Abstract for Weekly Colloquium on Thursday, October 12, 2017 at 4 pm in Harshbarger 206 ~ Refreshments at 3:45 pm
Dr. Jonathan H. Jiang is a Principal Scientist of Engineering and Science Directorate and Managing Supervisor of Aerosol and Cloud Group at Jet Propulsion Laboratory (JPL), California Institute of Technology.
Dr. Jiang is also an American Geophysical Union (AGU) appointed Editor, overseeing the reviewing processes for AGU’s journal of Earth and Space Science, as well as Journal of Geophysical Research –Atmosphere. His principle research interests lie in satellite remote sensing, with emphasis on pollution transport, clouds, water vapor, and their climate impacts. He joined JPL in 1999 with interest in developing cloud simulator and cloud ice retrieval algorithm for the EOS MLS project, as well as building collaborations between satellite observation and climate modeling groups to advance climate research on both observation and modeling fronts. Dr. Jiang has authored and co-authored ~150 peer-reviewed publications. He has been twice awarded the NASA Exceptional Achievement Medals in 2010 and in 2013 for his leadership and innovation in climate studies using NASA satellite observations.
The Coupled Model Intercomparison Project (CMIP) is a standard experimental protocol for studying the output of coupled atmosphere-ocean general circulation models (AOGCMs). CMIP provides a community-based infrastructure in support of model diagnosis, validation, inter-comparison, documentation and data access. Observational metrics based on NASA data have been developed and effectively applied in the previous CMIP5 and post-CMIP5 model evaluation and improvement projects. As new physics and parameterizations continue to be included in models for the upcoming CMIP6, we are developing new methodologies to better constrain models with NASA satellite observations and support CMIP6 model assessments. We target parameters and processes related to atmospheric clouds and water vapor, radiative budget, climate feedbacks, and water and energy cycles, and thus reduce uncertainties in climate models.