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  • View in gallery
    Fig. 1.

    Time series of changes in normalized winter Bering Sea ice area index (ΔBSI) and those in yields (ΔYield) of maize and rice, 1969–2008.

  • View in gallery
    Fig. 2.

    Geographical distribution of coefficients of correlation (a) between late winter ΔBSI and changes in spring sea level pressure (ΔSLP) and (b) between spring ΔBSI and spring ΔSLP. Dark (light) shading of either color indicates values that significantly exceed the 95% (90%) significance level.

  • View in gallery
    Fig. 3.

    Correlation maps of wind field at 850 hPa and SST with reference to changes in spring NPO (ΔNPO) in (a) spring and (b) summer for 1969–2008. Vectors are significant at the 95% level regression coefficients between wind and NPO. Contour lines indicate correlation coefficients between SST and NPO; regions with significant correlation (95%) are shaded.

  • View in gallery
    Fig. 4.

    As in Fig. 1, but showing changes in normalized summer Kuroshio SST (ΔKSST) index in place of ΔBSI.

  • View in gallery
    Fig. 5.

    Patterns of correlation of regional mean (left) ΔYield of maize with changes in diurnal temperature range (ΔDTR) and (right) and ΔYield of rice with changes in minimum temperature (ΔTmin) in summer. Dark (light) shading indicates values significantly exceeding the 95% (90%) significance level.

  • View in gallery
    Fig. 6.

    As in Fig. 5, but for ΔKSST index in place of the crop yield changes.

  • View in gallery
    Fig. 7.

    Composite differences of geopotential height at 500 hPa between positive and negative summer KSST index years. The shading illustrates the significance of differences at the 95% level.

  • View in gallery
    Fig. 8.

    Composite differences of vertically integrated moisture flux (kg m−1 s−1) and moisture flux divergence (10−5 kg m−2 s−1) between positive and negative summer KSST index years. Vectors are water vapor transport differences significant at the 95% level. Contour lines indicate composite differences in moisture flux divergence; shading represents the significantly different regions.

  • View in gallery
    Fig. 9.

    Characteristics of meridional mean (40°–50°N) vertical velocity differences. Vectors refer to composite differences of the meridional mean zonal wind and omega velocity between positive and negative summer KSST index years, and the contour lines indicate omega velocity differences. All omega velocity values were multiplied by −1000.

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Late Winter Sea Ice in the Bering Sea: Predictor for Maize and Rice Production in Northeast China

Mengzi ZhouNansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, and Climate Change Research Center, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China

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Huijun WangNansen-Zhu International Research Centre, Institute of Atmospheric Physics, and Climate Change Research Center, Chinese Academy of Sciences, Beijing, China

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Abstract

In this research, the linkage between late winter Bering Sea ice and maize/rice yields in northeastern China (NEC) is investigated. Results show that such ice influences NEC crop production and thus can be employed to predict harvest amounts. Further investigation reveals that positive anomalies of late winter sea ice cover can persist until spring and that spring sea ice can strengthen North Pacific Oscillation (NPO) positive-phase patterns, and vice versa. NPO significantly affects sea surface temperature (SST) over the North Pacific Ocean through sea–air interaction—in particular, in the Kuroshio region—that may persist until summer. In association with the positive SST anomalies, the polar vortex weakens and the western Pacific subtropical high strengthens, resulting in the convergence of southern and northern air masses over NEC. Moreover, both the southerly flow along the western flank of the western Pacific subtropical high and the easterly flow from the Japan Sea and the central Pacific region supply more water vapor transport; thus, an anomalous water vapor convergence center appears in NEC. With the anomalous updrafts, NEC exhibits positive precipitation anomalies. The greenhouse effect of water vapor results in an increase in minimum temperature, thereby leading to a decrease in diurnal temperature range (DTR). This increase in minimum temperature and decrease in DTR are primary factors favoring increases in rice and maize yields, respectively.

Corresponding author address: Mengzi Zhou, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China. E-mail: zhoumz@mail.iap.ac.cn

Abstract

In this research, the linkage between late winter Bering Sea ice and maize/rice yields in northeastern China (NEC) is investigated. Results show that such ice influences NEC crop production and thus can be employed to predict harvest amounts. Further investigation reveals that positive anomalies of late winter sea ice cover can persist until spring and that spring sea ice can strengthen North Pacific Oscillation (NPO) positive-phase patterns, and vice versa. NPO significantly affects sea surface temperature (SST) over the North Pacific Ocean through sea–air interaction—in particular, in the Kuroshio region—that may persist until summer. In association with the positive SST anomalies, the polar vortex weakens and the western Pacific subtropical high strengthens, resulting in the convergence of southern and northern air masses over NEC. Moreover, both the southerly flow along the western flank of the western Pacific subtropical high and the easterly flow from the Japan Sea and the central Pacific region supply more water vapor transport; thus, an anomalous water vapor convergence center appears in NEC. With the anomalous updrafts, NEC exhibits positive precipitation anomalies. The greenhouse effect of water vapor results in an increase in minimum temperature, thereby leading to a decrease in diurnal temperature range (DTR). This increase in minimum temperature and decrease in DTR are primary factors favoring increases in rice and maize yields, respectively.

Corresponding author address: Mengzi Zhou, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China. E-mail: zhoumz@mail.iap.ac.cn

1. Introduction

During the past century, technological advances have led to dramatic increases in crop yields. The global demand for agricultural crops will continually increase, perhaps for decades. Africa, for example, may not be able to increase its food production enough to keep pace with its population growth (Hazell and Wood 2008). In eastern and southeastern regions of Asia, population growth is 2%–3% per year (Delgado 2003). It is estimated that the global population will surpass 9 billion by 2050 and will require a food supply that is 70%–100% larger than that needed today (Pretty et al. 2010). Moreover, economic development drives rapid urbanization, which leads to environmental degradation and a decrease in cultivated land (Chen 2007), which in turn results in additional challenges for production demands. Climate is a main factor affecting the agriculture industry. In particular, the impact of extreme climate on agriculture is severe. For example, Adams et al. (1999) estimated that between $1.5 billion and $6.5 billion in U.S. agricultural losses can be attributed to each El Niño or La Niña event. The climate risk for crops in different regions showed wide variation (Lobell et al. 2008). Therefore, the accurate prediction of crop yields on a regional scale is important.

Northeastern China (NEC), one of the country’s most productive agricultural regions, is located in a transitional zone from temperate to frigid and is remarkably affected by climate change (Wang et al. 2011, 2012; Yang et al. 2007). Hence, accurate estimation of grain output for this region is critical for guaranteeing food security and stabilizing the provisions market.

A key issue in developing statistical models for predicting crop yields is the choice of forecast factors, which should include synoptic and biological significances. Sea ice is a critical component of the climate system. By mediating the exchange of radiation, sensible heat, and momentum between the atmosphere and the ocean, sea ice can affect the climate (Alexander et al. 2004; Deser et al. 2000; Li and Wang 2013). An accelerated decline in Arctic sea ice cover has gained recent attention (Kumar et al. 2010; Serreze et al. 2003; Stroeve et al. 2005). Liu et al. (2012) proposed that such a decrease played an important role in recent cold and snowy winters in Europe and North America. Herman and Johnson (1978) were among the first researchers to notice the effects of wintertime ice cover on general circulation in the subtropics. Some studies show that the atmospheric response imposed by sea ice forcing in the Pacific Ocean is stronger than that in the Atlantic Ocean (Dethloff et al. 2006). Li and Wang (2013) indicate that Bering Sea ice cover is significantly correlated with the East Asian winter monsoon. Moreover, application of satellite passive-microwave imagery makes it possible to generate more accurate sea ice datasets (Chapman and Walsh 1993; Comiso et al. 2008), which have been widely used. For example, they are used to address how the Arctic sea ice may influence climate change around the globe (Budikova 2009), to analyze the shift of a major ecosystem in the northern Bering Sea (Grebmeier et al. 2006), and so on. Also, these more reliable data could provide a guarantee for the prediction of crop yields in our study.

Thus far, however, few studies have focused on the influence of sea ice on yield. Therefore, the purpose of this research is to quantitatively analyze the relationship between the preceding sea ice area in the Bering Sea and maize and rice yields in NEC. Moreover, this paper discusses a possible mechanism of the effect of Bering Sea ice on summer climate in this area, which enables crop forecasting for NEC at least one season ahead.

2. Data and method

Restricted by meteorological factors, NEC abounds in spring maize and single-crop rice, which are also the focus of this research. The agricultural data used here are the same as those reported in our previous research (Zhou et al. 2012). Annual province-level data for crop area and production were obtained from China Agricultural Yearbooks, and yields were computed by dividing production by crop area. The ground observation station weather data were obtained from the China Meteorological Administration.The global gridded datasets for circulation fields were acquired from the National Centers for Environmental Prediction–National Center for Atmospheric Research monthly atmospheric reanalysis data (Kistler et al. 2001). The variables used include surface pressure, geopotential height, specific humidity, meridional wind, zonal wind, and vertical velocity. The total horizontal moisture flux, which reflects the characteristics of the vapor transport, can be obtained by vertical integration:
e1
where g is gravitational acceleration, q is specific humidity, V is the horizontal wind vector, p is pressure, Ps is surface pressure, and Pt is the top pressure above which the water vapor content is negligible. In this case, the value is 300 hPa. The vertically integrated horizontal moisture flux divergence A can be expressed as
e2
where u and υ are the zonal and meridional horizontal wind components. If A > 0, a net influx is indicated; otherwise, a net outflux is presumed.

The Met Office Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST1) with resolution 1° × 1° was used in this study (Rayner et al. 2003). The sea ice concentration, with values of 0–100, refers to the relative fraction of sea ice in each grid box. We defined the Bering Sea ice cover index (BSI) as the accumulation of sea ice concentration within the region of 55°–65°N, 160°E–160°W, which considers variation of the grid area with latitude. The time span of the actual available dataset is 40 yr, covering 1969–2008.

The year-to-year increment refers to the difference in any variable between the current year and the preceding year (Fan 2010; Fan and Wang 2009). The year-to-year-increment approach used in this study could minimize the influences of long-term-changing factors on yield, amplify the signals of the variables, and make the predictions more reliable (Wang et al. 2010).

3. The influence of late winter sea ice content in the Bering Sea on NEC yields

We examined the correlation coefficients between the year-to-year increment of late winter BSI and yields of maize and rice crops (Fig. 1). The results show that late winter BSI is a good indicator for yield forecasting, with correlation coefficients of 0.42 and 0.31, significant at the 99% and 95% levels, for maize and rice, respectively. We built a linear regression model that is based on the BSI for the increment of maize and rice yield. The predicted yield is obtained by adding the actual yield of the preceding year. The maize (rice) prediction model shows good predictive ability for the training period of 1969–2008, with a low relative root-mean-square error of 15% (13%), and the simulated maize (rice) yield could account for 78% (70%) of the total variance of the historical data. To evaluate the performance of the models further, we perform a cross-validation test for the period of 1969–2008 (Fan and Wang 2010). The correlation coefficient is 0.87 (0.82) between the predicted yield by the cross-validation test and the historical yield for maize (rice), which is significant at 99% levels. We also apply the independent-samples test (Fan 2010) to verify the prediction skill of the BSI. For instance, we use the 1969–2000 dataset to forecast the yield in 2001, and we employ the 1969–2001 dataset to predict the yield in 2002. By analogy, we have 10 hindcast years extended from 2001 to 2010. The predicted maize yield fits well with the historical yield, with a correlation coefficient of 0.59, which is significant at the 90% level. The correlation coefficient between the predicted rice yield from the independent-samples test and the actual yield is 0.52, which is significant at the 85% level (p = 0.1182), and the relative mean-square mean is 5.68%. Hence, the BSI could be a potential predictor for grain production in NEC.

Fig. 1.
Fig. 1.

Time series of changes in normalized winter Bering Sea ice area index (ΔBSI) and those in yields (ΔYield) of maize and rice, 1969–2008.

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-0242.1

To examine the mechanism through which the Bering Sea ice area affects the summer climate over NEC and thereby yield, it must be determined whether the late winter Bering Sea ice cover signal persists from late winter to summer. The sea ice content in the Bering Sea has strong seasonal variability. The advance of ice begins in November, reaches its maximum extent in March, retreats north with seasonal warming, and completely melts by early July (Niebauer 1980). It has been suggested that anomalous sea ice signals can be preserved from winter to spring (Fan 2007). Such persistence of sea ice anomalies is demonstrated by the correlation between late winter and springtime series of BSI with a coefficient of 0.73, significant at the 99% level. Spring sea ice affects the summer climate over NEC because it likely influences atmospheric general circulation, which could induce sea surface temperature (SST) variation at low to midlatitudes.

To examine the impact of springtime sea ice cover on atmospheric circulation, we analyzed the pattern of correlation between late winter BSI and spring BSI with spring sea level pressure. Figure 2 indicates that for both preceding and simultaneous relationships the spatial distribution displays a similar North Pacific Oscillation (NPO) pattern. Therefore, sea ice forcing the atmosphere is dominant over the Bering Sea, which is consistent with previous research (Agnew 1993). Figure 2b shows a shift in the abnormal low pressure system toward North America and an emergence of a weak high pressure system in the western Bering Sea. These results occurred because high albedo associated with heavy ice resulted in a reduction in solar radiation, which led to a decrease in local temperature and exchange of heat flux between the ocean and the atmosphere to create a weak high pressure system in the western Bering Sea. Extension of the sea ice also led to a steeper meridional temperature gradient. The westerly thermal wind anomalies led to warm advection in front of the original Aleutian low. As a result, the Aleutian low strengthened and was displaced downstream. To quantitatively express the influence of spring Bering Sea ice cover on the NPO, we defined an NPO index. There were several ways of defining the NPO index (Hebblewhite 2005; Rogers 1981; Wallace and Gutzler 1981; Yeh and Kirtman 2004). Here, the NPO index is the corresponding time coefficient of the first empirical orthogonal function mode performed on spring sea level pressure over the North Pacific (10°–75°N, 140°E–120°W). The first eigenmode explains 22.7% of the total variance. The correlation coefficient between the year-to-year increment of spring BSI and NPO index was 0.35, significant at the 95% level. This analysis demonstrates that Bering Sea ice cover can affect general circulation.

Fig. 2.
Fig. 2.

Geographical distribution of coefficients of correlation (a) between late winter ΔBSI and changes in spring sea level pressure (ΔSLP) and (b) between spring ΔBSI and spring ΔSLP. Dark (light) shading of either color indicates values that significantly exceed the 95% (90%) significance level.

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-0242.1

NPO is a major mode of atmospheric variability over the North Pacific and can initiate SST changes in the corresponding region (Cayan 1992; Vimont et al. 2003; Wang et al. 2007). We explored the NPO-related 850-hPa wind and SST anomalies from spring to summer (Fig. 3). An anomalous cyclone and anticyclone were present at high and low latitudes, respectively, which agrees well with NPO. Moreover, SST anomalies displayed a tripole pattern, which is in agreement with previous research results (Linkin and Nigam 2008). The SST distribution mode was more obvious in our study, however, because of application of the year-to-year increment approach. The cyclonic flow from the northern part strengthened the midlatitude westerlies, which accelerated the transmission of cold water from high latitudes eastward; the anomalous wind accompanied by anticyclonic flow resulted in more rapid transport of cold seawater from the California Current region to the subtropics. Thus, two cold-water centers appeared, each at high and low latitudes. What is more important is that an anomalous warm SST center appeared in the Kuroshio region through intensification of the Kuroshio. In addition, a weakened upwelling caused by the prevailing east wind anomaly contributed to the warming to some extent. To be specific, the anomalous warming could have persisted from spring to summer depending on its seasonal persistence. To verify this point, we defined a Kuroshio SST (KSST) index, which refers to the regionally averaged SST (15.5°–32.5°N, 120.5°–150.5°E). The correlation coefficients between the spring NPO index and the spring and summer KSSTs were 0.46 and 0.66, respectively, both significant at the 99% level. The correlation coefficient between spring KSST and summer KSST was 0.44, also significant at the 99% level. To summarize the above analysis, the Bering Sea ice cover may affect SST in the Kuroshio region via air–sea interaction through the NPO medium; thus, the abnormal signal related to late winter Bering Sea ice cover could be preserved until summer.

Fig. 3.
Fig. 3.

Correlation maps of wind field at 850 hPa and SST with reference to changes in spring NPO (ΔNPO) in (a) spring and (b) summer for 1969–2008. Vectors are significant at the 95% level regression coefficients between wind and NPO. Contour lines indicate correlation coefficients between SST and NPO; regions with significant correlation (95%) are shaded.

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-0242.1

To determine whether SST anomalies over the Kuroshio region could affect the yield over NEC by influencing certain meteorological variables, we first plotted the time series of summer KSST index, maize yield, and rice yield of NEC (Fig. 4). The results show that the summer SST anomalies over the Kuroshio region have a significant positive correlation with maize and rice yields. We next examined the spatial distribution of the correlation coefficient between regional mean yield and summer local meteorological variables. The primary factor that influences maize yield was determined to be diurnal temperature range (DTR), and the primary meteorological variable affecting rice yield was minimum temperature in summer (Fig. 5). Thus, we further determined whether the summer Kuroshio SST affects the aforementioned meteorological factors. As shown in Fig. 6, significant correlations were obvious among summer Kuroshio SST, summer DTR, and minimum temperature. The abnormal temperature change is well connected with increased precipitation. That is, in the daytime the increased evaporation caused by precipitation change consumes more heat, which leads to the maximum temperature decreases, whereas in the nighttime the greenhouse effect of water vapor in the atmosphere decreases ground effective radiation, which thus could increase the minimum temperature and decrease the DTR.

Fig. 4.
Fig. 4.

As in Fig. 1, but showing changes in normalized summer Kuroshio SST (ΔKSST) index in place of ΔBSI.

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-0242.1

Fig. 5.
Fig. 5.

Patterns of correlation of regional mean (left) ΔYield of maize with changes in diurnal temperature range (ΔDTR) and (right) and ΔYield of rice with changes in minimum temperature (ΔTmin) in summer. Dark (light) shading indicates values significantly exceeding the 95% (90%) significance level.

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-0242.1

Fig. 6.
Fig. 6.

As in Fig. 5, but for ΔKSST index in place of the crop yield changes.

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-0242.1

The Kuroshio is the western boundary current of the North Pacific subtropical gyre and transports abundant heat northward (Sugimoto et al. 2010). It plays an important role in air–sea interaction and can influence regional climate (Sakamoto et al. 2005; Zhang et al. 2007, 2008). To understand how anomalous SST affects simultaneous precipitation over NEC, we examined the variation of atmospheric circulation anomalies corresponding to SST anomalies in the Kuroshio region. Years greater or less than 0.75 of a standard deviation were selected for composite analysis on the basis of normalized summer KSST time series.

Figure 7 depicts the difference of geopotential height at 500 hPa between positive and negative KSST years. The apparent positive anomaly extends from Lake Baikal to Taymyr Peninsula, covering the central Siberian region. The polar vortex is weak, and the cold air mass over the Arctic more readily spills into midlatitudes. The positive anomaly over the Sea of Okhotsk is conducive to the maintenance of the blocking high and favors incursions of cold air masses from the northeast path in the Okhotsk region into NEC. It is certain that precipitation over NEC is related to circulation systems at subtropical regions in addition to those from mid–high latitudes (Jia and Wang 2006). Previous studies show that SST in the western Pacific warm pool can significantly affect convective activities near the South China Sea and the adjacent region (Hu 1997; Huang and Wu 1989). The intensification of meridional circulation associated with active convection enhances the western Pacific subtropical high and moves northward (Huang and Sun 1994). In addition, increased convective heating is favorable for westward extension of the western Pacific subtropical high by influencing the Walker circulation (Zhou et al. 2009). Consistent with these results, Fig. 7 shows positive anomalies in the western Pacific region, including southern Japan and eastern China. The distinct strong western Pacific subtropical high accompanied by the strong summer monsoon can induce a northward shift of the rainband, with convergence of warm moist air from the south and cold air from high latitudes over NEC.

Fig. 7.
Fig. 7.

Composite differences of geopotential height at 500 hPa between positive and negative summer KSST index years. The shading illustrates the significance of differences at the 95% level.

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-0242.1

To understand further the influence of the Kuroshio SST anomaly on summer precipitation over NEC, we analyzed the composite difference of vertically integrated moisture flux (Fig. 8). The results show that anomalous water vapor transport accompanying heavier rainfall over NEC had two major branches, including one from the Bohai Sea originating from the western North Pacific and one from the Japan Sea and the central Pacific region. The features of the moisture flux divergence field were consistent with the aforementioned anomalous water vapor transport. A significant convergence center was apparent in NEC, which is favorable for precipitation. The composite difference of the meridional mean (120°–130°E) zonal wind and omega velocity, illustrated in Fig. 9, showed an abnormal updraft within the longitude of NEC during the positive-KSST-index year, which favors active convection and increased rainfall.

Fig. 8.
Fig. 8.

Composite differences of vertically integrated moisture flux (kg m−1 s−1) and moisture flux divergence (10−5 kg m−2 s−1) between positive and negative summer KSST index years. Vectors are water vapor transport differences significant at the 95% level. Contour lines indicate composite differences in moisture flux divergence; shading represents the significantly different regions.

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-0242.1

Fig. 9.
Fig. 9.

Characteristics of meridional mean (40°–50°N) vertical velocity differences. Vectors refer to composite differences of the meridional mean zonal wind and omega velocity between positive and negative summer KSST index years, and the contour lines indicate omega velocity differences. All omega velocity values were multiplied by −1000.

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-0242.1

4. Conclusions

In this paper, we define an index for Bering Sea ice cover and find that the late winter BSI is positively correlated with the year-to-year increment of maize and rice yield in NEC. A possible mechanism was investigated. The signature related to late winter sea ice cover in the Bering Sea could be preserved to spring as a result of its own seasonal persistence. The increase (decrease) of the sea ice cover in spring could strengthen (weaken) the meridional temperature contrast, which causes the Aleutian low to become enhanced (weakened) and to shift eastward (westward) and finally lead to a positive-phase (negative phase) NPO pattern. The strengthened (weakened) anticyclone in the subtropics could induce a positive (negative) SST anomaly over the Kuroshio region by influencing oceanic currents. The positive (negative) SST anomaly could persist from spring to summer and develop via air–sea interaction. In a positive (negative) KSST year, the polar vortex is weak (strong) while the western Pacific subtropical high is strong (weak). Meanwhile, the water vapor transport from the Bohai Sea and the central Pacific increases (decreases). There exists an anomalous water vapor convergence (divergence) center and rising (sinking) motion over NEC. All of these conditions contribute to an increase (a decrease) of precipitation in NEC, which can result in an increase (a decrease) in daily minimum temperature and a decrease (an increase) in DTR. The climate anomalies that are described above significantly affect NEC crop yields.

Since its World Trade Organization accession, China faces more and more international agricultural trading issues. It has become more urgent to predict production accurately farther in advance. Our previous study showed that the spring NAO index is significantly correlated with crop yield (including maize and rice) in NEC (Zhou et al. 2012). Combined with this research, we will try to build an appropriate statistic model that is based on the spring NAO index, the Bering Sea ice area index, and other indices to predict production in NEC and to provide a valuable reference for policy makers.

Acknowledgments

This research was supported by the National Natural Science Foundation of China under Grants 41210007 and 41130103, and the CAS–CSIRO Cooperative Research Program (Grant GJHZ1223).

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