Articles | Volume 4-osr8
https://doi.org/10.5194/sp-4-osr8-16-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/sp-4-osr8-16-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
30 Sep 2024
| OSR8 | Chapter 4.4
| 30 Sep 2024 | OSR8 | Chapter 4.4
Baltic Sea surface temperature analysis 2022: a study of marine heatwaves and overall high seasonal temperatures
Anja Lindenthal
CORRESPONDING AUTHOR
Marine Sciences Department, Federal Maritime and Hydrographic Agency, 20539 Hamburg, Germany
Marine Sciences Department, Federal Maritime and Hydrographic Agency, 20539 Hamburg, Germany
Simon Jandt-Scheelke
Marine Sciences Department, Federal Maritime and Hydrographic Agency, 20539 Hamburg, Germany
Tim Kruschke
Marine Sciences Department, Federal Maritime and Hydrographic Agency, 20539 Hamburg, Germany
Priidik Lagemaa
Department of Marine Systems, Tallinn University of Technology, Tallinn, 12618, Estonia
Eefke M. van der Lee
Marine Sciences Department, Federal Maritime and Hydrographic Agency, 18057 Rostock, Germany
Ilja Maljutenko
Department of Marine Systems, Tallinn University of Technology, Tallinn, 12618, Estonia
Helen E. Morrison
Marine Sciences Department, Federal Maritime and Hydrographic Agency, 18057 Rostock, Germany
Tabea R. Panteleit
Marine Sciences Department, Federal Maritime and Hydrographic Agency, 20539 Hamburg, Germany
Urmas Raudsepp
Department of Marine Systems, Tallinn University of Technology, Tallinn, 12618, Estonia
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Tuomas Kärnä, Patrik Ljungemyr, Saeed Falahat, Ida Ringgaard, Lars Axell, Vasily Korabel, Jens Murawski, Ilja Maljutenko, Anja Lindenthal, Simon Jandt-Scheelke, Svetlana Verjovkina, Ina Lorkowski, Priidik Lagemaa, Jun She, Laura Tuomi, Adam Nord, and Vibeke Huess
Geosci. Model Dev., 14, 5731–5749, https://doi.org/10.5194/gmd-14-5731-2021, https://doi.org/10.5194/gmd-14-5731-2021, 2021
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We present Nemo-Nordic 2.0, a novel operational marine model for the Baltic Sea. The model covers the Baltic Sea and the North Sea with approximately 1 nmi resolution. We validate the model's performance against sea level, water temperature, and salinity observations, as well as sea ice charts. The skill analysis demonstrates that Nemo-Nordic 2.0 can reproduce the hydrographic features of the Baltic Sea.
Amirhossein Barzandeh, Marko Rus, Matjaž Ličer, Ilja Maljutenko, Jüri Elken, Priidik Lagemaa, and Rivo Uiboupin
EGUsphere, https://doi.org/10.5194/egusphere-2024-3691, https://doi.org/10.5194/egusphere-2024-3691, 2024
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We evaluated a deep-learning model, HIDRA2, for predicting sea levels along the Estonian coast and compared it to traditional numerical models. HIDRA2 performed better overall, offering faster forecasts and valuable uncertainty estimates using ensemble predictions.
Laura Schaffer, Andreas Boesch, Johanna Baehr, and Tim Kruschke
EGUsphere, https://doi.org/10.5194/egusphere-2024-3144, https://doi.org/10.5194/egusphere-2024-3144, 2024
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We developed a simple yet effective model to predict storm surges in the German Bight, using wind data and a multiple linear regression approach. Trained on historical data from 1959 to 2022, our storm surge model demonstrates high predictive skill and performs as well as more complex models, despite its simplicity. It can predict both moderate and extreme storm surges, making it a valuable tool for future climate change studies.
Jüri Elken, Ilja Maljutenko, Priidik Lagemaa, Rivo Uiboupin, and Urmas Raudsepp
State Planet, 4-osr8, 9, https://doi.org/10.5194/sp-4-osr8-9-2024, https://doi.org/10.5194/sp-4-osr8-9-2024, 2024
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Baltic deep water is generally warmer than surface water during winter when district heating is required. Depending on the location, depth, and oceanographic situation, bottom water of Tallinn Bay can be used as an energy source for seawater heat pumps until the end of February, covering the major time interval when heating is needed. Episodically, there are colder-water events when seawater heat extraction has to be complemented by other sources of heating energy.
Karina von Schuckmann, Lorena Moreira, Mathilde Cancet, Flora Gues, Emmanuelle Autret, Jonathan Baker, Clément Bricaud, Romain Bourdalle-Badie, Lluis Castrillo, Lijing Cheng, Frederic Chevallier, Daniele Ciani, Alvaro de Pascual-Collar, Vincenzo De Toma, Marie Drevillon, Claudia Fanelli, Gilles Garric, Marion Gehlen, Rianne Giesen, Kevin Hodges, Doroteaciro Iovino, Simon Jandt-Scheelke, Eric Jansen, Melanie Juza, Ioanna Karagali, Thomas Lavergne, Simona Masina, Ronan McAdam, Audrey Minière, Helen Morrison, Tabea Rebekka Panteleit, Andrea Pisano, Marie-Isabelle Pujol, Ad Stoffelen, Sulian Thual, Simon Van Gennip, Pierre Veillard, Chunxue Yang, and Hao Zuo
State Planet, 4-osr8, 1, https://doi.org/10.5194/sp-4-osr8-1-2024, https://doi.org/10.5194/sp-4-osr8-1-2024, 2024
Karina von Schuckmann, Lorena Moreira, Mathilde Cancet, Flora Gues, Emmanuelle Autret, Ali Aydogdu, Lluis Castrillo, Daniele Ciani, Andrea Cipollone, Emanuela Clementi, Gianpiero Cossarini, Alvaro de Pascual-Collar, Vincenzo De Toma, Marion Gehlen, Rianne Giesen, Marie Drevillon, Claudia Fanelli, Kevin Hodges, Simon Jandt-Scheelke, Eric Jansen, Melanie Juza, Ioanna Karagali, Priidik Lagemaa, Vidar Lien, Leonardo Lima, Vladyslav Lyubartsev, Ilja Maljutenko, Simona Masina, Ronan McAdam, Pietro Miraglio, Helen Morrison, Tabea Rebekka Panteleit, Andrea Pisano, Marie-Isabelle Pujol, Urmas Raudsepp, Roshin Raj, Ad Stoffelen, Simon Van Gennip, Pierre Veillard, and Chunxue Yang
State Planet, 4-osr8, 2, https://doi.org/10.5194/sp-4-osr8-2-2024, https://doi.org/10.5194/sp-4-osr8-2-2024, 2024
Urmas Raudsepp, Ilja Maljutenko, Priidik Lagemaa, and Karina von Schuckmann
State Planet Discuss., https://doi.org/10.5194/sp-2024-19, https://doi.org/10.5194/sp-2024-19, 2024
Preprint under review for SP
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Over the last three decades, the Baltic Sea has experienced rising temperature and salinity, reflecting broader atmospheric warming. Heat content fluctuations are driven by subsurface temperature changes in the upper 100 meters, including the thermocline and halocline, influenced by air temperature, evaporation, and wind stress. Freshwater content changes mainly result from salinity shifts in the halocline, with saline water inflow, precipitation, and wind stress as key factors.
Shakti Singh, Ilja Maljutenko, and Rivo Uiboupin
EGUsphere, https://doi.org/10.5194/egusphere-2024-1701, https://doi.org/10.5194/egusphere-2024-1701, 2024
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The sea ice statistics study highlights the bias in model estimations compared to satellite data and provides a simple approach to minimise that. During the study period, the model estimates sea ice forming slightly earlier but aligns well with the satellite data for ice season's end. Rapid decrease in the sea ice parameters is observed across the Baltic Sea, especially the ice thickness in the Bothnian Bay sub-basin. These statistics could be crucial for regional adaptation strategies.
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.
Alexandra Marki, Xin Li, and Simon Jandt-Scheelke
EGUsphere, https://doi.org/10.5194/egusphere-2023-3092, https://doi.org/10.5194/egusphere-2023-3092, 2024
Preprint archived
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Aim of the Oxygen Deficiency Index (ODI) blueprint is to inform about the water-quality in the North and Baltic Seas by using observations and model-simulations. The ODI helps us to calculate the probability if low-oxygen conditions might occur, with low ODI values indicating a low risk and vice-versa. We show that the ODI is able to forecast oxygen deficiency zones between 30 to 75 days. Important points in our study were the simplicity of application, deployment and an easy interpretation.
Urmas Raudsepp, Ilja Maljutenko, Amirhossein Barzandeh, Rivo Uiboupin, and Priidik Lagemaa
State Planet, 1-osr7, 7, https://doi.org/10.5194/sp-1-osr7-7-2023, https://doi.org/10.5194/sp-1-osr7-7-2023, 2023
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The freshwater content in the Baltic Sea has wide sub-regional variability characterized by the local climate dynamics. The total freshwater content trend is negative due to the recent increased inflows of saltwater, but there are also regions where the increase in runoff and decrease in ice content have led to an increase in the freshwater content.
Claudia Hinrichs, Peter Köhler, Christoph Völker, and Judith Hauck
Biogeosciences, 20, 3717–3735, https://doi.org/10.5194/bg-20-3717-2023, https://doi.org/10.5194/bg-20-3717-2023, 2023
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This study evaluated the alkalinity distribution in 14 climate models and found that most models underestimate alkalinity at the surface and overestimate it in the deeper ocean. It highlights the need for better understanding and quantification of processes driving alkalinity distribution and calcium carbonate dissolution and the importance of accounting for biases in model results when evaluating potential ocean alkalinity enhancement experiments.
Annika Drews, Wenjuan Huo, Katja Matthes, Kunihiko Kodera, and Tim Kruschke
Atmos. Chem. Phys., 22, 7893–7904, https://doi.org/10.5194/acp-22-7893-2022, https://doi.org/10.5194/acp-22-7893-2022, 2022
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Solar irradiance varies with a period of approximately 11 years. Using a unique large chemistry–climate model dataset, we investigate the solar surface signal in the North Atlantic and European region and find that it changes over time, depending on the strength of the solar cycle. For the first time, we estimate the potential predictability associated with including realistic solar forcing in a model. These results may improve seasonal to decadal predictions of European climate.
Urmas Raudsepp and Ilja Maljutenko
Geosci. Model Dev., 15, 535–551, https://doi.org/10.5194/gmd-15-535-2022, https://doi.org/10.5194/gmd-15-535-2022, 2022
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A model's ability to reproduce the state of a simulated object is always a subject of discussion. A new method for the multivariate assessment of numerical model skills uses the K-means algorithm for clustering model errors. All available data that fall into the model domain and simulation period are incorporated into the skill assessment. The clustered errors are used for spatial and temporal analysis of the model accuracy. The method can be applied to different types of geoscientific models.
Tuomas Kärnä, Patrik Ljungemyr, Saeed Falahat, Ida Ringgaard, Lars Axell, Vasily Korabel, Jens Murawski, Ilja Maljutenko, Anja Lindenthal, Simon Jandt-Scheelke, Svetlana Verjovkina, Ina Lorkowski, Priidik Lagemaa, Jun She, Laura Tuomi, Adam Nord, and Vibeke Huess
Geosci. Model Dev., 14, 5731–5749, https://doi.org/10.5194/gmd-14-5731-2021, https://doi.org/10.5194/gmd-14-5731-2021, 2021
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We present Nemo-Nordic 2.0, a novel operational marine model for the Baltic Sea. The model covers the Baltic Sea and the North Sea with approximately 1 nmi resolution. We validate the model's performance against sea level, water temperature, and salinity observations, as well as sea ice charts. The skill analysis demonstrates that Nemo-Nordic 2.0 can reproduce the hydrographic features of the Baltic Sea.
Klaus Wyser, Torben Koenigk, Uwe Fladrich, Ramon Fuentes-Franco, Mehdi Pasha Karami, and Tim Kruschke
Geosci. Model Dev., 14, 4781–4796, https://doi.org/10.5194/gmd-14-4781-2021, https://doi.org/10.5194/gmd-14-4781-2021, 2021
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This paper describes the large ensemble done by SMHI with the EC-Earth3 climate model. The ensemble comprises 50 realizations for each of the historical experiments after 1970 and four different future projections for CMIP6. We describe the creation of the initial states for the ensemble and the reduced set of output variables. A first look at the results illustrates the changes in the climate during this century and puts them in relation to the uncertainty from the model's internal variability.
Tian Tian, Shuting Yang, Mehdi Pasha Karami, François Massonnet, Tim Kruschke, and Torben Koenigk
Geosci. Model Dev., 14, 4283–4305, https://doi.org/10.5194/gmd-14-4283-2021, https://doi.org/10.5194/gmd-14-4283-2021, 2021
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Three decadal prediction experiments with EC-Earth3 are performed to investigate the impact of ocean, sea ice concentration and thickness initialization, respectively. We find that the persistence of perennial thick ice in the central Arctic can affect the sea ice predictability in its adjacent waters via advection process or wind, despite those regions being seasonally ice free during two recent decades. This has implications for the coming decades as the thinning of Arctic sea ice continues.
Jukka-Pekka Jalkanen, Lasse Johansson, Magda Wilewska-Bien, Lena Granhag, Erik Ytreberg, K. Martin Eriksson, Daniel Yngsell, Ida-Maja Hassellöv, Kerstin Magnusson, Urmas Raudsepp, Ilja Maljutenko, Hulda Winnes, and Jana Moldanova
Ocean Sci., 17, 699–728, https://doi.org/10.5194/os-17-699-2021, https://doi.org/10.5194/os-17-699-2021, 2021
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This modelling study describes a methodology for describing pollutant discharges from ships to the sea. The pilot area used is the Baltic Sea area and discharges of bilge, ballast, sewage, wash water as well as stern tube oil are reported for the year 2012. This work also reports the release of SOx scrubber effluents. This technique may be used by ships to comply with tight sulfur limits inside Emission Control Areas, but it also introduces a new pollutant stream from ships to the sea.
Mihhail Zujev, Jüri Elken, and Priidik Lagemaa
Ocean Sci., 17, 91–109, https://doi.org/10.5194/os-17-91-2021, https://doi.org/10.5194/os-17-91-2021, 2021
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The proposed method of data assimilation is capable of effectively correcting basin-scale mismatch of oceanographic models when the domain is under nearly coherent external forcing. The method uses basin-scale EOF modes, calculated from the long-term model statistics. These modes are used to reconstruct gridded fields from point observations, which are further fed to the model using relaxation. Tests with sea surface temperature and salinity in the NE Baltic Sea were successful.
Sabine Haase, Jaika Fricke, Tim Kruschke, Sebastian Wahl, and Katja Matthes
Atmos. Chem. Phys., 20, 14043–14061, https://doi.org/10.5194/acp-20-14043-2020, https://doi.org/10.5194/acp-20-14043-2020, 2020
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Ozone depletion over Antarctica was shown to influence the tropospheric jet in the Southern Hemisphere. We investigate the atmospheric response to ozone depletion comparing climate model ensembles with interactive and prescribed ozone fields. We show that allowing feedbacks between ozone chemistry and model physics as well as including asymmetries in ozone leads to a strengthened ozone depletion signature in the stratosphere but does not significantly affect the tropospheric jet position.
Lasse Johansson, Erik Ytreberg, Jukka-Pekka Jalkanen, Erik Fridell, K. Martin Eriksson, Maria Lagerström, Ilja Maljutenko, Urmas Raudsepp, Vivian Fischer, and Eva Roth
Ocean Sci., 16, 1143–1163, https://doi.org/10.5194/os-16-1143-2020, https://doi.org/10.5194/os-16-1143-2020, 2020
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Very little is currently known about the activities and emissions of private leisure boats. To change this, a new model was created (BEAM). The model was used for the Baltic Sea to estimate leisure boat emissions, also considering antifouling paint leach. When compared to commercial shipping, the modeled leisure boat emissions were seen to be surprisingly large for some pollutant species, and these emissions were heavily concentrated on coastal inhabited areas during summer and early autumn.
Markus Kunze, Tim Kruschke, Ulrike Langematz, Miriam Sinnhuber, Thomas Reddmann, and Katja Matthes
Atmos. Chem. Phys., 20, 6991–7019, https://doi.org/10.5194/acp-20-6991-2020, https://doi.org/10.5194/acp-20-6991-2020, 2020
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Modelling the response of the atmosphere and its constituents to 11-year solar variations is subject to a certain uncertainty arising from the solar irradiance data set used in the chemistry–climate model (CCM) and the applied CCM itself.
This study reveals significant influences from both sources on the variations in the solar response in the stratosphere and mesosphere.
However, there are also regions where the random, unexplained part of the variations in the solar response is largest.
Stefan Liersch, Julia Tecklenburg, Henning Rust, Andreas Dobler, Madlen Fischer, Tim Kruschke, Hagen Koch, and Fred Fokko Hattermann
Hydrol. Earth Syst. Sci., 22, 2163–2185, https://doi.org/10.5194/hess-22-2163-2018, https://doi.org/10.5194/hess-22-2163-2018, 2018
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Application-oriented regional impact studies require accurate simulations of future climate variables and water availability. We analyse the quality of global and regional climate projections and discuss potentials of correction methods that partly overcome this quality issue. The model ensemble used in this study projects increasing average annual discharges and a shift in seasonal patterns, with decreasing discharges in June and July and increasing discharges from August to November.
Katja Matthes, Bernd Funke, Monika E. Andersson, Luke Barnard, Jürg Beer, Paul Charbonneau, Mark A. Clilverd, Thierry Dudok de Wit, Margit Haberreiter, Aaron Hendry, Charles H. Jackman, Matthieu Kretzschmar, Tim Kruschke, Markus Kunze, Ulrike Langematz, Daniel R. Marsh, Amanda C. Maycock, Stergios Misios, Craig J. Rodger, Adam A. Scaife, Annika Seppälä, Ming Shangguan, Miriam Sinnhuber, Kleareti Tourpali, Ilya Usoskin, Max van de Kamp, Pekka T. Verronen, and Stefan Versick
Geosci. Model Dev., 10, 2247–2302, https://doi.org/10.5194/gmd-10-2247-2017, https://doi.org/10.5194/gmd-10-2247-2017, 2017
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The solar forcing dataset for climate model experiments performed for the upcoming IPCC report is described. This dataset provides the radiative and particle input of solar variability on a daily basis from 1850 through to 2300. With this dataset a better representation of natural climate variability with respect to the output of the Sun is provided which provides the most sophisticated and comprehensive respresentation of solar variability that has been used in climate model simulations so far.
Tobias Pardowitz, Robert Osinski, Tim Kruschke, and Uwe Ulbrich
Nat. Hazards Earth Syst. Sci., 16, 2391–2402, https://doi.org/10.5194/nhess-16-2391-2016, https://doi.org/10.5194/nhess-16-2391-2016, 2016
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This paper describes an approach to derive probabilistic predictions of local winter storm damage occurrences. Such predictions are subject to large uncertainty due to meteorological forecast uncertainty and uncertainties in modelling weather impacts. The paper aims to quantify these uncertainties and demonstrate that valuable predictions can be made on the district level several days ahead.
Edith Soosaar, Ilja Maljutenko, Rivo Uiboupin, Maris Skudra, and Urmas Raudsepp
Ocean Sci., 12, 417–432, https://doi.org/10.5194/os-12-417-2016, https://doi.org/10.5194/os-12-417-2016, 2016
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Remote sensing imagery and numerical model study of river bulge evolution and dynamics in a non-tidal sea showed an anti-cyclonically rotating bulge during the studied low wind period in the Gulf of Riga. In about 7–8 days the bulge grew up to 20 km in diameter, before being diluted. Both model and satellite images showed river water mainly contained in the bulge. The study shows significant effects of the wind in the evolution of the river bulge, even if the wind speed was moderate (3–4 m s−1).
Cited articles
Belkin, I. M.: Rapid warming of large marine ecosystems, Prog. Oceanogr., 81, 207–213, https://doi.org/10.1016/j.pocean.2009.04.011, 2009.
Buga, L., Sarbu, G., Fryberg, L., Magnus, W., Wesslander, K., Gatti, J., Leroy, D., Iona, S., Larsen, M., Koefoed Rømer, J., Østrem, A. K., Lipizer, M., and Giorgetti A.: EMODnet Chemistry Eutrophication and Acidity aggregated datasets v2018, EMODnet, Thematic Lot no. 4/SI2.749773, https://doi.org/10.6092/EC8207EF-ED81-4EE5-BF48-E26FF16BF02E, 2018.
Cheng, L., von Schuckmann, K., Abraham, J. P., Trenberth, K. E., Mann, M. E, Zanna, L., England, M. H., Zika, J. D., Fasullo, J. T., Yu, Y., Pan, Y., Zhu, J., Newsom, E. R., Bronselaer, B., and Lin, X.: Past and future ocean warming, Nature Reviews Earth and Environment, 3, 776–794, https://doi.org/10.1038/s43017-022-00345-1, 2022.
Chubarenko, I. P., Demchenko, N. Y., Esiukova, E. E., Lobchuk, O. I., Karmanov, K. V., Pilipchuk, V. A., Isachenko, I. A., Kuleshov, A. F., Chugaevich, V. Y., Stepanova, N. B., Krechik, V. A., and Bagaev, A. V.: Spring thermocline formation in the coastal zone of the southeastern Baltic Sea based on field data in 2010–2013, Oceanology, 57, 632–638, https://doi.org/10.1134/S000143701705006X, 2017.
Copernicus Climate Change Service/ECMWF: Surface air temperature for August 2022, Copernicus Climate Change Service/ECMWF, https://climate.copernicus.eu/surface-air-temperature-august-2022 (last access: 11 July 2023), 2022a.
Copernicus Climate Change Service/ECMWF: Surface air temperature for November 2022, Copernicus Climate Change Service/ECMWF, https://climate.copernicus.eu/surface-air-temperature-november-2022 (last access: 11 July 2023), 2022b.
Dutheil, C., Meier, H. E. M., Gröger, M., and Börgel, F.: Warming of Baltic Sea water masses since 1850, Clim. Dynam., 61, 1311–1331, https://doi.org/10.1007/s00382-022-06628-z, 2023.
EU Copernicus Marine Service Product: Global Ocean – In-Situ Near-Real-Time Observations, Mercator Ocean International [data set], https://doi.org/10.48670/moi-00036, 2023a.
EU Copernicus Marine Service Product: Baltic Sea – L3S Sea Surface Temperature Reprocessed, Mercator Ocean International [data set], https://doi.org/10.48670/moi-00312, 2023b.
EU Copernicus Marine Service Product: Baltic Sea Physics Reanalysis, Mercator Ocean International [data set], https://doi.org/10.48670/moi-00013, 2023c.
EUMETSAT: AVHRR Factsheet, Doc. No. EUM/OPS/DOC/09/5183, https://www-cdn.eumetsat.int/files/2020-04/pdf_avhrr_factsheet.pdf (last access: 24 April 2024), 2015.
Frölicher, T. L., Fischer, E. M., and Gruber, N.: Marine heatwaves under global warming, Nature, 560, 360–364, https://doi.org/10.1038/s41586-018-0383-9, 2018.
Giorgetti, A., Lipizer, M., Molina Jack, M. E., Holdsworth, N., Jensen, H. M., Buga, L., Sarbu, G., Iona, A., Gatti, J., Larsen, M., and Fyrberg, L.: Aggregated and Validated Datasets for the European Seas: The Contribution of EMODnet Chemistry, Front. Mar. Sci., 7, 583657, https://doi.org/10.3389/fmars.2020.583657, 2020.
Goebeler, N., Norkko, A., and Norkko, J.: Ninety years of coastal monitoring reveals baseline and extreme ocean temperatures are increasing off the Finnish coast, Commun. Earth Environ., 3, 215, https://doi.org/10.1038/s43247-022-00545-z, 2022.
Gröger, M., Placke, M., Meier, H. E. M., Börgel, F., Brunnabend, S.-E., Dutheil, C., Gräwe, U., Hieronymus, M., Neumann, T., Radtke, H., Schimanke, S., Su, J., and Väli, G.: The Baltic Sea Model Intercomparison Project (BMIP) – a platform for model development, evaluation, and uncertainty assessment, Geosci. Model Dev., 15, 8613–8638, https://doi.org/10.5194/gmd-15-8613-2022, 2022.
Gurvan, M., Bourdallé-Badie, R., Chanut, J., Clementi, E., Coward, A., Ethé, C., Iovino, D., Lea, D., Lévy, C., Lovato, T., Martin, N., Masson, S., Mocavero, S., Rousset, C., Storkey, D., Vancoppenolle, M., Müeller, S., Nurser, G., Bell, M., and Samson, G.: NEMO ocean engine, in: Notes du Pôle de modélisation de l'Institut Pierre-Simon Laplace (IPSL) (v4.0, Number 27), Zenodo, https://doi.org/10.5281/zenodo.3878122, 2019.
Hobday, A. J., Alexander, L. V., Perkins, S. E., Smale, D. A., Straub, S. C., Oliver, E. C. J., Benthuysen, J. A., Burrows, M. T., Donat, M. G., Feng, M., Holbrook, N. J., Moore, P. J., Scannell, H. A., Sen Gupta, A., and Wernberg, T.: A hierarchical approach to defining marine heatwaves, Prog. Oceanogr., 141, 227–238, https://doi.org/10.1016/j.pocean.2015.12.014, 2016.
Hobday, A. J., Oliver, E. C. J., Sen Gupta, A., Benthuysen, J. A., Burrows, M. T., Donat, M. G., Holbrook, N. J., Moore, P. J., Thomsen, M. S., Wernberg, T., and Smale, D. A.: Categorizing and naming marine heatwaves, Oceanography, 31, 162–173, https://doi.org/10.5670/oceanog.2018.205, 2018.
Holbrook, N. J., Scannell, H. A., Sen Gupta, A., Benthuysen, J. A., Feng, M., Oliver, E. C., Alexander, L. V., Burrows, M. T., Donat, M. G., Hobday, A. J., and Moore, P. J.: A global assessment of marine heatwaves and their drivers, Nat. Commun., 10, 2624, https://doi.org/10.1038/s41467-019-10206-z, 2019.
ICES Bottle and low-resolution CTD dataset: Extractions 22 DEC 2013 (for years 1990–2012), 25 FEB 2015 (for year 2013), 13 OCT 2016 (for year 2015), 15 JAN 2019 (for years 2016–2017), 22 SEP 2020 (for year 2018), 10 MAR 2021 (for years 2019–2020), 28 FEB 2022 (for year 2021), ICES, Copenhagen [data set], https://data.ices.dk (last access: 30 April 2024), 2022.
In Situ TAC partners: EU Copernicus Marine Service Product User Manual for the Global Ocean- In-Situ Near-Real-Time Observations, INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030, Issue 2.0, Mercator Ocean International, https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-INS-PUM-013-030-036.pdf (last access: 22 March 2024), 2023.
IPCC: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Pörtner, H.-O., Roberts, D. C., Tignor, M., Poloczanska, E. S., Mintenbeck, K., Alegría, A., Craig, M., Langsdorf, S., Löschke, S., Möller, V., Okem, A., and Rama, B., Cambridge University Press, Cambridge, UK and New York, NY, USA, 3056 pp., https://doi.org/10.1017/9781009325844, 2022.
Jain, A. K.: Data clustering: 50 years beyond K-means, Pattern Recogn. Lett., 31, 651–666, https://doi.org/10.1016/j.patrec.2009.09.011, 2010.
Kauppi, L. and Villnäs, A.: Marine heatwaves of differing intensities lead to distinct patterns in seafloor functioning, P. Roy. Soc. B, 289, 20221159, https://doi.org/10.1098/rspb.2022.1159, 2022.
Kauppi, L., Göbeler, N., Norkko, J., Norkko, A., Romero-Ramirez, A., and Bernard, G.: Changes in macrofauna bioturbation during repeated heatwaves mediate changes in biogeochemical cycling of nutrients, Front. Mar. Sci., 9, 1070377, https://doi.org/10.3389/fmars.2022.1070377, 2023.
Minnett, P. J., Alvera-Azcárate, A., Chin, T. M., Corlett, G. K., Gentemann, C. L., Karagali, I., Li, X., Marsouin, A., Marullo, S., Maturi, E., Santoleri, R., Saux Picart, S., Steele, M., and Vazquez-Cuervo, J.: Half a century of satellite remote sensing of sea-surface temperature, Remote Sens. Environ., 233, 111366, https://doi.org/10.1016/j.rse.2019.111366, 2019.
Oliver, E. C. J.: marineHeatWaves v0.16, GitHub [code], https://github.com/ecjoliver/marineHeatWaves (last access: 17 March 2023), 2016.
Oliver, E. C. J.: Mean warming not variability drives marine heatwave trends, Clim. Dynam., 53, 1653–1659, https://doi.org/10.1007/s00382-019-04707-2, 2019.
Panteleit, T., Verjovkina, S., Jandt-Scheelke, S., Spruch, L., and Huess, V.: EU Copernicus Marine Service Quality Information Document for the Baltic Sea Physics Reanalysis, BALTICSEA_MULTIYEAR_PHY_003_011, Issue 4.0, Mercator Ocean International, https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-BAL-QUID-003-011.pdf (last access: 12 April 2023), 2023.
Raudsepp, U. and Maljutenko, I.: A method for assessment of the general circulation model quality using the K-means clustering algorithm: a case study with GETM v2.5, Geosci. Model Dev., 15, 535–551, https://doi.org/10.5194/gmd-15-535-2022, 2022.
Raudsepp, U., Maljutenko, I., Haapala, J., Männik, A., Verjovkina, S., Uiboupin, R., von Schuckmann, K., and Mayer, M.: Record high heat content and low ice extent in the Baltic Sea during winter 2019/20, in: Copernicus Ocean State Report, Issue 6, J. Oper. Oceanogr., 15, Supplement, s175–s185, https://doi.org/10.1080/1755876X.2022.2095169, 2022.
Ringgaard, I., Korabel, V., Spruch, L., Lindenthal, A., and Huess, V.: EU Copernicus Marine Service Product User Manual for the Baltic Sea Physics Reanalysis, BALTICSEA_MULTIYEAR_PHY_003_011, Issue 1.0, Mercator Ocean International, https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-BAL-PUM-003-011_012.pdf (last access: 12 April 2023), 2023.
Sen Gupta, A., Thomsen, M., Benthuysen, J. A., Hobday, A. J., Oliver, E., Alexander, L. V., Burrows, M. T., Donat, M. G., Feng, M., Holbrook, N. J., Perkins-Kirkpatrick, S., Moore, P. J., Rodrigues, R. R., Scannell, H. A., Taschetto, A. S., Ummenhofer, C. C., Wernberg, T., and Smale, D. A.: Drivers and impacts of the most extreme marine heatwave events, Sci. Rep., 10, 19359, https://doi.org/10.1038/s41598-020-75445-3, 2020.
She, J., Su, J., and Zinck, A.-S.: Anomalous surface warming in the Baltic Sea in summer 2018 and mechanism analysis, in: Copernicus Marine Service Ocean State Report, Issue 4, J. Oper. Oceanogr., 13, Supplement, s125–s132, https://doi.org/10.1080/1755876X.2020.1785097, 2020.
Smale, D. A., Wernberg, T., Oliver, E. C. J., Thomsen, M., Harvey, B. P., Straub, S. C., Burrows, M. T., Alexander, L. V., Benthuysen, J. A., Donat, M. G., Feng, M., Hobday, A. J., Holbrook, N. J., Perkins-Kirkpatrick, S. E., Scannell, H. A., Sen Gupta, A., Payne, B. L., and Moore, P. J.: Marine heatwaves threaten global biodiversity and the provision of ecosystem services, Nat. Clim, Change, 9, 306–312, https://doi.org/10.1038/s41558-019-0412-1, 2019.
SMHI: Baltic Sea – Eutrophication and Acidity aggregated datasets 1902/2017 v2018, Swedish Meteorological and Hydrological Institute, EMODnet Chemistry [data set], https://doi.org/10.6092/595D233C-3F8C-4497-8BD2-52725CEFF96B, 2018.
SMHI: Meteorological observations, air temperature, station number 140480 (Umeå Flygplats), Swedish Meteorological and Hydrological Institute [data set], https://www.smhi.se/data/meteorologi/ladda-ner-meteorologiska-observationer/#param=airtemperatureInstant,stations=core,stationid=140480 (last access: 19 December 2023), 2023a.
SMHI: Meteorological observations, wind, station number 140480 (Umeå Flygplats), Swedish Meteorological and Hydrological Institute [data set], https://www.smhi.se/data/meteorologi/ladda-ner-meteorologiska-observationer/#param=wind,stations=core,stationid=140480 (last access: 19 December 2023), 2023b.
Smith, K. E., Burrows, M. T., Hobday, A. J., King, N. G., Moore, P. J., Gupta, A. S., Thomsen, M. S., Wernberg, T., and Smale, D. A.: Biological Impacts of Marine Heatwaves, Annu. Rev. Mar. Sci., 15, 119–145, https://doi.org/10.1146/annurev-marine-032122-121437, 2023.
Sun, D., Jing, Z., Li, F., and Wu, L.: Characterizing global marine heatwaves under a spatio-temporal framework, Prog. Oceanogr., 211, 102947, https://doi.org/10.1016/j.pocean.2022.102947, 2023.
The BACC Author Team: Assessment of Climate Change for the Baltic Sea Basin, Springer Berlin, Heidelberg, p. 88, https://doi.org/10.1007/978-3-540-72786-6, 2008.
Väli, G., Meier, H. 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.
Wehde, H., Schuckmann, K. V., Pouliquen, S., Grouazel, A., Bartolome, T., Tintore, J., De Alfonso Alonso-Munoyerro, M., Carval, T., Racapé, V., and the INSTAC team: EU Copernicus Marine Service Quality Information Document for the Global Ocean- In-Situ Near-Real-Time Observations, INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030, Issue 2.3, Mercator Ocean International, https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-030-036.pdf (last access: 22 March 2024), 2023.
WMO: State of the Global Climate 2022, WMO-No. 1316, World Meteorological Organization, 2023, 48 pp., ISBN 978-92-63-11347-4, https://library.wmo.int/idurl/4/66214 (last access: 2 May 2023), 2023.
Zhao, Z. and Marin, M.: A MATLAB toolbox to detect and analyze marine heatwaves, Journal of Open Source Software, 4, 1124, https://doi.org/10.21105/joss.01124, 2019a.
Zhao, Z. and Marin, M.: A MATLAB toolbox to detect and analyze marine heatwaves (MHWs), GitHub [code], https://github.com/ZijieZhaoMMHW/m_mhw1.0 (last access: 28 May 2024), 2019b.
Short summary
In 2022, large parts of the Baltic Sea experienced the third-warmest to warmest summer and autumn temperatures since 1997 and several marine heatwaves (MHWs). Using remote sensing, reanalysis, and in situ data, this study characterizes regional differences in MHW properties in the Baltic Sea in 2022. Furthermore, it presents an analysis of long-term trends and the relationship between atmospheric warming and MHW occurrences, including their propagation into deeper layers.
In 2022, large parts of the Baltic Sea experienced the third-warmest to warmest summer and...
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