SPATIOTEMPORAL ASSESSMENT OF GLOBAL SURFACE TEMPERATURE TRENDS UNDER CLIMATE CHANGE

Abstract

The long-term spatial and temporal trends in global land-surface temperature were evaluated by using historical monthly observations at the global, country, state/province and major-city level. The research methodology used was quantitative, retrospective and observational. All the temperature data were cleaned and processed using Python and then analysed using descriptive statistics, linear regression, the Mann–Kendall trend test and Sen's slope estimator at the annual, seasonal and decadal scales. The temperature anomalies were based on the 1951-1980 base period. The global dataset included data for 1750-2015; the spatial dataset included data for 242 countries, 241 states or provinces and 100 major cities up to 2013. Global land temperature showed statistically significant rising trend; linear regression showed a warming trend of 0.0458 °C/decade, whereas Sen's slope showed warming trend of 0.0516 °C/decade. The warmest decade was the 2010s and the warmest year was 2015 at 1.173 °C. Significant warming was found during all seasons with the greatest rate of warming occurring in autumn at 0.0652 °C per decade. The mean warming rates for countries, states or provinces and major cities were 0.0702 °C per decade, 0.0643 °C and 0.0694 °C respectively. These results confirm warming that has occurred, but also in a regionally variable manner, and point to the need for long-term monitoring for regionally based assessments of climate risk, adaptation planning and evidence-based policymaking.

The long-term spatial and temporal trends in global land-surface temperature were evaluated by using historical monthly observations at the global, country, state/province and major-city level. The research methodology used was quantitative, retrospective and observational. All the temperature data were cleaned and processed using Python and then analysed using descriptive statistics, linear regression, the Mann–Kendall trend test and Sen's slope estimator at the annual, seasonal and decadal scales. The temperature anomalies were based on the 1951-1980 base period. The global dataset included data for 1750-2015; the spatial dataset included data for 242 countries, 241 states or provinces and 100 major cities up to 2013. Global land temperature showed statistically significant rising trend; linear regression showed a warming trend of 0.0458 °C/decade, whereas Sen's slope showed warming trend of 0.0516 °C/decade. The warmest decade was the 2010s and the warmest year was 2015 at 1.173 °C. Significant warming was found during all seasons with the greatest rate of warming occurring in autumn at 0.0652 °C per decade. The mean warming rates for countries, states or provinces and major cities were 0.0702 °C per decade, 0.0643 °C and 0.0694 °C respectively. These results confirm warming that has occurred, but also in a regionally variable manner, and point to the need for long-term monitoring for regionally based assessments of climate risk, adaptation planning and evidence-based policymaking.