A statistical method for calculating the impact of climate change on future air quality over the Nor

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A statistical method for calculating the impact
of climate change on future air quality over the
Northeast United States.
Collaborators Cynthia Lin, Katharine Hayhoe,
Edwin Maurer, Christian Hogrefe, Pat Kinney,
Daniel Jacob
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Relationship of meteorology and ozone episodes
Northeast
Curves include effects of T-dependent biogenic
emissions, stagnation, and chemistry.
Lin et al., 2001
Curves represent the total derivative dO3/dT,
the sum of partial derivatives (dO3/dxi)(dxi/dT),
where x is the ensemble of ozone forcing
variables that are temperature-related.
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A statistical method to study effects of climate
change on air quality. Idea Use probabilities of
ozone exceedance daily GCM maximum temperatures
to predict number of exceedance days for each
summer in future. Step 1. Find exceedance
probability for each model days maximum
temperature Step 2. Sum up probabilities for
ensemble of summer days to get total exceedances
for that summer.
1.
2.

A1
Lin et al. 2001
future smog episodes
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• Assumptions
• Emissions of ozone precursors remain constant
  over time.
• For each region, the suite of conditions that
  lead to high ozone levels do not vary over time.
• Models and scenarios
• Statistically downscaled temperatures from the
  GFDL, PCM, Hadley Centre models.
• A1fi and B1 scenarios.

A1fi
Trends in CO2 emissions, 2000 to 2100
B1
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• Statistically downscaled temperatures use
  observations to train GCM.
• Interpolate observed 1961-1990 temperatures onto
  1/8o grid. Calculate monthly mean and variability
  at each gridpoint.
• 2. Apply bias correction to GCM temperatures and
  apply observed probability density functions so
  that model matches observations.
• T downscaled (x,t) T GCM
  gridsquare (t) a b(x) c(x,t)
• 3. Apply these same fixes to future GCM data
  possible problem?

Hayhoe et al., 2006. Apologies for unclear
temperature scales.
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Frequency distributions of present-day daily
maximum temperatures.
Hadley
Obs
GCM, Northeast U.S.
Historical means present-day. GCM data for
1961-1990 PCM, Hadley, and GFDL. Observations
are for 1980-1998.
GFDL
PCM
Obs
Downscaling makes GCM output look nearly
perfect. Mismatch at high end noted in Hayhoe et
al., 2006, but may also be due to our spatial
averaging. We are investigating.
Downscaled, Northeast U.S.
models
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Summertime exceedances averaged over the
Northeast, calculated using daily max
temperatures statistically downscaled from three
GCMs.

• Exceedance days averaged over the Northeast
• Increase by 10-30 by 2020s
• Double by 2050
• Increase beyond 2050 depending on climate
  scenario and on model
• Caveats
• Assumption is that anthropogenic emissions
  remain constant.
• Method does not capture high ozone episodes
  during recent past we are investigating.
• Use same technique for PM2.5?

A1Fi
CMAQ
B1
observations
Lin et al., 2007
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Deterministic approach
Statistical approach
Advantages day-by-day calculations, allows for
changes in anthropogenic emissions of ozone
precursors and for detailed diagnosis of causes
of ozone change (transport, VOCs, boundary layer
height, clouds. . .)
Advantages fast, takes into account many factors
that affect ozone levels, based on observations,
allows for easy model intercomparison across many
years, good for near-term outlook.
GCM
met BC
GCM
Regional climate model
Daily max Ts
met fields
statistical downscaling
downscaled met
Global chemistry model
Regional chemistry model (i.e., CMAQ)
Probabilities of ozone exceedances Exceedance f
(maxT)
chem BC
FUTURE AIR QUALITY
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Frequency distributions for daily max
temperatures comparison between future model and
observed present-day
Frequency distributions are for 2070-2099 model
results. Obs are from 1980-1999
Downscaled GCM output does not capture the change
in temperature probability distributions. This
could be a problem for calculating air quality.
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Advantage of method Quick gauge of ozone
sensitivity to climate and of climate
penalty. Limitations Assumes constant emissions
of ozone precursors, and unvarying temperature
met variable relationships Also may not capture
variability of exceedances.
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Emissions of carbon dioxide across the 21st
century for a range of scenarios.
Scenarios used by UCS A1F1 B1
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Unscaled GCM results are consistent with our
previous research with GISS GCM
Our research shows that higher maximum
temperatures are linked to longer stagnation
periods in future climate. Reduced meridional
temperature gradient Increased eddy transport
of latent heat
fewer cold fronts more
persistent heat waves