Impacts of Climate Change and Global Emissions on US Air Quality: Development of an Integrated Modeling Framework and Sensitivity AssessmentEPA Grant Number: R830961
Title: Impacts of Climate Change and Global Emissions on US Air Quality: Development of an Integrated Modeling Framework and Sensitivity Assessment
Investigators: Adams, Peter , Pandis, Spyros N.
Institution: Carnegie Mellon University
EPA Project Officer: Chung, Serena
Project Period: March 23, 2003 through March 22, 2006 (Extended to March 22, 2007)
Project Amount: $900,000
RFA: Assessing the Consequences of Global Change for Air Quality: Sensitivity of U.S. Air Quality to Climate Change and Future Global Impacts (2002) RFA Text | Recipients Lists
Research Category: Air Quality and Air Toxics , Climate Change , Air , Global Climate Change
A comprehensive modeling system for determining impacts of climate change on air quality will be developed. Novel approaches for separating the impact of climate change from climate variability will be investigated. Where feasible, we will propose simplified modeling approaches to capture such impacts in future assessment work.
Future changes in climate and global pollutant emissions may provide additional challenges to air quality management in the US. The goal of this study is to quantify the expected magnitude and range of these impacts on ozone, PM2.5 and PM10 concentrations, acid deposition, and visibility. The study will cover the whole country with emphasis on the Northeast, the Southwest, and South US. The impacts on both air pollution episodes and annual average conditions will be investigated.
A comprehensive modeling system will be developed combining the GISS global climate and chemistry model, with regional meteorological (MM5) and multiscale chemical transport (CTM) models (PMCAMx and CMAQ). The modeling system will describe atmospheric chemistry and dynamics in four scales: the global (4 x 5 degrees), the continental (36 x 36 km), the regional (12 x 12 km), and the urban (4 x 4 km). Predicted variables will include the concentrations of ozone and its precursors, the aerosol size/composition distribution, acid deposition fluxes, and visibility. An emissions processing system that accounts for climate-sensitive emissions (e.g., biogenic, evaporative emissions, etc.) will be developed as part of the comprehensive modeling system. The CTM will use the “relative-smart” aerosol, aqueous-phase, and gas-phase chemistry modules developed by our group to maximize computational efficiency without sacrificing accuracy. These modules decide and use the appropriate simplifying assumptions for each computational cell for each time-step based on the chemical and meteorological conditions in the cell. A set of future scenarios that spans permutations of emissions and climate changes will be simulated by this modeling system to quantify their effects individually and together.
Climate models simulate multiple decades to separate anthropogenic climate change from the natural variability of the system. Even with a computationally efficient CTM, the simulation of hundreds of years is not feasible. To overcome this major challenge we will use three approaches. First, we will develop a statistical method for the selection of a limited number of periods from the ensemble of climate simulations (without any chemistry) for subsequent air quality modeling. The second approach will use the global climate and global chemistry models for the simulation of multiple-decadal periods. Third, relationships between global and regional model output will be derived, via statistical and neural network approaches, such that regional-scale episodes can be predicted based on global model results alone.
We will investigate whether simpler models can be used to quantify the global emissions and climate change impacts. Candidate simplified models include a regression model based on the CTM sensitivity analysis, the CTM alone, the global chemistry model alone, or either in conjunction with statistical or neural network methods.