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Abstract This paper presents a new retrieval method for inferring the vertical profile of cirrus cloud effective particle size by using solar reflected line spectra in the 1.38‐μm band. The retrieval method is based on the maximum‐photon penetration principle coupled with the constrained linear inversion. This approach takes advantage of the vertical stratification of cirrus cloud effective particle size as well as absorption lines of water vapor of different intensity, which contain rich information on the vertical structure of cloud particle size. Reflected radiances at different wavenumbers provide the effective‐size information at different heights within cirrus associated with photon different penetration depths. Assuming a vertical profile of effective size monotonically decreasing toward cloud top and using results based on “exact” radiative transfer computations, we perform retrieval of the effective size for a number of model cirrus to check for algorithm accuracy. The retrieved profile of effective size is close to the model profile for cirrus optical depth less than about eight with an uncertainty range of 2.2–4.2 μm. In addition, we further carry out a sensitivity study involving the retrieved effective size in connection with different water vapor profiles and demonstrate that the difference from the model is only several percent except for the cloud top if an appropriate wavenumber set is selected. The results from this study suggest that the present method can be applied to realistic remote sensing of the vertical profile of cirrus cloud particle size. 

Abstract We extend a stochastic aerosol‐snow albedo model to explicitly simulate dust internally/externally mixed with snow grains of different shapes and for the first time quantify the combined effects of dust‐snow internal mixing and snow nonsphericity on snow optical properties and albedo. Dust‐snow internal/external mixing significantly enhances snow single‐scattering coalbedo and absorption at wavelengths of <1.0 μm, with stronger enhancements for internal mixing (relative to external mixing) and higher dust concentrations but very weak dependence on snow size and shape variabilities. Compared with pure snow, dust‐snow internal mixing reduces snow albedo substantially at wavelengths of <1.0 μm, with stronger reductions for higher dust concentrations, larger snow sizes, and spherical (relative to nonspherical) snow shapes. Compared to internal mixing, dust‐snow external mixing generally shows similar spectral patterns of albedo reductions and effects of snow size and shape. However, relative to external mixing, dust‐snow internal mixing enhances the magnitude of albedo reductions by 10%–30% (10%–230%) at the visible (near‐infrared) band. This relative enhancement is stronger as snow grains become larger or nonspherical, with comparable influences from snow size and shape. Moreover, for dust‐snow external and internal mixing, nonspherical snow grains have up to ~45% weaker albedo reductions than spherical grains, depending on snow size, dust concentration, and wavelength. The interactive effect of dust‐snow mixing state and snow shape highlights the importance of accounting for these two factors concurrently in snow modeling. For application to land/climate models, we develop parameterizations for dust effects on snow optical properties and albedo with high accuracy. 

Abstract We incorporate a parameterization to quantify the effect of three‐dimensional (3‐D) radiation‐topography interactions on the solar flux absorbed by the surfaces, including multiple reflections between surfaces and differences in sunward/shaded slopes, in the Community Climate System Model version 4 (CCSM4). A sensitivity experiment is carried out using CCSM4 with the prescribed sea surface temperature for year 2000 to investigate its impact on energy budget and surface temperature over the Tibetan Plateau (TP). The results show that the topographic effect reduces the upward surface shortwave flux and, at the same time, enhance snowmelt rate over the central and southern parts of TP. Comparing to observations and the ensemble of Coupled Model Intercomparison Project Phase 5 (CMIP5), we found that CMIP5 models have a strong cold bias of 3.9 K over TP, partially induced by the strong reflection of shortwave fluxes. We show that the inclusion of topographic effect reduces the substantial biases of upward shortwave fluxes and surface air temperatures over TP by 13% in the CCSM4 model. 

Abstract. In response to the coronavirus disease of 2019 (COVID-19),California issued statewide stay-at-home orders, bringing about abrupt anddramatic reductions in air pollutant emissions. This crisis offers us anunprecedented opportunity to evaluate the effectiveness of emissionreductions in terms of air quality. Here we use the Weather Research and Forecastingmodel with Chemistry (WRF-Chem) in combination with surface observations tostudy the impact of the COVID-19 lockdown measures on air quality insouthern California. Based on activity level statistics and satelliteobservations, we estimate the sectoral emission changes during the lockdown.Due to the reduced emissions, the population-weighted concentrations of fineparticulate matter (PM2.5) decrease by 15 % in southernCalifornia. The emission reductions contribute 68 % of the PM2.5concentration decrease before and after the lockdown, while meteorologyvariations contribute the remaining 32 %. Among all chemical compositions,the PM2.5 concentration decrease due to emission reductions isdominated by nitrate and primary components. For O3 concentrations, theemission reductions cause a decrease in rural areas but an increase in urbanareas; the increase can be offset by a 70 % emission reduction inanthropogenic volatile organic compounds (VOCs). These findings suggest thata strengthened control on primary PM2.5 emissions and a well-balancedcontrol on nitrogen oxides and VOC emissions are needed to effectively andsustainably alleviate PM2.5 and O3 pollution in southernCalifornia. 

Abstract. The interactions between aerosols and ice clouds represent one of the largest uncertainties in global radiative forcing from pre-industrial time to the present. In particular, the impact of aerosols on ice crystal effective radius (R_ei), which is a key parameter determining ice clouds' net radiative effect, is highly uncertain due to limited and conflicting observational evidence. Here we investigate the effects of aerosols on R_ei under different meteorological conditions using 9-year satellite observations. We find that the responses of R_ei to aerosol loadings are modulated by water vapor amount in conjunction with several other meteorological parameters. While there is a significant negative correlation between R_ei and aerosol loading in moist conditions, consistent with the "Twomey effect" for liquid clouds, a strong positive correlation between the two occurs in dry conditions. Simulations based on a cloud parcel model suggest that water vapor modulates the relative importance of different ice nucleation modes, leading to the opposite aerosol impacts between moist and dry conditions. When ice clouds are decomposed into those generated from deep convection and formed in situ, the water vapor modulation remains in effect for both ice cloud types, although the sensitivities of R_ei to aerosols differ noticeably between them due to distinct formation mechanisms. The water vapor modulation can largely explain the difference in the responses of R_ei to aerosol loadings in various seasons. A proper representation of the water vapor modulation is essential for an accurate estimate of aerosol–cloud radiative forcing produced by ice clouds.