A new indictor on the impact of large-scale A new indictor on the impact of large-scale circulation on wintertime particulate matter pollution over China

Extreme particulate matter (PM) air pollution of January 2013 in China was found to be associated with anomalous large-scale circulation patterns characterized by an eastward extension of the Siberian High (SH). We developed a Siberian High position index (SHPI), which depicts the mean longitudinal position of SH, as a new indicator of the 5 large-scale circulation pattern that controls wintertime air quality in China. This SHPI explains 58 % (correlation coe ﬃ cient of 0.76) of the interannual variability of wintertime aerosol optical depth (AOD) derived by MODIS over north China (NC) during 2000– 2013, whereas the intensity-based conventional Siberian High Index (SHI) shows es-sentially no skill in predicting the AOD variability. On the monthly scale, some high-AOD 10 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 standard deviation of the 2000–2013 mean. When the SH extends eastward, thus higher SHPI, prevailing northwesterly winds over NC are suppressed not only in the lower tropo-sphere but also in the middle troposphere, leading to reduced southward transport of 15 pollution from NC to south China (SC). As a consequence, the SHPI exhibits a signiﬁcantly negative correlation of − 0.82 with MODIS AOD over SC during 2000–2013, although the robustness of this correlation depends on that of satellite-derived AOD. The suppressed northwesterly winds during high-SHPI winters also lead to increased relative humidity (RH) over NC. Both the wind and RH changes are responsible for 20 enhanced PM pollution over north China during the high-SHPI winters.

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 Asia 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 signif- 15 icant role in the interannual variation of air pollutant over China Zhu et al., 2012), few study has examined the wintertime association between the variability of monsoon-related large-scale circulation patterns and air pollution over China. 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 20 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 Asian continent and the Aleutian Low over North Pacific causes strong northwesterly winds along the east flank of the Siberian High and the East Asia coast, which characterizes the East Asian winter monsoon (Chang et al., 2012). Wu et al. (2002) reported a signif-25 icant positive correlation between the intensity of the Siberian High and the East Asian winter monsoon on the interannual to interdecadal time-scales. The variation of the Siberian High may have an impact on wintertime air quality over east China, for exam- ple 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 2.5 in eastern China and large-scale circulations on the interannual scale during 2000-2013. Because long-term in situ observations of surface PM 2.5 are not available in China, we use 5 satellite-derived aerosol optical depth (AOD) as a proxy to represent the distribution and variability of atmospheric aerosols. The paper is organized as follows. Section 2 describes the data used in the analysis. In Sect. 3, we analyze the anomalous meteorological conditions of January 2013 and define our study regions. Section 4 examines the relationship of the Siberian High and AOD over China, and develops an index to 10 represent Siberian High variability which is able to explain the interannual variations of AOD over China. In Sect. 5, we discuss the robustness of the index we develop and compare it with other existing meteorological indices that affect wintertime air quality in China.

Aerosol optical depth
AOD products from satellites have been used to infer surface PM 2.5 concentrations at scales ranging from urban to regional and to global (Liu et al., 2007;H. Zhang et al., 2009;Lee et al., 2011;Hu et al., 2014;Boys et al., 2014;Donkelaar et al., 2014). To circumvent data scarcity of longer-term in situ measurements of surface PM 2.5 over ocean (±0.03 ± 0.05 × AOD) (Remer et al., 2005;Chu et al., 2012), previous comparisons of MODIS AOD and ground-based AOD measurements from Aerosol Robotic Network over land have shown tight correlations between the two, indicating that the MODIS AOD product is capable of providing quantitative information on the spatial and temporal variations of AOD over land (Levy et al., 2010;Prados et al., 2007). 10 Previous studies have indicated good correlations between the MODIS AOD and surface PM 2.5 concentrations over selected sites in China (Wang et al., 2003). Here we used the MODIS level-3 monthly gridded AOD (550 nm) data (Version 5.1) from December 2000 to February 2013 with a 1 • ×1 • resolution. The AOD values over bright surfaces were replaced by the deep blue aerosol retrieval (550 nm) at the same grid. 15 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 • × 0.5 • resolution at 558 nm for 2000-2013. MODIS has a large number of spectral bands, while MISR has the multiview-angle capabilities (Lyapustin et al., 2007).

Reanalysis data
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 Introduction 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. 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;. Given the anomalously weak SH in 20 January 2013, which was a heavily-polluted month in China, we hypothesize that SH variability can be a key indicator to represent the variability of large-scale circulation patterns which control the variability of wintertime PM pollutions over east China. To test this hypothesis, we conducted the following analysis to examine the association between the SH variability and regional PM pollution over China in winter 25 on a longer-term scale (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013), using MODIS-derived AOD as an indicator of aerosols levels. Figure 2a shows the 13-year mean winter AOD distribution over China Introduction  Fig. 2b) is among the regions with highest aerosol loadings and significant increases of AOD during the two averaging periods. According to current emission inventories, the emissions of SO 2 , NO x , and NH 3 from north China accounts for 25-35 % of total emissions in China, and SO 2 5 emissions from north China have increased faster than those from other regions of China (Lu et al., 2010;Q. Wang et al., 2009Q. Wang et al., , 2010. Therefore, north China (NC) is defined as the source region of aerosols. According to the climatology 850 hPa wind field (Fig. 1a), the pollution outflow from NC in winter follows southeastwards pathways and is expected to influence air quality over south China (SC), which is shown as the anthropogenic emissions over this region (Lu et al., 2011(Lu et al., , 2010Zhang et al., 2012). The departure of each winter's AOD from that depicted by the linear trend is assumed to represent the influence of meteorology. The years in which winter AOD lies above 30 % of the residual confidence interval of the linear trend line are referred to as the high-AOD winters (including 2001, 2003, 2007, 2008, 2013) and those below 30 % of Introduction  2009,2010,2012). Since the high-or low-AOD is defined relative to the trend line, the corresponding high-or low-AOD winters are expected to be driven by the interannual variability of meteorology. Mean meteorological conditions between the high-and low-AOD winters were compiled and compared to identify any significant differences in large-scale circulation pat-5 terns 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 locates (cf. Fig. 1a). Instead, significant changes of SLP are located over west of Mongolia (neg-10 ative differences) and over Japan (positive differences). The high-AOD winters also have a stronger component of southeasterly winds on 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 concentrations over NC. 15 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 • E and 40-65 • N (black rectangle in Figs. 1a and 4) (Jeong et al., 2011;Hasanean et al., 2013). However, as shown by Fig. 4, there is no significant difference in SLP over northern Mongolia between the high-and low-AOD winters, suggesting that this 20 conventional index of SHI may not be able to explain the interannual variability of PM in north China. As an example, Fig. 5 compares winter SLP and 850 hPa wind fields between 2003 (a high-AOD winter) and 2004 (a low-AOD winter). While winter-mean AOD over NC was significantly higher in 2003 (0.68) than that in 2004 (0.45), the SHI was almost the same between the two winters. The noticeable difference, however, is 25 that the high pressure isobars in the high-AOD winter of 2003 extended further east over the continent than those in 2004. Through linear regression, we found a poor correlation between SHI and detrended winter-mean AOD over NC (Fig. 6a), with SHI explaining only 4 % of the AOD variance. 15,2015 A new indictor on the impact of large-scale circulation on wintertime PM  Figure 4 manifests the displacement of the high SLP centers 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, it is further hypothesized that the position of the Siberian High is a more important factor than its intensity in 5 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

ACPD
• E and 30-65 • N (black rectangle in Fig. 5). The SHPI is defined by Eq. (1): where L i is the longitude of any eligible grid i within the 1023 hPa isobar and the definition domain, and P i is the SLP of the corresponding grid. The unit of SHPI is degree in longitude. Our definition of SHPI is similar to the longitude index of SH defined by Hou et al. (2003), but differs with regards to the region over which SHPI is calculated. They defined the index as the weighted mean longitudes of all the grids within the 1023 hPa 15 isobar which may extend westward to Europe and northward to the Arctic. Our definition of SHPI limits the spatial domain over which the 1023 hPa isobar is considered in the SHPI calculation because of our focus on East Asia and particularly China (Fig. 5). The 2001-2013 time series of winter SHPI is displayed in Fig. 6a Fig. 6a). 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 ( 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 (p < 0.01), which means that the position-based SHPI proposed here captures 58 % of the interannual variance in winter AOD over NC. This indicates that on the interannual scale, winter AOD over NC can be better 5 explained by SHPI, an index of the SH position, than the conventional SHI, an index of the SH intensity. According to Hou et al. (2008), the longitude index and intensity index of the Siberian High may not be significantly correlated. Our analysis supports this point since the SHI and SHPI have a weak correlation of only −0.32 (Fig. S1 in the Supplement). Figure 6d displays the time series of normalized SHPI and detrended NC AOD on the monthly scale. The corresponding raw data time series 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 (p < 0.01). Some 15 extremely high values of monthly AOD over NC have a clear association with higher values of SHPI. Taking January 2013 as an example, which has the highest AOD over NC among all the 39 winter months studied here, the SHPI of that month is also the highest (106.5 • E), lying above 2.6 standard deviation of the 2001-2013 January mean (99.8 • E). This association indicates that the anomalous feature of the Siberian High in 20 January 2013 was not only the weakening of its strength (cf. Fig. 1b) but also its more eastward extension, the latter being the primary factor contributing to high PM levels over NC. Another example is February 2011. Both AOD and SHPI of that month are among the highest values of the study period ( Fig. 6d and S2

Mechanism
To understand the mechanistic connection between SHPI and winter AOD over NC, we examine in this section how the SHPI variability is associated with the change of largescale circulation patterns using the NCEP reanalysis data which span 30 years from 1982 to 2011. The years with extremely high SHPI (beyond one standard deviation of 5 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 −1 over north China and Japan indicate the strong influence of Siberian High and East Asian winter monsoon. The area covered by the prevailing northwesterly winds 10 and the mean speed of those winds exhibit interannual variability that correlates with SHPI to some extent. For example, the winter of 1990 has the highest SHPI (105.9 • E) during the 30-year study period and that of 2004 has the lowest SHPI (96.3 • E). As shown in Fig. S3, the area of high northwesterly winds in 1990 is smaller with weaker northwesterly winds than 2004. Figure 7b displays the mean difference of 850 hPa 15 winds between the high-SHPI and low-SHPI winters. Mean wind speeds over NC during the high-SHPI winters are about 0.5 to 1 m s −1 lower than those during the low-SHPI years. Table 1 shows wintertime zonal and meridional wind speeds averaged over NC on different vertical levels for the 30-year mean, high-SHPI mean, and low-SHPI mean.
In high-SHPI winters, both zonal wind speed and meridional wind speed are lower not 20 only on 850 hPa but also on the upper levels. Lower wind speeds are conducive for pollution accumulation over the source region, which explains in part higher AOD in high-SHPI winters. To further illustrate the connections between SHPI and wind changes, Fig. 7c  by enhanced transport of moist air masses through the anomalous southerly winds. Higher RH during high-SHPI winters leads to higher mass concentrations and extinction of aerosols as a result of hygroscopic growth of aerosol species (Mu et al., 2014;Tai et al., 2010). Although high-SHPI is always associated with low northwesterly wind speed and high RH over NC, we found local wind speed or RH itself is not an indicator 5 as good as SHPI in explaining the interannual variation of NC AOD. One explanation is that SHPI represents the combined effects of large-scale circulation change on local meteorological conditions. In addition, systematic errors have been found for lowerlevel wind fields from NCEP reanalysis (Shi et al., 2006). To verify the above analysis of the mechanism, we tested the utility of SLP over 10 Japan (SLPJ, defined over 130-145 • E and 40-50 • N) as an alternative indicator of large-scale circulation to explain the interannual variations of AOD over NC. Figure 4 indicates a significant positive change of SLP over Japan during high-AOD winters.
The time series of SLPJ is shown in Fig. S4. SLPJ has a positive correlation with NC AOD and explains 38 % of the variance in detrended NC AOD (Fig. S4a). By compari- 15 son, SHPI explains 58 % of the AOD variance over NC. SLPJ also correlates well with SHPI (Fig. S4b), which indicates that in high-SHPI years the eastward extension of the SH leads to an increase of SLP over Japan and thus SLPJ may not be an indicator independent from SHPI. The anomalous high SLP over Japan influences the PM level over NC by reducing the prevailing northwesterly winds and increasing RH over NC, 20 which is consistent with the mechanism provided above.
To summarize, the SHPI indicator we 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 are suppressed by 13 % in the high-SHPI years and the surface relative humidity is enhanced by 12 % due to the 25 eastward extension of the SH. Since the suppressed wind speed is unfavorable for the diffusion of pollution and the enhanced surface relative humidity is favorable for secondary aerosol formation and hygroscopic growth, both of them lead to the accumulation of PM over NC in the high-SHPI years. Introduction

AOD variability in south China
The mechanism of SHPI influencing NC AOD suggests that the suppression of prevailing northwesterly winds and the enhancement of surface RH during the high-SHPI winters are the key factors resulting in enhanced PM levels over NC. The implication of this mechanism for wintertime PM over SC, which is the domestic receptor region of 5 NC outflow defined above, 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 influence local meteorological conditions over SC. In this section, we examine the extent to which the SHPI indicator developed in the previous 10 section can explain the interannual variability of AOD over SC. detrended SC AOD and normalized SHPI is −0.82, suggesting that SHPI explains 67 % of the variance in SC AOD. In the high-SHPI winters, the meridional wind speed over NC is reduced by 17, 16 and 19 % on 850, 700 and 500 hPa, respectively, compared to the low-SHPI winters ( Table 1). The suppressed northerly winds over NC directly suppressed the southward transport of pollution from NC to SC, resulting in lower AOD over 20 SC in the high-SHPI winters. Meanwhile, the 850 hPa wind speeds over SC does not show a significant difference between the high-SHPI and low-SHPI winters (Fig. 7b).
Although there is a 7.5 % enhancement of surface relative humidity over SC during the high-SHPI years (Fig. 7c) which might lead to higher AOD over this region, the overall significantly negative correlation between SC AOD and SHPI indicate that the sup-25 pressed pollution transport from NC to SC is the major reason to explain the influence of SHPI on AOD over SC.

Discussion
To test the robustness of the relationship between AOD and SHPI developed above on the basis of 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 between the different 5 datasets. 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 that we developed here in terms of explaining the 10 interannual variability of AOD over NC. However, the correlation between SHPI and AOD over SC displays a dependence on data sources. 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 of AOD interannual vari-15 ability retrieved by MODIS and MISR over SC. As shown in Fig. S5a, the correlation coefficient between the two AOD time series over SC is only 0.07, although neither shows a significant increasing trend during 2001-2013. However, 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, the inconsistency between MODIS and MISR over 20 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 leads to the inconsistency . Therefore, our conclusion on the association of SHPI and AOD variability in SC may require verification by later studies. In addition to the conventional SHI, the number of cold air surges has been used 25 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 middle or lower latitudes, and it is the main disastrous weather influencing China in the winter-half- year. Niu et al. (2011) reported that the number of cold air surges decreased significantly from 1976 to 2007, which coincided with increasing frequency of wintertime fog over eastern-central China. A variety of definitions has been used for cold air surges, such as changes in surface temperature, surface pressure, and wind speed (Wang et al., 2006). The definition of cold air surges we used is as follows. We took 8 sites in 5 north China (Jiuquan, Lanzhou, Beijing, Shenyang, Changchun, Haerbin, Xi'an, Ji'nan) and 7 sites in south China (Nanjing, Hankou, Chengdu, Changsha, Guiyang, Fuzhou, Guangzhou). If the 15-site mean daily temperature keeps decreasing for three days and the overall magnitude of temperature decrease during these three days is larger than 5 • C, we took it as a cold air surge. tive correlation between AOD and SHPI over SC is found only using AOD derived from MODIS and thus needs to be further confirmed.  15,2015 A new indictor on the impact of large-scale circulation on wintertime PM  15,2015 A new indictor on the impact of large-scale circulation on wintertime PM