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- ItemRegional projections of temperature and precipitation changes: Robustness and uncertainty aspects(Stuttgart : Gebrueder Borntraeger Verlagsbuchhandlung, 2017) Piniewski, M.; Mezghani, A.; Szczésniak, M.; Kundzewicz, Z.W.This study presents the analysis of bias-corrected projections of changes in temperature and precipitation in the Vistula and Odra basins, covering approximately 90% of the Polish territory and small parts of neighbouring countries in Central and Eastern Europe. The ensemble of climate projections consists of nine regional climate model simulations from the EURO-CORDEX ensemble for two future periods 2021-2050 and 2071-2100, assuming two representative concentration pathways (RCPs) 4.5 and 8.5. The robustness is measured by the ensemble models' agreement on significant changes.We found a robust increase in the annual mean of daily minimum and maximum temperature, by 1-1.4 °C in the near future and by 1.9-3.8 °C in the far future (areal-means of the ensemble mean values). Higher increases are consistently associated with minimum temperature and the gradient of change goes from SWto NE regions. Seasonal projections of both temperature variables reflect lower robustness and suggest a higher future increase in winter temperatures than in other seasons, notably in the far future under RCP 8.5 (by more than 1 °C). However, changes in annual means of precipitation are uncertain and not robust in any of the analysed cases, even though the climate models agree well on the increase. This increase is intensified with rising global temperatures and varies from 5.5% in the near future under RCP 4.5 to 15.2%in the far future under RCP 8.5. Spatial variability is substantial, although quite variable between individual climate model simulations. Although seasonal means of precipitation are projected to considerably increase in all four combinations of RCPs and projection horizons for winter and spring, the high model spread reduces considerably the robustness, especially for the far future. In contrast, the ensemble members agree well that overall, the summer and autumn (with exception of the far future under RCP 8.5) precipitation will not undergo statistically significant changes.
- ItemEnsemble simulations for the RCP8.5-Scenario(Stuttgart : Gebrueder Borntraeger Verlagsbuchhandlung, 2015) Gerstengarbe, F.-W.; Hoffmann, P.; Ă–sterle, H.; Werner, P.C.
- ItemMultivariate non-parametric Euclidean distance model for hourly disaggregation of daily climate data(Wien [u.a.] : Springer, 2021) Görner, Christina; Franke, Johannes; Kronenberg, Rico; Hellmuth, Olaf; Bernhofer, ChristianThe algorithm for and results of a newly developed multivariate non-parametric model, the Euclidean distance model (EDM), for the hourly disaggregation of daily climate data are presented here. The EDM is a resampling method based on the assumption that the day to be disaggregated has already occurred once in the past. The Euclidean distance (ED) serves as a measure of similarity to select the most similar day from historical records. EDM is designed to disaggregate daily means/sums of several climate elements at once, here temperature (T), precipitation (P), sunshine duration (SD), relative humidity (rH), and wind speed (WS), while conserving physical consistency over all disaggregated elements. Since weather conditions and hence the diurnal cycles of climate elements depend on the weather pattern, a selection approach including objective weather patterns (OWP) was developed. The OWP serve as an additional criterion to filter the most similar day. For a case study, EDM was applied to the daily climate data of the stations Dresden and Fichtelberg (Saxony, Germany). The EDM results agree well with the observed data, maintaining their statistics. Hourly results fit better for climate elements with homogenous diurnal cycles, e.g., T with very high correlations of up to 0.99. In contrast, the hourly results of the SD and the WS provide correlations up to 0.79. EDM tends to overestimate heavy precipitation rates, e.g., by up to 15% for Dresden and 26% for Fichtelberg, potentially due to, e.g., the smaller data pool for such events, and the equal-weighted impact of P in the ED calculation. The OWPs lead to somewhat improved results for all climate elements in terms of similar climate conditions of the basic stations. Finally, the performance of EDM is compared with the disaggregation tool MELODIST (Förster et al. 2015). Both tools deliver comparable and well corresponding results. All analyses of the generated hourly data show that EDM is a very robust and flexible model that can be applied to any climate station. Since EDM can disaggregate daily data of climate projections, future research should address whether the model is capable to respect and (re)produce future climate trends. Further, possible improvements by including the flow direction and future OWPs should be investigated, also with regard to reduce the overestimation of heavy rainfall rates.