Spatial pattern recognition of extreme temperature climatology: assessing HadCM3 simulations via NCEP reanalyses over Europe

Abstract

This article attempts to quantify the spatial uncertainties associated with extreme temperature's response, by assessing data derived from climate model. This is undertaken by a comparison of the spatial pattern of a long-term time-series aggregation (1960/61-1989/90) for extreme temperatures simulated by a particular GCM (the UK Met Offi ce -Hadley Centre climate model, HadCM3) to that of the USA NCAR NCEP Reanalyses, which are considered as 'truth', over the MICE (Modelling the Impacts of Climate Extremes -EU Project) spatial domain. Since evaluation of models is crucial to assessing future scenarios, the aim of this study is to investigate whether the extreme values predicted by the HadCM3 climate model can simulate those produced by NCEP Reanalyses, assuming that the extremes of both models are realizations of the same spatial stochastic process. To get more useful information about the uncertainties surrounding spatial climate projection, one also has to analyze the pattern of temperature extremes in terms of their anomalies. A common technical issue in the assessment of numerical spatial models is based on the Principal Components Analysis and Bayesian Classifi cation for spatial pattern recognition. These methodologies are very important and useful for guiding an evolutionary statistical model-building process. This study leads to the conclusion that the HadCM3 Simulations do not realistically reproduce the NCEP Reanalyses, despite the fact that the climatology of extremes has demonstrated very similar spatial patterns. It is likely therefore that such instability may persist in the future. Keywords: anomaly, empirical Bayesian classification; compositing; principal components loading.RESUMO: ANÁLISE DO PADRÃO ESPACIAL DA CLIMATOLOGIA DE TEMPERATURAS EXTREMAS: AVALIANDO SIMULAÇÕES DO HADCM3 VIA REANÁLISES DO NCEP PARA A EUROPA. Este artigo é um ensaio na quantifi cação de incertezas espaciais associadas à resposta de temperaturas extremas, avaliando dados derivados de um determinado modelo de clima. Isto é empreendido por uma comparação entre o padrão espacial de séries temporais agregadas de longo termo (1960/61-1989/90) para temperaturas extremas simuladas de um particular MCG, o "UK Met Offi ce -Hadley Centre" modelo de clima, HadCM3, e das reanálises do "USA NCAR NCEP", que são considerados como 'verdade', no domínio espacial do projeto MICE ("Modelling the Impacts of Climate Extremes" -Projeto Europeu). Posto que a avaliação de modelos é crucial na determinação de cenários futuros, a meta deste estudo é investigar se os valores extremos obtidos através do HadCM3 são hábeis para reproduzir as reanálises do NCEP, assumindo que os extremos de ambos os modelos sejam realizações do mesmo processo estocástico no espaço. Para adquirir informações mais úteis sobre as

A New Singular Value Decomposition Based Robust Graphical Clustering Technique and Its Application in Climatic Data

“…The Singular Value Decomposition can be applied to the analysis of climate data to identify patterns and maximize covariation (P.S. Lucio, F. C. Conde and A. M. Ramos 2007). A quasidecadal mode is isolated using singular value decomposition (SVD) (Mark R. Jury, 2009 ) applied to monthly smoothed and detrended rainfall, sea surface temperature (SST) (Wallace, J. M., Smith, C. and Bretherton, C. S. (1992)), sea level pressure (SLP).…”