Introduction
Human activities have important impacts on weather and climate at diverse spatiotemporal
scales by causing changes in the amounts of greenhouse gases, aerosols, and cloudiness in
the Earth's atmosphere. The changes in these factors alter the energy balance of the
Earth–atmosphere system. In particular, the large uncertainty involving the clouds,
precipitation, and radiation makes it difficult to accurately estimate the contributions
of these factors to long-term climate changes (Boucher et al., 2013). Recent estimation
indicates that in fast-developing China, anthropogenic emissions may have contributed
10%±4% in the current global radiative forcing (B. Li et al., 2016). Many
related studies have noted that short-term air pollution associated with intense
economical, political, and cultural events has discernible influences on local and/or
regional weather worldwide, where air pollution caused by regular or occasional
anthropogenic emissions significantly impacts atmospheric physics and chemistry (e.g.,
Zhao et al., 2006; Sanchez-Lorenzo et al., 2012; Williams et al., 2015). The involved
cloud-precipitation processes and interactions over monsoonal Asia remains the most
challenging topic (Z. Li et al., 2016).
Alternating weekdays and weekends may be the most apparent regular activity of human
beings. The so-called weekly cycle effects of atmospheric parameters, which are strongly
related to the aforementioned regular human activity, have been widely reported globally
(Gordon, 1994; Cerveny and Balling, 1998; Bäumer et al., 2008). At first, these
studies of the weekly cycles were focused on cities and industrialized areas. Later,
reportedly, the weekly cycles of atmospheric variables are not only local phenomena
confined within urban areas but also large-scale phenomena that are closely related to
regular human activities (Gordon, 1994). Over developed Europe, for example, Bäumer
et al. (2008) analyzed the station data from the Aerosol Robotic Network over central
Europe and reported that the aerosol optical thickness is higher midweek than on the
weekend. In rapidly industrializing Asia, there are also discernible weekly cycles in
meteorological parameters. For example, Gong et al. (2006) found that the weekend effect
in the diurnal temperature range (DTR) has the opposite signal between winter and summer,
where wintertime DTR tends to have a positive weekend effect (i.e., larger DTR on weekend
days compared to weekdays), which is in association with increased maximum temperature
and total irradiance but decreased relative humidity, whereas summertime DTR displays a
much stronger and significantly negative weekend effect. Evident weekly cycles are also
identified in other parameters, such as wind speed, precipitation frequency (Gong et al.,
2007), and thunderstorm frequency (Yang et al., 2016). In South Korea, Kim et al. (2009) found that there is more cloudiness and less
insolation for Wednesday–Thursday and less cloudiness and more insolation for
Monday–Tuesday. Furthermore, weekly periodicities are enhanced, especially in autumn,
more than 2–3 times as great as those of the annual mean with long-term surface
measurements of meteorology (1975–2005). The mechanisms of these weekly cycles are not
yet fully understood, though their strong covariations with human activities (e.g., air
pollutants) are widely measured. Gong et al. (2006, 2007) supposed that the phases of
weekly cycle may depend on the different climatological circumstances. Wang et al. (2012)
emphasized that during the autumn, the semi-direct aerosol effects likely play a dominant
role in the weekly cycle of cloud cover in southeastern China. Yang et al. (2016)
proposed the importance of human emission types.
In addition, there are occasional, intense human activities (e.g., societal and cultural
events) that also change atmospheric environments (particularly the air quality). For
example, Xin et al. (2012) found that the concentrations of PM2.5 in the
Beijing–Tianjin–Hebei area are reduced by approximately 50 % with the
implementation of emission controls during the 2008 Beijing summer Olympic Games. Similar
actions were also implemented when China hosted the 2014 Asia-Pacific Economic
Cooperation meeting and held the 2015 national military parade; as a result, the air
quality is considerably improved over northern China (Chen et al., 2015; Wang et al.,
2015; Xu et al., 2015, 2017; Wen et al., 2016). In contrast, setting off fireworks during
holiday celebrations can produce severe air pollution around the world, such as the
Fourth of July holiday in America (Carranza et al., 2001), the New Year's fireworks in
Mainz (Drewnick et al., 2006), the Diwali festival in India (Singh et al., 2014), and the
Chinese Spring Festival (CSF; Tang et al., 2016). The emissions may differ in magnitude
and type among these events (Wang et al., 2007; Zhang et al., 2010; Tang et al., 2016).
Compared with weekly cycles, the possible meteorological covariations in association with
these occasional, intense events are less studied.
Notably, the CSF holiday occurs annually in January–February across the whole country
and is the most important holiday in China. Moreover, the CSF is a cultural tradition
that is directly related to the daily activity of human beings, particularly economic
activities. Human emissions during the CSF holiday decrease evidently (Lin and McElroy,
2011; Gong et al., 2014). Some studies have tried to identify the possible covariations
in atmospheric physics and weather anomalies accompanying these local and regional
occasional societal and cultural events. Travis et al. (2002) noted a significant
increase in DTR in the United States during the 3 days after 11 September 2001 when civil
aviation planes were grounded. Gong et al. (2014) analyzed the temperature anomalies
during the CSF holiday from 2001 to 2012 and found that there are significant negative
anomalies of approximately -0.81 ∘C in eastern China, wherein
atmospheric feedbacks likely play a role in enhancing cooling.
The purpose of the present study is to investigate the possible changes in precipitation
over southern China during the CSF holiday. The paper is organized as follows. The data
and methods are presented in Sect. 2. The results, including the anomalous precipitation
frequency and amount; the relevant changes in humidity, cloud cover, and temperature; and
the anomalous atmospheric water vapor and circulation are described in Sect. 3. The
possible relevance of these results to human activity is discussed in Sect. 4. Finally, a
summary is presented in Sect. 5.
Results
Significant reduction of precipitation
We computed the daily anomalies of the precipitation frequencies for each station. To
determine how the precipitation might change before and after LNYD, we considered days
from -12 to +12. This method is similar to that used by Gong et al. (2014). We also
tried to use a longer time window and found that the conclusions remain the same. To
focus on southern China, we computed the means for all 155 stations within the target
area (Fig. 1). The regional mean ΔF is shown in Fig. 2a. Although the ΔF
before the LNYD is generally positive, the salient feature is the ΔF reduction
after the LNYD. The mean for day +3 to day +7 is -5.5 %, and a minimum of
-9.4 % occurs on day +5. Note that the largest reductions occur on days [+4,
+6], where the averaged anomalies are as low as -7.4 %. Precipitation is a
natural phenomenon. Thus, its frequency anomaly should be spatiotemporally random for a
certain day from 1979 to 2012 among all the stations over this large region. The standard
deviation of the stations' ΔF would provide information concerning further
uncertainties. A smaller standard deviation suggests that similar anomalies tend to be
observed, thus indicating a robust signal, whereas a larger standard deviation implies
diverse changes. We found that the standard deviation of days [+3, +7] is
approximately ±3.7 %, which is smaller than the standard deviation of most days
before the LNYD. Note that the means may be biased by outliers or skewness. Thus, to
determine the diverseness of these frequency anomalies among the 155 stations, we also
computed the medians and the lower and upper percentiles. In Fig. 2a, the ranges of the
10th and 90th percentiles are plotted as error bars and the medians are also shown. The 90th
percentiles for the days [+4, +6] are clearly -0.9 %, -3.9 %, and
-1.7 %, respectively, all being significantly below 0. Simultaneously, the medians
are generally similar to the means; for example, from day +3 to day +7, the means
(-3.1 %, -6.5 %, -9.4 %, -6.3 %, and -2.2 %, respectively)
and medians (-3.6 %, -7.0 %, -9.8 %, -7.0 %, and -1.8 %,
respectively) are almost identical, likely suggesting that the majority of the anomalies
tend to be negative and that the means are not skewed by large departures or outliers.
We further investigated the statistical significance of the precipitation
anomalies by employing a Monte Carlo approach (e.g., Stjern, 2011; Wang et
al., 2012). Here, the Monte Carlo test is performed by randomly rearranging
sequences of the lunar calendar days. For each experiment, we first
generated the random sequence and then estimated the frequencies for each
lunar day station by station. In addition, the regional anomaly mean is
calculated as described above. We repeated these steps 1000 times and
obtained the 10th and 90th percentiles of these experiments. By
comparing the observations with these Monte Carlo-yielded percentiles, we
may estimate whether the frequency anomalies are beyond those of random
chance. If the original observations are lower than the 10th percentile, these measurements are significant at the 0.1 level.
For the regional mean frequency anomalies, there are few cases where the simulated
anomaly is smaller than the observations during the holiday. Only 1 of 1000 random Monte
Carlo experiments is below the observation on day +5. On days +4 and +6, there are
10 and 14 cases, respectively, with values smaller than the observation. On days +3 and
+7, there are slightly more values smaller than the observation (135 and 190,
respectively). We found that the 10th percentiles during days +4 to +6 are
-3.6 %, -3.4 %, and -3.5 %, respectively. Note that the observations
(-6.5 %, -9.4 %, and -6.3 %) are all below the corresponding 10th
percentiles of the Monte Carlo experiments. Therefore, we may conclude that the observed
ΔF is significant at the 0.1 level from days +4 to day +6. We also examined
the medians of the 155 stations and found a similar result. For days +4, +5, and
+6, there are only 9, 1, and 7 cases, respectively, with values below the observations.
The observed medians during days +4, +5, and +6 are -7.0 %, -9.8 %, and
-7.0 %, respectively, which are all below the Monte Carlo 10th percentiles
(-3.7 %, -3.6 %, and -3.6 %, respectively).
Further, we investigated the possible changes in daily precipitation amounts. Here, all
trace precipitation records are excluded. The composite method is similar to that for
frequency. In other words, for a specific lunar calendar day, the anomalous amount is
calculated as the mean daily precipitation amount minus the climate reference, where the
climate reference is estimated based on the Gregorian calendar. Then, we averaged the
anomalies for all stations to obtain a regional mean. Figure 2b shows the results.
Interestingly, from day +2 to day +5, the amounts experience continuously negative
anomalies with a mean of -0.62 mmday-1. In particular, a minimum of
-1.0 mmday-1 occurs on day +3. During days +2, +4, and +5, the
anomalies are similar (being -0.54, 0.52, and -0.48 mmday-1,
respectively). Differing from the precipitation frequency, the amounts ranging from the
10th to the 90th percentiles are obviously larger. In addition, during days [+2, +5],
the upper bounds all exceed zero and imply larger uncertainties. Thus, the gradual
decrease in the amount after the LNYD is likely consistent with the significant reduction
in precipitation frequency. Note that the Monte Carlo test suggests a high confidence of
the negative amount anomalies during the holiday. Of 1000 Monte Carlo experiments, there
are 83, 14, 75, and 70 cases with values smaller than the observations during days [+2,
+5]. The Monte Carlo 10th percentiles for days [+2, +5] are -0.47, -0.45,
-0.42, and -0.40 mmday-1, respectively, whereas the corresponding
observations of -0.54, -0.95, -0.52, and -0.48 mmday-1 are all
smaller. We also examined the median of the 155 stations by employing the Monte Carlo
test. The results are similar. There are 70, 16, 66, and 43 experimental values that are
smaller than those observed. The medians of the observation for these days (-0.63,
-0.97, -0.65, and -0.70 mmday-1, respectively) are all smaller than
the corresponding Monte Carlo 10th percentiles (-0.56, -0.54, -0.52, and -0.47,
respectively). Therefore, the means and medians of the amount anomalies during days
[+2, +5] are significant at the 0.1 level. Note that the large error bars in Fig. 2b
may imply somewhat larger diversities or differences in the daily precipitation amounts
among the 155 stations. The Monte Carlo test likely provides a more reasonable estimation
of the significance. With the above analysis, we may conclude that there is a significant
reduction in the precipitation frequency and a decrease in the daily precipitation amount
after the LNYD.
Spatial distribution of the holiday precipitation anomalies
Although the precipitation reduction after the LNYD is evident in the composite analysis
based on the regional means (Fig. 2), this feature might differ among stations. In this
subsection, we investigated the spatial distribution of the precipitation anomalies. A
significant frequency reduction occurs on days [+4, +6], and a significant amount
reduction appears on days [+2, +5]. Here, we computed the mean frequency anomalies
during days [+4, +6] and the mean amount anomalies during days [+2, +5] for each
station. The statistical significance is also estimated using the Monte Carlo test at
each station. The method used is similar to that described in Sect. 3.1. Here, the Monte
Carlo test is performed for every station by randomly rearranging the lunar calendar, and
the simulation is repeated 1000 times. The 10th (90th) and 20th (80th) percentiles are
taken as the criteria. For the precipitation frequency, the mean value is regarded as
significant at the 0.1 (or the 0.2) level only when the anomalies in all 3 days (i.e.,
days +4, +5, and +6) are significant at the 0.1 (or 0.2) level. Similarly, when the
daily precipitation amounts on all four individual days (from day +2 to day +5) are
significant, their mean is regarded as a significant anomaly. The results are shown in
Fig. 3.
Anomalies of the precipitation frequency (a) and amount (b).
The significance values are estimated using a Monte Carlo approach; stations with circles
and dots denote that all days have values significant at the 0.1 and 0.2 levels,
respectively.
Figure 3a shows that the majority of the ΔF have negative signs. Among the
155 stations, only 7 stations have positive signs, and none of these positive anomalies
are significant. Of the 148 stations with ΔF reductions, 57 stations are
significant at the 0.1 level, and 108 are significant at the 0.2 level. Most stations
located in the north and east of the study area have a significant ΔF reduction.
The ΔF minimum reaches -13.5 % at the Sansui station (27∘ N,
108∘ E). Considering the magnitude of the anomaly, there are 55 stations with
ΔF reductions exceeding -9 %. Among these 55 stations, the anomalies for
45 stations are significant at the 0.1 level, and those at the other 10 stations are
significant at the 0.2 level.
Unlike the frequency, the spatial features of the amount anomalies differ
among stations (Fig. 3b), which is consistent with the relatively larger
span of the 10th–90th percentiles, as shown in Fig. 2b.
Nevertheless, there are still 117 stations with decreased amounts during
days [+2, +5]. Among these stations, 27 show significant decreases at
the 0.2 level, and 8 stations show significant decreases at the 0.1 level.
The largest reduction is -2.41 mmday-1, which appears at the Shouxian
station (32∘ N, 116∘ E).
Based on the above analysis of the temporal composites and spatial distributions, we
found that significant reductions of precipitation frequency and daily precipitation
amount occur regionally within an approximate 1week period after the LNYD.
Relative humidity and cloud anomalies
Relative humidity (RH) is the most important factor that directly controls
or influences
precipitation. In this subsection, we investigated the possible changes in the relative
humidity associated with the holiday precipitation decreases. Here, we examined the
station data to analyze the relative humidity by making composites. Note that the method
of estimating the anomalies is different from that for precipitation frequencies. The
composites of the relative humidity (as well as those of other meteorological parameters
analyzed in the following sections) are based on anomalies (ΔRH), which are
obtained by subtracting the 1979–2012 long-term mean from the daily relative humidity
values.
Station observational relative humidity anomalies (ΔRH)
estimated from all available days (a) and the daytime low cloud cover (LCC)
anomalies (b) during the holiday from 1979 to 2012. ERA-Interim ΔRH at
850 hPa (c) and the daily LCC anomalies (d) during the holiday. Standard
error is shown as error bars.
Station anomalies of relative humidity (RH) estimated from all available
days (a) and daytime low cloud cover (b) during days +4 to +6. The
significance is estimated using a t test; stations with circles and dots denote values
are significant at the 0.05 and 0.1 levels, respectively. Spatial distribution of
ERA-Interim ΔRH at 850 hPa (c) and the daily LCC
anomalies (d) during days +4 to +6. Only the significant values (at the
0.1 level) are plotted.
For each lunar calendar day, all 34 daily anomalies are collected and subjected to a
t test with the null hypothesis of their mean being not significantly different from
zero at the 0.05 (or 0.1) level. In Fig. 4, we plotted the means during days [-12,
+12]. To facilitate a comparison, the range of 1 standard error estimated from the 34
anomalies is shown via error bars. The ΔRH experiences an evident decrease from
day +2 to +7, with a mean value of -2.5 %. The driest value is -4.6 % at
day +5. The mean ΔRH of the lowest anomalies for days [+4, +6] is
-3.9 %, which is significant at the 0.1 level as estimated from a left-tail
t test. Note that the drying (wetting) may be the result of less (more) precipitation,
as demonstrated in the previous sections. For clarity, we also computed the ΔRH
using only the data from no-rain days. We found that the ΔRH for no-rain cases
shows a similar temporal feature (figure not shown). The lowest values still occur on
days [+4, +6], with anomalies of -0.97 %, -0.69 %, and -0.38 %,
respectively. However, these anomalies are not statistically significant. This result
could be caused by the relatively smaller data sample and exclusion of the precipitation
days. The sample number of no-rain days is 48.9 % of all days. In any case, the
similar drying features on no-rain days may provide support for the theory that the lower
ΔRH could be a cause of the precipitation reduction and may even help enhance the
drying.
We also examined the spatial distribution of the ΔRH. The station means of the
ΔRH during days [+4, +6] are plotted in Fig. 5. All 155 stations show negative
anomalies, with a maximum and a minimum of -0.6 % and -8.1 %, respectively.
When estimating the statistical significance, we considered the mean for days [+4,
+6]. Thus, we did not consider the significance of each of the 3 days. If the resulting
anomaly is significant at the 0.05 (or 0.1) level as calculated by a t test, this
station is regarded as significant during days [+4, +6]. As shown in Fig. 5, there
are 70 (98) stations that are significant at the 0.05 (0.1) level. Additionally, we
investigated the spatial features of no-rain days and found that most ΔRH values
during days [+4, +6] are also negative (the number of stations is 136, accounting for
approximately 88 % of the total stations), and the lowest anomaly is -7.6 %
(figure not shown). Again, this result suggests a drying atmosphere near the surface
during the New Year holiday.
For the stratus precipitation during the winter season, the relative humidity in the
lower-middle troposphere is more important than the surface humidity. The precipitation
reduction could occur with a drier and upper atmosphere. Then, we investigated the ΔRH in the
lower-middle troposphere using the ERA-Interim reanalysis datasets of the pressure levels
from 1979 to 2012. Here, we selected 99 grid points between 21–33∘ N and
108–123∘ E. We examined the ΔRH values at each level from 1000 to
500 hPa. Generally, the results are similar, showing drying tendencies throughout
as expected. Note that the ΔRH values in the low levels are more evident. At
1000 hPa, the ΔRH shows a continuous reduction during days [+3, +6],
with a mean decrease of -3.8 %. The negative anomalies on these days are
significant at the 0.1 level. The largest reduction occurs on day +5, being
-5.2 % (figure not shown). In Fig. 4, we plotted the regional mean ΔRH at
850 hPa. A similar reduction is observed during days [+2, +6], where the
lowest anomalies are still found on days [+4, +6]. In addition, the values on days
+4 and +5 (-4.0 % and -5.7 %) are both significant at the 0.1 level. In
the middle-upper troposphere, the ΔRH during the holiday is not evident.
The spatial distributions of ΔRH at 1000 and 850 hPa during days [+4,
+6] are also analyzed. At 1000 hPa, the majority of the significantly negative
ΔRH values are located over southern China and the neighboring western Pacific,
with a northeast–southwest distribution. The results at 850 hPa are plotted in
Fig. 5, and only the significant values (above the 0.1 level) are shown. At
850 hPa, the significant region is spatially smaller but has a greater magnitude
than that at 1000 hPa. Over the land, the majority of the anomalies are between
-4 % and -6 % at 1000 hPa, whereas the values are between
-6 % and -10 % at 850 hPa. The minimum grid point value at
1000 hPa is -6.5 %, while the minimum is as low as -9.7 % at
850 hPa.
The vertical profiles of the relative humidity (a),
temperature (b), and specific humidity (c) anomalies below
500 hPa. The significance level is estimated using a left-tail t test, and the
values significant at the 0.05 level are indicated with red dots.
The drying in the lower troposphere can be more clearly seen in the relative humidity
profiles. The vertical profile of the ΔRH during days +4 to +6, as averaged
over all 99 grids in southern China, is shown in Fig. 6. Because the meaningful changes
occurred in the lower troposphere, here we only plotted the ΔRH values below the
500 hPa level. Below 500 hPa, the anomalies all have negative signs. In
addition, the significant ΔRH values appear below the approximately
800 hPa level (Fig. 6a). The negative anomaly at 850 hPa is -3.9 %,
and that at 1000 hPa is -4.2 %. The mean for these layers (below
800 hPa) is -3.9 %. Generally, the significant drying of the relative
humidity appears in the lower-middle troposphere, which physically agrees with the
precipitation reduction.
As the relative humidity at the lower-middle level decreases, consistent changes in
clouds should occur, particularly in the low cloud cover (LCC). To confirm this, we first analyzed the surface-observed cloud data, which are
from the Global Surface Weather Dataset obtained from
the China Meteorological Administration. In this dataset, the observations are taken four
times per day (00:00, 06:00, 12:00, 18:00 UTC) during the years 1980–2012, except
during 2000, which is not available. Only those days with all four records are included.
Of the 304 stations in our study area, we selected 137 stations with overall data
availabilities above 30 %. When deriving the anomalies, we applied the same method as
that used for ΔRH. The results show that both the total cloud cover and LCC
reduce, the LCC especially experiences a significant decrease during the New Year
holiday. It seems the reduction of total cloud cover is contributed to by the decrease in
LCC. The mean anomaly of LCC during days [+1, +5] is -2.6 %. In addition, a
minimum of -3.1 % appears on day +5, which is significant at the 0.1 level. We
should note that sometimes the LCC cannot be completely distinguished from the middle
cloud cover in the station observations. Moreover, the cloud cover changes rapidly and
might depend on the observer. At nighttime, the uncertainty in the observations is much
greater. We would expect more reliable observations during the daytime. The mean LCC of
00:00 and 06:00 UTC is computed and regarded as the daytime observation. As shown in
Fig. 4b, the daytime LCC shows an evident reduction during days [+4, +6]. The values
of the 3 days are each significant at the 0.1 level, as estimated using a left-tail
t test. The mean for the 3 days is -6.1 %, and a minimum of -9.7 % occurs
on day +5. Compared to the climate daytime cloud cover of 78 % during January to
March in southern China, the LCC reduction during the New Year holiday has a considerably
large magnitude. Figure 5b shows the spatial distribution of the daytime LCC values
during days [+4, +6]. For a total of 137 stations, 119 (86.9 %) stations show
negative signs. In addition, there are 8 (27) stations with values significant at the
0.05 (0.1) level, as estimated via a both-tail t test. A minimum of -16.8 %
occurs at the Fuyang station (33∘ N, 115∘ E).
To assess the robustness
of the cloud cover changes, the ERA-Interim data about cloud cover are investigated. We
found that the high cloud cover and middle cloud cover show no significant changes
(figure not shown). Interestingly, there is a significant reduction in the LCC during the
CSF holiday. Figure 4d shows the temporal variations in the ERA-Interim LCC. The LCC
clearly shows an outstanding reduction during days [+4, +6]. The largest reduction,
i.e., -5.9 %, appears on day +5. The values from day +4 to day +5 are all
significant at the 0.1 level, as calculated by a left-tail t test. Averaging over days
[+4, +6], the mean value is -5.0 %. This magnitude is comparable to the
station-based daytime LCC anomaly (-6.1 %). The spatial distribution of the LCC
anomalies for the ERA-Interim data during days [+4, +6] is shown in Fig. 5d. The LCC
reduction center is located in the eastern region and has a magnitude between
approximately -8 % and -12 %. This reduction is almost identical to that of
the station observations (cf., Fig. 5b, d). Note that in the ERA-Interim data, the cloud
height is defined according to the corresponding sigma level. The low clouds correspond
to sigma 0.8–1.0. A significant decrease in the relative humidity occurs below
800 hPa (Fig. 6a), which physically agrees with the negative LCC anomalies.
Temperature and water vapor anomalies
The negative ΔRH during the holiday might be caused by anomalous temperatures, the
water vapor, or both, depending on certain conditions. In this subsection, we analyzed
the changes in the temperature and water vapor associated with the holiday precipitation
reduction. First, we examined the temporal changes in the temperature over southern China
using the same composite analysis as that used for the relative humidity. We found that
there is continuous cooling of the daily mean temperatures from day -3 to day +6,
which are all significant at the 0.1 level, as estimated by a left-tail t test (figure
not shown). The mean temperature anomaly of these days is -1.12 ∘C. The
maximum and minimum temperature anomalies are -0.70 and -1.42 ∘C for
days +6 and +3, respectively. Precipitation often causes a low temperature. To
exclude the possible influence of precipitation on temperature, we also examined the
daily temperatures for no-rain days and found a similar temperature anomaly of
-1.22 ∘C for days +1 to +6. In addition, there is a similar minimum
of -1.48 ∘C. These values for the no-rain days are significant at the
0.05 level.
Note that Gong et al. (2014) reported negative temperature anomalies over the whole of
eastern China around the LNYD when analyzing the shorter data period of 2001–2012. They
found that the most significant decreases appear on days -3 to +2, with a mean value
of -0.81 ∘C. However, the temperature beyond this period over southern
China is not addressed in their analysis. As demonstrated in the previous sections, the
most significant reductions of the ΔF and ΔRH are observed on days [+4,
+6]. Thus, we further computed the mean of the temperature anomalies (ΔT) for
days [+4, +6] and estimated their statistical significance using a t test for each
of the stations. The results show that all 155 stations have negative anomalies (figure
not shown). The maximum ΔT of -0.28 ∘C occurs at the Fengjie
station (31∘ N, 109∘ E), and a minimum of -2.14 ∘C
appears at the Jiuxian station (25∘ N, 118∘ E). The anomalies for 116
stations are statistically significant at the 0.1 level, and those for 95 stations are
significant at the 0.05 level. We also investigated the temperature anomalies during days
[+4, +6] using no-rain days and found similar results. All stations continue to show
negative anomalies, and more stations are significant (134 stations exceed the 0.1 level,
and 117 stations exceed the 0.05 level).
Means of station-specific humidity anomalies (ΔSH) during
the holiday from 1979 to 2012 as estimated from all available days (a) and
no-rain days (b). The corresponding ERA-Interim ΔSH at 1000 hPa (c)
and 850 hPa (d) are also plotted.
The above spatial distribution suggests that temperature anomalies are unlikely at the
local scale. For clarity, we also investigated the vertical profiles and the spatial
distributions of ΔT using the ERA-Interim pressure level data. The mean ΔT values below the 500 hPa level during days [+4, +6] are averaged for all
99 grid points over southern China and plotted in Fig. 6b. The significant negative
temperature anomalies below 700 hPa are evident, where all values are as low as
<-1 ∘C. The mean for these layers is -1.37 ∘C. To
determine whether the cooling of the lower-middle troposphere is a regional-scale
phenomenon, we further analyzed the spatial distribution of ΔT. Note that the
minimum of -1.56 ∘C occurs at the 850 hPa level in Fig. 6b.
Here, we computed the 850 hPa ΔT during days [+4, +6] for each of the
grid points (figure not shown), revealing that the majority of the study area experiences
significant cooling (exceeding -1 ∘C) that is almost identical to that
from the surface station observations. Based on these analyses, we may conclude that
during the holiday (particularly days [+4, +6]), there is an anomalous temperature
cooling over southern China from the surface to the middle troposphere (below
500 hPa).
Cooling favors condensation and precipitation. If the water vapor content
remains constant, a cooler temperature should result in a higher ΔRH.
During the period of 1979–2012, over southern China, the climatic mean
temperatures for days [+4, +6] at 1000 and 850 hPa are
9.3 and 3.3 ∘C, respectively. Meanwhile, the specific
humidities at 1000 and 850 hPa are 5.5 and 4.4 gkg-1,
respectively. According to the Clausius–Clapeyron equation (Murray, 1967),
the climatological relative humidities are 75.4 % at 1000 hPa and 77.5 %
at 850 hPa. The observed cooling of -1.15 ∘C at 1000 hPa and
-1.56 ∘C at 850 hPa (Fig. 6b) could result in corresponding
ΔRH increases of +6.0 % and +9.1 %, respectively. This
contradicts the observed negative ΔRH anomalies (cf., Fig. 5).
Therefore, the cooling temperature is not a direct factor causing the drier
ΔRH and the precipitation reduction.
Spatial distribution of the observational specific humidity
anomalies (ΔSH) during days [+4, +6] as estimated from all
available days (a) and no-rain days (b). The spatial distributions of the
ERA-Interim ΔSH at 1000 hPa (c) and 850 hPa (d) during days [+4,
+6]. Only the significant values (above the 0.05 level) are plotted.
Alternatively, the water vapor should be responsible for the anomalous ΔRH. We
analyzed the station specific humidity (SH). Here, the SH is estimated from the surface
pressure, relative humidity, and temperature, where the Tetens formula is employed to
estimate the saturated vapor pressure over water (Murray, 1967). Figure 7a shows the
specific humidity anomalies (ΔSH) before and after the LNYD. Evident negative
anomalies persist from days -3 to +7. The lowest values appear at days [+4, +5]
approximately. Note that the negative ΔSHs during days +2 to +6 are
significant at the 0.05 level, with a mean of -0.71 gkg-1. A minimum of
-0.82 gkg-1 occurs on day +4. In addition, the average ΔSH for
days [+4, +6] is -0.73 gkg-1. We repeated the composite analyses for
no-rain days and found that the ΔSH has a similar continuous reduction after LNYD
(Fig. 7b). The ΔSHs of days [+2, +5] are significant at the 0.1 level, and
their mean is -0.43 gkg-1. The minimum of -0.54 gkg-1 also
appears on day +4, while the mean from day +4 to +6 is -0.46 gkg-1,
being smaller than the values estimated for all days. Simultaneously, the spatial
distribution of ΔSH during days [+4, +6] reveals a regional-scale reduction
over the study area (Fig. 8). All stations clearly show negative anomalies, both when all
days are analyzed and when only no-rain days are analyzed. In the former instance, the
ΔSH values for the 140 stations are statistically significant at the 0.05 level,
and in the latter case the number of stations with statistically significant values
is 102.
Further, we investigated the ΔSH values in the lower-middle
troposphere using the ERA-Interim reanalysis data. The ΔSH at 1000 hPa
(850 hPa) during days [+2, +6] are all significant at the 0.05 (0.1)
level, with a mean of -0.75 (-0.65) gkg-1. It is clear that the
ΔSHs at 1000 and 850 hPa display features similar to those of
the surface observations in both temporal variations and magnitudes of
negative anomalies during the holiday (cf., Fig. 7). Furthermore, we
investigated the spatial distributions of ΔSH on days [+4, +6].
The evident ΔSH reduction covers nearly all of southern China, with
minimums extending from southeastern China to the western North Pacific,
south of approximately 30∘ N (Fig. 8). The anomaly center lies
between 110–130∘ E and 20–30∘ N.
The drying of the ΔSH in the lower-middle troposphere is more
obvious, as seen in the vertical profile (Fig. 6c). Significant negative
ΔSH values appear below 700 hPa. Below 800 hPa, the ΔSH
values are all less than -0.50 gkg-1. The mean for these layers (800
to 1000 hPa) is -0.70 gkg-1. Similarly, we also estimated the
variations of the relative humidity corresponding to these ΔSHs. The ΔSHs
at 1000 and 850 hPa are -0.78 and -0.69 gkg-1, respectively
(Fig. 6c). If the temperature remains unchanged, these values would reduce the relative
humidity by -10.9 % and -12.3 %, respectively. Obviously, the water vapor
reduction in the lower-middle troposphere plays the dominant role in causing anomalous
relative humidity, low-level cloud, and precipitation.
(a) Temporal anomalies of the total column water vapor (ΔTCWV)
during the holiday. (b) Spatial distribution of the ΔTCWV (in color
shading with units of kgm-2) and the 700–1000 hPa mean horizontal
wind anomalies (in vectors with units of ms-1) during days [+4, +6].
Stipples denote significant ΔTCWV (at the 0.05 level). Only the anomalous wind
vectors significant at the 0.05 level are plotted. (c) Spatial distribution of
the mean anomalies of the total precipitation (ΔTP; in color shading with units of
mm) and the significant (at the 0.05 level) 850 hPa mean horizontal wind
anomalies (in vectors with units of ms-1) during days [+1, +5].
The total column water vapor (TCWV), or the precipitable water, is also a large-scale
factor that correlates well with precipitation (e.g., Qian et al., 2009). The drying from
the surface to the mid-troposphere is suggestive of the reduction in the TCWV. For
clarification, we performed a composite analysis of the ERA-Interim TCWV. Unsurprisingly,
the ΔTCWV experiences a continuous reduction from day +1 to day +5 (Fig. 9a).
The ΔTCWV are significant at the 0.05 level during days [+2, +5], with a mean
of -1.93 kgm-2. In addition, the largest negative ΔTCWV values are
observed on days +4 and +5 (2.43 and 2.36 kgm-2, respectively). The
spatial distribution of the ΔTCWV values during days [+4, +6] is shown in
Fig. 9b. The TCWV decreases by approximately -1.00 kgm-2 or more over most
of southern China. The minimum is located over the neighboring western North Pacific
between 120–130∘ E and 20–30∘ N. Meanwhile, positive ΔTCWVs
with somewhat smaller magnitudes appear to the east (over approximately
150–175∘ E and 20–30∘ N). Generally, the reduction in the TCWV over
the western North Pacific and eastern China is physically consistent with the negative
anomalies of SH over southern China. The observed decrease in precipitable water during
the holiday is unfavorable for precipitation.
Based on the above analysis, we may conclude that, although the relative humidity (as
well as the relevant cloud cover and precipitation) would benefit from the cooling
temperatures, its reduction during days [+4, +6] is strongly dominated by the drying
water vapor in the lower troposphere. Therefore, the reduction of SH is likely to
contribute to the decrease in precipitation during the New Year holiday.
Water vapor budget and atmospheric circulation
Our analysis demonstrates that in association with the significant precipitation
reduction over southern China, an anomalous negative departure of the SH occurs. One
further question is what causes such water vapor deficits during the New Year holiday?
Here, we discussed the possible factors relevant to the atmospheric column water vapor
balance as well as their individual contributions. Regional changes in the column water
vapor are essentially related to two components. One is the budget between evaporation
and precipitation, and the other is the budget of moisture inflow and outflow from the
horizontal air motion crossing the four lateral boundaries. We investigated the
corresponding anomalies separately during the holiday.
First, we computed the differences between the evaporation and total precipitation
(evaporation minus precipitation, i.e., E-P) using the ERA-Interim data from 00:00 and
12:00 UTC. Note that both evaporation and precipitation are forecasted, and the
accumulated variables are collected at step 12. We analyzed the E-P anomaly (mm,
equivalent to kgm-2) during the holiday (figure not shown). From days +1 to
+5, the E-P anomalies are all positive values with a mean of 0.85 mm. In
addition, the anomalies on days +3, +4, and +5 are all significant at the 0.05
level. The maximum of the E-P anomaly (+1.25 mm) appears on day +4. We also
investigated the evaporation independently and found it is enhanced during the holiday,
with the mean of the evaporation anomalies reaching +0.39 mm (figure not
shown). Notably, the positive E-P anomalies occur concurrently with the precipitation
reductions. The E-P anomalies are likely dominated by precipitation changes. Although
the positive E-P anomaly implies a net gain, this effect cannot account for the total
net loss of the column water vapor.
Second, we investigated the column water vapor budget contributed by the
horizontal transport. Here, we simply estimated the moisture flux across the
borders (i.e., the box in Fig. 1). The vertically integrated moisture
transport (Q) crossing the border is defined as follows:
Q=∫L1g∫500hPa1000hPaq⋅Vdpdl,
where V is the horizontal wind vector, g is the gravitational acceleration, q
is the SH and L is the length of the border. Moisture transport above 500 hPa
is ignored because its contribution to the total column moisture transport is relatively
small, and large winds tend to cause errors in the upper troposphere, where the SH is
quite small (Qian et al., 2009). Along the northern and southern borders, we computed the
meridional transport, while along the eastern and western borders, we computed only the
zonal transport. The summation of the meridional and zonal transports yields the net
budget. The results show that the net water budgets are negative from day +1 to day
+5 (figure not shown), with a mean of -2.9×107 kgs-1. Note that
the anomalies of days +1, +3, and +4 are all significant at the 0.05 level. The
lowest water vapor budget is -4.4×107 kgs-1 (approximately
-1.96 kgm-2day-1), occurring on day +4. This simple calculation
suggests that the horizontal moisture transport dominates the anomalous variations in the
total net water vapor budget.
Because the negative anomaly in the water vapor budget may be caused by a
reduced inflowing water vapor transport or by an increased outflowing
transport, we investigated the transport crossing of each of the boundaries
during the holiday. We found that the zonal transports crossing the eastern
and western borders are negligible (figure not shown). In contrast, the
water vapor transport at the southern boundary has the greatest magnitude
(Fig. 9). The southern border shows continuous negative anomalies from day
+1 to day +5, and the largest departure of -5.5×107 kgs-1
occurs on day +4. The anomalies during days +2 to +5 are
significant at the 0.05 level and have a mean of -4.4×107 kgs-1.
Thus, the negative transport crossing the southern border implies
an enhanced outflow, and is largely responsible for the net loss of the
column water vapor during the holiday.
The water vapor transport over this large area should be closely related to the regional
atmospheric circulation. Previous studies have emphasized the importance of anomalous
atmospheric circulation over the western Pacific in modulating precipitation over
southern China by influencing moisture transport and convergence (e.g., He et al., 2007;
Li et al., 2013; among others). To elaborate on the details of the regional atmospheric
circulation around the New Year holiday, we investigated the tropospheric horizontal
winds over eastern Asia. The vertical mean horizontal winds averaged from 1000 to
700 hPa are plotted in Fig. 9b. Over southeastern China, there are clearly
dominant northerly wind anomalies. The northerly winds flow toward the east over the
western Pacific, south of approximately 25∘ N, and then the winds turn
northward. Consistent with this cyclonic circulation anomaly, eastern China and the
neighboring western Pacific experience remarkable reductions in the column water vapor.
This pattern remains stable when considering each pressure level in the mid-troposphere.
For example, at the 850 hPa level, the anomalous cyclone is more evident, and the
strong northerly winds over southeastern China are greater than the vertical means
(Fig. 9c). Interestingly, the precipitation reduction in southern China is well captured
in the ERA-Interim reanalysis, and such reduction is physically consistent with the
large-scale atmospheric circulation change. We repeated the composite analysis of the
atmospheric circulation using the NCEP/NCAR Reanalysis I and NCEP–DOE Reanalysis II
datasets and found a similar pattern (figures not shown). Employing the same ERA-Interim
data but a shorter data length (2001–2012), Gong et al. (2014) also reported a similarly
anomalous circulation pattern over East Asia around the Chinese New Year. This anomalous
cyclone is likely a robust signal of the holiday weather. Generally, this anomalous
cyclone plays a dominant role in bringing stronger northerly wind, causing less humidity
and lower water vapor, which finally results in less precipitation over southeastern
China.
The mean horizontal wind anomalies at 1000–500 hPa (in vectors with
units of ms-1) and the 1000–500 hPa thickness anomalies (in color
shading) during days [-4, 0]. Only significant (at the 0.1 level) winds and thickness
are plotted. The long-term mean horizontal winds between 1000 and 500 hPa during
days [-4, 0] are shown together as the streamlines for comparison. A red dashed
rectangle (140–145∘ E and 25–35∘ N) indicates the central location of
the anomalous cyclone, which is significant on day +5.
Discussion: time-lag correlation between the temperature and anomalous
cyclone
Note that in the observations, the holiday precipitation reduction is strongly related to
the significant drying of SH, which is caused by anomalous northerly winds. In fact, an
anomalous cyclone dominates East Asia and the western Pacific region during the CSF
holiday (Fig. 9). The anomalous northerly winds over southern China are just located in
the rear side of the anomalous cyclonic circulation. The anomalous cyclone is likely
correlated to the temperature cooling during the CSF holiday. Gong et al. (2014) reported
that during days [-4, 0], there is no anomalous cyclone over East Asia, but
simultaneously, the temperature cools significantly. These phenomena can be observed in
both the long-term (1979–2012) and short-term (2001–2012) data periods. The anomalous
cyclone appears in the troposphere after LNYD, moves eastward, and disappears after
approximately 12 days. This result is highly consistent with our analysis, as
demonstrated in Fig. 9. On days [-3, -1], the greatest temperature cooling in eastern
China was observed (cf., Gong et al., 2014, Fig. 4). As shown in Fig. 10, the
lower-middle troposphere shows no evident northerly wind anomaly over East Asia or the
western Pacific before LNYD. However, there is significant cooling in the lower-middle
troposphere, as indicated by the negative 1000–500 hPa thickness anomalies
during days [-4, 0]. The cooling center is located between 100–130∘ E and
30–40∘ N. In this case, the negative temperature anomalies can result in
anomalous cold advection due to the climatic northerly winds. This condition is helpful
for constructive baroclinic interaction between the upper and lower troposphere and
favors the midlatitude cyclone system (Hakim et al., 2003). We would expect a mature
cyclone anomaly approximately 1 week later if the cyclone develops from days
[-3, -1].
Correlations of the 500 hPa geopotential heights over the anomalous
cyclone center (140–145∘ E and 25–35∘ N) on day +5 with the
regional mean temperature over eastern (southern) China in varying lead or lag days.
We suppose that the intensity of the cyclone is correlated with the temperature cooling.
We computed the time-lag correlation between the holiday cyclone and regional mean
temperature. Because the anomalous cyclone is most robust during days [+4, +6], we
selected the corresponding center (140–145∘ E and 25–35∘ N) to
calculate a regional mean for the 500 hPa height. In eastern China, there are 394
available meteorological stations. We averaged the daily temperature anomalies of these
stations to derive a regional mean time series and then computed the correlation with the
500 hPa height on day +5. To suppress the noise and identify a stable signal,
we used a 3-day window when computing the means, i.e., the data on a specific day as well
as those of the day before and after are averaged. The results are plotted in Fig. 11;
note that a time-lag of 0 refers to day +5, a time-lag of -2 refers to day +3 and
so on. The results show that the cyclone is closely related to the temperature anomalies
that occurred approximately 5 days previously. A maximum correlation of 0.52 is observed
when temperature leads the pressure by 4 to 5 days. And it is statistically significant
at the 0.1 level. These significant positive correlations suggest that when the
temperature is lower around LNYD, the intensity of the anomalous cyclone on days [+4,
+6] over the western Pacific is likely to be stronger. Thus, the anomalous cyclone is
significantly related to the continental temperature that occurred approximately 1 week
previously. The regional temperature over southern China experiences a similar
cross-correlation with the following cyclone (Fig. 11). It should be pointed out that
these time-lag correlations should not be explained by the natural 1–2 week processes.
The natural synoptic system's occurrence and phases are random in time. Here, we prepared
the atmospheric circulation and temperature time series according to the lunar calendar
dates. If there is a natural cyclone around the CSF holiday, its random phase should be
offset by other cyclones when all years are put together. Anyway, these results
demonstrate that a stronger cyclone over the western Pacific on days [+4, +6] is
often accompanied by lower preceding temperature that occurs approximately 1 week
previously over eastern China.
CSF is a cultural event that is only related to human beings. Many studies show that air
pollution reduces significantly during the CSF holiday. For example, Tan et al. (2009)
investigated the 1994–2006 observations from 13 monitoring sites around the Taipei
metropolitan area and found that the concentrations of NOx, CO,
nonmethane hydrocarbon, SO2, and PM10 are lower during the New Year period
than during the non-New Year periods. Using three different approaches considering the
thermal power generation, satellite retrieval products, and statistical and modeling
attribution, Lin and McElroy (2011) estimated that the economic slowdown during the
celebrations of CSF is responsible for a notable reduction in anthropogenic emissions.
According to their estimations for 2005, 2007, 2008, and 2010, the CSF contributes a
NOx emission reduction of 12 %. In addition, the 2009 CSF
contributes a reduction of 10 %. These estimations are comparable to those given in
the work of Gong et al. (2014), in which they reported a PM10 reduction derived from
323 surface station measurements over eastern China and estimated the magnitude of the
PM10 concentration decrease to be approximately -9.24 % for the day -4 to
day +5 (excluding day 0 to rule out the New Year's Eve firework emissions). Aerosol is
the most likely factor affecting atmospheric physics during the CSF holiday.
In addition, it should be noted that the northerly winds are helpful in cooling the
temperature. We think that when considering the simultaneous temperature changes in
association with short-period aerosol anomalies, the atmospheric feedback would be
ignorable. However, during a moderate period (such as >3 days to 1 week), the
atmospheric feedback is likely discernible. The mechanism of how the concurrent aerosol
reduction influences temperature and further affects circulation needs more elaborate
observation and model simulation.
Summary
Briefly, the major findings of our analysis are summarized as follows.
The long-term station precipitation data from 1979 to 2012 are analyzed with a focus on
the possible changes during the CSF holiday. We found that the precipitation frequency
over southern China experiences a significant holiday reduction. The largest reduction
occurs on days [+4, +6] with a mean anomaly of -7.4 % and a minimum anomaly of
-9.4 % on day +5. At the same time, the daily precipitation amounts from day +2
to day +5 show continuous negative anomalies, and the mean is
-0.62 mmday-1. The Monte Carlo test implies that the holiday-related
frequency and amount anomalies are significantly different from random occurrences. The
spatial distributions of the mean frequency anomalies for days [+4, +6] show a clear
reduction across southern China. Among the 155 stations, negative anomalies are observed
at 148 stations.
The holiday precipitation anomalies are strongly linked to the relative humidity and
cloud cover. The station ΔRH shows an evident decrease from day +2 to +7, and
the lowest anomalies appear on days [+4, +6], with a mean of -3.9 %. When all
precipitation days are excluded, the ΔRH shows similar decreases, where the lowest
values also occur on days [+4, +6], with anomalies of -0.97 %, -0.69 %,
and -0.38 %, respectively. The ΔRH vertical profile demonstrates the
significant drying under approximately 800 hPa. The ERA-Interim reanalysis data
reveal that the negative anomaly at 850 hPa is -3.9 % and that at
1000 hPa is -4.2 %. The mean ΔRH of the layers between 850 and
1000 hPa is -3.9 %. The negative anomalies in the lower troposphere are
consistent with the significant decreases in the LCC. The daytime station LCC shows an
evident reduction during days [+4, +6], with a mean of -6.1 %. Meanwhile, the
daily ERA-Interim LCC also displays a notable reduction during days [+4, +6]. The
corresponding mean is -5.0 %, and a minimum of -5.9 % appears on day +5.
This magnitude as well as its spatial distribution is comparable to that of the
station-based daytime LCC anomaly.
The anomalous relative humidity is mainly caused by the drying of the water vapor in the
lower-middle troposphere over southern China during the holiday. Evident negative SH
anomalies persist from days -3 to +7 in the station observations. The lowest values
appear on days [+4, +5] approximately. The average ΔSH for days [+4, +6]
is -0.73 gkg-1. When the precipitation days are excluded, the ΔSH
shows a similar continuous reduction after LNYD. A minimum of -0.54 gkg-1
appears on day +4, while the mean for days [+4, +6] is -0.46 gkg-1.
Significant water vapor drying is observed for the entire lower troposphere below
700 hPa. Below 800 hPa, the ΔSH values are all less than
-0.50 gkg-1. The mean value between 800 and 1000 hPa is
-0.70 gkg-1. The water vapor reduction in the lower-middle troposphere
likely plays the dominant role in causing anomalous relative humidity, low-level cloud,
and precipitation. This water vapor deficit results from the anomalous meridional
horizontal moisture transport. During the holiday, a large-scale cyclonic circulation
appears over the western Pacific, which brings anomalous northerly winds to East Asia and
leads to the negative water vapor flux in the troposphere.
We calculated the correlations between holiday temperature and anomalous cyclone.
Cross-correlation demonstrates that approximately 1 week after a lower temperature over
eastern China, a stronger cyclone is observed over the western Pacific. The cooling is
likely to lead to anomalous cold advection, which provides a favorable condition for the
midlatitude cyclone system. The cyclone brings anomalous northerly wind to East Asia,
reduces the atmospheric SH, and consequently results in less precipitation over southern
China. The mechanisms need further clarification by elaborate observation and numerical
modeling.