Aerosol Effective Radiative Forcing in the Online Aerosol Coupled CAS-FGOALS-f3-L Climate Model

Effective Radiative Forcings Due To Anthropogenic Emission Changes Under Covid-19 and Post-Pandemic Recovery Scenarios

With the continuation of the Coronavirus Disease 2019 (Covid-19) pandemic, the impacts of this catastrophe on anthropogenic emissions are no longer limited to its early stage. This study quantitatively estimates effective radiative forcings (ERFs) due to anthropogenic well-mixed greenhouse gases (WMGHGs) and aerosols for the period 2020-2050 under the three latest Covid-19 economic-recovery scenarios using an aerosol-climate model. The results indicate that reductions in both WMGHG and aerosol emissions under the Covid-19 green recoveries lead to increases ranging from 0 to 0.3 W m^-2 in global annual mean anthropogenic ERF over the period 2020-2050 relative to the Shared Socioeconomic Pathway 2-4.5 scenario (the baseline case). These positive ERFs are mainly attributed to the rapid and dramatic decreases in atmospheric aerosol content that increase net shortwave radiative flux at the top of atmosphere via weakening the direct aerosol effect and low cloud cover. At the regional scale, reductions in aerosols contribute to positive ERFs throughout the Northern Hemisphere, while the decreased WMGHGs dominate negative ERFs over the areas away from aerosol pollution, such as the Southern Hemisphere oceans. This drives a strong interhemispheric contrast of ERFs. In contrast, the increased anthropogenic emissions under the fossil-fueled recovery scenario lead to an increase of 0.3 W m^-2 in global annual mean ERF in 2050 compared with the baseline case, primarily due to the contribution of WMGHG ERFs. The regional ERF changes are highly dependent on local cloud radiative effects.

A Multi-Year Study of GOES-13 Droplet Effective Radius Retrievals for Warm Clouds over South America and Southeast Pacific

Geostationary satellites can retrieve the cloud droplet effective radius (re) but suffer biases from cloud inhomogeneities, internal retrieval nonlinearities, and 3-D scattering/shadowing from neighboring clouds, among others. A 1-D retrieval method was applied to Geostationary Operational Environmental Satellite 13 (GOES-13) imagery, over large areas in South America (5∘ N–30∘ S; 20∘–70∘ W), the Southeast Pacific (5∘ N–30∘ S; 70∘–120∘ W), and the Amazon (2∘ N–7∘ S; 54∘–73∘ W), for four months in each year from 2014–2017. Results were compared against in situ aircraft measurements and the Moderate Resolution Imaging Spectroradiometer cloud product for Terra and Aqua satellites. Monthly regression parameters approximately followed a seasonal pattern. With up to 108,009 of matchups, slope, intercept, and correlation for Terra (Aqua) ranged from about 0.71 to 1.17, −2.8 to 2.5 μm, and 0.61 to 0.91 (0.54 to 0.78, −1.5 to 1.8 μm, 0.63 to 0.89), respectively. We identified evidence for re overestimation (underestimation) correlated with shadowing (enhanced reflectance) in the forward (backscattering) hemisphere, and limitations to illumination and viewing configurations accessible by GOES-13, depending on the time of day and season. A proposition is hypothesized to ameliorate 3-D biases by studying relative illumination and cloud spatial inhomogeneity.

Enhanced Simulation of an Asian Dust Storm by Assimilating GCOM-C Observations

Dust aerosols have great effects on global and regional climate systems. The Global Change Observation Mission-Climate (GCOM-C), also known as SHIKISAI, which was launched on 23 December 2017 by the Japan Aerospace Exploration Agency (JAXA), is a next-generation Earth observation satellite that is used for climate studies. The Second-Generation Global Imager (SGLI) aboard GCOM-C enables the retrieval of more precious global aerosols. Here, the first assimilation study of the aerosol optical thicknesses (AOTs) at 500 nm observed by this new satellite is performed to investigate a severe dust storm in spring over East Asia during 28–31 March 2018. The aerosol observation assimilation system is an integration of the four-dimensional local ensemble transform Kalman filter (4D-LETKF) and the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) coupled with the Non-Hydrostatic Icosahedral Atmospheric Model (NICAM). Through verification with the independent observations from the Aerosol Robotic Network (AERONET) and the Asian Dust and Aerosol Lidar Observation Network (AD-Net), the results demonstrate that the assimilation of the GCOM-C aerosol observations can significantly enhance Asian dust storm simulations. The dust characteristics over the regions without GCOM-C observations are better revealed from assimilating the adjacent observations within the localization length, suggesting the importance of the technical advances in observation and assimilation, which are helpful in clarifying the temporal–spatial structure of Asian dust and which could also improve the forecasting of dust storms, climate prediction models, and aerosol reanalysis.