The impacts of enhanced heterogeneous ice nucleation (HET) on the properties of deep convective clouds (DCCs) have been investigated in cloud-resolving simulations with the WRF-CHEM model. The study focuses on a case observed during the Deep Convective Clouds and Chemistry (DC3) field campaign. For the simulated DCCs, which had cold cloud-base temperatures, an inverse relationship exists between ice crystal mass produced through HET and anvil ice crystal number concentrations. This seems to be due to the indirect competition between HET and subsequent homogeneous freezing (HOM) for liquid droplets. Furthermore, our simulations suggest that HET enhancements at warmer temperatures are more efficient in depleting liquid droplets below and hence have larger impacts on anvil properties than HET enhancements at colder temperatures do. This temperature dependence indicates that similar increases in the number of ice nucleating particles (INPs) may potentially have different impacts on DCCs, depending on the INP type and at which temperatures they can nucleate ice crystals. We also found that the reduced anvil ice number concentrations due to the enhanced HET may lead to optically thinner anvil clouds. The reduction in cloud optical depth comes from a decrease in ice crystal mass concentrations, and in some runs also from an increase in ice crystal sizes. These results suggest potentially large impacts of INPs on the properties of DCCs, especially if precipitation is predominantly produced through ice processes in the DCCs. The results underscore the importance of fully understanding the temperature-dependent ability of aerosol particles to nucleate ice crystals.
Abstract. The year of 2015 was an extremely dry year for Southeast Asia where the direct impact of a strong El Niño was in play. As a result of this dryness and the relative lack of rainfall, an extraordinary quantity of aerosol particles from biomass burning remained in the atmosphere over the Maritime Continent during the fire season. This study uses the Weather Research and Forecasting model coupled with Chemistry to understand the impacts of these fire particles on cloud microphysics and radiation during the peak biomass burning season in September. Our simulations, one with fire particles and the other without them, cover the entire Maritime Continent region at a cloud-resolving resolution (4 km) for the entire month of September in 2015. The comparison of the simulations shows a clear sign of precipitation enhancement by fire particles through microphysical effects; smaller cloud droplets remain longer in the atmosphere to later form ice crystals, and/or they are more easily collected by ice-phase hydrometeors in comparison to droplets under no fire influences. As a result, the mass of ice-phase hydrometeors increases in the simulation with fire particles, and so does rainfall. On the other hand, the aerosol radiative effect weakly counteracts the invigoration of convection. Clouds are more reflective in the simulation with fire particles as ice mass increases. Combined with the direct scattering of sunlight by aerosols, the simulation with fire particles shows higher albedo over the simulation domain on average. The simulated response of clouds to fire particles in our simulations clearly differs from what was presented by two previous studies that modeled aerosol–cloud interaction in years with different phases of El Niño–Southern Oscillation (ENSO), suggesting a further need for an investigation on the possible modulation of fire–aerosol–convection interaction by ENSO.
Aerosol emissions from forest fires may impact cloud droplet activation through an increase in particle number concentrations (“the number effect”) and also through a decrease in the hygroscopicity κ of the entire aerosol population (“the hygroscopicity effect”) when fully internal mixing is assumed in models. This study investigated these effects of fire particles on the properties of simulated deep convective clouds (DCCs), using cloud‐resolving simulations with the Weather Research and Forecasting model coupled with Chemistry for a case study in a partly idealized setting. We found that the magnitude of the hygroscopicity effect was in some cases strong enough to entirely offset the number/size effect, in terms of its influence on modeled droplet and ice crystal concentrations. More specifically, in the case studied here, the droplet number concentration was reduced by about 37% or more due solely to the hygroscopicity effect. In the atmosphere, by contrast, fire particles likely have a much weaker impact on the hygroscopicity of the pre‐existing background aerosol, as such a strong impact would occur only if the fire particles mixed immediately and uniformly with the background. We also show that the differences in the number of activated droplets eventually led to differences in the optical thickness of the clouds aloft, though this finding is limited to only a few hours of the initial development stage of the DCCs. These results suggest that accurately and rigorously representing aerosol mixing and κ in models is an important step toward accurately simulating aerosol‐cloud interactions under the influence of fires.
<p>Processes that convert small cloud droplets, on the order of tens of micrometers, into raindrops, on the order of millimeters, consist of condensational growth and collision-coalescence: the former is efficient for small droplets, whereas the latter becomes predominant later in the growth stage when droplets are larger than about 30 micrometers. Thus, how droplets can quickly grow to 30 micrometers solely by inefficient condensation has been a topic of discussion for a long time. As a result, many parameterizations used in current models that cannot directly resolve these processes are actually based on empirical estimates. Recently, some studies have shown the impact of turbulences that can enhance collision-coalescence for droplets smaller than 30 micrometers, explaining the fast growth of cloud droplets into raindrops as observed. We have implemented these new equations of collision-coalescence in a parcel model where the activation of aerosol particles and their condensational growth are also explicitly calculated based on physical equations across numerous size bins. After the successful implementation of these processes, we have then applied machine-learning algorithms of training a machine to mimic the behavior of the explicit physical model to model-simulated mass and number of raindrops alongside ten dynamical and microphysical variables as input features. The machine-learned results are also compared with those from existing parameterizations frequently used in regional and climate models. Furthermore, the use of this new machine-learning-based parameterization, covering processes from aerosol activation to the formation of raindrops, in a regional model will be discussed.</p>
<p>The maritime continent in Southeast Asia is characterized by the frequent convective activities on a wide range of scales, as well as by the seasonal emissions of biomass-burning particles. The emission of biomass-burning particles in this region typically peaks in September and October, whereas its intensity varies considerably from year to year. Since the atmospheric circulation over the region is heavily influenced by a range of meteorological and climatological variabilities, such as ENSO, it is important to quantitatively examine the impacts of biomass-burning particles on clouds while taking weather/climate regimes into account. We investigate the effects of biomass-burning particles on clouds, especially convective ones, with cloud-resolving simulations by the WRF-CHEM model. Instead of focusing on a particular case, our simulations cover an extended period of time in the month of September, allowing us to examine both individual convection and an ensemble of convective clouds developing under different weather/climate regimes and hence different aerosol abundance and distributions. Such long-term and high-resolution simulations over the region will give us an insight into the climate-regime dependent two-way interaction between aerosols and clouds.</p>
Abstract. The year of 2015 was an extremely dry year for Southeast Asia where the direct impact of strong El Niño was in play. As a result of this dryness and the relative lack of rainfall, an extraordinary amount of aerosol particles from biomass burning remained in the atmosphere over the Maritime Continent during the fire season. This study uses the Weather Research and Forecasting model coupled with Chemistry to understand the impacts of these fire particles on cloud microphysics and radiation during the peak biomass burning season in September. Our simulations, one with fire particles and the other without them, cover the entire Maritime Continent region at a cloud-resolving resolution (4 km) for the entire month of September in 2015. The comparison of the simulations shows a clear sign of precipitation enhancement by fire particles through microphysical effects; smaller cloud droplets remain longer in the atmosphere to later form ice crystals, and/or they are more easily collected by ice-phase hydrometeors, in comparison to droplets under no fire influences. As a result, mass of ice-phase hydrometeors increases in the simulation with fire particles, so does rainfall. On the other hand, we see no clear sign of temperature differences between the two simulations that could stem from the semi-direct effects of aerosols by absorbing the incoming solar radiation. Clouds are more reflective in the simulation with fire particles as ice mass increases. Combined with the direct scattering of sunlight by aerosols, the simulation with fire particles shows higher albedo over the simulation domain on average. The simulated response of clouds to fire particles in our simulations clearly differs from what was presented by two previous studies that modeled aerosol-cloud interaction in years with different phases of El Niño-Southern Oscillation (ENSO), suggesting a further need for an investigation on the possible modulation of fire-aerosol-convection interaction by ENSO.
No abstract
<p><strong>Abstract.</strong> An increase in atmospheric aerosol loading could alter the microphysics, dynamics, and radiative characteristics of deep convective clouds. Earlier modeling studies have shown that the effects of increased aerosols on the amount of precipitation from deep convective clouds are model-dependent. This study aims to understand the effects of increased aerosol loading on a deep convective cloud throughout its lifetime with the use of the Weather Research and Forecasting (WRF) model as a cloud-resolving model (CRM). It simulates an idealized supercell thunderstorm with 8 different aerosol loadings, for three different cloud microphysics schemes. Variation in aerosol concentration is mimicked by varying either cloud droplet number concentration or the number of activated cloud condensation nuclei. We show that the sensitivity to aerosol loading is dependent on the choice of microphysics scheme. For the schemes that are sensitive to aerosols loading, the production of graupel via riming of snow is the key factor determining the precipitation response. The formulation of snow riming depends on the microphysics scheme and is usually a function of two competing effects, the size effect and the number effect. In many simulations, a decrease in riming is seen with increased aerosol loading, due to the decreased droplet size that lowers the riming efficiency drastically. This decrease in droplet size also results in a delay in the onset of precipitation, as well as so-called warm rain suppression. Although these characteristics of convective invigoration (Rosenfeld et al., 2008) are seen in the first few hours of the simulations, variation in the accumulated precipitation mainly stems from graupel production rather than convective invigoration. These results emphasize the importance of accurate representations of graupel formation in microphysics schemes.</p>
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