1. 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: