TY - GEN A1 - Murawski, Aline A1 - Bürger, Gerd A1 - Vorogushyn, Sergiy A1 - Merz, Bruno T1 - Can local climate variability be explained by weather patterns? BT - a multi-station evaluation for the Rhine basin T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - To understand past flood changes in the Rhine catchment and in particular the role of anthropogenic climate change in extreme flows, an attribution study relying on a proper GCM (general circulation model) downscaling is needed. A downscaling based on conditioning a stochastic weather generator on weather patterns is a promising approach. This approach assumes a strong link between weather patterns and local climate, and sufficient GCM skill in reproducing weather pattern climatology. These presuppositions are unprecedentedly evaluated here using 111 years of daily climate data from 490 stations in the Rhine basin and comprehensively testing the number of classification parameters and GCM weather pattern characteristics. A classification based on a combination of mean sea level pressure, temperature, and humidity from the ERA20C reanalysis of atmospheric fields over central Europe with 40 weather types was found to be the most appropriate for stratifying six local climate variables. The corresponding skill is quite diverse though, ranging from good for radiation to poor for precipitation. Especially for the latter it was apparent that pressure fields alone cannot sufficiently stratify local variability. To test the skill of the latest generation of GCMs from the CMIP5 ensemble in reproducing the frequency, seasonality, and persistence of the derived weather patterns, output from 15 GCMs is evaluated. Most GCMs are able to capture these characteristics well, but some models showed consistent deviations in all three evaluation criteria and should be excluded from further attribution analysis. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 525 KW - athmospheric circulation patterns KW - stochastic rainfall model KW - within-type variability KW - river Rhine KW - precipitation KW - temperature KW - trends KW - classification KW - Europe KW - scenarios Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-410155 SN - 1866-8372 IS - 525 ER - TY - GEN A1 - Delgado, José Miguel Martins A1 - Voss, Sebastian A1 - Bürger, Gerd A1 - Vormoor, Klaus Josef A1 - Murawski, Aline A1 - Rodrigues Pereira, José Marcelo A1 - Martins, Eduardo A1 - Vasconcelos Júnior, Francisco A1 - Francke, Till T1 - Seasonal drought prediction for semiarid northeastern Brazil BT - verification of six hydro-meteorological forecast products T2 - Hydrology and Earth System Sciences N2 - A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeastern Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Ceará’s research foundation for meteorology)and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches (empirical quantile mapping, extended downscaling and weather pattern classification) were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a multivariate linear regression to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation,(b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE), the Brier skill score (BSS) and the relative operating characteristic skill score (ROCSS). The tested forecasting products showed similar performance in the analyzed metrics. Forecasts of monthly precipitation had little or no skill considering RMSE and mostly no skill with BSS. A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and BSS and significant skill when discriminating hit rate and false alarm rate given by the ROCSS (forecasting drought events of, e.g., SPEI1 showed a ROCSS of around 0.5). Regarding the temporal variation of the forecast skill of the meteorological indices, it was greatest for April, when compared to the remaining months of the rainy season, while the skill of reservoir volume forecasts decreased with lead time. This work showed that a multi-model ensemble can forecast drought events of timescales relevant to water managers in northeastern Brazil with skill. But no or little skill could be found in the forecasts of monthly precipitation or drought indices of lower scales, like SPI1. Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeastern Brazil. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 476 KW - Hydrological drought KW - River-Basin KW - Model KW - Patterns KW - Precipitation KW - Variability KW - Nordeste Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-418461 ER - TY - JOUR A1 - Delgado, José Miguel Martins A1 - Voss, Sebastian A1 - Bürger, Gerd A1 - Vormoor, Klaus Josef A1 - Murawski, Aline A1 - Rodrigues Pereira, José Marcelo A1 - Martins, Eduardo A1 - Vasconcelos Júnior, Francisco A1 - Francke, Till T1 - Seasonal drought prediction for semiarid northeastern Brazil BT - verification of six hydro-meteorological forecast products JF - Hydrology and Earth System Sciences N2 - A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeastern Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Ceará’s research foundation for meteorology)and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches (empirical quantile mapping, extended downscaling and weather pattern classification) were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a multivariate linear regression to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation,(b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE), the Brier skill score (BSS) and the relative operating characteristic skill score (ROCSS). The tested forecasting products showed similar performance in the analyzed metrics. Forecasts of monthly precipitation had little or no skill considering RMSE and mostly no skill with BSS. A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and BSS and significant skill when discriminating hit rate and false alarm rate given by the ROCSS (forecasting drought events of, e.g., SPEI1 showed a ROCSS of around 0.5). Regarding the temporal variation of the forecast skill of the meteorological indices, it was greatest for April, when compared to the remaining months of the rainy season, while the skill of reservoir volume forecasts decreased with lead time. This work showed that a multi-model ensemble can forecast drought events of timescales relevant to water managers in northeastern Brazil with skill. But no or little skill could be found in the forecasts of monthly precipitation or drought indices of lower scales, like SPI1. Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeastern Brazil. KW - Hydrological drought KW - River-Basin KW - Model KW - Patterns KW - Precipitation KW - Variability KW - Nordeste Y1 - 2018 U6 - https://doi.org/10.5194/hess-22-5041-2018 SN - 1027-5606 SN - 1607-7938 VL - 22 IS - 9 SP - 5041 EP - 5056 PB - Copernicus Publ. CY - Göttingen ER -