Predicting decadal trends in cloud droplet number concentration using reanalysis and satellite data

Cloud droplet number concentration (CDNC) is the key state variable that moderates the relationship between aerosol and the radiative forcing arising from aerosol-cloud interactions. Uncertainty related to the effect of anthropogenic aerosol on cloud properties represents the largest uncertainty in total anthropogenic radiative forcing. Here we show that regionally-averaged time series of Moderate-Resolution Imaging Spectroradiometer (MODIS) observed CDNC are wellpredicted by MERRA2 reanalysis near-surface sulfate mass concentration over decadel timescales. A multiple linear regression between MERRA2 reanalysis masses of sulfate (SO₄), black carbon (BC), organic carbon (OC), sea salt (SS), and dust (DU) shows that CDNC across many different regimes can be reproduced by a simple power law fit to near-surface SO₄, with smaller contributions from BC, OC, SS, and DU. This confirms previous work using a less-sophisticated retrieval of CDNC at monthly time scales. The analysis is supported by examination of remotely-sensed sulfur dioxide (SO₂) over maritime volcanoes and the east coasts of North America and Asia, revealing that maritime CDNC responds to changes in SO₂ as observed by the Ozone Monitoring Instrument (OMI). This investigation of aerosol reanalysis and top-down remote sensing observations reveals that emission controls in Asia and North America have decreased CDNC in their maritime outflow on a decadal time scale.