Evaluating the impact of climate change and millennial variability on the last Neanderthal populations in Europe (Marine Isotope Stage 3) - Benjamin Albouy; Simon Paquin; Julien Riel-Salvatore; Masa Kageyama; Mathieu Vrac; Ariane Burke (Quaternary Science Reviews, 2024)

Anatomically modern human dispersals into Europe during MIS 3: Climate stability, paleogeography and habitat suitability - Simon Paquin; Benjamin Albouy; Masa Kageyama; Mathieu Vrac; Ariane Burke (Quaternary Science Reviews, 2024)

Correcting biases in tropical cyclone intensities in low-resolution datasets using dynamical systems metrics - Davide Faranda; Gabriele Messori; Stella Bourdin; Mathieu Vrac; Soulivanh Thao; Jacopo Riboldi; Sébastien Fromang; Pascal Yiou (Climate Dynamics, 2023)

More than agriculture: Analysing time-cumulative human impact on European land-cover of second half of the Holocene - Anhelina Zapolska; Maria Antonia Serge; Florence Mazier; Aurélien Quiquet; Hans Renssen; Mathieu Vrac; Ralph Fyfe; Didier M. Roche (Quaternary Science Reviews, 2023)

Changes in temperature–precipitation correlations over Europe: are climate models reliable? - Mathieu Vrac; Soulivanh Thao; Pascal Yiou (Climate Dynamics, 2023)
Inter-variable correlations (e.g., between daily temperature and precipitation) are key statistical properties to characterise probabilities of simultaneous climate events and compound events. Their correct simulations from climate models, both in values and in changes over time, is then a prerequisite to investigate their future changes and associated impacts. Therefore, this study first evaluates the capabilities of one 11-single run multi-model ensemble (CMIP6) and one 40-member single model initial-condition large ensemble (CESM) over Europe to reproduce the characteristics of a reanalysis dataset (ERA5) in terms of temperature–precipitation correlations and their historical changes. Next, the ensembles’ correlations for the end of the 21st century are compared. Over the historical period, both CMIP6 and CESM ensembles have season-dependent and spatially structured biases. Moreover, the inter-variable correlations from both ensembles mostly appear stationary. Thus, although reanalysis displays significant correlation changes, none of the ensembles can reproduce them, with internal variability representing only 30% on the inter-model variability. However, future correlations show significant changes over large spatial patterns. Yet, those patterns are rather different for CMIP6 and CESM, reflecting a large uncertainty in changes. In addition, for historical and future projections, an analysis conditional on atmospheric circulation regimes is performed. The conditional correlations given the regimes are found to be the main contributor to the biases in correlation over the historical period, and to the past and future changes of correlation. These results highlight the importance of the large-scale circulation regimes and the need to understand their physical relationships with local-scale phenomena associated to specific inter-variable correlations.

Combining global climate models using graph cuts - Soulivanh Thao; Mats Garvik; Gregoire Mariethoz; Mathieu Vrac (Climate Dynamics, 2022)
Global Climate Models are the main tools for climate projections. Since many models exist, it is common to use Multi-Model Ensembles to reduce biases and assess uncertainties in climate projections. Several approaches have been proposed to combine individual models and extract a robust signal from an ensemble. Among them, the Multi-Model Mean (MMM) is the most commonly used. Based on the assumption that the models are centered around the truth, it consists in averaging the ensemble, with the possibility of using equal weights for all models or to adjust weights to favor some models. In this paper, we propose a new alternative to reconstruct multi-decadal means of climate variables from a Multi-Model Ensemble, where the local performance of the models is taken into account. This is in contrast with MMM where a model has the same weight for all locations. Our approach is based on a computer vision method called graph cuts and consists in selecting for each grid point the most appropriate model, while at the same time considering the overall spatial consistency of the resulting field. The performance of the graph cuts approach is assessed based on two experiments: one where the ERA5 reanalyses are considered as the reference, and another involving a perfect model experiment where each model is in turn considered as the reference. We show that the graph cuts approach generally results in lower biases than other model combination approaches such as MMM, while at the same time preserving a similar level of spatial continuity.

Seasonal circulation regimes in the North Atlantic: Towards a new seasonality - Florentin Breton; Mathieu Vrac; Pascal Yiou; Pradeebane Vaittinada Ayar; Aglaé Jézéquel (International Journal of Climatology, 2022)
European climate variability is shaped by atmospheric dynamics over the North Atlantic and local processes. Better understanding their future seasonality is essential to anticipate changes in weather conditions for human and natural systems. We explore atmospheric seasonality over 1979–2017 and 1979–2100 with seasonal circulation regimes (SCRs), by clustering year‐round daily fields of Z500 from the ERA‐Interim reanalysis and 12 Coupled Model Intercomparison Project phase 5 (CMIP5) climate models (historical and RCP8.5 runs). The spatial and temporal variability of SCR structures and associated patterns of surface air temperature are investigated. Climate models have biases but reproduce structures and evolutions of SCRs similar to the reanalysis over 1979–2017: decreasing frequency of winter conditions (starting later and ending earlier in the year) and the opposite for summer conditions. These changes are stronger over 1979–2100 than over 1979–2017, associated with a large increase of North Atlantic seasonal mean Z500 and temperature. When using more SCRs (more freedom in definition of seasonality), the changes over 1979–2100 correspond to a long‐term swap between SCRs, resulting in similar structures (annual cycle and spatial patterns) relative to the evolution of seasonal mean Z500 and temperature. To understand whether the evolution of SCRs is linked to uniform warming, or to changes in circulation patterns, we remove the calendar trend in the Z500 regional average to define SCRs based on detrended data (d‐SCRs). The temporal properties of d‐SCRs appear almost constant whereas their spatial patterns change, indicating that the calendar Z500 regional trend drives the evolutions of SCRs, and that changing spatial patterns in d‐SCRs account for the heterogeneity of this trend. Our study suggests that historical winter conditions will continue to decrease in the future while historical summer conditions continue to increase. However, it also suggests that the spatial and temporal patterns of SCRs would remain similar, relatively to the year‐round Z500 increase.

Adjusting spatial dependence of climate model outputs with cycle-consistent adversarial networks - Bastien François; Soulivanh Thao; Mathieu Vrac (Climate Dynamics, 2021)
Climate model outputs are commonly corrected using statistical univariate bias correction methods. Most of the time, those 1d-corrections do not modify the ranks of the time series to be corrected. This implies that biases in the spatial or inter-variable dependences of the simulated variables are not adjusted. Hence, over the last few years, some multivariate bias correction (MBC) methods have been developed to account for inter-variable structures, inter-site ones, or both. As proof-of-concept, we propose to adapt a computer vision technique used for Image-to-Image translation tasks (CycleGAN) for the adjustment of spatial dependence structures of climate model projections. The proposed algorithm, named MBC-CycleGAN, aims to transfer simulated maps (seen as images) with inappropriate spatial dependence structure from climate model outputs to more realistic images with spatial properties similar to the observed ones. For evaluation purposes, the method is applied to adjust maps of temperature and precipitation from climate simulations through two cross-validation approaches. The first one is designed to assess two different post-processing schemes (Perfect Prognosis and Model Output Statistics). The second one assesses the influence of nonstationary properties of climate simulations on the performance of MBC-CycleGAN to adjust spatial dependences. Results are compared against a popular univariate bias correction method, a “quantile-mapping” method, which ignores inter-site dependencies in the correction procedure, and two state-of-the-art multivariate bias correction algorithms aiming to adjust spatial correlation structure. In comparison with these alternatives, the MBC-CycleGAN algorithm reasonably corrects spatial correlations of climate simulations for both temperature and precipitation, encouraging further research on the improvement of this approach for multivariate bias correction of climate model projections.

Observation system simulation experiments in the Atlantic Ocean for enhanced surface ocean p CO 2 reconstructions - Anna Denvil-Sommer; Marion Gehlen; Mathieu Vrac (Ocean Science, 2021)
To derive an optimal observation system for surface ocean pCO2 in the Atlantic Ocean and the Atlantic sector of the Southern Ocean, 11 observation system simulation experiments (OSSEs) were completed. Each OSSE is a feedforward neural network (FFNN) that is based on a different data distribution and provides ocean surface pCO2 for the period 2008–2010 with a 5 d time interval. Based on the geographical and time positions from three observational platforms, volunteering observing ships, Argo floats and OceanSITES moorings, pseudo-observations were constructed using the outputs from an online-coupled physical–biogeochemical global ocean model with 0.25∘ nominal resolution. The aim of this work was to find an optimal spatial distribution of observations to supplement the widely used Surface Ocean CO2 Atlas (SOCAT) and to improve the accuracy of ocean surface pCO2 reconstructions. OSSEs showed that the additional data from mooring stations and an improved coverage of the Southern Hemisphere with biogeochemical ARGO floats corresponding to least 25 % of the density of active floats (2008–2010) (OSSE 10) would significantly improve the pCO2 reconstruction and reduce the bias of derived estimates of sea–air CO2 fluxes by 74 % compared to ocean model outputs.

Comparison of statistical downscaling methods with respect to extreme events over Europe: Validation results from the perfect predictor experiment of the COST Action VALUE - Elke Hertig; Douglas Maraun; Judit Bartholy; Rita Pongracz; Mathieu Vrac; Ileana Mares; José M. Gutiérrez; Joanna Wibig; Ana Casanueva; Pedro M. M. Soares (International Journal of Climatology, 2019)
Credible information about the properties and changes of extreme events on the regional and local scales is of prime importance in the context of future climate change. Within the EU‐COST Action VALUE a comprehensive validation framework for downscaling methods has been developed. Here we present validation results for extremes of temperature and precipitation from the perfect predictor experiment that uses reanalysis‐based predictors to isolate downscaling skill. The raw reanalysis output reveals that there is mostly a large bias with respect to the extreme index values at the considered stations across Europe, clearly pointing to the necessity of downscaling. The performance of the downscaling methods is closely linked to their specific structure and setup. All methods using parametric distributions require non‐standard distributions to correctly represent marginal aspects of extremes. Also, the performance is much improved by explicitly including a seasonal component, particularly in case of precipitation. With respect to the marginal aspects of extremes the best performance is found for model output statistics (MOS), weather generators (WGs) as well as perfect prognosis (PP) methods using analogues. Spell‐length‐related extremes of temperature are best assessed by MOS and WGs, spell‐length‐related extremes of precipitation by MOS and PP methods using analogues. The skill of PP methods with transfer functions varies strongly across the methods and depends on the extreme index, region and season considered.

Which downscaled rainfall data for climate change impact studies in urban areas? Review of current approaches and trends - Eustache Gooré Bi; Philippe Gachon; Mathieu Vrac; Frédéric Monette (Theoretical and Applied Climatology, 2017)

A complete hydro-climate model chain to investigate the influence of sea surface temperature on recent hydroclimatic variability in subtropical South America (Laguna Mar Chiquita, Argentina) - Magali Troin; Mathieu Vrac; Myriam Khodri; Daniel Caya; Christine Vallet-Coulomb; Eduardo Piovano; Florence Sylvestre (Climate Dynamics, 2016)

Intercomparison of statistical and dynamical downscaling models under the EURO- and MED-CORDEX initiative framework: present climate evaluations - Pradeebane Vaittinada Ayar; Mathieu Vrac; Sophie Bastin; Julie Carreau; Michel Déqué; Clemente Gallardo (Climate Dynamics, 2016)

Assessing climate change impacts on European wind energy from ENSEMBLES high-resolution climate projections - Isabelle Tobin; Robert Vautard; Irena Balog; François-Marie Bréon; Sonia Jerez; Paolo Michele Ruti; Françoise Thais; Mathieu Vrac; Pascal Yiou (Climatic Change, 2015)

Trends and variability of seasonal weather regimes - Mathieu Vrac; Pradeebane Vaittinada Ayar; Pascal Yiou (International Journal of Climatology, 2014)
Seasons and seasonality are the main properties of extra‐tropical climate that affect ecosystems and society. For example, agriculture, tourism, energy consumption or ecosystem phenology are primarily dependent on seasonality and on the magnitude of the meteorological events associated within each season. Changes in the seasonality of variables like surface temperature during the last decades have been widely investigated but seasonal changes of the weather have received less quantification. This paper redefines the concept of seasonality based on the extra‐tropical atmospheric circulation, and on the notion that it can drive the evolution of temperature. We find that summer‐like atmospheric conditions have appeared earlier and ended later since 1948. Conversely, the period with winter patterns has reduced over that period. The temperatures associated with weather patterns allow to identify the sources of temperature trends.

Climate variability and trends in downscaled high-resolution simulations and projections over Metropolitan France - Robert Vautard; Thomas Noël; Laurent Li; Mathieu Vrac; Eric Martin; Philippe Dandin; Julien Cattiaux; Sylvie Joussaume (Climate Dynamics, 2013)

Ensemble reconstruction of the atmospheric column from surface pressure using analogues - Pascal Yiou; Tamara Salameh; Philippe Drobinski; Laurent Menut; Robert Vautard; Mathieu Vrac (Climate Dynamics, 2013)

Precipitation and temperature space–time variability and extremes in the Mediterranean region: evaluation of dynamical and statistical downscaling methods - Emmanouil Flaounas; Philippe Drobinski; Mathieu Vrac; Sophie Bastin; Cindy Lebeaupin-Brossier; Marc Stéfanon; Marco Borga; Jean-Christophe Calvet (Climate Dynamics, 2013)

Projected impacts of climate change on spatio‐temporal patterns of freshwater fish beta diversity: a deconstructing approach - Clément Tisseuil; Fabien Leprieur; Gäel Grenouillet; Mathieu Vrac; Sovan Lek (Global Ecology and Biogeography, 2012)
Aim To assess the potential impacts of future climate change on spatio‐temporal patterns of freshwater fish beta diversity. Location Adour–Garonne River Basin (France). Methods We first applied an ensemble modelling approach to project annually the future distribution of 18 fish species for the 2010–2100 period on 50 sites. We then explored the spatial and temporal patterns of beta diversity by distinguishing between its two additive components, namely species turnover and nestedness. Results Taxonomic homogenization of fish assemblages was projected to increase linearly over the 21st century, especially in the downstream parts of the river gradient. This homogenization process was almost entirely caused by a decrease in spatial species turnover. When considering the temporal dimension of beta diversity, our results reveal an overall pattern of decreasing beta diversity along the upstream–downstream river gradient. In contrast, when considering the turnover and nestedness components of temporal beta diversity we found significant U‐shaped and hump‐shaped relationships, respectively. Main conclusions Future climate change is projected to modify the taxonomic composition of freshwater fish assemblages by increasing their overall similarity over the Adour–Garonne River Basin. Our findings suggest that the distinction between the nestedness and turnover components of beta diversity is not only crucial for understanding the processes shaping spatial beta‐diversity patterns but also for identifying localities where the rates of species replacement are projected to be greatest. Specifically we recommend that future conservation studies should not only consider the spatial component of beta diversity but also its dynamic caused by climate warming.