Anwendung eines statistischen Downscaling-Verfahren zur Analyse von Klimawandel auf der nordwestlichen Iberischen Halbinsel
Creators: name: Brands, Swen
Publisher: name: Zenodo
Year: 2009
Subjects: Statistical Downscaling, Climate Change, Temperature, Iberian Peninsula

description: In this study the analog method is used to generate ensemble-projections of local Tmean, Tmax and Tmin in the northwestern part of the Iberian Peninsula (Galicia) that are based on a rigorous validation procedure. A three-step method is suggested: In a first step, a predictor screening under optimal conditions identifies T850MSLPSH850 as the the most skillful predictor-combination (estimated by the Spearman correlation coefficient) to downscale the three predictands. To simulate Tmean and Tmax (Tmin) SH850 (MSLP) can be omitted. Keeping the domain constant throughout the year, a seasonal validation of T850MSLP for Tmean and Tmax yields highest correlation in autumn (~0,8) and lowest in winter. The Tmin are reproduced with significantly lower skill in all seasons except winter, indicating that the disadvantage of not including the SH850 in the predictor-field is negligible during this season. In terms of reliability, and with the exception of a clear negative bias found for Tmax in spring, good results are obtained for all predictands and all seasons underlining that, under optimal conditions, local observations´ CDF are reproduced by T850MSLP. Assumed the best predictor combination under non-optimal conditions, in a second step, the T850MSLP of 5 control-runs of a multi-model, multi-initial condition-ensemble is used to downscale the predictands, obtaining 5 deterministic time series for each predictand and station. No bias adjusting, variance inflation or randomization is used to correct the downscaled series. While none of the deterministic downscalings alone is able to to reproduce local observations´ CDF, their combination performs quite well during summer and autumn, being less reliable in winter and spring. In a third step 14 scenario-runs of the formerly validated ensemble are used to generate summertime projections until 2050. Projections from JJA 2021-2050 (scenario period) are compared to observations from JJA 1973-2002 (reference period) in order to detect local climate change. As the CDF of the combined downscaled time series have been successfully validated in the control-period and are resistant to model uncertainties that clearly dominate the ensemble-spread in the scenario period, they give the most credible projections: The modelled mean relative summer warming is significant for all stations and predictands. The Tmax exhibit a significantly higher warming (+0,7 ±0,2°C to +1,8 ±0,3°C) than the Tmin (+0,3 ±0,1°C to +0,8 ±0,2°C), leaving the Tmean in between (+0,5 ±0,1°C to +1,2° ±0.2°C). It can be shown that the relationship between the probability of local daytime heat-events in the reference period (independent variable) and its increase in the scenario period (dependent variable) is described by a descending exponential function, whereas for nighttime warm-events a linear relationship can be assumed. Due to large individual model errors, deterministic downscalings should be considered with caution, especially if they are used to project the probability of future extreme events. They are even more dubious if their correspondent control-run versions are not validated and/or the projections are given in relative terms to the control-period. By expanding the current GCM-ensemble and optimizing the domain as a function of each members´ skillful scale, local CDF may be reproduced reliably throughout every season, opening the door to resistant projections that neither need to be corrected nor given in relative terms to the control-period.
description: Magister Thesis