Snowfall changes and climate sensitivity Perturbed Physics Ensemble Modeling: Linking Water Cycle to NASA Observations Investigators: Yongxiang Hu, Bruce Wielicki and Sharon Rodier Science Objectives: Assess the impact of NASA past and future observations on the improvement in understanding climate change; Link NASA global observations to prediction of changes in water cycle, both on global and regional scales Approach: Studying the relation between physical properties NASA can observe and temperature/rainfall changes predicted by models using outputs from thousands of models with perturbed parameterizations of physical processes. Models: Oxford group’s perturbed physics ensemble experiments with Hadley center coupled ocean/atmospheric climate models Progress: Model results: obtained model outputs from 1500 couple docean/atmospheric models, each with different physical parameterizations; Neural network mapping: established neural network based nonlinear relationships between observational metric (e.g., cloud, radiation, wind, …) and climate change metric (e.g., uncertainties in climate sensitivity, precipitation, …); Linking global cloud/radiation observations, climate sensitivity and changes in water cycle Snowfall changes and climate sensitivity NEWS linkages: Collaborated with Lin Lessons learned (Multi-satellite, multi-instrument, multi-model integration) What worked: Proposed tasks accomplished; What did not work: Suggestions: