Details

Autor(en) / Beteiligte

• Chen, Yun
• Xu, Tingbao
• Shui, Junfeng
• Liu, Rui
• Wahid, Shahriar
• Shi, Kaifang
• Yang, Haichang
• Cheng, Zhibo

Titel

Characterising spatiotemporal variability of South Asia's climate extremes in past decades

Ist Teil von

• Climate research, 2018-01, Vol.77 (3), p.249-265

Ort / Verlag

Oldendorf: Inter-Research Science Center

Erscheinungsjahr

2018

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• We systematically examined past spatiotemporal changes in climate variability to gain some cross-regional insights into South Asia's vulnerability to extreme conditions. Gridded Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) precipitation and Princeton Global Meteorological Forcing Dataset (PRINCETON) temperature data from 1975−2004 were used to derive a suite of annual extreme indices. Long-term mean and decadal variations of these indices were mapped. Long-term change tendencies were also detected from a suite of 'slope' maps composed by the 30 yr change trend at each grid cell in the region. Most precipitation indices indicated a tendency towards drier conditions, whereas all temperature indices marked a steady coherent warming trend. The extremely wet day precipitation index exhibited the largest change, indicating an increase in heavy precipitation in South Asia. The highest maximum temperature extreme showed increases, indicating more unbearable heatwaves in the region. These trends present a previously unrecognised regional picture of the patterns and trends in historical climate extremes, with each grid cell representing spatiotemporal characteristics of changes. The present study is superior to most studies that only summarise an averaged regional trend from tendencies over large areas, and therefore will improve trans-boundary understanding of extreme climates in South Asia. Our study also exemplifies the application of existing gridded regional/global data sets. It provides valuable means of cross-regional information for bridging gaps where gauging observations are unavailable, particularly in data-poor developing countries.