The cloud droplet number concentration (N d) is of central interest to improve the understanding of cloud physics and for quantifying the effective radiative forcing by aerosol‐cloud interactions. Current standard satellite retrievals do not operationally provide N d, but it can be inferred from retrievals of cloud optical depth (τ c) cloud droplet effective radius (r e) and cloud top temperature. This review summarizes issues with this approach and quantifies uncertainties. A total relative uncertainty of 78% is inferred for pixel‐level retrievals for relatively homogeneous, optically thick and unobscured stratiform clouds with favorable viewing geometry. The uncertainty is even greater if these conditions are not met. For averages over 1° ×1° regions the uncertainty is reduced to 54% assuming random errors for instrument uncertainties. In contrast, the few evaluation studies against reference in situ observations suggest much better accuracy with little variability in the bias. More such studies are required for a better error characterization. N d uncertainty is dominated by errors in r e, and therefore, improvements in r e retrievals would greatly improve the quality of the N d retrievals. Recommendations are made for how this might be achieved. Some existing N d data sets are compared and discussed, and best practices for the use of N d data from current passive instruments (e.g., filtering criteria) are recommended. Emerging alternative N d estimates are also considered. First, new ideas to use additional information from existing and upcoming spaceborne instruments are discussed, and second, approaches using high‐quality ground‐based observations are examined.

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.

Earth system models have been used for climate predictions in recent years due to their capabilities to include biogeochemical cycles, human impacts, as well as coupled and interactive representations of Earth system components (e.g., atmosphere, ocean, land, and sea ice). In this work, the Community Earth System Model (CESM) with advanced chemistry and aerosol treatments, referred to as CESM-NCSU, is applied for decadal (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) , and PM 10 are also reasonably well predicted over Europe with NMBs of 220.8 to 25.2%, so are predictions of SO 2 concentrations over the East Asia with an NMB of 218.2%, and the tropospheric ozone residual (TOR) over the globe with an NMB of 23.5%. Most meteorological and radiative variables predicted by CESM-NCSU agree well overall with those predicted by CESM-CMIP5. The performance of LWP and AOD predicted by CESM-NCSU is better than that of CESM-CMIP5 in terms of model bias and correlation coefficients. Large biases for some chemical predictions can be attributed to uncertainties in the emissions of precursor gases (e.g., SO 2 , NH 3 , and NO x ) and primary aerosols (black carbon and primary organic matter) as well as uncertainties in formulations of some model components (e.g., online dust and sea-salt emissions, secondary organic aerosol formation, and cloud microphysics). Comparisons of CESM simulation with baseline emissions and 20% of anthropogenic emissions from the baseline emissions indicate that anthropogenic gas and aerosol species can decrease downwelling shortwave radiation (FSDS) by 4.7 W m 22 (or by 2.9%) and increase SWCF by 3.2 W m 22 (or by 3.1%) in the global mean.

[1] Rapid economic growth over the last 30 years in China has led to a significant increase in aerosol loading, which is mainly due to the increased emissions of its precursors such as SO 2 and NO x . Here we show that these changes significantly affect wintertime clouds and precipitation over the East China Sea downwind of major emission sources. Satellite observations show an increase of cloud droplet number concentration from less than 200 cm −3 in the 1980s to more than 300 cm −3 in 2005. In the same time period, precipitation frequency reported by voluntary ship observers was reduced from more than 30% to less than 20% of the time. A back trajectory analysis showed the pollution in the investigation area to originate from the Shanghai-Nanjing and Jinan industrial areas. A model sensitivity study was performed, isolating the effects of changes in emissions of the aerosol precursors SO 2 and NO x on clouds and precipitation using a state-of-the-art mesocale model including chemistry and aerosol indirect effects. Similar changes in cloud droplet number concentration over the East China Sea were obtained when the current industrial emissions in China were reduced to the 1980s levels. Simulated changes in precipitation were somewhat smaller than the observed changes but still significant.

<p><strong>Abstract.</strong> 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 degree CDNC values plus associated uncertainties. All values are in-cloud values, that is, if the grid box is covered to 10 % with clouds, then the reported CDNC value will be valid for the cloudy part of the grid-box. Here, we provide an overview on 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 both in terms of its phase and magnitude. After an optimal screening was established a final CDNC climatology was generated. Resulting CDNC uncertainties for the climatology are in the order of 30 % in the stratocumulus regions and 60 % to 80 % elsewhere. The climatology is available in Network Common Data Format (netCDF) and adheres to the Climate and Forecast (CF) convention. The climatology is available via Digital Object Identifier (Bennartz and Rausch, 2016, <a href="http://dx.doi.org/10.15695/vudata.ees.1">doi:10.15695/vudata.ees.1</a>).</p>