Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods

Abstract

Abstract. The impact of climate change on water resources is usually assessed at the local scale. However, regional climate models (RCMs) are known to exhibit systematic biases in precipitation. Hence, RCM simulations need to be post-processed in order to produce reliable estimates of local scale climate. Popular post-processing approaches are based on statistical transformations, which attempt to adjust the distribution of modelled data such that it closely resembles the observed climatology. However, the diversity of suggested methods renders the selection of optimal techniques difficult and therefore there is a need for clarification. In this paper, statistical transformations for postprocessing RCM output are reviewed and classified into (1) distribution derived transformations, (2) parametric transformations and (3) nonparametric transformations, each differing with respect to their underlying assumptions. A real world application, using observations of 82 precipitation stations in Norway, showed that nonparametric transformations have the highest skill in systematically reducing biases in RCM precipitation.

Análisis integral del impacto del Cambio Climático enlos regímenes de agua, crecidas y sedimentos de una rambla mediterránea

“…Los métodos de corrección del sesgo en la información proveniente de los MCG y sus regionalizaciones generalmente están basados en transformaciones estadísticas, donde para una variable modelizada (U m ) correspondiente a una variable observada (U o ) se busca una función (h) tal que se consiga una relación como la representada en la Ecuación 1, corrigiendo a la primera variable. Si se conoce la función de distribución de probabilidades tanto para la variable observada (F o ) como para la simulada (F m ) se puede llegar a una transformación estadística a través de su probabilidad integral como la representada en la Ecuación 2 (Gudmundsson et al, 2012). Un ejemplo de la diferencia entre la información corregida y sin corregir se puede observar en la Figura 7.…”

Drought indices revisited – improving and testing of drought indices in a simulation of the last two millennia for Europe

“…The mapping has to be performed for each month of the year separately to account for the annual cycle. Quantile mapping is regularly used in downscaling and bias correction of model output (Raible et al, 2012;Themessl et al, 2011;Gudmundsson et al, 2012;Rajczak et al, 2016) and is similar to non-parametric approaches used in drought index definitions (Farahmand andAghaKouchak, 2015).…”

Framework for Incorporating Downscaled Climate Output into Existing Engineering Methods: Application to Precipitation Frequency Curves

“…Weather typing, or resampling, involves relating the weather patterns of the larger scale climate model to observed patterns in the local area (Prudhomme et al 2002;Onof and ArnbjergNielsen 2009). Quantile mapping, also used for bias-correction, matches the empirical quantiles of re-gridded historical data to those of the historical climate simulation, then adjusts the future climate simulation based on the difference between the historical data and simulation (Boé et al 2007;Laflamme et al 2016;Gudmundsson et al 2012).…”

“…Weather generators, which can be adapted to different anticipated changes in climate, have been used to simulate synthetic, rainfall time series at the station (point) scale at monthly, daily and hourly time steps (Wilks and Wilby 1999;Kilsby et al 2007;Willems et al 2013). Quantile mapping has been used to apply expected changes to the empirical distribution of observed rainfall events at the temporal and spatial resolution of the observations (Laflamme et al 2016;Boé et al 2007;Gudmundsson et al 2012;Wood et al 2004). Numerous studies utilize a "delta" or "change factor" technique, which applies the expected absolute (delta) or relative (ratio) change between current and future gridded projections to historical rainfall data (Wilks and Wilby 1999;Boé et al 2007;Wood et al 2004;K.…”