• Allen, R., and Coauthors, 2001: Is there an Indian Ocean dipole, and is it independent of the El Niño-Southern Oscillation? CLIVAR Exchanges, No. 6, International CLIVAR Project Office, Southampton, United Kingdom, 18–22.

  • Chen, P. Y., , Y. Q. Ni, , and Y. H. Yin, 2001: Diagnostic study on the impact of the global sea surface temperature anomalies on the winter temperature anomalies in eastern China in past 50 years (in Chinese). J. Trop. Meteor., 17, 371380.

    • Search Google Scholar
    • Export Citation
  • Chen, W. L., , Z. H. Jiang, , and L. Li, 2010: Simulation of regional climate change under the IPCC A2 scenario in southeast China. Climate Dyn., 36, 491–507, doi:10.1007/s00382-010-0910-3.

    • Search Google Scholar
    • Export Citation
  • Czaja, A., , and C. Frankignoul, 2002: Observed impact of North Atlantic SST anomalies on the North Atlantic Oscillation. J. Climate, 15, 606623.

    • Search Google Scholar
    • Export Citation
  • Ding, Y. H., , Z. Y. Wang, , and Y. F. Song, 2008: Causes of the unprecedented freezing disaster in January 2008 and its possible association with the global warming. Acta Meteor. Sin., 665, 809825.

    • Search Google Scholar
    • Export Citation
  • Fan, L., , Z. Liu, , and Q. Liu, 2011: Robust GEFA assessment of climate feedback to SST EOF modes. Adv. Atmos. Sci., 28, 907912.

  • Frankignoul, C., , and E. Kestenare, 2005: Observed Atlantic SST anomaly impact on the NAO: An update. J. Climate, 18, 40894094.

  • Frankignoul, C., , A. Czaja, , and B. L’Heveder, 1998: Air–sea feedback in the North Atlantic and surface boundary conditions for ocean models. J. Climate, 11, 23102324.

    • Search Google Scholar
    • Export Citation
  • Hong, C. C., , and T. Li, 2009: The extreme cold anomaly over Southeast Asia in February 2008: Roles of ISO and ENSO. J. Climate, 22, 37863801.

    • Search Google Scholar
    • Export Citation
  • Jiang, Z. H., , J. Song, , and L. Li, 2011: Extreme climate events in China: IPCC-AR4 model evaluation and projection. Climatic Change, 110, 385–401, doi:10.1007/s10584-011-0090-0.

    • Search Google Scholar
    • Export Citation
  • Jiang, Z. H., , T. T. Ma, , and Z. W. Wu, 2012: China cold wave duration in a warming winter: Change of the leading mode. Theor. Appl. Climatol., 110, 65–75, doi:10.1007/s00704-012-0613-2.

    • Search Google Scholar
    • Export Citation
  • Klein, S. A., , B. J. Soden, , and N.-C. Lau, 1999: Remote sea surface temperature variations during ENSO: Evidence for a tropical atmospheric bridge. J. Climate, 12, 917932.

    • Search Google Scholar
    • Export Citation
  • Kushnir, Y., , W. A. Robinson, , I. Blade, , N. M. J. Hall, , S. Peng, , and R. Sutton, 2002: Atmospheric GCM response to extratropical SST anomalies: Synthesis and evaluation. J. Climate, 15, 22332256.

    • Search Google Scholar
    • Export Citation
  • Lau, N. C., , A. Leetmaa, , and M. J. Nath, 2006: Attribution of atmospheric variations in the 1997–2003 period to SST anomalies in the Pacific and Indian Ocean basins. J. Climate, 19, 36073628.

    • Search Google Scholar
    • Export Citation
  • Lin, H., , and Z. W. Wu, 2012: Contribution of Tibetan Plateau snow cover to the extreme winter conditions of 2009/10. Atmos.–Ocean, 50, 86–94, doi:10.1080/07055900.2011.649036.

    • Search Google Scholar
    • Export Citation
  • Liu, Q., , N. Wen, , and Z. Liu., 2006: An observational study of the impact of the North Pacific SST on the atmosphere. Geophys. Res. Lett.,33, L18611, doi:10.1029/2006GL026082.

  • Liu, Z., , and N. Wen, 2008: On the assessment of nonlocal climate feedback. Part II: EFA-SVD and optimal feedback modes. J. Climate, 21, 54025416.

    • Search Google Scholar
    • Export Citation
  • Liu, Z., , Y. Liu, , L. Wu, , and R. Jacob, 2007: Seasonal and long-term atmospheric responses to reemerging North Pacific Ocean variability: A combined dynamical and statistical assessment. J. Climate, 20, 955980.

    • Search Google Scholar
    • Export Citation
  • Liu, Z., , N. Wen, , and Y. Liu, 2008: On the assessment of nonlocal climate feedback. Part I: The generalized equilibrium feedback assessment. J. Climate, 21, 134148.

    • Search Google Scholar
    • Export Citation
  • Ma, T. T., , Z. W. Wu, , and Z. H. Jiang, 2012: How does China coldwave frequency respond to a warming climate? Climate Dyn., 39, 2487–2496, doi:10.1007/s00382-012-1354-8.

    • Search Google Scholar
    • Export Citation
  • Newman, M., , G. Compo, , and M. Alexander, 2003: ENSO-forced variability of the Pacific decadal oscillation. J. Climate, 16, 38533857.

  • Notaro, M., , Z. Liu, , and J. W. Williams, 2006a: Observed vegetation climate feedbacks in the United States. J. Climate, 19, 763786.

  • Pu, B., , X. Y. Wen, , S. W. Wang, , and J. H. Zhu, 2007: Diagnostic and modeling study of the two basic modes of surface air temperature and its variation in China (in Chinese). Adv. Earth Sci., 22, 456467.

    • Search Google Scholar
    • Export Citation
  • Thompson, D. W. J., , and J. M. Wallace, 1998: The Arctic Oscillation signature in the wintertime geopotential height and temperature fields. Geophys. Res. Lett., 25, 12971300.

    • Search Google Scholar
    • Export Citation
  • Thompson, D. W. J., , and J. M. Wallace, 2000: Annular modes in the extratropical circulation. Part I: Month-to-month variability. J. Climate, 13, 10001016.

    • Search Google Scholar
    • Export Citation
  • Wang, B., , and Q. Zhang, 2002: Pacific–East Asian teleconnection. Part II: How the Philippine Sea anomalous anticyclone is established during El Niño development. J. Climate, 15, 32523265.

    • Search Google Scholar
    • Export Citation
  • Wang, B., , R. G. Wu, , and X. H. Fu, 2000: Pacific–East Asian teleconnection: How does ENSO affect East Asian climate. J. Climate, 13, 15171536.

    • Search Google Scholar
    • Export Citation
  • Wang, B., , Z. W. Wu, , and C. P. Chang, 2010: Another look at interannual-to-interdecadal variations of the East Asian winter monsoon: The northern and southern temperature modes. J. Climate, 23, 14951512.

    • Search Google Scholar
    • Export Citation
  • Wei, F. Y., 2007: Modern Climatic Statistical Diagnosis and Prediction Technology. China Meteorological Press, 110–111.

  • Wen, N., , Z. Liu, , and Q. Liu, 2010: Observed atmospheric responses to global SST variability modes: A united assessment using GEFA. J. Climate, 23, 17391759.

    • Search Google Scholar
    • Export Citation
  • Wu, B., , and J. Wang, 2002a: Possible impact of winter Arctic Oscillation on Siberian high, the East Asian winter monsoon and sea-ice-extent. Adv. Atmos. Sci., 19, 297320.

    • Search Google Scholar
    • Export Citation
  • Wu, B., , and J. Wang, 2002b: Winter Arctic Oscillation, Siberian high and East Asian winter monsoon. Geophys. Res. Lett., 29, 1897, doi:10.1029/2002GL015373.

    • Search Google Scholar
    • Export Citation
  • Wu, Z. W., , J. P. Li, , and Z. H. Jiang, 2010: Predictable climate dynamics of abnormal East Asian winter monsoon: Once-in-a-century snowstorms in 2007/2008 winter. Climate Dyn., 37, 16611669, doi:10.1007/s00382-010-0938-4.

    • Search Google Scholar
    • Export Citation
  • Yamagata, T., , S. Behera, , and S. Rao, 2003: Comments on “Dipoles, temperature gradients, and tropical climate anomalies.” Bull. Amer. Meteor. Soc.,84, 1418–1422.

  • Yang, H., 2011: The significant relationship between the Arctic Oscillation (AO) in December and the January climate over south China. Adv. Atmos. Sci., 28, 398407, doi:10.1007/s00376-010-0019-y.

    • Search Google Scholar
    • Export Citation
  • Yang, J. L., , Q. Y. Liu, , and S. P. Xie, 2007: Impact of the Indian Ocean SST basin mode on the Asian summer monsoon. Geophys. Res. Lett., 34, L02708, doi:10.1029/2006GL028571.

    • Search Google Scholar
    • Export Citation
  • Zhong, Y. F., , Z. Y. Liu, , and N. Michael, 2011: A GEFA Assessment of global ocean influence on U.S. precipitation variability: Attribution to regional SST variability modes. J. Climate, 24, 693707.

    • Search Google Scholar
    • Export Citation
  • View in gallery

    (a) Spatial pattern (color shadings, °C) and (b) the corresponding PC of the EOF2 mode of the winter temperature. In (b), the dotted lines denote the standard deviation σ = ±1.5 and the open bars are PC1 , while solid bars are PC2. The open and solid bars have no overlap.

  • View in gallery

    (a) Difference of winter air temperature (color shadings, °C; 90% significance shaded) between positive phase years (1965/66, 2000/01, and 2009/10) and negative phase years (1988/89 and 2007/08) of NE–SW pattern. (b) Vertical diagonal of winter air temperature anomaly (color shadings, °C) in 2009/10.

  • View in gallery

    Correlation coefficient between the PC2 of Chinese winter temperature and SST from 1958/59 to 2009/10 (90% significance shaded).

  • View in gallery

    The first three leading EOF modes of SST from each of the five ocean basins of 1958–2010: (a) TP1, TP2, and TP3; (b) TI1, TI2, and TI3; (c) TA1, TA2, and TA3; (d) NP1, NP2, and NP3; and (e) NA1, NA2, and NA3. Explained fractions of variance are given in parentheses. Contour interval (CI) = 0.3°C.

  • View in gallery

    The 1958/59–2009/10 GEFA feedback coefficient (bars, °C °C−1; 80% significance for dark bars, 90% significance for the dark bars with asterisks above/below) of responses of NE–SW temperature pattern to the first three leading SST EOF in five ocean basins.

  • View in gallery

    The (a) 2009/10 and (b) 1988/89 GEFA response amplitude () (bars, °C; dark bars are most import modes) of NE–SW positive and negative phase temperature pattern to the first three leading SST EOF in five ocean basins.

  • View in gallery

    China 2009/10 winter temperature (a) observed anomaly (color shadings, °C) and (b) its GEFA response sensitivity to TP1 and TA3 (color shadings, of °C). (c),(d) As in (a),(b), but for 1988/89 response to TP1 and TP2.

  • View in gallery

    The 2009/10 winter SLP observed anomaly field (color shadings, m °C−1) and its GEFA response sensitivity (contours, m °C−1) toTP1 and TA3.

  • View in gallery

    (a) GEFA responses of 500-hPa geopotential height (contours, m °C−1) and 850-hPa wind (vector, m s−1) to TP1 and TA3. (b) 500-hPa geopotential height (contours, m °C−1) and 850-hPa wind (vector, m s−1) observed anomaly in winter 2009/10 (90% significance shaded).

  • View in gallery

    The 2009/10 observed winter air temperature advection (color shadings, °C s−1) and 850-hPa wind field (vector, m s−1). (a),(b) Caused by the temperature turbulence (, ) and (c),(d) caused by the disturbance zonal () and meridional () wind, respectively.

  • View in gallery

    The changes of variation (°C °C−1) with the sample length alteration. The term is GEFA feedback coefficient of responses of NE–SW temperature pattern to the first three leading EOF modes of SST in five ocean basins. See text for explanation.

  • View in gallery

    The variation (°C °C−1) changes with the sample alteration. See text for explanation.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 17 17 13
PDF Downloads 4 4 4

Assessing the Influence of Regional SST Modes on the Winter Temperature in China: The Effect of Tropical Pacific and Atlantic

View More View Less
  • 1 Key Laboratory of Meteorological Disaster, Nanjing University of Information Science and Technology, Ministry of Education, Nanjing, China
  • 2 Center for Climatic Research, University of Wisconsin—Madison, Wisconsin
  • 3 Meteorological Bureau of Suzhou City, Suzhou, China
  • 4 Key Laboratory of Meteorological Disaster, Nanjing University of Information Science and Technology, Ministry of Education, Nanjing, China
© Get Permissions
Full access

Abstract

This study investigates the influence of different sea surface temperature (SST) modes on the winter temperature in China using the generalized equilibrium feedback assessment (GEFA). It is found that the second EOF mode of winter temperature in China during 1958–2010 shows a typical northeast–southwest (NE–SW) pattern, which is a major spatial mode of Chinese winter temperature at interannual scales. The winter temperature of the NE–SW pattern is forced mainly by SST modes in the tropical Pacific and Atlantic. For 2009/10, the tropical Pacific El Niño mode and tropical Atlantic tripole mode have the largest contribution to the response. The physical mechanism of the cold northeast–warm southwest (CNE–WSW) pattern is also explained in terms of GEFA of the responses of the atmospheric circulation. The northerly flow at the low level transports cold air to northern and northeastern China, resulting in a lower temperature there. Meanwhile, the anomaly meridional wind advects warm air from the southern oceans to southwestern China, leading to warming there.

Corresponding author address: Zhihong Jiang, Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, 219 Ningliu Rd., Nanjing, China. E-mail: zhjiang@nuist.edu.cn

Abstract

This study investigates the influence of different sea surface temperature (SST) modes on the winter temperature in China using the generalized equilibrium feedback assessment (GEFA). It is found that the second EOF mode of winter temperature in China during 1958–2010 shows a typical northeast–southwest (NE–SW) pattern, which is a major spatial mode of Chinese winter temperature at interannual scales. The winter temperature of the NE–SW pattern is forced mainly by SST modes in the tropical Pacific and Atlantic. For 2009/10, the tropical Pacific El Niño mode and tropical Atlantic tripole mode have the largest contribution to the response. The physical mechanism of the cold northeast–warm southwest (CNE–WSW) pattern is also explained in terms of GEFA of the responses of the atmospheric circulation. The northerly flow at the low level transports cold air to northern and northeastern China, resulting in a lower temperature there. Meanwhile, the anomaly meridional wind advects warm air from the southern oceans to southwestern China, leading to warming there.

Corresponding author address: Zhihong Jiang, Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, 219 Ningliu Rd., Nanjing, China. E-mail: zhjiang@nuist.edu.cn

1. Introduction

With the background global warming, the anomalous winter air temperature has received widespread concern in recent years (e.g., Wang and Zhang 2002; Yang et al. 2007; Lin and Wu 2012; Jiang et al. 2012). Especially, extreme climate events in China have received increasing attention during the past few years due to their frequent occurrences (e.g., Wu et al. 2010; Chen et al. 2010; Jiang et al. 2011; Ma et al. 2012). For instance, northern China witnessed cold winter with heavy snow disasters in 2009/10, during which Xinjiang Province experienced the most serious damage in the last five decades, while anomalous warming and drought occurred over the southwest. In 2008 (January–February), south China experienced continuous low temperature, accompanied by the most severe large range of continuous low temperature freezing rain and snowstorms in the last 50 years, leading to huge property losses and hundreds of human lives (e.g., Hong and Li 2009; Wu et al. 2010).

In the meantime, our understanding and prediction skill of these extreme events remain limited. Pu et al. (2007) showed that the two basic spatial patterns of air temperature changes over China are characterized by the empirical orthogonal function (EOF) modes, the EOF1 representing a consistent change in eastern China and EOF2 representing opposite changes between northeastern and southwestern China [northeast–southwest (NE–SW) pattern]. It was further shown that the two basic spatial patterns above are stable in different periods. However, it has remained unclear what caused the change of these temperature patterns. In particular, it has remained unclear how the abnormal winter temperature in China responds to global oceans. The impact of SST on Chinese winter temperature is receiving increasing attention. So far, the central and eastern equatorial Pacific SST has received the most attention. Many studies suggest that El Niño events give rise to cold northeast–warm southwest (CNE–WSW) pattern in China (e.g., Wang et al. 2000; Chen et al. 2001). Ding et al. (2008) suggested that the strong La Niña event in the 2007/08 winter provides a climate background for the invasion of cold air into southern China, and the persistence of circulation anomalies over the Eurasian continent is the direct cause of the snowstorms. Wang et al. (2010) found that the East Asian (EA) winter monsoon (EAWM) variability are different between the extratropical and tropical EA. El Niño–Southern Oscillation (ENSO), snow cover over northeastern Siberia, and SST of the North Atlantic and tropical Indian Oceans have been suggested to be the major factors impacting EAWM.

One challenge in previous observational studies is to isolate the atmospheric response to a specific oceanic variability mode. This is because the atmospheric response at a specific time usually consists of the responses to multiple concurrent SST forcings (Klein et al. 1999; Newman et al. 2003; Lau et al. 2006). Our study here is an attempt to addresses the questions: How do SST anomalies (SSTAs) impact the temperature variation in winter over China synthetically? How much does each SST mode contribute to the temperature anomaly?

Frankignoul et al. (1998) proposed a simple univariate statistical method, later called the equilibrium feedback assessment (EFA) (Liu and Wen 2008), for the assessment of climate feedback. This method has now been extended to the multivariate case as the generalized equilibrium feedback assessment (GEFA) method (Liu et al. 2008; Liu and Wen 2008). Using the GEFA method, Wen et al. (2010) investigated the impacts of global SST variability modes on geopotential height (GPH) in observations and showed that GEFA is able to distinguish the impacts from different SST modes in different oceans. Zhong et al. (2011) presented a comprehensive assessment of the observed influence of the global ocean on U.S. precipitation variability using the method of GEFA. However, no relevant analysis has yet been conducted on the climate variability in China.

Here, we will assess the impact of SST on winter temperature in China in the observation using GEFA. This paper is arranged as follows. The GEFA method is first reviewed and the observational data are described in section 2. Section 3 describes the characteristic winter temperature response pattern in China, the NE–SW pattern, in composite analysis and EOF analysis. The SST impact on the air temperature response pattern and the physical mechanism are further studied in sections 4 and 5, respectively. The last section summarizes the major findings.

2. Data and method

a. Data

The major datasets used in this study include 1) monthly-mean surface air temperature data at 160 gauge stations across China from the China Meteorological Administration, and 2) monthly-mean SST, GPH, sea level pressure (SLP), and wind fields data, gridded at 2.5° × 2.5° resolution taken from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) Global Reanalysis 1 (NCEP-1) data for the 1958–2010 period (available online at http://www.cdc.noaa.gov/cdc/data.ncep.reanalysis.derived.html). Here, winter refers to December–February.

b. Generalized equilibrium feedback assessment

GEFA is a multivariate generalization of the univariate EFA (Frankignoul et al. 1998; Liu et al. 2006; Notaro et al. 2006a) to facilitate distinguishing the impacts by interrelated oceanic forcings. A full formulation of GEFA has been demonstrated in Liu et al. (2008) and Liu and Wen (2008). For the convenience of the readers, we will briefly discuss GEFA here.

Assume the atmospheric variability at month t (t) consists of a stochastic part associated with the atmospheric internal variability N(t) and a SST-forced part × (t), such that
e1
or
e2
The SST field t consists of j points, representing j SST indices. Here, is the response sensitivity matrix with components bij judging the impact of the jth SST index on the temperature variability fields. Simultaneous association of SSTs and atmospheric variability primarily reflects the atmosphere driving the ocean, requiring that calculations aimed at quantifying oceanic feedbacks to the atmosphere use data with the atmosphere lagging the ocean (Frankignoul et al. 1998; Czaja and Frankignoul 2002). Therefore, is derived with atmosphere-lagged covariances as
e3
where is a SST lead time that is longer than the damping time scale of the atmosphere, is the lagged cross-covariance matrix between atmospheric variability and SST, and is the autocovariance matrix of SST. Discrimination of the impacts by interrelated SST forcings now becomes effortless by singling out each component bij. Here, is chosen to be 1 month in this study, as = 2 months tends to yield similar results (Wen et al. 2010). The significant test of is performed using the Monte Carlo bootstrap approach (Czaja and Frankignoul 2002). The computation of is repeated 500 times, each using randomly scrambled atmospheric time series. The atmospheric seasonality is retained, as only the order of the years is changed, not that of the months.

3. GEFA in the EOF space

a. The definition of NE–SW pattern

To discriminate the variation characteristics of winter air temperature, we perform EOF analysis of the winter-mean temperature over China from 1958/59 to 2009/10 (Wei 2007). The first EOF mode (explaining 55% of the total variance) mainly reflects the consistent warming trend (figure omitted). The second EOF mode (explaining 14% of the total variance) in Fig. 1a reflects a dipole temperature anomaly between northeastern China and southwestern China, which will be called the pattern of NE–SW. In particular, the positive center lies in southwestern China, reaching above 1°C, while the negative center appears in the northeast area, with −2°C. The corresponding principal components (PCs; Fig. 1b) show clear interannual variability. According to the first and second PCs (PC1 and PC2; Fig. 1b), years with PC1 < 0.5 standard deviation (σ = 0.5) and PC2 > 1.5 (σ = 1.5) are defined as the NE–SW positive phases (CNE–WSW) years. In contrast, years with PC1 lower than 0.5, and PC2 less than −1.5, are defined as the NE–SW negative phases (warm northeast–cold southwest) years. According to the definition, the NE–SW positive phases years include 1965/66, 2000/01, and 2009/10. The negative phases years include 1988/89 and 2007/08. Figure 2a shows the differences of the winter air temperature anomaly between these two groups of typical years (the positive years minus negative years). In addition, the vertical section (along the diagonal section from southwestern to northeastern China as marked in Fig. 2a) of the winter air temperature anomaly in 2009/10 (Fig. 2b) reveals that the dipole temperature anomaly is a shallow structure confined in the lower atmosphere, below 700 hPa, especially in the north.

Fig. 1.
Fig. 1.

(a) Spatial pattern (color shadings, °C) and (b) the corresponding PC of the EOF2 mode of the winter temperature. In (b), the dotted lines denote the standard deviation σ = ±1.5 and the open bars are PC1 , while solid bars are PC2. The open and solid bars have no overlap.

Citation: Journal of Climate 27, 2; 10.1175/JCLI-D-12-00847.1

Fig. 2.
Fig. 2.

(a) Difference of winter air temperature (color shadings, °C; 90% significance shaded) between positive phase years (1965/66, 2000/01, and 2009/10) and negative phase years (1988/89 and 2007/08) of NE–SW pattern. (b) Vertical diagonal of winter air temperature anomaly (color shadings, °C) in 2009/10.

Citation: Journal of Climate 27, 2; 10.1175/JCLI-D-12-00847.1

b. Regional SST forcing modes

To gain some perspective on the relation between Chinese winter temperature and global SST variability, we first show the simultaneous correlation coefficients between PC2 (in Fig. 1b) and SSTA over the globe (Fig. 3). Significantly positive correlations are found in the eastern equatorial Pacific Ocean, equatorial and northern Indian Ocean, as well as central tropical North Atlantic, while the negative correlation is mainly located in the central North Pacific. The correlation seems to suggest that Chinese winter temperatures are potentially related to five regional SSTAs, over the equatorial eastern and central Pacific, tropical Indian Ocean, North Atlantic, North Pacific, and equatorial Atlantic. However, the traditional correlation analysis cannot identify the impact of each SST forcing on temperature anomaly exclusively because the SST variability in different regions may not be independent.

Fig. 3.
Fig. 3.

Correlation coefficient between the PC2 of Chinese winter temperature and SST from 1958/59 to 2009/10 (90% significance shaded).

Citation: Journal of Climate 27, 2; 10.1175/JCLI-D-12-00847.1

To better represent the SST forcing, following Wen et al. (2010), we will use the regional SST EOFs. The EOFs tend to reduce the correlation among the forcing modes and therefore reduce sampling errors in GEFA (Fan et al. 2011). Furthermore, even though sometimes physically not fully justified (Allen et al. 2001; Yamagata et al. 2003), many of the leading EOF SST modes have been often used to refer to classical SST variability modes, such as ENSO and others, making them convenient for analysis. As in Wen et al. (2010), the global ocean north of 25°S is divided into nonoverlapping subbasins: the tropical Pacific (TP; 20°S–20°N, 120°E–60°W), tropical Indian Ocean (TI; 20°S–20°N, 35°–120°E), tropical Atlantic (TA; 25°S–25°N, 65°W–15°E), North Pacific (NP; 20°–60°N, 120°E–60°W), and North Atlantic (NA; 20°–60°N, 100°W–20°E). Regional SST EOFs are then obtained for each subbasin. Figure 4 shows the first three leading EOFs in TP, TI, TA, NP, and NA, respectively. The patterns are shown as follows: the first pattern of TP (TP1), including El Niño/La Niña modes, zonal dipole (warm east–cold west; TP2) mode, and tripole (TP3) mode (most of tropical Pacific shows negative anomaly except central equatorial Pacific; Fig. 4a); Indian Ocean Basin mode (IOB) mode (TI1), Indian Ocean dipole (IOD) mode (TI2), and zonal dipole Indian Ocean (TI3) mode (Fig. 4b); tropical Atlantic Niño (TA1) mode, meridional dipole (TA2) mode, and the tripole (TA3) mode (northern and southern tropical Atlantic are positive anomaly but the central area is negative; Fig. 4c); North Pacific horseshoe (NP1) mode, North Pacific meridional (NP2) mode, and zonal dipole North Pacific (NP3) mode (Fig. 4d); and North Atlantic tripole (NA1) mode, North Atlantic dipole (NA2) mode, and tripole (NA3) mode (Fig. 4e). Explained variance fractions of the first three leading SST modes in five ocean basins exceed 50% (Table 1).

Fig. 4.
Fig. 4.

The first three leading EOF modes of SST from each of the five ocean basins of 1958–2010: (a) TP1, TP2, and TP3; (b) TI1, TI2, and TI3; (c) TA1, TA2, and TA3; (d) NP1, NP2, and NP3; and (e) NA1, NA2, and NA3. Explained fractions of variance are given in parentheses. Contour interval (CI) = 0.3°C.

Citation: Journal of Climate 27, 2; 10.1175/JCLI-D-12-00847.1

Table 1.

Explained variance fractions of the three leading SST modes from five ocean basins.

Table 1.

It should be pointed out that our study of the response stability and sensitivity to different SST variability modes is important (see appendix). Then, we can obtain the GEFA feedback coefficient of the observed Chinese winter temperature response to SST EOF modes from 1958 to 2010. Figure 5 shows that the NE–SW type of winter air temperature has a robust response to tropical Pacific SST EOF2 (TP2), tropical Indian Ocean SST EOF3 (TI3), and North Pacific SST EOF3 (NP3) (90% significant level). Their corresponding response values are 0.97°, 1.64°, and −1.21°C °C−1, respectively. This means that, for example for TP2, the response value of temperature CNE–WSW is 0.97°C when the TP2 SST changes by 1°C. For tropical Pacific SST EOF1 (TP1), tropical Atlantic SST EOF3 (TA3), and other SST modes the response significant levels are lower but still exceed 80%.

Fig. 5.
Fig. 5.

The 1958/59–2009/10 GEFA feedback coefficient (bars, °C °C−1; 80% significance for dark bars, 90% significance for the dark bars with asterisks above/below) of responses of NE–SW temperature pattern to the first three leading SST EOF in five ocean basins.

Citation: Journal of Climate 27, 2; 10.1175/JCLI-D-12-00847.1

4. Winter air temperature response to SST mode based on GEFA method

In this section, we demonstrate the GEFA temperature response of the NE–SW pattern to SST modes using two years as examples. One is the positive year (2009/10) and the other negative year (1988/89) (see Fig. 1b). Similar results are found for other NE–SW pattern years (1965/66, 2000/01, and 2007/08) and therefore are not shown here. For the forcing of the observed SST variability, the magnitude of the winter air temperature response to each SST mode is further derived by a multiplication of the standard deviation of the PC and feedback coefficient .

The 2009/10 and 1988/89 are typical positive and negative phase years of the NE–SW response, respectively. With the feedback coefficient (Fig. 5) and winter SSTA (here represented by ), we estimate the corresponding amplitude of the Chinese winter air temperature response to different SST EOF modes. As shown in Fig. 6a, in 2009/10 the GEFA response amplitude to TP1 and TA3 make the most significant contribution among all of the SST forcings. The corresponding amplitude of winter temperature to TP1 and TA3 are 0.3° and 0.26°C, respectively. This means that the contribution of SST mode forcing depends not only on the response coefficient, but also on the intensity of the force field (SST anomaly). Here, although the TP1 response coefficient (Fig. 5) is not large, the SST anomaly of the TP1 pattern (El Niño) is very significant in 2009/10 and therefore leads to the maximum response amplitude on winter 2009/10 temperature of CNE–WSW. Similarly, in winter 1988/89 (Fig. 6b), for the NE–SW negative phase year, the most robust GEFA response amplitude are forced by the tropical Pacific La Niña mode (TP1) and tropical Pacific zonal (warm east–cold west) mode (TP2), with the temperature response amplitudes of −0.33° and −0.24°C, respectively. For the other NE–SW pattern years, the TP1 is also one of the main SST modes except 2000/01 (Table 2).

Fig. 6.
Fig. 6.

The (a) 2009/10 and (b) 1988/89 GEFA response amplitude () (bars, °C; dark bars are most import modes) of NE–SW positive and negative phase temperature pattern to the first three leading SST EOF in five ocean basins.

Citation: Journal of Climate 27, 2; 10.1175/JCLI-D-12-00847.1

Table 2.

The main GEFA SST modes of each NE–SW pattern year.

Table 2.

Considering the winter 2009/10, the temperature anomalies are mainly presented like the EOF2 mode structure (Fig. 7a); the forced field of winter 2009/10 air temperature anomaly over China response to SST field TP1 and TA3 is given in Fig. 7b, so as to further assess the impact of the SST forcing field. It shows that the eastern equatorial Pacific El Niño mode and tropical Atlantic tripole mode lead to a warming in southwestern China of 0.9°C and a cooling over 1.5°C in the northeast of northern China. Compared with the observed temperature anomaly (Fig. 7a), the combined contributions from TP1 and TA3 exceed 50%. Comprehensive analysis from above indicates that winter 2009/10 air temperature type CNE–WSW over China is mainly forced by the combined forcing of the eastern equatorial Pacific El Niño mode and tropical Atlantic tripole mode. Since the winter 1988/89 air temperature exhibits mainly the negative phase of the NE–SW pattern, Fig. 7d shows the response to both TP1 and TP2, so as to further assess the effects by SST forcings. It can be seen that in 1988/89, the equatorial Pacific anti-El Niño (La Niña) mode and the tropical Pacific zonal (warm east–cold west) mode have led to southwest cooling of 0.7°C and an increase of more than 1.5°C in northeastern and northern China. Compared with the observational anomaly field (Fig. 7c), the forcing contribution is larger than 70%.

Fig. 7.
Fig. 7.

China 2009/10 winter temperature (a) observed anomaly (color shadings, °C) and (b) its GEFA response sensitivity to TP1 and TA3 (color shadings, of °C). (c),(d) As in (a),(b), but for 1988/89 response to TP1 and TP2.

Citation: Journal of Climate 27, 2; 10.1175/JCLI-D-12-00847.1

5. The physical mechanisms of CNE–WSW temperature pattern

a. The relationship between CNE–WSW and SLP in 2009/10

Here, we further study the mechanism for the air temperature response to SST modes in terms of the atmospheric dynamics. In particular, we will focus on the responses of the lower-tropospheric circulation system, which is represented by the SLP. For the CNE–WSW pattern, we first study the GEFA response on SLP. Our study above (section 4) shows that the 2009/10 winter temperature anomaly is forced mainly by the SST modes, such as the TP1 and TA3 modes.

Since the responses of temperature anomalies to SST forcing are realized by the atmospheric teleconnection, the temperature response should be accompanied by the response in the atmospheric dynamic fields, such as the SLP. The SLP responses to SST modes are therefore estimated the same way as air temperature. Figure 8 (contours) shows GEFA response sensitivity of 2009/10 winter SLP to tropical Pacific SST EOF1 (TP1) and tropical Atlantic SST EOF3 (TA3). Under the forcing of El Niño and the tropical Atlantic tripole mode, a robust positive response is located in western Siberia (Siberian high), with the ridge extending into the Ural Mountain range; a negative response is forced over Mongolia and northeastern China. Thus, cold air from Siberia can be advected into northeastern China by the anomalous northerly winds, leading to cooling there. In the midlatitudes (30°–60°N), clear negative responses are found in the areas of the Azores and the East Coast of the United States, while a positive response is found in the northwestern Pacific. As such, warm air mass can be advected into southwestern China from tropical oceans.

Fig. 8.
Fig. 8.

The 2009/10 winter SLP observed anomaly field (color shadings, m °C−1) and its GEFA response sensitivity (contours, m °C−1) toTP1 and TA3.

Citation: Journal of Climate 27, 2; 10.1175/JCLI-D-12-00847.1

Figure 8 (shadings) further shows that the 2009/10 winter SLP observation anomaly is positive at high latitudes (north of 60°N) and negative in midlatitudes (30°–60°N). Obviously, the GEFA response pattern of SLP is consistent with the observed anomaly, although it does not agree very well over China. Moreover, the magnitudes between these two fields are almost the same. The SLP anomalies in the midlatitudes (30°–60°N) and high latitudes have atypical “− +” dipole structure distribution, which is a negative phase mode of the Arctic Oscillation (AO) (Thompson and Wallace 1998, 2000; Wu and Wang 2002a,b; Frankignoul and Kestenare 2005; Yang 2011).

In consequence, the combined actions of the eastern equatorial Pacific El Niño mode and tropical Atlantic “+ − +” tripole mode generate the 2009/10 winter temperature CNE–WSW pattern in China. The SLP GEFA response pattern in Northern Hemisphere presents a negative phase of AO. In this kind of situation, the Siberian high is conducive to strengthen, which brings cold air to the northeast China.

b. The relationship between CNE–WSW and U850/V850 in 2009/10

The above studies verify that the air temperature anomalies’ response to SST forcing could be achieved through SLP coarsely. To further clarify the formation mechanism of the CNE–WSW pattern, we also discuss temperature advection by the GEFA response of zonal and meridional 850-hPa winds (U850 and V850) in this section.

The GEFA response sensitivity of 2009/10 winter 500-hPa GPH (Z500) and U850/V850 toTP1 and TA3 (Fig. 9a) reproduces the classical atmosphere response pattern to El Niño, which is characterized by a robust teleconnection in the extratropics, such as the Pacific–North American (PNA) pattern response. The east of Lake Baikal in eastern Siberia and subtropical western Pacific exhibits the pattern of “− +” responses. Obviously, this GPH response situation is convenient for polar cold air occupying high latitudes. When the East Asian trough deepens, the cold air intrudes into the eastern ocean, leading to lower temperature in northeastern China, but it cannot reach farther south to influence southwestern China. Simultaneously, the positive abnormal response over the subtropical western Pacific and southern Asia generates southeasterly into southwest of China, resulting in a winter warmer than normal. As discussed above, the SLP field in section 5a, the GEFA responses of Z500, and U850/V850 are similar with observed anomaly field in 2009/10 (Fig. 9b). It shows again that the forcings of TP1 and TA3 are the major factors influencing the CNE–WSW temperature pattern.

Fig. 9.
Fig. 9.

(a) GEFA responses of 500-hPa geopotential height (contours, m °C−1) and 850-hPa wind (vector, m s−1) to TP1 and TA3. (b) 500-hPa geopotential height (contours, m °C−1) and 850-hPa wind (vector, m s−1) observed anomaly in winter 2009/10 (90% significance shaded).

Citation: Journal of Climate 27, 2; 10.1175/JCLI-D-12-00847.1

Based on the GEFA response of Z500 and U850/V850, we find the circulation system is the cause of the CNE–WSW pattern. To further understand how such a temperature change is generated, we further calculate the observed temperature advection. Here, we calculate each term of temperature advection at 850 hPa. It is obvious that the cold anomaly in northeast China is mainly caused by temperature turbulence (, ; Figs. 10a,b). There is a large and robust cold advection distributed along the Baikal, northeastern China, and Sea of Japan. The reason is that in winter 2009/10, an abnormal cold source was located in northwestern China. The prevailing westerly carries the cold air masses into northeastern China. However, over southwestern China, the warm advection is not stimulated by temperature turbulence but by the disturbance of zonal and meridional wind (, ; Figs. 10c,d). The warm flow from the southern oceans irrupts into southwestern China along with the stronger southwesterly winds. It should be noted that the wind turbulence in the meridional direction is more powerful than the zonal direction.

Fig. 10.
Fig. 10.

The 2009/10 observed winter air temperature advection (color shadings, °C s−1) and 850-hPa wind field (vector, m s−1). (a),(b) Caused by the temperature turbulence (, ) and (c),(d) caused by the disturbance zonal () and meridional () wind, respectively.

Citation: Journal of Climate 27, 2; 10.1175/JCLI-D-12-00847.1

Overall, the GEFA responses of Z500 and U850/V850 in 2009/10 indicate that the interaction of the cyclonic anomaly over the Asian continent and anticyclonic anomaly in the northwestern Pacific bring a straight westerly wind north of 30°N and transport the cold air from Siberia into northeastern China, leading to a lower winter air temperature. While these anomaly systems hinder the cold air from moving farther south, they guide the warm air from the south into southwestern China. Such a circulation response is mainly forced by the eastern equatorial Pacific El Niño mode (TP1) and tropical Atlantic tripole mode (TA3).

The above analysis of NE–SW positive phase pattern year also led us to wonder about the situation for negative phase years. In section 4a, we showed that the equatorial Pacific anti-El Niño mode (TP1) and the tropical Pacific zonal (warm east–cold west) mode (TP2) force the most remarkable contribution to the winter 1988/89 air temperature anomaly. With the above method, the GEFA response of atmospheric circulation is approximately opposite the year 2009/10 in which the decrease of the East Asian trough weakened the influence of cold air in the northern area of China. Meanwhile, under the control of the South Asian high and Ural Mountains low, the cold wave from the Siberian area bypasses the northeast area and flows straight to southwestern China, leading to the negative temperature phase of NE–SW pattern (figures not shown).

6. Summary and discussion

To assess the influence of global SST variability on Chinese winter temperature variability, we examined the response of the Chinese winter temperature anomaly of NE–SW pattern to the leading EOF modes of various oceanic SSTs using the GEFA method. While confirming some previous results, our assessment also unveils some new features of global oceanic forcing on Chinese winter temperatures. The main conclusions are as follows.

The second EOF mode (in the period 1958–2010) of the Chinese winter air temperature anomaly presents a robust spatial pattern of NE–SW. The main SST forcing modes for Chinese NE–SW winter temperature are from the tropical Pacific and Atlantic. For different years, the relative importance of the SST modes is different. For 2009/10, the tropical Pacific El Niño mode (TP1) and tropical Atlantic tripole mode (TA3) have the greatest contribution to the forcing. The GEFA responses of the atmospheric circulation anomaly suggest that in East Asia the combinations of TP1 and TA3 generates westerly flow and transports cold air from Siberia into northeastern China, leading to the lower winter temperatures. Meanwhile, in southwestern China the abnormal disturbance of meridional wind advects warm air from the south over the oceans, resulting in warmer temperature there.

Finally, there are many further issues to be studied. As a pilot study here, we only analyzed a few typical years in detail here. It is not very perfect for some results. In the future, it will be important to further diagnose and analyze other years so that we may find a more systematic and robust response. The GEFA response of SLP is largely consistent with the AO pattern. However, the relationship among SST, AO, and Chinese temperature remain to be further studied. Finally, in addition to SST forcing, there may be other factors influencing China temperature, such as snow coverage and vegetation.

Acknowledgments

We thank Drs. Zhiwei Wu and Peng Liu for helpful discussions. We also thank the editor and anonymous reviewers for their constructive comments. This work is supported jointly by the National Basic Research Program 973 (Grant 2010CB950401 and 2012CB955204), Special Research Program for Public Welfare (Meteorology) of China (GYHY200906016), the Research and Innovation Project for College Graduates of Jiangsu Province (782002156), and a Project Funded by the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions.

APPENDIX

The Stability and Sensitivity of Response Estimate

The three leading modes of these regional EOFs are combined into a grand set of EOF modes to represent the ocean forcing in the forcings matrix t; we then estimate the PC2 of China’s winter temperature (of the NE–SW pattern) to each SST mode in GEFA response matrix t.

Using winters from December 1958 to February 1967 (30 months) as the starting sample length, we examine the variation of the response value as a function of sample size N [N = 3i, where i = 10, 11, …, 51 (yr)]. As seen in Fig. A1, all of the values of tend to be stable after N exceeds approximately 90. For most leading modes, the stabilizing sample lengths are even shorter. For example, the coefficient stabilizes at about 45 months and 63 months for the first mode of tropical Pacific Ocean (TP1) and equatorial Indian Ocean mode (TI1), respectively.

Fig. A1.
Fig. A1.

The changes of variation (°C °C−1) with the sample length alteration. The term is GEFA feedback coefficient of responses of NE–SW temperature pattern to the first three leading EOF modes of SST in five ocean basins. See text for explanation.

Citation: Journal of Climate 27, 2; 10.1175/JCLI-D-12-00847.1

For the same sample length, we further study the sensitivity of to the removal of some sample years. The sample year of 1958 is first removed in the 51 sample years from 1958 to 2008, with the remaining 50 years used for GEFA analysis. The estimated value of is denoted as 1958. Similarly, the sample year of 1959 is removed and denoted as 1959, and so on. The estimated (1958, 1959, …, 2008), in which different sample years are removed, is given in Fig. A2. They are relatively steady. To test the sensitivity of in different sea areas, the variation of the coefficient (mean square standard deviation/mean value; Table A1) is further calculated. It is found that all the GEFA feedback coefficients to tropical SST modes are lower than 0.4, which means that is stable. But the variation coefficient of for high-latitude SST modes are much larger, especially in the third North Atlantic mode (larger than 2). A careful comparison also suggests that 1998, 1999, and 2000 are diverse from other years’ variation coefficients. Thus, the GEFA response to the atmosphere is insensitive to the sample selection of tropical SST modes but may be unstable for some mid- and high-latitude SST modes. The latter may be related to a more subtle response of atmosphere to extratropical SSTs (e.g., Kushnir et al. 2002; Liu et al. 2007) and require further studies in the future.

Fig. A2.
Fig. A2.

The variation (°C °C−1) changes with the sample alteration. See text for explanation.

Citation: Journal of Climate 27, 2; 10.1175/JCLI-D-12-00847.1

Table A1.

The coefficient of variation (CV; mean square standard deviation/mean value) of GEFA response value of the three leading SST modes from five ocean basins.

Table A1.

REFERENCES

  • Allen, R., and Coauthors, 2001: Is there an Indian Ocean dipole, and is it independent of the El Niño-Southern Oscillation? CLIVAR Exchanges, No. 6, International CLIVAR Project Office, Southampton, United Kingdom, 18–22.

  • Chen, P. Y., , Y. Q. Ni, , and Y. H. Yin, 2001: Diagnostic study on the impact of the global sea surface temperature anomalies on the winter temperature anomalies in eastern China in past 50 years (in Chinese). J. Trop. Meteor., 17, 371380.

    • Search Google Scholar
    • Export Citation
  • Chen, W. L., , Z. H. Jiang, , and L. Li, 2010: Simulation of regional climate change under the IPCC A2 scenario in southeast China. Climate Dyn., 36, 491–507, doi:10.1007/s00382-010-0910-3.

    • Search Google Scholar
    • Export Citation
  • Czaja, A., , and C. Frankignoul, 2002: Observed impact of North Atlantic SST anomalies on the North Atlantic Oscillation. J. Climate, 15, 606623.

    • Search Google Scholar
    • Export Citation
  • Ding, Y. H., , Z. Y. Wang, , and Y. F. Song, 2008: Causes of the unprecedented freezing disaster in January 2008 and its possible association with the global warming. Acta Meteor. Sin., 665, 809825.

    • Search Google Scholar
    • Export Citation
  • Fan, L., , Z. Liu, , and Q. Liu, 2011: Robust GEFA assessment of climate feedback to SST EOF modes. Adv. Atmos. Sci., 28, 907912.

  • Frankignoul, C., , and E. Kestenare, 2005: Observed Atlantic SST anomaly impact on the NAO: An update. J. Climate, 18, 40894094.

  • Frankignoul, C., , A. Czaja, , and B. L’Heveder, 1998: Air–sea feedback in the North Atlantic and surface boundary conditions for ocean models. J. Climate, 11, 23102324.

    • Search Google Scholar
    • Export Citation
  • Hong, C. C., , and T. Li, 2009: The extreme cold anomaly over Southeast Asia in February 2008: Roles of ISO and ENSO. J. Climate, 22, 37863801.

    • Search Google Scholar
    • Export Citation
  • Jiang, Z. H., , J. Song, , and L. Li, 2011: Extreme climate events in China: IPCC-AR4 model evaluation and projection. Climatic Change, 110, 385–401, doi:10.1007/s10584-011-0090-0.

    • Search Google Scholar
    • Export Citation
  • Jiang, Z. H., , T. T. Ma, , and Z. W. Wu, 2012: China cold wave duration in a warming winter: Change of the leading mode. Theor. Appl. Climatol., 110, 65–75, doi:10.1007/s00704-012-0613-2.

    • Search Google Scholar
    • Export Citation
  • Klein, S. A., , B. J. Soden, , and N.-C. Lau, 1999: Remote sea surface temperature variations during ENSO: Evidence for a tropical atmospheric bridge. J. Climate, 12, 917932.

    • Search Google Scholar
    • Export Citation
  • Kushnir, Y., , W. A. Robinson, , I. Blade, , N. M. J. Hall, , S. Peng, , and R. Sutton, 2002: Atmospheric GCM response to extratropical SST anomalies: Synthesis and evaluation. J. Climate, 15, 22332256.

    • Search Google Scholar
    • Export Citation
  • Lau, N. C., , A. Leetmaa, , and M. J. Nath, 2006: Attribution of atmospheric variations in the 1997–2003 period to SST anomalies in the Pacific and Indian Ocean basins. J. Climate, 19, 36073628.

    • Search Google Scholar
    • Export Citation
  • Lin, H., , and Z. W. Wu, 2012: Contribution of Tibetan Plateau snow cover to the extreme winter conditions of 2009/10. Atmos.–Ocean, 50, 86–94, doi:10.1080/07055900.2011.649036.

    • Search Google Scholar
    • Export Citation
  • Liu, Q., , N. Wen, , and Z. Liu., 2006: An observational study of the impact of the North Pacific SST on the atmosphere. Geophys. Res. Lett.,33, L18611, doi:10.1029/2006GL026082.

  • Liu, Z., , and N. Wen, 2008: On the assessment of nonlocal climate feedback. Part II: EFA-SVD and optimal feedback modes. J. Climate, 21, 54025416.

    • Search Google Scholar
    • Export Citation
  • Liu, Z., , Y. Liu, , L. Wu, , and R. Jacob, 2007: Seasonal and long-term atmospheric responses to reemerging North Pacific Ocean variability: A combined dynamical and statistical assessment. J. Climate, 20, 955980.

    • Search Google Scholar
    • Export Citation
  • Liu, Z., , N. Wen, , and Y. Liu, 2008: On the assessment of nonlocal climate feedback. Part I: The generalized equilibrium feedback assessment. J. Climate, 21, 134148.

    • Search Google Scholar
    • Export Citation
  • Ma, T. T., , Z. W. Wu, , and Z. H. Jiang, 2012: How does China coldwave frequency respond to a warming climate? Climate Dyn., 39, 2487–2496, doi:10.1007/s00382-012-1354-8.

    • Search Google Scholar
    • Export Citation
  • Newman, M., , G. Compo, , and M. Alexander, 2003: ENSO-forced variability of the Pacific decadal oscillation. J. Climate, 16, 38533857.

  • Notaro, M., , Z. Liu, , and J. W. Williams, 2006a: Observed vegetation climate feedbacks in the United States. J. Climate, 19, 763786.

  • Pu, B., , X. Y. Wen, , S. W. Wang, , and J. H. Zhu, 2007: Diagnostic and modeling study of the two basic modes of surface air temperature and its variation in China (in Chinese). Adv. Earth Sci., 22, 456467.

    • Search Google Scholar
    • Export Citation
  • Thompson, D. W. J., , and J. M. Wallace, 1998: The Arctic Oscillation signature in the wintertime geopotential height and temperature fields. Geophys. Res. Lett., 25, 12971300.

    • Search Google Scholar
    • Export Citation
  • Thompson, D. W. J., , and J. M. Wallace, 2000: Annular modes in the extratropical circulation. Part I: Month-to-month variability. J. Climate, 13, 10001016.

    • Search Google Scholar
    • Export Citation
  • Wang, B., , and Q. Zhang, 2002: Pacific–East Asian teleconnection. Part II: How the Philippine Sea anomalous anticyclone is established during El Niño development. J. Climate, 15, 32523265.

    • Search Google Scholar
    • Export Citation
  • Wang, B., , R. G. Wu, , and X. H. Fu, 2000: Pacific–East Asian teleconnection: How does ENSO affect East Asian climate. J. Climate, 13, 15171536.

    • Search Google Scholar
    • Export Citation
  • Wang, B., , Z. W. Wu, , and C. P. Chang, 2010: Another look at interannual-to-interdecadal variations of the East Asian winter monsoon: The northern and southern temperature modes. J. Climate, 23, 14951512.

    • Search Google Scholar
    • Export Citation
  • Wei, F. Y., 2007: Modern Climatic Statistical Diagnosis and Prediction Technology. China Meteorological Press, 110–111.

  • Wen, N., , Z. Liu, , and Q. Liu, 2010: Observed atmospheric responses to global SST variability modes: A united assessment using GEFA. J. Climate, 23, 17391759.

    • Search Google Scholar
    • Export Citation
  • Wu, B., , and J. Wang, 2002a: Possible impact of winter Arctic Oscillation on Siberian high, the East Asian winter monsoon and sea-ice-extent. Adv. Atmos. Sci., 19, 297320.

    • Search Google Scholar
    • Export Citation
  • Wu, B., , and J. Wang, 2002b: Winter Arctic Oscillation, Siberian high and East Asian winter monsoon. Geophys. Res. Lett., 29, 1897, doi:10.1029/2002GL015373.

    • Search Google Scholar
    • Export Citation
  • Wu, Z. W., , J. P. Li, , and Z. H. Jiang, 2010: Predictable climate dynamics of abnormal East Asian winter monsoon: Once-in-a-century snowstorms in 2007/2008 winter. Climate Dyn., 37, 16611669, doi:10.1007/s00382-010-0938-4.

    • Search Google Scholar
    • Export Citation
  • Yamagata, T., , S. Behera, , and S. Rao, 2003: Comments on “Dipoles, temperature gradients, and tropical climate anomalies.” Bull. Amer. Meteor. Soc.,84, 1418–1422.

  • Yang, H., 2011: The significant relationship between the Arctic Oscillation (AO) in December and the January climate over south China. Adv. Atmos. Sci., 28, 398407, doi:10.1007/s00376-010-0019-y.

    • Search Google Scholar
    • Export Citation
  • Yang, J. L., , Q. Y. Liu, , and S. P. Xie, 2007: Impact of the Indian Ocean SST basin mode on the Asian summer monsoon. Geophys. Res. Lett., 34, L02708, doi:10.1029/2006GL028571.

    • Search Google Scholar
    • Export Citation
  • Zhong, Y. F., , Z. Y. Liu, , and N. Michael, 2011: A GEFA Assessment of global ocean influence on U.S. precipitation variability: Attribution to regional SST variability modes. J. Climate, 24, 693707.

    • Search Google Scholar
    • Export Citation
Save