1. Hot extremes in Macao: dynamics and predictability Cheng QIAN ( 钱诚 ) 1 Wen ZHOU 2, Soi Kun FONG 3, and Ka Cheng LEONG 3 1 Key Laboratory of Regional Climate-Environment for Temperate East Asia & LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China 2 Guy Carpenter Asia-Pacific Climate Impact Centre, School of Energy and Environment, City University of Hong Kong, Hong Kong, China 3 Macao Meteorological and Geophysical Bureau, Macao, China Qian, C., W. Zhou, S. K. Fong, and K. C. Leong, 2015: Two approaches for statistical prediction of non-Gaussian climate extremes: a case study of Macao hot extremes during 1912−2012. J. Climate, 28(2), 623−636, doi: 10.1175/JCLI-D-14-00159.1 International Workshop on High Impact Weather Research 2015.1.22

2. Introduction Changes in extreme climate events, especially hot extremes, could have notable impacts on human mortality, regional economies, and natural ecosystems Climate change adaptation research requires spatially fine information understanding historical variations and changes in regional or even local hot extremes and predicting future changes will be beneficial for human adaptation to climate change

3. Introduction The Gaussian/normal assumption( 正态分布假定 ) has been widely used in many previous studies on climate variability and change that have used traditional statistical methods (e.g. regression) to estimate linear trends, diagnose physical mechanisms, or construct statistical prediction/downscaling models.

4. Introduction However, climate extremes sometimes, if not often, have a non-Gaussian distribution (highly skewed or kurtotic, or with substantial outliers) (e.g., Klein Tank et al. 2009), which will distort relationships and significance tests. skewed Kurtotic outliers

5. Introduction The aim of this study is to propose two approaches to statistically predict the future occurrence of non- Gaussian climate extremes the construction of a physically based statistical prediction/downscaling model

6. Location of Macao ( 澳门 ) before 1999: a colony of Portugal Now: a special administrative region of the People's Republic of China continuous observations since 1901 and even during World War II relatively unaffected by urbanization dynamic downscaling is difficult for such a coastal city

7. Data Daily maximum and minimum temperature observations in Macao during 1912-2012 the NCEP/NCAR reanalysis data during 1948–2012: sea level pressure (SLP), winds at 850 hPa (UV850), air temperature at 850 hPa (T850), geopotential height at 500 hPa and 200 hPa (GHT500 and GHT200), and zonal wind at 200 hPa (U200) The monthly NOAA extended reconstructed sea surface temperature (SST) dataset version 3 (ERSSTv3) for the period 1948–2012

8. Methods Three approaches for normality test: the histogram, Quantile- Quantile plotting, and the Jarque-Bera test (Qian and Zhou, 2014) Pearson /Spearman correlation coefficient effective degrees of freedom (EDOF) generalized linear model (GLM)

9. Hot extreme indices hot day (TX>33 ℃ ) – HD33 hot night (TN>28 ℃ ) – HN28 95% percentile (33.2 ºC) 99% percentile (27.8 ºC) mostly in JJAS According to Macao Meteorological and Geophysical Bureau

10. Statistical downscaling for hot days (>33 ℃ ) (1) Weather typing; (2) Weather generators; (3) Regression methods Solution: Transform: to become quasi-Gaussian and use multiple LM Distribution of HD33: non-Guassian After transformation

11. Interannual variability Mostly in JJAS Gaussian distribution (a<0.05)

12. Associated with the interannual variability of occurrence of hot days at Macao (1948-2005)  Anomalous more HD33 year corresponds to El Niño Modoki developing stage. Macao is located on the northwest edge of the cyclonic circulation system and thus is controlled by anomalous northerly wind, favoring high temperatures from mainland China moving southward El Niño Modoki

13. Associated with the interdecadal variability of occurrence of hot days at Macao (1948-2005) higher tropospheric temperature in northern Asia warmer SSTA in North Atlantic Ocean warmer JJAS mean temperature in Macao

14. Statistical prediction/downscaling model for HD33 combining the influence factors for the interannual and interdecadal variability, a physically based multiple linear regression model: Schematic diagram extreme temperature index transform to normal distribution find interannual predictors find interdecadal predictors multiple regression model RCP85 RCP26 training projection

15. hot nights (HN28) far from Gaussian Multiple linear regression is not appropriate. Transform to Gaussian is difficult. Solution: the non-parametric Spearman's rank correlation coefficient Generalized Linear Model

16. Associated with hot nights (HN28) at Macao non-parametric Spearman’s rank correlation (1948-2005)  a positive PDO-like SSTA pattern can weaken the East Asian summer monsoon through weakening the land-sea thermal contrast and reduce JJAS rainfall in Macao, favoring higher temperature in Macao Pacific decadal oscillation (PDO)-like + -

17. Statistical prediction/downscaling model for HN28 a physically based generalized linear regression model: link function is Possion Schematic diagram extreme temperature index find predictors generalized linear regression model RCP85 RCP26 training projection using Spearman’s correlation

18. Summary Two approaches are proposed to statistically predict/downscaling non-Gaussian temperature extremes: one uses a multiple linear regression model after transforming the non-Gaussian predictant to a quasi-Gaussian variable, and uses Pearson’s correlation test to identify potential predictors; the other uses a generalized linear model when the transformation is difficult, and uses a non- parametric Spearman’s correlation test to identify potential predictors. Hot extremes in Macao is associated with the interannual and interdecadal variability of a coupled El Niño-Southern Oscillation (ENSO)-East Asian summer monsoon system. It is important to test the assumed distribution of climate extremes and to apply appropriate statistical approaches. Thank you for your attention!