The response of the Indian summer monsoon (ISM) circulation and precipitation
to Middle East dust aerosols on sub-seasonal timescales is studied using
observations and the Weather Research and Forecasting model coupled with
online chemistry (WRF-Chem). Satellite data show that the ISM rainfall in
coastal southwest India, central and northern India, and Pakistan is closely
associated with the Middle East dust aerosols. The physical mechanism behind
this dust–ISM rainfall connection is examined through ensemble simulations
with and without dust emissions. Each ensemble includes 16 members with
various physical and chemical schemes to consider the model uncertainties in
parameterizing short-wave radiation, the planetary boundary layer, and
aerosol chemical mixing rules. Experiments show that dust aerosols increase
rainfall by about 0.44 mm day
Aerosols, from both natural sources (e.g., mineral dust, sea salt, and volcanic eruptions) and anthropogenic emissions (e.g., black carbon and sulfate), can influence climate by scattering and absorbing solar and terrestrial radiation (the direct effect) and by serving as cloud condensation nuclei and ice nuclei and altering cloud microphysical properties (the indirect effect). The Indian summer monsoon (ISM) region and its surrounding areas have been identified as having high aerosol concentrations (e.g., Kuhlmann and Quaas, 2010) and this large aerosol loading has been increasing dramatically during the past decade due to population growth and more frequent dust activity (Hsu et al., 2012). The potential impacts of aerosols on ISM is an important issue because about one-third of the world's population rely heavily on the ISM rainfall for water supply and agricultural production.
Both observational and modeling studies suggest that local anthropogenic aerosols, especially black carbon over northern India, have major impacts on ISM through the “solar dimming effect” and the “elevated heat pump” (EHP) effect on different timescales (Ramanathan et al., 2005; Lau et al., 2006; Wang et al., 2009b; Kuhlmann and Quaas, 2010; Nigam and Bollasina, 2010; Bollasina et al., 2011; Lau and Kim, 2011; Bollasina et al., 2013). The “solar dimming effect” proposes that the anthropogenic aerosol-induced reduction of north–south land–sea thermal contrast through aerosols' surface cooling effect contributes to a weaker meridional monsoon circulation. In contrast, the EHP effect hypothesizes that the anthropogenic and desert dust aerosols stacked up on the southern slope of the Tibetan Plateau can heat the air in the mid-to-upper troposphere due to their high elevation, which in turn results in the earlier onset of the Indian summer monsoon and more precipitation during monsoon season. In addition to local anthropogenic aerosols, remote mineral dust aerosols, which dominate the aerosol concentrations in the Middle East and the Arabian Sea (AS), can play an important role in altering the ISM rainfall. Wang et al. (2009a) found that dust aerosols can absorb solar radiation in a way similar to black carbon in the ISM and nearby regions. Using CALIPSO satellite retrievals, Kuhlmann and Quaas (2010) examined the aerosol constituents and concluded that the AS has heavy dust loading, with up to 80 % of measurements identified as either dust or polluted dust during the Asian summer monsoon season. Jin et al. (2014; hereafter J2014) found that dust aerosols contribute 53 % of the total aerosol optical depth (AOD) over the AS and the Iranian Plateau (IP) during the ISM season based on aerosol reanalysis.
The above studies have documented spatiotemporal features of mineral dust in the Middle East and the ISM surrounding regions, but have not focused on the impacts of remote Middle East dust on the ISM system. Until recently, general circulation model (GCM) experiments and observational analyses have demonstrated significant impacts of remote Middle East dust aerosols on the ISM rainfall. Vinoj et al. (2014; hereafter V2014) found a positive relationship between the ISM rainfall in southern India and dust aerosols over the AS, west Asia and the Arabian Peninsula (AP) using a GCM. They proposed that dust-induced convergence over eastern North Africa and the AP by heating the atmosphere increases moisture transfer over India, which in turn modulates monsoon rainfall over south India within a week. Based on satellite-retrieved AOD and rainfall and meteorological reanalysis, J2014 proposed an AOD–ISM rainfall hypothesis based on a dust-induced EHP effect centered over the IP and extending southward to the AS. By connecting the dust and ISM, this hypothesis explains the observed positive correlation between Middle East dust aerosols and ISM rainfall. Although V2014 and J2014 proposed a similar physical mechanism for the AOD–ISM rainfall correlation, the spatial patterns of increased ISM rainfall from their studies are quite different. In V2014, the rainfall response was found only in southern and central India and mainly located in south India, with only a minor increase or even decrease in rainfall in central India. However, in J2014, increased rainfall was observed in Pakistan and all of India except for southeast India; the largest increase in rainfall was located in the Indo-Gangetic Plain (IGP) region. In addition, V2014 stated no cross-correlation between AOD and ISM rainfall within a week in observations, but J2014 found a significant cross-correlation, with its maximum occurring when AOD leads the ISM rainfall response by 13 days. Most recently, Solmon et al. (2015; hereafter S2015) studied the interaction between Middle East dust and ISM rainfall on interannual to decadal timescales using a regional climate model (RCM). They found that the dust aerosols could increase rainfall in southern India, while it decreased rainfall in central and northern India (CNI) and Pakistan during the period of 2000 to 2009. All three studies have focused on the dynamic impact of dust radiative forcing on the ISM rainfall, but their results differ or have opposite signs in terms of spatial distributions of the rainfall response in central and north India and Pakistan.
Our study uses observations and model experiments to understand the discrepancies among the above studies. We have three research questions. First, in what areas is rainfall sensitive to Middle East dust aerosols? The AOD–ISM rainfall relationship based on observations can provide a baseline for model evaluations. Secondly, how are the observed AOD–ISM interactions represented in the Weather Research and Forecasting model (Skamarock et al., 2008) coupled with online chemistry (WRF-Chem) (Grell et al., 2005), and how do the modeling uncertainties affect our conclusions? In V2014, 19 ensemble simulations were conducted during a short period (10 days). In S2015, three ensemble simulations were created by perturbing the boundary conditions. In our study, 16 pairs of ensemble simulations are conducted using a perturbed physics and chemistry ensemble (PPCE) method during the boreal summer 2008, a period with strong dust emissions. We believe that by using PPCE members, we can better capture the uncertainties in the monsoon response to dust because the AOD–ISM rainfall hypothesis is based on both chemical properties (e.g., aerosol chemical mixing rules) of dust and their impact on atmospheric physical processes (e.g., radiation and circulations) (McFiggans et al., 2006). The dust-induced impact is then examined by the ensemble mean differences. Finally, is the 13-day maximum cross-correlation found in observations in J2014 captured by WRF-Chem? This question is critical because if the AOD–ISM rainfall hypothesis is true, AOD must lead the ISM rainfall response in the model.
WRF-Chem simultaneously simulates the evolution of trace gases and aerosols and their interactions with meteorological fields. It incorporates the second-generation Regional Acid Deposition Model (RADM2) gas-phase chemical mechanism (Stockwell et al., 1997) and the Modal Aerosol Dynamics model for Europe (MADE) primary aerosol scheme (Schell et al., 2001) coupling the Secondary Organic Aerosol Model (SORGAM) aerosol scheme for simulating secondary organic aerosol formation from biogenic and anthropogenic emissions (Ackermann et al., 1998). RADM2 uses a simplified lumped molecular approach with surrogate species that classifies species based on similarity in oxidation reactivity and emissions magnitudes (Middleton et al., 1990) to represent atmospheric chemical compositions. RADM2 resolves 63 gas phase species, including 21 inorganic and 42 organic species. It also includes 21 photolysis and 124 thermal reactions to simulate the primary gas-phase chemical reactions.
The major aerosol species treated in the MADE-SORGAM aerosol scheme include
sulfate, nitrite, ammonium, soil-derived dust, organic carbon (OC), black
carbon (BC), sea salt, and water. Three overlapping modal modes are used to
represent aerosol size distribution in MADE-SORGAM: Aitken (0.01–0.1
Aerosol dynamics implemented in the MADE-SORGAM aerosol scheme include particle formation, condensational growth, coagulation, and deposition. Particles are formed by direct particle emissions and secondary formation of nucleation. Direct emissions includes biomass burning, anthropogenic emissions, soil-derived dust, sea salt, and so on. Nucleation dynamics is incorporated to consider the formation of secondary aerosols in sulfuric acid–water conditions (Kulmala et al., 1998). The growth of aerosol particle size by vapor condensation is calculated based on the rate of change of the third moment of aerosol size log-normal distribution while neglecting the Kelvin effect (Binkowski and Shankar, 1995). Because aerosol particle size is defined as dry particle radius, the condensation does not cause particle shift between modes. Coagulation caused by Brownian motion is considered in MADE. The collision of particles within one mode can form a new particle in that mode; the collision of particles from two different modes can form a new particle in the mode with larger diameter (Whitby and McMurry, 1997). Dry deposition of trace gases is calculated using dry deposition velocity, which is parameterized by an aerodynamic sub-layer and surface resistance. Dry deposition of aerosols is calculated using gravitational sedimentation velocity (Wesely, 1989), resistance due to interception (Ruijgrok et al., 1995; Zhang et al., 2001), impaction (Peters and Eiden, 1992), and Brownian motion (Binkowski and Shankar, 1995). Wet deposition of both aerosols and trace gases in-cloud and below-cloud are also treated in the model. Cloud-borne aerosols and cloud water-dissolved trace gases are assumed to be removed immediately from the atmosphere by collection of rainfall, ice, snow, and graupel. The removal rate of cloud-borne aerosols and trace gases is approximately identical to the removal rate of cloud water by precipitation, which is calculated by a microphysics scheme in the model (e.g., Lin microphysics scheme). Below-cloud scavenging of aerosols and trace gases by precipitation are also treated following Easter et al. (2004).
Each aerosol species is assigned a complex refractive index, with its real
part indicating phase velocity of scattering and its imaginary part
indicating absorption when solar radiation propagates through the
atmosphere. In the released version of WRF-Chem, mineral dust aerosols are
assigned the wavelength-independent refractive index (1.550
The Goddard Chemistry Aerosol Radiation and Transport (GOCART) dust emissions
scheme (Ginoux et al., 2001) coupled with the MADE-SORGAM aerosol scheme is
used to simulate dust emissions. In GOCART, dust emissions are calculated based
on wind speed and an erodibility map (Prospero et al., 2002; Zender et al.,
2003b) as
The anthropogenic emissions for WRF-Chem come from the mixture of the
Reanalysis of the Tropospheric chemical composition emissions inventory
(
In this study, WRF-Chem 3.5 is configured over the Middle East and the ISM
region (
All simulations cover a 104-day period from 20 May 2008 to 31 August 2008 without nudging. The first 12 days for each integration are discarded as “spin up” to reduce the impact of initial conditions, and the analysis focuses on the monsoon season from 1 June 2008 to 31 August 2008. Meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-I) global reanalysis data are prescribed as lateral and lower boundary conditions (e.g., sea surface temperature, SST) and initial conditions.
WRF-Chem provides multiple options for physical and chemical parameterizations. For physical parameterizations, we used the Noah land surface model (Chen et al., 2001), the RRTMG SW and long-wave (LW) radiation schemes, the Yonsei University (YSU) planetary boundary layer (PBL) scheme (Hong et al., 2006), and Lin's double-moment microphysical scheme (Lin et al., 1983). An updated version of the Grell–Devenyi (Grell and Devenyi, 2002) cumulus parameterization scheme is used, which includes feedback from the parameterized convection to the atmospheric RRTMG radiation scheme and the Fast-J photolysis scheme. For the chemical parameterizations, we used RADM2 gas-phase chemistry, MOSAIC-SORGAM aerosol chemistry with aqueous reactions, and the Fast-J photolysis scheme. Table 1 summarizes the model schemes used in this study.
Configuration options of WRF-Chem used in this study.
To understand how the model uncertainties and errors affect our results, additional alternative physical and chemical parameterization schemes are used to create ensemble simulations. V2014 and S2015 created ensemble members by perturbing initial and boundary conditions, respectively. In this study, two groups of simulations were designed based on the presence and absence of dust emissions. The reference group considers all aerosol forcing (including mineral dust, sea salt, biomass burning, biogenic emissions, and anthropogenic emissions; ALLF) and the perturbed group is identical to the reference group but without dust emissions (NDST). Within each group, 16 ensemble members were created using the PPCE method because we are more interested in how differences in the aerosol chemical mixing rules, aerosol diffusion in the atmospheric boundary layer, and radiation schemes may affect the simulations of the ISM rainfall and variability.
Four different aerosol chemical mixing rules are used to calculate the aerosol optical properties: volume approximation, Maxwell–Garnett approximation, exact volume, and exact Maxwell–Garnett schemes (Fast et al., 2006; Barnard et al., 2010). The volume approximation assumption calculates refractive indices based on the volume average of each aerosol species. The Maxwell–Garnett method assumes a random distribution of black carbon in spherical particles. Both of the volume and Maxwell–Garnett schemes call for the full Mie calculation only at the first time step (Ghan et al., 2001). However, the exact volume and exact Maxwell–Garnett schemes call for the full Mie calculation at each time step.
Two SW radiation schemes – RRTMG and Goddard, and two PBL schemes – YSU and
Bougeault–Lacarrère (BouLac) are employed. The sub-grid cloud
parameterization in RRTMG can simulate interactions between aerosol
radiative forcing and sub-grid clouds. YSU and BouLac represent two types of
PBL schemes – turbulent kinetic energy and first-order closure schemes (Shin
and Hong, 2011), respectively. In total, there are 32 ensemble members, which
are comprised of 2 PBL
Various schemes employed to create the ensemble members.
UV, PAR, and McICA stand for ultraviolet, photosynthetically active radiation, and the Monte Carlo Independent Column Approximation, respectively.
The Moderate-resolution Imaging Spectroradiometer (MODIS) instrument aboard
the National Aeronautics and Space Administration (NASA) Terra and Aqua
platforms is uniquely designed to observe and monitor atmospheric trace
gases, clouds, and tropospheric aerosols. MODIS provides two kinds of AOD
data, “dark target” and “deep-blue” daily and monthly AOD at the
wavelength of 550 nm with resolution of 1
The Multi-angle Imaging Spectroradiometer (MISR) instrument provides detailed aerosol properties on the global scale. MISR onboard Terra, NASA's first Earth Observing System spacecraft, is designed to improve our understanding of the regional and global impacts of different types of atmospheric particles and clouds on climate (Diner et al., 1998). With nine cameras, MISR views Earth in nine different directions, and each piece of Earth's surface below is successively imaged by all nine cameras, at each of the four wavelengths (blue, green, red, and near infrared). This specific feature of MISR can help estimate aerosol particle size and composition with unprecedented accuracy. Based on the particle size information, the aerosol's effects on climate caused by natural sources and human activities can be isolated. The swath for MISR is only 360 km, which gives MISR a longer global span time of 9 days.
The ECMWF Monitoring Atmospheric Composition and Climate (MACC)
(0.5
Four precipitation data sets are used. The Tropical Rainfall Measuring
Mission (TRMM) (0.25
ERA-I (0.5
Figure 2a–c illustrate the spatial patterns of the observed and modeled rainfall averaged for June-July-August (JJA) 2008. During the ISM season, TRMM and GPCP observed heavy rainfall in coastal southwest India (CSWI) and CNI. The WRF-Chem ensemble mean rainfall in 16 ALLF members (Fig. 2c) shows a spatial pattern quite consistent with that of TRMM and GPCP. Note that the model overestimates rainfall in CSWI and underestimates rainfall in CNI. Similar rainfall differences between model simulations and observations were also found in both RCM (e.g., Solmon et al., 2015) and GCM (e.g., Levine and Turner, 2012) studies. The underestimated rainfall in CNI can be partly attributed to the lack of representation of agricultural irrigation in the model. Intensive irrigation activities occurring during JJA over the IGP can increase local evapotranspiration, and thus increase rainfall (Douglas et al., 2009; Guimberteau et al., 2012). Figure 2d–f show the GPH and circulation at 850 hPa from reanalysis and WRF-Chem. The ISM system is featured by strong cross-equator southerly winds in the tropical Indian Ocean and southwesterly winds in the lower troposphere in the AS and the Bay of Bengal. Another ISM feature is the deeper low-pressure centered over north India and the IP. In general, the model can capture the ISM features quite well.
Left: precipitation (mm day
A reliable representation of the spatial distribution of dust concentration in the model is essential for examining dust impacts on the ISM rainfall. The modeled AOD at 300, 400, 600, and 999 nm are converted to 550 nm using the Ångström exponent and evaluated using satellite-retrieved AOD, which is usually 550 nm. By tuning the empirical proportionality in Eq. (1) against satellite AOD, the spatial pattern of modelled AOD is quite consistent with multiple satellite retrievals and aerosol reanalysis results. Figure 3 demonstrates that WRF-Chem captures the observed high dust loading in the AP, the Thar Desert, and the IP. However, the model underestimates AOD over the northern AS and overestimates AOD in the southern AP in comparison to the other four data sets. Note that MISR, MODIS Aqua, and MACC show much higher AOD over the AS than the southern AP, whereas modeled AOD shows the opposite. This discrepancy between the modeled and satellite data could be attributed to two potential contributors. First, the assumption regarding dust mass distribution on dust particle size in the MADE-SORGAM aerosol scheme is not suitable for Middle East dust aerosols. In MADE-SORGAM, only 7 % of the mass of total dust emissions are assigned to the accumulation mode, whereas the other 93 % percent goes into the coarse mode; consequently, most dust emissions are deposited quickly in the dust source regions in the AP and only very little is transported long-distance to the AS. However, in the Mineral Dust Entrainment and Deposition model (Zender et al., 2003a), 17 % of the mass of dust emissions are assigned to the accumulation mode. Note also that the underestimation of fine particles reduces dust-induced atmospheric heating because fine particles absorb 3 to 5 times the solar radiation absorbed by coarse particles (Mahowald et al., 2014) and have a longer lifetime. Secondly, the model does not adequately represent the impact of relative humidity on AOD calculations. Increased relative humidity can lead to higher AOD because more water vapor can be taken up by dust particles, an effect known as aerosol humidification (Myhre et al., 2007).
Spatial patterns of AOD (unitless) from
The observed relationship between AOD and the ISM rainfall is studied using regression analysis. Figure 4 shows the spatial patterns of AOD regressed on the area-averaged ISM rainfall in WHI using their JJA monthly anomalies during 2000 and 2013. Figure 4a shows the regressed AOD using MISR and NOAA observations. Positive anomalies of the ISM rainfall in WHI are associated with heavy aerosol loading over the northern AS, the southern AP, and the IP. This spatial pattern of regressed AOD persists or becomes stronger in other AOD and rainfall data sets, as shown in Fig. 4b–d. Over northeastern India, dust is negatively correlated with rainfall because local dust is removed through wet deposition. This spatial pattern is consistent with the modeled atmospheric heating pattern induced by dust in Fig. 14a, which will be discussed later.
Spatial patterns of AOD regressed on area-averaged ISM
rainfall in WHI (box in
To evaluate the modeled rainfall responses, the regressed rainfall on the area-averaged AOD in the DST region is calculated based on satellite retrievals, as shown in Fig. 5. In general, the various data sets show a consistent spatial pattern. The positive response of rainfall to AOD is primarily located in the IGP, central India, and CSWI, while a weak negative response is seen in southeast India. These observed north–positive and southeast–negative correlation patterns differ from the results of V2014 (Figure 4b in V2014) and are almost opposite to those of S2015 (Figure 5b in S2015), but are very similar to our observation-based analysis in J2014 (Figure 2c in J2014). See Table 3 for a summary.
Rainfall response in various regions of India to Middle East dust in this study and others.
“
Same as Fig. 4, but for the spatial patterns of
precipitation (mm day
The regression analyses of the AOD–ISM rainfall relationship based on observations provide a baseline for evaluating the model results.
The ISM rainfall response to the Middle East dust is represented by the
ensemble mean differences in rainfall from 16 ALLF and 16 NDST simulations.
Figure 6 shows the spatial pattern of rainfall response averaged during JJA
2008. In general, rainfall increases over most of India with a magnitude of
0.44
Spatial pattern of WRF-Chem ensemble mean
differences in rainfall (mm day
Figure 7a shows the spatial correlations between the regressed rainfall pattern in Figure 5c and rainfall responses in each of the 16 ensemble pairs (ALLF minus NDST) as well as their ensemble mean. It can be seen that 15 out of 16 members show positive spatial correlations between the modeled rainfall response and regressed rainfall in observations, with a magnitude of 0.1 to 0.5, which indicates that most of the members can capture the observed spatial patterns of dust-induced rainfall changes. Interestingly, the rainfall response from the ensemble mean shows a spatial correlation of about 0.6, much larger than any other ensemble members. This indicates that the ensemble mean may cancel out and reduce model errors raised from various parameterization schemes.
The spatial correlation coefficients between the regressed rainfall
change pattern (Figure 5c) and the modeled rainfall response (Fig. 6) in
Figure 7b illustrates the centered spatial correlations between the regressed rainfall pattern in Fig. 5c and the ensemble means of rainfall responses in several subgroups of the ensemble members. Figure 7b shows a higher correlation coefficient of the regressed rainfall with the ensemble means of the ensemble members using the BouLac PBL scheme (PB8) than those using the YSU scheme (PB1). A higher correlation coefficient is also found when using the RRTMG SW radiation scheme (SW4) than using the Goddard scheme (SW2). However, those correlation coefficients using the different aerosol chemical mixing rules show very few differences.
Figure 8 shows the scatter plot of the area-averaged rainfall in WHI and CNI
in each ALLF ensemble member (
Scatter plot of area-averaged rainfall (mm day
Figure 9 shows the time series of WRF-Chem simulated daily rainfall in each ensemble member and the ensemble mean of rainfall response and AOD in ALLF simulations. In general, the model can capture the temporal variation of rainfall in July and August, with two peaks in early July and the first half of August in both the model runs and observations. The model has a notable low bias in June, which is larger in CNI than in WHI. This low bias could be attributed to irrigation, which occurs in spring and summer with maximum irrigation in May and June in IGP (Douglas et al., 2009). Ensemble means underestimate mean rainfall by one-third in NDST members during JJA 2008 in both WHI and CNI, as shown in Fig. 9. However, the ensemble spread (shadings in light blue and red in Fig. 9a and b) do overestimate rainfall during specific days in WHI and CNI. The daily ensemble mean of rainfall response is shown in Figures 9c and 9d. In both WHI and CNI, increased rainfall is illustrated in most days during the entire period. However, the rainfall response does not change significantly in CNI until late June or early July, which is due to a late monsoon onset in CNI at the end of June (Moron and Robertson, 2014). The daily ensemble mean of the modeled area-averaged AOD in DST is also shown in Figures 9c and 9d for comparison with the rainfall response.
Left: time series of rainfall (mm day
Figure 10 shows the cross-correlation between the daily ensemble mean of rainfall response in WHI and CNI and dust AOD (ALLF minus NDST) in DST. Both correlation coefficients become significant when dust AOD leads rainfall response by 10 to 11 days, generally consistent with the observed 13-day lag shown in J2014. This provides further evidence for the causal relationship between Middle East dust and ISM rainfall.
Cross-correlation coefficients between WRF-Chem
simulated rainfall responses in WHI and CNI and dust AOD (ALLF minus NDST)
in remote DST regions. All correlations are calculated based upon daily
anomalies obtained by subtracting the 21-day running mean from the daily
data. The filled markers represent that the correlation coefficients with a
95 % confidence level based on the
The direct radiative forcing of dust at all-sky conditions is calculated at
the top of the atmosphere (i.e., 50 hPa; hereafter TOA), in the atmosphere
(i.e., the atmospheric layers between TOA and the surface), and at the
surface. Figure 11 shows the ensemble mean of dust radiative forcing (ALLF
minus NDST) for SW, LW, and net (SW
Spatial patterns of dust direct radiative forcing (W m
For LW, at the TOA, Fig. 11d shows that dust causes positive radiative
forcing between 1 and 5 W m
It is obvious that the LW and SW radiative effects of dust have opposite signs, and the SW forcing has a much greater magnitude than LW forcing (Fig. 11a–f). Therefore, the dust net radiative forcing is dominated by the SW forcing (Fig. 11g–i). The area-averaged radiative forcing of dust over the entire domain is summarized in Table 4. By simple comparison of values in Table 4, we can conclude that a quarter to one-third of the SW radiative forcing is counterbalanced by LW radiative effects, which is consistent with a previous study (Huang et al., 2009).
Area-averaged direct radiative forcing of dust simulated
by WRF-Chem over the entire model domain for JJA 2008 at all-sky and cloudy
conditions. Downward radiation is defined as negative. The acronyms have the same
meaning as in Fig. 11. Units: W m
Figure 12 shows the ensemble means of cloud fraction responses (ALLF minus NDST) between various atmospheric layers to Middle East dust aerosols in JJA 2008. Cloud fraction in the entire atmospheric column (i.e., 1000–50 hPa) increases in the north Indian Ocean, Somalia, the north Arabian Sea, CSWI, northwest India, and the Bay of Bengal from a magnitude of 0.02 to 0.05 (Fig. 12a). In contrast, it decreases in magnitude in the central Arabian Sea and Sudan from 0.01 to 0.04. Figure 12b illustrates the similar spatial patterns of the cloud fraction responses in the lower troposphere (i.e., 1000–700 hPa) to those in the entire atmospheric column, but with a larger magnitude and larger significant areas in CSWI and northwest India. However, cloud fraction changes in the middle troposphere (i.e., 700–500 hPa; Fig. 12c) is very small. Figure 12d demonstrates increased magnitude of the cloud fraction in the upper troposphere (i.e., 500–200 hPa) in the western part of the north Indian Ocean, Somalia, and the Bay of Bengal from 0.02 to 0.04 (Fig. 12d). Cloud fraction responses in the stratosphere (i.e., 200–50 hPa) are similar to those in the upper troposphere (Fig. 12e).
Spatial patterns of the ensemble means of cloud fraction responses
(ALLF minus NDST; scale factor: 10
Figure 13 illustrates the radiative effects at various atmospheric layers
due to changes in cloud fraction calculated by subtracting the radiative
effects at the clear-sky conditions from those at the all-sky conditions.
The SW radiative effect at TOA decreases (Fig. 13a) in areas where cloud
fraction in the entire atmospheric column increases (Fig. 12a), which is
because more cloud can scatter more SW radiation to space. Increased SW
radiation at TOA is also seen in the central Arabian Sea and Sudan where
cloud fraction decreases. At the surface, the spatial distribution of the SW
radiative effect displays a very similar pattern to that at TOA, but with a
smaller magnitude (Fig. 13c), which results in a positive radiative effect
in the atmosphere (Fig. 13b) over the north Arabian Sea and CSWI. The LW
radiation increases at TOA (Fig. 13d) in areas where cloud fraction in the
upper troposphere or stratosphere increases, because clouds emit less LW
radiation to space than the surface due to their lower temperature. At the
surface, the LW radiation effect is determined by changes in cloud fraction in
the lower troposphere through a cloud blocking effect of LW radiation from the
surface, which decreases in the central Arabian Sea and increases in the
Indian subcontinent (Fig. 13f). Figure 13e shows the increased LW radiation
effect in the south Arabian Sea. Figure 13g–i demonstrate the net
(LW
Same as Fig. 11, but for radiative effects (W m
Dust aerosols can change large-scale circulations through their surface cooling effects and atmospheric warming effects. The resultant circulation change depends on the net effect of the two.
Figure 14a illustrates the spatial pattern of the ensemble mean thickness differences in ALLF and NDST simulations between 800 and 500 hPa averaged for JJA 2008. According to the hypsometric equation, the thickness between two isobaric surfaces is proportional to the mean temperature of the layer. Figure 14a illustrates the increased thickness over the AS and southeast AP from a magnitude of 15 to 25 m. The spatial pattern of the thickness differences generally follows the AOD spatial pattern (Fig. 3e). Figure 14b shows the vertical profiles of area-averaged atmospheric heating sources at all-sky conditions in DST. LW radiative forcing and sensible heating contribute to the atmospheric cooling from the surface to about 600 hPa. The SW radiative forcing is the only source of atmospheric warming effect, which is strongest near 950 hPa and diminishes to zero near 400 hPa. The latent heat shows few changes. Net atmospheric heating, which is the sum of LW, SW, sensible heat, and latent heat, demonstrates the atmospheric heating effect of dust from the near surface to 400 hPa except for an anomalous cooling effect at 900 hPa, which is caused by cloud effects.
Dai et al. (2013) showed that the south–north ocean–land thermal contrast in the mid-upper troposphere is more important for the ISM formation than in the lower troposphere. The dust-induced atmospheric heating in the mid-upper troposphere is studied by selecting two subgroups of simulations with “wet” and “dry” rainfall responses in the WHI region. The ensemble members in the “wet” and “dry” are selected based on the rainfall responses in Fig. 8a, and include ensemble members of 1, 5, 12, and 14, and 2, 4, 6, and 8, respectively. Figure 15 shows the temperature responses at 700 and 500 hPa in the “wet” and “dry” subgroups. The dust-induced atmospheric heating at 700 hPa is strong over the Iranian Plateau and the Arabian Sea in the “wet” members, which is consistent with the observational results in Jin et al. (2014), but the “dry” members show very weak heating or even a cooling effect. Furthermore, the dust-induced high-level (i.e., 500 hPa) heating, also known as the “EHP” effect, has a larger spatial coverage than the low-level (i.e., 700 hPa) heating and is mainly located in the Iranian Plateau rather than the Tibetan Plateau, which is different from the original “EHP” hypothesis proposed by Lau et al. (2006).
Spatial patterns of WRF-Chem simulated temperature differences (ALLF
minus NDST; unit: K) in the “wet” subgroup at
Due to dust-induced atmospheric heating in the troposphere, a low-pressure system at 850 hPa can be observed (shown as contours in Fig. 16a) over the AS, shamal wind regions, the IP, and the Caspian Sea and nearby regions. A convergence region centered over the north AS and western India at 850 hPa is associated with the low-pressure system, illustrated by arrows in Fig. 16a. In this convergence area, the strengthened southwesterly winds transport more water vapor from the AS northeastward to the Indian subcontinent. When moist airflows meet the mountains in CSWI, CNI, the Tibetan Plateau, and north Pakistan, they are lifted, converged and cooled, which forms the orographic rainfall (Fig. 6). Additionally, the strengthened northwesterly winds over the AP can result in more dust emissions over the AP and transport them from the AP to the AS, thus forming a positive feedback. Figure 16b shows that dust can also modulate the atmospheric circulation in the upper troposphere, e.g., 500 hPa. There are two dust-induced convergence regions at 500 hPa: the IP and Iraq, CNI and North China.
Spatial patterns of WRF-Chem ensemble mean differences in GPH
(shading; unit: m) and winds (arrows; units: m s
By disturbing large-scale circulations, dust can also modulate the moisture
transport of ISM. Shadings in Fig. 17a show the spatial distribution of the
ensemble mean of precipitable water differences in the entire atmospheric
column in ALLF and NDST experiments during JJA 2008. Increased precipitable
water (PW) is simulated over the entire Indian subcontinent with maximum
increases of 2 mm in CSWI and IGP. The increased PW is attributed to the
strengthened moisture transport by the southwesterly winds over the AS and
southeasterly winds over the IGP, as indicated by arrows in Fig. 17a. A minor
PW increase occurs in east India around 20
The maximum moist static energy (MSE) in the sub-cloud layer has been
demonstrated to be closely related to the poleward boundary of the monsoon
circulation and rainfall (Prive and Plumb, 2007a, b). Following the method
of Wang et al. (2009), the mean MSE is calculated in the three lowest model
layers to represent the sub-cloud MSE. Figure 17b shows the spatial
distribution of the ensemble mean of MSE differences between ALLF and NDST
experiments for JJA 2008. We found increased MSE in Pakistan and India, with
a magnitude between 1 and 2 kJ kg
Spatial patterns of WRF-Chem ensemble mean differences in
Frequent dust storms develop in the boreal summer due to the strong shamal winds in the AP and IP. After long-distance transport, these dust storms can reach the AS and interact with the ISM. Using observational data sets, we found a positive correlation between the ISM rainfall and the remote Middle East dust aerosols. To disclose the physical mechanism responsible for this correlation, a regional meteorological model coupled with online chemistry, WRF-Chem, is used to examine the radiative effects of Middle East dust on ISM. The primary conclusions are drawn below.
WRF-Chem is capable of simulating the major ISM features and heavy dust loading in the Middle East and the AS during the boreal summer. The model can capture the ISM circulations quite well, e.g., the cross-equator circulation and the southwesterly winds over the AS. It also reproduces precipitation patterns quite similar to the observations, with heavy precipitation located in CSWI and IGP. The low bias of precipitation in central India is partly attributed to the lack of representation of agriculture irrigation. In addition, by tuning the empirical proportionality constant in the GOCART dust emissions scheme against satellite observations, the model can capture the main spatiotemporal features of dust aerosols in the Middle East and its surrounding regions.
Satellite retrievals show that AOD in DST is positively correlated with the
ISM rainfall in CSWI, CNI, and Pakistan. This correlation is examined here
by using WRF-Chem. Two groups of experiments with the presence and absence
of dust emissions are designed to isolate dust impacts on ISM rainfall.
Ensemble model results based on PPCE show that mineral dust increases the
ISM rainfall by 0.44
Cross-correlation analysis of the modeled daily dust AOD in DST and the ISM
rainfall response in WHI and CNI shows a maximum cross-correlation when dust
AOD leads rainfall response by 11 days (Fig. 10). This finding is very
similar to the 13 days found in observational data sets by J2014. Note that
the model experiments here have separated the rainfall response due to dust
for the correlation analysis, while observations include various forcings
and responses. Considering that a typical dust event usually lasts 3 to 5
days, and the time for water vapor to be transported from the equator to CNI
is about a week, assuming a wind speed of 10 m s
Dust-induced ISM rainfall increase can be explained by the dynamic impacts of dust radiative forcing on water vapor transport from the AS to the ISM region. By absorbing solar radiation in the atmosphere (Fig. 11h), dust heats the lower troposphere (800–500 hPa) by about 1 K over the AS, the south AP, and the IP (Fig. 14). Dust-induced atmospheric heating is further enhanced by the positive radiative effect due to cloud changes, which in turn causes a low-pressure system at 850 hPa over the AS and surrounding regions that is associated with a convergence anomaly over the AS and north India (Fig. 16). The southwestern branch of the convergence anomaly transports more water vapor from the AS to the Indian subcontinent, resulting in more precipitable water in the atmospheric column (Fig. 17a). This strengthened southwesterly wind due to dust-induced heating is responsible for the dust–ISM rainfall correlation observed in both satellite data and model simulations. Furthermore, the northwestern branch of the convergence anomaly over the AP can create more dust emissions and transport these dust particles from the AP to the AS, building a positive feedback. Lastly, Middle East dust aerosols tend to increase the sub-cloud MSE in the Indian subcontinent by absorbing solar radiation. The overall chains of the physical mechanism are illustrated in a schematic diagram in Fig. 18.
Schematic diagram shows the physical mechanism for the Middle East dust–ISM rainfall connection. Red plus signs represent positive responses. The positive feedback between dust emissions, atmospheric heating, and shamal winds is seen as a cycle over the Middle East and the Arabian Sea.
Three issues warrant further discussion. First, the hypothesis of the Middle East dust–ISM rainfall connection largely relies on the dust-induced atmospheric heating, which is primarily determined by the imaginary refractive index of dust aerosols in the climate model. However, the retrieved imaginary refractive index of dust aerosols is found to span a wide range from 0.001 to 0.008 at 600 nm (e.g., Colarco et al., 2014), while only one constant value (i.e., 0.003) is used in the released version of WRF-Chem model. The uncertainties associated with the dust imaginary refractive index could add uncertainties to the rainfall responses in the model simulations. This issue will be examined in more detail in our future studies. Secondly, SST is prescribed during the 3-month simulation period. The SST in the AS has been shown to play an important role in modulating the ISM rainfall (e.g., Levine and Turner, 2012). The surface cooling effect of dust can decrease the SST, which may influence the ISM rainfall response to Middle East dust. Thirdly, the aerosol indirect effect is parameterized only in the microphysics scheme on the grid scale (i.e., stratiform rainfall) in current WRF-Chem (e.g., Lim et al., 2014), therefore experiments with a relatively coarse horizontal resolution of 54 km that cannot resolve convective clouds (typically 1–5 km wide) fails to consider the aerosol indirect effects on the ISM convective rainfall. In future studies, we suggest the use of a high-resolution RCM with grid spacing at 1–5 km coupled with an ocean model to quantify the impacts of dust-induced SST change and aerosol indirect effects on the ISM rainfall. Overall, this study highlights the thermodynamic and hydrological impacts of the remote mineral dust aerosols in the Middle East on the ISM rainfall. This dust–ISM rainfall relationship should be examined in the models participating in the fifth and future phases of the Coupled Model Intercomparison Project on interannual to decadal timescales.
Q. Jin and J. Wei designed the WRF-Chem experiments. Q. Jin carried out all the simulations with contributions from J. Wei and Z.-L. Yang. Q. Jin prepared the manuscript with contributions from all co-authors.
This research was supported by King Abdullah University of Science and Technology (KAUST). We wish to thank the Texas Advanced Computing Center for providing powerful computing resources. We also thank Patricia Bobeck and Alex Resovsky for proofreading and Lei Yin for his plotting of the 3-D elevation map in Fig. 18. Thanks to the editor and two reviewers for evaluating this paper. Edited by: A. B. Guenther