Hot extremes in Macao: dynamics and predictability

Cheng QIAN (钱诚 )1

Wen ZHOU2, Soi Kun FONG3, and Ka Cheng LEONG3

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 Research2015.1.22

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

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.

xy

m

iiixy

1

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

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

Location of Macao (澳门 )

before 1999: a colony of PortugalNow: 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

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

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)

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

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

)(...)(5.0 110 ipredictorbipredictorbbx ppi 5.0 xy

x 5.0 xy

Distribution of HD33: non-Guassian After transformation

Interannual variability

Mostly in JJAS

5.0 xy

Gaussian distribution (a<0.05)

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

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

Statistical prediction/downscaling model for HD33

TxbAMObGHTbVbUbMEMIbbx 6543210 2008508505.0

combining the influence factors for the interannual and interdecadal variability, a physically based multiple linear regression model:

Schematic diagramextreme temperature index

transform to normal distribution

find interannual predictorsfind interdecadal predictors

multiple regression model

RCP85

RCP26

training projection

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

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

+

-

Statistical prediction/downscaling model for HN28a physically based generalized linear regression model:

200850)25.0log( 43210 GHTbVbSLPbSSTAbbx link function is Possion

Schematic diagramextreme temperature index

find predictors

generalized linear regression model

RCP85

RCP26

training projection

using Spearman’s correlation

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!