ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-8173-2018Sensitivity of atmospheric aerosol scavenging to precipitation intensity and
frequency in the context of global climate changeSensitivity of atmospheric aerosol scavengingHouPeihttps://orcid.org/0000-0002-5543-2375WuShiliangslwu@mtu.eduMcCartyJessica L.GaoYanghttps://orcid.org/0000-0001-6444-6544Atmospheric Sciences Program, Michigan Technological University,
Houghton, MI 49931, USADepartment of Geological and Mining Engineering and Sciences, Michigan
Technological University, Houghton, MI 49931, USACollege of Environmental Science and Engineering, Ocean University of
China, Qingdao, ChinaMichigan Tech Research Institute, Ann Arbor, MI 48105, USADepartment of Geography, Miami University, Oxford, OH 45056, USAShiliang Wu (slwu@mtu.edu)13June20181811817381829October201712December20177April201821May2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/8173/2018/acp-18-8173-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/8173/2018/acp-18-8173-2018.pdf
Wet deposition driven by precipitation is an important sink
for atmospheric aerosols and soluble gases. We investigate the sensitivity of
atmospheric aerosol lifetimes to precipitation intensity and frequency in the
context of global climate change. Our sensitivity model simulations, through
some simplified perturbations to precipitation in the GEOS-Chem model, show
that the removal efficiency and hence the atmospheric lifetime of aerosols
have significantly higher sensitivities to precipitation frequencies than to
precipitation intensities, indicating that the same amount of precipitation
may lead to different removal efficiencies of atmospheric aerosols. Combining
the long-term trends of precipitation patterns for various regions with the
sensitivities of atmospheric aerosol lifetimes to various precipitation
characteristics allows us to examine the potential impacts of precipitation
changes on atmospheric aerosols. Analyses based on an observational dataset
show that precipitation frequencies in some regions have decreased in the
past 14 years, which might increase the atmospheric aerosol lifetimes in
those regions. Similar analyses based on multiple reanalysis meteorological
datasets indicate that the changes of precipitation intensity and frequency
over the past 30 years can lead to perturbations in the atmospheric aerosol
lifetimes by 10 % or higher at the regional scale.
Introduction
Wet scavenging is a major removal process for aerosols and soluble trace
gases (Atlas and Giam, 1988; Radke et al., 1980). Global climate change implies significant
perturbations of precipitation, which can directly affect the wet scavenging
process. Salzmann (2016) found that the global mean precipitation did not change
significantly since 1850 with climate models, while Trenberth et al. (2007) reported that
the total precipitation amount increased over land north of 30∘ N
in the past century and decreased in the tropical region after the 1970s
based on observational data. Trenberth (2011) also noted that theoretically a warmer
climate could lead to less frequent but more intense precipitation.
The impacts of long-term changes in precipitation characteristics on air
quality have not been well studied. Most previous studies focused on the
correlation between air pollution and the total precipitation amount or
precipitation intensity (Cape et al., 2012; Pye et al., 2009; Tai et al., 2012).
For example, Dawson et al. (2007) found
a strong sensitivity of the PM2.5 (particulate matter with diameters
less than 2.5 µm) concentrations to precipitation intensity over a
large domain of the eastern United States with perturbation tests. Only a few studies
focused on precipitation frequency. Jacob and Winner (2009) noted that precipitation
frequency could be more important than precipitation intensity for air
quality because the wet scavenging process due to precipitation is very
efficient (Balkanski et al., 1993). Fang et al. (2011) projected with the Geophysical Fluid Dynamics
Laboratory chemistry–climate model (AM3) that wet deposition has a stronger
spatial correlation with precipitation frequency than intensity over the
United States in January, although they concluded that frequency has a minor effect on wet
deposition in the context of climate change. Mahowald et al. (2011) also discussed the
importance of precipitation frequency in wet deposition, based on simulations
showing a large removal rate of dust in precipitation events.
In this study, we first use GEOS-Chem, a global 3-D chemical transport model
(CTM), to examine the sensitivities of atmospheric aerosol lifetimes to
various precipitation characteristics, including the precipitation
intensity, frequency, and total amount. By isolating these precipitation
characteristics from other meteorological fields through a suite of
perturbation simulations, we are able to better understand the sensitivities
of atmospheric aerosols to various precipitation characteristics. We focus
on black carbon (BC) as a proxy for atmospheric aerosols to examine the
impacts of changes in precipitation characteristics. BC is nearly inert in
the atmosphere (Ramanathan and Carmichael, 2008), making it a good tracer for studying the transport
and deposition of atmospheric species. We also analyze the long-term trends
of the precipitation characteristics over various regions around the world,
based on the observational and reanalysis meteorological datasets for the
past decades. We then combine the long-term trends in the precipitation
patterns for various regions with the sensitivities of BC to precipitation
characteristics to quantify their potential impacts on atmospheric aerosols
in the context of global climate change.
Methods
We utilize a global 3-D CTM, GEOS-Chem (Bey et al.,
2001) version 9-02-01 (www.geos-chem.org, last access: 8 June 2018), to carry out a suite of perturbation
tests to examine the sensitivities of atmospheric aerosols to precipitation
characteristics. As a chemical transport model, the GEOS-Chem model does not
simulate meteorology prognostically; instead, it is driven by assimilated
meteorological data from the Goddard Earth Observing System (GEOS) of NASA
GMAO. We use the GEOS-5 meteorological dataset in this study. We conduct
global simulations with a horizontal resolution of 4∘ latitude by
5∘ longitude and 47 vertical layers. All the model simulations in
this study run from 1 July 2005 to 1 January 2007, i.e., for one and half
years, with the first half year serving as the model spin-up.
The wet deposition scheme in GEOS-Chem includes scavenging in convective
updrafts, in-cloud scavenging (rainout), and below-cloud scavenging
(washout), which were described in detail by Liu et al. (2001) and Wang et al. (2011). In
the GEOS-Chem simulation, the BC aerosols are classified into two types based on
their hygroscopicity (hydrophobic vs. hydrophilic), and wet scavenging is
more efficient for hydrophilic BC. GEOS-Chem assumes the ratio between
hydrophobic and hydrophilic BC to be 4 : 1 in fresh emissions, and hydrophobic
BC converts to hydrophilic BC with an e-folding lifetime of 1.15 days.
The washout rate constant (k) is affected by the particle size and the
form of precipitation. For washout by rain with a precipitation rate P
(mm h-1), k=1.1×10-3P0.61 for the accumulation mode (aerosols
with diameters between 0.04 and 2.5 µm) and k=0.92P0.79
for the coarse mode (aerosols with diameter between 2.5 and 16 µm);
for washout by snow with precipitation rate P, k=2.8×10-2P0.96 for the accumulation mode and k=1.57P0.96 for the coarse
mode (Feng, 2007, 2009). The coefficients for the accumulation mode are used in
calculating k for fine particles including BC in GEOS-Chem.
Our study focuses on three precipitation characteristics: the precipitation
intensity, frequency, and total amount. We define precipitation events as
the data points with “significant” (we use a precipitation rate of more than 1 mm day-1
as the criterion in this study) precipitation. Precipitation
intensity is the average precipitation rate of precipitation events, with a
unit of mm day-1. Precipitation frequency is the fraction of precipitation
events during the study period (i.e., the probability of any given data
points with more than 1 mm day-1 precipitation rate), which is dimensionless.
Total precipitation amount is defined as the average amount of precipitation
rate during the study period, with a unit of mm day-1. Assuming that
precipitation is negligible on data points with no precipitation events,
we would have
totalprecipitationamount≅precipitationintensity⋅precipitationfrequency.
For sensitivity tests focused on precipitation intensity, we scale the base
GEOS-5 precipitation values from the control run by a uniform factor for
each grid box. For the sensitivity tests focused on precipitation frequency,
we use a stochastic function to turn off the precipitation at a given data
point. For example, in a simulation where we reduce the precipitation
frequency by 25 %, for a data point i,j,t, we modify
the initial precipitation rate P0i,j,t to
Pi,j,t=P0i,j,t;Ri,j,t≥0.250;Ri,j,t<0.25,
where R is a random function with a range of (0, 1). In this way, we
decrease the precipitation frequency of each grid box to 75 % of its base
value across the whole study domain and keep the base spatiotemporal
precipitation patterns over each specific region.
Impacts of the precipitation characteristics on the atmospheric
lifetime of BC under a given (a) constant precipitation frequency,
(b) constant precipitation intensity, and (c) constant
precipitation amount. The top x axis reflects the precipitation frequency
set in each perturbation test, shown as fractions of base precipitation
frequency. Base precipitation frequency is the precipitation frequency used
in the control case. Similarly, the bottom x axis reflects the settings of
precipitation intensity in the perturbation tests. The box plot shows the
probability distribution of BC lifetime for each case, where the top and
bottom edges of each box show the third and first quartiles, respectively;
the green central bar shows the median; the whisker shows the range of the
non-outliers that cover 99.3 % of the data, assuming normally distributed
data; and the red plus shows the outliers.
Model-calculated BC atmospheric lifetime as a function of
precipitation intensity and frequency. The dashed contour lines indicate the
atmospheric lifetimes of the black carbon aerosols from the interpolation of
20 cases, which show the potential changes of BC lifetimes from the base BC
lifetime (in the control run) driven by the changes of precipitation
intensity and frequency. The blue solid line represents a total precipitation
equal to that of the base simulation (control run). The pink solid line
indicates the conditions leading to atmospheric black carbon aerosol
lifetimes that match the base simulation (control run).
For convenience in identifying and describing all the sensitivity tests, we
name them after their precipitation frequency and intensity scaling factors.
For instance, the case f0.5i2 represents the simulation with half the base
precipitation frequency and twice the base precipitation intensity, while
the case f1i1 indicates the control simulation with a base frequency and
intensity. We carry out more than 20 sensitivity model simulations to cover
various precipitation intensities and frequencies as shown in Table 1.
Series of sensitivity model simulations carried out in this
study.
Model simulationsObjectiveCase namesConstant precipitation frequency (Fig. 1a)To study the sensitivity of BC lifetime to precipitation intensityf1i0.25,f1i0.5,f1i1,f1i2, and f1i4Constant precipitation intensity (Fig. 1b)To study the sensitivity of BC lifetime to precipitation frequencyf0.1i1,f0.25i1,f0.5i1,f0.75i1, and f1i1Constant precipitation amount (Fig. 1c)To compare the sensitivity of BC lifetime to precipitation intensityf0.1i10,f0.25i4,f0.5i2,f0.75i1.33, and f1i1and precipitation frequencyHygroscopicity of aerosols (100 % vs. 20 % BCTo examine the impacts on wet deposition from the parameterizationf1i1 and f0.75i1.33in fresh emissions are assumed to be hydrophilic)on the hygroscopicity of aerosolsAerosol size (BC aerosols are assumed to beTo examine the impacts on wet scavenging from the parameterizationf1i1 and f0.75i1.33in coarse mode vs accumulation mode)on the size of aerosolsContour of BC lifetime (Figs. 2, 4–6)To plot BC lifetime as a function of the precipitation intensityf0.25i0.5,f0.25i1,f0.25i1.33,f0.25i2,f0.25i4,and frequencyf0.5i0.5,f0.5i1,f0.5i1.33,f0.5i2,f0.5i4,f0.75i0.5,f0.75i1,f0.75i1.33,f0.75i2,f0.75i4,f1i0.5,f1i1,f1i1.33,f1i2, and f1i4
The abundance of atmospheric aerosols is determined by both the aerosol
emission rates and their atmospheric residence times, i.e., their lifetimes.
The average atmospheric lifetimes of aerosols are calculated as
lifetime=burdenremovalrate=burdendrydepositionrate+wetdepositionrate.
Therefore, more efficient wet scavenging would lead to shorter atmospheric
aerosol lifetimes.
We then examine the long-term changes in precipitation characteristics for
various regions around the world in past decades. We first analyze changes
in the precipitation between two 7-year periods (2008–2014 vs. 2001–2007)
based on an observational dataset, the 3 h Realtime Tropical Rainfall
Measuring Multi-Satellite Precipitation Analysis version 7 (TRMM3B42v7,
short for TRMM, https://pmm.nasa.gov/TRMM, last access: 8 June 2018). TRMM (3B42v7)
performs better than the previous version of satellite products
(3B42v6), though there are still problems in detecting precipitation events
with low precipitation rates (Maggioni et al., 2016). We then examine three reanalysis
datasets with longer temporal coverage (2001–2010 vs. 1981–1990): the
National Centers for Environmental Prediction (NCEP) reanalysis dataset
(Kalnay et al., 1996), the NCEP-DOE AMIP-II (NCEP2) reanalysis dataset (Kanamitsu et al., 2002), and
NASA's Modern-Era Retrospective analysis for Research and Applications
(MERRA) dataset (Rienecker et al., 2011). These datasets have different resolutions and
spatial coverage. TRMM only covers 60∘ N–60∘ S, while other datasets
cover the whole globe. The resolutions (∘ long ×∘ lat × h) for
TRMM, NCEP, NCEP2, and MERRA are 0.25 × 0.25 × 3, 2.5 × 2.5 × 6,
2.5 × 2.5 × 6, and 2.5 × 2 × 1, respectively. We regrid the TRMM dataset from 0.25 × 0.25
to 2.5 × 2.5 (∘ long ×∘ lat) to reduce the computational
cost and the relative errors at small precipitation rates (Huffman et al., 2007; Gehne et al., 2016).
By combining the resulting sensitivities of BC lifetimes to precipitation
characteristics with the results of the long-term trends in precipitation
characteristics, we then estimate the impacts of long-term changes in
precipitation characteristics on the atmospheric lifetime of BC.
Results
The global annual mean lifetime of BC is calculated at 5.29 days in our
control simulation (Fig. 1). This value is similar to the results of a
previous study, which stated that the lifetime of BC would be around 1
week (Ramanathan and Carmichael, 2008). Our result also agrees with the lifetime of 5.8 ± 1.8
days simulated by the GEOS-Chem model (Park et al., 2005) and the 5.4 days result
simulated by the ECHAM5-HAM model (Stier et al., 2005). For 13 models in AeroCom, the
lifetimes of BC from anthropogenic fossil fuel and biofuel sources are
simulated to be from 3.5 to 17.1 days, with 5.9 days as the median value
(Samset et al., 2014).
The definitions of the continental regions in this study. The
uppercase letters in the region names represent the names of their
continents: North America (NA), South America (SA), Europe (EU), Africa (AF),
Asia (AS), and Oceania (OC). The lowercase letters in the region names
represent the subregions inside the continent: northern (n), southern (s), western (w),
eastern (e), middle (m), northwestern (nw), northeastern (ne), southwestern (sw),
and southeastern (se).
The potential change of atmospheric BC aerosol lifetime driven by
the changes between the two periods (2008–2014 and 2001–2007) in
precipitation characteristics based on the TRMM meteorological dataset. The
dashed contours are the same as in Fig. 2, which indicate the atmospheric
lifetimes of the black carbon aerosols from the interpolation of 20 cases and
show the potential changes of BC lifetimes from the base BC lifetime (in the
control run) driven by the changes of precipitation intensity and frequency.
Red blocks show the changes of precipitation intensities and frequencies,
with the size of the block showing the standard error of the percentage
changes.
We first compare the results of the control run with other simulations with
the same precipitation frequency (f1i0.25,f1i0.5,f1i1,f1i2, and f1i4) to
examine the sensitivity of BC lifetime to precipitation intensity (Fig. 1a).
We find that an increase in precipitation intensity leads to decreases in
both the BC lifetime and the sensitivity of the BC lifetime to precipitation
intensity; that is, the impact of precipitation intensity on BC aerosols is
saturated when the intensity is very high, which is consistent with a
previous study (Fang et al., 2011). We then compare the control run with other
simulations with the same precipitation intensity (f0.1i1,f0.25i1,f0.5i1,
f0.75i1, and f1i1) to study the sensitivities of the BC lifetime to
precipitation frequency (Fig. 1b). Again, the BC lifetime responds
nonlinearly to the changes in precipitation frequency, and the sensitivity
decreases with increases in precipitation frequency.
When we compare the simulations with a common precipitation amount (f0.1i10,f0.25i4,f0.5i2,f0.75i1.33, and f1i1), we find that the BC lifetime
increases with increasing precipitation intensity (Fig. 1c). For example,
case f0.1i10 has an annual average BC lifetime of 7.86 days, which is much
longer than the 5.29 days of the control simulation (case f1i1). This
indicates that the sensitivity of the BC lifetime to precipitation frequency
is stronger than to the precipitation intensity.
The calculated efficiency of wet scavenging can be affected by model
parameterizations. We first examine the possible impacts on our results from
the parameterization on the hygroscopicity of aerosols. With the default
parameterization in GEOS-Chem, 20 % of the fresh BC emissions are assumed
to be hydrophilic. We set up sensitivity runs with another parameterization,
where all BC is assumed to be hydrophilic. With these two different
parameterization schemes, we examine the changes in the BC lifetime between
two scenarios (f1i1 vs. f0.75i1.33) respectively. We find that with the
default setting in GEOS-Chem, the atmospheric lifetime of BC under the
f0.75i1.33 scenario is slightly higher than the f1i1 scenario by 0.4 %. In
comparison, if all the BC is assumed to be hydrophilic, the BC lifetime
under the f0.75i1.33 scenario would be 3.6 % higher. This implies that for
hydrophilic aerosols, the sensitivity to precipitation frequency would be
even higher.
We also evaluate the impacts on wet scavenging from aerosol size with
sensitivity simulations. If we assume the aerosols to be in coarse mode, we
find that it would lead to more efficient scavenging and consequently much
shorter lifetime (compared to the default setting in GEOS-Chem that all BC
aerosols are in accumulation mode). However, there are no significant
effects on the relative sensitivities to precipitation frequency vs.
intensity – the percentage change in BC lifetime between the f1i1 and
f0.75i1.33 scenarios is very similar to the cases with parameterization for
the accumulation mode (0.3 % vs. 0.4 %). This indicates that the relative
sensitivity of the BC lifetime to precipitation frequency and precipitation
intensity is not significantly affected by the parameterization of particle
size in the wet scavenging scheme in GEOS-Chem. It is worth noting that our
model does not resolve the size of the precipitation droplet, which can also
affect the efficiency of wet scavenging.
The stronger sensitivity of the BC lifetime to precipitation frequency than
that to intensity implies that an increase in the total precipitation amount
does not necessarily lead to a decrease in the BC lifetime. This is better
illustrated in Fig. 2, which shows the BC lifetime as a function of the
precipitation intensity and frequency based on 20 cases (f0.25,f0.5,f0.75,f1 versus i0.5,i1,i1.33,i2,i4). Compared with the control scenario
(i.e., f1i1, the base precipitation intensity and frequency, as labeled by
the black star), any point in the area between the two solid curves (the
blue one shows a constant total precipitation amount, and the pink one shows
a constant BC lifetime) would have a higher total precipitation amount and a
longer BC lifetime. This indicates that, even with an increased total
precipitation, the BC lifetime (and hence the atmospheric concentrations of
BC) can still increase if the precipitation frequency decreases
significantly. This feature may help explain the decrease of the wet
deposition flux found in wetter future climate simulations, despite their
slightly increased total precipitation amounts (Xu et al., 2018).
The lifetime contour plot in Fig. 2 can be employed as a simple tool to help
us understand the impacts of long-term changes in precipitation on
atmospheric aerosols, so we also investigate the long-term trends in the
precipitation characteristics over the past decades for various regions
around the world. In considering the spatial variations of precipitation
patterns and their long-term trends, we divide the global continental
regions into multiple subcontinental areas to better resolve the spatial
variations (Fig. 3). We first carry out an analysis based on precipitation
data from the TRMM dataset. The changes in the average precipitation
intensities and frequencies between the periods of 2008–2014 and 2001–2007
for each region are shown as ratios in Fig. 4, with the width and height of
the blocks indicating the standard errors of the calculated percentage
changes in precipitation frequency and intensity, respectively. Although
these TRMM data only cover 14 years, the standard errors as shown in Fig. 4
indicate that the changes in precipitation intensity and frequency over most
regions are statistically significant. We find that during these 14 years,
the average precipitation intensity has increased over most regions, but the
average precipitation frequency has decreased over more than one-third of
the total regions including western North America (nwNA and swNA), southern
South America (sSA), western Europe (wEU), southern Africa (sAF), and
southwestern Asia (swAS). Based on the TRMM dataset, we find that almost all
(five out of six) of the regions with decreasing precipitation frequency are
expected to experience longer atmospheric aerosol lifetimes.
The potential change of atmospheric BC aerosol lifetime driven by
the changes between the two periods (2001–2010 and 1981–1990) in
precipitation characteristics based on multiple meteorological datasets:
(a) NCEP, (b) NCEP2, and (c) MERRA. The dashed
contours are the same as in Fig. 2, which indicate the atmospheric lifetimes
of the black carbon aerosols from the interpolation of 20 cases and show the
potential changes of BC lifetimes from the base BC lifetime (in the control
run) driven by the changes of precipitation intensity and frequency. Red
blocks show the changes of precipitation intensities and frequencies, with
the size of the block showing the standard error of the percentage changes.
Comparison of the contours calculated on global and regional scales:
(a) global, (b) southeastern North America (seNA), and (c) northeastern Asia (neAS). The contours indicate the atmospheric
lifetimes of the black carbon aerosols from the interpolation of 20 cases and
show the potential changes of BC lifetimes from the base BC lifetime (in the
control run) driven by the changes of precipitation intensity and frequency.
The contour calculated on the global scale is the same as Fig. 2. The seNA and
neAS regions are two most extreme cases among all regions, with the smallest and
largest sensitivities between BC lifetimes and precipitation changes.
Since the TRMM data only cover a relatively short period, we conduct similar
analyses with three reanalysis datasets (NCEP, NCEP2, and MERRA) to cover a
longer time period (2001–2010 vs. 1981–1990) (Fig. 5). We find that, similar
to the TRMM data, all the three reanalysis datasets show increasing trends
for precipitation intensity over most regions but more divergent trends for
precipitation frequency in the past decades. The NCEP data show that
precipitation frequency has decreased over about two-thirds of the total
regions, while NCEP2 and MERRA data show decreasing precipitation frequency
over one-third and one-half of the total regions, respectively. In addition,
even when the different datasets indicate the same direction for the
precipitation change over a specific region, the magnitude of the changes
may vary significantly across datasets. For example, the derived changes in
the average precipitation intensity over neNA (northeastern North America)
based on NCEP, NCEP2, and MERRA data are +8, +12, and +3 %
respectively. These variations across different data sources reflect the
significant uncertainties associated with these datasets, as reported
earlier (e.g., Trenberth and Christian, 1998; Trenberth et al., 2011;
Gehne et al., 2016).
On the other hand, previous analysis on global land-average precipitation
showed that various reanalysis datasets have similar trends and interannual
variability with other gauge- and satellite-based datasets during 2001–2010,
though the estimated trend of precipitation varies based on temporal and
spatial scales (Gehne et al., 2016). In addition, our study focuses on the changes over
continental regions, where the precipitation data in the reanalysis datasets
are found to be more reliable than over the ocean regions (Trenberth et al., 2011).
Therefore despite the uncertainties associated with each meteorological
dataset, we can use Fig. 5 to estimate the expected changes in the
atmospheric BC lifetimes for certain regions, especially for those regions
showing consistent trends across different datasets. Assuming the effects of
precipitation on wet deposition is the only factor that affects the
atmospheric BC aerosol lifetimes, all three datasets indicate that
atmospheric BC aerosol lifetimes could have decreased in the northern
regions of North America (neNA and nwNA), the northwestern and southern
regions of South America (nwSA and sSA), southern Africa (sAF), and northern
Oceania (nOC). All three meteorological datasets show increasing trends in
aerosol lifetimes over southwestern North America (swNA), middle Africa
(mAF), and southern Oceania (sOC), which imply increasing trends for the
concentrations of particulate matter (PM) over these regions, driven by
changes in precipitation. At the regional scale, precipitation changes over
the past 30 years can easily lead to perturbations in atmospheric BC
lifetimes by 10 % or higher.
We should note that there are some caveats for our idealized sensitivity
simulations. The way we reduce precipitation frequency in the model (based
on a stochastic function as discussed in Sect. 2) can be very different
from climate-driven precipitation change in the real world. The globally
uniform scaling factors applied to precipitation intensity do not account
for the spatial variations. As a consequence, the sensitivities of BC
lifetime to precipitation changes over a specific region may be different
from those shown in Fig. 2. To partly address this issue, we have
constructed some regional contour plots similar to that in Fig. 2 but based
on sensitivities of BC lifetime for those specific regions (Fig. 6).
Comparison of these regional contours with the global one indicate some
differences in the sensitivity of BC to precipitation changes, but generally
less than 3 %. In addition, to clearly demonstrate that the BC lifetime
has different sensitivities to precipitation intensity and frequency, our
sensitivity simulations cover a wide range of precipitation intensities and
frequencies. Some of these applied perturbations are significantly larger than
those induced by climate change, especially at large (such as regional or
global) scales. Therefore, simple interpolation of some results from this
study in examining the effects of climate change may introduce some
uncertainties. Our results are also affected by the limitations of the
meteorology datasets. Although the TRMM and the reanalysis datasets used in
this study represent some of the best meteorological datasets available,
each of them has their own shortcomings – the observational datasets are
more reliable, but only cover a relatively short period of 14 years; the
reanalysis datasets cover longer periods, but are less reliable due to known
issues such as the bias in moisture budget (e.g., Trenberth and Christian, 1998;
Trenberth et al., 2011; Gehne et al., 2016).
Conclusions and discussion
The efficiency of the wet scavenging of atmospheric aerosols is affected
not only by the precipitation amount but also the precipitation patterns. Our
results, based on sensitivity simulations with the GEOS-Chem model, show
that the atmospheric lifetimes of BC are more sensitive to precipitation
frequency than precipitation intensity, and as a consequence, increases in
the total precipitation amount do not always lead to a more efficient wet
scavenging of atmospheric aerosols. The sensitivities of the atmospheric
lifetimes of aerosols to the precipitation characteristics derived from our
model simulations offer a simple and convenient tool for us to better
examine the implications of long-term changes in precipitation (including
the total amounts and patterns) for atmospheric aerosols in various regions.
Analysis of satellite data (TRMM) for the past 14 years (2001–2014) reveals
that precipitation intensity has increased in most regions. On the other
hand, decreasing precipitation frequency are found in some regions such as
western North America, southern South America, western Europe, southern
Africa, and southwestern Asia. The decreases in precipitation frequency
could lead to increases in atmospheric aerosol lifetimes over these regions.
Our further analyses based on three meteorological datasets (NCEP, NCEP2,
and MERRA) for the past decades (1981–2010) show increases in precipitation
intensities over most continental regions, but significant decreases in
precipitation frequency are identified over some regions. These changes in
precipitation characteristics affect the wet deposition of aerosols and
consequently the total burdens of aerosols and their atmospheric lifetimes.
Despite the significant uncertainties associated with meteorological data,
we find that the changes in precipitation intensity and frequency over the
past 30 years could have led to perturbations in the regional atmospheric
aerosol lifetimes by 10 % or higher. Our results are consistent with
Kloster et al. (2010) and Fang et al. (2011) who reported increasing atmospheric aerosol burden due to
climate change, although their results are based on future climate change.
We also find that all three meteorological databases consistently show
that the changes in precipitation intensity and frequency over the past
decades have led to decreases in atmospheric aerosol lifetimes over the
northern regions of North America, northwestern and southern regions of
South America, southern Africa, and northern Oceania. They are also consistent in
indicating increasing trends of atmospheric aerosol lifetimes in the
southwestern region of North America, middle Africa, and southern Oceania. The
increasing trends in atmospheric aerosol lifetimes over these regions driven
by the changes in precipitation intensity and frequency in the context of
global climate change could pose challenges for the local PM air qualities.
It should be noted that the results from this work can be affected by the
parameterization in the GEOS-Chem model and have certain limitations. Our
study does not account for the impacts of precipitation on wildfires which
can emit a massive amount of aerosols including BC (Dawson et al., 2014).
Data used in this study can be provided upon request to the corresponding
author.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Chemistry-Climate
Modelling Initiative (CCMI) (ACP/AMT/ESSD/GMD inter-journal SI)”. It is not
associated with a conference.
Acknowledgements
We thank all those who have contributed to the datasets we used in this
study. The NCEP reanalysis data and NCEP-DOE AMIP-II reanalysis data were
provided by NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, and were accessed
through their website at http://www.esrl.noaa.gov/psd/ (last access: 8 June 2018). The MERRA data used
in this study were provided by the Global Modeling and Assimilation Office
(GMAO) at the NASA Goddard Space Flight Center through the NASA GES DISC
online archive. We thank Hongyu Liu and Bo Zhang for fruitful
discussions. Superior, a high performance computing cluster at Michigan
Technological University, was used to obtain the results presented in this
publication. Shiliang Wu acknowledges a sabbatical fellowship from the Ocean
University in China. This publication was made possible by a US EPA grant
(grant 83518901). Its contents are solely the responsibility of the grantee
and do not necessarily represent the official views of the US EPA.
Further, the US EPA does not endorse the purchase of any commercial
products or services mentioned in this publication.
Edited by: Peter Hess
Reviewed by: three anonymous referees
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