@misc{SunyerHundechaLawrenceetal.2015, author = {Sunyer, M. A. and Hundecha, Y. and Lawrence, D. and Madsen, H. and Willems, Patrick and Martinkova, M. and Vormoor, Klaus Josef and B{\"u}rger, Gerd and Hanel, Martin and Kriaučiūnienė, J. and Loukas, A. and Osuch, M. and Y{\"u}cel, I.}, title = {Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {512}, issn = {1866-8372}, doi = {10.25932/publishup-40892}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-408920}, pages = {21}, year = {2015}, abstract = {Information on extreme precipitation for future climate is needed to assess the changes in the frequency and intensity of flooding. The primary source of information in climate change impact studies is climate model projections. However, due to the coarse resolution and biases of these models, they cannot be directly used in hydrological models. Hence, statistical downscaling is necessary to address climate change impacts at the catchment scale. This study compares eight statistical downscaling methods (SDMs) often used in climate change impact studies. Four methods are based on change factors (CFs), three are bias correction (BC) methods, and one is a perfect prognosis method. The eight methods are used to downscale precipitation output from 15 regional climate models (RCMs) from the ENSEMBLES project for 11 catchments in Europe. The overall results point to an increase in extreme precipitation in most catchments in both winter and summer. For individual catchments, the downscaled time series tend to agree on the direction of the change but differ in the magnitude. Differences between the SDMs vary between the catchments and depend on the season analysed. Similarly, general conclusions cannot be drawn regarding the differences between CFs and BC methods. The performance of the BC methods during the control period also depends on the catchment, but in most cases they represent an improvement compared to RCM outputs. Analysis of the variance in the ensemble of RCMs and SDMs indicates that at least 30\% and up to approximately half of the total variance is derived from the SDMs. This study illustrates the large variability in the expected changes in extreme precipitation and highlights the need for considering an ensemble of both SDMs and climate models. Recommendations are provided for the selection of the most suitable SDMs to include in the analysis.}, language = {en} } @misc{MurawskiBuergerVorogushynetal.2016, author = {Murawski, Aline and B{\"u}rger, Gerd and Vorogushyn, Sergiy and Merz, Bruno}, title = {Can local climate variability be explained by weather patterns?}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {525}, issn = {1866-8372}, doi = {10.25932/publishup-41015}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-410155}, pages = {24}, year = {2016}, abstract = {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.}, language = {en} } @misc{DelgadoVossBuergeretal.2018, author = {Delgado, Jos{\´e} Miguel Martins and Voss, Sebastian and B{\"u}rger, Gerd and Vormoor, Klaus Josef and Murawski, Aline and Rodrigues Pereira, Jos{\´e} Marcelo and Martins, Eduardo and Vasconcelos J{\´u}nior, Francisco and Francke, Till}, title = {Seasonal drought prediction for semiarid northeastern Brazil}, series = {Hydrology and Earth System Sciences}, journal = {Hydrology and Earth System Sciences}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-418461}, pages = {16}, year = {2018}, abstract = {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 (Cear{\´a}'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.}, language = {en} } @article{DelgadoVossBuergeretal.2018, author = {Delgado, Jos{\´e} Miguel Martins and Voss, Sebastian and B{\"u}rger, Gerd and Vormoor, Klaus Josef and Murawski, Aline and Rodrigues Pereira, Jos{\´e} Marcelo and Martins, Eduardo and Vasconcelos J{\´u}nior, Francisco and Francke, Till}, title = {Seasonal drought prediction for semiarid northeastern Brazil}, series = {Hydrology and Earth System Sciences}, volume = {22}, journal = {Hydrology and Earth System Sciences}, number = {9}, publisher = {Copernicus Publ.}, address = {G{\"o}ttingen}, issn = {1027-5606}, doi = {10.5194/hess-22-5041-2018}, pages = {5041 -- 5056}, year = {2018}, abstract = {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 (Cear{\´a}'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.}, language = {en} }