Strong Precipitation Suppression by Aerosols in Marine Low Clouds

The adjustment of cloud amount to aerosol effects occurs to a large extent in response to the aerosol effect on precipitation. Here the marine boundary layer clouds were studied by analyzing the dependence of rain intensity measured by Global Precipitation Measurement on cloud properties. We showed that detectable rain initiates when the drop effective radius at the cloud top (re) exceeds 14 μm, and precipitation is strongly suppressed with increasing cloud drop concentration (Nd), which contributes to the strong dependence of cloud amount on aerosols. The rain rate increases sharply with cloud thickness (CGT) and re when re > 14 μm. The dependence of rain rate on re and CGT presents a simple framework for precipitation susceptibility to aerosols, which explains other previously observed relationships. We showed that sorting data by CGT and using alternative cloud condensation nuclei proxy rather than aerosol optical depth are critical for studying aerosol‐cloud‐precipitation interactions.Plain Language SummaryAerosol‐cloud interaction remains the greatest uncertainty in future climate projection. Precipitation is a key process that mediates how the cloud amount responds to aerosol perturbations. Here we combined precipitation measured by the radar onboard the satellite of Global Precipitation Measurement (GPM) and cloud properties retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua satellite for studying the dependence of rain intensity on cloud properties for marine boundary layer water clouds over the Southern Hemisphere Ocean. Our results showed that rain is sharply intensified when droplets at the cloud top grow larger than 14 μm, and precipitation decreases with increasing cloud drop number concentration (Nd). A simple framework to explain the relationship between precipitation and aerosols is proposed here by showing the dependence of precipitation on Nd and cloud geometric thickness. We also discussed why using aerosol optical depth (AOD) as CCN proxy in previous studies could lead to great uncertainties and why sorting cloud geometrical thickness is necessary.Key PointsPrecipitation is strongly suppressed with increasing cloud drop concentrationSorting data by cloud thickness and using alternative CCN proxy rather than AOD are critical for studying aerosol‐cloud interactionsDetectable rain initiates when the drop effective radius at the cloud top exceeds 14 μmPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154630/1/grl60407.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154630/2/grl60407_am.pd

Uncertainties in global aerosol simulations: Assessment using three meteorological data sets

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94950/1/jgrd13664.pd

Constraining cloud lifetime effects of aerosols using A‐Train satellite observations

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95434/1/grl29404.pd

Influence of anthropogenic sulfate and black carbon on upper tropospheric clouds in the NCAR CAM3 model coupled to the IMPACT global aerosol model

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95099/1/jgrd14985.pd

Coupled IMPACT aerosol and NCAR CAM3 model: Evaluation of predicted aerosol number and size distribution

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94609/1/jgrd14974.pd

Have Australian rainfall and cloudiness increased due to the remote effects of Asian anthropogenic aerosols?

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94749/1/jgrd13340.pd

Improving BC Mixing State and CCN Activity Representation With Machine Learning in the Community Atmosphere Model Version 6 (CAM6)

Abstract Representing mixing state of black carbon (BC) is challenging for global climate models (GCMs). The Community Atmosphere Model version 6 (CAM6) with the four‐mode version of the Modal Aerosol Module (MAM4) represents aerosols as fully internal mixtures with uniform composition within each aerosol mode, resulting in high degree of internal mixing of BC with non‐BC species and large mass ratio of coating to BC (RBC, the mass ratio of non‐BC species to BC in BC‐containing particles). To improve BC mixing state representation, we coupled a machine learning (ML) model of BC mixing state index trained on particle‐resolved simulations to the CAM6 with MAM4 (MAM4‐ML). In MAM4‐ML, we use RBC to partition accumulation mode particles into two new modes, BC‐free particles and BC‐containing particles. We adjust RBC to make the modeled BC mixing state index (χmode) match the one predicted by the ML model (χML). On a global average, the mass fraction of BC‐containing particles in accumulation mode decreases from 100% (MAM4‐default) to 48% (MAM4‐ML). The globally averaged χmode decreases from 78% (MAM4‐default) to 63% (MAM4‐ML, 19% reduction) and agrees well with χML (66%). The RBC decreases by 52% for accumulation mode and better agrees with observations. The hygroscopicity drops by 9% for BC‐containing particles in accumulation mode, leading to a 20% reduction in the BC activation fraction. The surface BC concentration increases most (6.9%) in the Arctic, and the BC burden increases by 4%, globally. Our study highlights the application of the ML model for improving key aerosol processes in GCMs

Anthropogenic–biogenic interaction amplifies warming from emission reduction over the southeastern US

A decline of surface biogenic secondary organic aerosols through the mediation of reduced anthropogenic aerosols has been recognized as an air quality co-benefit of anthropogenic emission control over the southeastern US. However, the climate impacts of this anthropogenic–biogenic interaction remain poorly understood. Here, we identified a substantial decline of summertime aerosol loading aloft over the southeastern US in recent decades through the interaction, which leads to a stronger decline in column-integrated aerosol optical depth and a greater increase in radiative fluxes over the southeastern than northeastern US, different from trends of anthropogenic emissions and near-surface aerosol loading. The anthropogenic–biogenic interaction is shown to explain more than 60% of the coherent increasing trend of 5.3 Wm ^−2 decade ^−1 in clear-sky surface downward radiative fluxes. We show that current climate models fail to represent this interaction. The interaction is further projected to amplify the positive radiative forcing from emission control by 42.3% regionally over the southeastern US and globally by 5.4% in 2050 under RCP4.5 compared to 2005. This amplification effect implies greater challenges to achieving the Paris Agreement temperature targets with continuous emission control in future