Fuzzy Inference Systems as Geographic Patterns of Climatic Warming Over Mexico

摘要

Local and regional temperature response to the global temperature rising is a matter of relevance in terms of climate change impacts assessment. However, in developing countries, the estimation of this response has been hampered, mainly, due to the lack of regional climate models and of higher computational power. This work analyzes the high-resolution warming signal over Mexico as function of the global mean temperature using Adaptive Neuro-Fuzzy Inference Systems. The geographical array of Fuzzy Inference Systems are presented as warming patterns that were used to project the temperature trend, in the 21st century, under the four Representative Concentration Pathways. We based on the assumptions that the global temperature increase is the dominant influence on future climate and that the local response is determined by the local geographic conditions. The resulting scenarios shows that the northwestern and south central regions present the highest warming values, likewise, all maps display a region where the projected warming remains uncertain. The proposed methodology is presented as an alternative pattern scaling technique whose results pretend to serve as an analysis tool of potential impacts of regional warming over Mexico, and lead to the generation and improvement of adaptation and mitigation strategies.