Climate Change and Ozone Air Quality: Climate Change and Ozone Air Quality: Applications of a Coupled Applications of a Coupled

GCM/MM5/CMAQ Modeling SystemGCM/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

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

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

The New York Climate and The New York Climate and Health Project (NYCHP)Health Project (NYCHP)

Global ClimateNASA-GISS

GCMRegional Climate

MM5, RAMS

Air QualitySMOKE, CMAQ

Public HealthRisk

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 ClimateNASA-GISS

GCM

Air QualitySMOKE, 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)

Worldwide Total Annual CO2 Emissions

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35.00

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A2 ASFB2 MESSAGEA1B AIMA1G MINICAMA1T MESSAGEB1 IMAGE

Model SetupModel 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 DomainModeling 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 How Well Do the Models Do for the 1990’s?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 Summertime Average Observed and Predicted Observed and Predicted

Daily Maximum Daily Maximum TemperaturesTemperatures

• 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 Cumulative Distribution Functions of Summertime Daily Maximum Observed And Predicted Daily Maximum Observed And Predicted

Temperatures in the Entire Modeling DomainTemperatures 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) 27.3/29.3 26.2/27.7

Variance (ºC)2 17.8/27.5 23.6/27.7

Median (ºC) 27.8/29.4 26.1/27.5

2.5th Percentile (ºC) 17.2/19.4 16.3/18.3

25th Percentile (ºC) 23.9/26.7 22.9/24.1

75th Percentile (ºC) 30.6/32.8 29.6/31.5

97.5th Percentile (ºC) 34.4/36.7 35.2/37.2

Variance of Five Annual Median Values (ºC)2

0.39 0.36

Variance of Summer Temperature Time Series on Variance of Summer Temperature Time Series on Different Scales, Averaged Over the DomainDifferent Scales, Averaged Over the Domain

• Variance of longer-term fluctuations is captured by MM5

• Variance of shorter-term fluctuations is underestimated by MM5

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Observations

MM5

Daily maximum 1-hr ozone concentrations, averaged over all Daily maximum 1-hr ozone concentrations, averaged over all summer days 1993 – 1997, for observations (top) and CMAQ summer days 1993 – 1997, for observations (top) and CMAQ

predictions (bottom).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 Cumulative Distribution Functions of Hourly and Daily Maximum Observed And Predicted Ozone Daily Maximum Observed And Predicted Ozone

Concentrations in the Greater NYC AreaConcentrations 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 Variance of Average Summer Ozone Time Series on Different Time ScalesSeries 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

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CMAQ

Kirchhofer Map-Typing AnalysisKirchhofer 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

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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’sA 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?

Predicted Changes in Summertime Daily Predicted Changes in Summertime Daily Average Temperature for the 2050s “A2” Average Temperature for the 2050s “A2”

ScenarioScenarioGISS-GCM (left) and GISS-MM5 (right)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 Changes in Average Daily Maximum 1-hr Ozone ConcentrationsOzone 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 ChangeClimate Change vs. Emissions Change(VOC+8%, NO(VOC+8%, NOxx+29.5%)+29.5%)

Climate Change vs. Emissions Change:Climate Change vs. Emissions Change:Factor SeparationFactor Separation

Climate Emissions

1990s 1990s Base

1990s 2050s A2 Base + E

2050s 1990s Base + C

2050s 2050s 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

SummarySummary 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 StepsNext StepsSimulate different emissions scenarios

and decadesHigher resolution modeling for selected

episodesSimulate the effect of climate change on

the efficacies of U.S. emission control policies (e.g. CSA)

Include changes in land use / land coverPublic health impacts analysis