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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 Office - 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 Classification 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.Este artigo é um ensaio na quantificação 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 Office - 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 incertezas acerca da projeção espacial de clima, deve-se também analisar o padrão de extremos de temperatura em termos de suas anomalias. Uma técnica comum na avaliação de modelos numéricos está baseada na Análise de Componentes Principais ou na Classificação Bayesiana Empírica para o reconhecimento do padrão espacial. Estas metodologias são muito importante e úteis para guiar o processo de estatístico evolutivo na construção de modelos. Este estudo conduz à conclusão que as simulações do HadCM3 não reproduzem realisticamente as reanálises do NCEP, apesar da climatologia de extremos ter demonstrado padrões espaciais semelhantes. É provável, então, que tal instabilidade possa persistir no futuro.

Climatological records users, frequently, request time series for geographical locations where there is no observed meteorological attributes. Climatological conditions of the areas or points of interest have to be calculated interpolating observations in the time of neighboring stations and climate proxy. The aim of the present work is the application of reliable and robust procedures for monthly reconstruction of precipitation time series. Time series is a special case of symbolic regression and we can use Artificial Neural Network (ANN) to explore the spatiotemporal dependence of meteorological attributes. The ANN seems to be an important tool for the propagation of the related weather information to provide practical solution of uncertainties associated with interpolation, capturing the spatiotemporal structure of the data. In practice, one determines the embedding dimension of the time series attractor (delay time that determine how data are processed) and uses these numbers to define the network's architecture. Meteorological attributes can be accurately predicted by the ANN model architecture: designing, training, validation and testing; the best generalization of new data is obtained when the mapping represents the systematic aspects of the data, rather capturing the specific details of the particular training set. As illustration one takes monthly total rainfall series recorded in the period 1961–2005 in the Rio Grande do Sul – Brazil. This reliable and robust reconstruction method has good performance and in particular, they were able to capture the intrinsic dynamic of atmospheric activities. The regional rainfall has been related to high-frequency atmospheric phenomena, such as El Niño and La Niña events, and low frequency phenomena, such as the Pacific Decadal Oscillation.