2016 Progress Report: Project 3: Air Quality and Climate Change Modeling: Improving Projections of the Spatial and Temporal Changes of Multipollutants to Enhance Assessment of Public Health in a Changing World

EPA Grant Number: R835871C003
Subproject: this is subproject number 003 , established and managed by the Center Director under grant R835871
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).

Center: Solutions for Energy, AiR, Climate and Health Center (SEARCH)
Center Director: Bell, Michelle L.
Title: Project 3: Air Quality and Climate Change Modeling: Improving Projections of the Spatial and Temporal Changes of Multipollutants to Enhance Assessment of Public Health in a Changing World
Investigators: Zhang, Yang , Bell, Michelle L. , Leung, Lai-yung Ruby , Streets, David G.
Institution: North Carolina State University , Argonne National Laboratory , Battelle Memorial Institute, Pacific Northwest Division , Yale University
EPA Project Officer: Callan, Richard
Project Period: October 1, 2015 through September 30, 2020
Project Period Covered by this Report: October 1, 2015 through September 30,2016
RFA: Air, Climate And Energy (ACE) Centers: Science Supporting Solutions (2014) RFA Text |  Recipients Lists
Research Category: Air , Climate Change , Airborne Particulate Matter Health Effects , Particulate Matter , Global Climate Change


The main goal of Project 3 is to make critical improvements to online-coupled air quality models (AQMs) and their inputs and outputs, and apply the improved AQMs to estimate the concentrations resulting from energy and emission scenarios (Project 1) to be used in health risk assessments (Project 4). During this reporting period, we have three specific objectives: (1) perform 2008-2012 simulations using one global model and several regional AQMs and evaluate model performance using surface and satellite observations; (2) develop and apply methods to analyze extreme events such as heat waves in observations and climate model outputs for evaluating model ability to capture extreme events important for air quality; and (3) develop new strategies to generate high resolution snapshots of several air pollutants (NO2 and PM2.5) from satellites.

Progress Summary:

During this reporting period, we have completed the initial applications of a global model and three regional AQMs for a 5-year period (2008-2012) over the continental United States (CONUS). The global simulation results are used to provide initial and boundary conditions for regional simulations. We also performed a comprehensive evaluation using observational data from various surface networks and satellites. Our preliminary evaluation of global simulation shows that climatic variables are overall well predicted except for wind speed at 10-m and some cloud variables such as cloud optical depth and cloud droplet number concentrations, suggesting uncertainties in representing surface/cloud processes, and satellite retrievals. Chemical concentrations are well predicted for inorganic particulate matter over CONUS and Europe, black carbon, total carbon, PM2.5, and PM10 over CONUS, and for column CO over the globe. However, moderate-to-large biases exist for other species over various regions and most column variables. Our preliminary evaluation of regional simulations using three AQMs shows an overall good model performance in surface O3 and PM2.5 predictions. There is a good agreement in the simulated maximum 1-hr and 8-hr O3 mixing ratios among the three models, but larger differences exist in their simulated concentrations of PM2.5 and major PM species. Our evaluation indicates a need to diagnose the reasons underlying differences between model results and observations and among the three sets of regional model results to improve these models’ skill in reproducing observed O3, PM2.5, and other related variables.

We have performed analysis of heat wave duration using daily surface temperature from an ensemble of climate simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) for the present (1981-2000) and future (2030s and 2090s) with the RCP scenarios. Our analysis shows the largest change in heat wave duration in the 2090s with the RCP8.5 scenario in China, Southeast Asia, and Southeast United States. We also have analyzed the CESM Large Ensemble experiments to capture model internal variability. Both CMIP5 and CESM Large Ensemble simulations show consistent increase in temperature skewness in southeastern China and southeastern United States in the future. Increased temperature skewness is highly correlated with heat wave frequency.

We also have developed a new high resolution NO2 data set based on the standard NASA Ozone Monitoring Instrument NO2 product for the eastern United States. Our new product, derived from a high resolution simulation and observations, yields NO2 columns that are twice as large in urban areas and NO2 columns in rural areas that are 20-40% lower. Furthermore, the new NO2 retrievals are now able to capture the magnitude of the concentration gradient between urban and rural areas, which is in better agreement with the current EPA NO2 monitoring network. In addition, we have derived high resolution snapshots of PM2.5 from MODIS aerosol optical depth with reasonable correlation with ground monitors.

Future Activities:

We will perform diagnostic evaluation and sensitivity simulations using global and regional models to pinpoint the likely causes of the large model biases for potential improvement of the model’s skills. Improved global model results will be used to provide improved initial and boundary conditions to drive regional modeling. We will set up regional simulations using the 4th regional AQM. The results from the four regional AQMs will be intercompared and analyzed to identify reasons for large model biases. These regional models will be improved to obtain the best possible performance for air quality and human exposure studies under current climate/emission conditions. We will perform similar analysis of temperature skewness and heat wave using outputs from regional AQMs for the United States. We also will develop methods to quantify compound extreme events, such as drought and heat waves and apply the methods to understand and estimate changes in compound extreme events projected by CMIP5 models and regional AQMs, with a focus over the United States. We intend to compare our high resolution satellite product with the high density NO2 and PM2.5 measurements from Project 2. Furthermore, we will compare high-resolution satellite data with model results to suggest improvements to the emission inventory.

Supplemental Keywords:

Air quality, climate ensemble analysis, climate extreme, downscaling, global and regional modeling, heat wave, model evaluation, model improvement, O3, PM2.5, satellite retrieval

Relevant Websites:

SEARCH (Solutions for Energy, Air, Climate, & Health) Center | Yale School of Forestry and Environmental Studies Exit

Progress and Final Reports:

Original Abstract
  • 2017

  • Main Center Abstract and Reports:

    R835871    Solutions for Energy, AiR, Climate and Health Center (SEARCH)

    Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
    R835871C001 Project 1: Modeling Emissions from Energy Transitions
    R835871C002 Project 2: Assessment of Energy-Related Sources, Factors and Transitions Using Novel High-Resolution Ambient Air Monitoring Networks and Personal Monitors
    R835871C003 Project 3: Air Quality and Climate Change Modeling: Improving Projections of the Spatial and Temporal Changes of Multipollutants to Enhance Assessment of Public Health in a Changing World
    R835871C004 Project 4: Human Health Impacts of Energy Transitions: Today and Under a Changing World