Articles | Volume 28, issue 2
https://doi.org/10.5194/npg-28-231-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-28-231-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 May 2021
Research article |
| 17 May 2021
| 17 May 2021
Identification of droughts and heatwaves in Germany with regional climate networks
Gerd Schädler
CORRESPONDING AUTHOR
Institute for Meteorology and Climate Research – Department Troposphere Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
Marcus Breil
Institute for Meteorology and Climate Research – Department Troposphere Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
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Marcus Breil, Emanuel Christner, Alexandre Cauquoin, Martin Werner, Melanie Karremann, and Gerd Schädler
Clim. Past, 17, 1685–1699, https://doi.org/10.5194/cp-17-1685-2021, https://doi.org/10.5194/cp-17-1685-2021, 2021
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For the first time an isotope-enabled regional climate simulation for Greenland is performed for the mid-Holocene. Simulation results are compared with observed isotope ratios in ice cores. Compared to global climate simulations, a regional downscaling improves the agreement with measured isotope concentrations. Thus, an isotope-enabled regional climate simulation constitutes a useful supplement to reconstruct regional paleo-climate conditions during the mid-Holocene in Greenland.
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
Short summary
Short summary
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.
Lukas Hubert Leufen and Gerd Schädler
Geosci. Model Dev., 12, 2033–2047, https://doi.org/10.5194/gmd-12-2033-2019, https://doi.org/10.5194/gmd-12-2033-2019, 2019
Short summary
Short summary
An artificial neural network was used to calculate the scaling quantities u* and T*. To train and test the network, a large set of worldwide observations was used. Extensive sensitivity studies showed that a relatively small 6–3–2 network with six input parameters and one hidden layer yields satisfying results. An implementation of this network in a stand-alone land surface model showed that the neural network gives results equivalent to and sometimes better than the standard implementation.
Mark Reyers, Hendrik Feldmann, Sebastian Mieruch, Joaquim G. Pinto, Marianne Uhlig, Bodo Ahrens, Barbara Früh, Kameswarrao Modali, Natalie Laube, Julia Moemken, Wolfgang Müller, Gerd Schädler, and Christoph Kottmeier
Earth Syst. Dynam., 10, 171–187, https://doi.org/10.5194/esd-10-171-2019, https://doi.org/10.5194/esd-10-171-2019, 2019
Short summary
Short summary
In this study, the regional MiKlip decadal prediction system is evaluated. This system has been established to deliver highly resolved forecasts for the timescale of 1 to 10 years for Europe. Evidence of the general potential for regional decadal predictability for the variables temperature, precipitation, and wind speed is provided, but the performance of the prediction system depends on region, variable, and system generation.
Gerhard Krinner, Chris Derksen, Richard Essery, Mark Flanner, Stefan Hagemann, Martyn Clark, Alex Hall, Helmut Rott, Claire Brutel-Vuilmet, Hyungjun Kim, Cécile B. Ménard, Lawrence Mudryk, Chad Thackeray, Libo Wang, Gabriele Arduini, Gianpaolo Balsamo, Paul Bartlett, Julia Boike, Aaron Boone, Frédérique Chéruy, Jeanne Colin, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Jeff Derry, Agnès Ducharne, Emanuel Dutra, Xing Fang, Charles Fierz, Josephine Ghattas, Yeugeniy Gusev, Vanessa Haverd, Anna Kontu, Matthieu Lafaysse, Rachel Law, Dave Lawrence, Weiping Li, Thomas Marke, Danny Marks, Martin Ménégoz, Olga Nasonova, Tomoko Nitta, Masashi Niwano, John Pomeroy, Mark S. Raleigh, Gerd Schaedler, Vladimir Semenov, Tanya G. Smirnova, Tobias Stacke, Ulrich Strasser, Sean Svenson, Dmitry Turkov, Tao Wang, Nander Wever, Hua Yuan, Wenyan Zhou, and Dan Zhu
Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, https://doi.org/10.5194/gmd-11-5027-2018, 2018
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This paper provides an overview of a coordinated international experiment to determine the strengths and weaknesses in how climate models treat snow. The models will be assessed at point locations using high-quality reference measurements and globally using satellite-derived datasets. How well climate models simulate snow-related processes is important because changing snow cover is an important part of the global climate system and provides an important freshwater resource for human use.
Katrin Sedlmeier, Sebastian Mieruch, Gerd Schädler, and Christoph Kottmeier
Nonlin. Processes Geophys., 23, 375–390, https://doi.org/10.5194/npg-23-375-2016, https://doi.org/10.5194/npg-23-375-2016, 2016
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Compound extreme events (e.g., simultaneous occurrence of hot and dry days) are likely to have a big impact on society. In our paper, we propose a new method to analyze the temporal succession of compound extreme events, an aspect that has been largely neglected so far. We analyze past and future changes and identify regions within Europe, which are probably susceptible to a future change in the succession of heavy precipitation and cold days in winter and hot and dry days in summer.
Michael Weimer, Sebastian Mieruch, Gerd Schädler, and Christoph Kottmeier
Nonlin. Processes Geophys., 23, 307–317, https://doi.org/10.5194/npg-23-307-2016, https://doi.org/10.5194/npg-23-307-2016, 2016
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This paper is the first time that a complex network approach has been used for analysis of decadal climate predictions. We have developed an alternative estimator of heat periods based on network statistics, which turns out to be superior for parts of Europe. This paper opens the perspective that network measures have the potential to improve decadal predictions.
S. Mieruch, H. Feldmann, G. Schädler, C.-J. Lenz, S. Kothe, and C. Kottmeier
Geosci. Model Dev., 7, 2983–2999, https://doi.org/10.5194/gmd-7-2983-2014, https://doi.org/10.5194/gmd-7-2983-2014, 2014
Jan Wohland, Peter Hoffmann, Daniela C. A. Lima, Marcus Breil, Olivier Asselin, and Diana Rechid
Earth Syst. Dynam., 15, 1385–1400, https://doi.org/10.5194/esd-15-1385-2024, https://doi.org/10.5194/esd-15-1385-2024, 2024
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We evaluate how winds change when humans grow or cut down forests. Our analysis draws from climate model simulations with extreme scenarios where Europe is either fully forested or covered with grass. We find that the effect of land use change on wind energy is very important: wind energy potentials are twice as high above grass as compared to forest in some locations. Our results imply that wind profile changes should be better incorporated in climate change assessments for wind energy.
Marcus Breil, Vanessa K. M. Schneider, and Joaquim G. Pinto
Biogeosciences, 21, 811–824, https://doi.org/10.5194/bg-21-811-2024, https://doi.org/10.5194/bg-21-811-2024, 2024
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The general impact of afforestation on the regional climate conditions in Europe during the period 1986–2015 is investigated. For this purpose, a regional climate model simulation is performed, in which afforestation during this period is considered, and results are compared to a simulation in which this is not the case. Results show that afforestation had discernible impacts on the climate change signal in Europe, which may have mitigated the local warming trend, especially in summer in Europe.
Marcus Breil, Annabell Weber, and Joaquim G. Pinto
Biogeosciences, 20, 2237–2250, https://doi.org/10.5194/bg-20-2237-2023, https://doi.org/10.5194/bg-20-2237-2023, 2023
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A promising strategy for mitigating burdens of heat extremes in Europe is to replace dark coniferous forests with brighter deciduous forests. The consequence of this would be reduced absorption of solar radiation, which should reduce the intensities of heat periods. In this study, we show that deciduous forests have a certain cooling effect on heat period intensities in Europe. However, the magnitude of the temperature reduction is quite small.
Efi Rousi, Andreas H. Fink, Lauren S. Andersen, Florian N. Becker, Goratz Beobide-Arsuaga, Marcus Breil, Giacomo Cozzi, Jens Heinke, Lisa Jach, Deborah Niermann, Dragan Petrovic, Andy Richling, Johannes Riebold, Stella Steidl, Laura Suarez-Gutierrez, Jordis S. Tradowsky, Dim Coumou, André Düsterhus, Florian Ellsäßer, Georgios Fragkoulidis, Daniel Gliksman, Dörthe Handorf, Karsten Haustein, Kai Kornhuber, Harald Kunstmann, Joaquim G. Pinto, Kirsten Warrach-Sagi, and Elena Xoplaki
Nat. Hazards Earth Syst. Sci., 23, 1699–1718, https://doi.org/10.5194/nhess-23-1699-2023, https://doi.org/10.5194/nhess-23-1699-2023, 2023
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The objective of this study was to perform a comprehensive, multi-faceted analysis of the 2018 extreme summer in terms of heat and drought in central and northern Europe, with a particular focus on Germany. A combination of favorable large-scale conditions and locally dry soils were related with the intensity and persistence of the events. We also showed that such extremes have become more likely due to anthropogenic climate change and might occur almost every year under +2 °C of global warming.
Marcus Breil, Felix Krawczyk, and Joaquim G. Pinto
Earth Syst. Dynam., 14, 243–253, https://doi.org/10.5194/esd-14-243-2023, https://doi.org/10.5194/esd-14-243-2023, 2023
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We provide evidence that biogeophysical effects of afforestation can counteract the favorable biogeochemical climate effect of reduced CO2 concentrations. By changing the land surface characteristics, afforestation reduces vegetation surface temperatures, resulting in a reduced outgoing longwave radiation in summer, although CO2 concentrations are reduced. Since forests additionally absorb a lot of solar radiation due to their dark surfaces, afforestation has a total warming effect.
Anne Sophie Daloz, Clemens Schwingshackl, Priscilla Mooney, Susanna Strada, Diana Rechid, Edouard L. Davin, Eleni Katragkou, Nathalie de Noblet-Ducoudré, Michal Belda, Tomas Halenka, Marcus Breil, Rita M. Cardoso, Peter Hoffmann, Daniela C. A. Lima, Ronny Meier, Pedro M. M. Soares, Giannis Sofiadis, Gustav Strandberg, Merja H. Toelle, and Marianne T. Lund
The Cryosphere, 16, 2403–2419, https://doi.org/10.5194/tc-16-2403-2022, https://doi.org/10.5194/tc-16-2403-2022, 2022
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Snow plays a major role in the regulation of the Earth's surface temperature. Together with climate change, rising temperatures are already altering snow in many ways. In this context, it is crucial to better understand the ability of climate models to represent snow and snow processes. This work focuses on Europe and shows that the melting season in spring still represents a challenge for climate models and that more work is needed to accurately simulate snow–atmosphere interactions.
Priscilla A. Mooney, Diana Rechid, Edouard L. Davin, Eleni Katragkou, Natalie de Noblet-Ducoudré, Marcus Breil, Rita M. Cardoso, Anne Sophie Daloz, Peter Hoffmann, Daniela C. A. Lima, Ronny Meier, Pedro M. M. Soares, Giannis Sofiadis, Susanna Strada, Gustav Strandberg, Merja H. Toelle, and Marianne T. Lund
The Cryosphere, 16, 1383–1397, https://doi.org/10.5194/tc-16-1383-2022, https://doi.org/10.5194/tc-16-1383-2022, 2022
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We use multiple regional climate models to show that afforestation in sub-polar and alpine regions reduces the radiative impact of snow albedo on the atmosphere, reduces snow cover, and delays the start of the snowmelt season. This is important for local communities that are highly reliant on snowpack for water resources and winter tourism. However, models disagree on the amount of change particularly when snow is melting. This shows that more research is needed on snow–vegetation interactions.
Giannis Sofiadis, Eleni Katragkou, Edouard L. Davin, Diana Rechid, Nathalie de Noblet-Ducoudre, Marcus Breil, Rita M. Cardoso, Peter Hoffmann, Lisa Jach, Ronny Meier, Priscilla A. Mooney, Pedro M. M. Soares, Susanna Strada, Merja H. Tölle, and Kirsten Warrach Sagi
Geosci. Model Dev., 15, 595–616, https://doi.org/10.5194/gmd-15-595-2022, https://doi.org/10.5194/gmd-15-595-2022, 2022
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Afforestation is currently promoted as a greenhouse gas mitigation strategy. In our study, we examine the differences in soil temperature and moisture between grounds covered either by forests or grass. The main conclusion emerged is that forest-covered grounds are cooler but drier than open lands in summer. Therefore, afforestation disrupts the seasonal cycle of soil temperature, which in turn could trigger changes in crucial chemical processes such as soil carbon sequestration.
Marcus Breil, Emanuel Christner, Alexandre Cauquoin, Martin Werner, Melanie Karremann, and Gerd Schädler
Clim. Past, 17, 1685–1699, https://doi.org/10.5194/cp-17-1685-2021, https://doi.org/10.5194/cp-17-1685-2021, 2021
Short summary
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For the first time an isotope-enabled regional climate simulation for Greenland is performed for the mid-Holocene. Simulation results are compared with observed isotope ratios in ice cores. Compared to global climate simulations, a regional downscaling improves the agreement with measured isotope concentrations. Thus, an isotope-enabled regional climate simulation constitutes a useful supplement to reconstruct regional paleo-climate conditions during the mid-Holocene in Greenland.
Marcus Breil, Edouard L. Davin, and Diana Rechid
Biogeosciences, 18, 1499–1510, https://doi.org/10.5194/bg-18-1499-2021, https://doi.org/10.5194/bg-18-1499-2021, 2021
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The physical processes behind varying evapotranspiration rates in forests and grasslands in Europe are investigated in a regional model study with idealized afforestation scenarios. The results show that the evapotranspiration response to afforestation depends on the interplay of two counteracting factors: the transpiration facilitating characteristics of a forest and the reduced saturation deficits of forests caused by an increased surface roughness and associated lower surface temperatures.
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
Short summary
Short summary
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.
Edouard L. Davin, Diana Rechid, Marcus Breil, Rita M. Cardoso, Erika Coppola, Peter Hoffmann, Lisa L. Jach, Eleni Katragkou, Nathalie de Noblet-Ducoudré, Kai Radtke, Mario Raffa, Pedro M. M. Soares, Giannis Sofiadis, Susanna Strada, Gustav Strandberg, Merja H. Tölle, Kirsten Warrach-Sagi, and Volker Wulfmeyer
Earth Syst. Dynam., 11, 183–200, https://doi.org/10.5194/esd-11-183-2020, https://doi.org/10.5194/esd-11-183-2020, 2020
Lukas Hubert Leufen and Gerd Schädler
Geosci. Model Dev., 12, 2033–2047, https://doi.org/10.5194/gmd-12-2033-2019, https://doi.org/10.5194/gmd-12-2033-2019, 2019
Short summary
Short summary
An artificial neural network was used to calculate the scaling quantities u* and T*. To train and test the network, a large set of worldwide observations was used. Extensive sensitivity studies showed that a relatively small 6–3–2 network with six input parameters and one hidden layer yields satisfying results. An implementation of this network in a stand-alone land surface model showed that the neural network gives results equivalent to and sometimes better than the standard implementation.
Mark Reyers, Hendrik Feldmann, Sebastian Mieruch, Joaquim G. Pinto, Marianne Uhlig, Bodo Ahrens, Barbara Früh, Kameswarrao Modali, Natalie Laube, Julia Moemken, Wolfgang Müller, Gerd Schädler, and Christoph Kottmeier
Earth Syst. Dynam., 10, 171–187, https://doi.org/10.5194/esd-10-171-2019, https://doi.org/10.5194/esd-10-171-2019, 2019
Short summary
Short summary
In this study, the regional MiKlip decadal prediction system is evaluated. This system has been established to deliver highly resolved forecasts for the timescale of 1 to 10 years for Europe. Evidence of the general potential for regional decadal predictability for the variables temperature, precipitation, and wind speed is provided, but the performance of the prediction system depends on region, variable, and system generation.
Gerhard Krinner, Chris Derksen, Richard Essery, Mark Flanner, Stefan Hagemann, Martyn Clark, Alex Hall, Helmut Rott, Claire Brutel-Vuilmet, Hyungjun Kim, Cécile B. Ménard, Lawrence Mudryk, Chad Thackeray, Libo Wang, Gabriele Arduini, Gianpaolo Balsamo, Paul Bartlett, Julia Boike, Aaron Boone, Frédérique Chéruy, Jeanne Colin, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Jeff Derry, Agnès Ducharne, Emanuel Dutra, Xing Fang, Charles Fierz, Josephine Ghattas, Yeugeniy Gusev, Vanessa Haverd, Anna Kontu, Matthieu Lafaysse, Rachel Law, Dave Lawrence, Weiping Li, Thomas Marke, Danny Marks, Martin Ménégoz, Olga Nasonova, Tomoko Nitta, Masashi Niwano, John Pomeroy, Mark S. Raleigh, Gerd Schaedler, Vladimir Semenov, Tanya G. Smirnova, Tobias Stacke, Ulrich Strasser, Sean Svenson, Dmitry Turkov, Tao Wang, Nander Wever, Hua Yuan, Wenyan Zhou, and Dan Zhu
Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, https://doi.org/10.5194/gmd-11-5027-2018, 2018
Short summary
Short summary
This paper provides an overview of a coordinated international experiment to determine the strengths and weaknesses in how climate models treat snow. The models will be assessed at point locations using high-quality reference measurements and globally using satellite-derived datasets. How well climate models simulate snow-related processes is important because changing snow cover is an important part of the global climate system and provides an important freshwater resource for human use.
Andreas Will, Naveed Akhtar, Jennifer Brauch, Marcus Breil, Edouard Davin, Ha T. M. Ho-Hagemann, Eric Maisonnave, Markus Thürkow, and Stefan Weiher
Geosci. Model Dev., 10, 1549–1586, https://doi.org/10.5194/gmd-10-1549-2017, https://doi.org/10.5194/gmd-10-1549-2017, 2017
Short summary
Short summary
We present a coupled regional climate system model. The COSMO CLM regional climate model is two-way coupled via OASIS3-MCT to the land surface, regional ocean for the Mediterranean Sea, North and Baltic seas and an earth system model. The direct coupling costs of communication and horizontal interpolation are shown to be negligible even for a frequent exchange of 450 2-D fields. A procedure of finding an optimum processor configuration is presented and successfully applied to all couplings.
Katrin Sedlmeier, Sebastian Mieruch, Gerd Schädler, and Christoph Kottmeier
Nonlin. Processes Geophys., 23, 375–390, https://doi.org/10.5194/npg-23-375-2016, https://doi.org/10.5194/npg-23-375-2016, 2016
Short summary
Short summary
Compound extreme events (e.g., simultaneous occurrence of hot and dry days) are likely to have a big impact on society. In our paper, we propose a new method to analyze the temporal succession of compound extreme events, an aspect that has been largely neglected so far. We analyze past and future changes and identify regions within Europe, which are probably susceptible to a future change in the succession of heavy precipitation and cold days in winter and hot and dry days in summer.
Michael Weimer, Sebastian Mieruch, Gerd Schädler, and Christoph Kottmeier
Nonlin. Processes Geophys., 23, 307–317, https://doi.org/10.5194/npg-23-307-2016, https://doi.org/10.5194/npg-23-307-2016, 2016
Short summary
Short summary
This paper is the first time that a complex network approach has been used for analysis of decadal climate predictions. We have developed an alternative estimator of heat periods based on network statistics, which turns out to be superior for parts of Europe. This paper opens the perspective that network measures have the potential to improve decadal predictions.
S. Mieruch, H. Feldmann, G. Schädler, C.-J. Lenz, S. Kothe, and C. Kottmeier
Geosci. Model Dev., 7, 2983–2999, https://doi.org/10.5194/gmd-7-2983-2014, https://doi.org/10.5194/gmd-7-2983-2014, 2014
Related subject area
Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Big data and artificial intelligence
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Characterisation of Dansgaard–Oeschger events in palaeoclimate time series using the matrix profile method
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Integrated hydrodynamic and machine learning models for compound flooding prediction in a data-scarce estuarine delta
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The blessing of dimensionality for the analysis of climate data
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Extracting statistically significant eddy signals from large Lagrangian datasets using wavelet ridge analysis, with application to the Gulf of Mexico
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Applications of matrix factorization methods to climate data
Detecting dynamical anomalies in time series from different palaeoclimate proxy archives using windowed recurrence network analysis
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Marko Zoldoš, Tomislav Džoić, Jadran Jurković, Frano Matić, Sandra Jambrošić, Ivan Ljuština, and Maja Telišman Prtenjak
Nonlin. Processes Geophys., 32, 89–106, https://doi.org/10.5194/npg-32-89-2025, https://doi.org/10.5194/npg-32-89-2025, 2025
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Fog can disrupt aviation by causing accidents and delays due to low visibility, yet it remains underresearched in Croatia. This study examined fog and mist at Pula Airport using 20 years of data and machine learning techniques. There is a declining trend in fog that is linked to changing weather patterns. Fog mainly occurs from October to March. These findings enhance knowledge about fog in Croatia and can improve weather forecasts, increasing safety at the airport.
Francesco Martinuzzi, Miguel D. Mahecha, Gustau Camps-Valls, David Montero, Tristan Williams, and Karin Mora
Nonlin. Processes Geophys., 31, 535–557, https://doi.org/10.5194/npg-31-535-2024, https://doi.org/10.5194/npg-31-535-2024, 2024
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We investigated how machine learning can forecast extreme vegetation responses to weather. Examining four models, no single one stood out as the best, though "echo state networks" showed minor advantages. Our results indicate that while these tools are able to generally model vegetation states, they face challenges under extreme conditions. This underlines the potential of artificial intelligence in ecosystem modeling, also pinpointing areas that need further research.
Tobias Sebastian Finn, Lucas Disson, Alban Farchi, Marc Bocquet, and Charlotte Durand
Nonlin. Processes Geophys., 31, 409–431, https://doi.org/10.5194/npg-31-409-2024, https://doi.org/10.5194/npg-31-409-2024, 2024
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We train neural networks as denoising diffusion models for state generation in the Lorenz 1963 system and demonstrate that they learn an internal representation of the system. We make use of this learned representation and the pre-trained model in two downstream tasks: surrogate modelling and ensemble generation. For both tasks, the diffusion model can outperform other more common approaches. Thus, we see a potential of representation learning with diffusion models for dynamical systems.
Susana Barbosa, Maria Eduarda Silva, and Denis-Didier Rousseau
Nonlin. Processes Geophys., 31, 433–447, https://doi.org/10.5194/npg-31-433-2024, https://doi.org/10.5194/npg-31-433-2024, 2024
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The characterisation of abrupt transitions in palaeoclimate records allows understanding of millennial climate variability and potential tipping points in the context of current climate change. In our study an algorithmic method, the matrix profile, is employed to characterise abrupt warmings designated as Dansgaard–Oeschger (DO) events and to identify the most similar transitions in the palaeoclimate time series.
John Bjørnar Bremnes, Thomas N. Nipen, and Ivar A. Seierstad
Nonlin. Processes Geophys., 31, 247–257, https://doi.org/10.5194/npg-31-247-2024, https://doi.org/10.5194/npg-31-247-2024, 2024
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During the last 2 years, tremendous progress has been made in global data-driven weather models trained on reanalysis data. In this study, the Pangu-Weather model is compared to several numerical weather prediction models with and without probabilistic post-processing for temperature and wind speed forecasting. The results confirm that global data-driven models are promising for operational weather forecasting and that post-processing can improve these forecasts considerably.
Bin Shi and Junjie Ma
Nonlin. Processes Geophys., 31, 165–174, https://doi.org/10.5194/npg-31-165-2024, https://doi.org/10.5194/npg-31-165-2024, 2024
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Different from traditional deterministic optimization algorithms, we implement the sampling method to compute the conditional nonlinear optimal perturbations (CNOPs) in the realistic and predictive coupled ocean–atmosphere model, which reduces the first-order information to the zeroth-order one, avoiding the high-cost computation of the gradient. The numerical performance highlights the importance of stochastic optimization algorithms to compute CNOPs and capture initial optimal precursors.
David Docquier, Giorgia Di Capua, Reik V. Donner, Carlos A. L. Pires, Amélie Simon, and Stéphane Vannitsem
Nonlin. Processes Geophys., 31, 115–136, https://doi.org/10.5194/npg-31-115-2024, https://doi.org/10.5194/npg-31-115-2024, 2024
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Identifying causes of specific processes is crucial in order to better understand our climate system. Traditionally, correlation analyses have been used to identify cause–effect relationships in climate studies. However, correlation does not imply causation, which justifies the need to use causal methods. We compare two independent causal methods and show that these are superior to classical correlation analyses. We also find some interesting differences between the two methods.
Louis Le Toumelin, Isabelle Gouttevin, Clovis Galiez, and Nora Helbig
Nonlin. Processes Geophys., 31, 75–97, https://doi.org/10.5194/npg-31-75-2024, https://doi.org/10.5194/npg-31-75-2024, 2024
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Forecasting wind fields over mountains is of high importance for several applications and particularly for understanding how wind erodes and disperses snow. Forecasters rely on operational wind forecasts over mountains, which are currently only available on kilometric scales. These forecasts can also be affected by errors of diverse origins. Here we introduce a new strategy based on artificial intelligence to correct large-scale wind forecasts in mountains and increase their spatial resolution.
Florian Dupuy, Pierre Durand, and Thierry Hedde
Nonlin. Processes Geophys., 30, 553–570, https://doi.org/10.5194/npg-30-553-2023, https://doi.org/10.5194/npg-30-553-2023, 2023
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Forecasting near-surface winds over complex terrain requires high-resolution numerical weather prediction models, which drastically increase the duration of simulations and hinder them in running on a routine basis. A faster alternative is statistical downscaling. We explore different ways of calculating near-surface wind speed and direction using artificial intelligence algorithms based on various convolutional neural networks in order to find the best approach for wind downscaling.
Valérian Jacques-Dumas, René M. van Westen, Freddy Bouchet, and Henk A. Dijkstra
Nonlin. Processes Geophys., 30, 195–216, https://doi.org/10.5194/npg-30-195-2023, https://doi.org/10.5194/npg-30-195-2023, 2023
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Computing the probability of occurrence of rare events is relevant because of their high impact but also difficult due to the lack of data. Rare event algorithms are designed for that task, but their efficiency relies on a score function that is hard to compute. We compare four methods that compute this function from data and measure their performance to assess which one would be best suited to be applied to a climate model. We find neural networks to be most robust and flexible for this task.
Domenico Giaquinto, Warner Marzocchi, and Jürgen Kurths
Nonlin. Processes Geophys., 30, 167–181, https://doi.org/10.5194/npg-30-167-2023, https://doi.org/10.5194/npg-30-167-2023, 2023
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Despite being among the most severe climate extremes, it is still challenging to assess droughts’ features for specific regions. In this paper we study meteorological droughts in Europe using concepts derived from climate network theory. By exploring the synchronization in droughts occurrences across the continent we unveil regional clusters which are individually examined to identify droughts’ geographical propagation and source–sink systems, which could potentially support droughts’ forecast.
Joko Sampurno, Valentin Vallaeys, Randy Ardianto, and Emmanuel Hanert
Nonlin. Processes Geophys., 29, 301–315, https://doi.org/10.5194/npg-29-301-2022, https://doi.org/10.5194/npg-29-301-2022, 2022
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In this study, we successfully built and evaluated machine learning models for predicting water level dynamics as a proxy for compound flooding hazards in a data-scarce delta. The issues that we tackled here are data scarcity and low computational resources for building flood forecasting models. The proposed approach is suitable for use by local water management agencies in developing countries that encounter these issues.
Benjamin Walleshauser and Erik Bollt
Nonlin. Processes Geophys., 29, 255–264, https://doi.org/10.5194/npg-29-255-2022, https://doi.org/10.5194/npg-29-255-2022, 2022
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As sea surface temperature (SST) is vital for understanding the greater climate of the Earth and is also an important variable in weather prediction, we propose a model that effectively capitalizes on the reduced complexity of machine learning models while still being able to efficiently predict over a large spatial domain. We find that it is proficient at predicting the SST at specific locations as well as over the greater domain of the Earth’s oceans.
Raphael Kriegmair, Yvonne Ruckstuhl, Stephan Rasp, and George Craig
Nonlin. Processes Geophys., 29, 171–181, https://doi.org/10.5194/npg-29-171-2022, https://doi.org/10.5194/npg-29-171-2022, 2022
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Our regional numerical weather prediction models run at kilometer-scale resolutions. Processes that occur at smaller scales not yet resolved contribute significantly to the atmospheric flow. We use a neural network (NN) to represent the unresolved part of physical process such as cumulus clouds. We test this approach on a simplified, yet representative, 1D model and find that the NN corrections vastly improve the model forecast up to a couple of days.
Bo Christiansen
Nonlin. Processes Geophys., 28, 409–422, https://doi.org/10.5194/npg-28-409-2021, https://doi.org/10.5194/npg-28-409-2021, 2021
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In geophysics we often need to analyse large samples of high-dimensional fields. Fortunately but counterintuitively, such high dimensionality can be a blessing, and we demonstrate how this allows simple analytical results to be derived. These results include estimates of correlations between sample members and how the sample mean depends on the sample size. We show that the properties of high dimensionality with success can be applied to climate fields, such as those from ensemble modelling.
Camille Besombes, Olivier Pannekoucke, Corentin Lapeyre, Benjamin Sanderson, and Olivier Thual
Nonlin. Processes Geophys., 28, 347–370, https://doi.org/10.5194/npg-28-347-2021, https://doi.org/10.5194/npg-28-347-2021, 2021
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This paper investigates the potential of a type of deep generative neural network to produce realistic weather situations when trained from the climate of a general circulation model. The generator represents the climate in a compact latent space. It is able to reproduce many aspects of the targeted multivariate distribution. Some properties of our method open new perspectives such as the exploration of the extremes close to a given state or how to connect two realistic weather states.
Jonathan M. Lilly and Paula Pérez-Brunius
Nonlin. Processes Geophys., 28, 181–212, https://doi.org/10.5194/npg-28-181-2021, https://doi.org/10.5194/npg-28-181-2021, 2021
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Long-lived eddies are an important part of the ocean circulation. Here a dataset for studying eddies in the Gulf of Mexico is created through the analysis of trajectories of drifting instruments. The method involves the identification of quasi-periodic signals, characteristic of particles trapped in eddies, from the displacement records, followed by the creation of a measure of statistical significance. It is expected that this dataset will be of use to other authors studying this region.
Cristian Lussana, Thomas N. Nipen, Ivar A. Seierstad, and Christoffer A. Elo
Nonlin. Processes Geophys., 28, 61–91, https://doi.org/10.5194/npg-28-61-2021, https://doi.org/10.5194/npg-28-61-2021, 2021
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An unprecedented amount of rainfall data is available nowadays, such as ensemble model output, weather radar estimates, and in situ observations from networks of both traditional and opportunistic sensors. Nevertheless, the exact amount of precipitation, to some extent, eludes our knowledge. The objective of our study is precipitation reconstruction through the combination of numerical model outputs with observations from multiple data sources.
Dylan Harries and Terence J. O'Kane
Nonlin. Processes Geophys., 27, 453–471, https://doi.org/10.5194/npg-27-453-2020, https://doi.org/10.5194/npg-27-453-2020, 2020
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Different dimension reduction methods may produce profoundly different low-dimensional representations of multiscale systems. We perform a set of case studies to investigate these differences. When a clear scale separation is present, similar bases are obtained using all methods, but when this is not the case some methods may produce representations that are poorly suited for describing features of interest, highlighting the importance of a careful choice of method when designing analyses.
Jaqueline Lekscha and Reik V. Donner
Nonlin. Processes Geophys., 27, 261–275, https://doi.org/10.5194/npg-27-261-2020, https://doi.org/10.5194/npg-27-261-2020, 2020
Moritz N. Lang, Sebastian Lerch, Georg J. Mayr, Thorsten Simon, Reto Stauffer, and Achim Zeileis
Nonlin. Processes Geophys., 27, 23–34, https://doi.org/10.5194/npg-27-23-2020, https://doi.org/10.5194/npg-27-23-2020, 2020
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Statistical post-processing aims to increase the predictive skill of probabilistic ensemble weather forecasts by learning the statistical relation between historical pairs of observations and ensemble forecasts within a given training data set. This study compares four different training schemes and shows that including multiple years of data in the training set typically yields a more stable post-processing while it loses the ability to quickly adjust to temporal changes in the underlying data.
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Short summary
We used regional climate networks (RCNs) to identify past heatwaves and droughts in Germany. RCNs provide information for whole areas and can provide many details of extreme events. The RCNs were constructed on the grid of the E-OBS data set. Time series correlation was used to construct the networks. Network metrics were compared to standard extreme indices and differed considerably between normal and extreme years. The results show that RCNs can identify severe and moderate extremes.
We used regional climate networks (RCNs) to identify past heatwaves and droughts in Germany....
