Tropical cyclones (TCs) are important weather systems that may become major natural disasters. Well-known factors impacting TCs are the sea surface temperature and atmospheric circulation. Aerosols can affect TC thermodynamics, precipitation, and microphysics (Rosenfeld et al., 2008; Evan et al., 2011). As cloud condensation nuclei (CCN), aerosols can contribute to cloud formation and can alter the cloud droplet number concentration and size distribution, thereby influencing microphysical processes and other hydrometeors (e.g., rain, snow, cloud ice, and graupel). Aerosols may affect the track, intensity, thermodynamic structure, microphysical processes, and precipitation of TCs by activating as CCN. Various CCN concentrations have a slight effect on TC tracks (Hazra et al., 2013; Xu et al., 2013). However, the effects of CCN on TC intensity are complex. Cotton et al. (2007) indicated that anthropogenic aerosol ingestion during the mature phase of the TC weakens TC intensity. Herbener et al. (2014) proposed that an increase in aerosol concentration in the rainband area leads to storm intensification, while Carrio and Cotton (2011) suggested that an aerosol increase in the rainband region weakens a storm. Rosenfeld et al. (2011) discovered that anthropogenic aerosols decrease TC intensity by suppressing the autoconversion of cloud water. Evan et al. (2011) stated that continental aerosols decreased vertical wind shear over the Arabian Sea, favoring TC intensification. Aerosols regulate TC microphysical processes and precipitation. An increase in aerosol concentration and activation as CCN can add to the number concentration of cloud water and decrease cloud water effective radii, thereby suppressing the autoconversion of cloud water (e.g., Li, 2004; Zhang et al., 2007; Lin et al., 2011), and weakening the rain formation (e.g., Khain et al., 2005; Rosenfeld et al., 2012). An augment in the number concentration of small cloud water would promote the transport of water to upper levels, thereby enhancing convection at the periphery of a TC (Rosenfeld and Woodley, 2003). For example, Jiang et al. (2016) found that the existence of numerous anthropogenic aerosols could increase convective precipitation at the periphery of a TC, and Khain et al. (2008b) indicated that aerosols also promote lightning there. Aerosol radiative effects may also affect TC activity. Wang et al. (2013) pointed out that light absorbing aerosols produce a greater convective available potential energy above the convection condensation level, facilitating convective motion development. Dust aerosols impact radiation, vertical wind shear, and the hydrological cycle, which are all relevant to TC activity over the Atlantic (Pan et al., 2018).

Sea-salt aerosol (SSA), which is a type of large-radius aerosol, originates from sea spray. Its production flux is a function of wind speed (Monahan et al., 1986; Gong et al., 1997a, b). TCs occur over oceans with strong wind speeds, where large amounts of SSA are emitted into the atmosphere. Studies on sea spray and TCs have focused on the influence of heat flux and momentum transport caused by sea spray on TCs (e.g., Miller et al., 1992; Li, 2004; Mueller and Veron, 2014). This paper only addresses the impacts on TCs of SSA originating from sea spray and activated as CCN, and not the impacts on TCs of the air–sea transport of momentum and heat caused by sea spray.

Compared with anthropogenic aerosols, SSAs have larger radii and higher hygroscopicity. SSAs can grow readily by collecting vapor, which supports condensation. Second, because SSA emission depends on the wind speed at the sea surface, it is at its maximum in the TC eyewall region where wind speeds are the highest. Anthropogenic aerosols mainly come from land, and influence TCs through their initial drift into the TC periphery. Sea-salt and anthropogenic aerosols influence different parts of the TCs, causing different effects. Sea-salt and anthropogenic aerosols also have substantially different properties, which may lead to different effects on TCs. Anthropogenic aerosols suppress warm cloud processes and collision efficiency. However, large-nuclei aerosols such as SSAs are able to enhance warm cloud precipitation (Johnson, 1982). Rosenfeld et al. (2002) indicated that aerosols from sea spray may aid in the collection of cloud droplets formed on pollution particles, therefore reducing air pollution. For the effects of aerosols on TCs, researchers have typically focused on the influence of anthropogenic aerosols on TCs (Rosenfeld et al., 2007; Khain et al., 2008b; Evan et al., 2011; Lin et al., 2011). The effects of SSA as CCN on TCs have been given little attention. The present work addresses the impact of SSA on TC structure, cloud microphysical processes, thermodynamic structure, and precipitation, using the fully coupled WRF-Chem (Weather Research and Forecasting with Chemistry) model (Grell et al., 2005).

We used WRF-Chem, a non-hydrostatic and fully compressible model, to couple atmospheric chemistry with atmospheric processes such as transport, cloud microphysics, and radiation. The model contains a prognostic variable of CCN concentration. The two nested domains D01 and D02 were used (Fig. 1). The horizontal resolution, time step, and grid size for domain D01 were 9 km, 45 s, and 391 × 320, respectively, and 3 km, 15 s, and 598 × 343 for domain D02. Data from D02 were used to assess the effects of SSA. All experiments began at 0000 UTC 20 September 2013 and ended at 0000 UTC 23 September 2013. The first 12 h were regarded as the spin-up time. The period from 1200 UTC 20 September to 0000 UTC 23 September was used for analysis. Initial conditions were from the NCEP-FNL 1° × 1° dataset (the NCEP Final Operational Model Global Tropospheric Analysis dataset; http://rda.ucar.edu/datasets/ds083.2). Simulated was tropical Cyclone Usagi, which traversed the northwestern Pacific Ocean in September 2013, and was the first tropical super cyclone in 2013 over the northwestern Pacific, making landfall in Guangdong Province of China. The cumulus parameterization scheme introduces more errors than the explicit cloud scheme, and the latter scheme performs better in the description of cloud microphysical processes and the representation of cloud structure. Therefore, similar to Wang et al. (2014), neither domain D01 nor D02 employed the cumulus parameterization scheme. The model used a terrain-following vertical coordinate system with 31 vertical layers and a pressure of 50 hPa at the top of the model. Twelve layers were established below a height of 1 km to better resolve the boundary layer.

Figure 1 Two nested domains with horizontal resolutions of 9 km (D01) and 3 km (D02).

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The microphysical scheme used was that of Lin (Lin et al., 1983), which predicts cloud droplet number and size by determining aerosol activation in the WRF-Chem model (Liu and Daum, 2004). The other major physical schemes are the Rapid Radiative Transfer Model for general circulation models (Mlawer et al., 1997; Iacono et al., 2000), a medium-range forecast planetary boundary-layer scheme (Hong et al., 2006), and the Noah land surface model (Chen and Dudhia, 2001; Ek et al., 2003).

In the WRF-Chem model, SSA is mainly composed of sodium chloride, which is highly hydrophilic. The production of SSA was computed by the Goddard Chemistry Aerosol Radiation and Transport model (Chin et al., 2000), and the particle size distribution was assumed as log-normal for each mode. SSAs have a broad size distribution. In the WRF-Chem model, the dry radius of SSA ranges from 0.1 to 10 microns with very low number concentrations for large radii; radii of most SSA are below 1 micron. SSAs as CCN can impact cloud droplet number concentration and cloud condensation. Radii of SSA, which vary depending on the relative humidity of the surrounding environment, were calculated by using an empirical equation proposed by Gerber (1985). SSAs occur with sea spray, and are subject to transport and dry and wet deposition. Continuous flow across the sea surface forces waves to break into spray. Because SSAs are large and very hydrophilic, they can be easily removed from the atmosphere. Their average lifetime is approximately 0.6 days (Chin et al., 2002), and their production flux is often considered as a function of the sea surface wind speed. The generation rate of sea-salt particle per increment of particle radius, per unit area of sea surface (the units are particles m^–2 s^–1 μm^–1) was proposed by Gong et al. (1997b) and expressed as

where A = 4.7(1 + Θr)C, B = (0.433 – logr)/0.433, C = –0.017r^–1.44, W₁₀ is sea surface wind speed, r is the SSA radius, Θ is a parameter to adjust for the shape of the submicron size distribution, and n is the emission coefficient, with a default value of 1.373.

To investigate the influence of SSA on a TC, we designed three simulation experiments, with Eq. (1) evaluated in the control (CTL) experiment. The emission coefficient n was set to 0.137 and 13.73 in the first (LOW) and second (HIGH) sensitivity experiment, respectively. In the analysis, we compare the results from the control experiment and those from the low and high emission scenarios. The LOW scenario has an emission coefficient of magnitude one-tenth that in the CTL, while the high emission scenario has an emission coefficient 10 times that in the CTL. To exclude the disturbance of anthropogenic aerosol effects, anthropogenic aerosols were not included in the simulations. A fixed horizontal distribution of SST was used in these simulations.

Figure 2a shows simulation results as well as observed data from a Chinese typhoon website (http://tcdata.typhoon.org.cn/zjljsjj_zlhq.html). Simulated tracks are consistent with the observed tracks, with simulated tracks lying to the south of the observed tracks (Fig. 2a). Translation speeds of simulated TCs reduce over the latter part of the simulation period. There are no apparent differences in TC tracks between the sensitivity experiments and the control experiment (CTL), indicating the lack of influence of SSA.

Figure 2 (a) Observed and simulated 3-h tracks of Tropical Cyclone Usagi, (b) temporal evolution of Usagi’s maximum surface wind speed, and (c) minimum sea level pressure. Observations are marked by black, the control experiment (CTL) by red, the low emission experiment (LOW) by blue, and the high emission experiment (HIGH) by green.

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The simulated and observed maximum surface wind speeds and minimum sea level pressure are revealed in Figs. 2b, c. The simulated TC intensity is greater than the observed intensity, possibly because of the relatively poor resolution, model setting, and simulation schemes in the initial NCEP-FNL dataset. However, the simulated evolution of TC intensity is consistent with observations. Around 1200 UTC 22 September, the TC weakened rapidly after landfall. The rate of weakening is lower in the simulations than in observations, likely because landfall is simulated to occur at a time that was later than the actual landfall. There are no distinct differences in TC intensity between the sensitivity experiments and CTL, supporting the argument that SSA has little impact on TC intensity.

To confirm the simulation results, we compare simulated regional accumulated rainfall in domain D02 with the Tropical Rainfall Measuring Mission (TRMM) dataset (Huffman et al., 2007), which has a grid resolution of 0.25° × 0.25° and time interval of 3 h. Figure 3 indicates that the simulated accumulated rainfall distribution from the CTL experiment is consistent with TRMM data, although the simulated rainfall is higher than the rainfall according to the TRMM data. This discrepancy may be the result of a number of factors. The grid resolution of the D02 domain is 3 km, while the TRMM dataset provides grid-averaged precipitation at a resolution of only 0.25°. Thus, the WRF-Chem simulations provide information on precipitation at a higher resolution. Furthermore, the WRF-Chem model employs the Lin cloud scheme, which is a bulk scheme for describing the cloud microphysical properties using a semi-empirical gamma or exponential size distribution. Moreover, the rain mixing ratio and precipitation may be overestimated in the simulations, as bulk schemes tend to produce higher cloud droplet numbers (Fan et al., 2012).

Figure 3 Accumulated precipitation (mm) from (a) TRMM satellite measurements and (b) the CTL simulation. The period ranges from 1200 UTC 20 September to 0000 UTC 23 September 2013.

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Figure 4 shows a large density of fine particulate matter (PM_2.5) in the eyewall region and a decrease of PM_2.5 concentration with altitude. A greater SSA production flux generates a higher CCN number concentration. The vertically integrated CCN number concentration is 0.72, 1.33, and 2.39× 10⁶ cm^–2 in the LOW, CTL, and HIGH simulations, respectively. The vertically integrated cloud droplet number concentration is correspondingly 2.10, 2.31, and 2.62 × 10¹⁰ cm^–2. The higher cloud droplet number concentration contributes to the condensation process, which mainly takes place in the eyewall region, but the maximum condensation is found at a height of approximately 5 km (Fig. 4d). In the LOW experiment, owing to the low SSA production, the PM_2.5 concentration is much less than that in the CTL experiment. The condensation in the LOW experiment is suppressed compared with that in the CTL (Fig. 4e). In the HIGH experiment, the SSA production is high, so the PM_2.5 concentration is substantially greater than that in the CTL experiment, which strengthens the condensation and increases the cloud droplet number concentration.

Figure 4 Height–radius cross-sections of temporally and azimuthally averaged (a–c) PM2.5 concentration and (d–f) condensation rates. (a) and (d) show the results of the CTL simulation; (b) and (e) show the results of LOW minus CTL (LOW – CTL) experiments; (c) and (f) show the results of the HIGH minus CTL (HIGH – CTL) experiments. The period ranges from 1200 UTC 20 September to 0000 UTC 23 September 2013.

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In the TC system, the latent heating rate mainly depends on the condensation process. Figure 5a shows that the maximum rate of latent heat release occurs at a height of approximately 5 km and in the eyewall region, where the heating rate exceeds 7 K h^–1. In the HIGH experiment, because of the enhancement of condensation, heating rate is higher than that in the CTL experiment. Conversely, because condensation in the LOW experiment is weak compared with that in the CTL experiment, the heating rate is also low. The sensitivity experiments demonstrate that variations in latent heating rate have a direct influence on the temperature. Figure 5d shows that the TC clearly has a warm core, with the maximum temperature occurring between the altitudes of 5 and 8 km. Temperature in the core and at low altitude is clearly lower in the LOW experiment than that in CTL, because reduced latent heating rate in the LOW experiment diminishes the transport of latent heat to the TC center, which weakens the warm-core structure. In the HIGH experiment, the high latent heat release produces a clear warm-core structure. However, at the periphery of the TC, the high latent heat release is accompanied by a low temperature.

Figure 5 Height–radius cross-sections of temporally and azimuthally averaged (a–c) latent heating rate and (d–f) temperature deviation. (a) and (d) show the results of the CTL simulation; (b) and (e) show the differences of the results between the LOW and CTL experiments (LOW – CTL); (c) and (f) show the differences of the results between the HIGH and CTL experiments (HIGH – CTL). The period ranges from 1200 UTC 20 September to 0000 UTC 23 September 2013.

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The sensitivity experiments also demonstrate that the variation in latent heat release affects the flow field. In the HIGH experiment, the high latent heat release enhances the vertical velocity components and convection in the eyewall region, invigorating the descending air around the eye (Fig. 6f). In the LOW experiment, the low latent heat release reduces the latent heat transport to the TC center. Thus, the temperature declines at the TC center, increasing descending air around the TC core (Fig. 6b). Because of conservation of mass, the vertical velocity components near the eyewall increase slightly.

Figure 6 Height–radius cross-sections of temporally and azimuthally averaged (a–c) vertical and (d–f) radial wind speeds. (a) and (d) show the results of the CTL simulation; (b) and (e) show the results of the LOW minus CTL experiments (LOW – CTL); (c) and (f) show the results of the HIGH minus CTL experiments (HIGH – CTL). The period ranges from 1200 UTC 20 September to 0000 UTC 23 September 2013.

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The presence of SSA regulates the CCN concentration, affecting microphysical processes and hydrometeor structure. The cloud water mixing ratio is larger in the HIGH experiment than in the CTL (Fig. 7b) because of intensified condensation. The mixing ratio is lower in the LOW experiment than in the CTL because of the reduction in condensation. Figure 7a also shows that the maximum cloud water mixing ratio occurs at an altitude of about 1 km. However, the largest condensation rate is found at an altitude of about 5 km (Fig. 4d) because the cloud water mixing ratio is affected not only by condensation but also by autoconversion into rain and the accretion of cloud water by rain. Figure 8 shows that maximum autoconversion into rain and the accretion of cloud water by rain occur at an altitude of about 5 km, indicating that more cloud water is converted to rain at this altitude. In the HIGH experiment, the high mixing ratio and number concentration of cloud water enhance the accretion of cloud water by rain (Fig. 8c). In contrast, owing to the lower mixing ratio and number concentration of cloud water, the accretion of cloud water by rain is suppressed in the LOW experiment. Studies have revealed that the augmentation in anthropogenic aerosol concentration generates higher number concentrations of cloud water and smaller cloud particle effective radii, which augments the accretion of cloud water by rain and suppresses cloud water collisions (Rosenfeld et al., 2007; Jiang et al., 2016). The radii of SSA are larger than those of anthropogenic aerosols. Large-radius CCN contribute to the formation of large-radius cloud water embryos, enhancing cloud water autoconversion into rain. Figure 8 shows that the high rates of production of SSA enhance autoconversion.

Figure 8 Height–radius cross-sections of temporally and azimuthally averaged (a–c) rates of cloud water autoconversion into rain (RAUT) and (d–f) accretion of cloud water by rain (RACW). (a) and (d) show results of the CTL simulation; (b) and (e) show the results of the LOW minus CTL experiments (LOW – CTL); (c) and (f) show the results of the HIGH minus CTL experiments (HIGH – CTL). The period ranges from 1200 UTC 20 September to 0000 UTC 23 September 2013.

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In the HIGH experiment, enhanced accretion and autoconversion result in an increase in the rain mixing ratio (Fig. 7f). In the eyewall region, the amount of ascending air increases, which intensifies convection and increases the mixing ratios of snow, graupel, and rain. In the LOW experiment, both accretion and autoconversion are suppressed, reducing the rain mixing ratio (Fig. 7e). However, at a horizontal distance of 90 km from the center, ascending air slightly increases and contributes to the increase in rain, snow, and graupel mixing ratios. Rain can interact with snow/graupel, while the snow/graupel and rain mixing ratios can influence each other. The graupel/snow mixing ratio is affected by advection, vertical motion, as well as ice-phase microphysical processes, such as melting, freezing, deposition, sublimation and collision. However, we are unable to conclude which ice-phase microphysical process determines the graupel/snow mixing ratio, but infer from Fig. 7 that ascending air increases in the eyewall region, which intensifies convection, and, therefore, increases the mixing ratios of snow/graupel and rain.

Figure 7 Height–radius cross-sections of temporally and azimuthally averaged (a–c) cloud ice mixing ratio (contour; dashed lines correspond to negative values) and cloud water mixing ratio (shaded), and (d–f) rain mixing ratio (shaded) and the sum of graupel and snow mixing ratios (contour; dashed lines correspond to negative values). (a) and (d) show the results of the CTL simulation; (b) and (e) show the results of the LOW minus CTL experiments (LOW – CTL); (c) and (f) show the results of the HIGH minus CTL experiments (HIGH – CTL). The red line indicates 0°C. The period ranges from 1200 UTC 20 September to 0000 UTC 23 September 2013.

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To investigate the relationship between SSA emissions and precipitation, a DOUBLE experiment was conducted, whereby the emission coefficient n was set to twice that in the CTL experiment. Figure 9 shows that the average precipitation rate decreases from the low emission experiment to the CTL experiment, and then increases with increasing SSA emission, indicating a nonlinear relationship between the precipitation rate and SSA emission.

Figure 9 Time- and domain-averaged precipitation rates for the LOW, CTL, DOUBLE, and HIGH experiments. The period ranges from 1200 UTC 20 September to 0000 UTC 23 September 2013.

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We investigated the impact of SSA on TC Usagi using the online fully coupled model WRF-Chem. Sea-salt aerosols, with their large radii and high hygroscopicity, are expected to influence microphysical processes, the thermodynamic structure, and the precipitation of TCs. Following Gong et al. (1997a), the rate of sea-salt droplet generation was computed as a function of sea surface wind speed. Three experiments—low SSA emission (LOW), the control (CTL), and high SSA emission (HIGH)—were conducted. According to the simulations, the SSA has no obvious influence on the TC track and intensity; the concentrations of SSA decrease rapidly with height. As the presence of SSA promotes the autoconversion of cloud water, the accretion of cloud water by rain and autoconversion of cloud water into rain are highest in the HIGH experiment. In contrast, the LOW experiment has fewer CCN, and the accretion and autoconversion are weakest in this simulation. Because rain formation is mainly affected by the processes of accretion and autoconversion, the simulation results indicate that SSA can enhance rain formation.

Higher levels of SSA lead to the intensification of condensation and latent heat release, producing small increases in horizontal wind speed in the TC. The latent heating rate is highest in the HIGH experiment, and lowest in the LOW experiment.

Variations in latent heating rate also have a direct influence on the thermodynamics of the TC. In the LOW experiment, a low latent heating rate leads to less latent heat being transported to the TC eye. Therefore, temperature in the eye is reduced and downdrafts are strengthened, resulting in a small increase of the vertical velocity component near the eyewall. In the HIGH experiment, a high latent heating rate substantially strengthens the updrafts around the eyewall, intensifying convection and increasing precipitation.

The relationship between aerosols and precipitation is complex, and the effect of aerosols on precipitation remains uncertain. Using the WRF model, Li et al. (2008) investigated the influence of aerosols on clouds and precipitation, indicating that the precipitation increases with an increase of aerosol concentration. However, when the concentration is extremely high, precipitation begins to decrease. The variation of precipitation with aerosol concentration is nonlinear. Under various conditions of humidity, wind shear, and background fields, aerosols have different impacts on precipitation (Khain et al., 2008a; Fan et al., 2009). Varying concentrations, properties, and radii of aerosols have variable effects on precipitation. Some studies have indicated that high CCN concentrations increase cloud water number concentration, reduce the cloud particle effective radii, and reduce the collision efficiency of cloud droplets (Rosenfeld et al., 2007; Khain et al., 2008b), but these have mainly focused on anthropogenic aerosols and submicron-scale CCN. First, SSA is a large aerosol. Acting as CCN, SSAs increase the rates of accretion of cloud water by rain and cloud water collision, and have positive effects on rain formation. Second, SSA is strongly hydrophilic and can readily grow under high humidity conditions. Therefore, this enhances the condensation of water vapor into cloud droplets with relatively large radii, as well as the autoconversion of cloud water.

Tropical cyclones are weather systems of enormous complexity. Different concentrations of SSA alter the thermodynamic structure and microphysical processes of TCs. In the present work, we find that SSA contributes to the formation of rain. However, precipitation is also influenced by the thermodynamic structure in TCs. In the LOW experiment, because of the increase of downdrafts around the eye, updrafts in the eyewall increase slightly as a result of the conservation of mass. Thus, the eyewall convection in the LOW experiment slightly intensifies. In summary, there is a nonlinear relationship between the precipitation rate and SSA emission.

Acknowledgments. We are grateful to the NCAR Mesoscale and Microscale Meteorology Division for making the WRF-Chem model available at http://www.mmm.ucar.edu/wrf/users. We thank NCAR’s Research Data Archive for making the NCEP Final Operational Model Global Tropospheric Analysis (NCEP-FNL; http://rda.ucar.edu/datasets/ds083.2) dataset available.