Articles | Volume 18, issue 12
https://doi.org/10.5194/cp-18-2643-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/cp-18-2643-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Dec 2022
Research article |
| 20 Dec 2022
| 20 Dec 2022
Evaluation of statistical climate reconstruction methods based on pseudoproxy experiments using linear and machine-learning methods
Zeguo Zhang
CORRESPONDING AUTHOR
Institute of Coastal Systems, Helmholtz-Zentrum Hereon, 21502 Geesthacht, Germany
Sebastian Wagner
Institute of Coastal Systems, Helmholtz-Zentrum Hereon, 21502 Geesthacht, Germany
Marlene Klockmann
Institute of Coastal Systems, Helmholtz-Zentrum Hereon, 21502 Geesthacht, Germany
Eduardo Zorita
Institute of Coastal Systems, Helmholtz-Zentrum Hereon, 21502 Geesthacht, Germany
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Eva Hartmann, Sebastian Wagner, Jürg Luterbacher, and Elena Xoplaki
EGUsphere, https://doi.org/10.5194/egusphere-2026-1636, https://doi.org/10.5194/egusphere-2026-1636, 2026
This preprint is open for discussion and under review for Climate of the Past (CP).
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The historically important regions Middle East and Nile Basin are transiently simulated with a paleo-adjusted regional climate model for the last 2500 years. Particularly striking is the climatic response to the volcanic eruptions in 536 and 540CE. Detailed examination confirms the extreme widespread cooling, especially in the first years after the eruptions, while hydroclimatic anomalies strongly depend on the season and the respective location, accompanied by large-scale circulation changes.
Proshonni Aziz, Birgit Hünicke, and Eduardo Zorita
EGUsphere, https://doi.org/10.5194/egusphere-2026-1225, https://doi.org/10.5194/egusphere-2026-1225, 2026
This preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).
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By analysing data from 1940 to 2024, we found that upper atmosphere conditions in early winter directly influence the number of North Sea storms occurring weeks or months later. We used a climate model and machine learning to improve these forecasts. Results show that December patterns can predict January storminess with high accuracy, with effects persisting for up to 60 days. Stratospheric data and machine learning together improve winter storm prediction for this region.
Nele Tim, Birgit Hünicke, and Eduardo Zorita
EGUsphere, https://doi.org/10.5194/egusphere-2026-806, https://doi.org/10.5194/egusphere-2026-806, 2026
This preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).
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We analysed extreme precipitation over Southern Africa by use of high-resolution atmospheric simulations. The strongest precipitation occurs over Madagascar, the Mozambique Channel, and the adjacent mainland. Extremes are generally stronger in the Summer Rainfall Zone than in the Winter Rainfall Zones, are projected to become more intense along the South African coast, and the Agulhas Current does not have a discernible impact on rainfall events along the South African coast.
Kai Bellinghausen, Birgit Hünicke, and Eduardo Zorita
EGUsphere, https://doi.org/10.5194/egusphere-2026-523, https://doi.org/10.5194/egusphere-2026-523, 2026
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Storm extremes across Northern Hemisphere land areas have a structured synchronised pattern. Using ERA5 data (1940–2023) and a storm index based on local wind‑speed extremes, we find that northern regions above 50° N vary together, opposite to more southern areas. Links to SST, SKT and pressure fields point to climate modes like the NAO as possible drivers. ACE2 emulator experiments confirm that surface‑temperature patterns can drive jet‑stream shifts possibly altering the mode of storminess.
Eva Hartmann, Mingyue Zhang, Sebastian Wagner, Muralidhar Adakudlu, Jürg Luterbacher, and Elena Xoplaki
Clim. Past, 21, 1699–1724, https://doi.org/10.5194/cp-21-1699-2025, https://doi.org/10.5194/cp-21-1699-2025, 2025
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For the first time, we provide a regional climate model with past climate forcings. The model's sensitivity to each forcing is tested in a case study around the large Samalas volcanic eruption in 1257. The orbital forcing shows the largest effect with an amplification of the seasons in the past compared with the standard model. The volcanic forcing has a strong short-term cooling effect after the volcanic eruption. This model is later used for a transient simulation of the last 2500 years.
Kai Bellinghausen, Birgit Hünicke, and Eduardo Zorita
Nat. Hazards Earth Syst. Sci., 25, 1139–1162, https://doi.org/10.5194/nhess-25-1139-2025, https://doi.org/10.5194/nhess-25-1139-2025, 2025
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We designed a tool to predict the storm surges at the Baltic Sea coast with satisfactory predictability (80 % correct predictions), using lead times of a few days. The proportion of false warnings is typically as low as 10 % to 20 %. We were able to identify the relevant predictor regions and their patterns – such as low-pressure systems and strong winds. Due to its short computing time, the method can be used as a pre-warning system to trigger the application of more sophisticated algorithms.
Marlene Klockmann, Udo von Toussaint, and Eduardo Zorita
Geosci. Model Dev., 17, 1765–1787, https://doi.org/10.5194/gmd-17-1765-2024, https://doi.org/10.5194/gmd-17-1765-2024, 2024
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Reconstructions of climate variability before the observational period rely on climate proxies and sophisticated statistical models to link the proxy information and climate variability. Existing models tend to underestimate the true magnitude of variability, especially if the proxies contain non-climatic noise. We present and test a promising new framework for climate-index reconstructions, based on Gaussian processes, which reconstructs robust variability estimates from noisy and sparse data.
Mingyue Zhang, Eva Hartmann, Sebastian Wagner, Muralidhar Adakudlu, Niklas Luther, Christos Zerefos, and Elena Xoplaki
Clim. Past Discuss., https://doi.org/10.5194/cp-2023-77, https://doi.org/10.5194/cp-2023-77, 2023
Manuscript not accepted for further review
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A transient regional paleoclimate simulation with all external forcings (solar, orbital, volcanic, GHG, and land use), at 0.44° resolution is presented. The mean climate between Early Roman Period and pre-industrial over Eastern Mediterranean & Nile River basin is compared. The availability of this modelling data enables us to compare the simulated output with proxy records, further link the climate events with societal change and provide insights into their impact on societal and human history.
Nele Tim, Birgit Hünicke, and Eduardo Zorita
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-147, https://doi.org/10.5194/nhess-2023-147, 2023
Manuscript not accepted for further review
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Our study analyses extreme precipitation over southern Africa in regional high-resolution atmospheric simulations of the past and future. We investigated heavy precipitation over Southern Africa, coastal South Africa, Cape Town, and the KwaZulu-Natal province in eastern South Africa. Coastal precipitation extremes are projected to intensify, double in intensity in KwaZulu-Natal, and weaken in Cape Town. Extremes are not projected to occur more often in the 21st century than in the last decades.
Nele Tim, Eduardo Zorita, Birgit Hünicke, and Ioana Ivanciu
Weather Clim. Dynam., 4, 381–397, https://doi.org/10.5194/wcd-4-381-2023, https://doi.org/10.5194/wcd-4-381-2023, 2023
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As stated by the IPCC, southern Africa is one of the two land regions that are projected to suffer from the strongest precipitation reductions in the future. Simulated drying in this region is linked to the adjacent oceans, and prevailing winds as warm and moist air masses are transported towards the continent. Precipitation trends in past and future climate can be partly attributed to the strength of the Agulhas Current system, the current along the east and south coasts of southern Africa.
Kai Bellinghausen, Birgit Hünicke, and Eduardo Zorita
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-21, https://doi.org/10.5194/nhess-2023-21, 2023
Manuscript not accepted for further review
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The prediction of extreme coastal sea level, e.g. caused by a storm surge, is operationally carried out with dynamical computer models. These models are expensive to run and still display some limitations in predicting the height of extremes. We present a successful purely data-driven machine learning model to predict extreme sea levels along the Baltic Sea coast a few days in advance. The method is also able to identify the critical predictors for the different Baltic Sea regions.
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.
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.
Ralf Weisse, Inga Dailidienė, Birgit Hünicke, Kimmo Kahma, Kristine Madsen, Anders Omstedt, Kevin Parnell, Tilo Schöne, Tarmo Soomere, Wenyan Zhang, and Eduardo Zorita
Earth Syst. Dynam., 12, 871–898, https://doi.org/10.5194/esd-12-871-2021, https://doi.org/10.5194/esd-12-871-2021, 2021
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The study is part of the thematic Baltic Earth Assessment Reports – a series of review papers summarizing the knowledge around major Baltic Earth science topics. It concentrates on sea level dynamics and coastal erosion (its variability and change). Many of the driving processes are relevant in the Baltic Sea. Contributions vary over short distances and across timescales. Progress and research gaps are described in both understanding details in the region and in extending general concepts.
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Short summary
A bidirectional long short-term memory (LSTM) neural network was employed for the first time for past temperature field reconstructions. The LSTM method tested in our experiments using a limited calibration and validation dataset shows worse reconstruction skills compared to traditional reconstruction methods. However, a certain degree of reconstruction performance achieved by the nonlinear LSTM method shows that skill can be achieved even when using small samples with limited datasets.
A bidirectional long short-term memory (LSTM) neural network was employed for the first time for...
