Nonstationary extreme value analysis of temperature extremes in China
- 130 Downloads
In a changing climate, the common assumption of stationarity of climate extremes has been increasingly challenged, raising the need to incorporate non-stationarity in extreme value modeling. In this study, quantile regression is used to identify the trends of annual temperature extremes and their correlations with two large climate patterns, the western Pacific subtropical high (WPSH) and the Arctic Oscillation (AO) at 357 stations in China. Statistical significant positive trends and correlations between warm (or cold) temperature extremes and WPSH (or AO) have been detected at most stations. The influence of WPSH on warm extremes is significant in southern China, while the AO mainly affects the cold extremes in northern and eastern China. Then, annual temperature extremes are fitted to generalized extreme value (GEV) distributions with time-varying parameters. The summer (or winter) mean daily maximum (or minimum) temperatures and two climate indices, the WPSH index and the AO index, are chosen as covariates. In total, 16 candidate GEV distribution models are constructed, and the best fitting model with the lowest Bayesian information criterion (BIC) is selected. The 20-year return levels of annual warm (or cold) extremes in the period 1961–1980 and 1991–2010 are computed and compared. The changes of 20-year return levels of annual warm and cold extremes are jointly determined by trend and distributional changes of annual temperature extremes. Analysis of large scale atmospheric circulation changes indicate that a strengthening anticyclonic circulation and increasing geopotential height in recent decades may have contributed to the changes in temperature extremes in China.
KeywordsTemperature extremes GEV Non-stationarity Return level Atmospheric circulation
We sincerely acknowledge the editor and two anonymous reviewers whose kind and valuable comments greatly improved the quality of this manuscript. This work was partly supported by the Youth Innovation Promotion Association of CAS (2016195), CAS Knowledge Innovation Project (KZCX2-EW-QN209), and National Natural Science Foundation of China (31570423). The authors also appreciated Dr. Reich for sharing the R-function “NoCrossQuant.R” to implement the non-crossing quantile regression.
- Alexander LV, Zhang X, Peterson TC, Caesar J, Gleason B, Klein Tank AMG, Haylock M, Collins D, Trewin B, Rahimzadeh F, Tagipour A, Rupa Kumar K, Revadekar J, Griffiths G, Vincent L, Stephenson DB, Burn J, Aguilar E, Brunet M, Taylor M, New M, Zhai P, Rusticucci M, Vazquez-Aguirre JL (2006) Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res 111:D05109Google Scholar
- IPCC (2007) Climate change 2007: the physical science basis. contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
- IPCC (2012) Special report on managing the risks of extreme events and disasters to advance climate change adaptation. A report of working groups I and II of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
- Leadbetter MR (1983) Extremes and local dependence in stationary sequences. Probab Theory Relat Fields 65(2):291–306Google Scholar
- Wigley TML (1988) The effect of climate change on the frequency of absolute extreme events. Clim Monit 17:44–55Google Scholar
- Yu K, Lu Z, Stander J (2003) Quantile regression: application and current research areas. Statistician 52:331–350Google Scholar
- Zhang X, Zwiers FW (2013) Statistical indices for the diagnosing and detecting changes in extremes. In: Easterling D, Hsu K, Schubert S, Sorooshian S, AghaKouchak A (eds) Extremes in a changing climate. Springer, Berlin, pp 1–14Google Scholar