Spatial and interannual variations of spring rainfall over eastern China in association with PDO–ENSO events

Abstract

The spatio-temporal variations of eastern China spring rainfall are identified via empirical orthogonal function (EOF) analysis of rain-gauge (gridded) precipitation datasets for the period 1958–2013 (1920–2013). The interannual variations of the first two leading EOF modes are linked with the El Niño–Southern Oscillation (ENSO), with this linkage being modulated by the Pacific Decadal Oscillation (PDO). The EOF1 mode, characterized by predominant rainfall anomalies from the Yangtze River to North China (YNC), is more likely associated with out-of-phase PDO–ENSO events [i.e., El Niño during cold PDO (EN_CPDO) and La Niña during warm PDO (LN_WPDO)]. The sea surface temperature anomaly (SSTA) distributions of EN_CPDO (LN_WPDO) events induce a significant anomalous anticyclone (cyclone) over the western North Pacific stretching northward to the Korean Peninsula and southern Japan, resulting in anomalous southwesterlies (northeasterlies) prevailing over eastern China and above-normal (below-normal) rainfall over YNC. In contrast, EOF2 exhibits a dipole pattern with predominantly positive rainfall anomalies over southern China along with negative anomalies over YNC, which is more likely connected to in-phase PDO–ENSO events [i.e., El Niño during warm PDO (EN_WPDO) and La Niña during cold PDO (LN_CPDO)]. EN_WPDO (LN_CPDO) events force a southwest–northeast oriented dipole-like circulation pattern leading to significant anomalous southwesterlies (northeasterlies) and above-normal (below-normal) rainfall over southern China. Numerical experiments with the CAM5 model forced by the SSTA patterns of EN_WPDO and EN_CPDO events reproduce reasonably well the corresponding anomalous atmospheric circulation patterns and spring rainfall modes over eastern China, validating the related mechanisms.

Appendix

Fisher’s exact test (Conover 1980)

	Column 1	Column 2	Column total
Row 1	a	b	C 1 (a + b)
Row 2	c	d	C 2 (c + d)
Row total	R 1 (a + c)	R 2 (b + d)	N (a + b + c + d)

N observations are summarized in the table above, whose row totals are fixed as R ₁ and R ₂ , and column totals are fixed as C ₁ and C ₂. The exact distribution of the test statistics of this table is given by the hypergeometric distribution, as follows:

$$ P\left({T}_2=a\right)=\left\{\begin{array}{l}\frac{\left(\begin{array}{c}a+b\\ {}a\end{array}\right)\left(\begin{array}{c}c+d\\ {}c\end{array}\right)}{\left(\begin{array}{c}n\\ {}a+c\end{array}\right)}=\frac{\left(a+b\right)!\left(c+d\right)!\left(a+c\right)!\left(b+d\right)!}{n!a!b!c!d!},a=0,1,\cdots, \min \left({R}_1,{C}_1\right)\\ {}0,a= other\end{array}\right. $$

Here, $\left(\begin{array}{l}n\\ {}k\end{array}\right)$ is the binomial coefficient and the symbol. ! indicates the factorial operator.

1.1 Lower tailed test

$$ {\displaystyle \begin{array}{l}{H}_0:{p}_1\ge {p}_2\\ {}{H}_1:{p}_1<{p}_2\end{array}} $$

where p ₁ is the probability of an observation in row 1 being classified into column 1, p ₂ is the probability of an observation in row 2 being classified into column 1, and t _obs is the observed value of T ₂.

If P(T₂ ≤ t _obs ) ≤ α, reject H ₀ at the level of significance α. Use this when there is a negative association between the variables.

1.2 Upper tailed test

$$ {\displaystyle \begin{array}{l}{H}_0:{p}_1\le {p}_2\\ {}{H}_1:{p}_1>{p}_2\end{array}} $$

If P(T₂ ≥ t _obs ) ≤ α, reject H ₀ at the level of significance α. Use this when there is a positive association between the variables.