Results from the VALUE perfect predictor experiment: process-based evaluation

Comunicación presentada en: EGU General Assembly 2016 celebrada del 17 al 22 de abril de 2016 en Viena, Austria

COST Action 733. Harmonization and application of weather type classifications for European regions. Final scientific report

Freely available via Official URL link

The VALUE perfect predictor experiment: evaluation of temporal variability

Temporal variability is an important feature of climate, comprising systematic vari-ations such as the annual cycle, as well as residual temporal variations such asshort-term variations, spells and variability from interannual to long-term trends.The EU-COST Action VALUE developed a comprehensive framework to evaluatedownscaling methods. Here we present the evaluation of the perfect predictorexperiment for temporal variability. Overall, the behaviour of the differentapproaches turned out to be as expected from their structure and implementation.The chosen regional climate model adds value to reanalysis data for most consid-ered aspects, for all seasons and for both temperature and precipitation. Bias cor-rection methods do not directly modify temporal variability apart from the annualcycle. However, wet day corrections substantially improve transition probabilitiesand spell length distributions, whereas interannual variability is in some cases dete-riorated by quantile mapping. The performance of perfect prognosis (PP) statisticaldownscaling methods varies strongly from aspect to aspect and method to method,and depends strongly on the predictor choice. Unconditional weather generatorstend to perform well for the aspects they have been calibrated for, but underrepre-sent long spells and interannual variability. Long-term temperature trends of thedriving model are essentially unchanged by bias correction methods. If precipita-tion trends are not well simulated by the driving model, bias correction furtherdeteriorates these trends. The performance of PP methods to simulate trendsdepends strongly on the chosen predictors.VALUE has been funded as EU COST Action ES1102

Validation of spatial variability in downscaling results from the VALUE perfect predictor experiment

The spatial dependence of meteorological variables is crucial for many impacts, for example, droughts, floods, river flows, energy demand, and crop yield. There is thus a need to understand how well it is represented in downscaling (DS) products. Within the COST Action VALUE, we have conducted a comprehensive analysis of spatial variability in the output of over 40 different DS methods in a perfect predictor setup. The DS output is evaluated against daily precipitation and temperature observations for the period 1979?2008 at 86 sites across Europe and 53 sites across Germany. We have analysed the dependency of correlations of daily temperature and precipitation series at station pairs on the distance between the stations. For the European data set, we have also investigated the complexity of the downscaled data by calculating the number of independent spatial degrees of freedom. For daily precipitation at the German network, we have additionally evaluated the dependency of the joint exceedance of the wet day threshold and of the local 90th percentile on the distance between the stations. Finally, we have investigated regional patterns of European monthly precipitation obtained from rotated principal component analysis. We analysed Perfect Prog (PP) methods, which are based on statistical relationships derived from observations, as well as Model Output Statistics (MOS) approaches, which attempt to correct simulated variables. In summary, we found that most PP DS methods, with the exception of multisite analog methods and a method that explicitly models spatial dependence yield unrealistic spatial characteristics. Regional climate model?based MOS methods showed good performance with respect to correlation lengths and the joint occurrence of wet days, but a substantial overestimation of the joint occurrence of heavy precipitation events. These findings apply to the spatial scales that are resolved by our observation network, and similar studies with higher resolutions, which are relevant for small hydrological catchment, are desirable.Funding Information: EU. Grant Number: EU COST Action ES110

A comparison of statistical downscaling techniques for daily precipitation: results from the CORDEX flagship pilot study in South America

Southeast South America (SESA) is one of the regions of the planet where extreme precipitation events occur and have high impact on human activities. These extreme events result from the complex interactions of a broad range of scales, therefore their study, modelling and projections in a changing climate continue to be a challenging task. The CORDEX Flagship Pilot Study in South America (FPSSESA) addresses this topic in order to advance in the understanding and modelling of extreme precipitation events based on coordinated experiments using different downscaling approaches. In this work we present the results from the collaborative action to intercompare different statistical downscaling techniques in simulating daily precipitation in SESA with special focus on extremes. To this end, seven statistical downscaling models based on the regression and analog families were evaluated over SESA. The sensitivity to the different predictor and predictand datasets were tested using two reanalyses (ECMWF ERA-Interim and Japanese 55-year Reanalysis JRA-55) and two daily precipitation (station data and MSWEP) datasets. The models were calibrated and cross-validated during the 1979-2009 period and also evaluated in the independent warm season of 2009-2010. This season, with record of extreme precipitation events, is the target season chosen in the FPS-SESA to perform the dynamical downscaling simulations as well, and therefore it allows for comparisons between both approaches. The results show that the methods are more skillful when combined predictors including circulation variables at middle levels and local humidity at low levels of the atmosphere are considered. The performance of the models is also sensitive to reanalysis choice. The methods show overall good performance in simulating daily precipitation characteristics over the region, but no single model performs best over all validation metrics and aspects evaluated.Fil: Bettolli, Maria Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; ArgentinaFil: Gutiérrez Llorente, José Manuel. Universidad de Cantabria; EspañaFil: Iturbide, Maialen. Universidad de Cantabria; EspañaFil: Baño Medina, Jorge. Universidad de Cantabria; EspañaFil: Huth, Radan. Karlova Univerzita (cuni); República ChecaFil: Solman, Silvina Alicia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Fernández, Jesús. Universidad de Cantabria; EspañaFil: da Rocha, Rosmeri Porfirio. Universidade de Sao Paulo; BrasilFil: Llopart, Marta. Universidad Estadual de Sao Paulo; BrasilFil: Lavín Gullón, Álvaro. Universidad de Cantabria; EspañaFil: Coppola, Erika. The Abdus Salam; ItaliaFil: Chou, Sin Chan. Centro de Previsao de Tempo e Estudos Climaticos; BrasilFil: Doyle, Moira Evelina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Olmo, Matías Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; ArgentinaFil: Feijoó, Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaInternational Conference on Regional ClimateBeijingChinaWorld Climate Research ProgrammeInstituto Sueco de Meteorología e Hidrologí

Research on possible climate change impacts

Brief information about the projects on climate change impact research running at the Academy of Sciences. Their common part is the construction of climate change scenarios for experimental sites

North Atlantic Oscillation - definitions, manifestations, influence on European climate

The paper reviews basic knowledge on the North Atlantic Oscillation (NAO), including its manifestations in the atmospheric pressure and geopotential height fields, definitions, methods of detection, effects on surface climate elements (temperature, precipitation) mainly in Europe, connections to phenological phases of some plant species, and relationships with solar activity

Comparison of solar and geomagnetic effects on the atmospheric circulation variability modes

The shapes, spatial extent, and intensity of modes of low-frequency variability of atmospheric circulation in the Northern Hemisphere in winter are significantly affected by the phase of the 11-yr solar cycle. Here we extend the analysis to the geomagnetic activity, and compare the results with the solar activity