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.

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.