Bastien Francois

• PhD
• Climate Scientist at Koninklijk Nederlands Meteorologisch Instituut

Hydrological climate change impact studies typically rely on hydrological projections generated by hydrological models driven with bias adjusted climate simulations. Such hydrological projections are influenced by internal climate variability, which can mask the emergence of robust climate trends. To account for this internal variability in climate...

Atmospheric variables simulated from climate models often present biases relative to the same variables calculated by reanalysis in 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. Currently, the bias correction methods used operationally correct one-dim...

Compound wind and precipitation (CWP) extremes often cause severe impacts on human society and ecosystems, such as damage to agricultural crops and infrastructure. High regional frequencies of CWP extremes across multiple regions in the same winter, referred to as spatially compounding events, can further impact the global economy and the reinsuran...

Climate projections from global circulation models (GCMs), part of the Coupled Model Intercomparison Project 6 (CMIP6), are often employed to study the impact of future climate on ecosystems. However, especially at regional scales, climate projections display large biases in key forcing variables such as temperature and precipitation. These biases...

Many climate-related disasters often result from a combination of several climate phenomena, also referred to as “compound events’’ (CEs). By interacting with each other, these phenomena can lead to huge environmental and societal impacts, at a scale potentially far greater than any of these climate events could have caused separately. Marginal and...

Climate is a complex system resulting from various interactions between its different components and its multiple variables. This thesis aims to assess whether and how the use of multivariate statistical approaches for the study of climate simulations can contribute to a deeper understanding of climate change and high-impact climate events. To answ...

Climate projections from global circulation models (GCMs) part of the Coupled Model Intercomparison Project 6 (CMIP6) are often employed to study the impact of future climate on ecosystems. However, especially at regional scales, climate projections display large biases in key forcing variables such as temperature and precipitation, which hamper pr...

Many climate-related disasters often result from a combination of several climate phenomena, also referred to as "compound events" (CEs). By interacting with each other, these phenomena can lead to huge environmental and societal impacts, at a scale potentially far greater than any of these climate events could have caused separately. Marginal and...

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...

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...

Climate models are the major tools to study the climate system and its evolutions in the future. However, climate simulations often present statistical biases and have to be corrected against observations before being used in impact assessments. Several bias correction (BC) methods have therefore been developed in the literature over the last 2 dec...

Climate models are the major tools to estimate climate variables evolutions in the future. However, climate simulations often present statistical biases and have to be corrected against observations before being used in impact assessments. Several bias correction (BC) methods have therefore been developed in the literature over the last two decades...

Abstract. Climate models are the major tools to estimate climate variables evolutions in the future. However, climate simulations often present statistical biases and have to be corrected against observations before being used in impact assessments. Several bias correction (BC) methods have therefore been developed in the literature over the last t...

Climate models present statistical biases. There is a huge need for climatologists to correct climate model data against observations before using it in impact studies. To do so, many univariate and multivariate methods of bias correction have been developed in the scientific literature. Univariate bias correction methods (1D-BC), based largely on...