Cloud-climate feedbacks represent a major uncertainty in projections of climate change. Proxies for climate change (e.g., seasonal cycle, interannual variability, volcanic eruptions) have thus far been found to be unfit to quantify cloud feedbacks. Improved understanding of the cloud-climate feedback processes may lead to a better exploitation of existing data. Compositing techniques to separate feedback mechanisms, as well as the combination of model results with observational data, have already enhanced our understanding of cloud feedbacks. New analysis methods of cloud responses to forcing in models, and of natural variability, may yield further insights. A very promising, though demanding pathway is to improve cloud process parameterizations in large-scale models through the use of observations and LES or CRM, as well as by exploiting NWP experiences. Sustained observations from existing and additional satellites and ground-based sites are needed to support these approaches.

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