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 in-cloud 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.
Highlights
One of the largest sources of uncertainty in predicting future climate is the degree to which clouds will alter the Earth’s radiative balance (Stocker et al, 2013)
We further investigate dependencies of the retrieved cloud droplet number concentration (CDNC) and effective radii on scattering angle and sunglint angle and provide a discussion of uncertainties derived alongside the CDNC climatology
The climatology described in this publication provides a number of incremental improvements over earlier climatologies published by us
Summary
One of the largest sources of uncertainty in predicting future climate is the degree to which clouds will alter the Earth’s radiative balance (Stocker et al, 2013). Stratiform boundary layer clouds play an important role in modulating earth albedo and are “at the heart of tropical feedback uncertainties in climate models” (Bony and Dufresne, 2005). Satellite observations play a critical role in understanding current-day variability of clouds, in validating and constraining climate models, and in furthering our understanding of cloud processes. A variety of observational case studies and process studies were published using similar approaches for deriving CDNC (Boers et al, 2006; George and Wood, 2010; Painemal and Zuidema, 2010; Rausch et al, 2010; Bennartz et al, 2011). Various authors have addressed shortcomings and issues related to CDNC climatologies (Merk et al, 2016; Grosvenor and Wood, 2014) as well as issues related to the cloud retrievals underlying the CDNC climatologies (Zhang and Platnick, 2011; Nakajima et al, 2010; Maddux et al, 2010; Horvath et al, 2014). Painemal and Zuidema (2011)
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