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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

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climate change and ozone air quality applications of a coupled gcm mm5 cmaq modeling system

Climate Change and Ozone Air Quality: Applications of a Coupled GCM/MM5/CMAQ Modeling System

C. Hogrefe1, J. Biswas1, K. Civerolo2, J.-Y. Ku2, B. Lynn3, J. Rosenthal3, K. Knowlton3, R. Goldberg4, C. Rosenzweig4,and P.L. Kinney3

1Atmospheric Sciences Research Center, State University of NY at Albany, 2NYS Dept. of Environmental Conservation,3Columbia University,4NASA-Goddard Institute for Space Studies

Models-3 Users’ Workshop, October 27, 2003, RTP

This project is supported by the U.S. Environmental Protection Agency under STAR grant R-82873301

the new york climate and health project nychp

Changing Greenhouse Gas Emissions

IPCC SRES Scenarios

Changing Greenhouse Gas Emissions

IPCC SRES Scenarios

Global Climate

NASA-GISS GCM

Global Climate

NASA-GISS GCM

Regional Climate

MM5, RAMS

Regional Climate

MM5, RAMS

Public Health

Risk Assessment

Changing Regional Land Use / Land Cover

SLEUTH, Remote Sensing, IPCC SRES Scenarios

Air Quality

SMOKE, CMAQ

Air Quality

SMOKE, CMAQ

Changing Ozone Precursor Emissions

IPCC SRES Scenarios

Changing Ozone Precursor Emissions

IPCC SRES Scenarios

The New York Climate and Health Project (NYCHP)
slide3

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
Model Setup
  • GISS coupled global ocean/atmosphere model driven by IPCC greenhouse gas scenarios (“A2” high CO2 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 1993-1997

June – August 2053-2057

modeling domain
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
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
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
slide8
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
variance of summer temperature time series on different scales averaged over the domain
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
slide10
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)
slide11
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 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
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 1996-2000 were used to evaluate the “1993-1997” 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
slide14

23%

14%

15%

13%

11%

17%

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
A Model Look Into the 2050’s
  • How will modeled temperature and ozone in the northeastern U.S. change under the “A2” (high CO2 growth) scenario (assume constant VOC and NOx 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%, NOx+29.5%) are included in the simulation?
slide16
Predicted Changes in Summertime Daily Average Temperature for the 2050s “A2” ScenarioGISS-GCM (left) and GISS-MM5 (right)
slide17

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
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 factor separation
Climate Change vs. Emissions Change:Factor Separation

E: Pure effect of anthropogenic emission changes

C: Pure effect of climate change (biogenic emissions, temperatures, flow patterns)

EC: Synergistic effects

summary
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
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
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