Statistical downscaling of precipitation using a stochastic rainfall model conditioned on circulation patterns – an evaluation of assumptions

Abstract

For climate impact assessment regarding hydrology, the availability of long precipitation time series with high temporal and spatial resolution is essential. A possible approach to obtain this data is the statistical downscaling of precipitation simulated by a global climate model (GCM) using a stochastic rainfall model with parameters conditioned on circulation patterns (CP). This approach requires: (1) the existence of a strong relationship between CP and precipitation, (2) the sufficient reproduction of CPs by the GCM, (3) the adequate simulation of precipitation by the rainfall model and (4) either stationarity of the relationship between precipitation and CPs or an approach to account for non‐stationarity. The objective of this research is the careful evaluation and discussion of the above stated four hypotheses. For this purpose, a case study for the Aller–Leine river basin in Northern Germany has been created. It has been found that CPs can be defined which show significant differences in precipitation behaviour. The CPs derived from re‐analysis data are well reproduced by the GCM simulations. In addition, the hourly stochastic rainfall model simulates the observed precipitation characteristics well, except for a certain overestimation of the extremes. However, the change in rainfall between past and future time periods as predicted by a regional climate model could not be explained by the change in CP frequency, due to the non‐stationarity of the relationship between rainfall and CP. This can be best accounted for by re‐estimating the parameters of the stochastic rainfall model for future conditions based on corrected observations using a delta change approach regarding simulated rainfall from a regional climate model.

Can local climate variability be explained by weather patterns? A multi-station evaluation for the Rhine basin

“…Instationarities in the relationship between weather types and local variables are a long-debated issue in downscaling (IPCC, 2007), and several studies indicated their presence (e.g. Widmann and Schär, 1997;Beck et al, 2007;Haberlandt et al, 2015). Those classifications were, however, based on sea level pressure only Haberlandt et al, 2015) or additionally included geopotential height (Widmann and Schär, 1997).…”

“…Widmann and Schär, 1997;Beck et al, 2007;Haberlandt et al, 2015). Those classifications were, however, based on sea level pressure only Haberlandt et al, 2015) or additionally included geopotential height (Widmann and Schär, 1997). The addition of temperature and specific humidity might provide a better classification also in terms of capturing transient changes in local climate by changes in weather pattern sequencing.…”

“…The weather generator is then parameterized separately for each class of weather patterns (e.g. Plate, 1991, 1992;Corte-Real et al, 1999;Fowler et al, 2005;Haberlandt et al, 2015). Statistical downscaling tends to underestimate the variance of regional or local climate if the contribution of local processes is not considered, and may poorly represent extremes.…”

“…Other studies often consider only one or two variables (e.g. Beck et al, 2007;Kyselý, 2007;Anagnostopoulou et al, 2008;Beck and Philipp, 2010;Łupikasza, 2010;Haberlandt et al, 2015) and only a few studies are available with an extended list of up to eight variables (e.g. Kidson, 1994;Enke et al, 2005a;Cahynová and Huth, 2010).…”

Circulation pattern-based assessment of projected climate change for a catchment in Spain

“…[Bardossy and Plate 1992;Esteban et al 2005;Stahl et al, 2006;Huth et al, 2008;Demuzere et al 2009;Lee and Sheridan 2011]. Recent studies [Bardossy and Pegram 2011;SaprizaAzuri et al, 2013;Haberlandt et al, 2014] have illustrated the usefulness of incorporating ACPs in downscaling and bias correction methodologies. In particular Sapriza- demonstrated that several aspects of the spatio-temporal non-stationarity of catchment scale spatially-distributed rainfall fields can be reproduced by decomposing the rainfall generating process into several, time-stationary, stochastic rainfall generating processes (SRGPs).…”