(6am) Climate Change, Air Quality and Public Health: An Uncertainty Analysis Conference: AIChE Annual MeetingYear: 2014Proceeding: 2014 AIChE Annual MeetingGroup: Education DivisionSession: Poster Session: Meet the Faculty Candidate Time: Sunday, November 16, 2014 - 2:00pm-4:00pm Authors: Garcia Menendez, F., Massachusetts Institute of Technology The central goal of this research is to explore the connections between climate change, air quality and public health through numerical modeling, with an emphasis on uncertainty in simulations of human-climate interactions. Climate change is expected to significantly influence meteorology and impact future air quality. In addition, climate change mitigation strategies will also affect emissions and concentrations of air pollutants. Earth system models are vital to advance our understanding of atmospheric processes and are used as key tools to steer environmental policies with enormous economic and social implications. However, there are large uncertainties associated with climate modeling that further propagate into estimates of future air quality, as well as projections of associated health and economic impacts. These uncertainties must be identified and quantified to adequately assess the changes brought about by different climate policies. Furthermore, characterizing uncertainty across the complete climate change problem is essential to target the largest potential reductions to uncertainty in existing modeling systems and allow for improved impact and adaptation analyses. Towards this end, a modeling framework to simulate air quality and associated health impacts under different climate scenarios has been developed using global earth systems modeling with atmospheric chemistry capabilities. Future pollutant concentrations are simulated with the Community Earth System Model (CESM), while the MIT Integrated Global System Model is used to predict climate change during the 21st century and beyond. The framework can be used to explore the most important sources of uncertainty in climate projections: emissions of climate forcers and conventional air pollutants under changing policy scenarios; model response due to structural uncertainty; and natural climate variability. By using this modeling framework to simulate changes in air quality for ensemble simulations of 21st century climate change, the propagation of climate uncertainties into atmospheric pollutant concentrations predictions, as well as associated health and economic impacts can be evaluated through probabilistic analyses. This information will assist in developing more meaningful climate impacts and risk assessments and better guide decision-making.