Evaluating the Contribution of Natural Variability and Climate Model Response to Uncertainty in Projections of Climate Change Impacts on U.S. Air Quality

Thursday, 18 December 2014
Fernando Garcia Menendez1, Erwan Monier1 and Noelle E Selin2, (1)Massachusetts Institute of Technology, Center for Global Change Science, Cambridge, MA, United States, (2)Massachusetts Institute of Technology, Engineering Systems Division, Cambridge, MA, United States
We examine the effects of internal variability and model response in projections of climate impacts on U.S. ground-level ozone across the 21st century using integrated global system modeling and global atmospheric chemistry simulations. The impact of climate change on air pollution has been explored by using general circulation model simulations to drive air quality models and project atmospheric pollutant concentrations. However, the uncertainties associated with climate projections and their propagation into air quality simulations must be well understood when undertaking these assessments. Here, we investigate the influence of climate uncertainty on projections of U.S. ground-level ozone beyond greenhouse gas emissions scenario by evaluating the roles of natural variability and climate sensitivity. The Community Atmosphere Model with Chemistry is used to simulate ozone concentrations and driven by meteorological fields generated through the MIT Integrated Global System Model (IGSM-CAM). Under unconstrained and stabilization greenhouse gas emissions scenarios, 30-year simulations centered around the years 2000, 2050 and 2100 are carried out using multiple initial conditions to assess the influence of internal variability on projected ozone changes. The climate penalty on U.S. air quality is estimated by fixing anthropogenic emissions of conventional air pollutants and precursors at year 2000 levels. The effect of climate model response is evaluated by perturbing climate sensitivity within the IGSM-CAM through a cloud radiative adjustment method and comparing ozone projections under climate sensitivities equal to 3.0 and 4.5˚C. In addition, we explore the propagation of these uncertainties into population-weighted pollutant concentrations and their potential impact on health impact assessments. In the ensemble simulation of 21st century climate we identify an important influence of natural variability and climate model response on ozone projections and find that they contribute significantly to the uncertainty associated with climate penalty estimates.