What is the added value of using downscaled CMIP5 data for the study of climate extremes under climate change?

In the beginning of this century, impacts studies due to climate change were carried out directly with the outputs of the general circulation models of the Atmosphere and the Ocean (AOGCM). However, these models had very low resolutions in the order of several degrees and the climate of some areas, such as monsoon regions, was poorly reproduced. These two disadvantages make it difficult to study the evolution of extremes. Recently, more impact studies are using outputs from multiple AOGCM models that are downscaled and unbiased. The ISIMIP consortium (https://www.isimip.org/) participates in the dissemination of this practice by proposing several AOGCM models with a resolution of 0.5° X 0.5°.

In our study, a high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP5 experiment using the ERA5 reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.25°x 0.25°, comprises 21 climate models and includes 5 surface daily variables at monthly resolution: air temperature (mean, minimum, and maximum), precipitation, and mean near-surface wind speed  (Noël et al. accepted). This dataset is obtained by using the quantile – quantile method Cumulative Distribution Function transform (CDFt) (Vrac et al. 2012, 2016,, developed over  10 years to bias correct or downscale climate model output, and ERA5 land data as a reference . T

We propose in this communication to present the climate variability by the end of the century in terms of extreme climate indicators such as heat waves or heavy rainfall at the local/grid point level (e.g. city level). Particular attention will be paid to the magnitude of the changes as well as the associated uncertainty.

 

References

Vrac, M., Drobinski, P., Merlo, A., Herrmann, M., Lavaysse, C., Li, L., & Somot, S. (2012). Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment.Nat. Hazards Earth Syst. Sci., 12, 2769–2784.

Vrac, M., Noël, T., & Vautard, R. (2016). Bias correction of precipitation through Singularity Stochastic Removal: Because occurrences matter. Journal of Geophysical Research: Atmospheres, 121(10), 5237-5258.

Noël, T., Loukos, H., Defrance, D., Vrac, M., & Levavasseur, G. (2020). High-resolution downscaled CMIP5 projections dataset of essential surface climate variables over the globe coherent with ERA5 reanalyses for climate change impact assessments. Data in Brief (accepted, https://doi.org/10.31223/X53W3F)