Articles | Volume 12, issue 4
https://doi.org/10.5194/esd-12-1253-2021
© 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/esd-12-1253-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Nov 2021
Research article |
| 25 Nov 2021
| 25 Nov 2021
Is time a variable like the others in multivariate statistical downscaling and bias correction?
Météo-France, 42 avenue Gaspard-Coriolis, 31057, Toulouse, France
Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL), CEA/CNRS/UVSQ, Université Paris-Saclay, Centre d'Etudes de Saclay, Orme des Merisiers, 91191 Gif-sur-Yvette, France
Related authors
Guillaume Evin, Benoit Hingray, Guillaume Thirel, Agnès Ducharne, Laurent Strohmenger, Lola Corre, Yves Tramblay, Jean-Philippe Vidal, Jérémie Bonneau, François Colleoni, Joël Gailhard, Florence Habets, Frédéric Hendrickx, Louis Héraut, Peng Huang, Matthieu Le Lay, Claire Magand, Paola Marson, Céline Monteil, Simon Munier, Alix Reverdy, Jean-Michel Soubeyroux, Yoann Robin, Jean-Pierre Vergnes, Mathieu Vrac, and Eric Sauquet
EGUsphere, https://doi.org/10.5194/egusphere-2025-2727, https://doi.org/10.5194/egusphere-2025-2727, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
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.
Robin Noyelle, Davide Faranda, Yoann Robin, Mathieu Vrac, and Pascal Yiou
Weather Clim. Dynam., 6, 817–839, https://doi.org/10.5194/wcd-6-817-2025, https://doi.org/10.5194/wcd-6-817-2025, 2025
Short summary
Short summary
Properties of extreme meteorological and climatological events are changing under human-caused climate change. Extreme event attribution methods seek to estimate the contribution of global warming in the probability and intensity changes of extreme events. Here we propose a procedure to estimate these quantities for the flow analogue method, which compares the observed event to similar events in the past.
Yoann Robin, Mathieu Vrac, Aurélien Ribes, Occitane Barbaux, and Philippe Naveau
EGUsphere, https://doi.org/10.5194/egusphere-2025-1121, https://doi.org/10.5194/egusphere-2025-1121, 2025
Short summary
Short summary
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.
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
Nat. Hazards Earth Syst. Sci., 23, 1045–1058, https://doi.org/10.5194/nhess-23-1045-2023, https://doi.org/10.5194/nhess-23-1045-2023, 2023
Short summary
Short summary
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 %].
Yoann Robin and Aurélien Ribes
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 205–221, https://doi.org/10.5194/ascmo-6-205-2020, https://doi.org/10.5194/ascmo-6-205-2020, 2020
Short summary
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.
Guillaume Evin, Benoit Hingray, Guillaume Thirel, Agnès Ducharne, Laurent Strohmenger, Lola Corre, Yves Tramblay, Jean-Philippe Vidal, Jérémie Bonneau, François Colleoni, Joël Gailhard, Florence Habets, Frédéric Hendrickx, Louis Héraut, Peng Huang, Matthieu Le Lay, Claire Magand, Paola Marson, Céline Monteil, Simon Munier, Alix Reverdy, Jean-Michel Soubeyroux, Yoann Robin, Jean-Pierre Vergnes, Mathieu Vrac, and Eric Sauquet
EGUsphere, https://doi.org/10.5194/egusphere-2025-2727, https://doi.org/10.5194/egusphere-2025-2727, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
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.
Robin Noyelle, Davide Faranda, Yoann Robin, Mathieu Vrac, and Pascal Yiou
Weather Clim. Dynam., 6, 817–839, https://doi.org/10.5194/wcd-6-817-2025, https://doi.org/10.5194/wcd-6-817-2025, 2025
Short summary
Short summary
Properties of extreme meteorological and climatological events are changing under human-caused climate change. Extreme event attribution methods seek to estimate the contribution of global warming in the probability and intensity changes of extreme events. Here we propose a procedure to estimate these quantities for the flow analogue method, which compares the observed event to similar events in the past.
Duncan Pappert, Alexandre Tuel, Dim Coumou, Mathieu Vrac, and Olivia Martius
Weather Clim. Dynam., 6, 769–788, https://doi.org/10.5194/wcd-6-769-2025, https://doi.org/10.5194/wcd-6-769-2025, 2025
Short summary
Short summary
This study compares the dynamical structures that characterise long-lasting (persistent) and short hot spells in Western Europe. We find differences in large-scale atmospheric flow patterns during the events and particular soil moisture evolutions, which can account for the variation in event duration. There is variability in how drivers combine in individual events. Understanding persistent heat extremes can help improve their representation in models and ultimately their prediction.
Germain Bénard, Marion Gehlen, and Mathieu Vrac
Earth Syst. Dynam., 16, 1085–1102, https://doi.org/10.5194/esd-16-1085-2025, https://doi.org/10.5194/esd-16-1085-2025, 2025
Short summary
Short summary
We introduce a novel approach to compare Earth system model output using a causality-based approach. The analysis of interactions between atmospheric, oceanic and biogeochemical variables in the North Atlantic subpolar gyre highlights the dynamics of each model. This method reveals potential underlying causes of model differences, offering a tool for enhanced model evaluation and improved understanding of complex Earth system dynamics under past and future climates.
Ségolène Crossouard, Soulivanh Thao, Thomas Dubos, Masa Kageyama, Mathieu Vrac, and Yann Meurdesoif
EGUsphere, https://doi.org/10.5194/egusphere-2025-1418, https://doi.org/10.5194/egusphere-2025-1418, 2025
Short summary
Short summary
Current atmospheric models are limited by the computational time required for physical processes, known as physical parameterizations. To address this, we developed neural network-based emulators to replace these parameterizations in the IPSL climate model, using a simplified aquaplanet setup. We found that incorporating some physical knowledge, such as latent variables, into the learning process can improve predictions.
Yoann Robin, Mathieu Vrac, Aurélien Ribes, Occitane Barbaux, and Philippe Naveau
EGUsphere, https://doi.org/10.5194/egusphere-2025-1121, https://doi.org/10.5194/egusphere-2025-1121, 2025
Short summary
Short summary
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.
Pradeebane Vaittinada Ayar, Stella Bourdin, Davide Faranda, and Mathieu Vrac
EGUsphere, https://doi.org/10.5194/egusphere-2025-252, https://doi.org/10.5194/egusphere-2025-252, 2025
Short summary
Short summary
The tracking of Tropical cyclones (TCs) remains a matter of interest for the investigation of observed and simulated tropical cyclones. In this study, Random Forest (RF), a machine learning approach, is considered to track TCs. RF associates TC occurrence or absence to different atmospheric configurations. Compared to trackers found in the literature, it shows similar performance for tracking TCs, better control over false alarm, more flexibility and reveal key variables allowing to detect TCs.
Paul C. Astagneau, Raul R. Wood, Mathieu Vrac, Sven Kotlarski, Pradeebane Vaittinada Ayar, Bastien François, and Manuela I. Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2024-3966, https://doi.org/10.5194/egusphere-2024-3966, 2025
Short summary
Short summary
To study floods and droughts are likely to change in the future, we use climate projections from climate models. However, we first need to adjust the systematic biases of these projections at the catchment scale before using them in hydrological models. Our study compares statistical methods that can adjust these biases, but specifically for climate projections that enable a quantification of internal climate variability. We provide recommendations on the most appropriate methods.
Joséphine Schmutz, Mathieu Vrac, Bastien François, and Burak Bulut
EGUsphere, https://doi.org/10.5194/egusphere-2025-461, https://doi.org/10.5194/egusphere-2025-461, 2025
Short summary
Short summary
In recent years, Europe has faced severe hot and dry events affecting biodiversity, agriculture, and health. Understanding past significant variation in their occurrence is key for adaptation. This paper identifies emerging hotspots in Europe and North Africa. Since the 1970s, the Iberian Peninsula, Maghreb, and Central Europe have seen more frequent events, driven by rising temperature maxima, while Eastern Europe has experienced a decline due to changes in drought.
Denis Allard, Mathieu Vrac, Bastien François, and Iñaki García de Cortázar-Atauri
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-102, https://doi.org/10.5194/hess-2024-102, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Atmospheric variables from climate models often present biases relative to the past. In order to use these models to assess the impact of climate change on processes of interest, it is necessary to correct these biases. We tested several Multivariate Bias Correction Methods (MBCMs) for 5 physical variables that are input variables for 4 process models. We provide recommendations regarding the use of MBCMs when multivariate and time dependent processes are involved.
Davide Faranda, Gabriele Messori, Erika Coppola, Tommaso Alberti, Mathieu Vrac, Flavio Pons, Pascal Yiou, Marion Saint Lu, Andreia N. S. Hisi, Patrick Brockmann, Stavros Dafis, Gianmarco Mengaldo, and Robert Vautard
Weather Clim. Dynam., 5, 959–983, https://doi.org/10.5194/wcd-5-959-2024, https://doi.org/10.5194/wcd-5-959-2024, 2024
Short summary
Short summary
We introduce ClimaMeter, a tool offering real-time insights into extreme-weather events. Our tool unveils how climate change and natural variability affect these events, affecting communities worldwide. Our research equips policymakers and the public with essential knowledge, fostering informed decisions and enhancing climate resilience. We analysed two distinct events, showcasing ClimaMeter's global relevance.
Mathieu Vrac, Denis Allard, Grégoire Mariéthoz, Soulivanh Thao, and Lucas Schmutz
Earth Syst. Dynam., 15, 735–762, https://doi.org/10.5194/esd-15-735-2024, https://doi.org/10.5194/esd-15-735-2024, 2024
Short summary
Short summary
We aim to combine multiple global climate models (GCMs) to enhance the robustness of future projections. We introduce a novel approach, called "α pooling", aggregating the cumulative distribution functions (CDFs) of the models into a CDF more aligned with historical data. The new CDFs allow us to perform bias adjustment of all the raw climate simulations at once. Experiments with European temperature and precipitation demonstrate the superiority of this approach over conventional techniques.
Moctar Dembélé, Mathieu Vrac, Natalie Ceperley, Sander J. Zwart, Josh Larsen, Simon J. Dadson, Grégoire Mariéthoz, and Bettina Schaefli
Proc. IAHS, 385, 121–127, https://doi.org/10.5194/piahs-385-121-2024, https://doi.org/10.5194/piahs-385-121-2024, 2024
Short summary
Short summary
This study assesses the impact of climate change on the timing, seasonality and magnitude of mean annual minimum (MAM) flows and annual maximum flows (AMF) in the Volta River basin (VRB). Several climate change projection data are use to simulate river flow under multiple greenhouse gas emission scenarios. Future projections show that AMF could increase with various magnitude but negligible shift in time across the VRB, while MAM could decrease with up to 14 days of delay in occurrence.
Cedric Gacial Ngoungue Langue, Christophe Lavaysse, Mathieu Vrac, and Cyrille Flamant
Nat. Hazards Earth Syst. Sci., 23, 1313–1333, https://doi.org/10.5194/nhess-23-1313-2023, https://doi.org/10.5194/nhess-23-1313-2023, 2023
Short summary
Short summary
Heat waves (HWs) are climatic hazards that affect the planet. We assess here uncertainties encountered in the process of HW detection and analyse their recent trends in West Africa using reanalysis data. Three types of uncertainty have been investigated. We identified 6 years with higher frequency of HWs, possibly due to higher sea surface temperatures in the equatorial Atlantic. We noticed an increase in HW characteristics during the last decade, which could be a consequence of climate change.
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
Nat. Hazards Earth Syst. Sci., 23, 1045–1058, https://doi.org/10.5194/nhess-23-1045-2023, https://doi.org/10.5194/nhess-23-1045-2023, 2023
Short summary
Short summary
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 %].
Bastien François and Mathieu Vrac
Nat. Hazards Earth Syst. Sci., 23, 21–44, https://doi.org/10.5194/nhess-23-21-2023, https://doi.org/10.5194/nhess-23-21-2023, 2023
Short summary
Short summary
Compound events (CEs) result from a combination of several climate phenomena. In this study, we propose a new methodology to assess the time of emergence of CE probabilities and to quantify the contribution of marginal and dependence properties of climate phenomena to the overall CE probability changes. By applying our methodology to two case studies, we show the importance of considering changes in both marginal and dependence properties for future risk assessments related to CEs.
Antoine Grisart, Mathieu Casado, Vasileios Gkinis, Bo Vinther, Philippe Naveau, Mathieu Vrac, Thomas Laepple, Bénédicte Minster, Frederic Prié, Barbara Stenni, Elise Fourré, Hans Christian Steen-Larsen, Jean Jouzel, Martin Werner, Katy Pol, Valérie Masson-Delmotte, Maria Hoerhold, Trevor Popp, and Amaelle Landais
Clim. Past, 18, 2289–2301, https://doi.org/10.5194/cp-18-2289-2022, https://doi.org/10.5194/cp-18-2289-2022, 2022
Short summary
Short summary
This paper presents a compilation of high-resolution (11 cm) water isotopic records, including published and new measurements, for the last 800 000 years from the EPICA Dome C ice core, Antarctica. Using this new combined water isotopes (δ18O and δD) dataset, we study the variability and possible influence of diffusion at the multi-decadal to multi-centennial scale. We observe a stronger variability at the onset of the interglacial interval corresponding to a warm period.
Moctar Dembélé, Mathieu Vrac, Natalie Ceperley, Sander J. Zwart, Josh Larsen, Simon J. Dadson, Grégoire Mariéthoz, and Bettina Schaefli
Hydrol. Earth Syst. Sci., 26, 1481–1506, https://doi.org/10.5194/hess-26-1481-2022, https://doi.org/10.5194/hess-26-1481-2022, 2022
Short summary
Short summary
Climate change impacts on water resources in the Volta River basin are investigated under various global warming scenarios. Results reveal contrasting changes in future hydrological processes and water availability, depending on greenhouse gas emission scenarios, with implications for floods and drought occurrence over the 21st century. These findings provide insights for the elaboration of regional adaptation and mitigation strategies for climate change.
Cedric G. Ngoungue Langue, Christophe Lavaysse, Mathieu Vrac, Philippe Peyrillé, and Cyrille Flamant
Weather Clim. Dynam., 2, 893–912, https://doi.org/10.5194/wcd-2-893-2021, https://doi.org/10.5194/wcd-2-893-2021, 2021
Short summary
Short summary
This work assesses the forecast of the temperature over the Sahara, a key driver of the West African Monsoon, at a seasonal timescale. The seasonal models are able to reproduce the climatological state and some characteristics of the temperature during the rainy season in the Sahel. But, because of errors in the timing, the forecast skill scores are significant only for the first 4 weeks.
Anna Denvil-Sommer, Marion Gehlen, and Mathieu Vrac
Ocean Sci., 17, 1011–1030, https://doi.org/10.5194/os-17-1011-2021, https://doi.org/10.5194/os-17-1011-2021, 2021
Short summary
Short summary
In this work we explored design options for a future Atlantic-scale observational network enabling the release of carbon system estimates by combining data streams from various platforms. We used outputs of a physical–biogeochemical global ocean model at sites of real-world observations to reconstruct surface ocean pCO2 by applying a non-linear feed-forward neural network. The results provide important information for future BGC-Argo deployment, i.e. important regions and the number of floats.
Yoann Robin and Aurélien Ribes
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 205–221, https://doi.org/10.5194/ascmo-6-205-2020, https://doi.org/10.5194/ascmo-6-205-2020, 2020
Short summary
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.
Mathieu Vrac and Soulivanh Thao
Geosci. Model Dev., 13, 5367–5387, https://doi.org/10.5194/gmd-13-5367-2020, https://doi.org/10.5194/gmd-13-5367-2020, 2020
Short summary
Short summary
We propose a multivariate bias correction (MBC) method to adjust the spatial and/or inter-variable properties of climate simulations, while also accounting for their temporal dependences (e.g., autocorrelations).
It consists on a method reordering the ranks of the time series according to their multivariate distance to a reference time series.
Results show that temporal correlations are improved while spatial and inter-variable correlations are still satisfactorily corrected.
Cited articles
Bárdossy, A. and Pegram, G.: Multiscale spatial recorrelation of RCM
precipitation to produce unbiased climate change scenarios over large areas
and small, Water Resour. Res., 48, 9502, https://doi.org/10.1029/2011WR011524, 2012. a, b
Bartók, B., Tobin, I., Vautard, R., Vrac, M., Jin, X., Levavasseur, G.,
Denvil, S., Dubus, L., Parey, S., Michelangeli, P.-A., Troccoli, A., and
Saint-Drenan, Y.-M.: A climate projection dataset tailored for the European
energy sector, Clim. Serv., 16, 100138, https://doi.org/10.1016/j.cliser.2019.100138,
2019. a
Bevacqua, E., Maraun, D., Vousdoukas, M. I., Voukouvalas, E., Vrac, M.,
Mentaschi, L., and Widmann, M.: Higher probability of compound flooding from
precipitation and storm surge in Europe under anthropogenic climate change,
Sci. Adv., 5, eaaw5531, https://doi.org/10.1126/sciadv.aaw5531, 2019. a
Bhuiyan, M. A. E., Nikolopoulos, E. I., Anagnostou, E. N., Quintana-Seguí, P., and Barella-Ortiz, A.: A nonparametric statistical technique for combining global precipitation datasets: development and hydrological evaluation over the Iberian Peninsula, Hydrol. Earth Syst. Sci., 22, 1371–1389, https://doi.org/10.5194/hess-22-1371-2018, 2018. a
Boé, J., Terray, L., Habets, F., and Martin, E.: Statistical and dynamical downscaling of the Seine basin climate for hydro‐meteorological studies, Int. J. Climatol., 27, 1643–1655, https://doi.org/10.1002/joc.1602, 2007. a
Bürger, G., Murdock, T. Q., Werner, A. T., Sobie, S. R., and Cannon, A. J.: Downscaling Extremes – An Intercomparison of Multiple Statistical Methods for Present Climate, J. Climate, 25, 4366–4388, https://doi.org/10.1175/JCLI-D-11-00408.1, 2012. a
Cannon, A. J.: Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables, Clim. Dynam., 50, 31–49, https://doi.org/10.1007/s00382-017-3580-6, 2018. a, b, c
Cannon, A. J., Sobie, S. R., and Murdock, T. Q.: Bias Correction of GCM
Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in
Quantiles and Extremes?, J. Climate, 28, 6938–6959, https://doi.org/10.1175/JCLI-D-14-00754.1, 2015. a
Charles, S. P., Bates, B. C., Smith, I. N., and Hughes, J. P.: Statistical
downscaling of daily precipitation from observed and modelled atmospheric
fields, Hydrol. Process., 18, 1373–1394, https://doi.org/10.1002/hyp.1418, 2004. a
Chen, J., Brissette, F. P., and Leconte, R.: Uncertainty of downscaling method in quantifying the impact of climate change on hydrology, J. Hydrol., 401, 190–202, https://doi.org/10.1016/j.jhydrol.2011.02.020, 2011. a
Chen, J., Brissette, F. P., Chaumont, D., and Braun, M.: Finding appropriate
bias correction methods in downscaling precipitation for hydrologic impact
studies over North America, Water Resour. Res., 49, 4187–4205,
https://doi.org/10.1002/wrcr.20331, 2013. a
Chen, J., Arsenault, R., Brissette, F. P., and Zhang, S.: Climate Change Impact Studies: Should We Bias Correct Climate Model Outputs or Post-Process Impact Model Outputs?, Water Resour. Res., 57, e2020WR028638,
https://doi.org/10.1029/2020WR028638, 2021. a
Christensen, J. H., Boberg, F., Christensen, O. B., and Lucas-Picher, P.: On
the need for bias correction of regional climate change projections of
temperature and precipitation, Geophys. Res. Lett., 35, L20709, https://doi.org/10.1029/2008GL035694, 2008. a
Dekens, L., Parey, S., Grandjacques, M., and Dacunha-Castelle, D.: Multivariate distribution correction of climate model outputs: A generalization of quantile mapping approaches: Multivariate distribution correction of climate model outputs, Environmetrics, 28, e2454,
https://doi.org/10.1002/env.2454, 2017. a
Delrieu, G., Nicol, J., Yates, E., Kirstetter, P.-E., Creutin, J.-D., Anquetin, S., Obled, C., Saulnier, G.-M., Ducrocq, V., Gaume, E., Payrastre, O., Andrieu, H., Ayral, P.-A., Bouvier, C., Neppel, L., Livet, M., Lang, M., du Châtelet, J. P., Walpersdorf, A., and Wobrock, W.: The Catastrophic
Flash-Flood Event of 8–9 September 2002 in the Gard Region, France: A First
Case Study for the Cévennes– Vivarais Mediterranean Hydrometeorological
Observatory, J. Hydrometeorol., 6, 34–52, https://doi.org/10.1175/JHM-400.1, 2005. a
Déqué, M.: Frequency of precipitation and temperature extremes over
France in an anthropogenic scenario: Model results and statistical correction according to observed values, Global Planet. Change, 57, 16–26,
https://doi.org/10.1016/j.gloplacha.2006.11.030, 2007. a
Dufresne, J.-L., Foujols, M.-A., Denvil, S., Caubel, A., Marti, O., Aumont, O., Balkanski, Y., Bekki, S., Bellenger, H., Benshila, R., Bony, S., Bopp, L., Braconnot, P., Brockmann, P., Cadule, P., Cheruy, F., Codron, F., Cozic, A., Cugnet, D., de Noblet, N., Duvel, J.-P., Ethé, C., Fairhead, L., Fichefet, T., Flavoni, S., Friedlingstein, P., Grandpeix, J.-Y., Guez, L., Guilyardi, E., Hauglustaine, D., Hourdin, F., Idelkadi, A., Ghattas, J., Joussaume, S., Kageyama, M., Krinner, G., Labetoulle, S., Lahellec, A., Lefebvre, M.-P., Lefevre, F., Levy, C., Li, Z. X., Lloyd, J., Lott, F., Madec, G., Mancip, M., Marchand, M., Masson, S., Meurdesoif, Y., Mignot, J., Musat, I., Parouty, S., Polcher, J., Rio, C., Schulz, M., Swingedouw, D., Szopa, S., Talandier, C., Terray, P., Viovy, N., and Vuichard, N.: Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5, Clim. Dynam., 40, 2123–2165, https://doi.org/10.1007/s00382-012-1636-1, 2013. a
Eden, J., Widmann, M., Grawe, D., and Rast, S.: Skill, Correction, and
Downscaling of GCM-Simulated Precipitation, J. Climate, 25, 3970–3984,
https://doi.org/10.1175/JCLI-D-11-00254.1, 2012. a
Finney, D. J.: Dimensions of Statistics, J. Roy. Stat. Soc. Ser. C, 26,
285–289, https://doi.org/10.2307/2346969, 1977. a
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S., Collins, W.,
Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P.,
Guilyardi, E., Jakob, C., Kattsov, V., Reason, C., and Rummukainen, M.:
Evaluation of Climate Models, book section 9, Cambridge University Press, Cambridge, UK and New York, NY, USA, 741–866, https://doi.org/10.1017/CBO9781107415324.020, 2013. a
Frieler, K., Lange, S., Piontek, F., Reyer, C. P. O., Schewe, J., Warszawski,
L., Zhao, F., Chini, L., Denvil, S., Emanuel, K., Geiger, T., Halladay, K.,
Hurtt, G., Mengel, M., Murakami, D., Ostberg, S., Popp, A., Riva, R.,
Stevanovic, M., Suzuki, T., Volkholz, J., Burke, E., Ciais, P., Ebi, K.,
Eddy, T. D., Elliott, J., Galbraith, E., Gosling, S. N., Hattermann, F.,
Hickler, T., Hinkel, J., Hof, C., Huber, V., Jägermeyr, J., Krysanova, V., Marcé, R., Müller Schmied, H., Mouratiadou, I., Pierson, D., Tittensor, D. P., Vautard, R., van Vliet, M., Biber, M. F., Betts, R. A., Bodirsky, B. L., Deryng, D., Frolking, S., Jones, C. D., Lotze, H. K., Lotze-Campen, H., Sahajpal, R., Thonicke, K., Tian, H., and Yamagata, Y.: Assessing the impacts of 1.5 ∘C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b), Geosci. Model Dev., 10, 4321–4345, https://doi.org/10.5194/gmd-10-4321-2017, 2017. a
Frost, A. J., Charles, S. P., Timbal, B., Chiew, F. H. S., Mehrotra, R.,
Nguyen, K. C., Chandler, R. E., McGregor, J. L., Fu, G., Kirono, D. G. C.,
Fernandez, E., and Kent, D. M.: A comparison of multi-site daily rainfall
downscaling techniques under Australian conditions, J. Hydrol., 408, 1–18,
https://doi.org/10.1016/j.jhydrol.2011.06.021, 2011. a
Galmarini, S., Cannon, A., Ceglar, A., Christensen, O., de Noblet-Ducoudré, N., Dentener, F., Doblas-Reyes, F., Dosio, A., Gutierrez, J., Iturbide, M., Jury, M., Lange, S., Loukos, H., Maiorano, A., Maraun, D., McGinnis, S., Nikulin, G., Riccio, A., Sanchez, E., Solazzo, E., Toreti, A., Vrac, M., and Zampieri, M.: Adjusting climate model bias for agricultural impact assessment: How to cut the mustard, Clim. Serv., 13, 65–69, https://doi.org/10.1016/j.cliser.2019.01.004, 2019. a
Grouillet, B., Ruelland, D., Vaittinada Ayar, P., and Vrac, M.: Sensitivity
analysis of runoff modeling to statistical downscaling models in the western
Mediterranean, Hydrol. Earth Syst. Sci., 20, 1031–1047,
https://doi.org/10.5194/hess-20-1031-2016, 2016. a
Gudmundsson, L., Bremnes, J. B., Haugen, J. E., and Engen-Skaugen, T.:
Technical Note: Downscaling RCM precipitation to the station scale using
statistical transformations; a comparison of methods, Hydrol. Earth Syst. Sci., 16, 3383–3390, https://doi.org/10.5194/hess-16-3383-2012, 2012. a
Haddad, Z. S. and Rosenfeld, D.: Optimality of empirical Z–R relations, Q. J. Roy. Meteorol. Soc., 123, 1283–1293, https://doi.org/10.1002/qj.49712354107, 1997. a
Hempel, S., Frieler, K., Warszawski, L., Schewe, J., and Piontek, F.: A
trend-preserving bias correction – the ISI-MIP approach, Earth Syst. Dynam., 4, 219–236, https://doi.org/10.5194/esd-4-219-2013, 2013. a
IPCC: Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems, edited by: Shukla, P. R., Skea, J., Calvo Buendia, E., Masson-Delmotte, V., Pörtner, H.-O., Roberts, D. C., Zhai, P., Slade, R., Connors, S., van Diemen, R., Ferrat, M., Haughey, E., Luz, S., Neogi, S., Pathak, M., Petzold, J., Portugal Pereira, J., Vyas, P., Huntley, E., Kissick, K., Belkacemi, M., and Malley, J., in press, 2019. a
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer,
L. M., Braun, A., Colette, A., Déqué, M., Georgievski, G.,
Georgopoulou, E., Gobiet, A., Nikulin, G., Haensler, A., Hempelmann, N.,
Jones, C., Keuler, K., Kovats, S., Kröner, N., Kotlarski, S., Kriegsmann,
A., Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S., Preuschmann, S.,
Radtke, K., Rechid, D., Rounsevell, M., Samuelsson, P., Somot, S., Soussana,
J.-F., Teichmann, C., Valentini, R., Vautard, R., and Weber, B.: EURO-CORDEX: New high-resolution climate change projections for European impact research, Reg. Environ. Change, 14, 563–578, https://doi.org/10.1007/s10113-013-0499-2, 2014. a
Jeong, D., St-Hilaire, A., Ouarda, T., and Gachon, P.: Multisite statistical
downscaling model for daily precipitation combined by multivariate multiple
linear regression and stochastic weather generator, Climatic Change, 114,
567–591, https://doi.org/10.1007/s10584-012-0451-3, 2012. a
Johnson, F. and Sharma, A.: A nesting model for bias correction of variability at multiple time scales in general circulation model precipitation simulations, Water Resour. Res., 48, W01504, https://doi.org/10.1029/2011WR010464, 2012. a, b
Kallache, M., Vrac, M., Naveau, P., and Michelangeli, P.-A.: Non-stationary
probabilistic downscaling of extreme precipitation, J. Geophys. Res., 116,
D05113, https://doi.org/10.1029/2010JD014892, 2011. a
Kirtman, B., Power, S., Adedoyin, J., Boer, G., Bojariu, R., Camilloni, I.,
Doblas-Reyes, F., Fiore, A., Kimoto, M., Meehl, G., Prather, M., Sarr, A.,
Schär, C., Sutton, R., van Oldenborgh, G., Vecchi, G., and Wang, H.:
Near-term Climate Change: Projections and Predictability, book section 11,
Cambridge University Press, Cambridge, UK and New York, NY, USA, 953–1028, https://doi.org/10.1017/CBO9781107415324.023, 2013. a
KNMI: Cordex EUR-11 KNMI RACMO22E, available at:
http://cera-www.dkrz.de/WDCC/ui/Compact.jsp?acronym=CXEU11KNRA (last access: 15 November 2021), 2017. a
Leonard, M., Westra, S., Phatak, A., Lambert, M., Hurk, B., Mcinnes, K.,
Risbey, J., Schuster, S., Jakob, D., and Stafford Smith, M.: A compound event
framework for understanding extreme impacts, Wiley Interdisciplin. Rev. Clim.
Change, 5, 113–128, 2014. a
Maraun, D. and Widmann, M.: Statistical Downscaling and Bias Correction for
Climate Research, Cambridge University Press, Cambridge, https://doi.org/10.1017/9781107588783, 2018. a, b
Marti, O., Braconnot, P., Dufresne, J., Bellier, J., R., B., Bony, S., Brockmann, P., Cadule, P., Caubel, A., Codron, F., de Noblet, N., Denvil, S.,
Fairhead, L., Fichefet, T., Foujols, M., Friedlingstein, P., Goosse, H.,
Grandpeix, J., Guilyardi, E., Hourdin, F., Idelkadi, A., Kageyama, M.,
Krinner, G., Lévy, C., Madec, G., Mignot, J., Musat, I., Swingedouw, D.,
and Talandier, C.: Key features of the IPSL ocean atmosphere model and its
sensitivity to atmospheric resolution, Clim. Dynam., 34, 1–26,
https://doi.org/10.1007/s00382-009-0640-6, 2010. a
McFarlane, N.: Parameterizations: representing key processes in climate models without resolving them, Wiley Interdisciplin. Rev. Clim. Change, 2, 482–497, https://doi.org/10.1002/wcc.122, 2011. a
Mehrotra, R. and Sharma, A.: Correcting for systematic biases in multiple raw
GCM variables across a range of timescales, J. Hydrol., 520, 214–223,
https://doi.org/10.1016/j.jhydrol.2014.11.037, 2015. a, b
Mehrotra, R. and Sharma, A.: A Multivariate Quantile-Matching Bias Correction
Approach with Auto- and Cross-Dependence across Multiple Time Scales:
Implications for Downscaling, J. Climate, 29, 3519–3539,
https://doi.org/10.1175/JCLI-D-15-0356.1, 2016. a, b, c
Piani, C. and Haerter, J.: Two dimensional bias correction of temperature and
precipitation copulas in climate models, Geophys. Res. Lett., 39, L20401,
https://doi.org/10.1029/2012GL053839, 2012. a
Raimonet, M., Oudin, L., Thieu, V., Silvestre, M., Vautard, R., Rabouille, C., and Le Moigne, P.: Evaluation of Gridded Meteorological Datasets for
Hydrological Modeling, J. Hydrometeorol., 18, 3027–3041,
https://doi.org/10.1175/JHM-D-17-0018.1, 2017. a
Rizzo, M. L. and Székely, G. J.: Energy distance, Wiley Interdisciplin. Rev. Comput. Stat., 8, 27–38, https://doi.org/10.1002/wics.1375, 2016. a
Robin, Y.: yrobink/SBCK: Version 0.4.0, Zenodo [code], https://doi.org/10.5281/zenodo.5483134, 2021. a
Santambrogio, F.: Optimal Transport for Applied Mathematicians: Calculus of
Variations, PDEs, and Modeling, in: Progress in Nonlinear Differential Equations and Their Applications, 1st Edn., Birkhäuser, Cham,
https://doi.org/10.1007/978-3-319-20828-2, 2015. a, b, c
Schmidli, J., Frei, C., and Vidale, P.: Downscaling from GCM precipitation: a
benchmark for dynamical and statistical downscaling methods, Int. J. Climatol., 26, 679–689, https://doi.org/10.1002/joc.1287, 2006. a
Teutschbein, C. and Seibert, J.: Bias correction of regional climate model
simulations for hydrological climate-change impact studies: Review and
evaluation of different methods, J. Hydrol., 456–457, 12–29,
https://doi.org/10.1016/j.jhydrol.2012.05.052, 2012. a, b
Thrasher, B., Maurer, E. P., McKellar, C., and Duffy, P. B.: Technical Note:
Bias correcting climate model simulated daily temperature extremes with
quantile mapping, Hydrol. Earth Syst. Sci., 16, 3309–3314,
https://doi.org/10.5194/hess-16-3309-2012, 2012. a
Vaittinada Ayar, P., Vrac, M., Bastin, S., Carreau, J., Déqué, M., and Gallardo, C.: Intercomparison of statistical and dynamical downscaling models under the EURO- and MED-CORDEX initiative framework: Present climate
evaluations, Clim. Dynam., 46, 1301–1329, https://doi.org/10.1007/s00382-015-2647-5, 2015. a
Villani, C.: Optimal Transport: Old and New, in: Grundlehren der mathematischen Wissenschaften, 1st Edn., Springer-Verlag, Berlin, Heidelberg,
https://doi.org/10.1007/978-3-540-71050-9, 2008. a, b
Vrac, M. and Thao, S.: R2D2 v2.0: accounting for temporal dependences in multivariate bias correction via analogue rank resampling, Geosci. Model Dev., 13, 5367–5387, https://doi.org/10.5194/gmd-13-5367-2020, 2020. a, b, c
Vrac, M. and Vaittinada Ayar, P.: Influence of bias correcting predictors on
statistical downscaling models, J. Appl. Meteorol. Clim., 56, 5–26,
https://doi.org/10.1175/JAMC-D-16-0079.1, 2017. a
Vrac, M., Stein, M. L., Hayhoe, K., and Liang, X.-Z.: A general method for
validating statistical downscaling methods under future climate change,
Geophys. Res. Lett., 34, L18701, https://doi.org/10.1029/2007GL030295, 2007. a
Vrac, M., Drobinski, P., Merlo, A., Herrmann, M., Lavaysse, C., Li, L., and
Somot, S.: Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment, Nat. Hazards Earth Syst. Sci., 12, 2769–2784, https://doi.org/10.5194/nhess-12-2769-2012, 2012. a, b
Vrac, M., Noël, T., and Vautard, R.: Bias correction of precipitation
through Singularity Stochastic Removal: Because occurrences matter, J. Geophys. Res., 121, 5237–5258, https://doi.org/10.1002/2015JD024511, 2016. a
Wilcox, R. H.: Adaptive control processes – A guided tour, by Richard Bellman, Princeton University Press, Princeton, New Jersey, 1961, 255 pp., 6.50, Nav. Res. Logist. Q., 8, 315–316, https://doi.org/10.1002/nav.3800080314, 1961. a
Wilks, D. S.: Stochastic weather generators for climate-change downscaling,
part II: multivariable and spatially coherent multisite downscaling, Wiley
Interdisciplin. Rev. Clim. Change, 3, 267–278, https://doi.org/10.1002/wcc.167, 2012.
a
Xiaoli, L., Coulibaly, P., and Evora, N.: Comparison of data-driven methods for downscaling ensemble weather forecasts, Hydrol. Earth Syst. Sci., 12,
615–624, https://doi.org/10.5194/hess-12-615-2008, 2008. a
Xu, C.-Y.: From GCMs to river flow: a review of downscaling methods and
hydrologic modelling approaches, Prog. Phys. Geogr., 23, 229–249, https://doi.org/10.1177/030913339902300204, 1999. a
Zscheischler, J., Westra, S., Hurk, B., Seneviratne, S., Ward, P., Pitman, A., AghaKouchak, A., Bresch, D., Leonard, M., Wahl, T., and Zhang, X.: Future
climate risk from compound events, Nat. Clim. Change, 8, 469–477,
https://doi.org/10.1038/s41558-018-0156-3, 2018. a
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.
We propose a new multivariate downscaling and bias correction approach called
time-shifted...
Altmetrics
Final-revised paper
Preprint