The European Centre for Medium-range Weather Forecast (ECMWF) provides an aerosol re-analysis starting from year 2003 for the Monitoring Atmospheric Composition and Climate (MACC) project. The re-analysis assimilates total aerosol optical depth retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) to correct for model departures from observed aerosols. The reanalysis therefore combines satellite retrievals with the full spatial coverage of a numerical model. Re-analysed products are used here to estimate the shortwave direct and first indirect radiative forcing of anthropogenic aerosols over the period 2003–2010, using methods previously applied to satellite retrievals of aerosols and clouds. The best estimate of globally-averaged, all-sky direct radiative forcing is −0.7±0.3Wm−2. The standard deviation is obtained by a Monte-Carlo analysis of uncertainties, which accounts for uncertainties in the aerosol anthropogenic fraction, aerosol absorption, and cloudy-sky effects. Further accounting for differences between the present-day natural and pre-industrial aerosols provides a direct radiative forcing estimate of −0.4±0.3Wm−2. The best estimate of globally-averaged, all-sky first indirect radiative forcing is −0.6±0.4Wm−2. Its standard deviation accounts for uncertainties in the aerosol anthropogenic fraction, and in cloud albedo and cloud droplet number concentration susceptibilities to aerosol changes. The distribution of first indirect radiative forcing is asymmetric and is bounded by −0.1 and −2.0Wm−2. In order to decrease uncertainty ranges, better observational constraints on aerosol absorption and sensitivity of cloud droplet number concentrations to aerosol changes are required.

To date, no observation-based proxy for climate change has been successful in quantifying the feedbacks between clouds and climate. The most promising, yet demanding, avenue to gain confi dence in cloud–climate feedback estimates is to utilize observations and large-eddy simulations (LES) or cloud-resolving modeling (CRM) to improve cloud process parameterizations in large-scale models. Sustained and improved satellite observations are essential to evaluate large-scale models. A reanalysis of numerical prediction models with assimilation of cloud, aerosol, and precipitation observations would provide a valuable dataset for examining cloud interactions. The link between climate modeling and numerical weather prediction (NWP) may be exploited by evaluating how accurate cloud characteristics are represented by the parameterization schemes in NWP models. A systematic simplifi cation of large-scale models is an important avenue to isolate key processes linked to cloud–climate feedbacks and would guide the formulation of testable hypotheses for fi eld studies. Analyses of observation-derived correlations between cloud and aerosol properties in combination with modeling studies may allow aerosol–cloud interactions to be detected and quantifi ed. Reliable representations of cloud dynamic and physical processes in large-scale models are a prerequisite to assess aerosol indirect effects on a large scale with confi dence. To include aerosol indirect effects in a consistent manner, we recommend that a “radiative fl ux perturbation” approach be considered as a complement to radiative forcing.