Tignor, M., Allen, S., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 741–866, 2013.
Flato, G. M. and Hibler III., W. D.: Ridging and strength in modeling the thickness distribution of Arctic sea ice, J. Geophys. Res., 100, 18611–18626, 1995.
Gálos, B., Hänsler, A., Kindermann, G., Rechid, D., Sieck, K., and Jacob, D.: The role of forests in mitigating climate change – A case study for Europe, Acta Silv. Lign. Hung., 8, 87–102, http://pure.iiasa.ac.at/id/eprint/9930/1/The%20Role%20of%20 Forests%20in%20Mitigating%20Climate%20Change.pdf
(last access: 14 January 2021), 2012.
Gao, Y., Markkanen, T., Backman, L., Henttonen, H. M., Pietikäinen, J.-P., Mäkelä, H. M., and Laaksonen, A.: Biogeophysical impacts of peatland forestation on regional climate changes in Finland, Biogeosciences, 11, 7251–7267, https://doi.org/10.5194/bg-11-7251-2014, 2014.
Geyer, B., Ludwig, T. and von Storch, H: Limits of reproducibility and hydrodynamic noise in atmospheric regional modelling, Communications Earth and Environment, 2, 17, https://doi.org/10.1038/s43247-020-00085-4, 2021.
Giorgi, F.: Thirty years of regional climate modeling: Where are we and where are we going next?, J. Geophys. Res.-Atmos., 124, 5696–5723, https://doi.org/10.1029/2018JD030094, 2019.
Giorgi, F. and Bi, X.: A study of internal variability of a regional climate model, J. Geophys. Res., 105, 29503–29521, https://doi.org/10.1029/2000JD900269, 2000.
Giorgi, F. and Gao, X. J.: Regional earth system modeling: review and future direction, Atmospheric and Oceanic Science Letters, 11, 1674–2834, https://doi.org/10.1080/16742834.2018.1452520, 2018.
Gröger, M., Maier-Reimer, E., Mikolajewicz, U., Moll, A., and Sein, D.: NW European shelf under climate warming: implications for open ocean – shelf exchange, primary production, and carbon absorption, Biogeosciences, 10, 3767–3792, https://doi.org/10.5194/bg-10-3767-2013, 2013.
Gröger, M., Dieterich, C., Meier, H. E. M., and Schimanke, S.: Thermal air–sea coupling in hindcast simulations for the North Sea and Baltic Sea on the NW European shelf, Tellus A, 67, 1, https://doi.org/10.3402/tellusa.v67.26911, 2015.
Gröger, M., Arneborg, L., Dieterich, C., Höglund, A., and Meier, H. E. M.: Summer hydrographic changes in the Baltic Sea, Kattegat and Skagerrak projected in an ensemble of climate scenarios downscaled with a coupled regional ocean–sea ice–atmosphere model, Clim. Dynam., 53, 5945–5966 https://doi.org/10.1007/s00382-019-04908-9, 2019.
Gröger, M., Dieterich, C., and Meier, H. E. M.: Is interactive air sea coupling relevant for simulating the future climate of Europe?, Clim. Dynam., 56, 491–514, https://doi.org/10.1007/s00382-020-05489-8, 2021.
Guimberteau, M., Drapeau, G., Ronchail, J., Sultan, B., Polcher, J., Martinez, J.-M., Prigent, C., Guyot, J.-L., Cochonneau, G., Espinoza, J. C., Filizola, N., Fraizy, P., Lavado, W., De Oliveira, E., Pombosa, R., Noriega, L., and Vauchel, P.: Discharge simulation in the sub-basins of the Amazon using ORCHIDEE forced by new datasets, Hydrol. Earth Syst. Sci., 16, 911–935, https://doi.org/10.5194/hess-16-911-2012, 2012.
Gustafsson, B. G., Schenk, F., Blenckner, T., Eilola, K., Meier, H. E. M., Müller-Karulis, B., Neumann, T., Ruoho-Airola, T., Savchuk, O. P., and Zorita, E.: Reconstructing the development of Baltic Sea eutrophication 1850–2006, Ambio, 41, 534–548, https://doi.org/10.1007/s13280-012-0317-y, 2012.
Gustafsson, N., Nyberg, L., and Omstedt, A.: Coupling of a High-Resolution Atmospheric Model and an Ocean Model for the Baltic Sea, Mon. Weather Rev., 126, 2822–2846, 1998.
Haapala, J., Meier, H. E. M., and Rinne, J.: Numerical Investigations of Future Ice Conditions in the Baltic Sea, Ambio, 30, 237–2004, 2001.
Haapala, J., Lönnroth, N., and Stössel, A.: A numerical study of open water formation in sea ice, J. Geophys. Res., 110, C09011, https://doi.org/10.1029/2003JC002200, 2005.
Hagemann, S. and Dümenil, L.: A parametrization of the lateral waterflow for the global scale, Clim. Dynam., 14, 17–31, https://doi.org/10.1007/s003820050205, 1998.
Hagemann, S., Stacke, T., and Ho-Hagemann, H. T. M.: High resolution discharge simulations over Europe and the Baltic Sea catchment, Front. Earth Sci., 8, https://doi.org/10.3389/feart.2020.00012, 2020.
Hall, A.: Projecting regional change, Science, 346, 1461–1462, https://doi.org/10.1126/science.aaa0629, 2014.
Hanke, M., Redler, R., Holfeld, T., and Yastremsky, M.: YAC 1.2.0: new aspects for coupling software in Earth system modelling, Geosci. Model Dev., 9, 2755–2769, https://doi.org/10.5194/gmd-9-2755-2016, 2016.
Hantson, S., Knorr, W., Schurgers, G., Pugh, T. A. M., and Arneth, A.: Global isoprene and monoterpene emissions under changing climate, vegetation, CO2 and land use, Atmos. Environ., 155, 35–45, https://doi.org/10.1016/j.atmosenv.2017.02.010, 2017.
Haverd, V., Smith, B., Cook, G. D., Briggs, P. R., Nieradzik, L., Roxburgh, S. H., Liedloff, A., Meyer, C. P., and Canadell, J. G.: A stand-alone tree demography and landscape structure module for Earth system models, Geophys. Res. Lett., 40, 5234–5239, 2013.
Haverd, V., Smith, B., Nieradzik, L. P., and Briggs, P. R.: A stand-alone tree demography and landscape structure module for Earth system models: integration with inventory data from temperate and boreal forests, Biogeosciences, 11, 4039–4055, https://doi.org/10.5194/bg-11-4039-2014, 2014.
Haverd, V., Smith, B., Nieradzik, L., Briggs, P. R., Woodgate, W., Trudinger, C. M., Canadell, J. G., and Cuntz, M.: A new version of the CABLE land surface model (Subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis, Geosci. Model Dev., 11, 2995–3026, https://doi.org/10.5194/gmd-11-2995-2018, 2018.
Hay, L. E., Wilby, R. L., and Leavesley, G. H.: A Comparison of delta change and downscaled GCM sceanrios for three mountainous basins in the United States, J. Am. Water Resour. As., 36, 387–397, https://doi.org/10.1111/j.1752-1688.2000.tb04276.x, 2000.
Heinze, C., Eyring, V., Friedlingstein, P., Jones, C., Balkanski, Y., Collins, W., Fichefet, T., Gao, S., Hall, A., Ivanova, D., Knorr, W., Knutti, R., Löw, A., Ponater, M., Schultz, M. G., Schulz, M., Siebesma, P., Teixeira, J., Tselioudis, G., and Vancoppenolle, M.: ESD Reviews: Climate feedbacks in the Earth system and prospects for their evaluation, Earth Syst. Dynam., 10, 379–452, https://doi.org/10.5194/esd-10-379-2019, 2019.
Heinze, R., Dipankar, A., Henken, C. C., Moseley, C., Sourdeval, O., Trömel, S., Xie, X., Adamidis, P., Ament, F., Baars, H., Barthlott, C., Behrendt, A., Blahak, U., Bley, S., Brdar, S., Brueck, M., Crewell, S., Deneke, H., Di Girolamo, P., Evaristo, R., Fischer, J., Frank, C., Friederichs, P., Göcke, T., Gorges, K., Hande, L., Hanke, M., Hansen, A., Hege, H.-C., Hoose, C., Jahns, T., Kalthoff, N., Klocke, D., Kneifel, S., Knippertz, P., Kuhn, A., van Laar, T., Macke, A., Maurer, V., Mayer, B., Meyer, C. I., Muppa, S. K., Neggers, R. A. J., Orlandi, E., Pantillon, F., Pospichal, B., Röber, N., Scheck, L., Seifert, A., Seifert, P., Senf, F., Siligam, P., Simmer, C., Steinke, S., Stevens, B., Wapler, K., Weniger, M., Wulfmeyer, V., Zängl, G., Zhang, D., and Quaas, J.:, Large-eddy simulations over Germany using ICON: a comprehensive evaluation, Q. J. Roy. Meteor. Soc., 143, 69–100, https://doi.org/10.1002/qj.2947, 2017.
Hertig, E. and Jacobeit, J.: Variability of weather regimes in the North Atlantic-European area: past and future, Atmos. Sci. Lett., 15, 314–320, https://doi.org/10.1002/asl2.505, 2014.
Hibler, W. D.: A dynamic thermodynamic sea ice model, J. Phys. Oceanogr., 9, 815–846, 1979.
Hibler, W. D.: Modeling a variable thickness ice cover, Mon. Weather Rev., 108, 1943–1973, 1980.
Hickler, T., Vohland, K., Feehan, J., Miller, P. A., Smith, B., Costa, L., Giesecke, T., Fronzek, S., Carter, T. R., Cramer, W., Kühn, I., and Sykes, M. T.: Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species-based dynamic vegetation model, Global Ecol. Biogeogr., 21, 50–63, https://doi.org/10.1111/j.1466-8238.2010.00613.x, 2012.
Hill, C., DeLuca, C., Balaji, V., Suarez, M., and da Silva, A.: Archi-
tecture of the Earth System Modeling Framework, Comput. Sci.
Eng., 6, 18–28, 2004.
Ho-Hagemann, H. T. M. and Rockel, B.: Einfluß von Atmosphäre-Ozean Wechselwirkungen auf Starkniederschläge über Europ, in: Warnsignal Klima: Extremereignisse, edited by: Lozìn, J. L., Breckle, S.-W., Graßl, H., Kasang, D., and Weisse, R., 161–168, available at: https://www.klima-warnsignale.uni-hamburg.de/wp-content/uploads/pdf/de/extremereignisse/warnsignal_klima-extremereignisse-kapitel-4_2.pdf (last access: 13 September 2021), 2018.
Ho-Hagemann, H. T. M., Hagemann, S., and Rockel, B.: On the role of soil moisture in the generation of heavy rainfall during the Oder flood event in July 1997, Tellus A, 67, 28661, https://doi.org/10.3402/tellusa.v67.28661, 2015.
Ho-Hagemann, H. T. M., Gröger, M., Rockel, B., Zahn, M., Geyer, B., and Meier, H. E. M.: Effects of air–sea coupling over the North Sea and the Baltic Sea on simulated summer precipitation over Central Europe, Clim. Dynam., 49, 3851–3876, https://doi.org/10.1007/s00382-017-3546-8, 2017.
Ho-Hagemann, H. T. M., Hagemann, S., Grayek, S., Petrik, R., Rockel, B., Staneva, J., Feser, F., and Schrum, C.: Internal model variability in the regional coupled system model GCOAST-AHOI, Atmosphere, 11, 227, https://doi.org/10.3390/atmos11030227, 2020.
Holt, J., Schrum, C., Cannaby, H., Daewel, U., Allen, J. I., Artioli, Y., Bopp, L., Butenschon, M., Fach, B. A., Harle, J., Pushpadas, D., Salihoglu, B., and Wakelin, S.: Potential impacts of climate change on the primary production of regional seas: a comparative analysis of five European seas, Prog. Oceanogr., 140, 91–115, https://doi.org/10.1016/j.pocean.2015.11.004, 2016.
Hordoir, R. and Meier, H. E. M.: Freshwater fluxes in the Baltic Sea: A model study, J. Geophys. Res., 115, C08028, https://doi.org/10.1029/2009jc005604, 2010.
Hordoir, R., Axell, L., Höglund, A., Dieterich, C., Fransner, F., Gröger, M., Liu, Y., Pemberton, P., Schimanke, S., Andersson, H., Ljungemyr, P., Nygren, P., Falahat, S., Nord, A., Jönsson, A., Lake, I., Döös, K., Hieronymus, M., Dietze, H., Löptien, U., Kuznetsov, I., Westerlund, A., Tuomi, L., and Haapala, J.: Nemo-Nordic 1.0: a NEMO-based ocean model for the Baltic and North seas – research and operational applications, Geosci. Model Dev., 12, 363–386, https://doi.org/10.5194/gmd-12-363-2019, 2019.
Hundecha, Y., Arheimer, B., Donnelly, C., and Pechlivanidis, I.: A regional parameter estimation scheme for a pan-European multi-basin model, J. Hydrol., 6, 90–111, https://doi.org/10.1016/j.ejrh.2016.04.002, 2016.
Hunke, E. C. and J. K. Dukowicz: An elastic-viscous-plastic model for sea ice dynamics, J. Phys. Oceanogr., 27, 1849–1867, 1997.
Hurrell, J. W.: Decadal trends in the North Atlantic Oscillation: Regional temperatures and precipitation, Science, 269, 676–679, https://doi.org/10.1126/science.269.5224.676, 1995.
Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B. L., Calvin, K., Doelman, J. C., Fisk, J., Fujimori, S., Klein Goldewijk, K., Hasegawa, T., Havlik, P., Heinimann, A., Humpenöder, F., Jungclaus, J., Kaplan, J. O., Kennedy, J., Krisztin, T., Lawrence, D., Lawrence, P., Ma, L., Mertz, O., Pongratz, J., Popp, A., Poulter, B., Riahi, K., Shevliakova, E., Stehfest, E., Thornton, P., Tubiello, F. N., van Vuuren, D. P., and Zhang, X.: Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6, Geosci. Model Dev., 13, 5425–5464, https://doi.org/10.5194/gmd-13-5425-2020, 2020.
IPCC: 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, 1535 pp., 2013.
IPCC: Summary for Policymakers, in: Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems, edited by: Shukla, P. R., Skea, J., Calvo Buendia, E., Masson-Delmotte, V., Pörtner, H.-O., Roberts, D. C., Zhai, P., Slade, R., Connors, S., van Diemen, R., Ferrat, M., Haughey, E., Luz, S., Neogi, S., Pathak, M., Petzold, J., Portugal Pereira, J., Vyas, P., Huntley, E., Kissick, K., Belkacemi, M., Malley, J., available at: https://www.ipcc.ch/site/assets/uploads/sites/4/2020/02/SPM_Updated-Jan20.pdf (last access: 10 September 2021), 2019.
Jacob, D.: A note to the simulation of the annual and inter-annual variability of the water budget over the Baltic Sea drainage basin, Meteorol. Atmos. Phys., 77, 61–73, https://doi.org/10.1007/s007030170017, 2001.
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer, L. M., Braun, A., Colette, A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner, N., Kotlarski, S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D., Rounsevell, M., Samuelsson, P., Somot, S., Soussana, J.-F., Teichmann, C., Valentini, R., Vautard, R., Weber, B., and Yiou, P.: EURO-CORDEX: new high-resolution climate change projections for European impact research, Reg. Environ. Change, 14, 563–578, https://doi.org/10.1007/s10113-013-0499-2, 2014.
Jacob, D., Teichmann, C., Sobolowski, S., Katragkou, E., Anders, I., Belda, M., Benestad, R., Boberg, F., Buonomo, E., Cardoso, R. M., Casanueva, A., Christensen, O. B., Christensen, J. H., Coppola, E., De Cruz, L., Davin, E. L., Dobler, A., Domínguez, M., Fealy, R., Fernandez, J., Gaertner, M. A., García-Díez, M., Giorgi, F., Gobiet, A., Goergen, K., Gómez-Navarro, J. J., Alemán, J. J. G., Gutiérrez, C., Gutiérrez, J. M., Güttler, I., Haensler, A., Halenka, T., Jerez, S., Jiménez-Guerrero, P., Jones, R. G., Keuler, K., Kjellström, E., Knist, S., Kotlarski, S., Maraun, D., van Meijgaard, E., Mercogliano, P., Montávez, J. P., Navarra, A., Nikulin, G., de Noblet-Ducoudré, N., Panitz, H.-J., Pfeifer, S., Piazza, M., Pichelli, E., Pietikäinen, J.-P., Prein, A. F.,Preuschmann, S., Rechid, D., Rockel, B., Romera, R., Sánchez, E., Sieck, K., Soares, P. M. M., Somot, S., Srnec, L., Sørland, S. L., Termonia, P., Truhetz, H., Vautard, R., Warrach-Sagi, K., and Wulfmeyer, V.: Regional climate downscaling over Europe: perspectives from the EURO-CORDEX community, Reg. Environ. Change, 20, 51, https://doi.org/10.1007/s10113-020-01606-9, 2020.
Jacob, R., Larson, J., and Ong, E.: MxN Communication and Parallel Interpolation in CCSM3 Using the Model
Coupling Toolkit, Int. J. High Perf. Comp. App., 19, 293–307, https://doi.org/10.1177/1094342005056116, 2005.
Jakacki, J. and Meler, S.: An evaluation and implementation of the regional coupled ice-ocean model of the Baltic Sea, Ocean Dynam., 69, 1–19, https://doi.org/10.1007/s10236-018-1219-8, 2018.
Janecki, M., Nowicki, A., Kańska, A., Golenko, M., and Dzierzbicka-Głowacka, L.: Numerical simulations of sea-ice conditions in the Baltic Sea for 2010–2016 winters using the 3D CEMBS model, Pol. Marit. Res., 25, 35–43, https://doi.org/10.2478/pomr-2018-0094, 2018.
Janssen, P. A. and Viterbo, P.: Ocean waves and the atmospheric climate, J. Climate, 9, 1269–1287, 1996.
Jeworrek, J., Wu, L., Dieterich, C., and Rutgersson, A.: Characteristics of convective snow bands along the Swedish east coast, Earth Syst. Dynam., 8, 163–175, https://doi.org/10.5194/esd-8-163-2017, 2017.
Jöckel, P., Kerkweg, A., Pozzer, A., Sander, R., Tost, H., Riede, H., Baumgaertner, A., Gromov, S., and Kern, B.: Development cycle 2 of the Modular Earth Submodel System (MESSy2), Geosci. Model Dev., 3, 717–752, https://doi.org/10.5194/gmd-3-717-2010, 2010.
Jones, C. D., Arora, V., Friedlingstein, P., Bopp, L., Brovkin, V., Dunne, J., Graven, H., Hoffman, F., Ilyina, T., John, J. G., Jung, M., Kawamiya, M., Koven, C., Pongratz, J., Raddatz, T., Randerson, J. T., and Zaehle, S.: C4MIP – The Coupled Climate–Carbon Cycle Model Intercomparison Project: experimental protocol for CMIP6, Geosci. Model Dev., 9, 2853–2880, https://doi.org/10.5194/gmd-9-2853-2016, 2016.
Jones, P. W.: First- and Second-Order Conservative Remapping Schemes for Grids in Spherical Coordinates, Mon. Weather Rev., 127, 2204–2210, 1999.
Jungclaus, J. H., Keenlyside, N., Botzet, M., Haak, H., Luo, J.-J., Latif, M., Marotzke, J., Mikolajewicz, U., and Roeckner, E.: Ocean circulation and tropical variability in the coupled model ECHAM5/MPI-OM, J. Climate, 19, 3952, https://doi.org/10.1175/JCLI3827.1, 2006.
Kelemen, F. D., Primo, C., Feldmann, H., and Ahrens, B.: Added Value of atmosphere–ocean Coupling in a Century-Long Regional Climate Simulation, Atmosphere, 10, 537, https://doi.org/10.3390/atmos10090537, 2019.
Kerkweg, A., Hofmann, C., Jöckel, P., Mertens, M., and Pante, G.: The on-line coupled atmospheric chemistry model system MECO(n) – Part 5: Expanding the Multi-Model-Driver (MMD v2.0) for 2-way data exchange including data interpolation via GRID (v1.0), Geosci. Model Dev., 11, 1059–1076, https://doi.org/10.5194/gmd-11-1059-2018, 2018.
Kerr, R.: Forecasting Regional Climate Change Flunks Its First Test, Science, 339, 6120, https://doi.org/10.1126/science.339.6120.638, 2013.
Kjellström, E., Döscher, R., and Meier, H. E. M.: Atmospheric response to different sea surface temperatures in the Baltic Sea: coupled versus uncoupled regional climate model experiments, Hydrol. Res., 36, 397–409, https://doi.org/10.2166/nh.2005.0030, 2005.
Kjellström, E., Bärring, L., Nikulin, G., Nilsson, C., Persson, G., and Strandberg, G.: Production and use of regional climate model projections – a Swedish perspective on building climate services, Climate Services, 2–3, 15–29, 2016.
Kjellström, E., Nikulin, G., Strandberg, G., Christensen, O. B., Jacob, D., Keuler, K., Lenderink, G., van Meijgaard, E., Schär, C., Somot, S., Sørland, S. L., Teichmann, C., and Vautard, R.: European climate change at global mean temperature increases of 1.5 and 2 ∘C above pre-industrial conditions as simulated by the EURO-CORDEX regional climate models, Earth Syst. Dynam., 9, 459–478, https://doi.org/10.5194/esd-9-459-2018, 2018.
Knudsen, M.:. Ein hydrographischer Lehrsatz, Annalen der Hydrographie und Maritimen Meteorologie, 28, 316–320, 1900.
Koca, D., Smith, B., and Sykes, M. T.: Modelling regional climate change effects on potential natural ecosystems in Sweden, Climate Change, 78, 381–406, https://doi.org/10.1007/s10584-005-9030-1, 2006.
Köppen, W.: Die Klimate der Erde, Walter de Gruyter, Berlin, available at: http://koeppen-geiger.vu-wien.ac.at/pdf/Koppen_1936.pdf (last access: 10 September 2021), 1923.
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.
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World Map of the Köppen-Geiger, climate classification updated, Meteorol. Z., 15, 259–263, https://doi.org/10.1127/0941-2948/2006/0130, 2006.
Koven, C. D., Knox, R. G., Fisher, R. A., Chambers, J. Q., Christoffersen, B. O., Davies, S. J., Detto, M., Dietze, M. C., Faybishenko, B., Holm, J., Huang, M., Kovenock, M., Kueppers, L. M., Lemieux, G., Massoud, E., McDowell, N. G., Muller-Landau, H. C., Needham, J. F., Norby, R. J., Powell, T., Rogers, A., Serbin, S. P., Shuman, J. K., Swann, A. L. S., Varadharajan, C., Walker, A. P., Wright, S. J., and Xu, C.: Benchmarking and parameter sensitivity of physiological and vegetation dynamics using the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) at Barro Colorado Island, Panama, Biogeosciences, 17, 3017–3044, https://doi.org/10.5194/bg-17-3017-2020, 2020.
Kudryavtsev, V. N. and Makin, V. K.: Impact of ocean spray on the dynamics of the marine atmospheric boundary layer, Bound.-Lay. Meteorol., 140, 383–410, 2011.
Kulmala, M., Nieminen, T., Nikandrova, A., Lehtipalo, K., Manninen, H. E., Kajos, M. K., Kolari, P., Lauri, A., Petaja, T., Krejci, R., Hansson, H.-C., Swietlicki, E., Lindroth, A., Christensen, T., Arneth, A., Hari, P., Back, J., Vesala, T., and Kerminen, V.-M.: CO2-induced terrestrial climate feedback mechanism: From carbon sink to aerosol source and back, Boreal Environ. Res., 19, 122–131, 2014.
Kumkar, Y., Astrup, R., Stordal, F., and Bright, R. M.: Quantifying Regional Surface Energy Responses to Forest Structural Change in Nordic Fennoscandia, J. Geophys. Res.-Atmos., 125, 17, https://doi.org/10.1029/2019jd032092, 2020.
Lagergren, F. and Jönsson, A. M.: Ecosystem model analysis of multi-use forestry in a changing climate, Ecosyst. Serv., 26, 209–224, https://doi.org/10.1016/j.ecoser.2017.06.007, 2017.
Larsen, M. A. D., Refsgaard, J. C., Drews, M., Butts, M. B., Jensen, K. H., Christensen, J. H., and Christensen, O. B.: Results from a full coupling of the HIRHAM regional climate model and the MIKE SHE hydrological model for a Danish catchment, Hydrol. Earth Syst. Sci., 18, 4733–4749, https://doi.org/10.5194/hess-18-4733-2014, 2014.
Larsén, X. G., Du, J., Bolaños, R., Imberger, M., Kelly, M. C., Badger, M., and Larsen, S.: Estimation of offshore extreme wind from wind-wave coupled modeling, Wind Energy, https://doi.org/10.1002/we.2339, 2019.
Larson, J., Jacob, R., and Ong, E.: The Model Coupling Toolkit: A New Fortran90 Toolkit for Building
Multiphysics Parallel Coupled Models, Int. J. High Perf. Comp. App., 19, 277–292,
https://doi.org/10.1177/1094342005056116, 2005.
Lawrence, D. M., Oleson, K. W., Flanner, M. G., Thornton, P. E., Swenson, S. C., Lawrence, P. J., Zeng, X., Yang, Z.-L., Levis, S., Sakaguchi, K., Bonan, G. B., and Slater, A. G.: Parameterization improvements and functional and structural advances in version 4 of the community land model, J. Adv. Model. Earth Sy., 3, M03001, https://doi.org/10.1029/2011MS00045, 2011.
Lee, J.-W., Hong, S.-Y., Kim, J.-E. E., Yoshimura, K., Ham, S., and Joh, M.: Development and implementation of river-routing process module in a regional climate model and its evaluation in korean river basins, J. Geophys. Res.-Atmos., 120, 4613–4629, https://doi.org/10.1002/2014JD022698, 2015.
Lehmann, A. and Hinrichsen, H.-H.: On the thermohaline variability of the Baltic Sea, J. Marine Syst., 25, 333–357, https://doi.org/10.1016/s0924-7963(00)00026-9, 2000.
Lehmann, A. and Myrberg, K.: Upwelling in the baltic sea review, J. Marine Syst., 74, S3–S12, 2008.
Lemieux J. F., Dupont, F., Blaine, P., Roy, F., Smith, G. C., and Flato, G. M.: Improving the simulation of landfast ice by combining tensile strength and a parameterization for grounded ridges, J. Geophys. Res.-Oceans, 121, 7354–7368, 2016.
Lemmen, C., Hofmeister, R., Klingbeil, K., Nasermoaddeli, M. H., Kerimoglu, O., Burchard, H., Kösters, F., and Wirtz, K. W.: Modular System for Shelves and Coasts (MOSSCO v1.0) – a flexible and multi-component framework for coupled coastal ocean ecosystem modelling, Geosci. Model Dev., 11, 915–935, https://doi.org/10.5194/gmd-11-915-2018, 2018.
Lengaigne, M., Madec, G., Bopp, L., Menkes, C., Aumont, O., and Cadule, P.: Bio-physical feedbacks in the Arctic Ocean using an Earth system model, Geophys. Res. Lett., 36, L21602, https://doi.org/10.1029/2009GL040145, 2009.
Leppäranta, M. and Myrberg, K.: Physical Oceanography of the Baltic Sea, Geophysical Sciences, Springer-Verlag, Berlin, Heidelberg, 378 pp., https://doi.org/10.1007/978-3-540-79703-6, 2009.
Leppäranta, M., Sun, Y., and Haapala, J.: Comparisons of Sea-Ice Velocity Fields from ERS-1 SAR and a dynamic model, J. Glaciol., 44, 248–262, https://doi.org/10.3189/S0022143000002598, 1998.
Li, L. Z. X.: Atmospheric GCM response to an idealized anomaly of the Mediterranean sea surface temperature, Clim. Dynam., 27, 543–552, https://doi.org/10.1007/s00382-006-0152-6, 2006.
Liepert, B. G. and Lo, F.: CMIP5 update of “Inter-model variability and biases of the global water cycle in CMIP3 coupled climate models”, Environ. Res. Lett., 8, 029401, https://doi.org/10.1088/1748-9326/8/2/029401, 2013.
Lind, P. and Kjellström, E.: Water budget in the Baltic Sea drainage basin: Evaluation of simulated fluxes in a regional climate model, Boreal Environ. Res., 14, 56–67, 2008.
Lindeskog, M., Arneth, A., Bondeau, A., Waha, K., Seaquist, J., Olin, S., and Smith, B.: Implications of accounting for land use in simulations of ecosystem carbon cycling in Africa, Earth Syst. Dynam., 4, 385–407, https://doi.org/10.5194/esd-4-385-2013, 2013.
Liu, L., Yang, G., Wang, B., Zhang, C., Li, R., Zhang, Z., Ji, Y., and Wang, L.: C-Coupler1: a Chinese community coupler for Earth system modeling, Geosci. Model Dev., 7, 2281–2302, https://doi.org/10.5194/gmd-7-2281-2014, 2014.
Lorenz, P. and Jacob, D.: BALTIMOS – a coupled modelling system for the Baltic Sea and its drainage basin, Theor. Appl. Climatol., 118, 715, https://doi.org/10.1007/s00704-014-1276-y, 2014.
Luyssaert, S., Jammet, M., Stoy, P. C., Estel, S., Pongratz, J., Ceschia, E., Churkina, G., Don, A., Erb, K.-H., Ferlicoq, M., Gielen, B., Grünwald, T., Houghton, R. A., Klumpp, K., Knohl, A., Kolb, T., Kuemmerle, T., Laurila, T., Lohila, A., Loustau, D., McGrath, M. J., Meyfroidt, P., Moors, E. J., Naudts, K., Novick, K., Otto, J., Pilegaard, K., Pio, C. A., Rambal, S., Rebmann, C., Ryder, J., Suyker, A. E., Varlagin, A., Wattenbach, M., and Dolman, A. J.: Land management and land-cover change have impacts of similar magnitude on surface temperature, Nat. Clim. Change, 4, 389–393, https://doi.org/10.1038/nclimate2196, 2014.
Luyssaert, S., Marie, G., Valade, A., Chen, Y.-Y., Djomo, S. N., Ryder, J., Otto, J., Naudts, K., Lansø, A. S., Ghattas, J., and McGrath, M. J.: Trade-offs in using European forests to meet climate objectives, Nature, 562, 259–262, https://doi.org/10.1038/s41586-018-0577-1, 2018.
Mahmood, R., Pielke Sr., R. A.,, Hubbard, K. G., Niyogi, D., Dirmeyer, P. A., McAlpine, C., Carleton, A. M., Hale, R., Gameda, S., Beltrán-Przekurat, A., Baker, B., McNider, R., Legates, D. R., Shepherd, M., Du, J., Blanken, P. D., Frauenfeld, O. W., Nair, U., and Fall, S.: Land cover changes and their biogeophysical effects on climate, Int. J. Climatol., 34, 929–953, https://doi.org/10.1002/joc.3736, 2014.
Mathis, M., Elizalde, A., and Mikolajewicz, U.: Which complexity of regional climate system models is essential for downscaling anthropogenic climate change in the Northwest European Shelf?, Clim. Dynam., 50, 2637–2659, https://doi.org/10.1007/s00382-017-3761-3, 2018.
Mauser, W. and Bach, H.: PROMET – Large scale distributed hydrological modelling to study the impact of climate change on the water flows of mountain watersheds, J. Hydrol., 376, 362–377, https://doi.org/10.1016/j.jhydrol.2009.07.046, 2009.
May, W., Ganske, A., Leckebusch, G. C., Rockel, B., Tinz, B., and Ulbrich, U.: Projected Change – Atmosphere, North Sea Region Climate Change Assessment, Part of the series Regional Climate Studies, Springer, Berlin Heidelberg, 149–173, https://doi.org/10.1007/978-3-319-39745-0_5, 2016.
May, W., Miller, P. A., and Smith, B.: The importance of land–atmosphere biophysical interactions for regional climate and terrestrial ecosystem change: Improved understanding to inform Swedish national climate action, CEC Synthesis Report no. 5, Centre for Environmental and Climate Research, Lund University, 152, available at: https://www.cec.lu.se/sv/sites/cec.lu.se.sv/files/2020-12/CEC{%}20synthesis{%}20no{%}205%202020_TG2%20%28tillg{%}C3%A4nglighetsanpassad{%}20pdf{%}29.pdf (last access: 22 February 2021), 2020.
Maykut, G. A. and Untersteiner, N.: Some results from a time-dependent thermodynamic model of sea ice, J. Geophys. Res., 76, 1550–1575, https://doi.org/10.1029/JC076i006p01550, 1971.
McWilliams, J. C. and Sullivan, P. P.: Vertical mixing by langmuir circulations, Spill Sci. Technol. B., 6, 225–237, 2008.
Meier, H. E. M.: Regional ocean climate simulations with a 3D ice-ocean model for the Baltic Sea: 1. Model experiments and results for temperature and salinity, Clim. Dynam., 19, 237–253, 2002a.
Meier, H. E. M.: Regional ocean climate simulations with a 3D ice-ocean model for the Baltic Sea: 2. Results for sea ice, Clim. Dynam., 19, 255–266, https://doi.org/10.1007/s00382-001-0225-5, 2002b.
Meier, H. E. M.: Projected change – Marine Physics, Second Assessment of Climate Change for the Baltic Sea Basin, Part of the series Regional Climate Studies, 243–252, https://doi.org/10.1007/978-3-319-16006-1_13, 2015.
Meier, H. E. M. and Döscher, R.: Simulated water and heat cycles of the Baltic Sea using a 3D coupled atmosphere-ice-ocean model, Boreal Environ. Res., 7, 327–334, 2002.
Meier, H. E. M. and Kauker, F.: Modeling decadal variability of the Baltic Sea: 2. Role of freshwater inflow and large-scale atmospheric circulation for salinity, J. Geophys. Res., 108, 3368, https://doi.org/10.1029/2003JC001799, 2003.
Meier, H. E. M., Döscher, R., and Halkka, A.: Simulated distributions of Baltic seaice in warming climate and consequences for the winter habitat of the Baltic ringed seal, Ambio, 33, 249–256, 2004.
Meier, H. E. M., Höglund, A., Döscher, R., Andersson, A., Löptien, U., and Kjellström, E.: Quality assessment of atmospheric surface fields over the Baltic Sea of an ensemble of regional climate model simulations with respect to ocean dynamics, Oceanologia, 53, 193–227, 2011.
Meier, H. E. M., Edman, M., Eilola, K., Placke, M., Neumann, T., Andersson, H., Brunnabend, S.-E., Dieterich, C., Frauen, C., Friedland, R., Gröger, M., Gustafsson, B., Gustafsson, E., Isaev, A., Kniebusch, M., Kuznetsov, I., Müller-Karulis, B., Omstedt, A., Ryabchenko, V., Saraiva, S., and Savchuk, O. P.: Assessment of eutrophication abatement scenarios for the Baltic Sea by multi-model ensemble simulations, Frontiers in Marine Science, 5, 440, https://doi.org/10.3389/fmars.2018.00440, 2018.
Meier, H. E. M., Dieterich, C., Eilola, K. Gröger, M., Höglund, A., Radtke, H., Saraiva, S., and Wåhlström, I.: Future projections of record-breaking sea surface temperature and cyanobacteria bloom events in the Baltic Sea, Ambio, 48, 1362–1376, https://doi.org/10.1007/s13280-019-01235-5, 2019a.
Meier, H. E. M., Edman, M., Eilola, K., Placke, M., Neumann, T., Andersson, H., Brunnabend, S., Dieterich, C., Frauen, C., Friedland, R., Gröger, M.,Gustafsson, B., Gustafsson, E., Isaev, A., Kniebusch, M., Kuznetsov, I., Müller-Karulis, B., Naumann, M., Omstedt, A., Ryabchenko, V., Saraiva, S., and Savchuk, O.: Assessment of uncertainties in scenario simulations of biogeochemical cycles in the Baltic Sea, Frontiers in Marine Science, 6, 46, https://doi.org/10.3389/fmars.2019.00046, 2019b.
Meier, H. E. M., Dieterich, C., Gröger, M., Dutheil, C., Börgel, F., Safonova, K., Christensen, O. B., and Kjellström, E.: Oceanographic regional climate projections for the Baltic Sea until 2100, Earth Syst. Dynam. Discuss. [preprint], https://doi.org/10.5194/esd-2021-68, in review, 2021.
Melville, W. K., Veron, F., and White, C. J.: The velocity field under breaking waves: coherent structures and turbulence, J. Fluid Mech., 454, 203–233, 2002.
Metzger, M. J., Bunce, R. G. H., Jongman, R. H. G., Mücher, C. A., and Watkins, J. W.: A climatic stratification of the environment of Europe, Glob. Ecol. Biogeogr., 14, 549–563, https://doi.org/10.1111/j.1466-822X.2005.00190.x, 2005.
Mikolajewicz, U., Sein, D. V., Jacob, D., Konigk, T., Podzun, R., and Semmler, T.: Simulating Arctic sea ice variability with a coupled regional atmosphere–ocean–sea ice model, Meteorol. Z., 14, 793–800, https://doi.org/10.1127/0941-2948/2005/0083, 2005.
Miller, P. A., Giesecke, T., Hickler, T., Bradshaw, R. H. W., Smith, B., Seppä, H., Valdes, P. J., and Sykes, M. T.: Exploring climatic and biotic controls on Holocene vegetation change in Fennoscandia, J. Ecol., 96, 247–259, https://doi.org/10.1111/j.1365-2745.2007.01342.x, 2008.
Milly, P. C. D., Malyshev, S. L., Shevliakova, E., Dunne, K. A., Findell, K. L., Gleeson, T., Liang, Z., Phillipps, P., Stouffer, R. J., and Svenson, S: An enhanced model of land water and energy for global hydrologic and earth-system studies, J. Hydrometeorol., 15, 1739–1761, https://doi.org/10.1175/jhm-d-13-0162.1, 2014.
Moorcroft, P. R., Hurtt, G. C., and Pacala. S. W.: A method for scaling vegetation dynamics: the ecosystem demography model ED, Ecol. Monogr., 71, 557–586, https://doi.org/10.1890/0012-9615(2001)071[0557:AMFSVD]2.0.CO;2, 2001.
Nguyen-Quang, T., Polcher, J., Ducharne, A., Arsouze, T., Zhou, X., Schneider, A., and Fita, L.: ORCHIDEE-ROUTING: revising the river routing scheme using a high-resolution hydrological database, Geosci. Model Dev., 11, 4965–4985, https://doi.org/10.5194/gmd-11-4965-2018, 2018.
Nilsson, E. O., Rutgersson, A., Smedman, A. S., and Sullivan, P. P.: Convective boundary-layer structure in the presence of wind-following swell, Q. J. Roy. Meteor. Soc., 138, 1476–1489, https://doi.org/10.1002/qj.1898, 2012.
Oikkonen, A., Haapala, J., Lensu, M., Karvonen, J., and Itkin, P.: Small-scale sea ice deformation during N-ICE2015: From compact pack ice to marginal ice zone, J. Geophys. Res.-Oceans, 122, 5105–5120, https://doi.org/10.1002/2016JC012387, 2017.
Oki, T. and Sud, Y. C.: Design of total runoff integrating pathways (TRIP) – A global river channel network, Earth Interact., 2, 1–37, https://doi.org/10.1175/1087-3562(1998)002<0001:dotrip>2.3.co;2, 1998.
Olin, S., Lindeskog, M., Pugh, T. A. M., Schurgers, G., Wårlind, D., Mishurov, M., Zaehle, S., Stocker, B. D., Smith, B., and Arneth, A.: Soil carbon management in large-scale Earth system modelling: implications for crop yields and nitrogen leaching, Earth Syst. Dynam., 6, 745–768, https://doi.org/10.5194/esd-6-745-2015, 2015a.
Olin, S., Schurgers, G., Lindeskog, M., Wårlind, D., Smith, B., Bodin, P., Holmér, J., and Arneth, A.: Modelling the response of yields and tissue C:N to changes in atmospheric CO2 and N management in the main wheat regions of western Europe, Biogeosciences, 12, 2489–2515, https://doi.org/10.5194/bg-12-2489-2015, 2015b.
Österblom, H., Hansson, S., Larsson, U., Hjerne, O., Wulff, F., Elmgren, R., and Folke, C.: Human-induced trophic cascades and ecological regime shifts in the Baltic Sea, Ecosystems, 10, 877–889, https://doi.org/10.1007/s10021-007-9069-0, 2007.
Palosuo, E.: The Gulf of Bothnia in winter. II. Freezing and ice forms, Merentutkimuslaitoksen Julkaisu/Havsforskningsinstitutets Skrift, 208, Finnish Institute of Marine Research, Helsinki, 1963.
Pemberton, P., Löptien, U., Hordoir, R., Höglund, A., Schimanke, S., Axell, L., and Haapala, J.: Sea-ice evaluation of NEMO-Nordic 1.0: a NEMO–LIM3.6-based ocean–sea-ice model setup for the North Sea and Baltic Sea, Geosci. Model Dev., 10, 3105–3123, https://doi.org/gmd-10-3105-2017, 2017.
Penduff, T., Sérazin, G., Leroux, S., Close, S.,Molines, J.-M., Barnier, B., Bessières, L., Terray, L., and Maze, G.: Chaotic variability of ocean heat content: climate-relevant features and observational implications, Oceanography, 31, 63–71, 2018.
Perlin, N., Skyllingstad, E. D., Samelson, R. M., and Barbour, P. L.: Numerical simulation of Air –Sea Coupling during Coastal Upwelling, Am. Met. Soc., 37, 2081–2093, 2007.
Perugini, L., Caporaso, L., Marconi, S., Cescatti, A., Quesada, B., de Noblet-Ducoudré, N., House, J. I., and Arneth, A.: Biophysical effects on temperature and precipitation due to land cover change, Environ. Res. Lett., 12, 053002, https://doi.org/10.1088/1748-9326/aa6b3f, 2017.
Pielke Sr., R. A., Pitman, A., Niyogi, D., Mahmood, R., McAlpine, C., Hossain, F., Goldewijk, K. K., Nair, U., Betts, R., Fall, S., Reichstein, M., Kabat, P., and de Noblet, N.: Land use/land cover changes and climate: modeling analysis and observational evidence, WiRes Clim. Change, 2, 828–850, https://doi.org/10.1002/wcc.144, 2011.
Placke, M., Meier, H. E. M., and Neumann, T.: Sensitivity of the Baltic Sea overturning circulation to long-term atmospheric and hydrological changes, J. Geophys. Res.-Oceans, 126, e2020JC016079, https://doi.org/10.1029/2020JC016079, 2021.
Polton, J. A., Lewis, D. M., and Belcher, S. E.: The role of wave-induced coriolis–stokes forcing on the wind-driven mixed layer, J. Phys. Oceanogr., 35, 444–457, https://doi.org/10.1175/JPO2701.1, 2005.
Prentice, I. C., Bondeau, A., Cramer, W., Harrison, S. P., Hickler, T., Lucht, W., Sitch, S., Smith, B., and Sykes, M. T.: Dynamic global vegetation modelling: quantifying terrestrial ecosystem responses to large-scale environmental change, in: Terrestrial Ecosystems in a Changing World, edited by: Canadell, J. G., Pataki, D. E., and Pitelka, L. F., 175–192, Springer, Heidelberg, https://doi.org/10.1007/978-3-540-32730-1_15, 2007.
Primo, C., Kelemen, F. D., Feldmann, H., Akhtar, N., and Ahrens, B.: A regional atmosphere–ocean climate system model (CCLMv5.0clm7-NEMOv3.3-NEMOv3.6) over Europe including three marginal seas: on its stability and performance, Geosci. Model Dev., 12, 5077–5095, https://doi.org/10.5194/gmd-12-5077-2019, 2019.
Pugh, T. A. M., Lindeskog, M., Smith, B., Poulter, B., Arneth, A., Haverd, V., and Calle, L.: Role of forest regrowth in global carbon sink dynamics, P. Natl. Acad. Sci. USA, 116, 4382–4387, https://doi.org/10.1073/pnas.1810512116, 2019.
Qiao, F., Yuan, Y., Yang, Y., Zheng, Q., Xia, C., and Ma, J.: Wave-induced mixing in the upper ocean: Distribution and application to a global ocean circulation model, Geophys. Res. Lett., 31, L11303, https://doi.org/10.1029/2004GL019824, 2004.
Qiao, W., Wu, L., Song, J., Li, X., Qiao, F., and Rutgersson, A.: Momentum flux balance at the air–sea interface, J. Geophys. Res.-Oceans, 126, e2020JC016563, https://doi.org/10.1029/2020JC016563, 2021.
Radtke, H., Brunnabend, S.-E., Gräwe, U., and Meier, H. E. M.: Investigating interdecadal salinity changes in the Baltic Sea in a 1850–2008 hindcast simulation, Clim. Past, 16, 1617–1642, https://doi.org/10.5194/cp-16-1617-2020, 2020.
Räisänen, J. Hansson, U., Ullerstig, A., Döscher, R., Graham, L., Jones, C., Meier, H. E. M., Samuelsson, P., and Willén, U.: European climate in the late twenty-first century: Regional simulations with two driving global models and two forcing scenarios, Clim. Dynam., 22, 13–31, https://doi.org/10.1007/s00382-003-0365-x, 2004.
Ratnam, J. V., Giorgi, F., Kaginalkar, A. S., and Cozzini, S.: Simulation of the Indian monsoon using the RegCM3-ROMS regional coupled model, Clim. Dynam., 33, 119–139, 2009.
Reckermann, M., Omstedt, A., Soomere, T., Aigars, J., Akhtar, N., Bełdowska, M., Bełdowski, J., Cronin, T., Czub, M., Eero, M., Hyytiäinen, K. P., Jalkanen, J.-P., Kiessling, A., Kjellström, E., Kuliński, K., Larsén, X. G., McCrackin, M., Meier, H. E. M., Oberbeckmann, S., Parnell, K., Pons-Seres de Brauwer, C., Poska, A., Saarinen, J., Szymczycha, B., Undeman, E., Wörman, A., and Zorita, E.: Human impacts and their interactions in the Baltic Sea region, Earth Syst. Dynam. Discuss. [preprint], https://doi.org/10.5194/esd-2021-54, in review, 2021.
Roeckner, E., Bäuml, G., Bonaventura, L., Brokopf, R., Esch, M., Giorgetta, M., Hagemann, S.,
Kirchner, I.,
Kornblueh, L.,
Manzini, E., and
Schulzweida, U.: The atmospheric general circulation model ECHAM 5. PART I: Model description. Max Planck Institute for Meteor. Rep. 349, MPI for Meteorology, Hamburg, Germany, available at: http://hdl.handle.net/11858/00-001M-0000-0012-0144-5 (last access: 10 Sptember 2021), 2003.
Ronkainen, I., Lehtiranta, J., Lensu, M., Rinne, E., Haapala, J., and Haas, C.: Interannual sea ice thickness variability in the Bay of Bothnia, The Cryosphere, 12, 3459–3476, https://doi.org/10.5194/tc-12-3459-2018, 2018.
Rousi, E., Rust, H. W., Ulbrich, U., and Anagnostopoulou, C.: Implications of Winter NAO Flavors on Present and Future European Climate, Climate, 8, 13, https://doi.org/10.3390/cli8010013, 2020.
Rousset, C., Vancoppenolle, M., Madec, G., Fichefet, T., Flavoni, S., Barthélemy, A., Benshila, R., Chanut, J., Levy, C., Masson, S., and Vivier, F.: The Louvain-La-Neuve sea ice model LIM3.6: global and regional capabilities, Geosci. Model Dev., 8, 2991–3005, https://doi.org/10.5194/gmd-8-2991-2015, 2015.
Rutgersson, A., Nilsson, E., and Kumar, R.: Introducing surface waves in a coupled wave-atmosphere regional climate model: Impact on atmospheric mixing length, J. Geophys. Res.-Oceans, 117, C00J15, https://doi.org/10.1029/2012JC007940, 2012.
Rutgersson, A., Kjellström, E., Haapala, J., Stendel, M., Danilovich, I., Drews, M., Jylhä, K., Kujala, P., Guo Larsén, X., Halsnæs, K., Lehtonen, I., Luomaranta, A., Nilsson, E., Olsson, T., Särkkä, J., Tuomi, L., and Wasmund, N.: Natural Hazards and Extreme Events in the Baltic Sea region, Earth Syst. Dynam. Discuss. [preprint], https://doi.org/10.5194/esd-2021-13, in review, 2021.
Samuelsson, P., Jones, C. G., Willen, U., Ullerstig, A., Gollvik, S., Hansson, U., Jansson, Kjellström, C., Nikulin, G., and Wyser, K.: The Rossby Centre Regional Climate model RCA3: model description and performance, Tellus A, 63, 4–23, https://doi.org/10.1111/j.1600-0870.2010.00478.x, 2011.
Saraiva, S., Meier, H. E. M., Andersson, H., Höglund, A., Dieterich, C., Hordoir, R., and Eilola, K.: Baltic Sea ecosystem response to various nutrient load scenarios in present and future climates, Clim. Dynam., 52, 3369, https://doi.org/10.1007/s00382-018-4330-0, 2019a.
Saraiva, S., Meier, H. E. M., Andersson, H. C., Höglund, A., Dieterich, C., Gröger, M., Hordoir, R., and Eilola, K.: Uncertainties in projections of the Baltic Sea ecosystem driven by an ensemble of global climate models, Front. Earth Sci., 6, 244, https://doi.org/10.3389/feart.2018.00244, 2019b.
Sato, H., Itoh, A., and Kohyama, T.: SEIB-DGVM: A new Dynamic Global Vegetation Model using a spatially explicit individual-based approach, Ecol. Model., 200, 279–307, 2007.
Scaife, A. A., Folland, C. K., Alexander, L. V., Moberg, A., and Knight, J. R.: European climate extremes and the North Atlantic Oscillation, J. Climate, 21, 72–83, https://doi.org/10.1175/2007JCLI1631.1, 2008.
Schrum, C.: Regional climate modeling and air–sea coupling, Climate Science: Oxford Research Encyclopedias, https://doi.org/10.1093/acrefore/9780190228620.013.3, 2017.
Sein, D. V., Koldunov, N. V., Pinto, J. G., and Cabos, W: Sensitivity of simulated regional Arctic climate to the choice of coupled model domain, Tellus A, 66, 23966, https://doi.org/10.3402/tellusa.v66.23966, 2014.
Sein, D. V., Mikolajewicz, U., Gröger, M., Fast, I., Cabos, W., Pinto, J. G., Hagemann S., Semmler T., Izquierdo A, and Jacob D.: Regionally coupled atmosphere–ocean–sea ice–marine biogeochemistry model ROM: 1. description and validation, J. Adv. Model. Earth Sy., 7, 268–304, https://doi.org/10.1002/2014ms000357, 2015.
Sein, D. V., Gröger, M., Cabos, W., Alvarez, F., Hagemann, S., de la Vara, A., Pinto, J. G., Izquierdo, A., Koldunov, N. V., Dvornikov, A. Y., Limareva, N., Martinez, 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.
Sein, D. V., Dvornikov, A. Y., Martyanov, S. D., Cabos, W., Ryabchenko, V. A., Gröger, M., Jacob, D., Kumar Mishra, A., and Kumar, P.: Indian ocean marine biogeochemical variability and its feedback on simulated South Asia climate, Earth Syst. Dynam. Discuss. [preprint], https://doi.org/10.5194/esd-2021-64, in review, 2021.
Senatore, A., Mendicino, G., Gochis, D. J., Yu, W., Yates, D. N., and Kunstmann, H.: Fully coupled atmosphere-hydrology simulations for the central Mediterranean: Impact of enhanced hydrological parameterization for short and long time scales, J. Adv. Model. Earth Sy., 7, 1693–1715, https://doi.org/10.1002/2015ms000510, 2015.
Sevault, F., Somot, S., Alias, A., Dubois, C., Lebeaupin-Brossier, C., Nabat, P., Adloff, F., Deque, M., and Decharmes, 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.
Shrestha, P., Sulis, M., Masbou, M., Kollet, S., and Simmer, C.: A scale-consistent terrestrial systems modeling platform based on COSMO, CLM, and ParFlow, Mon. Weather Rev., 142, 3466–3483, https://doi.org/10.1175/mwr-d-14-00029.1, 2014.
Sieck, K. and Jacob, D.: Influence of the boundary forcing on the internal variability of a regional climate model, American Journal of Climate Change, 5, 373–382, https://doi.org/10.4236/ajcc.2016.53028, 2016.
Sitz, L. E., Sante, F. D., Farneti, R., Fuentes-Franco, R., Coppola, E., Mariotti, L., Reale, M., Sannino, G., Barreiro, M., Nogherotto, R., Giuliani, G., Graffino, G., Solidoro, C., Cossarini, G., and Giorgi, F.: Description and evaluation of the earth system regional climate model (Reg CM-ES), J. Adv. Model. Earth Sy., 9, 1863–1886, https://doi.org/10.1002/2017ms000933, 2017.
Smith, B., Prentice, I. C., and Sykes M. T.: Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space, Global Ecol. Biogeogr., 10, 621–637, 2001.
Smith, B., Samulelsson, P., Wramneby, A., and Rummukainen, M.: A model of the coupled dynamics of climate, vegetation and terrestrial ecosystem biogeochemistry for regional applications, Tellus A, 63, 87–106, https://doi.org/10.1111/j.1600-0870.2010.00477.x, 2011.
Smith, B., Wårlind, D., Arneth, A., Hickler, T., Leadley, P., Siltberg, J., and Zaehle, S.: Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model, Biogeosciences, 11, 2027–2054, https://doi.org/10.5194/bg-11-2027-2014, 2014.
Smith, E. T. and Sheridan, S. C.: Where do cold air outbreaks occur, and how have they changed over time? Geophys. Res. Lett., 47, e2020GL086983, https://doi.org/10.1029/2020GL086983, 2020.
Somot, S., Sevault, F., Déque, M., and Crépon, M.: 21st century climate change scenario for the Mediterranean using a coupled atmosphere ocean regional climate model, Global Planet. Change, 63, 112–126, https://doi.org/10.1016/j.gloplacha.2007.10.003, 2008.
Soto-Navarro, J., Jordá, G., Amores, A. W. 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.
Squire, V. A.: A fresh look at how ocean waves and sea ice interact, Philos. T. R. Soc. A, 376, https://doi.org/10.1098/rsta.2017.0342, 2018.
Staneva, J., Wahle, K., Günther, H., and Stanev, E.: Coupling of wave and circulation models in coastal–ocean predicting systems: a case study for the German Bight, Ocean Sci., 12, 797–806, https://doi.org/10.5194/os-12-797-2016, 2016.
Staneva, J., Alari, V., Breivik, Ø., Bidlot, J. R., and Mogensen, K.: Effects of wave-induced forcing on a circulation model of the North Sea, Ocean Dynam., 67, 81–101, https://doi.org/10.3390/w13040415, 2017.
Stevens, B., Fiedler, S., Kinne, S., Peters, K., Rast, S., Müsse, J., Smith, S. J., and Mauritsen, T.: MACv2-SP: a parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6, Geosci. Model Dev., 10, 433–452, https://doi.org/10.5194/gmd-10-433-2017, 2017.
Stokes, G. G.: On the theory of oscillatory waves, Transactions of the Cambridge Philosophical Society, 1880.
Strandberg, G. and Kjellström, E.: Climate impacts from afforestation and deforestation in Europe, Earth Interact. 23, 1–27, https://doi.org/10.1175/EI-D-17-0033.1, 2019.
Strandberg, G., Kjellström, E., Poska, A., Wagner, S., Gaillard, M.-J., Trondman, A.-K., Mauri, A., Davis, B. A. S., Kaplan, J. O., Birks, H. J. B., Bjune, A. E., Fyfe, R., Giesecke, T., Kalnina, L., Kangur, M., van der Knaap, W. O., Kokfelt, U., Kuneš, P., Latałowa, M., Marquer, L., Mazier, F., Nielsen, A. B., Smith, B., Seppä, H., and Sugita, S.: Regional climate model simulations for Europe at 6 and 0.2 k BP: sensitivity to changes in anthropogenic deforestation, Clim. Past, 10, 661–680, https://doi.org/10.5194/cp-10-661-2014, 2014.
Strandberg, G., Bärring, L., Hansson, U., Jansson, C., Jones, C., Kjellström, E., Kolax, M., Kupiainen, M., Nikulin, G., Samuelsson, P., Ullerstig A., and Wang, S.: CORDEX scenarios for Europe from the Rossby Centre regional climate model RCA4, SMHI Meteorology and Climatology Rep., 116, 84, 2015.
Stuecker, M. F., Bitz, C. M., Armour, K. C., Proistosescu, C., Kang, S. M., Xie, S. P., Kim, D., McGregor, S., Zhang, W., Sen Zhao, Cai, W., Dong Y., and Jin, F. F.: Polar amplification dominated by local forcing and feedbacks, Nat. Clim. Change, 8, 1076–1081, https://doi.org/10.1038/s41558-018-0339-y, 2018.
Suzuki, N. and Fox-Kemper, B.: Understanding stokes forces in the wave-averaged equations, J. Geophys. Res.-Oceans, 121, 3579–3596, 2016.
Tang, J., Yurova, A. Y., Schurgers, G., Miller, P. A., Olin, S.,Smith, B., Siewert, M. B., Olefeldt, D., Pilesjö, P., and Poska, A.: Drivers of dissolved organic carbon export in a subarctic catchment: Importance of microbial decomposition, sorption-desorption, peatland and lateral flow, Sci. Total Environ., 622–623, 260–274, https://doi.org/10.1016/j.scitotenv.2017.11.252, 2018.
Teichmann, C., Bülow, K., Otto, J., Pfeifer, S., Rechid, D., Sieck, K., and Jacob, D.: Avoiding Extremes: Benefits of Staying below +1.5 ∘C Compared to +2.0 ∘C and +3.0 ∘C Global Warming, Atmosphere, 9, 115, https://doi.org/10.3390/atmos9040115, 2018.
Thorndike, A. S., Rothrock, D. A., Maykut, G. A., and Colony, R.: The thickness distribution of sea ice, J. Geophys. Res., 80, 4501–4513, https://doi.org/10.1029/JC080i033p04501, 1975.
Thurow, F.: Estimation of the total fish biomass in the Baltic Sea during the 20th century, ICES J. Mar. Sci., 54, 444–461, https://doi.org/10.1006/jmsc.1996.0195, 1997.
Tian, T., Boberg, F., Christensen, O. B., Hesselbjerg Christensen, J., She, J., and Vihma, T.: Resolved complex coastlines and land–sea contrasts in a high-resolution regional climate model: a comparative study using prescribed and modelled SSTs, Tellus A, 65, 19951, https://doi.org/10.3402/tellusa.v65i0.19951, 2013.
Uppala, S. M., KÅllberg, P. W., Simmons, A. J., Andrae, U., Bechtold, V. D. C., Fiorino, M., Gibson, J. K., Haseler, J., Hernandez, A., Kelly, G. A., Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R. P., Andersson, E., Arpe, K., Balmaseda, M. A., Beljaars, A. C. M., Berg, L. V. D., Bidlot, J., Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher, M., Fuentes, M., Hagemann, S., Hólm, E., Hoskins, B. J., Isaksen, L., Janssen, P. A. E. M., Jenne, R., Mcnally, A. P., Mahfouf, J.-F., Morcrette, J.-J., Rayner, N. A., Saunders, R. W., Simon, P., Sterl, A., Trenberth, K. E., Untch, A., Vasiljevic, D., Viterbo, P., and Woollen, J.: The ERA-40 re-analysis, Q. J. Roy. Meteor. Soc., 131, 2961–3012, https://doi.org/10.1256/qj.04.176, 2005.
Valcke, S., Craig, T., and Coquart, L.: OASIS3-MCT User Guide, OASIS3-MCT 3.0, Tech. Rep., 1875, CERFACS/CNRS SUCURA, Toulouse, France, available at: https://www.cerfacs.fr/oa4web/oasis3-mct_3.0/oasis3mct_UserGuide.pdf (last access: 10 September 2021), 2015.
Väli, G., Meier, H. E. M., and Elken, J.: Simulated halocline variability in the Baltic Sea and its impact on hypoxia during 1961–2007, J. Geophys. Res.-Oceans, 118, 6982–7000, https://doi.org/10.1002/2013jc009192, 2013.
Vancoppenolle, M., Fichefet, T., Goosse, H., Bouillon, S., Madec, G., and Maqueda, M. A.: Simulating the mass balance and salinity of Arctic and Antarctic sea ice: I. Model description and val-idation, Ocean Model., 27, 33–53, https://doi.org/10.1016/j.ocemod.2008.10.005, 2009.
Van Pham, T., Brauch, J., Dieterich, C., Früh, B., and Ahrens, B.: New coupled atmosphere–ocean-ice system COSMO-CLM/NEMO: Assessing air temperature sensitivity over the North and Baltic Seas, Oceanologia, 56, 167–189, https://doi.org/10.5697/oc.56-2.167, 2014.
Vidale, P. L., Lüthi, D., Frei, C., Seneviratne, S. I., and Schär, C.: Predictability and uncertainty in a regional climate model, J. Geophys. Res., 108, 4586, https://doi.org/10.1029/2002JD002810, 2003.
Vogel, M. M., Orth, R., Cheruy, F., Hagemann, S., Lorenz, R., van den Hurk, B. J. J. M., and Seneviratne, S. I.: Regional amplification of projected changes in extreme temperatures strongly controlled by soil moisture-temperature feedbacks, Geophys. Res. Lett., 44, 1511– 1519, https://doi.org/10.1002/2016GL071235, 2017.
von Storch, H., Langenberg, H., and Feser, F.: A Spectral Nudging Technique for Dynamical Downscaling Purposes, Mon. Weather Rev., 128, 3664–3673, https://doi.org/10.1175/1520-0493(2000)128<3664:ASNTFD>2.0.CO;2, 2000.
Wahle, K., Staneva, J., Koch, W., Fenoglio-Marc, L., Ho-Hagemann, H., and Stanev, E. V.: An atmosphere–wave regional coupled model: improving predictions of wave heights and surface winds in the southern north sea, Ocean Sci., 13, 289–301, 2017.
Wang, S., Dieterich, C., Döscher, R., Höglund, A., Hordoir, R., Meier, H. E. M., Samuelsson, P., and Schimanke, S.: Development and evaluation of a new regional coupled atmosphere–ocean model in the North Sea and Baltic Sea, Tellus A, 67, 1, https://doi.org/10.3402/tellusa.v67.24284, 2015.
Warner, J. C., Sherwood, C. R., Signell, R. P., Harris, C. K., and Arango, H. G.: Development of a three-dimensional, regional, coupled wave, current, and sediment-transport model, Comput. Geosci., 34, 1284–1306, 2008.
Warner, J. C., Ganju, N. K., Sherwood, C. R., Tarandeep, K., Aretxabaleta, A., He, R., Zambon, J., and Kumar, N.: Coupled-Ocean-Atmosphere-Wave-Sediment Transport (COAWST) Modeling System: US Geological Survey Software Release, https://doi.org/10.5066/P9NQUAOW, 2020.
Wei, J., Malanotte-Rizzoli, P., Eltahir, E. A. B., Xue, P., and Xu, D.: Coupling of a regional atmospheric model (RegCM3) and a regional oceanic model (FVCOM) over the maritime continent, Clim. Dynam., 43, 1575–1594, 2014.
Weisse, R. and Feser, F.:, Evaluation of a method to reduce uncertainty in wind hindcasts performed with regional atmosphere models, Coast. Eng., 48, 211–225, https://doi.org/10.1016/S0378-3839(03)00027-9, 2003.
Weisse, R., Heyen, H., and von Storch, H.: Sensitivity of a regional atmospheric model to a sea statedependent roughness and the need for ensemble calculations, Mon. Weather Rev., 128, 3631–3642, 2000.
Wiese, A., Staneva, J., Ho-Hagemann, H. T. M., Grayek, S., Koch, W., and Schrum, C.: Internal Model Variability of Ensemble Simulations With a Regional Coupled Wave-Atmosphere Model GCOAST, Frontiers in Marine Science, 7, 596843, https://doi.org/10.3389/fmars.2020.596843, 2020.
Wilcke, R. A. I. and Bärring, L.: Selecting regional climate scenarios for impact modelling studies, Environ. Modell. Softw., 78, 191–201 https://doi.org/10.1016/j.envsoft.2016.01.002, 2016.
Winckler J., Reick C. H., Bright R. M., and Pongratz J.: Importance of surface roughness for the local biogeophysical effects of deforestation, J. Geophys. Res., 124, 8605–8616, https://doi.org/10.1029/2018JD030127, 2019.
Wramneby, A., Smith, B., and Samuelsson, P.: Hot spots of vegetation-climate feedbacks under future greenhouse forcing in Europe, J. Geophys. Res., 115, D21119, https://doi.org/10.1029/2010JD014307, 2010.
Wu, L., Rutgersson, A., Sahlée, E., and Larsén, X. G.: The impact of waves and sea spray on modelling storm track and development, Tellus A, 67, 27967, https://doi.org/10.3402/tellusa.v67.27967, 2015.
Wu, L., Rutgersson, A., Sahlée, E., and Guo Larsén, X.: Swell impact on wind stress and atmospheric mixing in a regional coupled atmosphere-wave model, J. Geophys. Res.-Oceans, 121, 4633–4648, 2016.
Wu, L., Rutgersson, A., and Nilsson, E.: Atmospheric boundary layer turbulence closure scheme for wind-following swell conditions, J. Atmos. Sci. 74, 2363–2382, 2017a.
Wu, L., Sproson, D., Sahlée, E., and Rutgersson, A.: Surface wave impact when simulating midlatitude storm development, J. Atmos. Ocean. Tech. 34, 233–248, 2017b.
Wu, L., Breivik, Ø., and Rutgersson, A.: Ocean-wave-atmosphere interaction processes in a fully coupled modelling system, J. Adv. Model. Earth Sy., 11, 3852–3874, https://doi.org/10.1029/2019MS001761, 2019a.
Wu, L., Staneva, J., Breivik, Ø., Rutgersson, A., Nurser, A. G., Clementi, E., and Madec, G.: Wave effects on coastal upwelling and water level, Ocean Model., 140, 101405, https://doi.org/10.1016/j.ocemod.2019.101405, 2019b.
Xie, S. P. and Philander, S. G. H.: A coupled ocean-atmosphere model of relevance to the ITCZ in the eastern Pacific, Tellus A, 46, 340–350, 1994.
Yang, B., Zhang, Y., Qian, Y, Song, F., Leung, L. R., Wu, P., Guo, Z., and Huang, A.: Better monsoon precipitation in coupled climate models due to bias compensation, Clim. Atmos. Sci., 2, 43, https://doi.org/10.1038/s41612-019-0100-x, 2019.
Zhang, W., Jansson, C., Miller, P. A., Smith, B., and Samuelsson, P.: Biogeophysical feedbacks enhance the Arctic terrestrial carbon sink in regional Earth system dynamics, Biogeosciences, 11, 5503–5519, https://doi.org/10.5194/bg-11-5503-2014, 2014.
Zhang, W., Miller, P. A., Jansson, C., Samuelsson, P., Mao, J., and Smith, B.: Self-amplifying feedbacks accelerate greening and warming of the Arctic, Geophys. Res. Lett., 45, 7102–7111, https://doi.org/10.1029/2018GL077830, 2018.
Zhang, W., Döscher, R., Koenigk, T., Miller, P. A., Jansson, C., Samuelsson, P., Wu, M., and Smith, B.: The Interplay of Recent Vegetation and Sea Ice Dynamics – Results From a Regional Earth System Model Over the Arctic, Geophys. Res. Lett., 47, e2019GL085982, https://doi.org/10.1029/2019GL085982, 2020.
Zhao, D., Toba, Y., Sugioka, K.i., and Komori, S.: New sea spray generation function for spume droplets, J. Geophys. Res.-Oceans, 111, C02007, https://doi.org/10.1029/2005JC002960, 2006.
Zhao, X., Shen, H. H., and Cheng, S.: Modeling ocean wave propagation under sea ice covers, Acta Mech. Sinica, 31, 1–15, https://doi.org/10.1007/s10409-015-0017-5, 2015.
Short summary
Regional climate studies are typically pursued by single Earth system component models (e.g., ocean models and atmosphere models). These models are driven by prescribed data which hamper the simulation of feedbacks between Earth system components. To overcome this, models were developed that interactively couple model components and allow an adequate simulation of Earth system interactions important for climate. This article reviews recent developments of such models for the Baltic Sea region.
Regional climate studies are typically pursued by single Earth system component models (e.g.,...
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