Extreme weather tendencias in Hungary: one empirical and two model approaches

Ponencia presentada en: VI Congreso Internacional de la Asociación Española de Climatología celebrado en Tarragona del 8 al 11 de octubre de 2008.[ES]La frecuencia e intensidad de los eventos extremos constituyen el componente más cuestionable de las proyecciones regionales de cambio climático. En este trabajo se comparan los resultados de tres aproximaciones científicas: la modelizació via GCM, procedente de las compilaciones del IPCC AR4, los modelos de mesoescala, compilados a partir del proyecto PRUDENCE y un método empírico denominado experimento NATURAL. Esta última aproximación facilita los coeficientes de regresión entre ls variables locales y globales durante la fase de calentamiento monotónico entre 1976-2005. La aproximación a través de modelos globales incluye resultados procedentes de 9 AOGCMs, mientras que PRUDENCE analiza en detalle 5 salidas.[EN]The frequency and intensity of weather extremes are the most questionable components of the projected regional climate changes. Results by three scientific approaches, the raw GCMs, from the IPCC AR4 compilations, the mesoscale models, compiled from the PRUDENCE project, and an empirical method, called Natural experiment are compared. The latter approach provides regression coefficients between the local and global variables in the warming phase during the 1976-2005 period. The global model results comprise results of 9 AOGCMs, whereas in the PRUDENCE set of 5 model outputs are analysed in detail

Benchmarking homogenization algorithms for monthly data

The COST (European Cooperation in Science and Technology) Action ES0601: Advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies. The algorithms were validated against a realistic benchmark dataset. Participants provided 25 separate homogenized contributions as part of the blind study as well as 22 additional solutions submitted after the details of the imposed inhomogeneities were revealed. These homogenized datasets were assessed by a number of performance metrics including i) the centered root mean square error relative to the true homogeneous values at various averaging scales, ii) the error in linear trend estimates and iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that currently automatic algorithms can perform as well as manual ones