SPIE Proceedings [SPIE SPIE Asia-Pacific Remote Sensing - Kyoto, Japan (Monday 29 October 2012)] Remote Sensing and Modeling of the Atmosphere, Oceans, and Interactions IV - Spatio-temporal

Spatio-temporal variability of drought over northern

China and its relationships with Indian–Pacific sea

surface temperatures

Qing Dong，CunjinXue，Yongzheng Ren

Center for Earth Observation and Digital Earth, Chinese Academy of Sciences

Beijing 10094, China

[email protected]

abstract

Monthly precipitation data from 66 rain gauge stations in northern China are analyzed for the

period 1976–2011. Variations in droughts and wet spells are described using the standardized

precipitation index (SPI). Empirical orthogonal functions and a global wavelet spectral analysis

are applied to capture modes of spatio-temporal variability in droughts over northern China. Time

series of monthly sea surface temperatures (SST) and the Multivariate El Niño Southern

Oscillation Index (MEI) are presented, and cross wavelet and wavelet coherence transforms are

carried out to investigate possible mechanisms behind variations in droughts and wet spells. From

1976 onwards, the northern parts of northern China have experienced an increase in the frequency

of droughts, while the southern parts of northern China have experienced a decrease in the

frequency of droughts. The north–south variability of droughts and wet spells is characterized by

interannual timescales of 3.3 years and 7.0–11.0 years. The former timescale is closely related

with the MEI, while the latter is closely related with sea surface temperature anomalies (SSTA)

over the North Pacific. Most parts of northern China experienced an increase in the frequency of

droughts during the periods 1980–2000 and 2004–present, and a decrease in the frequency of

droughts during the period 2000–2004. The variability of drought in northern China peaks at

timescales of 16.0–32.0 and 3.5–4.0 years. The first of these timescales shows a significant

correlation with SSTA over the Indian Ocean. The eastern parts of northern China have

experienced a decrease in the frequency of droughts since 1976, while the western parts of

northern China have experienced an increase in the frequency of droughts. The east–west

variability of droughts and wet spells is characterized by interannual timescales of 3.3–8.0 years,

which are related with SSTA over the Indian Ocean warm pool.

Keywords: droughts; spatio-temporal variability; northern China; Indian–

Pacific; SST

Remote Sensing and Modeling of the Atmosphere, Oceans, and Interactions IV, edited by Michio Kawamiya, Tiruvalam N. Krishnamurti, Shamil Maksyutov, Proc. of SPIE Vol. 8529, 85290J · © 2012 SPIE · CCC code:

0277-786/12/$18 · doi: 10.1117/12.976019

Proc. of SPIE Vol. 8529 85290J-1

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1 Introduction Droughts in China have inflicted tremendous social and economic damage, and have received

much scientific attention (Qian et al, 2003; Song and Qi, 2004; Shen et al., 2007; Huang and

Du, 2010). Due to the influence of global climate change, northern China has experienced

frequent and intense droughts during the last three decades (Fu and An, 2002; Bordi et al.,

2004; Su and Wang, 2007). In response to the repeated occurrence of these severe droughts,

great effort has been devoted toward understanding the factors that control precipitation

variability over northern China, with the goal of improving predictions of rainfall at

interannual and interdecadal timescales (Lau and Weng, 2001; Wei et al., 2003; Bordi et al.,

2004).

Previous studies based on instrumental and historical data have focused mainly on

characteristics of drought (such as frequency, spatial pattern, and decadal to centennial

variability), and on elucidating relationships between drought and temperature (Qian et al.,

2003; Li et al., 2006; Shen et al., 2007). Empirical orthogonal function (EOF) analysis has

revealed that spatio-temporal variations in droughts and wet spells over northern China are

characterized by different modes, each of which reveals different interannual and interdecadal

timescales (Bordi et al., 2004; Li et al., 2006; Su and Wang, 2007). Relationships between the

variability of droughts and wet spells with El Niño Southern Oscillation (ENSO) phenomena

and global sea surface temperature (SST) have also been examined for insight into the

initiation, duration, and termination of droughts (Gong and Wang, 1999; Huang et al., 1999;

Hoerling and Kumar, 2003; Jiang et al., 2006; Li et al., 2006; Dai, 2011). A better

understanding of the underlying causes of variations in droughts and wet spells is likely to

improve the long-term predictability of droughts in northern China. EOF modes are mutually

orthogonal, indicating that the relationship that each mode reveals is distinct; however, few

studies have investigated the mechanisms causing variations in droughts and wet spells

according to individual EOF modes.

The standardized precipitation index (SPI), which was developed by McKee et al. (1993), is

used in this study to account for the basic effect of precipitation changes on droughts and wet

conditions. Owing to its simplicity, temporal flexibility, and versatility, SPI has been widely

used for regional and global drought monitoring (Hayes et al., 1999; Paulo and Pereira, 2006;

Cancelliere et al., 2007). Also previous studies have demonstrated the utility of SPI for

monitoring droughts in China (Bordi et al., 2004; Zhang et al., 2009; Bai et al., 2010; Zhai

and Feng, 2011). The present study analyzes possible mechanisms behind the spatio-temporal

variability of droughts, and evaluates the potential of the results for improving long-term

prediction of droughts and wet spells in northern China. The methodologies used in this study

are described in Section 2. The study area and data are shown in Section 3. Spatio-temporal

variations in droughts and wet spells in northern China are examined according to each EOF



mode in Section 4, and possible mechanisms behind these variations and comparative analysis

with previous works are discussed. Conclusions are presented in Section 5.

2 Methodology

2.1 Standardized precipitation index (SPI)

SPI allows an analyst to determine the rarity of a drought and an anomalously wet event at

multiple time scales of interest for a station with historic data. And calculation of the SPI

begins with building a frequency distribution from precipitation data at a location for a

specified time period. Then a gamma probability density function is fitted to the precipitation

data, which is done through a process of maximum likelihood estimation of the scale and

shape parameters of gamma distribution to find a cumulative distribution of precipitation. As

the gamma distribution is the suited to evaluate goodness-of-fit (Thom, 1996; Guttman,

1999). Finally, An equiprobability transformation is made from the cumulative distribution to

the standard normal distribution with mean of zero and standard deviation of one (WU, et al.,

2001; GB/T 20481-2006). This transformed probability is the SPI value, which varies

between +2.0 and −2.0, with extremes outside this range occurring 5% of the time (Edwards

and McKee, 1997). SPI can be calculated for any timescale (e.g., 1-, 3-, 6-, 9-, 12-, or 24-

month window lengths) and for all kinds of drought monitoring systems. 1-, 3-, 6-, and 9-

month window lengths describe high-frequency variability within the annual cycle, while 12-

and 24-month window lengths are suitable for capturing the characteristics of interannual and

interdecadal variability (Bordi et al., 2001). SPI is calculated using a 12-month window in this

study.

2.2 Empirical orthogonal function (EOF)

An EOF analysis is a multivariate statistical modal to reduce a large quantity of data to a

smaller and more comprehensible number of components that still contain the essential

information of the original data set (Svensson 1999). In this paper, the EOF analysis is

conducted to decompose the spatio-temporal co-variability of patterns of SPI at different

stations over northern China. The spatial patterns are ordered with respect to their relative

contributions to the total space–time variability, and the time score reveals the primary

temporal mode of the time series (Bordi et al., 2004).

2.3 Wavelet transformation

Wavelet analysis is becoming a common tool for analyzing localized variations of power

within a time series. In this study, the Morlet wavelet transform is applied as described in

Torrence and Compo (1998), Jevrejeva et al. (2003), and Grinsted et al. (2004). This



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transform is used to capture the main time cycles using the global wavelet spectra, to show

covariance between the time series at different timescales using the cross wavelet transform,

and to measure the cross-correlation between two time series at changing timescales using the

wavelet coherence transform. And the statistical significance of the results is tested against

red noise backgrounds using Monte Carlo methods.

3 Study area and Data 3.1 Study area and Rain gauge station

Fig. 1. Study area and locations of rain gauge stations.

In the last continuous several years, Hebei province, Beijing, Tianjin, Shanxi province,

Shaanx province, Henan province, Shandong province, Jiangsu province, Anhui Province and

Hubei province have been exposed to severely droughts. Then we use this area, regarded as

northern China, as the study area to analyze the spatio-temporal variability of droughts

(Fig.1). In this area, an annual precipitation varies from 300 mm to 1,600 mm according to the

monthly precipitation from 1976-2000. This paper uses monthly precipitation data from 66



rain gauge stations over northern China to calculate the monthly SPI for the period January

1976–March 2011. The locations of these stations are shown in the right panel of Fig. 1.

3.2 Sea surface temperature and its anomalies index

Monthly means of SST, as observed by NOAA AVHRR from January 1982 to April 2011,

are used to calculate the North Pacific Ocean temperature Decadal variation Index (INPD)

and the Southern Indian Ocean Dipole Index (SIODI). The INPD, defined as the standardized

difference of SSTA between the region (30°N–45°N, 160°E–150°W) and the region (5°N–

20°N, 180°–130°W), describes interdecadal variability of SST in both the western Pacific and

the tropical Pacific. The SIODI, defined as the standardized difference of SSTA between the

region (25°S–40°S, 65°E–80°E) and the region (10°S–20°S, 95°–105°E), describes the

interannual and interdecadal variations of SST in the Southern Indian Ocean (Li et al., 2006).

The SSTA of the Indian Ocean warm pool is also computed to quantify the interannual

variability of Indian Ocean SST, where the Indian Ocean warm pool is defined as the region

(5°S–10°N, 60°E–100°E).

The Multivariate El Niño Southern Oscillation Index (MEI) is also used to investigate

relationships between ENSO phenomena and variations in droughts and wet spells over

northern China. The MEI combines sea-level pressure, zonal and meridional velocity of the

surface wind, SST, surface air temperature, and total cloud fraction (Wolter and Timlin,

1993). The MEI may therefore provide a more complete and flexible description of ENSO

variability than any single variable ENSO index (Mazzarella et al., 2010; Wolter and Timlin,

2011; see http://www.cdc.noaa.gov/Correlation/mei.data).

4 Results and discussion An EOF analysis of SPI over the research area for the period 1976–2011 is used to capture the

spatio-temporal co-variability of droughts over northern China. The first, second, and third

EOF modes explain 17.5%, 12.3%, and 9.6% of the cumulative variance, respectively, and

together depict the most important characteristics of variations in droughts and wet spells over

northern China. Significance testing indicates that the fourth and higher modes cannot explain

the physical meaning of variations in droughts and wet spells. The following sections present

and discuss the spatial patterns and temporal characteristics of droughts over northern China

according to the first three EOF modes.

4.1 The north–south dipole pattern and its relationships with MEI and

Pacific SSTA

The spatial distribution of the first EOF mode, which explains 17.5% of the total variance, is

shown in Fig. 2a. This mode consists of a north–south dipole pattern over northern China,



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with the positive pole covering the southern and eastern parts of Hubei province and the

southern parts of Anhui and Jiangsu provinces, and the negative pole covering Beijing,

Tianjin, the central and eastern parts of Hebei province, and the northern parts of Shanxi and

Shaanxi provinces. The corresponding principal component time score, shown in Fig. 2b,

indicates a slight positive trend from 1976 to 2011. This trend denotes an increase in the

frequency of drought events in the northern parts of the focus region, and a decrease in the

frequency of drought events in the southern parts. The global wavelet spectral analysis of the

principal component time score of the first EOF mode shows two peaks in the co-variability

of the north–south dipole pattern, located at timescales of 7.0–11.0 years and 3.3 years (Fig.

2c).

Fig. 2. (a) Spatial pattern, (b) time scores, and (c) global wavelet spectral analysis of the principal

components of the first mode of the standardized precipitation index calculated with a 12-month

window (SPI-12) over northern China. The contour interval in panel (a) is 0.25 and the red line in

panel (c) represents the 95% significance level.

Potential links between the north–south dipole pattern of variability in droughts and wet spells

over northern China, and both ENSO phenomena and Pacific Ocean SST are explored in Figs

3 and 4. Figure 3 shows the similarity (cross wavelet transform) between the period patterns

of MEI, INPD, and the first time score of SPI (FTS), while Figure 4 shows the coincidence

(wavelet coherence transform) between these period patterns. The wavelet analysis indicates a

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time series in the 1.0–3.0 years band for the period 1985–1998. Between 1985 and 1992, FTS

and MEI are highly positively correlated and in phase, while in 1995–1998, the phase of FTS

lags that of MEI by 45° in an unstable phase relationship. In this period and at this timescale,

variations in the north–south dipole pattern of variability in droughts and wet spells over

northern China are related to ENSO events. In periods of low coherence, the relationship

between these two time series is poor.

Fig. 3. (a) Cross wavelet transform of the first time score of the standardized precipitation index

(FTS) and Multivariate El Niño Southern Oscillation Index (MEI) time series, and (b) cross

wavelet transform of the FTS and North Pacific Ocean temperature Decadal variability Index

(INPD) time series. The thick solid line denotes the 5% significance level using the red noise

model, and the thin solid line indicates the cone of influence. The vectors indicate phase

differences between the two time series (with in-phase pointing right, anti-phase pointing left, and

FTS lagging the other time series by 90° pointing straight up).

There is good agreement in power (Fig. 3b) and coherence (Fig. 4b) between the FTS and

INPD time series in the 1.5–2.5 years and 7.5–12 years bands during the periods of 1988–

1995 and 1990–2007 (Figs 3b and 4b). The two time series are generally in an unstable anti-

phase relationship in the 1.5–2.5 years band, while they are in a stable in-phase relationship in

the 7.5–12 years bands between 1990 and 2007. This result suggests that the variations in in

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linked with Pacific Ocean SSTA at the timescale of 7.5–12 years.

Fig. 4. (a) Wavelet coherence transform of the first time score of the standardized precipitation

index (FTS) and Multivariate El Niño Southern Oscillation Index (MEI) time series, and (b)

wavelet coherence transform of the FTS and North Pacific Ocean temperature Decadal variability

Index (INPD) time series. The thick solid line denotes the 5% significance level using the red

noise model, and the thin solid line indicates the cone of influence. The vectors indicate phase

differences between the two time series (with in-phase pointing right, anti-phase pointing left, and

FTS lagging the other time series by 90° pointing straight up).

4.2 Coherence pattern and its relationships with MEI and Indian

Ocean SSTA

The spatial pattern of the second EOF mode, which explains 12.3% of the total variance, is

shown in Fig. 5a. This mode is negative in most parts of northern China, and the

corresponding principal component time score (Fig. 5b) shows piecewise linear trends,

indicating that most parts of northern China have been affected by time-varying changes in

the frequency of drought events during the analysis period. Most parts of northern China

experienced an increasing frequency of drought events during the periods 1980–2000 and

2004–present, but a decreasing frequency of drought events during the period 2000–2004.

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mode of variability peaks at an interannual timescale of 3.5–4.0 years and an interdecadal

timescale of 16.0–32.0 years (Fig. 5c).


components of the second mode of standardized precipitation index calculated with a 12-month


panel (c) represents the 95% significance level.

Analysis of the cross wavelet and wavelet coherence transforms of the time series of the

second time score of SPI and MEI (Figs 6a and 7a) indicates little agreement in power or

coherence at the interannual timescale of 3.5–4.0 years or the interdecadal timescale of 16.0–

32.0 years. This result suggests that ENSO has little influence on variations in droughts and

wet spells over northern China at these timescales. Analysis of the time series of the second

time score of SPI and SIODI (Figs 6b and 7b), however, indicates good agreement in both

power and coherence at the 1.5–2.0, 6.0–8.0, and 16.0–32.0 years timescales for the periods

1984–1993, 2003–present, and the whole time period. In the 1.5–2.0 years band, the second

time score of SPI lags SIODI by approximately 90°, with a small instability. In the 6.0–8.0

years band, the second time score of SPI lags SIODI by 45° in a stable phase relationship. In

the 16.0–24.0 years band, the second time score of SPI lags the SIODI by 135° in a stable

phase relationship. The variations in droughts and wet spells over northern China are

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Fig. 6. (a) Cross wavelet transform of the second time score of the standardized precipitation index

(STS) and Multivariate El Niño Southern Oscillation Index (MEI) time series, and (b) cross

wavelet transform of the STS and Southern Indian Ocean Dipole Index (SIODI) time series. The

thick solid line denotes the 5% significance level using the red noise model, and the thin solid line

indicates the cone of influence. The vectors indicate phase differences between the two time series

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index (STS) and Multivariate El Niño Southern Oscillation Index (MEI) time series, and (b)

wavelet coherence transform of the STS and Southern Indian Ocean Dipole Index (SIODI) time

series. The thick solid line denotes the 5% significance level using the red noise model, and the

thin solid line indicates the cone of influence. The vectors indicate phase differences between the

two time series (with in-phase pointing right, anti-phase pointing left, and STS lagging the other

time series by 90° pointing straight up).

4.3 East–west dipole pattern and its relationship with MEI and Pacific

SSTA

The spatial pattern of the third EOF mode, which explains 9.6% of the total variance, is

shown in Fig. 8a. This mode consists of an east–west dipole pattern over northern China, with

the positive pole over the Shaanxi province and western parts of Shanxi province, and the

negative pole over Beijing, Tianjin, the northeastern part of Hebei province, most of

Shandong province, and the northern parts of Jiangsu province. The corresponding principal

component time score (Fig. 8b) shows a slight decreasing trend over the three most recent

decades. This trend means that the western parts of northern China have experienced an

increase in the frequency of drought events, while the eastern parts of northern China have

experienced a decrease in the frequency of drought events. Global wavelet spectral analysis of

the principal component time score shows that the east–west pattern of variations in droughts

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and wet spells over northern China is characterized by an interannual timescale of 3.3–8.0

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components of the third mode of the standardized precipitation index calculated with a 12-month


(c) represents the 95% significance level.

Analysis of the cross wavelet and wavelet coherence transforms of the time series of the third

time score of SPI and MEI (Figs 9a and 10a) indicates little agreement in either power or

coherence in the 3.3–8.0 years bands. This result suggests that ENSO has little influence on

variations in droughts and wet spells over northern China at this timescale.

Analysis of the time series of the third time score of SPI and SSTA of the Indian Ocean warm

pool (Figs 9b and 10b), however, indicates good agreement in both power and coherence at

two bands within the 3.3–8.0 years band for the periods 1983–1997 and 2006–present: 3.3–

4.0 and 6.5–7.0 years. The relationship between the two time series is anti-phase and stable in

the 3.3–8.0 years band, while the third time score of SPI leads SSTA of the Indian Ocean

warm pool by approximately 45° in the 6.5–7.0 years band. These results suggest that east–

west variations in droughts and wet spells over northern China may be driven by SSTA in the

Indian Ocean warm pool at timescales of 3.3–8.0 years, while the relationship between these

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east–west variations and SSTA in the Indian Ocean warm pool changes from anti-phase to

45°-phase at timescales of 6.5–7.0 years.

Fig. 9. (a) Cross wavelet transform of the third time score of the standardized precipitation index

(TTS) and Multivariate El Niño Southern Oscillation Index (MEI) time series, and (b) cross

wavelet transform of the TTS and SSTA of the Indian ocean warm pool time series. The thick

solid line denotes the 5% significance level using the red noise model, and the thin solid line


(with in-phase pointing right, anti-phase pointing left, and TTS lagging the other time series by

90° pointing straight up).

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Fig. 10. (a) Wavelet coherence transform of the third time score of the standardized precipitation

index (TTS) and Multivariate El Niño Southern Oscillation Index (MEI) time series, and (b)

wavelet coherence transform of TTS and SSTA of the Indian Ocean warm pool time series. The

thick solid line denotes the 5% significance level using the red noise model, and the thin solid line


(with in-phase pointing right, anti-phase pointing left, and TTS lagging the other time series by

90° pointing straight up).

4.4 Comparative analysis with previous works

The first EOF mode of monthly SPI-12 corresponds to north–south variations in droughts and

wet spells over north China (Fig. 2). This result corresponds well to the first EOF mode of

monthly palmer drought severity index (PDSI) for the period 1951–2000 reported by Su and

Wang (2007), the first EOF mode of monthly SPI-24 from 1951 to 2000 reported by Brodi et

al. (2004), and the first REOF mode of the DW-Index from 1900 to 1999 reported by Qian et

al. (2003). The second EOF mode of SPI-12 is coherent throughout most of northern China

(Fig. 5), and corresponds well with the second EOF mode reported by Brodi et al. (2004) and

the third EOF mode reported by Qian et al. (2003).

Global wavelet spectral analysis of the principal component time scores reveals that the first

(north–south dipole pattern) and third (east–west dipole pattern) EOF modes are characterized

(a)

(b)



by interannual timescales, while the second (regionally coherent) EOF mode is imposed on

interdecadal timescales. Cross wavelet and wavelet coherence transforms of the principal

component time score time series and the MEI and SSTA over the Indian and Pacific Oceans

time series indicates strong relationships. The first (north–south dipole) EOF mode is strongly

linked with SSTA over Pacific Ocean (INPD), the second (regionally coherent) EOF mode is

strongly linked with SSTA over Indian Ocean (SIODI), and the third (east–west dipole) EOF

mode is strongly linked with SSTA in the Indian Ocean warm pool.

Previous studies have suggested close relationships between decadal and interdecadal

variations of oceanic temperature and precipitation over China (Huang et al., 1999; Lau and

Weng, 2001; Li et al., 2006; Huang and Du, 2010; Dai, 2011), and between ENSO

phenomena and droughts over China at interannual timescales (Song and Qi, 2004; Hao et al.,

2007; Su and Wang, 2007). The present analysis indicates that droughts and wet spells over

northern China vary on interdecadal timescales of 16.0–32.0 years, although the statistical

significance of this result cannot yet be ascertained due to the unavailability of sufficiently

long time series. Huang et al. (1999) pointed out that interdecadal variations in droughts and

wet spells over northern China since the late 1970s have been primarily induced by SST

anomalies over the central and eastern equatorial Pacific, and Li et al. (2006) suggested that

interdecadal variability of SSTA in the northern Pacific Ocean and interdecadal variability of

SSTA in the southern Indian Ocean are closely correlated with variations of precipitation in

eastern and southeastern Asia in the 25–35 years and 18–25 years bands, respectively. Other

than the 2.0–3.0 years band, there is little similarity or coincidence between the first three

principal component time scores and the MEI time series at timescales between 2.0 and 7.0

years, the typical periodicity of ENSO (McPhaden et al., 2006). This result suggests that

ENSO contributes to variations in droughts and wet spells over northern China on the

timescales of 2.0–3.0 years, but exerts little influence at other timescales.

5 Conclusions This study presented a quantitative evaluation of variability in droughts and wet spells over

northern China. Monthly precipitation data from 66 rain gauge stations across northern China

were used to calculate the standardized precipitation index (SPI) for the period 1976–2011.

The results and significance testing suggest that SPI adequately portrays the interannual and

interdecadal variations of droughts and wet spells over northern China. In addition, this study

used time series of MEI and SSTA over the Pacific and Indian Oceans to explore possible

causes of variations of droughts and wet spells over northern China. The analyses yielded the

following conclusions.

1) The first (north–south dipole) EOF mode shows that the northern parts of northern China

have experienced an increase in the frequency of drought events from 1976 to the present,

while the southern parts of northern China have experienced a decrease in the frequency of



drought events. The north–south variability of droughts and wet spells over northern China is

characterized by interannual timescales of 3.3 years and 7.0–11.0 years. ENSO appears to

contribute to the peak at 3.3 years, while the peak at 7.0–11.0 years may be driven by SSTA

over the north Pacific.

2) The second (regionally coherent) EOF mode reveals that most parts of northern China

experienced an increase in the frequency of drought events for the periods 1980–2000 and

2004–present, contrasted with a decrease in the frequency of drought events for the period

2000–2004. These east–west variations in droughts and wet spells are characterized by an

interannual timescale of 3.5–4.0 years and an interdecadal timescale of 16.0–32.0 years. This

mode of variability is significantly correlated with SSTA over the Indian Ocean (SIODI) at

both timescales, especially at the interdecadal period. MEI does not correlate strongly with

this mode of variability at either timescale.

3) The third (east–west dipole) EOF mode shows that the eastern parts of northern China have

experienced a decrease in the frequency of drought events in recent decades, while the

western parts of northern China have experienced an increase in the frequency of drought

events. The east–west variability of droughts and wet spells is characterized by one significant

timescale of 3.3–8.0 years. The temporal variability of east–west variations in droughts and

wet spells over northern China correlates well with SSTA in the Indian Ocean warm pool, but

does not correlate significantly with MEI.

4) The first three EOF modes (i.e., the north–south dipole pattern, the regionally coherent

pattern, and the east–west dipole pattern) of spatio-temporal variations of droughts and wet

spells over northern China in recent decades are identified, and their possible causes are

discussed. To our knowledge, this identification and discussion of the potential causal

relationships between sea surface temperatures in the Indian and Pacific Oceans and

variations of droughts and wet spells over northern China is unprecedented. These results may

provide a useful resource for improving long-term predictions of droughts and wet spells in

northern China.

Acknowledgements The research was partially funded by the project 2009CB723903 supported by the National Key

Basic Research Program of China, the Project Y2ZZ18101B supported by CEODE , the projects

40901194 supported by the National Natural Science Foundation of China, and the project

DESP01-04-03 supported by CEODE. The authors also wish to thank PO.DAAC for providing the

AVHRR Oceans Pathfinder SST data.



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SPIE Proceedings [SPIE SPIE Asia-Pacific Remote Sensing - Kyoto, Japan (Monday 29 October 2012)] Remote Sensing and Modeling of the Atmosphere, Oceans, and Interactions IV - Spatio-temporal