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    Anomaly correlations of the preceding (a) September–November (SON), (b) DJF, (c) MAM, and (d) simultaneous JJA rainfall with rainfall averaged over northeastern China (42.5°–50°N, 118°–130°E) in JJA. All the data are high-pass filtered with time scales shorter than 9 yr. The green rectangles in (c) and (d) represent the Huang-Huai and northeastern China regions, respectively. (e) Time series of JJA rainfall (mm day−1) averaged over northeastern China in 1948–2013 (bars) and the low-pass filtered component with time scales longer than 9 yr (curve).

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    (a) Latitudinally varying correlations of monthly mean rainfall anomaly averaged over 105°–130°E with rainfall anomaly averaged over northeastern China in JJA. All the data are high-pass filtered with time scales shorter than 9 yr (shading). The contour represents the significant correlation at confidence level of 95% using a t test (absolute correlation larger than 0.24). (b) Latitudinally varying monthly mean climatology (contour) and wet/dry composite (shading) of rainfall averaged over 105°–130°E. A wet (dry) year refers to a rainfall anomaly averaged over northeastern China larger than 0.5 mm day−1 (smaller than −0.5 mm day−1); see the time series in Fig. 1e. The contour interval is 2 mm day−1 in (b).

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    As in Fig. 2, but for rainfall anomaly averaged over the Huang-Huai region (32°–38°N, 105°–120°E; green rectangle in Fig. 1c) in April–May. A wet (dry) year refers to a rainfall anomaly averaged over the Huang-Huai region in April–May larger than 0.5 mm day−1 (smaller than −0.5 mm day−1) (see line in Fig. 4b).

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    Anomalies of rainfall (mm day−1) averaged over northeastern China (green rectangle in Fig. 1d) in JJA (bars) and over the Huang-Huai region (32°–38°N, 105°–120°E; green rectangle in Fig. 1c) in April–May (curve) for (a) the raw data, (b) the interannual component with time scales shorter than 9 years, and (c) the interdecadal component with time scales longer than 9 yr. The correlations between the two series shown in (a)–(c) are 0.37, 0.42, and 0.27, respectively.

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    Mean rainfall anomaly (shading; mm day−1) averaged in (a) April–May 2007, (b) JJA 2007, (c) April–May 2013, and (d) JJA 2013. The red rectangles in (a),(c) and (b),(d) represent the Huang-Huai and northeastern China regions, respectively.

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    Correlations of monthly mean anomalies of H850 (shading) and uv850 (vector) in (a)–(f) March–August with rainfall anomaly averaged over the Huang-Huai region in April–May in 1949–2013. All the data are high-pass filtered for time scales shorter than 9 yr.

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    Simultaneous correlations of anomaly of H850 and uv850 with rainfall anomaly averaged over northeastern China in JJA. The green contours represent the significance of the corresponding correlations at 95% confidence level using the t test. All the data are high-pass filtered with time scales shorter than 9 yr.

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    (a) Simultaneous regressions of SSTA onto rainfall [°C (mm day−1)−1 (std dev)−1] averaged over the Huang-Huai region in April–May (shading), (b) simultaneous regressions [m °C−1 (std dev)−1] of zonal mean departure of H850 onto SSTA averaged over the tropical Indian Ocean [10°S–10°N, 60°–95°E, purple rectangle in (a)] in April–May, and (c) regressions [m °C−1 (std dev)−1] of zonal mean departure of H850 in JJA onto SSTA averaged over the tropical Indian Ocean in April–May. The green contours represent the significance of the corresponding correlations at 95% confidence level using a t test. All the data span 1949–2013 and are high-pass filtered with time scales shorter than 9 yr. The purple rectangle is the region to be used to represent SSTA in the tropical Indian Ocean.

  • View in gallery

    Anomalies of rainfall (bars; mm day−1) averaged over (a) the Huang-Huai region (32°–38°N, 105°–120°E; green rectangle in Fig. 1c) in April–May and (b) northeastern China (green rectangle in Fig. 1d) in JJA, and SST anomalies (curves; °C) averaged over the tropical Indian Ocean (10°S–10°N, 60°–95°E; purple rectangle in Fig. 8a) in April–May. All data are high-pass filtered with time scales shorter than 9 yr. The correlations between the two series shown in (a) and (b) are 0.42 and 0.21, respectively.

  • View in gallery

    (a) Simultaneous correlations between rainfall anomaly averaged over the Huang-Huai region and soil moisture anomaly in April–May and (b) correlations between soil moisture anomaly at 0–10 cm averaged over the Huang-Huai region in April–May and H850 and uv850 in JJA. The green contours represent the significance of the corresponding correlations at a 95% confidence level using a t test for soil moisture at 0–10 cm in (a) and H850 in (b). All the data are high-pass filtered with time scales shorter than 9 yr. The global zonal mean of H850 was removed prior to the calculation.

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    (a) Averaged correlations of 12 individual ensemble members between rainfall averaged over the Huang-Huai region in April–May and rainfall in JJA, (b) averaged correlations of 12 individual ensemble members between rainfall averaged over northeastern China in JJA and rainfall anomaly in April–May, (c) correlations of the 12-ensemble-member mean between rainfall averaged over the Huang-Huai region in April–May and rainfall in JJA, and (d) correlations of the 12-ensemble-member mean between rainfall averaged over northeastern China in JJA and rainfall anomaly in April–May. The rectangle box represents northeastern China in (a),(c) and the Huang-Huai region in (b),(d). The hatched regions are the significance at a 90% confidence level using a t test.

  • View in gallery

    Anomaly correlations of the 12-ensemble-member mean (a) between rainfall averaged over the Huang-Huai region in April–May and SST in April–May and (b) between rainfall averaged over northeastern China in JJA and SST in April–May. The rectangle box represents a key region of SST in the Indian Ocean. The hatched regions are the significance at a 90% confidence level using a t test.

  • View in gallery

    Sliding correlations (a) between rainfall anomalies averaged over the Huang-Huai region in April–May and over northeastern China in JJA, (b) between the rainfall anomalies averaged over the Huang-Huai region in April–May and SSTA over the Indian Ocean in April–May, and (c) between the rainfall anomalies averaged over northeastern China in JJA and the SSTA over the Indian Ocean in April–May. Bars represent the correlations for raw data and lines are the correlations for high-pass filtered data with time scales shorter than 9 yr. The sliding correlations are computed using a 31-yr running window.

  • View in gallery

    As in Fig. 13, but for sliding correlations (a) between rainfall anomalies averaged over northeastern China in JJA and soil moisture anomaly averaged over the Huang-Huai region in April–May and (b) between rainfall anomalies and soil moisture anomaly averaged over the Huang-Huai region in April–May.

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Variability of Summer Rainfall in Northeast China and Its Connection with Spring Rainfall Variability in the Huang-Huai Region and Indian Ocean SST

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  • 1 * Institute of Meteorological Sciences of Jilin Province, and Laboratory of Research for Middle-High Latitude Circulation and East Asian Monsoon, Changchun, China
  • | 2 Climate Prediction Center, NOAA/NWS/NCEP, College Park, Maryland
  • | 3 Center for Ocean–Land–Atmosphere Studies, George Mason University, Fairfax, Virginia
  • | 4 School of Environmental Science and Engineering, Sun Yat-sen University, Guangdong, China
  • | 5 Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qing Dao, China, and Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland
  • | 6 ** China Meteorological Administration Training Center, WMO Regional Training Center, Beijing, China
  • | 7 Climate Prediction Center, NOAA/NWS/NCEP, College Park, and Innovim, LLC, Greenbelt, Maryland
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Abstract

In this work, the variability of summer [June–August (JJA)] rainfall in northeast China is examined and its predictors are identified based on observational analyses and atmospheric modeling experiments. At interannual time scales, the summer rainfall anomaly in northeast China is significantly correlated with the rainfall anomaly over the Huang-Huai region (32°–38°N, 105°–120°E) in late spring (April–May). Compared with climatology, an earlier (later) rainy season in the Huang-Huai region favors a wet (dry) summer in northeast China. Also, this connection has strengthened since the late 1970s. In addition to the impact of the sea surface temperature anomaly (SSTA) in the tropical Indian Ocean, the local soil moisture anomalies caused by the rainfall anomaly in the Huang-Huai region in late spring generate summer general circulation anomalies, which contribute to the rainfall anomaly in northeast China. As a result, when compared with the SSTA, the rainfall anomaly in the Huang-Huai region in late spring can be used as another and even better predictor for the summer rainfall anomaly in northeast China.

The results from atmospheric general circulation model experiments forced by observed SST confirm the diagnostic results to some extent, including the connection of the rainfall anomaly between the Huang-Huai region in April–May and northeastern China in JJA as well as the influence from SSTA in the tropical Indian Ocean. It is shown that eliminating the internal dynamical processes by using the ensemble mean intensifies the connection, implying that the connection of rainfall variation in the two different seasons/regions may be partially caused by the external forcing (e.g., SSTA in the tropical Indian Ocean).

Corresponding author address: Zeng-Zhen Hu, Climate Prediction Center, NOAA/NWS/NCEP, 5830 University Research Court, College Park, MD 20740. E-mail: zeng-zhen.hu@noaa.gov

Abstract

In this work, the variability of summer [June–August (JJA)] rainfall in northeast China is examined and its predictors are identified based on observational analyses and atmospheric modeling experiments. At interannual time scales, the summer rainfall anomaly in northeast China is significantly correlated with the rainfall anomaly over the Huang-Huai region (32°–38°N, 105°–120°E) in late spring (April–May). Compared with climatology, an earlier (later) rainy season in the Huang-Huai region favors a wet (dry) summer in northeast China. Also, this connection has strengthened since the late 1970s. In addition to the impact of the sea surface temperature anomaly (SSTA) in the tropical Indian Ocean, the local soil moisture anomalies caused by the rainfall anomaly in the Huang-Huai region in late spring generate summer general circulation anomalies, which contribute to the rainfall anomaly in northeast China. As a result, when compared with the SSTA, the rainfall anomaly in the Huang-Huai region in late spring can be used as another and even better predictor for the summer rainfall anomaly in northeast China.

The results from atmospheric general circulation model experiments forced by observed SST confirm the diagnostic results to some extent, including the connection of the rainfall anomaly between the Huang-Huai region in April–May and northeastern China in JJA as well as the influence from SSTA in the tropical Indian Ocean. It is shown that eliminating the internal dynamical processes by using the ensemble mean intensifies the connection, implying that the connection of rainfall variation in the two different seasons/regions may be partially caused by the external forcing (e.g., SSTA in the tropical Indian Ocean).

Corresponding author address: Zeng-Zhen Hu, Climate Prediction Center, NOAA/NWS/NCEP, 5830 University Research Court, College Park, MD 20740. E-mail: zeng-zhen.hu@noaa.gov

1. Introduction

Precipitation in northeast China is largely concentrated in summer. Extreme rainfall events in summer can sometimes cause many human casualties and huge damage to industry and agriculture in the region. For example, according to Wikipedia (http://en.wikipedia.org/wiki/2013_China%E2%80%93Russia_floods), the floods in northeast China in August 2013 led to 81 deaths and 97 people unaccounted for in the region as of 19 August 2013. Also, 360 000 people were displaced and 3.74 million people were affected. The total damage was estimated to be CNY16 billion (approximately USD2.6 billion). It is obvious that accurate climate prediction would greatly benefit the disaster relief effort in the region.

Unfortunately, the seasonal and interannual predictability of the climate variability in the region is reported to be quite low (Liang et al. 2009; Gao et al. 2014), which may be a result of the geographic location and climatic features of the region. Geographically, northeastern China is located in the mid-to-high latitudes of East Asia and on the northern side of the East Asian jet stream. Climatologically, the summer climate in this region is affected by the atmospheric general circulation both in the extratropics and tropics. Exerting a critical influence on summer climate in northeastern Asia (Sun et al. 1994; Zhu et al. 2007; Liu et al. 2010; Chen and Lu 2014), blocking and an associated cold vortex occur very often and are largely unpredictable at interseasonal and interannual time scales because of their nonlinearly and internal dynamically driven features. The northward migration of the subtropical high over the northwestern Pacific and the associated East Asian summer monsoon also affect the summer climate in northeast China (Guo 1983; Gao 2007; Shen et al. 2011).

In addition to local impact, remote factors also play a role in the summer climate variability in northeast China. El Niño–Southern Oscillation (ENSO), the largest interannual signal in the climate system (e.g., Zhang et al. 2013), can be one of the potential factors (Wang and Zhu 1985; Lian and An 1998). Nevertheless, there are no robust and significant correlations between ENSO and seasonal mean precipitation in northeast China during 1951–2000 (Wu et al. 2003). Wu et al. (2010) reported that the relation between ENSO and the climate variation in northeast China varied with time. They showed that the summer temperature in northeast China tended to be warmer (cooler) than normal in El Niño (La Niña) developing years during 1950s through the mid-1970s and the relationship weakened or even became opposite in 1980s and 1990s. Besides ENSO, the convective activity over the tropical western Pacific associated with the heat condition of the warm pool is another forcing source to affect the climate variability in East China, Korea, and Japan, by exciting the so-called Pacific–Japan pattern (Nitta 1987; Nitta and Hu 1996).

Furthermore, there are also possible connections of climate variation in northeast China with sea surface temperature (SST) anomalies (SSTAs) in other ocean basins such as the North Atlantic Ocean (Wu et al. 2011) and the Indian Ocean (Hu et al. 2003; Yang et al. 2007, 2009; Cao et al. 2013; Song and Zhou 2014). For example, Wu et al. (2010) reported that a tripole SSTA pattern in the North Atlantic Ocean has a weak correlation with northeastern China summer temperature during the 1950s through the mid-1970s, in sharp contrast to the 1980s and 1990s. They further argued that the North Atlantic SSTA can have an impact on northeastern China summer temperature independent of ENSO.

For the impact of the Indian Ocean on East Asian climate, Yang et al. (2009) suggested that an Indian Ocean basin mode of SSTA (IOBM) following ENSO can induce robust climate anomalies over the Indo-western Pacific region and also affects the midlatitude atmospheric circulation in summer. They noted that the IOBM persists from spring to summer, and a warm IOBM can induce a new atmospheric heating source in South Asia through a positive feedback. This new atmospheric heating source generates an anomalous high to its northwest over western Central Asia, which in turn generates an eastward downstream atmospheric wave train, affecting the midlatitude atmospheric circulation. This wave train (see Fig. 2 of Yang et al. 2009) to some extent is analogous to the wave train associated with the ENSO-unrelated portion of the Indian summer monsoon variation shown in Hu et al. (2005; see their Fig. 4), implying the possible impact of the Indian Ocean and/or Indian summer monsoon on East Asian climate variability. Recently, Li et al. (2013) showed that the effects of spring Indian Ocean and Pacific SSTAs on summer precipitation in China are qualitatively opposite. When the ENSO-unrelated part of SST in the Indian Ocean is above normal, rainfall decreases south of the Yangtze River, in most areas of Inner Mongolia, and in some parts of Liaoning Province, and rainfall increases in the Yangtze River valley, parts of southwestern and northern China, northeastern Inner Mongolia, and Heilongjiang Province. The rainfall anomaly pattern appears with opposite signs when the Pacific SST (particularly those in the Niño-3.4 region) is above normal.

Overall, the internal dynamical processes in the mid-to-high latitudes seem to play a dominant role in summer climate variability in northeast China at interseasonal and interannual time scales and external remote forcings from the mid-to-low latitudes may play a secondary role (Gao et al. 2014). As such, accurate prediction of summer climate variability in northeast China is extremely challenging. It is thus necessary to seek some regional precedent signals that may help to enhance the prediction skill. In this work, we demonstrate the connection of summer rainfall variability in northeast China with precedent spring rainfall variability in the Huang-Huai region. It is suggested that the latter can be used as another and even better predictor for the former besides the SSTA in the tropical Indian Ocean. The analyses are based on observational data and model simulations. The paper is organized as follows. Datasets are described in section 2, followed by the results in section 3. Conclusions with some discussion are given in section 4.

2. Data

The following monthly mean data are adopted in this work: (i) analyzed precipitation from gauge observations (PREC; Chen et al. 2002); (ii) geopotential height and wind at 850 hPa (H850 and uv850) and soil moisture at 0–10 cm from the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis (Kalnay et al. 1996); and (iii) National Oceanic and Atmospheric Administration (NOAA) extended reconstructed SST version 3b (ERSSTv3b; Smith et al. 2008). The precipitation data span from January 1948 to August 2013, and the NCEP–NCAR reanalysis data and SST cover the period January 1949–August 2013.

To confirm results diagnosed from the observational and reanalysis data, Atmospheric Model Intercomparison Project (AMIP)-type experiments are used in this work, which are forced by observed monthly mean SST and sea ice, as well as time-evolving greenhouse gas concentrations. There are 12 ensemble members with slightly different atmosphere initial conditions; each of them is integrated from January 1950 to December 2010, as analyzed in Gao et al. (2014). The model used in these experiments is the atmospheric component of the NCEP Climate Forecast System version 2 (Kumar et al. 2012, Saha et al. 2010, 2014). The model has a horizontal resolution of T126 spectral truncation and 64 vertical levels extending from the surface to 0.26 hPa. A detailed description of the model is provided in Saha et al. (2010, 2014).

Anomalies are computed with reference to climatologies for the entire respective data periods, except for the sliding correlations in which anomalies are referred to the climatology based on each 31-yr time window (see Figs. 13 and 14 for details). For the observational and reanalysis data, anomalies are decomposed into two temporal bands using Fourier analysis: interannual (time scales shorter than 9 yr) and interdecadal (time scales longer than 9 yr) components, respectively.

3. Results

a. Northward propagation of rainfall anomaly

To represent the variability of summer rainfall in northeast China, the regional average over 42.5°–50°N, 118°–130°E is computed, according to previous works (e.g., Sun et al. 2000; Wu et al. 2010, 2011; Gao et al. 2014). Consistent with Gao et al. (2014), the regional mean has significant correlation with summer [June–August (JJA)] rainfall variability in most regions of northeastern China (Fig. 1d), confirming the rationality of using this regional mean to represent the coherent variation of summer rainfall in the region. From the time series shown in Fig. 1e, we note that superimposed on interannual variability are also interdecadal variations with time scales longer than 9 yr. At interdecadal time scales, the wet periods are 1955–66, 1983–96, and 2010–13, and the dry periods are 1967–81 and 1999–2009.

Fig. 1.
Fig. 1.

Anomaly correlations of the preceding (a) September–November (SON), (b) DJF, (c) MAM, and (d) simultaneous JJA rainfall with rainfall averaged over northeastern China (42.5°–50°N, 118°–130°E) in JJA. All the data are high-pass filtered with time scales shorter than 9 yr. The green rectangles in (c) and (d) represent the Huang-Huai and northeastern China regions, respectively. (e) Time series of JJA rainfall (mm day−1) averaged over northeastern China in 1948–2013 (bars) and the low-pass filtered component with time scales longer than 9 yr (curve).

Citation: Journal of Climate 27, 18; 10.1175/JCLI-D-14-00217.1

At interannual time scales, interestingly, the JJA rainfall anomaly in northeast China is significantly correlated with the rainfall anomaly to its south in previous seasons (Figs. 1b,c). For example, in the preceding winter [December–February (DJF)], the maximum positive correlations present in the lower reach of the Yangtze River (Fig. 1b). The maximum positive correlations move northward into the Huang-Huai region by the preceding spring [March–May (MAM)] (Fig. 1c) and into northeast China in JJA (Fig. 1d). This relationship is also confirmed by meteorological station data (not shown). The anomalous correlations between the rainfall averaged for stations in northeast China in JJA and the spring rainfall averaged for stations in the Huang-Huai region during 1961–2013 can reach as high as 0.5.

To further demonstrate this seasonal northward movement of the correlations, we examine the latitudinally and monthly varying correlations between monthly mean rainfall anomalies averaged over 105°–130°E and the regional mean rainfall anomalies in northeast China in JJA at interannual time scales (Fig. 2a). Significant correlations emerge around 30°N (the lower reach of Yangtze River) in January, move northward to the region around 32°–38°N (the Huang-Huai region) and north in April–May, and then move farther towards northeastern China around 42°–52°N in JJA. This northward movement is consistent with the results shown in Figs. 1b–d. To examine whether the significant correlations shown in Fig. 2a appear during the entire JJA season or just in one or two months over northeastern China, Fig. 3a shows the correlations of rainfall anomaly averaged over the Huang-Huai region in April–May with monthly rainfall anomaly averaged over 105°–130°E. From Fig. 3a, we note that the significant correlations over northeastern China present during all of JJA with a maximum in July, suggesting that the rainfall anomaly in the Huang-Huai region in April–May is connected with the entire summer rainfall anomaly over northeastern China.

Fig. 2.
Fig. 2.

(a) Latitudinally varying correlations of monthly mean rainfall anomaly averaged over 105°–130°E with rainfall anomaly averaged over northeastern China in JJA. All the data are high-pass filtered with time scales shorter than 9 yr (shading). The contour represents the significant correlation at confidence level of 95% using a t test (absolute correlation larger than 0.24). (b) Latitudinally varying monthly mean climatology (contour) and wet/dry composite (shading) of rainfall averaged over 105°–130°E. A wet (dry) year refers to a rainfall anomaly averaged over northeastern China larger than 0.5 mm day−1 (smaller than −0.5 mm day−1); see the time series in Fig. 1e. The contour interval is 2 mm day−1 in (b).

Citation: Journal of Climate 27, 18; 10.1175/JCLI-D-14-00217.1

Fig. 3.
Fig. 3.

As in Fig. 2, but for rainfall anomaly averaged over the Huang-Huai region (32°–38°N, 105°–120°E; green rectangle in Fig. 1c) in April–May. A wet (dry) year refers to a rainfall anomaly averaged over the Huang-Huai region in April–May larger than 0.5 mm day−1 (smaller than −0.5 mm day−1) (see line in Fig. 4b).

Citation: Journal of Climate 27, 18; 10.1175/JCLI-D-14-00217.1

The composites based on wet/dry conditions in JJA over northeastern China (shading in Fig. 2b) and wet/dry conditions in April–May over the Huang-Huai region (shading in Fig. 3b) further confirm the connection of rainfall variations between the Huang-Huai region in April–May and northeastern China in JJA. From the two composites (shadings in Figs. 2b and 3b), we note that the large positive anomalies are present in the both composites around 32°–38°N (the Huang-Huai region) somewhat to the north in April–May and also over northeastern China in JJA, suggesting that above (below) normal rainfall in the Huang-Huai region during late spring precedes wet (dry) summer conditions in northeast China. Climatologically, the rainy season in the Huang-Huai region is late June–August (contours in Figs. 2b and 3b) (see also Tao and Chen 1987; Ding 1994). The composite rainfall anomalies in the Huang-Huai region are mainly in late March–June (shadings in Figs. 2b and 3b). Thus, the composites suggest that an earlier (later) rainy season in the Huang-Huai region favors above normal (below normal) rainfall in northeast China in summer.

The statistical relation of rainfall anomalies between the Huang-Huai region in April–May and northeastern China in JJA is further confirmed in Fig. 4, which shows the time series of rainfall anomalies averaged over the Huang-Huai region (32°–38°N, 105°–120°E; the green rectangle in Fig. 1c) in April–May and over northeastern China (42.5°–50°N, 118°–130°E; the green rectangle in Fig. 1d) in JJA. The correlations are 0.37 for raw data, 0.42 for the interannual component, and 0.28 for the interdecadal component, suggesting that the connection between the Huang-Huai region in April–May and northeastern China in JJA is mainly at an interannual time scale.

Fig. 4.
Fig. 4.

Anomalies of rainfall (mm day−1) averaged over northeastern China (green rectangle in Fig. 1d) in JJA (bars) and over the Huang-Huai region (32°–38°N, 105°–120°E; green rectangle in Fig. 1c) in April–May (curve) for (a) the raw data, (b) the interannual component with time scales shorter than 9 years, and (c) the interdecadal component with time scales longer than 9 yr. The correlations between the two series shown in (a)–(c) are 0.37, 0.42, and 0.27, respectively.

Citation: Journal of Climate 27, 18; 10.1175/JCLI-D-14-00217.1

After confirming the statistical relation, we further examine the connection between rainfall anomalies over the Huang-Huai region in April–May and northeastern China in JJA for some extreme years. Figure 5 shows the rainfall anomalies averaged in April–May and JJA for 2007 and 2013, which are the most extreme dry and wet summers in northeast China during 1948–2013 (Fig. 1e). It is seen that the dry (wet) condition in the Huang-Huai region in April–May corresponds to the dry (wet) condition in northeast China in 2007 (2013), consistent with the statistical relation shown in Figs. 14. These results indicate that the connection between rainfall anomalies over the Huang-Huai region in April–May and northeastern China in JJA exists statistically and also in extreme years. Thus, the rainfall anomaly in the Huang-Huai region in April–May has the potential for being used as a predictor for the rainfall anomaly in northeast China in JJA.

Fig. 5.
Fig. 5.

Mean rainfall anomaly (shading; mm day−1) averaged in (a) April–May 2007, (b) JJA 2007, (c) April–May 2013, and (d) JJA 2013. The red rectangles in (a),(c) and (b),(d) represent the Huang-Huai and northeastern China regions, respectively.

Citation: Journal of Climate 27, 18; 10.1175/JCLI-D-14-00217.1

b. Why are the rainfall anomalies connected in the two regions?

To understand the underlying atmospheric circulation anomalies for the connection of rainfall anomalies between the Huang-Huai region in April–May and northeastern China in JJA, we examine the anomalies of H850 in the Eastern Hemisphere associated with the corresponding rainfall anomaly (Figs. 6 and 7). Figure 6 shows the correlations of uv850 and H850 anomaly evolution during March–August with rainfall anomaly averaged over the Huang-Huai region in April–May. In March (Fig. 6a), negative H850 anomaly and cyclonic-like uv850 anomaly present in regions from the Mediterranean Sea to eastern Siberia via western China, and positive H850 anomaly exists in regions from the Arabian Sea to northeastern Asia and an anticyclonic-like uv850 anomaly controls regions around Japan and the Korean Peninsula. In April (Fig. 6b), the negative H850 anomaly over the Eurasian continent strengthens and moves southeastward and the negative H850 anomaly over eastern Siberia moves eastward. Meanwhile, the positive H850 anomaly and anticyclonic-like uv850 anomaly around Japan and the Korean Peninsula become stronger. The zonal anomaly gradient along the East Asian coast with a high in the east and low in the west prefers moisture transport from the ocean to the Huang-Huai region, and causes low-level convergence and above normal rainfall in the Huang-Huai region. By May (Fig. 6c), the anomalous circulation persists, except that the negative H850 anomaly weakens over the inland region of China and strengthens and moves slightly southwestward over eastern Siberia.

Fig. 6.
Fig. 6.

Correlations of monthly mean anomalies of H850 (shading) and uv850 (vector) in (a)–(f) March–August with rainfall anomaly averaged over the Huang-Huai region in April–May in 1949–2013. All the data are high-pass filtered for time scales shorter than 9 yr.

Citation: Journal of Climate 27, 18; 10.1175/JCLI-D-14-00217.1

Fig. 7.
Fig. 7.

Simultaneous correlations of anomaly of H850 and uv850 with rainfall anomaly averaged over northeastern China in JJA. The green contours represent the significance of the corresponding correlations at 95% confidence level using the t test. All the data are high-pass filtered with time scales shorter than 9 yr.

Citation: Journal of Climate 27, 18; 10.1175/JCLI-D-14-00217.1

In summer (Figs. 6d–f), the overall anomalies associated with rainfall anomaly in the Huang-Huai region in April–May are weaker, and the anomaly distribution pattern is also different compared with that in spring (Figs. 6a–c). For example, the zonal gradient is dominant in spring, but the meridional contrast is more pronounced in East Asia in summer. For the three months in summer, the anomaly distribution over East Asia is generally similar (Figs. 6d–f) with a positive H850 anomaly and anticyclonic-like uv850 anomaly in northeastern Siberia and a negative H850 anomaly and cyclonic-like uv850 anomaly to its south. This kind of anomaly distribution prefers moisture transport from the northwestern Pacific Ocean to northeastern China and causes above normal rainfall in northeast China (Gao 2007; Gao et al. 2014).

Compared with the correlations shown in Figs. 6b and 6c, the simultaneous correlations of anomalies of H850 and uv850 in the Eastern Hemisphere with rainfall anomaly averaged over northeastern China in JJA are much stronger and also have similar spatial distribution (Fig. 7). Significantly positive correlations present in northern Eurasia, and significantly negative correlations exist in northeast China. This kind of correlation distribution favors (does not favor) moisture transport from the northwestern Pacific Ocean to northeastern China and causes above (below) normal rainfall in northeast China (Gao 2007; Gao et al. 2014). The correlation pattern in East Asia shown in Fig. 7 is similar to that in Figs. 6d–f, suggesting that the atmospheric circulation evolution in East Asia associated with rainfall anomaly in the Huang-Huai region in April–May can generate the circulation anomaly in summer contributing to rainfall anomalies in northeast China.

To further understand why the above (below) normal rainfall in the Huang-Huai region in April–May can generate circulation anomaly favoring wet (dry) conditions in northeast China in JJA, Fig. 8a shows the simultaneous regressions of SSTA onto the rainfall anomaly averaged over the Huang-Huai region in April–May (shading) and the corresponding correlations at a significance level of 95% using a t test (contours). Although some significant regressions and correlations present over the Pacific, the most systematical and significant regressions and correlations exist in the tropical Indian Ocean. The coherent regression and correlation distributions over the Indian Ocean seem to imply an impact from the basinwide mode, that is, the IOBM discussed in Yang et al. (2007, 2009). The correlations of Fig. 8a suggest that warming (cooling) in the tropical Indian Ocean may strengthen the subtropical high over the western Pacific (Hu et al. 2003) and prefer wet (dry) condition in the Huang-Huai region in April–May. The influence of the tropical Indian Ocean on the subtropical high over the western Pacific is also confirmed by the regressions of zonal mean departures of H850 onto the SSTA averaged over the tropical Indian Ocean in April–May (shading in Fig. 8b) and the corresponding significant correlations (contours in Fig. 8b).

Fig. 8.
Fig. 8.

(a) Simultaneous regressions of SSTA onto rainfall [°C (mm day−1)−1 (std dev)−1] averaged over the Huang-Huai region in April–May (shading), (b) simultaneous regressions [m °C−1 (std dev)−1] of zonal mean departure of H850 onto SSTA averaged over the tropical Indian Ocean [10°S–10°N, 60°–95°E, purple rectangle in (a)] in April–May, and (c) regressions [m °C−1 (std dev)−1] of zonal mean departure of H850 in JJA onto SSTA averaged over the tropical Indian Ocean in April–May. The green contours represent the significance of the corresponding correlations at 95% confidence level using a t test. All the data span 1949–2013 and are high-pass filtered with time scales shorter than 9 yr. The purple rectangle is the region to be used to represent SSTA in the tropical Indian Ocean.

Citation: Journal of Climate 27, 18; 10.1175/JCLI-D-14-00217.1

In fact, the SSTA in the tropical Indian Ocean in April–May is associated with an apparent circulation anomaly (Fig. 8b), which is similar to the one causing the rainfall anomaly in the Huang-Huai region (Figs. 6b,c). For example, negative correlations (regressions) appear over the regions from Central Asia to eastern Siberia and positive correlations (regressions) emerge over the regions around Japan and the Korean Peninsula in both correlation (regression) maps (Figs. 6b,c and 8b). Such similarities between Figs. 6b,c and 8b suggest that, associated with the rainfall anomaly in the Huang-Huai region in April–May, the atmospheric circulation anomalies in East Asia may, to some extent, be forced by the SSTA in the tropical Indian Ocean.

Furthermore, the SSTA in the tropical Indian Ocean in April–May is also associated with the atmospheric circulation anomalies in JJA (Fig. 8c), which are analogous to the circulation anomalies causing the rainfall anomaly in northeast China in JJA (Fig. 7). For instance, both the SSTA in the tropical Indian Ocean in April–May (Fig. 8c) and the rainfall anomaly in northeast China in JJA (Fig. 7) are associated with anomalies of H850 in the regions from the northern Japan to the high latitudes of eastern Siberia and opposite-signed anomalies in northern China, the Korean Peninsula, and central and southern Japan. As mentioned previously, such anomalies favor (do not favor) moisture transport from oceans and its convergence in northeast China, resulting in above (below) normal rainfall.

At interannual time scales, the correlation of the rainfall anomaly in northeast China in JJA is 0.42 with the rainfall anomaly in the Huang-Huai region in April–May (Fig. 4b), and 0.21 with the SSTA in the Indian Ocean in April–May (Fig. 9b). Also, the rainfall anomaly in the Huang-Huai region in April–May is correlated with SSTA in the Indian Ocean in April–May with a correlation coefficient of 0.42 (Fig. 9a). Now the question is: For predicting JJA rainfall anomaly in northeast China, why is the rainfall anomaly in the Huang-Huai region in April–May a better predictor than the SSTA in the Indian Ocean in April–May? It will be shown that other factors than SST in the Indian Ocean may also be influencing the summer rainfall anomaly in northeast China.

Fig. 9.
Fig. 9.

Anomalies of rainfall (bars; mm day−1) averaged over (a) the Huang-Huai region (32°–38°N, 105°–120°E; green rectangle in Fig. 1c) in April–May and (b) northeastern China (green rectangle in Fig. 1d) in JJA, and SST anomalies (curves; °C) averaged over the tropical Indian Ocean (10°S–10°N, 60°–95°E; purple rectangle in Fig. 8a) in April–May. All data are high-pass filtered with time scales shorter than 9 yr. The correlations between the two series shown in (a) and (b) are 0.42 and 0.21, respectively.

Citation: Journal of Climate 27, 18; 10.1175/JCLI-D-14-00217.1

Soil moisture feedback may be a candidate for connecting rainfall anomaly between the Huang-Huai region in April–May and northeastern China in JJA (Yang and Lau 1998). It is noted that rainfall anomaly in the Huang-Huai region in April–May is highly correlated with soil moisture anomaly in the region (Fig. 10a), suggesting that the soil moisture anomaly is largely driven by the rainfall anomaly. In return, the soil moisture in the Huang-Huai region in April–May affects the atmospheric circulation in a broad region in the following season. For example, the correlations between the soil moisture anomaly averaged over the Huang-Huai region in April–May and H850 and uv850 in JJA (Fig. 10b) suggest that an anomalous cyclone (anticyclone)-like anomalies at 850 hPa appear in the central and eastern China and an opposite-signed anomaly appears in the high latitudes in JJA when soil moisture is above (below) normal in the Huang-Huai region in April–May.

Fig. 10.
Fig. 10.

(a) Simultaneous correlations between rainfall anomaly averaged over the Huang-Huai region and soil moisture anomaly in April–May and (b) correlations between soil moisture anomaly at 0–10 cm averaged over the Huang-Huai region in April–May and H850 and uv850 in JJA. The green contours represent the significance of the corresponding correlations at a 95% confidence level using a t test for soil moisture at 0–10 cm in (a) and H850 in (b). All the data are high-pass filtered with time scales shorter than 9 yr. The global zonal mean of H850 was removed prior to the calculation.

Citation: Journal of Climate 27, 18; 10.1175/JCLI-D-14-00217.1

The correlation distribution pattern in East Asia shown in Fig. 10b is similar to that shown in Figs. 6d–f and 7. This similarity suggests that rainfall anomaly in the Huang-Huai region in April–May is connected to the rainfall anomaly in northeast China in JJA, via the soil moisture and atmospheric circulation feedback. Thus, in addition to the fact that both regions are affected by SSTA in the Indian Ocean, the land feedback seems to enhance the connection between rainfall anomaly in the Huang-Huai region in April–May and the rainfall anomaly in northeast China in JJA. As a result, rainfall anomaly in the Huang-Huai region in April–May becomes a better predictor for the summer rainfall anomaly in northeast China than SSTA in the Indian Ocean. Clearly, the detailed physical processes associated with the rainfall–soil moisture–atmospheric circulation–rainfall interaction need to be verified through carefully designed experiment using land–atmosphere coupled models.

c. Confirmation with AMIP experiments

To verify the statistical relations of summer rainfall anomalies in northeast China with the rainfall anomalies in the Huang-Huai region in April–May and with SSTA in the tropical Indian Ocean in April–May, here we further examine the results from the AMIP runs. In terms of the averaged correlations from individual ensemble, both the correlations are positive between the rainfall anomalies averaged over the Huang-Huai region in April–May and summer rainfall anomalies (Fig. 11a) and between the rainfall anomalies averaged over northeastern China in JJA and the rainfall anomalies in April–May (Fig. 11b), confirming the positive correlation of rainfall variability between northeastern China in JJA and the Huang-Huai region in April–May. Nevertheless, the correlations are small and not significant at the level of 90%.

Fig. 11.
Fig. 11.

(a) Averaged correlations of 12 individual ensemble members between rainfall averaged over the Huang-Huai region in April–May and rainfall in JJA, (b) averaged correlations of 12 individual ensemble members between rainfall averaged over northeastern China in JJA and rainfall anomaly in April–May, (c) correlations of the 12-ensemble-member mean between rainfall averaged over the Huang-Huai region in April–May and rainfall in JJA, and (d) correlations of the 12-ensemble-member mean between rainfall averaged over northeastern China in JJA and rainfall anomaly in April–May. The rectangle box represents northeastern China in (a),(c) and the Huang-Huai region in (b),(d). The hatched regions are the significance at a 90% confidence level using a t test.

Citation: Journal of Climate 27, 18; 10.1175/JCLI-D-14-00217.1

On the other hand, when comparing with the averaged correlations of individual ensemble members between the rainfall anomalies averaged over the Huang-Huai region in April–May and summer rainfall anomalies (Fig. 11a), it is seen that the positive correlations in northeast China obviously increase and even become significant in some areas of northeastern China and the Huang-Huai region when the ensemble mean is taken to calculate the correlations (Fig. 11c). That is also the case for the correlations calculated between the rainfall anomalies averaged over northeastern China in JJA and those in April–May (cf. Figs. 11b and 11d). The increases of the positive correlations suggest that eliminating the internal dynamical processes by using the ensemble mean enhances the connection between the rainfall anomalies in northeast China in JJA and those in the Huang-Huai region in April–May. That also indicates that the connection between these two regions may be partially realized through the external forcing (e.g., SSTA). That is, to some extent, confirmed by the correlations between SSTA in April–May and the ensemble mean rainfall anomaly averaged over northeastern China in JJA in the AMIP runs (Fig. 12b).

Fig. 12.
Fig. 12.

Anomaly correlations of the 12-ensemble-member mean (a) between rainfall averaged over the Huang-Huai region in April–May and SST in April–May and (b) between rainfall averaged over northeastern China in JJA and SST in April–May. The rectangle box represents a key region of SST in the Indian Ocean. The hatched regions are the significance at a 90% confidence level using a t test.

Citation: Journal of Climate 27, 18; 10.1175/JCLI-D-14-00217.1

From Fig. 12b, positive correlations are noted in the tropical Indian Ocean, suggesting that the positive (negative) SSTA in the tropical Indian Ocean in April–May favors above normal (below normal) rainfall in northeast China in JJA. That is consistent with the observed evidence discussed in section 3b. However, the correlations are not as significant as in the observations. Also, the AMIP runs even did not reproduce the simultaneous correlations between SSTA in the tropical Indian Ocean and the rainfall anomaly averaged over the Huang-Huai region in April–May (cf. Figs. 8a and 12a). In fact, some significant positive correlations are presented in the midlatitudes of Pacific Ocean, and some negative correlations are seen in the tropical Pacific Ocean (although insignificantly). That implies that ENSO may also play a role in the rainfall variations in the Huang-Huai region in April–May. Some other possible reasons causing the differences between the AMIP runs and the observations will be discussed in the next section.

d. Strengthening of the connection of rainfall in the two regions

Interestingly, the connection of rainfall variabilities in the two regions has strengthened in the later period (since about the late 1970s) as compared to the earlier period (Fig. 13a). The sliding correlations between rainfall variability in the Huang-Huai region and SSTA in the Indian Ocean in April–May display a slight increase after about 1983 (Fig. 13b). The sliding correlation between summer rainfall variability in northeast China and SSTA in the Indian Ocean in April–May shows a different evolution: large positive correlations between the late 1970s and early 1990s and small correlations in the other periods (Fig. 13c). Thus, it seems that the time-varying impact of SSTA in the Indian Ocean in April–May on the rainfall variabilities in two regions may partially contribute to the intensification of the connection of rainfall variability in two regions/seasons (Fig. 13a).

Fig. 13.
Fig. 13.

Sliding correlations (a) between rainfall anomalies averaged over the Huang-Huai region in April–May and over northeastern China in JJA, (b) between the rainfall anomalies averaged over the Huang-Huai region in April–May and SSTA over the Indian Ocean in April–May, and (c) between the rainfall anomalies averaged over northeastern China in JJA and the SSTA over the Indian Ocean in April–May. Bars represent the correlations for raw data and lines are the correlations for high-pass filtered data with time scales shorter than 9 yr. The sliding correlations are computed using a 31-yr running window.

Citation: Journal of Climate 27, 18; 10.1175/JCLI-D-14-00217.1

On the other hand, the strengthening connection of rainfall variabilities in the two regions may be also due to the enhancement of the impact from soil moisture in the Huang-Huai region in April–May on summer rainfall variation in northeast China (Fig. 14a). Particularly, the correlation increases clearly since around 1983. Also, the connection between soil moisture and rainfall anomalies in the Huang-Huai region in April–May increases in the early 1970s (Fig. 14b).

Fig. 14.
Fig. 14.

As in Fig. 13, but for sliding correlations (a) between rainfall anomalies averaged over northeastern China in JJA and soil moisture anomaly averaged over the Huang-Huai region in April–May and (b) between rainfall anomalies and soil moisture anomaly averaged over the Huang-Huai region in April–May.

Citation: Journal of Climate 27, 18; 10.1175/JCLI-D-14-00217.1

It is speculated that the intensifying connection of the rainfall variabilities in the Huang-Huai region in April–May with that in northeast China in JJA since the late 1970s (Fig. 13a) may be due to multiple factors. These factors may include the enhancement of the impact of SSTA in the Indian Ocean in April–May on rainfall variabilities in the Huang-Huai region in April–May since around 1983 (Fig. 13b) and in northeast China in JJA between the late 1970s and early 1990s (Fig. 13c), the increased impact of soil moisture in the Huang-Huai region in April–May on summer rainfall variability in northeast China since around 1983 (Fig. 14a), and the strengthened coupling between rainfall and soil moisture in the Huang-Huai region in April–May since the early 1970s (Fig. 14b). Overall, the strengthening connection of the rainfall variabilities in the Huang-Huai region in April–May with that in northeast China in JJA may be due to enhanced land–atmosphere coupling and increased impact of the Indian Ocean SSTA.

4. Summary

In this work, we have examined the variability of summer rainfall in northeast China and its connection with the preceding spring rainfall variability in the Huang-Huai region and SSTA in the tropical Indian Ocean. It is noted that at interannual time scales, the rainfall anomaly during summer in northeast China has significant correlation with that around 32°–38°N (the Huang-Huai region) in April–May. However, there is no significant correlation at interdecadal time scales. It is suggested that at interannual time scales above (below) normal rainfall in the Huang-Huai region in April–May favors wet (dry) summer in northeast China. In other words, considering its climatological seasonality, an earlier (later) rainy season in the Huang-Huai region favors above normal (below normal) rainfall in northeast China in summer. Both the rainfall anomalies in the Huang-Huai region in April–May and in northeast China in JJA are associated with SSTA in the tropical Indian Ocean in April–May. Thus, the SSTA in the tropical Indian Ocean and the rainfall anomaly in the Huang-Huai region in April–May can be used as predictors for the summer rainfall anomaly in northeast China.

However, at interannual time scales, the correlation of summer rainfall anomalies in northeast China has much higher correlation with the rainfall anomaly in the Huang-Huai region in April–May than with SSTA in the tropical Indian Ocean in April–May. Thus, the rainfall field in the Huang-Huai region in April–May is a better predictor than SSTA in the tropical Indian Ocean in April–May for predicting the summer rainfall anomaly in northeast China. In fact, the rainfall anomaly in the Huang-Huai region in April–May can also generate an atmospheric circulation anomaly in summer and correspondingly contribute to a rainfall anomaly in northeast China, via soil moisture and atmospheric circulation feedback. Thus, in addition to the fact that both regions are affected by SSTA in the Indian Ocean, the land feedback seems to enhance the connection between the rainfall anomaly in the Huang-Huai region in April–May and the rainfall anomaly in northeast China in JJA.

The results from AMIP experiments forced by observed SST, to some extent, confirm the possible links between rainfall anomalies in the Huang-Huai region in April–May and over northeastern China in JJA at interannual time scales. It is further suggested that eliminating the internal dynamical processes by using the ensemble mean enhances the connection. That may imply that the connection of the rainfall anomaly in two different seasons/regions is partially realized through some external forcing like SSTA. The AMIP experiments confirm the observational results that the positive (negative) SSTA in the tropical Indian Ocean in April–May favors above (below) normal rainfall conditions in northeast China in JJA.

However, the correlations are much weaker in the AMIP runs than in the observations, and the AMIP runs did not reproduce the simultaneous correlations between SSTA in the tropical Indian Ocean and rainfall anomaly averaged over the Huang-Huai region in April–May. There are multiple possible reasons that could result in the differences between the AMIP experiments and observations. One of them is the lack of feedback from the atmosphere to the ocean in the AMIP experiments. Previous works (e.g., Wang et al. 2005; Zhu and Shukla 2013) have demonstrated the importance of air–sea coupling in the variability and prediction of the Asian–Pacific summer monsoon. Another is related to the model bias in simulating the East Asian summer monsoon. For example, Song and Zhou (2014) noted that statistically state-of-the-art coupled models cannot reproduce some critical climatological features of the East Asian summer monsoon system, such as the location and intensity of the monsoon-induced rain band over 28°–38°N, 105°–150°E in East Asia and the seasonal northward shift of the western Pacific subtropical high.

Interestingly, the connection of JJA rainfall variability in northeast China with that in the Huang-Huai region in April–May has strengthened since the late 1970s. This strengthening may be due to both enhanced land–atmosphere coupling and the increased impact of the Indian Ocean SSTA. The strengthening connection is overall consistent with results of Xie et al. (2010) and Huang et al. (2010). They noted that the influence of the tropical Indian Ocean on the summer northwestern Pacific has strengthened since the mid-1970s. They argued that during late spring to early summer, El Niño–induced warming in the tropical Indian Ocean decays rapidly prior to the 1970s shift but grows and persists through summer after it. This difference in the evolution of the warming in the tropical Indian Ocean determines the strength of the teleconnection from the tropical Indian Ocean to the northwestern Pacific in the subsequent summer.

Acknowledgments

This work was jointly supported by the National Basic Research Program of China (Grant 2012CB955303, 2014CB953904), the R&D Special Fund for Public Welfare Industry (Meteorology) (Grant GYHY201106015), and the National Natural Science Foundation of China (Grants 41275096, 41175083, and 41175051). While conducting this work, Dr. Peitao Peng gave us some constructive suggestions. We appreciate the constructive comments and valuable suggestions from three reviewers, which are very helpful in improving the manuscript significantly.

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