Climate Change and Ozone Air Quality: Applications of a Coupled GCM/MM5/CMAQ Modeling System C. Hogrefe 1, J. Biswas 1, K. Civerolo 2, J.-Y. Ku 2, B. Lynn 3, J. Rosenthal 3, K. Knowlton 3, R. Goldberg 4, C. Rosenzweig 4, and P.L. Kinney 3 1 Atmospheric Sciences Research Center, State University of NY at Albany, 2 NYS Dept. of Environmental Conservation, 3 Columbia University, 4 NASA-Goddard Institute for Space Studies This project is supported by the U.S. Environmental Protection Agency under STAR grant R Models-3 Users’ Workshop, October 27, 2003, RTP

The New York Climate and Health Project (NYCHP) Global Climate NASA-GISS GCM Regional Climate MM5, RAMS Air Quality SMOKE, CMAQ Public Health Risk Assessment Changing Regional Land Use / Land Cover SLEUTH, Remote Sensing, IPCC SRES Scenarios Changing Greenhouse Gas Emissions IPCC SRES Scenarios Changing Ozone Precursor Emissions IPCC SRES Scenarios Regional Climate MM5, RAMS Global Climate NASA-GISS GCM Air Quality SMOKE, CMAQ Changing Greenhouse Gas Emissions IPCC SRES Scenarios Changing Ozone Precursor Emissions IPCC SRES Scenarios

SRES A2: “A very heterogeneous world. The underlying theme is that of strengthening regional cultural identities, with an emphasis on family values and local traditions, high population growth, and less concern for rapid economic development.” SRES B2: “A world in which the emphasis is on local solutions to economic, social, and environmental sustainability. It is again a heterogeneous world with less rapid, and more diverse technological change but a strong emphasis on community initiative and social innovation to find local, rather than global solutions.” (IPCC Data Distribution Center)

Model Setup GISS coupled global ocean/atmosphere model driven by IPCC greenhouse gas scenarios (“A2” high CO 2 scenario presented here) MM5 regional climate model takes initial and boundary conditions from GISS GCM MM5 is run on 2 nested domains of 108km and 36km over the U.S. CMAQ 4.2 is run at 36km to simulate ozone (CB-IV) 1996 U.S. Emissions processed by SMOKE and – for some simulations - scaled by IPCC scenarios BEIS2 for biogenic emissions and Mobile5b for mobile source emissions Simulations periods : June – August June – August

Modeling Domain 36 km MM5/CMAQ domain and NYCHP 31- county area of interest around New York City About 400 ozone and temperature monitors in the entire domain About 20 ozone and temperature monitors in the 31-county area

How Well Do the Models Do for the 1990’s? Compare MM5/CMAQ predictions for temperature and ozone to observations Examine spatial patterns and different aspects of variability: – Cumulative Distribution Functions (CDFs) – Extreme values (exceedance of thresholds) – Variance on different time scales Compare observed and predicted ozone concentrations under different synoptic regimes

Summertime Average Observed and Predicted Daily Maximum Temperatures The GCM-driven MM5 captures the spatial temperature gradients oriented along lines of latitude Daily maximum temperatures tend to be underestimated in the northern portion of the modeling domain, while they are overestimated in the southern portion of the modeling domain

Cumulative Distribution Functions of Summertime Daily Maximum Observed And Predicted Temperatures in the Entire Modeling Domain Good agreement between observed and modeled CDF, but: Interannual variability slightly underestimated Predicted daily maxima generally lower than observed Observations (lowest/highest) MM5 (lowest/highest) Mean (ºC)27.3/ /27.7 Variance (ºC) / /27.7 Median (ºC)27.8/ / th Percentile (ºC)17.2/ / th Percentile (ºC)23.9/ / th Percentile (ºC)30.6/ / th Percentile (ºC)34.4/ /37.2 Variance of Five Annual Median Values (ºC)

Variance of Summer Temperature Time Series on Different Scales, Averaged Over the Domain Variance of longer-term fluctuations is captured by MM5 Variance of shorter-term fluctuations is underestimated by MM5

Daily maximum 1-hr ozone concentrations, averaged over all summer days 1993 – 1997, for observations (top) and CMAQ predictions (bottom). The GCM/MM5 driven CMAQ captures the spatial pattern of summertime average daily maximum ozone concentrations (R~0.7)

Cumulative Distribution Functions of Hourly and Daily Maximum Observed And Predicted Ozone Concentrations in the Greater NYC Area CMAQ captures observed interannual ozone variability Overestimation of low observed daily maximum 8-hr ozone concentrations

Variance of Average Summer Ozone Time Series on Different Time Scales Variance of longer-term fluctuations is captured by CMAQ Variance of shorter-term fluctuations is underestimated by CMAQ, presumably because of the fairly coarse horizontal and vertical grid spacing used

Kirchhofer Map-Typing Analysis Method computes correlations between maps of gridded sea level pressure to find the most representative patterns Gridded “Observations” from the archived 40 km ETA surface analysis for were used to evaluate the “ ” GCM/MM5 predictions After determining the most representative observed sea level pressure patterns, each observed and predicted day is assigned to one of these patterns and the average daily maximum observed and predicted ozone concentration associated with each pattern is determined

14% 17% 13% 23% 15% 11% Observed and CMAQ-Predicted Daily Maximum Ozone Concentrations for the Five Most Frequently Observed Summertime Sea Level Pressure Patterns (Left) Correlation coefficient between observed and predicted patterns ~0.75 GCM-MM5- CMAQ system captures the influence of synoptic-scale meteorology on ozone concentrations

A Model Look Into the 2050’s How will modeled temperature and ozone in the northeastern U.S. change under the “A2” (high CO 2 growth) scenario (assume constant VOC and NO x emissions)? – Which aspects of distributions will be subject to changes (means, extremes)? – Will changes be distinguishable from interannual variability in the modeled 1990’s? How will CMAQ ozone predictions change when IPCC “A2” projected changes in ozone precursor emissions (VOC+8%, NO x +29.5%) are included in the simulation?

Predicted Changes in Summertime Daily Average Temperature for the 2050s “A2” Scenario GISS-GCM (left) and GISS-MM5 (right)

Distribution of Predicted Daily Maximum Temperatures in the 1990s and 2050s Future distributions are shifted upward The shift is larger than the predicted interannual variability for the 1990s

Changes in Average Daily Maximum 1-hr Ozone Concentrations CMAQ predicts an increase of ozone concentrations over large areas of the modeling domain as a result of the changed regional climate

Climate Change vs. Emissions Change (VOC+8%, NO x +29.5%)

Climate Change vs. Emissions Change: Factor Separation ClimateEmissions 1990s Base 1990s2050s A2 Base +  E 2050s1990s Base +  C 2050s2050s A2 Base +  E +  C +  EC  E: Pure effect of anthropogenic emission changes  C: Pure effect of climate change (biogenic emissions, temperatures, flow patterns)  EC: Synergistic effects

Summary The GCM/MM5/CMAQ system captures synoptic-scale and interannual variability of summertime temperatures and ozone CMAQ paints a plausible picture of summertime ozone concentrations and variability Predicted temperature and ozone changes are larger than 1990’s interannual variability Even with constant anthropogenic precursor emissions, CMAQ predicts an increase in average and extreme ozone concentrations Increasing precursor emissions cause a further deterioration of predicted ozone air quality, but the relative impact of climate change vs. emission changes varies from region to region

Next Steps Simulate different emissions scenarios and decades Higher resolution modeling for selected episodes Simulate the effect of climate change on the efficacies of U.S. emission control policies (e.g. CSA) Include changes in land use / land cover Public health impacts analysis