Global and regional estimates of warm cloud droplet number concentration based on 13 years of AQUA-MODIS observations

Abstract

Abstract. We present and evaluate a climatology of cloud droplet number concentration (CDNC) based on 13 years of Aqua-MODIS observations. The climatology provides monthly mean 1 × 1 • CDNC values plus associated uncertainties over the global ice-free oceans. All values are incloud values, i.e. the reported CDNC value will be valid for the cloudy part of the grid box. Here, we provide an overview of how the climatology was generated and assess and quantify potential systematic error sources including effects of broken clouds, and remaining artefacts caused by the retrieval process or related to observation geometry. Retrievals and evaluations were performed at the scale of initial MODIS observations (in contrast to some earlier climatologies, which were created based on already gridded data). This allowed us to implement additional screening criteria, so that observations inconsistent with key assumptions made in the CDNC retrieval could be rejected. Application of these additional screening criteria led to significant changes in the annual cycle of CDNC in terms of both its phase and magnitude. After an optimal screening was established a final CDNC climatology was generated. Resulting CDNC uncertainties are reported as monthly-mean standard deviations of CDNC over each 1 × 1 • grid box. These uncertainties are of the order of 30 % in the stratocumulus regions and 60 to 80 % elsewhere.

Global Estimates of Changes in Shortwave Low‐Cloud Albedo and Fluxes Due to Variations in Cloud Droplet Number Concentration Derived From CERES‐MODIS Satellite Sensors

“…In terms of S a , hemispheric seasonal maxima occur in their corresponding winter, with the SH yielding the highest global values (Figure 2b). This trait is consistent with more pristine conditions in the SH, with vast areas featuring N d less than 50 cm À3 (Bennartz & Rausch, 2017).…”

Improved Aerosol Processes and Effective Radiative Forcing in HadGEM3 and UKESM1

“…The simulated N d distributions from GA7 and GA7.1 are in very good agreement with Grosvenor et al (2018) with highest N d concentrations over land and downwind of key source regions. The stratocumulus cloud regions also appear well represented although N d is lower in this region in Bennartz and Rausch (2017). Notwithstanding, the differences in the satellite data sets both have higher N d across the Southern Ocean than in GA7, with this low bias being somewhat improved in GA7.1.…”

Synergetic Satellite Trend Analysis of Aerosol and Warm Cloud Properties ver Ocean and Its Implication for Aerosol‐Cloud Interactions

“…More specifically, for a given 1°× 1°grid box, we get the counts of pixel-level cloud retrievals with respect to 108 joint COT-CER bins at 3.7 μm from MYD08_D3 product (Hubanks et al, 2016). For each COT-CER bin, CDNC is estimated by the following formula (Bennartz, 2007;Bennartz & Rausch, 2017):…”