Statistical downscaling of the French Mediterranean climate: assessment for present and projection in an anthropogenic scenario

Abstract

<jats:p>&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; The Mediterranean basin is a particularly vulnerable region to climate change, featuring a sharply contrasted climate between the North and South and governed by a semi-enclosed sea with pronounced surrounding topography covering parts of the Europe, Africa and Asia regions. The physiographic specificities contribute to produce mesoscale atmospheric features that can evolve to high-impact weather systems such as heavy precipitation, wind storms, heat waves and droughts. The evolution of these meteorological extremes in the context of global warming is still an open question, partly because of the large uncertainty associated with existing estimates produced by global climate models (GCM) with coarse horizontal resolution (~200 km). Downscaling climatic information at a local scale is, thus, needed to improve the climate extreme prediction and to provide relevant information for vulnerability and adaptation studies. In this study, we investigate wind, temperature and precipitation distributions for past recent climate and future scenarios at eight meteorological stations in the French Mediterranean region using one statistical downscaling model, referred as the "Cumulative Distribution Function transform" (CDF-t) approach. A thorough analysis of the uncertainty associated with statistical downscaling and bi-linear interpolation of large-scale wind speed, temperature and rainfall from reanalyses (ERA-40) and three GCM historical simulations, has been conducted and quantified in terms of Kolmogorov-Smirnov scores. CDF-t produces a more accurate and reliable local wind speed, temperature and rainfall. Generally, wind speed, temperature and rainfall CDF obtained with CDF-t are significantly similar with the observed CDF, even though CDF-t performance may vary from one station to another due to the sensitivity of the driving large-scale fields or local impact. CDF-t has then been applied to climate simulations of the 21st century under B1 and A2 scenarios for the three GCMs. As expected, the most striking trend is obtained for temperature (median and extremes), whereas for wind speed and rainfall, the evolution of the distributions is weaker. Mean surface wind speed and wind extremes seem to decrease in most locations, whereas the mean rainfall value decreases while the extremes seem to slightly increase. This is consistent with previous studies, but if this trend is clear with wind speed and rainfall data interpolated from GCM simulations at station locations, conversely CDF-t produces a more uncertain trend.&lt;/p&gt;</jats:p>

Scaling precipitation extremes with temperature in the Mediterranean: past climate assessment and projection in anthropogenic scenarios

“…There is, however, no consensus on the evolution of the frequency and intensity of the extreme events over the Mediterranean, even though a decrease of average precipitation and an increase in precipitation variability is expected (Pal et al 2004;Giorgi 2006; Fig. 1 Target region of the HyMeX/MED-CORDEX regional climate simulations with colors indicating the topography and white circles the locations of the weather stations used in this study Lavaysse et al 2012;Önol et al 2014) and an increased probability of occurrence of events conducive to floods has been suggested (Gao et al 2006).…”

Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R&lt;sup&gt;2&lt;/sup&gt;D&lt;sup&gt;2&lt;/sup&gt;) bias correction

“…The reference data employed here are daily temperature and precipitation time series from the "Systeme d'Analyze Fournissant des Renseignements Atmosphériques à la Neige" (SAFRAN) reanalysis data (Quintana-Segui et al, 2008) over the southeast region of France (2 − 7.5 • E × 42 − 45 • N) corresponding to 1506 continental grid cell with an approximate 8 km × 8 km spatial resolution. SAFRAN has been described, validated and employed in many studies (e.g., Quintana-Segui et al, 2008;Lavaysse et al, 2012;Vrac et al, 2012). The ERA-Interim (hereafter ERA-I; Dee et al, 2011) daily reanalysis data with a 0.75 • by 0.75 • spatial resolution are used here as model data to be corrected.…”

“…For the precipitation variable, CDF-t has been applied based on the relatively common "threshold adaptation" procedure. It consists of first defining a threshold th for which model data below th are set to zero (e.g., Schmidli et al, 2006;Lavaysse et al, 2012). This threshold is chosen such that the frequency of days with model precipitation greater than th is the same as the frequency of rainy days in the reference (observed) precipitation dataset.…”

Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment

“…Moreover, the SDM evaluated here is named the "cumulative distribution function transform" (CDF-t) approach. CDF-t has originally been developed for wind downscaling (Michelangeli et al, 2009) but recently applied to temperature and precipitation (Vigaud et al, 2012;Lavaysse et al, 2012). This method aims at modelling local-scale statistical characteristics using a probabilistic downscaling model.…”