Using a new high-resolution air quality reanalysis dataset for China for
five winters from December 2013 to February 2018, we examine the influence
of large-scale circulation on daily PM2.5 variability through its
direct effect on key regional meteorological variables over three major
populated regions of China: Beijing–Tianjin–Hebei (BTH), the Yangtze River
Delta (YRD) and the Pearl River Delta (PRD). In BTH, a shallow East Asian
trough curbs northerly cold and dry air from the Siberian High, enhancing
PM2.5 pollution levels. Weak southerly winds in eastern and southern
China, associated with a weakened Siberian High, suppress horizontal
dispersion, contributing to air pollution accumulation over YRD. In PRD,
weak southerly winds and precipitation deficits over southern China are
conducive to high PM2.5 pollution levels. To account for these
dominant large-scale circulation–PM2.5 relationships, we propose
three new circulation-based indices for predicting different levels of air
pollution based on regional PM2.5 concentrations in each region: a
500 hPa geopotential height-based index for BTH, a sea level pressure-based
index for YRD and an 850 hPa meridional wind-based index for PRD. These
three indices can effectively distinguish clean days from heavily polluted
days in these regions, assuming variation is solely due to meteorology. We
also find that including the most important regional meteorological variable
in each region improves the performance of the circulation-based indices in
predicting daily PM2.5 concentrations on the regional scale. These
results are beneficial to understanding and forecasting the occurrence of
heavily polluted PM2.5 days in BTH, YRD and PRD from a large-scale
perspective.
Introduction
Over the past few decades, rapid economic progress and urbanisation in China
have raised a number of environmental challenges. These challenges include sharp
increases in the atmospheric concentrations of particulate matter with an
aerodynamic diameter of 2.5 µm or less (PM2.5), and are of
the utmost concern for public health (Xu et al., 2013; Zheng et al., 2015).
Episodes of haze and smog pollution with high levels of PM2.5, in
particular during winter, have become common in the most developed and
highly populated city clusters in China (Zhang et al., 2007; Chan and Yao,
2008; Zhang et al., 2014). Although emissions of pollutant precursors
strongly influence air pollution levels, meteorology also plays a major role
in air quality variability and trends through a combination of transport,
transformation and deposition processes (e.g. Wang et al., 2009; Hou et
al., 2018, 2020). For instance, the extreme haze event in January 2013 in
Beijing when the maximum instantaneous PM2.5 value exceeded
500 µg m-3, one of the worst air pollution events on record in China, has
been attributed to unfavourable atmospheric dispersion conditions with weak
surface winds and high humidity (Wang et al., 2014; Yang et al., 2015). In
contrast, during winter and spring in 2015, PM2.5 concentrations were
much lower at most Chinese monitoring stations because of more favourable
atmospheric dispersion conditions compared with those of the previous year
(Wang et al., 2016).
While regional meteorological conditions are known to strongly influence air
pollution levels, the responses of PM2.5 concentrations to different
meteorological variables are complex (e.g. Tai et al., 2010; Barmpadimos et
al., 2012; Dawson et al., 2014; Han et al., 2016; Garrido-Perez et al., 2017,
2021). Key processes and the relevant regional meteorological variables
influencing PM2.5 levels have been identified in recent studies.
These processes include: (a) secondary aerosol formation and hygroscopic
growth associated with high relative humidity (RH; Sun et al., 2013; Wang et
al., 2014); (b) sulphate and secondary organic aerosol formation and the
volatilisation of ammonium nitrate and semi-volatile organics favoured by
high temperature (Dawson et al., 2007; Aksoyoglu et al., 2011); (c) wet
deposition due to precipitation (Koch et al., 2003; Tai et al., 2010); (d)
horizontal dispersion of polluted air under high wind speeds (WSPD; Wang et
al., 2012; Zhang et al., 2014); and (e) vertical ventilation and dilution of
the boundary layer via mechanically generated turbulence associated with
strong wind shear (WSHR; Wang et al., 2018, 2019a) and via thermodynamically
generated turbulence as measured by inversion intensity (INV; Zhao et al.,
2013; Wang et al., 2014). Specifically, high temperature and RH, weak WSPD,
strong INV and weak WSHR have been found to contribute to the accumulation
and growth of pollutants in a shallow and stable boundary layer over the
North China Plain (e.g. Wang et al., 2014; Leung et al., 2018). However, RH
can also be associated with precipitation and therefore removal of aerosols
by wet deposition (Zhu et al., 2012; Leung et al., 2018), and may also be an
indicator of air masses from different origins.
These key regional meteorological factors have been found to be affected by
circulation patterns at larger scales over different regions of the globe
(Tai et al., 2012; Garrido-Perez et al., 2017; Pei et al., 2018). Prominent
large-scale circulation patterns over China during winter include the East
Asian winter monsoon (EAWM; Chang et al., 2006; Wang and Chen, 2010) and El
Niño–Southern Oscillation (ENSO; Wang et al., 2000; Zhang et al., 2017).
The EAWM resulting from the development of the cold-core Siberian High
system is mainly characterised by dry cold low-level northerlies along the
East Asian coast, the mid-tropospheric East Asian trough and the
upper-tropospheric westerly jet stream (Jhun and Lee, 2004; Li and Yang,
2010; Wang and Lu, 2017). The EAWM has a significant impact on China's
regional meteorological conditions, including air temperature, wind speed,
RH and atmospheric stability (Jeong and Park, 2017; Wang et al., 2019b), and
hence influences PM2.5 levels as noted above. ENSO, as the dominant
mode of global ocean-atmosphere interaction, also substantially modulates
regional meteorological conditions in China, through changes in atmospheric
circulation patterns. The regional meteorological variables affected include
RH and precipitation over southeastern China, and wind speed over northern
China (Sun et al., 2018; He et al., 2019).
Previous studies of how the large-scale winter circulation modulates air
quality in China through its effect on regional meteorology have been
primarily focused on Beijing and the North China Plain, the regions with the
most severe PM2.5 pollution (e.g. Wang et al., 2014, 2019b; Zhang et
al., 2019). Broader regions in northern and southern China also show clear
relationships between PM2.5 concentrations and the EAWM intensity
(e.g. Jeong and Park, 2017), aerosol optical depth and the position of the
Siberian High (Jia et al., 2015), as well as the number of haze days and
ENSO intensity (e.g. He et al., 2019). However, the major city clusters in
northern, eastern and southern China, i.e. Beijing–Tianjin–Hebei (BTH),
the Yangtze River Delta (YRD) and the Pearl River Delta (PRD), respectively,
have been considered jointly only in a few studies (e.g. Leung et al.,
2018; Hou et al., 2019). Furthermore, most of the existing large-scale
circulation indices, such as the EAWM indices (Wang et al., 2010), the
Siberian High index (Wu and Wang, 2002) and the Haze Weather index (Cai et
al., 2017), have been proposed for the North China Plain. Consequently, they
do not reflect the link between the large-scale circulation and PM2.5 levels over YRD and PRD. Indeed, Leung et al. (2018) found that different
distinct meteorological modes could explain the variability of PM2.5 in BTH, YRD and PRD, but simple large-scale circulation indices have not
been defined for the latter two regions as yet.
Understanding the impact of the large-scale circulation on PM2.5 air
qualityin these three major populated regions of China during
winter, therefore, requires consideration of regional differences in the
dominant large-scale circulation features. In order to understand and
predict the occurrence of days with high PM2.5 concentrations, it is
critical to investigate the relationship between the large-scale circulation
and PM2.5 levels on daily timescales. This study examines the
dominant large-scale circulation–PM2.5 relationships separately for
BTH, YRD and PRD during winter, and further proposes novel circulation-based
indices to explain the day-to-day variability of PM2.5 levels in each
region. We first explore the relationship of daily PM2.5 concentrations with specific regional meteorological variables across BTH,
YRD and PRD (Sect. 3). We then identify the dominant large-scale
circulation associated with heavily polluted days for the three regions
through its effect on the most important regional meteorological variables,
and propose specific circulation-based indices for these three regions
(Sect. 4). Furthermore, we assess the performance of these
circulation-based indices in distinguishing different levels of air
pollution (Sect. 5) and examine the joint effect of the circulation-based
indices and regional meteorology on the day-to-day variability of PM2.5
(Sect. 6). Finally, Sect. 7 summarises the main results.
Data and methodology
We use daily meteorological data from the fifth-generation atmospheric
reanalysis ERA5 provided by the European Centre for Medium-Range Weather
Forecasts at a spatial resolution of 0.25∘ (Copernicus Climate
Change Service, 2017; Hersbach et al., 2020). These data include zonal
wind at 300, 900 and 1000 hPa (U300, U900, U1000); meridional wind
at 850, 900 and 1000 hPa (V850, V900, V1000); geopotential height at
500 hPa (Z500); air temperature at 900 and 1000 hPa; RH at 900 and
1000 hPa; sea level pressure (SLP) and sea surface temperatures (SSTs).
Hourly data are used to calculate daily averages for 450 d during the
five winters from 1 December 2013–28 February 2014 to 1 December
2017–28 February 2018 (hereafter referred to as DJF 2013–2017). Daily
precipitation is from the Global Precipitation Climatology Project (GPCP;
Huffman et al., 2001) 1∘ daily precipitation product. These
meteorological fields are used to investigate both the large-scale
circulation features and regional meteorological conditions modulating
PM2.5 concentrations. Four meteorological fields representing
relevant processes affecting PM2.5 in the boundary layer (RH, WSPD,
WSHR and INV) are evaluated, following Ge et al. (2019). RH and WSPD are
used at 1000 hPa. Wind shear, WSHR, is calculated as
WSHR=(U900-U1000)2+(V900-V1000)2.
Inversion intensity, INV, is calculated as
INV=θν,900hPa-θν,1000hPa,
where θν is virtual potential
temperature and the subscripts 900 hPa and 1000 hPa specify the vertical levels
at which θν is evaluated from air
temperature and RH.
The 6-year-long high-resolution Chinese air quality reanalysis dataset
(CAQRA; Kong et al., 2021) is the latest long-term air quality reanalysis
for China. It contains surface fields of conventional pollutants, including
PM2.5, at high spatial (15 km × 15 km) and temporal (1 h)
resolution for the period 2013–2018. This dataset has been developed by
assimilating pollutant concentrations from over 1000 surface air quality
monitoring sites from the China National Environmental Monitoring Centre.
CAQRA has been validated against independent datasets, yielding a good
performance in reproducing the magnitude and variability of surface air
pollutants in China on a regional scale (Kong et al., 2021). We use
PM2.5 hourly concentrations from this dataset to calculate daily
averages for the same time period as the daily meteorological data (DJF
2013–2017, 450 d). PM2.5 concentrations show a decreasing trend
over the period of analysis, consistent with the primary emission reductions
and PM2.5 concentration decreases reported by many previous studies
(e.g. Li et al., 2019; Cheng et al., 2019). Therefore, to eliminate the
influence of changing anthropogenic emissions, the daily PM2.5 data are
de-trended by removing the linear trend from the December 2013–February 2018 (1550 d) time series. To understand how meteorology drives clean vs. polluted
conditions in a consistent way, percentile thresholds of the de-trended
daily PM2.5 data are used. We choose the 10th percentile (p10) of
PM2.5 concentrations as the clean threshold and the 90th
percentile (p90) of PM2.5 concentrations as the heavily polluted
threshold. We then group all the days below p10 and above p90 and classify
them as clean or heavily polluted days (45 d each).
Statistical significance is assessed at the 95 % confidence level
throughout this paper, unless otherwise stated. The effective numbers of
degrees of freedom are calculated in order to assess the significance of
correlations considering the effect of temporal autocorrelation (Allen and
Smith, 1994; Hu et al., 2017). A non-parametric bootstrap resampling method is
used to assess the significance of differences between meteorological
variables under heavily polluted and average conditions, as these variables
do not necessarily follow normal distributions. This bootstrap resampling
method generates random samples of meteorological variables for the whole
period of analysis. Each random sample comprises 45 d, i.e. the total
number of heavily polluted days. Then the difference between the mean of
each sample and all the data is calculated. This procedure is repeated
10 000 times to create a random distribution of meteorological variable
differences. Following this, differences calculated for heavily polluted
days are compared with the distribution of meteorological variable
differences. The differences calculated for heavily polluted days are
considered significantly negative or positive (at 95 % confidence level)
when they are below or above the 2.5 % and 97.5 % tails, respectively.
Influence of regional meteorological variables on daily PM2.5 variability
We first identify the meteorologically coherent regions representing BTH,
YRD and PRD by searching for reanalysis grid cells where the daily
PM2.5 concentrations are highly correlated (r≥ 0.7)
with those in the grid cells corresponding to Beijing, Shanghai and
Guangzhou, respectively (Fig. 1). This accounts for the regional nature of
PM2.5 pollution and provides a more robust result than using the
closest grid cells containing the cities or some arbitrary rectangular
regions as previous studies have done (e.g. Leung et al., 2018; Hou et al.,
2019). Daily regional PM2.5 concentrations are then calculated by
averaging the data over these three homogeneous regions. Note that, as the
90th percentiles (p90) of daily average PM2.5 differ for the three
regions, heavily polluted days defined on p90 correspond to concentrations >97µg m-3 for BTH, >110µg m-3
for YRD and >68µg m-3 for PRD. The value of p90
PM2.5 is higher in YRD than in BTH, because the smaller size of YRD is
more representative of a coherent urban environment (Fig. 1). For
consistency, the gridded meteorological fields described in Sect. 2 are
averaged over the same regions to construct daily regional meteorological
variables.
Correlation coefficients of daily mean PM2.5 concentrations
over all reanalysis grid cells with those in the grid cells corresponding to
(a) Beijing, (b) Shanghai and (c) Guangzhou during DJF 2013–2017. Regions
where correlations are higher than 0.7 (dark red shading) are selected to
represent the Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD) and
Pearl River Delta (PRD) regions, separately.
Figure 2 shows the lagged relationship of daily regional PM2.5 concentrations with specific regional meteorological variables in these
three homogeneous regions for the entire DJF 2013–2017 period. There are
positive correlations for INV and negative correlations for WSHR and WSPD
with PM2.5 concentrations for all three regions. This occurs even
when daily PM2.5 concentrations are lagged by a few days. This
suggests that high PM2.5 days are associated with poor vertical
ventilation (increased INV and reduced WSHR) and reduced horizontal
dispersion (weak WSPD) for several days preceding the high PM2.5 levels. In particular, WSPD is the variable with the highest correlation
with PM2.5 concentrations in YRD, appearing for a 1 d lag (r=-0.43) (Fig. 2b). Unlike the other three variables considered, the
relationship between RH and PM2.5 concentration varies across BTH,
YRD and PRD. A positive correlation is seen between RH and PM2.5 concentrations for BTH, with the highest value at zero lag (r=0.66)
(Fig. 2a). This highlights the general contrast between clean, dry air
reaching BTH from the northwest and more polluted, humid air reaching BTH
from central and eastern China. However, RH is negatively correlated with
PM2.5 concentrations in the other two regions, with larger
correlations in PRD than in YRD. The high correlations in PRD persist over
the previous 4 d (with the highest value of r=-0.52 for a 2 d
lag) (Fig. 2c). This reflects the association of high RH with cleaner
oceanic air and precipitation, and hence wet deposition in PRD (e.g. Zhu et
al., 2012; Jeong and Park, 2017). RH is the meteorological variable
presenting the highest correlation value with PM2.5 concentrations
over both BTH and PRD. These results are consistent with previous findings
of the different patterns in PM2.5–RH relationships over northern and
southern China (Leung et al., 2018; He et al., 2019).
Lagged correlations between daily mean PM2.5 concentrations
and regional meteorological variables including relative humidity (RH; red
bars), wind speed (WSPD; green bars), vertical wind shear (WSHR; gold bars) and inversion intensity (INV; blue bars) over (a) BTH, (b) YRD and (c)
PRD during DJF 2013–2017. Horizontal dashed black lines indicate the 95 %
confidence level using the two-tailed Student's t test.
Consequently, RH on the same day (r=0.66), WSPD 1 d before (r=-0.43) and RH 2 d before (r=-0.52) are identified as the most
important regional meteorological variables contributing to the day-to-day
variability of PM2.5 concentrations over BTH, YRD and PRD,
respectively. Among the second most relevant meteorological variables, WSPD
and INV stand out for BTH and PRD, respectively, with absolute correlation
coefficients close to 0.5 for some time lags. Following previous analyses
(e.g. Tai et al., 2010, 2012; Leung et al., 2018; Ge et al., 2019), we now
investigate how the relationships between PM2.5 concentrations and
the most important regional meteorological variables described above, and
considering the same time lags, are caused by common association with
large-scale circulation systems.
Modulation of daily PM2.5 by the large-scale circulation
Using ERA-5 reanalysis data for DJF 2013–2017, we find that the winter
large-scale circulation over East Asia is dominated by the Siberian High as
seen from the high sea level pressure (SLP) values centred over northwestern
Mongolia (Fig. 3a). The Siberian High induces northerly near-surface winds
along its eastern edge, which bring cold, clean air to northern and central
China as indicated by negative values of meridional wind at 850 hPa (V850)
(Fig. 3b). This northerly near-surface flow is also associated with the
middle tropospheric East Asian trough, characterised by low geopotential
heights at 500 hPa (Z500) over Northeast China as seen in Fig. 3c. Over
eastern and southern China, wet and warm southerly winds blow from the South
China Sea (Fig. 3b), bringing precipitation (Fig. 3d).
Winter mean (a) sea level pressure (SLP; hPa), (b) 850 hPa meridional wind (V850; m s-1), (c) 500 hPa geopotential
height (Z500; m, green and brown shading) and 850 hPa wind (arrows), and (d)
precipitation (mm d-1) during DJF 2013–2017.
Previous studies have introduced a variety of large-scale circulation
indices to characterise atmospheric circulation in East Asia. Here we apply
three commonly used EAWM indices (IYang (V850): Yang et al., 2002;
ISun (Z500): Sun and Li, 1997; and IJhun (U300): Jhun and Lee, 2004) and
a widely used Siberian High index (ISH; Wu and Wang, 2002) to test their
relationship with daily PM2.5 concentrations separately for the three
meteorologically coherent regions using reanalysis data (Table S1 in the Supplement). We
reverse IYang and ISun by multiplying them by -1 so that a high
index value represents a strong EAWM. The three EAWM indices have been
selected because they reflect the circulation characteristics of the EAWM in
the lower, middle and upper troposphere, respectively (e.g. Wang et al.,
2019b). Linear correlations of all three EAWM indices with the daily
PM2.5 concentrations for the whole period of analysis are significant
(at 99 % confidence level) only for BTH (r ranging from -0.54 to -0.36),
whereas absolute correlation coefficients do not exceed 0.12 for YRD and
PRD. This suggests that these three typical EAWM indices do not capture well
the relationship between the large-scale circulation and daily PM2.5 concentrations over the YRD and PRD regions used in this study. The
Siberian High index (ISH) is significantly correlated with daily
PM2.5 concentrations for all three regions, although the correlations
are not strong (r ranging from -0.19 to -0.13).
As the correlations of the daily PM2.5 concentrations with the
mentioned indices are low for YRD and PRD, we further investigate the
influence of large-scale circulation on daily PM2.5 variability
through its direct effect on the most important regional meteorological
variables identified separately for the three regions. For this purpose, we
first examine the dominant large-scale circulation features associated with
heavily polluted days for each region, then identify the correlation
patterns of daily PM2.5 concentrations with these circulation variables
for the whole period of analysis and define circulation-based indices
separately for the three regions. These analyses will be carried out
considering the same time lags as those for the most important regional
meteorological variables identified in Sect. 3. The daily meteorological
reanalysis data are normalised by subtracting the means of individual
variables and dividing by their standard deviations to yield fields with
zero means and unit variance before calculating these indices.
Beijing–Tianjin–Hebei (BTH)
As shown in Fig. 2a, the strongest correlations between daily PM2.5 concentrations and regional meteorological variables over BTH are found
for RH with no time lag. In this section, we examine circulation variables
during heavily polluted days (PM2.5 above p90; daily PM2.5
concentrations >97µg m-3 for BTH) over this region.
Figure 4 shows the average composites of circulation variables (SLP, V850
and Z500) for heavily polluted days over BTH (upper panels), along with the
difference (lower panels) between heavily polluted days and the winter (DJF)
mean (as displayed in Fig. 3) during 2013–2017. Heavily polluted days are
characterised by a weak and eastward-extended Siberian High, weak northerly
winds at 850 hPa over North China and a shallow East Asian trough at 500 hPa, reflecting a weak EAWM circulation (Jia et al., 2015; Ge et al., 2019).
Following these results, we calculate daily correlations of the PM2.5
concentrations with SLP, V850 and Z500 for the whole period of analysis to
assess to what extent the observed circulation anomalies can be used to
represent the day-to-day variability of PM2.5. The resulting
circulation–PM2.5 correlation patterns are displayed in Fig. 5. The
daily PM2.5 concentrations for BTH have negative correlations with
SLP over mainland China (and positive correlations centred over Japan; Fig. 5a), positive correlations with V850 over eastern China (Fig. 5b) and
positive correlations with Z500 centred over Northeast China (Fig. 5c), in
accord with the observed departures of heavily polluted days from the winter
mean.
Average (a) SLP (hPa), (b) V850 (m s-1), (c) Z500 (m,
shading) and 850 hPa wind (m s-1, vector) on heavily polluted days
(24 h PM2.5 above the regional 90th percentile), and the difference
(heavily polluted days minus winter mean) for (d) SLP, (e) V850 and (f) Z500
and 850 hPa wind during DJF 2013–2017 over BTH. For V850 (b, e), blue
regions represent northerlies and red regions represent southerlies. Dotted
regions in (d–f) mark statistically significant differences at the
95 % level (determined through a bootstrap resampling method). Grey
shading represents the BTH region.
Correlation coefficients of daily PM2.5 concentrations in
BTH with (a) SLP, (b) V850 and (c) Z500 during DJF 2013–2017 (dotted
regions indicate significant correlations at the 95 % level from the
two-tailed Student's t test). Grey shading represents the BTH region. The
broad region presenting the highest correlation with BTH is marked by a
yellow rectangle in each panel. The region used for the definition of a
circulation-based index (Eq. 3) is marked by a thick yellow rectangle in (c).
Based on these circulation-PM2.5 correlation patterns, we now select
broad regions (yellow rectangles in Fig. 5) which represent the highest
correlations with PM2.5 concentrations in BTH and then construct
spatial averages of the daily meteorological fields over these regions. The
area-weighted averages of daily normalised SLP, V850 and Z500 show
significant correlations with daily PM2.5 concentrations in BTH (at
99 % confidence level), especially for Z500 (r=0.67), followed by V850
(r=0.59) and SLP (r=0.54) (Table 1). Note that these correlations are
stronger than those using the EAWM indices and the Siberian High index from
the literature (see Table S1). We therefore use Z500 averaged over Northeast
China, Korea and the Sea of Japan [118–139∘ E,
33–50∘ N] (rectangle in Fig. 5c) to build a
Z500-based index for BTH (IZ500_BTH) for all days in DJF
2013–17). IZ500_BTH is calculated as the mean of daily
normalised Z500 in that region with a reversed sign (Eq. 3) so that negative
values of IZ500_BTH indicate a shallow East Asian trough:
IZ500_BTH=-Z500(33–50∘ N, 118–139∘ E)‾.IZ500_BTH is significantly correlated both with
PM2.5 concentrations (r=-0.67 in Table 2) and with RH (r=-0.64
in Table 2) in BTH on daily timescales. These results point to a shallow
East Asian trough as the dominant large-scale circulation pattern favouring
high PM2.5 concentrations and high RH in BTH. The shallow East Asian
trough in the middle troposphere inhibits the invasion of northerly cold air
from the rear of the trough to northern and central China, yielding
southerly wind anomalies (Fig. 4e, f), as found in other studies (e.g.
Zhang et al., 2014). This anomalous warm and humid air from the south
therefore creates appropriate conditions for the accumulation and possibly
the growth of fine aerosols and also suppresses the southward transport of
aerosols away from BTH. (See positive correlations for RH and negative
correlations for WSPD in Fig. 2a.)
Correlation of the area-weighted averages of daily
normalised circulation variables over the regions marked by yellow
rectangles in Figs. 5, 7 and 9 with daily PM2.5 concentrations over BTH, YRD and PRD, respectively, during DJF
2013–2017. All correlation values are significant at the 99 % confidence
level. The highest correlation (absolute value) for each region is shown in
bold.
CorrelationV850Z500SLPPrecipitationcoefficient (450 d)PM2.5 (BTH)0.590.670.54PM2.5 (YRD)0.250.21-0.33PM2.5 (PRD)-0.430.36-0.29Yangtze River Delta (YRD)
As shown in Fig. 2b, the correlations between daily PM2.5 concentrations and regional meteorological variables over YRD are highest
for the most important regional meteorological variable (WSPD) when daily
PM2.5 concentrations are lagged by 1 d. Hence, in this section,
we focus on the circulation variables (SLP, V850 and Z500) 1 d before
heavily polluted days over this region. Heavily polluted days in YRD
(PM2.5 above p90; daily PM2.5
concentrations >110µg m-3) are mainly characterised by reduced SLP over eastern China,
indicating a weak Siberian High (Fig. 6a and d) and a shallow East Asian
trough with positive Z500 anomalies centred over Japan (Fig. 6c and f).
This weakened intensity of the Siberian High is associated with a northerly
wind anomaly over both North and South China, as well as a significant
southerly wind anomaly over Northeast China and Japan (Fig. 6e). The
northerly wind anomaly implies a weakening of the winter mean southerly wind
over southern China and a strengthening of the winter mean northerly wind
over northern China (Fig. 6b, e). This different pattern in southern
vs. northern China is further supported by the daily wind speed at 850 hPa (WSPD850)–PM2.5 correlation features for the whole period of
analysis, where daily PM2.5 concentrations in YRD are negatively
correlated with WSPD850 over southern China and positively correlated over
northern China (Fig. S1). Furthermore, the daily PM2.5 concentrations
for YRD have negative correlations with SLP centred over Northeast China
(Fig. 7a), negative correlations with V850 over both southern China and
northern China (and positive correlations over Northeast China and Japan;
Fig. 7b), and positive correlations with Z500 centred over Northwest China
(Fig. 7c). These circulation-PM2.5 correlation patterns for the whole
period of analysis are consistent with the circulation anomalies shown for
heavily polluted days in Fig. 6.
Average (a) SLP (hPa), (b) V850 (m s-1), (c) Z500 (m,
shading) and 850 hPa wind (m s-1, vector) 1 d before heavily
polluted days (24 h PM2.5 above the regional 90th percentile),
and difference (1 d before heavily polluted days minus winter mean) for
(d) SLP, (e) V850, (f) Z500 and 850 hPa wind during DJF 2013–2017 over YRD.
For V850 (b, e), blue regions represent northerlies and red regions
represent southerlies. Dotted regions in (d–f) mark statistically
significant differences at the 95 % level (determined through a bootstrap
resampling method). Grey shading represents the YRD region.
Correlation coefficients of daily PM2.5 concentrations in
YRD with 1 d before (a) SLP, (b) V850 and (c) Z500 during DJF 2013–2017
(dotted regions indicate significant correlations at the 95 % level from
the two-tailed Student's t test). Grey shading represents the YRD region. The
broad region presenting the highest correlation with YRD is marked by a
yellow rectangle in each panel. The region used for the definition of a
circulation-based index (Eq. 4) is marked by a yellow thick rectangle in (a).
We then identify the regions with the highest correlations of area-weighted
average daily normalised meteorological fields with daily PM2.5 concentrations in YRD. Among these three meteorological fields (i.e. SLP,
V850 and Z500), for the regions that show the highest correlations with
PM2.5 concentrations in YRD (yellow rectangles in Fig. 7), SLP is found
to have the highest correlation (r=-0.33) (Table 1). We therefore use SLP
averaged over Northeast China [30–49∘ N,
111–131∘ E] (rectangle in Fig. 7a) to build a
normalised SLP-based index for YRD (ISLP_YRD) for all
days in DJF 2013–17 (Eq. 4). Negative values of ISLP_YRD
indicate a weak Siberian High:
ISLP_YRD=SLP(30–49∘ N, 111–131∘ E)‾.ISLP_YRD is significantly correlated both with
PM2.5 concentrations (r=-0.33 in Table 2) and with WSPD (r=0.29
in Table 2) in YRD on daily timescales. This suggests a weakened Siberian
High as the dominant large-scale circulation pattern contributing to higher
concentrations of PM2.5 and reduced WSPD in YRD. The
associated reduction in the southerly wind reported above for southern and
eastern China together with reduced WSPD implies a greater suppression of
horizontal dispersion, contributing to air pollution accumulation over YRD.
Moreover, strengthened northerly winds in northern China may lead to
southward transport of aerosols emitted from sources over northern China to
YRD, as also indicated by previous studies (Li et al., 2012; Jeong and Park,
2017).
Correlation of circulation-based indices defined in this study
(Eqs. 3–5) with daily PM2.5 concentrations over BTH, YRD and PRD,
and with the most important regional meteorological variable in each region
during DJF 2013–2017. All correlations are significant at the 99 %
confidence level.
We repeated the analysis above to examine the sensitivity to different time
lags. The observed circulation anomaly patterns without a lag resemble those
found for a 1 d lag, although they are displaced to the east because of
the eastward movement of synoptic systems in the midlatitudes (Fig. S2 in the Supplement). The
region that shows the highest correlations with PM2.5 concentrations in
YRD on the SLP-PM2.5 correlation pattern is slightly less significant
without a lag, again with an eastward shift (Fig. S3 in the Supplement).
Pearl River Delta (PRD)
In contrast to BTH and YRD, the highest correlations of daily PM2.5 concentrations over PRD with the two most important regional
meteorological variables (RH and INV) persist when PM2.5 is lagged by
several days (Fig. 2c). As the maximum correlations are found with a lag of
2 d, we examine composites of two circulation variables (SLP and V850)
and precipitation 2 d before the occurrence of heavily polluted days
over PRD (PM2.5 above p90; daily PM2.5 concentrations >68µg m-3) (Fig. 8). These variables are mainly characterised by reduced SLP
centred over northern China and increased SLP over southwestern China, weak
southerly winds at 850 hPa over South China, as well as precipitation
deficits over southern China. Correlation patterns of PM2.5 with the
same fields (Fig. 9) for the whole period of analysis further support these
circulation anomalies for heavily polluted days. Daily PM2.5 concentrations over PRD have negative correlations with SLP over northern
China (and positive correlations over southern China; Fig. 9a) and negative
correlations with V850 over South China and the South China Sea (Fig. 9b),
as well as with precipitation over southern China (Fig. 9c). There are also
negative correlations between daily PM2.5 concentrations and SSTs
over the central and eastern equatorial Pacific (and positive correlations
over the western equatorial Pacific), as well as negative correlations for
SLP over the western North Pacific (Fig. S4). These circulation–PM2.5 correlation features display characteristic ENSO-related patterns over the
Pacific and East Asia (e.g. Wang et al., 2000). La Niña events are
associated with warm SSTs in the western Pacific and cold SSTs in the
central and eastern equatorial Pacific, reduced SLP over the western North
Pacific and descending motion on the northwestern flank of this reduced SLP.
The opposite relationships are seen for El Niño (Fig. S4 in the Supplement). This anomalous
subsidence with suppressed precipitation (Fig. 9) has been found to play a
major role in high PM2.5 concentrations over southern China (e.g. He
et al., 2019; Sun et al., 2018). We also found that more than 80 % (37 out
of 45) of heavily polluted days in PRD are in La Niña years, considered
here as those when the Niño 3.4 index (area-weighted averages of SSTs
anomaly over 5∘ S–5∘ N, 120–170∘ W) is less than -0.5. Nonetheless, these
results should be treated with caution because of the relatively short time
series considered (only five winters with PM2.5 data).
Average (a) SLP (hPa), (b) V850 (m s-1) and (c) precipitation (mm d-1) 2 d before heavily
polluted days, and the difference (2 d before heavily polluted days minus
winter mean) for (d) SLP, (e) V850 and (f) precipitation during DJF 2013–2017 over PRD. For
V850 (b, e), blue regions represent northerlies and red regions represent
southerlies. Dotted regions in (d–f) mark statistically
significant differences at the 95 % level (determined through
a bootstrap resampling method). Grey shading represents the PRD
region.
Correlation coefficients of daily PM2.5 concentrations in
PRD with 2 d before (a) SLP, (b) V850 and (c) precipitation during DJF
2013–2017 (dotted regions indicate significant correlations at the 95 %
level from the two-tailed Student's t test). Grey shading represents the PRD
region. The broad region presenting the highest correlation with PRD is
marked by a yellow rectangle in each panel. The region used for the
definition of a circulation-based index (Eq. 5) is marked by a thick
yellow rectangle in (b).
Comparing the correlations of the area-weighted average daily normalised
meteorological fields with daily PM2.5 concentrations, V850 is found to
have the highest value (r=-0.43 in Table 1), followed by SLP and
precipitation (r<0.4) over the regions showing the highest
correlation with PM2.5 concentrations in PRD (yellow rectangles in
Fig. 9). We therefore build a normalised daily V850-based index for PRD
(IV850_PRD) by averaging V850 over the region of South
China and the South China Sea [100–118∘ E,
10–22∘ N] (rectangle in Fig. 9b) (Eq. 5).
Negative values of IV850_PRD indicate weak southerly
winds over South China:
IV850_PRD=V850(10-22∘N,100-118∘E)‾.
Weak southerly winds over southern China as the dominant large-scale
circulation pattern are associated with greater polluted continental flow
and precipitation deficits under weak cleaner oceanic winds (Fig. 8e, f)
that are conducive to air pollution over PRD via reduced wet deposition.
Consequently, IV850_PRD is not only negatively correlated
with PM2.5 concentrations (r=-0.43 in Table 2) but also positively
correlated with regional RH in PRD (r=0.64 in Table 2). The anomalous
subsidence yielding precipitation deficits over southern China is also
associated with a shallow and stable boundary layer where the vertical
dilution capacity of the lower atmosphere decreases (see negative correlations
for RH and positive correlations for INV in Fig. 2c). Overall, the observed
circulation patterns for smaller and zero lag are broadly similar to those
found for a 2 d lag (Fig. S5 in the Supplement), although the V850-PM2.5 correlations weaken as the lag is reduced (Fig. S6 in the Supplement).
Performance of circulation-based indices for differing air pollution
levels
Our analyses confirm that the proposed circulation-based indices are
significantly correlated with the most important regional meteorological
variables and the PM2.5 concentrations on daily timescales during DJF
2013–2017. The correlations are significant at the 99 % confidence level
(Table 2). To further examine the performance of circulation-based indices
for distinguishing different levels of air quality, we show the
distributions of IZ500_BTH,ISLP_YRD
and IV850_PRD for several percentile thresholds of
daily PM2.5: above p90 (heavily polluted), p50–90 (moderately
polluted), p10–50 (moderately clean) and below p10 (clean) (Fig. 10). Note
that the sample size for moderate events is larger than for heavily
polluted or clean events and also that daily PM2.5 concentrations are
lagged by 1 and 2 d in the case of YRD and PRD, respectively, for
consistency with the previous analysis.
Frequency distributions of circulation-based indices for
different percentile thresholds of daily mean PM2.5 concentrations
over (a) BTH, (b) YRD and (c) PRD during DJF 2013–2017. PM2.5
concentration data are lagged by 1 and 2 d with respect to the
circulation indices for YRD and PRD, respectively. The vertical lines and
shading represent the averages and the associated 95 % confidence
intervals, respectively. Averages are calculated using Tukey's trimean
(e.g. Ge et al., 2019):
X‾=14(Q1+2Q2+Q3),
where Q1 is the lower quartile, Q2 is the median, and Q3 is the upper quartile.
The confidence intervals for these averages are estimated by using bootstrap
resampling (e.g. Wang, 2001). This method generates samples by randomly
choosing daily values of circulation-based indices (resampling with
replacement) and then calculating Tukey's trimean. This process is
repeated 10 000 times to get robust replicates of the mean. Ultimately, the
lower and upper limits of the 95 % confidence intervals are calculated as
the values corresponding to the 2.5th and 97.5th percentiles.
For BTH, the average value of IZ500_BTH with associated
95 % confidence intervals are IZ500_BTH=-1.04±0.20 for heavily polluted days, IZ500_BTH=-0.28±0.10 for moderately polluted days,
IZ500_BTH=0.35±0.10 for moderately
clean days and IZ500_BTH=0.83±0.23 for
clean days (Fig. 10a). The values of IZ500_BTH for
these four categories differ (i.e. the confidence intervals do not overlap)
at the 95 % confidence level and IZ500_BTH can
distinguish between different levels of air quality, not just extreme
heavily polluted or clean conditions. Ge et al. (2019) used a Siberian High
index (ISH; Wu and Wang, 2002), which we tested as described in Sect. 4, and a
potential vorticity-based EAWM index (IPV; Huang et al., 2016) to
distinguish different PM2.5 pollution levels in Beijing. They found
that ISH can effectively distinguish clean days (daily PM2.5 concentrations ≤75µg m-3) from polluted days (daily
PM2.5 concentrations ≥75µg m-3), but could not
distinguish between moderate and severe (daily concentrations PM2.5≥150µg m-3) PM2.5 pollution. The IPV index
exhibited the reverse problem. This shows that IZ500_BTH
performs better than existing circulation indices, both in capturing the
relationship between the dominant large-scale circulation and daily
PM2.5 concentrations (Tables 2 and S1) and in distinguishing
pollution levels in BTH (Fig. 10a). In the case of YRD (Fig. 10b),
ISLP_YRD can effectively distinguish heavily polluted
days (ISLP_YRD=-0.32±0.28) from clean
days (ISLP_YRD=0.51±0.19). However,
differences are not significant between heavily and moderately polluted days
(ISLP_YRD=-0.19±0.11) and are not
highly significant between clean and moderately clean days
(ISLP_YRD=0.24±0.11). For PRD (Fig. 10c), IV850_PRD can distinguish well between heavily
polluted days (IV850_PRD=-0.31±0.16),
moderately clean days (IV850_PRD=0.22±0.10) and clean days (IV850_PRD=0.83±0.19), but not between heavily polluted and moderately polluted days
(IV850_PRD=-0.28±0.09).
To further illustrate the relationships between the dominant large-scale
circulation, as represented by these circulation-based indices, and the
severity of PM2.5 pollution at daily timescales, we show the joint
frequency distributions of daily values of circulation-based indices
compared with daily PM2.5 concentrations (Fig. 11). We show the linear
relationship between each respective index and PM2.5 concentrations, as
given in Table 2, with higher PM2.5 concentrations and smaller
(negative) index values on heavily polluted days, and vice versa. Moderately
polluted days (PM2.5 above p50; daily PM2.5 concentrations >43µg m-3 for BTH, >59µg m-3 for
YRD and >39µg m-3 for PRD) tend to occur when the
circulation-based indices are negative. This is more often the case for
heavily polluted days (PM2.5 above p90; daily PM2.5 concentrations >97µg m-3 for BTH, >110µg m-3
for YRD and >68µg m-3 for PRD), in particular for BTH
where 98 % (44 of 45) of those days have negative values of
IZ500_BTH compared with 66 % (119 of 180) of moderately
polluted days (p50–90 PM2.5). However, there is no such apparent
distinction in the other two regions, since around 62 % of both heavily
and moderately polluted days in YRD have negative values of
ISLP_YRD, and 70 % of these days in PRD have negative
values of IV850_PRD. Alternatively, 51 % (23 of 45),
16 % (7 of 45) and 13 % (6 of 45) of heavily polluted days in BTH, YRD and
PRD, respectively, occur when circulation-based indices are below -1.
Joint distributions of circulation-based indices against
de-trended daily PM2.5 concentrations for different percentile
thresholds (colour coded), including the corresponding linear fits with
95 % prediction intervals, over (a) BTH, (b) YRD and (c) PRD during DJF
2013–2017. PM2.5 concentrations data are lagged by 1 and 2 d behind the circulation indices in the case of YRD and PRD,
respectively.
By contrast, moderately clean days (PM2.5 below p50) and, to a greater
extent, clean days (PM2.5 below p10; daily PM2.5 concentrations <16µg m-3 for BTH, <29µg m-3 for YRD and
<15µg m-3 for PRD) tend to occur when circulation-based
indices are positive. About 91 % (41 of 45), 80 % (36 of 45) and 89 % (40 of
45) of clean days in BTH, YRD and PRD have positive indices values. As
expected, this tendency is even more pronounced for larger values of the
indices, as 93 % (52 of 56), 88 % (37 of 42) and 93 % (37 of 40) of
days with IZ500_BTH, ISLP_YRD and
IV850_PRD exceeding 1 are classified as moderately
clean or clean. The share of days with positive values of the circulation
indices generally decreases with increasing PM2.5 pollution levels
for all three regions, especially for PRD where the percentage of days with
positive values of IV850_PRD decreases from 89 % of
clean days to only 61 % (110 of 180) of moderately clean days. The results
of the analyses conducted so far show that the daily circulation-based
indices proposed in this study can capture most of the day-to-day
variability of PM2.5 and also identify days with different pollution
levels, although with poorer performance for YRD than for the other two
regions.
Joint effect of large-scale circulation and regional meteorology
The relatively moderate correlation between daily circulation-based index
and daily PM2.5 concentrations in YRD reflects the complex mix of
factors affecting the day-to-day variability of this pollutant. We have also
found that regional meteorological variables (the most relevant ones
are identified in Sect. 3) influence the PM2.5 concentrations for the
three regions (e.g. r=0.66 for RH in BTH, r=-0.43 for WSPD in YRD and
r=-0.52 for RH in PRD). On the other hand, there are significant
correlations between the circulation-based indices and the most relevant
regional meteorological variables in each region, indicating that the effect
of circulation on PM2.5 occurs through modulation of the regional
meteorology. The relationship between the daily circulation-based index and
the most important daily regional meteorological variable is weaker in YRD
(r=0.29) than for the other two regions (r=-0.64 for BTH; r=0.64
for PRD) (Table 2). This shows that the daily circulation-based index is not
solely capable of capturing the regional meteorological variability driving
day-to-day PM2.5 changes in YRD.
While there is some co-variation in the large-scale circulation with the
regional meteorology, they can be combined to reproduce the day-to-day
variability of PM2.5 with improved performance. We therefore build
multiple regression models including a linear combination of the most
important regional meteorological field and the large-scale circulation
index in each region (Table 3). The inclusion of regional meteorology
explains more of the variance in the PM2.5 concentrations for all three
regions (R2(IZ500_BTH+RH)=0.54,
R2(ISLP_YRD+WSPD)=0.23 and R2(IV850_PRD+RH)=0.30) than the large-scale
circulation index alone (R2(IZ500_BTH)=0.45, R2(ISLP_YRD)=0.11 and R2(IV850_PRD)=0.18). However, if we consider the
regional meteorological variable alone, we see that its relationship with
daily PM2.5 concentrations explains more of the variance than the
large-scale circulation variable for the YRD and PRD regions. Hence,
compared with a linear model on the most relevant regional meteorological
field, these multiple models do not bring major improvements for YRD and
PRD, where the increase in explained variance is relatively small (0.18 vs.
0.23 for YRD and 0.27 vs. 0.30 for PRD). As expected, the signs of the
regression coefficients for the most important regional meteorological field
and the large-scale circulation index (Table S2) are consistent with those
of their respective correlation coefficients with PM2.5.
Linear relationship (explained variance) of daily
PM2.5 concentrations in BTH, YRD and PRD, with the
circulation-based index of Table 2, the most important regional
meteorological field in each region and the linear combination of both
during DJF 2013–2017. All the linear relationships are significant at the
99 % confidence level.
BTHIZ500_BTHRHIZ500_BTH+RHR20.450.440.54YRDISLP_YRDWSPDISLP_YRD+WSPDR20.110.180.23PRDIV850_PRDRHIV850_PRD+RHR20.180.270.30Discussion and conclusions
This study investigates the modulation of daily PM2.5 concentrations by
regional meteorological conditions and large-scale circulation in three
major populated regions of China during winter. Using a new high-resolution
Chinese air quality reanalysis dataset, major regions associated with BTH,
YRD and PRD are identified where daily PM2.5 concentrations are
spatially coherent. For these three regions, we find that the regional
meteorological variables most correlated with daily PM2.5 concentrations are different: RH on the same day for BTH (r=0.66), WSPD
1 d before for YRD (r=-0.43) and RH 2 d before for PRD (r=-0.52). We identify the dominant large-scale circulation patterns associated
with heavily polluted days (PM2.5 above p90) considering the same
time lags. In BTH, we find that a shallow East Asian trough has the
strongest relationship with both PM2.5 concentrations (r=-0.67) and
RH (r=-0.64). This suggests a strong contribution of warm, humid air from
the south and weak transport of northerly cold, dry air associated with the
shallow East Asian trough to air pollution accumulation in BTH. In YRD, a
weak Siberian High shows the largest correlation with PM2.5 concentrations (r=-0.33) and WSPD (r=0.29). This reflects the
relationship between weak southerly winds over southern China, associated
with a weak Siberian High, and poor horizontal dispersion of polluted air in
YRD. In PRD, weak southerly winds over southern China have the largest
correlations with PM2.5 concentrations (r=-0.43) and RH (r=0.64). This illustrates the influence of flow from more polluted continental
regions and of precipitation deficits under weak humid southerly winds on
PM2.5 pollution through reduced wet deposition in PRD.
Based on these dominant large-scale circulation features, we propose three
new circulation-based indices that can be used both to explain the
day-to-day variability of the PM2.5 concentrations and to predict the
occurrence of heavily polluted days and clean days (PM2.5 below p10)
in each region: a 500 hPa geopotential height-based index for BTH
(IZ500_BTH), a sea level pressure-based index for YRD
(ISLP_YRD) and a meridional wind-based index for PRD
(IV850_PRD). These indices capture the relationship
between the dominant large-scale circulation and daily PM2.5 concentrations better than existing EAWM indices (Yang et al., 2002; Sun
and Li, 1997; Jhun and Lee, 2004) and the Siberian High index (Wu and Wang,
2002). They improve on the capability of current circulation-based indices
(e.g. Wu and Wang, 2002; Huang et al., 2016) to distinguish PM2.5
pollution levels in BTH, and are the first daily circulation-based indices
specifically derived for YRD and PRD. Furthermore, consideration of regional
meteorology improves the performance of these large-scale circulation-based
indices to predict the day-to-day evolution of the regional PM2.5 concentrations in these regions, raising the explained variance from 0.45
to 0.54 for BTH, from 0.11 to 0.23 for YRD and from 0.18 to 0.30 for PRD.
These results demonstrate the benefits of considering the large-scale
circulation for air quality studies over China. Although the circulation
indices explain less variance than the most relevant regional meteorological
fields for YRD and PRD, we expect climate models to represent these features
of the large-scale circulation better than regional meteorological fields
that depend on subgrid scale processes. Indeed, current climate models have
a limited capability to represent some regional signals (e.g. RH: Xu et
al., 2021; surface wind speed: Zha et al., 2020). On the other hand, climate
model projections of the inter-annual variability, decadal oscillations and
long-term trends of circulation indices are appropriate to represent the
future evolution of the PM2.5 concentrations under climate change
(e.g. Cai et al., 2017; Zhao et al., 2021), considering different degrees
of pollution control. Such an approach could be applied to guide air quality
policies aimed at keeping future PM2.5 concentrations below current
levels.
There are, however, two limitations inherent in this work. First, the
relationships between atmospheric circulation and daily PM2.5 concentrations may not be linear, as assumed in this study. Although we
have improved the explained daily variability of PM2.5 by linearly
combining the most important regional meteorological field and the
large-scale circulation index, non-linear models that account for the
covariance of meteorological fields (e.g. Barmpadimos et al., 2011, 2012,
Garrido-Perez et al., 2021) or dimensionality reduction techniques, such as
principal component analysis (e.g. Tai et al., 2012; Shen et al., 2015;
Leung et al., 2018), merit further consideration. In addition, these
large-scale relationships are based on only five winters of data, because
high spatiotemporal coverage of air pollution measurements are only
available in China from 2013. Hence, whilst our results are encouraging
(e.g. we find that more than 80 % of heavily polluted days in PRD occur
in La Niña years), the robustness of these results needs to be verified
using longer-term data. Despite these limitations, the results of this study
are beneficial to understanding and forecasting the occurrence of air
pollution episodes in the three regions from a large-scale perspective.
Data availability
No new data were created in this study. The data used in this study are
introduced with details in Sect. 2 and web links of these publicly
available datasets are as follows: ERA-5 (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels, Hersbach et al., 2018),
GPCP (https://rda.ucar.edu/datasets/ds728.3/, Huffman et al., 2016) and CAQRA
(http://cstr.cn/31253.11.sciencedb.00053, Tang et al., 2021).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-22-6471-2022-supplement.
Author contributions
ZJ, RMD, CO, CL and OW designed the study. ZJ processed and analysed the
data. XT provided the CAQRA reanalysis data. ZJ, RMD, CO, CL and OW prepared
the paper with contributions from all co-authors.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
Oliver Wild and Ruth M. Doherty thank the Natural
Environment Research Council (NERC) for funding under grant nos. NE/N006925/1, NE/N006976/1 and NE/N006941/1. Carlos Ordóñez thanks the Spanish Ministerio de Economía y Competitividad (grant no. RYC-2014-15036). Chaofan Li thanks the National Key Research and Development Program of China (grant No. 2018YFA0606501).
Financial support
This research has been supported by the Natural Environment Research Council (NERC; grant nos. NE/N006925/1, NE/N006976/1 and NE/N006941/1).
Review statement
This paper was edited by Lea Hildebrandt Ruiz and reviewed by three anonymous referees.
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