Upload
tobias-gregory
View
24
Download
1
Embed Size (px)
DESCRIPTION
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 - PowerPoint PPT Presentation
Citation preview
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
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
1990
2000
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Decade
To
tal C
O2
Em
issi
on
s (G
tC)
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
0123456789
10
Var
ian
ce (
C^
2)
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
00.050.10.150.20.250.30.350.4
Intra-Day
Diurnal
Synoptic
Baseline
Var
ianc
e
Observations
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
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’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