(42e) Evaluating the Role of Natural Variability in Assessments of Climate Change Impacts on Air Quality

Climate change is expected to significantly influence meteorology and may therefore impact future air quality. Several studies have attempted to estimate the effect of climate change on air pollution by using meteorological fields derived from general circulation model simulations to drive atmospheric chemistry and transport models and project future pollutant concentrations. However, large uncertainties are associated with climate simulations and may propagate into predictions of future air quality. Beyond uncertainty in emissions and model response, climate projections are significantly influenced by natural variability. However, little attention has been given to the role of natural climate fluctuations in modeling analyses aimed at quantifying the effect of climate change on air pollution. As internal variability intrinsically limits the ability of models to predict climate on time scales smaller than a decade, multiyear or multidecadal mean data may be necessary to adequately capture the climate signal and should be considered for air quality assessments.

In this study we investigate the effect of natural variability inherent to climate projections on future estimates of U.S. air quality. Future ozone and particulate matter concentrations are simulated with the Community Earth System Model driven by meteorological fields derived from an ensemble simulation of 21st century climate change carried out with the MIT Integrated Global System Model–Community Atmosphere Model. The influence of natural variability is explored by carrying out a series of multidecadal atmospheric chemistry simulations under present and future climates, using fixed anthropogenic emissions levels and multiple initial conditions. By modeling the changes in surface O₃ and PM_2.5 concentrations across the climate ensemble, we identify the magnitude of unforced interannual variations in air quality and the adequate time scales for climate-related impacts assessments. In addition, the analysis further examines how uncertainty in air quality projections due to internal variability may propagate into health effects estimates and influence the findings of climate impacts studies.