Intercomparison of statistical and dynamical downscaling models under the EURO- and MED-CORDEX initiative framework: present climate evaluations

Abstract

International audienceGiven the coarse spatial resolution of General Circulation Models, finer scale projections of variables affected by local-scale processes such as precipitation are often needed to drive impacts models, for example in hydrology or ecology among other fields. This need for high-resolution data leads to apply projection techniques called downscaling. Downscaling can be performed according to two approaches: dynamical and statistical models. The latter approach is constituted by various statistical families conceptually different. If several studies have made some intercomparisons of existing downscaling models, none of them included all those families and approaches in a manner that all the models are equally considered. To this end, the present study conducts an intercomparison exercise under the EURO- and MED-CORDEX initiative hindcast framework. Six Statistical Downscaling Models (SDMs) and five Regional Climate Models (RCMs) are compared in terms of precipitation outputs. The downscaled simulations are driven by the ERAinterim reanalyses over the 1989–2008 period over a common area at 0.44° of resolution. The 11 models are evaluated according to four aspects of the precipitation: occurrence, intensity, as well as spatial and temporal properties. For each aspect, one or several indicators are computed to discriminate the models. The results indicate that marginal properties of rain occurrence and intensity are better modelled by stochastic and resampling-based SDMs, while spatial and temporal variability are better modelled by RCMs and resampling-based SDM. These general conclusions have to be considered with caution because they rely on the chosen indicators and could change when considering other specific criteria. The indicators suit specific purpose and therefore the model evaluation results depend on the end-users point of view and how they intend to use with model outputs. Nevertheless, building on previous intercomparison exercises, this study provides a consistent intercomparison framework, including both SDMs and RCMs, which is designed to be flexible, i.e., other models and indicators can easily be added. More generally, this framework provides a tool to select the downscaling model to be used according to the statistical properties of the local-scale climate data to drive properly specific impact models

Projected changes in future climate over the Midwest and Great Lakes region using downscaled CMIP5 ensembles

“…Several studies have compared different statistical and dynamical downscaling approaches and have illustrated that appropriate selection of approach needs to consider the advantages and limitations of each technique, availability of data and computational resources, and the kind of impacts being considered (e.g. Murphy, 1999;Gutmann et al, 2012;Hamlet et al, 2013;Giorgi and Gutowski, 2015;Ayar et al, 2016;Tang et al, 2016). In our case, to support multi-scale analysis of temperature (T) and precipitation (P) across the region, we have chosen to downscale the GCM projections to 1/16 ∘ resolution (∼5 ×7 km at mid-latitudes) at daily timescales.…”

Which downscaled rainfall data for climate change impact studies in urban areas? Review of current approaches and trends

“…The validity of many of these methods has not yet been tested in urban areas, and work published in the literature to date provides very little information on their effectiveness at a smaller scale (1-10 km 2 ), such as in urban watersheds (Arnbjerg-Nielsen et al 2013;Willems et al 2012). Since downscaling is not an end in itself, but a process that provides necessary data and information to end-users (e.g., Vaittinada Ayar et al 2015), such global approaches are not very useful for estimating the impacts of CC on hydrologic budgets at a local scale.…”

“…It is correct that analogsbased methods (e.g., Zorita and von Storch 1999) are unable by construction to generate values out of the range of the calibration dataset. However, stochastic downscaling models-or more generally, any model whose parameters or projections can evolve with the predictors-are able to generate downscaled values that have never been observed (e.g., Vaittinada Ayar et al 2015). For example, a recent study using linear statistical downscaling method (from the tool developed by Hessami et al 2008), realized on both temperature and precipitation variables, has demonstrated the capacity to generate plausible future changes in the severity and occurrence of droughts (an increase over the twenty-first century never observed over the historical period) in the Canadian Prairies.…”

“…These studies highlight the fact that statistical downscaling is not very costly from a numerical standpoint and yields very satisfactory results in view of its complexity (Piazza et al 2011, Vaittinada Ayar et al 2015. However, result quality is directly dependent on the quality of the observational databases used and/or of the relationships between predictand and predictor derived over the current period.…”

Sensitivity analysis of runoff modeling to statistical downscaling models in the western Mediterranean

“…SDMs are based on a static relationship; i.e., the mathematical formulation of the relation between predictands (i.e., the localscale variable to be simulated) and predictors (i.e., the largescale information or data used as inputs in the SDMs) has to be valid not only for the current climate on which the relationship is calibrated, but also for future climates, for example. Most state-of-the-art SDMs belong to one of the four following families (Vaittinada Ayar et al, 2015): transfer functions, weather typing, methods based on stochastic weather generators and model output statistics (MOS) models, which generally work on cumulative distribution functions (CDFs). Many studies demonstrated that caution is required when interpreting the results of climate change impact studies based on only one downscaling model (e.g., Chen et al, 2011).…”