The Climate Change group addresses real problems related to climate and environment by using mathematical and statistical tools. Current research interest includes studying the impacts of climate change at local spatial scales and at fine time resolution and also extreme events. This entails exploring the various methodologies of downscaling meteorological variables from global climate models (GCM) or regional climate models (RCM). Among the methods used are regression-based statistical downscaling methods (SDSM), canonical correlation analysis (CCA), artificial neural networks.
Research Alliance: Resource Sustainability.
Global or Regional Climate Model
Canonical Correlation Analysis
Artificial Neural Network
CLIMATE CHANGE RESEARCH GROUP
Department of Mathematical Sciences,
Faculty of Science, UTM,
81310 Johor Bahru,