AQ2 4 Liang

Slide1: 

Application of an Integrated Modeling System for Climate and Air Quality Change Studies at Regional to Local Scales Xin-Zhong Liang (PI), xliang@uiuc.edu Illinois State Water Survey (ISWS) Co-PIs: Ho-Chun Huang, Allen Williams, Michael Caughey, Kenneth Kunkel (ISWS) Donald J. Wuebbles Department of Atmospheric Sciences (DAS) Other Investigators: Jinhong Zhu, Zhining Tao, Min Xu (ISWS) Kenneth O. Patten, Jintai Lin (DAS) University of Illinois at Urbana-Champaign

Objective: 

Objective To quantify, and understand the uncertainties of, the individual and combined impacts of global climate and emission changes on U.S. air quality, from the present to 2050 and 2100.

Hypothesis: 

Hypothesis U.S. air quality is determined by complex interactions over a range of spatial and temporal scales from chemical processes and emissions on local to regional scales long-range transport of global pollutants and precursors global and regional climate changes and variability

Slide4: 

Regional Model System Resolution: 30 km Present Climate andamp; GCM Projections Intercontinental Chemical Transport Model Global Emissions andamp; Projections Regional Air Quality Model Regional Climate Model Emissions Model U.S. NEI99 andamp; Projections Focal Areas Northeast Midwest West Coast Texas O3 NOx VOCs CO PM2.5 SO42- NO3- NH3 EC OC Global Model System Resolution: 150-300 km The nested global-regional modeling system: its components and their interactions Target Areas Resolution: 10 km

Slide5: 

D2c D1 Regional computational domain design. The global models provide the regional models with lateral boundary conditions in the buffer zones (shaded outer edges). The climate model uses a grid spacing of 30-km over domain D1, while the air quality model uses 90-km in D1 and 30-km in subdomains D2a-d. D2b D2d D2a

Uncertainty: 

Uncertainty Biases in the climate and chemistry arising from the driving global models and the incomplete physics of the regional models Different climate sensitivities of the driving global models and the regional models Inconsistencies in the coupled global-regional modeling system Pollutant emissions error Natural variability

Global Climate Models: 

Global Climate Models LOW climate sensitivity: Parallel Climate Model (PCM) of the National Center for Atmospheric Research/U.S. Department of Energy HIGH climate sensitivity: Hadley Centre third generation climate model (HAD)

Future Projections: 

Future Projections PCM simulations for 2050 and 2100 A1Fi : high emissions scenario B1: low emissions scenario HAD simulations for 2100 A2: intermediate high emissions scenario B2: intermediate low emissions scenario

Climate Model Results: 

Climate Model Results The following examples show the difference between GCMs in simulating the present climate and projecting future changes the effect of the RCM downscaling on regional climate simulations and projections

Slide10: 

Summary for Climate Simulations: 

Summary for Climate Simulations GCMs produce substantial regional climate biases and inter-model differences RCM downscaling reduces GCM climate biases and inter-model differences RCM downscaling projects smaller warming trends, particularly in the central U.S.

Air Quality Model Results: 

The following examples show the AQM ozone projections for 2100 under different emissions scenarios and incorporating climate changes the effect of the RCM climate sensitivity on AQM simulations and projections Air Quality Model Results

Slide13: 

Summary for AQ Simulations: 

Summary for AQ Simulations Ozone concentration depends sensitively on temperature as expected, but this sensitivity is regionally variable This sensitivity is particularly high in the Southeast U.S. where the difference between high and low emissions scenarios is pronounced If the high emissions scenarios are realized in the future, the Southeast U.S. may have a difficult time meeting the NAAQS O3 standard as a result of substantial O3 increase due to the projected warmer climate and more biogenic VOC emissions

Overall Regional Uncertainty: 

The following examples show the spread among models of Midwest temperature and ozone biases and future projections under various emissions scenarios the sensitivity of ozone temperature-dependence to regional emission changes Overall Regional Uncertainty

Slide16: 

U.S. Midwest regional temperature biases and future projections by GCMs (PCM, HAD) and their downscaling RCMs (GR, KF)

Slide17: 

U.S. Midwest regional ozone biases and future projections by GCMs (PCM, HAD) and their downscaling RCMs (GR, KF)

Slide18: 

The chemical LBCs predicted by MOZART cause AQM to produce overall more O3 than assuming clean air boundaries. The effect is especially large in northwest U.S., where 20-35 ppb more O3 may result from the long-range transport. Long-range transport can exacerbate local and regional air quality problems by elevating the background and loading during episodes. Long-Range Transport Impact on Local-Regional AQ

Conclusions: 

Conclusions The regional model downscaling can significantly reduce biases of the driving global models in simulating the present climate/air quality patterns and that this improvement has important consequences for future projections of regional climate/air quality changes. For both the present and future climate simulations, the regional model results are sensitive to the planetary forcings imposed by outputs from different global models as well as to its own physical process representations, especially different cumulus parameterizations, with strong regional dependence.

Conclusions: 

Conclusions Due to these sensitivities, there are large uncertainties involved in application of model projections of future climate/air quality changes for decision making at regional to local scales. Given significant uncertainties in estimating/projecting surface emissions and important contributions from long-range pollutant transport, decision making on U.S. air quality regulation is challenging.