Extreme particulate matter (PM) air pollution of January 2013 in China was
found to be associated with an anomalous eastward extension of the Siberian
High (SH). We developed a Siberian High position index (SHPI), which depicts
the mean longitudinal position of the SH, as a new indicator of the
large-scale circulation pattern that controls wintertime air quality in
China. This SHPI explains 58 % (correlation coefficient of 0.76) of the
interannual variability of wintertime aerosol optical depth (AOD) retrieved
by MODIS over North China (NC) during 2001–2013. By contrast, the
intensity-based conventional Siberian High index (SHI) shows essentially no
skill in predicting this AOD variability. On the monthly scale, some
high-AOD months for NC are accompanied with extremely high SHPIs; notably,
extreme PM pollution of January 2013 can be explained by the SHPI value
exceeding 2.6 times the standard deviation of the 2001–2013 January mean.
When the SH extends eastward, thus higher SHPI, prevailing northwesterly
winds over NC are suppressed not only in the lower troposphere but also in
the middle troposphere, leading to reduced southward transport of pollution
from NC to South China (SC). The SHPI hence exhibits a significantly
negative correlation of
January 2013 saw persistent and severe haze outbreaks in China, with monthly
mean fine particulate matter (PM
The aforementioned studies did not address the question whether extreme air pollution of January 2013 over China is connected with the anomaly of large-scale circulation patterns at a temporal scale broader than that of the episodic cases. The East Asian monsoon is the most prominent feature of large-scale circulation patterns over the Eurasia continent. While the summer monsoon has been shown to play a significant role in regulating the interannual variation of air pollution over China (L. Zhang et al., 2010; Zhu et al., 2012), few studies have examined the wintertime association between the variability of monsoon-related large-scale circulation patterns and air pollution. As the most important large-scale circulation patterns in winter, the Siberian High has a significant influence on winter climate in Northern Eurasia, East Asia, and even the whole Northern Hemisphere (e.g., Cohen et al., 2001; Gong et al., 2002; Chernokulsky et al., 2013). The sea level pressure difference between the Siberian High over the Asian continent and the Aleutian Low over North Pacific causes strong northwesterly winds along the east flank of the Siberian High and the East Asian Coast, which characterizes the East Asian winter monsoon (Chang et al., 2012). Wu et al. (2002) reported a significant positive correlation between the intensity of the Siberian High and the East Asian winter monsoon on the interannual to interdecadal timescales. The variation of the Siberian High may have an impact on wintertime air quality over east China, for example by ways of influencing large-scale wind fields and local meteorological conditions which control pollutant transport and transformation.
This study investigates the possible connections between wintertime
PM
AOD products from satellites have been used to infer surface PM
Previous studies have indicated good correlations between the MODIS AOD and
surface PM
To verify the robustness of our analysis using MODIS AOD, we also analyzed
level-3 monthly gridded AOD from Multi-angle Imaging SpectroRadiometer
(MISR) aboard of Terra. The MISR standard AOD products have a 0.5
The meteorological variables used to explore the mechanism behind the
variations of SH and AOD are obtained from National Centers for
Environmental Prediction (NCEP) reanalysis (Kalnay et al., 1996), including
sea level pressure (SLP), relative humidity (RH), geopotential heights, and
winds. The NCEP/NCAR reanalysis data provide a historical record of more
than 50 years (Kistler et al., 2001) and are available on the 2.5
To verify the robustness of NCEP reanalysis in characterizing large-scale circulation patterns, we also analyzed the reanalysis data from European Centre for Medium-Range Weather Forecasts (ECMWF) Re-analysis Interim (ERA-Interim), the latest global atmospheric reanalysis produced by ECMWF (Simons et al., 2007). NCEP and ERA-Interim are the two widely used reanalysis products with relatively long periods.
Figure 1a shows the mean January SLP and 850 hPa wind fields during 2001–2012 from NCEP. The Siberian High (SH) is a semi-permanent anticyclone high-pressure system centered over Mongolia and eastern Siberia (black rectangle in Fig. 1a) that is formed by radiative cooling in winter. Driven by the pressure gradient between the Siberian High and the Aleutian Low over northwest Pacific, the prevailing winds over east China are northwesterly in winter. Figure 1b displays the January 2013 SLP and the 850 hPa wind anomalies compared to the 2001–2012 mean. The SLP was significantly lower over Mongolia in January 2013, indicating a significantly weaker Siberian High and consequently a weaker East Asian winter monsoon during this month. This anomalous SLP distribution of January 2013 is associated with anomalous southerly winds in the lower atmosphere over east China (Fig. 1b) and coincident with higher temperatures and RH (not shown), which all present as favorable meteorological conditions for the buildup and recirculation of air pollutants over this region (Sun et al., 2013; Zhang et al., 2014; Y. S. Wang et al., 2014). Given the anomalously weak SH in January 2013, which was a heavily polluted month in China, we hypothesize that SH variability is a key indicator of the variability in large-scale circulation patterns which control the variability of wintertime PM pollution over east China.
To test this hypothesis, we investigated if significant association exists in
winter between the SH variability and regional PM pollution over China on a
longer-term scale (2001–2013), using MODIS-derived AOD as an indicator of
aerosol levels. Figure 2a shows the 13-year mean winter AOD distribution
over China and Fig. 2b displays the mean change of AOD from 2001–2006 to
2007–2013. North China (30–42
Figure 3 depicts the time series of winter AOD averaged over NC, showing a
significant increase in AOD from about 0.5 in 2001 to about 0.8 in 2013. A
linear regression of the time series gives a trend of 1.5 % year
Time series of winter mean AOD over North China (solid thick line)
and the fitted linear regression line (dotted thin line). The insert shows
the correlation coefficient (
Mean meteorological conditions between the high- and low-AOD winters were compiled and compared to identify any significant differences in large-scale circulation patterns between them. The differences in winter-mean SLP and 850 hPa wind fields are shown in Fig. 4 (high-AOD winters minus low-AOD winters). Surprisingly, Fig. 4 does not reveal any significant decrease of SLP from low-AOD to high-AOD winters over Mongolia where the climatological center of the Siberian High is located (cf. Fig. 1a). Instead, significant changes of SLP are located over west of Mongolia (negative differences) and over Japan (positive differences). The high-AOD winters also have a stronger component of southeasterly winds at 850 hPa over North China. This change of wind directions not only suppresses the northwesterly flow that brings cleaner continental background air, but also reduces the transport of pollution from NC to SC, both of which lead to higher pollution levels over NC.
Difference of SLP (shaded, hPa) and 850 hPa wind vectors (m s
Distribution of winter SLP (shaded) and anomalous (minus 13-year
mean) 850 hPa wind fields (vector) in
The index widely used in the literature to describe the SH variability is
the Siberian High intensity (SHI), defined as the mean SLP over northern
Mongolia between 80–120
Time series of wintertime AOD over North China (red lines) with
Figure 4 manifests the displacement of the high SLP center during the
high-AOD winters from northern Mongolia where the conventional SHI is
defined. Figure 5 further illustrates that the main difference in SH between
the two specific winters of largely varying AODs lies in its spatial
extension. Given this feature, we further hypothesized that the position of
the Siberian High is a more important factor than its intensity in terms of
affecting PM concentrations over NC. We thus proposed a Siberian High
position index (SHPI) as the weighted mean of the longitudes of all the
grids within the 1023 hPa isobar over the broad region of 60–145
Figure 6b shows the time series of winter-mean SHPI and NC AOD from 2001 to
2013. They exhibit a positive correlation of 0.39, which is not significant
due to the confounding effect of the increasing trend in AOD. Since the focus
here is on variability, the AOD time series were detrended by removing any
significant linear trend (detrended AOD) and the SHPI time series were
normalized by their climatological mean and standard deviation. As shown in
Fig. 6c, the detrended NC AOD and normalized SHPI display a strong
correlation of 0.76 (
Figure 6d displays the time series of normalized SHPI and detrended NC AOD
on the monthly scale. The corresponding raw data prior to the detrending and
normalization are provided in Fig. S2. Here the normalization of SHPI is
conducted separately for November, December, and January to retain its
intraseasonal variability. At the monthly scale, the correlation between
normalized SHPI and detrended NC AOD is also significant at 0.45 (
Geographic distributions of
To understand the mechanistic connection between SHPI and winter AOD over NC,
we examine in this section how the SHPI variability is linked with the change
of large-scale circulation patterns using the NCEP reanalysis data which span
30 years (1982–2011). The years with extremely high SHPI (beyond one
standard deviation of the mean) in winter are defined to be high-SHPI years
and those below one standard deviation of the mean as low-SHPI years.
Figure 7a displays the climatological distribution of 850 hPa wind fields
during 1982–2011. The northwesterly winds larger than 5 m s
Mean zonal (
To verify the above analysis of the mechanism, we tested the utility of SLP
over Japan (SLPJ, defined over 130–145
To summarize, the SHPI indicator developed here is able to capture the interannual variations of winter-mean and monthly-mean NC AOD to a large extent. Comparing to the climatology, 850 hPa wind speeds over NC during the high-SHPI years are suppressed by 13 % and the surface relative humidity is enhanced by 12 % as a result of the eastward extension of the SH. Since the suppressed wind speed is unfavorable for the dispersion of air pollution and higher surface relative humidity enhances secondary aerosol formation and hygroscopic growth, both factors lead to higher PM levels over NC in the high-SHPI years.
Our above analysis suggests that the suppression of prevailing northwesterly winds and the enhancement of surface RH are the key meteorological features during the high-SHPI winters. The implication of such conditions for wintertime PM over SC, the domestic receptor region of wintertime NC outflow, is not straightforward. On one hand, suppressed northwesterly winds are unfavorable meteorological conditions for the export of pollution from NC, which may lead to reduced PM levels over SC. On the other hand, the Siberian High variability is expected to have an influence on local meteorological conditions over SC. In this section, we examine the extent to which the SHPI indicator developed in the previous section can explain the interannual variability of AOD over SC.
Figure 8 displays the time series of winter mean AOD over SC from MODIS. The
multi-year mean AOD over SC is about 0.4, with a positive but not
significant trend of increase of 0.13 % year
Time series of AOD over South China and normalized SHPI.
To test the robustness of the relationship between AOD and SHPI developed above using MODIS AOD and NCEP reanalysis, we conducted the same analysis using AOD derived from MISR (MISR AOD) and SHPI derived from the ERA-Interim reanalysis (ERA SHPI). Table 2 compares the correlation coefficients derived using the different data sets. Significant positive correlations are consistently found between the SHPI and AOD over NC, regardless of the data sources from which the SHPI and AOD are derived. For example, the ERA SHPI has a correlation of 0.65 with MISR AOD over NC, compared to that of 0.76 between NCEP SHPI and MODIS AOD. This indicates the robustness of the SHPI indicator developed here with regard to explaining the interannual variability of AOD over NC. However, the correlation between SHPI and AOD over SC displays a dependence on the data source. The ERA SHPI has a similarly strong negative correlation with MODIS AOD over SC as the NCEP SHPI does, but neither NCEP SHPI nor ERA SHPI correlates well with MISR AOD over this region. This discrepancy can be partly explained by the inconsistency in the interannual variability of AOD between MODIS and MISR over SC. As shown in Fig. S5a, the correlation coefficient between the two AOD time series is only 0.07 over SC during 2001–2013, although neither shows a significant increasing trend. By comparison, the AOD time series from MODIS and MISR show a strong correlation of 0.7 over NC (Fig. S5b). Since SC has more cloud coverage than NC (Li et al., 2004), the inconsistency between MODIS and MISR over SC may lie in the different cloud-screening algorithms between MODIS and MISR. In addition, MISR has a lower sampling frequency than MODIS which may also lead to the inconsistency (Y. Zhang et al., 2010). Therefore, our conclusion on the association of SHPI with AOD variability over SC may require verification by later studies.
Correlation coefficients between SHPI and AOD derived from different data sets: NCEP and ERA-Interim for SHPI, and MODIS and MISR for AOD.
In addition to the conventional SHI, the number of cold air surges has been
used as an indicator of the strength of the SH in winter. A cold air surge is
an influx of unusually cold continental air from the Arctic Ocean and Siberia
into the middle or lower latitudes, and it is the main disastrous weather
influencing China in the winter half-year. Niu et al. (2010) reported that
the number of cold air surges decreased significantly from 1976 to 2007,
which coincided with the increasing frequency of wintertime fog over
eastern-central China. Varieties of definitions have been used for cold air
surges, such as changes in surface temperature, surface pressure, and wind
speed (Wang, B 2006). The definition of cold air surges we used is as
follows. We took eight sites in North China (Jiuquan, Lanzhou, Beijing,
Shenyang, Changchun, Haerbin, Xi'an, Ji'nan) and seven sites in South China
(Nanjing, Hankou, Chengdu, Changsha, Guiyang, Fuzhou, Guangzhou). If the
15-site mean daily temperature keeps decreasing for 3 days and the overall
magnitude of this temperature decrease is larger than 5
To summarize, through analyzing the anomalous meteorological conditions during January 2013, we have revealed not only the weakening of the strength of the Siberian High over Mongolia, but also its more eastward extension, the latter being the key factor contributing to high PM levels over NC. Thus, the SHPI depicting the mean longitudinal position of the Siberian High is developed, and this index captures 58 % of the interannual variance in winter AOD over NC during 2001–2013. The SHPI is able to indicate the occurrence of high PM pollution levels over NC on the monthly scale; notably, the extreme PM pollution of January 2013 over NC is associated with an extremely high value of SHPI (above 2.6 times standard deviation of the 2001–2013 January mean). Mechanistic analysis indicates that high SHPI is often associated with suppressed prevailing northwesterly winds and higher relative humidity over NC, both of which are favorable for secondary formation and accumulation of PM over NC. The suppressed prevailing winds over NC also weaken the southward transport of pollution to SC, resulting in lower PM levels over SC. The positive correlations between NC AOD and SHPI also exist among different data sets we tested, including NCEP and ERA-Interim for SHPI and MODIS and MISR for AOD. However, the negative correlation between AOD and SHPI over SC is significant only when using AOD derived from MODIS and thus needs to be further confirmed.
This research was supported by the National Key Basic Research Program of China (2013CB956603 and 2014CB441302) and the CAS Strategic Priority Research Program (grant no. XDA05100403). We thank Lu Shen for helpful discussion. Edited by: S. Gong