A toolkit for climate change analysis and pattern recognition for extreme weather conditions e Case study: California-Baja California Peninsula

Abstract

This paper describes the development of a Climate Change Toolkit (CCT) to perform tasks needed in a climate change study plus projection of extreme weather conditions by analyzing historical weather patterns. CCT consists of Data Extraction, Global Climate Data Management, Bias Correction and Statistical Downscaling, Spatial Interpolation, and Critical Consecutive Day Analyzer (CCDA). CCDA uses a customized data mining approach to recognize spatial and temporal patterns of extreme events. CCT is linked to an archive of 0.5 historical global daily dataset (CRU, 1970-2005), and GCM data (1960e2099) for five models and four carbon scenarios. Application of CCT in California using ensemble results of scenario RCP8.5 showed a probable increase in the frequency of dry periods in the southern part of the region, while decreasing in the north. The frequency of wet periods may suggest higher risks of flooding in the north and coastal strips.We further found that every county in northern California may experience flooding conditions of 1986 at least once between 2020 and 2050.