Articles | Volume 6, issue 2
https://doi.org/10.5194/ascmo-6-205-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-6-205-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Nonstationary extreme value analysis for event attribution combining climate models and observations
CNRM, Université de Toulouse, Météo-France, CNRS, 42 avenue Gaspard-Coriolis, 31057, Toulouse, France
CNRM, Université de Toulouse, Météo-France, CNRS, 42 avenue Gaspard-Coriolis, 31057, Toulouse, France
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A deep frost occurred in early April 2021, inducing severe damages in grapevine and fruit trees in France. We found that such extreme frosts occurring after the start of the growing season such as those of April 2021 are currently about 2°C colder [0.5 °C to 3.3 °C] in observations than in preindustrial climate. This observed intensification of growing-period frosts is attributable, at least in part, to human-caused climate change, making the 2021 event 50 % more likely [10 %–110 %].
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We propose a new multivariate downscaling and bias correction approach called
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Explore2 provides hydrological projections for 1,735 French catchments. Using QUALYPSO, this study assesses uncertainties, including internal variability. By the end of the century, low flows are projected to decline in southern France under high emissions, while other indicators remain uncertain. Emission scenarios and regional climate models are key uncertainty sources. Internal variability is often as large as climate-driven changes.
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Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
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Climate model simulations of the response to human and natural influences together, natural climate influences alone and greenhouse gases alone are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies and to underpin the next IPCC report.
Piers M. Forster, Chris Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Christophe Cassou, Mathias Hauser, Zeke Hausfather, June-Yi Lee, Matthew D. Palmer, Karina von Schuckmann, Aimée B. A. Slangen, Sophie Szopa, Blair Trewin, Jeongeun Yun, Nathan P. Gillett, Stuart Jenkins, H. Damon Matthews, Krishnan Raghavan, Aurélien Ribes, Joeri Rogelj, Debbie Rosen, Xuebin Zhang, Myles Allen, Lara Aleluia Reis, Robbie M. Andrew, Richard A. Betts, Alex Borger, Jiddu A. Broersma, Samantha N. Burgess, Lijing Cheng, Pierre Friedlingstein, Catia M. Domingues, Marco Gambarini, Thomas Gasser, Johannes Gütschow, Masayoshi Ishii, Christopher Kadow, John Kennedy, Rachel E. Killick, Paul B. Krummel, Aurélien Liné, Didier P. Monselesan, Colin Morice, Jens Mühle, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Jan C. Minx, Matthew Rigby, Robert Rohde, Abhishek Savita, Sonia I. Seneviratne, Peter Thorne, Christopher Wells, Luke M. Western, Guido R. van der Werf, Susan E. Wijffels, Valérie Masson-Delmotte, and Panmao Zhai
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Yoann Robin, Mathieu Vrac, Aurélien Ribes, Occitane Barbaux, and Philippe Naveau
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We describe an improved method and the associated free licensed package ANKIALE (ANalysis of Klimate with bayesian Inference: AppLication to extreme Events) for estimating the statistics of temperature extremes. This method uses climate model simulations (including multiple scenarios simultaneously) to provide a prior of the real-world changes, constrained by the observations. The method and the tool are illustrated via an application to temperature over Europe until 2100, for four scenarios.
Piers M. Forster, Chris Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Bradley Hall, Mathias Hauser, Aurélien Ribes, Debbie Rosen, Nathan P. Gillett, Matthew D. Palmer, Joeri Rogelj, Karina von Schuckmann, Blair Trewin, Myles Allen, Robbie Andrew, Richard A. Betts, Alex Borger, Tim Boyer, Jiddu A. Broersma, Carlo Buontempo, Samantha Burgess, Chiara Cagnazzo, Lijing Cheng, Pierre Friedlingstein, Andrew Gettelman, Johannes Gütschow, Masayoshi Ishii, Stuart Jenkins, Xin Lan, Colin Morice, Jens Mühle, Christopher Kadow, John Kennedy, Rachel E. Killick, Paul B. Krummel, Jan C. Minx, Gunnar Myhre, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Carl-Friedrich Schleussner, Sonia I. Seneviratne, Sophie Szopa, Peter Thorne, Mahesh V. M. Kovilakam, Elisa Majamäki, Jukka-Pekka Jalkanen, Margreet van Marle, Rachel M. Hoesly, Robert Rohde, Dominik Schumacher, Guido van der Werf, Russell Vose, Kirsten Zickfeld, Xuebin Zhang, Valérie Masson-Delmotte, and Panmao Zhai
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This paper tracks some key indicators of global warming through time, from 1850 through to the end of 2023. It is designed to give an authoritative estimate of global warming to date and its causes. We find that in 2023, global warming reached 1.3 °C and is increasing at over 0.2 °C per decade. This is caused by all-time-high greenhouse gas emissions.
Piers M. Forster, Christopher J. Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Mathias Hauser, Aurélien Ribes, Debbie Rosen, Nathan Gillett, Matthew D. Palmer, Joeri Rogelj, Karina von Schuckmann, Sonia I. Seneviratne, Blair Trewin, Xuebin Zhang, Myles Allen, Robbie Andrew, Arlene Birt, Alex Borger, Tim Boyer, Jiddu A. Broersma, Lijing Cheng, Frank Dentener, Pierre Friedlingstein, José M. Gutiérrez, Johannes Gütschow, Bradley Hall, Masayoshi Ishii, Stuart Jenkins, Xin Lan, June-Yi Lee, Colin Morice, Christopher Kadow, John Kennedy, Rachel Killick, Jan C. Minx, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Carl-Friedrich Schleussner, Sophie Szopa, Peter Thorne, Robert Rohde, Maisa Rojas Corradi, Dominik Schumacher, Russell Vose, Kirsten Zickfeld, Valérie Masson-Delmotte, and Panmao Zhai
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This is a critical decade for climate action, but there is no annual tracking of the level of human-induced warming. We build on the Intergovernmental Panel on Climate Change assessment reports that are authoritative but published infrequently to create a set of key global climate indicators that can be tracked through time. Our hope is that this becomes an important annual publication that policymakers, media, scientists and the public can refer to.
Adrien Guérou, Benoit Meyssignac, Pierre Prandi, Michaël Ablain, Aurélien Ribes, and François Bignalet-Cazalet
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Based on the latest satellite observations published by the French space agency CNES, we present the current state of the sea level at the scale of the planet and assess its rise and acceleration over the past 29 years. To support scientific research we provide updated estimations of our confidence in our estimations and highlight key technological and scientific fields. Making progress on that will help to better characterize the sea level in the future.
Robert Vautard, Geert Jan van Oldenborgh, Rémy Bonnet, Sihan Li, Yoann Robin, Sarah Kew, Sjoukje Philip, Jean-Michel Soubeyroux, Brigitte Dubuisson, Nicolas Viovy, Markus Reichstein, Friederike Otto, and Iñaki Garcia de Cortazar-Atauri
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A deep frost occurred in early April 2021, inducing severe damages in grapevine and fruit trees in France. We found that such extreme frosts occurring after the start of the growing season such as those of April 2021 are currently about 2°C colder [0.5 °C to 3.3 °C] in observations than in preindustrial climate. This observed intensification of growing-period frosts is attributable, at least in part, to human-caused climate change, making the 2021 event 50 % more likely [10 %–110 %].
Aurélien Ribes, Julien Boé, Saïd Qasmi, Brigitte Dubuisson, Hervé Douville, and Laurent Terray
Earth Syst. Dynam., 13, 1397–1415, https://doi.org/10.5194/esd-13-1397-2022, https://doi.org/10.5194/esd-13-1397-2022, 2022
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We use a novel statistical method to combine climate simulations and observations, and we deliver an updated assessment of past and future warming over France. As a key result, we find that the warming over that region was underestimated in previous multi-model ensembles by up to 50 %. We also assess the contribution of greenhouse gases, aerosols, and other factors to the observed warming, as well as the impact on the seasonal temperature cycle, and we discuss implications for climate services.
Yoann Robin and Mathieu Vrac
Earth Syst. Dynam., 12, 1253–1273, https://doi.org/10.5194/esd-12-1253-2021, https://doi.org/10.5194/esd-12-1253-2021, 2021
Short summary
Short summary
We propose a new multivariate downscaling and bias correction approach called
time-shifted multivariate bias correction, which aims to correct temporal dependencies in addition to inter-variable and spatial ones. Our method is evaluated in a
perfect model experimentcontext where simulations are used as pseudo-observations. The results show a large reduction of the biases in the temporal properties, while inter-variable and spatial dependence structures are still correctly adjusted.
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Short summary
We have developed a new statistical method to describe how a severe weather event, such as a heat wave, may have been influenced by climate change. Our method incorporates both observations and data from various climate models to reflect climate model uncertainty. Our results show that both the probability and the intensity of the French July 2019 heatwave have increased significantly in response to human influence. We find that this heat wave might not have been possible without climate change.
We have developed a new statistical method to describe how a severe weather event, such as a...