GEOPHYSICAL RESEARCH LETTERS, VOL. 38, LXXXXX, doi:10.1029/2011GL047235, 2011
Pollution from China increases cloud droplet number,
2 suppresses rain over the East China Sea
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Ralf Bennartz,1 Jiwen Fan,2 John Rausch,1 L. Ruby Leung,2 and Andrew K. Heidinger3
4 Received 22 February 2011; revised 27 March 2011; accepted 3 April 2011; published XX Month 2011.
[1] Rapid economic growth over the last 30 years in China
has led to a significant increase in aerosol loading, which is
mainly due to the increased emissions of its precursors such
as SO2 and NOx. Here we show that these changes significantly affect wintertime clouds and precipitation over the
East China Sea downwind of major emission sources. Satellite observations show an increase of cloud droplet number
concentration from less than 200 cm−3 in the 1980s to more
than 300 cm−3 in 2005. In the same time period, precipitation frequency reported by voluntary ship observers was
reduced from more than 30% to less than 20% of the time.
A back trajectory analysis showed the pollution in the investigation area to originate from the Shanghai‐Nanjing and
Jinan industrial areas. A model sensitivity study was performed, isolating the effects of changes in emissions of
the aerosol precursors SO2 and NOx on clouds and precipitation using a state‐of‐the‐art mesocale model including
chemistry and aerosol indirect effects. Similar changes in
cloud droplet number concentration over the East China
Sea were obtained when the current industrial emissions in
China were reduced to the 1980s levels. Simulated changes
in precipitation were somewhat smaller than the observed
changes but still significant. Citation: Bennartz, R., J. Fan,
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J. Rausch, L. R. Leung, and A. K. Heidinger (2011), Pollution from
China increases cloud droplet number, suppresses rain over the
East China Sea, Geophys. Res. Lett., 38, LXXXXX, doi:10.1029/
2011GL047235.
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1. Introduction
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[2] Increased air pollution levels in China and downwind
have received great attention over the last decades. The
effects of air pollution on solar radiation budget at the surface are quite significant and straightforward to observe
[Nakajima et al., 2003]. The impact of rising pollution levels
on clouds and precipitation is equally important but much
harder to assess [Rosenfeld et al., 2008; Stevens and Feingold,
2009]. Lumped together in the term ‘aerosol indirect effects’
various suggested mechanisms of aerosol‐cloud‐precipitation
interactions constitute the largest uncertainties in current estimates of global anthropogenic climate forcing [Forster et al.,
2007]. Previous studies linking aerosols with clouds and precipitation were often focusing on subtropical stratocumulus
1
Atmospheric and Oceanic Sciences Department, University of
Wisconsin‐Madison, Madison, Wisconsin, USA.
2
Pacific Northwest National Laboratory, Richland, Washington,
USA.
3
Center for Satellite Applications and Research, NESDIS, NOAA,
Camp Springs, Maryland, USA.
Copyright 2011 by the American Geophysical Union.
0094‐8276/11/2011GL047235
cloud regimes off the west coast of the continents. Changes in
those stratocumulus regimes have important climatic implications. However, those cloud systems do not produce much
precipitation, and cover only small fractions of the globe.
Other studies have inferred the potential influence of aerosols on precipitation in other climate regimes [Qian et al.,
2009; Rosenfeld and Givati, 2006], but quantitative relationships between changes in clouds and precipitation and aerosol
source regions have not yet been firmly established [Stevens
and Feingold, 2009].
[3] Here we study the impact of pollution on clouds and
precipitation up to more than 1000 km downwind of the
sources of pollution over the East China Sea. During winter,
cold high passages occur over China approximately once
every 4 days. As the high pressure moves to the East China
Sea, it undergoes a significant transformation due to the
strong sensible and latent heat fluxes from the warm ocean
surface. Active cumulus convection develops and clouds
form in the moist and unstable stratification above the sea
surface [Ding and Krishnamurti, 1987]. Rapid cyclogenesis
can also occur in the baroclinic zone as cold air moves over
the warm East China Sea and the Kuroshio Current, leading
to explosive cyclogenesis and large amounts of precipitation
[Hanson and Long, 1985]. How aerosols affect cloud and
precipitation in these varied cloud regimes is not clear. We
evaluate a 30‐year time series of observations of liquid
cloud properties and precipitation. Satellite observations allow
for an estimation of cloud droplet number concentration
(CDNC) [Bennartz, 2007; Brenguier et al., 2000], a key
variable in determining the impact of pollution on cloud
microphysics. Precipitation frequency is studied based on voluntary ship observer reports [Kent et al., 2007]. The strong
correlative evidence of indirect aerosol effects found in the
observational datasets is substantiated with a modeling study
suggesting a causal relation between changes in air pollution
and subsequent changes in clouds and precipitation.
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2. Observed Changes in Cloud Droplet Number
Concentration and Precipitation
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[4] Figure 1a, 1b, and 1e show the wintertime cloud droplet
number concentration (CDNC) derived from Patmos‐x
satellite observations. The dataset used spans the winters
between 9/1981 and 9/2009 with two shorter time periods
excluded for technical reasons (low solar zenith angle and
inconsistencies in instrument channel settings, respectively).
For a detailed discussion on the dataset, see the auxiliary
material as well as Rausch et al. [2010].1 Between 1982
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1
Auxiliary materials are available in the HTML. doi:10.1029/
2011GL047235.
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Figure 1. (a) The PATMOS‐x derived CDNC averaged over five winter seasons (1982–1986). (b) Same as Figure 1a but
for the winter seasons 2004–2009. (c and d) The frequency of occurrence of all liquid rain events reported by ship observers
for the same winter seasons as in Figures 1a and 1b. The rectangular box in Figures 1a–1d highlights the investigation area.
(e) Monthly mean CDNC averaged over the investigation area for December, January, and February 1982–2009. (f) The
time series of winter liquid rain frequency. The black solid lines in Figures 1e and 1f correspond to the long‐term mean
value.
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and 2009 CDNC in the investigation area have increased
in the investigation area with an average rate of increase of
+42 cm−3/10 yrs (linear regression slope). Values in the early
1980s are on the order of 200 cm−3 while in the late 2000s
values are in excess of 300 cm−3. These trends are statistically significant at the 99%+ level (Two‐sided Student’s
t‐test) and also clearly exceed what can be attributed to satellite drifts and inter‐calibration issues [Rausch et al., 2010].
Are the trends observed in CDNC correlated with trends in
precipitation? If so, is the increase in CDNC causing changes
in precipitation? Arguments can be made for both questions to be answered in the affirmative. Qian et al. [2009]
have made a case for pollution suppressing light rain over
China. The combined modeling and observational analysis
performed in their paper suggested that indeed elevated CDNC
values caused by pollution inhibit collision‐coalescence processes in warm rain. Using TRMM observations and observed
aerosol distributions Berg et al. [2006] show that elevated
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BENNARTZ ET AL.: POLLUTION OVER EAST CHINA SEA
aerosol concentrations might lead to reduced collision‐
coalescence over the East China Sea as well. A similar analysis by Jin and Shepherd [2008] revealed strong effects of
pollution on clouds over the East China Sea. Addressing the
issue from a cloud microphysical standpoint, various observational and modeling studies use combinations of cloud
droplet number concentration and liquid water path to establish drizzle rates for warm cloud fields [Bennartz, 2007;
Comstock et al., 2004; Geoffroy et al., 2008; Pawlowska and
Brenguier, 2003; vanZanten et al., 2005]. For light warm
rain, these studies find roughly an inverse relation between
CDNC and light rain (drizzle) rate. Extending this 1/CDNC‐
scaling argument to our case one would predict a decrease
in warm rain by roughly 30% (relative) between 1980 and
2010, inversely proportional to an increase in CDNC from
200 cm−3 around 1980 to 300 cm−3 around 2010 (see Figure 1).
This back‐of‐the‐envelope calculation would suggest a
decrease in light precipitation dominated by warm cloud
processes.
[5] Actual precipitation trends are difficult to assess, especially over the oceans, where conventional data are sparse.
Satellite‐based precipitation estimates are currently not an
option. Existing passive microwave climatologies, e.g., from
Special Sensor Microwave/Imager (SSM/I), are not suitable
for cloud‐aerosol interaction studies because of technical
issues associated with the separation of rain from cloud
liquid water. By design this separation eliminates potential
sensitivities [Berg et al., 2006; O’Dell et al., 2008; Seethala
and Horváth, 2010]. Space‐borne radar observations do not
yet provide long enough coverage (CloudSat) or are insensitive to light rain (TRMM‐PR). While trends in precipitation intensity cannot be assessed reliably, some information
about precipitation frequency is available from weather
observations made by voluntary ship observers provided via
the International Comprehensive Ocean‐Atmosphere Dataset (ICOADS) [Kent et al., 2007].
[6] Based on ICOADS, Figures 1c, 1d, and 1f show precipitation frequency plots for the years 1982–1986 and 2004–
2009. Large differences between the earlier and later years
are seen in a latitude belt between roughly 15 N and 40 N.
The average rain frequency in the investigation area is
reduced from about 30% (absolute) for 1982–1986 to about
20% (absolute) for 2004–2009, or by about 40% relative to
the long‐term mean value. A negative trend is apparent
in all three precipitation classes (see Table S3 of Text S1).
In order to eliminate spurious trends we performed a series
of tests, the results of which are also summarized in the
auxiliary material (Table S3 of Text S1). In particular,
we address the following three issues: (1) Are the trends
statistically significant? We performed two‐sided Student
t‐tests for all trends. We find the trends in all three precipitation classes to be statistically significant at the 99% confidence level. Obviously, this statistical significance by itself
does not allow for any conclusion on the physical significance of the trend, i.e., it does not answer the question
whether the trend in observations is caused by a trend in
actual precipitation frequency. (2) Might the trends then be
caused by outliers toward the beginning or the end of the
time series? As noted earlier, the ICOADS time series is
only quality controlled until 2007. Also, from Figure 1f, it is
apparent that for the years 1982–1985 precipitation frequency
appears to be exceptionally high. We therefore re‐calculated
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the trends for the years 1986–2007 only. The resulting
trends for the intermediate and low rain classes were similar
to those found for the entire period. The trends in light
and intermediate rain remain high at −16.7%/10 yrs and
−17.4%/10 yrs and at confidence levels of higher than 99%.
Only the trend in heavy precipitation is reduced significantly and the confidence level drops to 66%. (3) Could the
trends perhaps be introduced by changes in observer habits,
reporting differences, or postprocessing of the ICOADS data?
Changes in observer routine have been reported in earlier
studies [Petty, 1995]. If trends were caused by such changes,
we would expect the trends to appear in a similar manner
globally. To test this hypothesis, we selected a reference
area in the South Pacific where large changes in aerosol
load are not to be expected and CDNC is virtually constant
over time [Rausch et al., 2010]. For the South Pacific reference area we do not find any statistically significant negative trend although an apparent positive trend in intermediate
rain is found for the shorter time interval (1986–2007). As
a result of these tests we might cautiously conclude that
the large trends in light and intermediate precipitation in
winter over the China Sea are possibly physical. Given there
are no similar trends identified in the reference area systematic changes caused by observer habits and/or changes
in data processing do not appear to be causing the trends in
precipitation.
[7] Obviously, there are various possible candidate physical explanations including the cloud‐aerosol interaction
hypothesis, but possibly also correlations with changes in
the Asian Monsoon, or changes in large‐scale circulation
patterns. While changes in large scale flow cannot be completely ruled out, recent work [Li and Yang, 2010; S. Yang,
personal communication, 2011] suggests there are no significant trends in the Asian Winter Monsoon. Our own analysis of wind patterns based on NCEP reanalysis supports
this assessment (not shown). In wintertime, the atmospheric
flow over China and the East China Sea is dominated by a
very stable high pressure system over northern Asia inducing a strong near‐zonal continental outflow in the middle
atmosphere and a northwesterly boundary layer wind. This
flow pattern provides an effective mechanism for transporting pollution originating in the large industrial areas
onto the East China Sea. We use a back‐trajectory model
[Draxler, 1999] in combination with a SO2 emission inventory [Ohara et al., 2007] to determine the source regions of
pollution reaching the investigation area. To further disentangle the effects of local pollution from changes in, e.g.,
large‐scale circulation patterns and other possible sources of
trends, we have performed a modeling sensitivity study
using WRF‐Chem (see auxiliary material for details on the
model setup).
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3. Airmass Origin and Source Regions
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[8] Back trajectory origins and SO2 source regions are
given in Figure 2. For each back trajectory, the uptake is
calculated as the product of SO2 emission from the REAS
database multiplied by uptake efficiency (function of height
for each back trajectory) and then summed over all back
trajectories that originate in the grid box. The Shanghai‐
Nanjing and Jinan industrial areas can be clearly identified
as hot spots for emissions transported into the investigation
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Figure 2. (a) The relative density of origins of back‐trajectories that end in the investigation area after 48 hours. (b) The
SO2 uptake derived from 48 h back‐trajectory origins and the SO2 emission database. Values in Figures 2a and 2b are normalized to a peak value of one. (c) Comparison of the total China SO2 emissions (green curve), the 48 h SO2 uptake (red
curve) with rain frequency (black curve, same as in Figure 1e) and CDNC−1 (blue curve). All values in Figure 2c are
normalized to zero mean and unit standard deviation. For visualization purposes the SO2 uptake (red curve) and total China
emission (green curve) are multiplied by −1.
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area within 48 hours. Figure 2c shows time series of all‐
China SO2 emissions, back‐trajectory SO2 uptake for the
investigation area at 48 hours lead‐time, ICOADS rain
frequency and 1/CDNC. All four curves are normalized to
zero mean and unit standard deviation. In addition the SO2
emissions and uptake are multiplied by −1 in order to compare slopes and variability to the other two datasets. Correlation between Chinese SO2 emissions and 1/CDNC (rain
frequency) is 0.83 (0.80). Correlations for SO2 uptake for
individual back‐trajectories lead times peaked at 48 hours
with values of 0.72 (0.70) for 1/CDNC (rain frequency).
Correlation between rain frequency and 1/CDNC is 0.85.
All correlations were found to be significant at the 99.9% or
higher level.
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4. Modeling Results
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[9] The observational results presented here suggest a
strong impact of pollutant emissions on cloud properties and
precipitation frequency over the East China Sea. In a modeling study using a state‐of‐the‐art mesoscale meteorological and chemical model, WRF‐Chem, we investigated the
causality of the relation between changes in mainland China
pollutant emissions (see the auxiliary material for details)
and changes in downwind cloud and precipitation properties
by isolating the impact of SO2. A two month simulation was
performed with current day emissions (‘POLLUTED’ run,
see auxiliary material) and emissions scaled back to the
1980s (‘CLEAN’ run, see auxiliary material). Everything
else, in particular the meteorological conditions, remained
unchanged between the two runs. The model results in the
change of CDNC are in good agreement with the observations spatially and on average as shown in Figure 3. The
average CDNC increased from 208 cm−3 to 295 cm−3
between the CLEAN and POLLUTED case. A reduction in
precipitation frequency from CLEAN to POLLUTED is simulated as well but less pronounced than the observed decreases
in precipitation frequency. The absolute reduction in precipitation of 5% between the CLEAN and POLLUTED experiments is about half of the observed change. The resulting
relative changes show a decrease in the order of one third
of the observations. There might be various reasons for the
differences between observed and simulated precipitation
response, including the aforementioned uncertainties in the
observational datasets, unaccounted changes in precipitation
response to factors other than CDNC, possible re‐routing of
ship traffic away from (heavy) precipitation in the later years
(due to improved forecasting capabilities), the coarse scale
of the simulations, and shortcomings in the representation of
precipitation and aerosol indirect effects in general.
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5. Conclusions
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[10] The long‐term observations studied here imply a
strong impact of pollution from China on clouds and precipitation over large parts of the East China Sea during
winter causing increases in CDNC and a reduction in precipitation frequency. With the more widespread use of filter
technology in energy generation the observed effects would
be expected to become weaker in subsequent years. Continuing satellite observations will allow monitoring of these
changes. Marked by a convectively mixed layer and frequent cyclogenesis producing clouds and precipitation, the
East China Sea might serve as a natural testbed, in contrast
to the more extensively studied stratocumulus regimes, for
assessing the ability of global climate models to reproduce
aerosol indirect effects, which constitute a major uncertainty
in our current understanding of anthropogenic climate forc-
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Figure 3. The spatial distribution of CDNC averaged over the two months of WRF‐CHEM simulations for (a) CLEAN
and (b) POLLUTED. (c) The corresponding time series of CDNC averaged over the investigation area (black box in
Figures 3a and 3b).
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ing. The observed response of the clouds and precipitation
to changes in pollution might also help assess the validity
and potential environmental consequences of geoengineering approaches that alter cloud albedo by providing additional cloud condensation nuclei to reduce surface warming.
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[11] Acknowledgments. This project was partly supported by NASA
grant NNX08AF92G to the first author and the bilateral agreement between
Department of Energy and China Ministry of Sciences and Technology
that supports regional climate research at PNNL. PNNL is operated for
the US Department of Energy by Battelle Memorial Institute under contract
DE‐AC06‐76RLO1830. We thank Chun Zhao and Yi Gao at PNNL for
providing the emission data used in our model simulations and Andi
Walther of UW‐Madison/CIMSS for his help in processing the satellite
observations. The views and opinions contained in this report are those
of the authors and should not be construed as an official National Oceanic
and Atmospheric Administration or U.S. Government position, policy,
or decision.
[12] The Editor thanks two anonymous reviewers for their assistance in
evaluating this paper.
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R. Bennartz and J. Rausch, Atmospheric and Oceanic Sciences
Department, University of Wisconsin‐Madison, 1225 W. Dayton St.,
Madison, WI 53706, USA. (bennartz@aos.wisc.edu)
J. Fan and L. R. Leung, Pacific Northwest National Laboratory, PO Box
999, Richland, WA 99352, USA.
A. K. Heidinger, Center for Satellite Applications and Research,
NESDIS, NOAA, 5200 Auth Rd., Camp Springs, MD 20746, USA.
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414
6 of 6