Título:	Exploring spatial patterns of GCM projection bias via model based geostatistics.
Autoria:	MAIA, A. de H. N. HAMADA, E. GONDIM, R. S.
Afiliação:	ALINE DE HOLANDA NUNES MAIA, CNPMA; EMILIA HAMADA, CNPMA; RUBENS SONSOL GONDIM, CNPAT.
Ano de publicação:	2013
Referência:	In: REUNIÃO ANUAL DA REGIÃO BRASILEIRA DA SOCIEDADE INTERNACIONAL DE BIOMETRIA, 58.; SIMPÓSIO DE ESTATÍSTICA APLICADA À EXPERIMENTAÇÃO AGRONÔMICA, 15., 2013, Campina Grande. Modelagem estatística em áreas multidisciplinares: impactos causados pelas mudanças climáticas na Região Nordeste: [anais]. Campina Grande: Sociedade Internacional de Biometria, 2013. 5 p.
Conteúdo:	General Circulation Models (GCMs) are numerical models developed to represent physical processes in the atmosphere, ocean, cryosphere and land surface. They constitute the most advanced tool currently available for simulating future climate scenarios as a response to increasing greenhouse gas concentrations. GCMs, possibly in conjunction with nested regional climate models (RCMs), have the potential to provide consistent estimates of regional climate change which are required in climate impact assessments. The characterization of model bias in terms of magnitude and spatial patterns is part of the process of evaluating the model performance via hindcast skill analysis, an important preliminary step in climate change impact assessments. In this paper, we discuss how Model Based Geostatistics can be applied for exploring bias patterns, key information for performing bias correction of GCM or RCM projections in future time slices. As example, we present an assessment of annual rainfall projection bias for three GCMs across the Northeast Brazil.
Thesagro:	Modelo de simulação Clima
NAL Thesaurus:	General Circulation Models Climate change
Palavras-chave:	Gestatistics
Tipo do material:	Artigo em anais e proceedings
Acesso:	openAccess
Aparece nas coleções:	Artigo em anais de congresso (CNPMA)