Gauging Uncertainty in Climate Models: Implications for atmospheric chemistry and health
Loretta J. MickleyNovember 29, 2012School of Public Health
GCM = General Circulation Model, Global Climate Model
2
Basic working of climate models
All climate models depend on basic physics to describe motions and thermodynamics of the atmosphere:
E.g., vertical structure of pressure is described by hydrostatic equation
( ) ( ) a adPP z P z dz gdz gdz
Climate models also depend on parameterizations for many processes.
E.g., microphysics of cloud droplet formation, vegetation processes.
Tilt of earth, geography, greenhouse gas content
Weather + Climate
InputPhysics + Parameterized processes
Climate model Output
1950 2000 2010 2050
2. Initialize with observed sea surface temperatures, then let run free. Force with observed greenhouse gas trends.
1. Continually nudge model with observations from satellites, surface
3. Run model forced by scenarios of greenhouse gases and aerosols.
Three ways to run a climate model. There are many variations!
4. Start centuries earlier with estimated ocean Ts.5. Run with carbon cycle online.
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Detour: how 3-D chemistry models work.
emissionstransportdilutionchemistry
particulate matter (PM)and ozone pollution
population
GEOS-Chem chemical transport model: Global 3-D model describes the transport and chemical evolution of atmospheric pollutants
winds Winds carry pollutants to other boxes.Emissions + chemistry
calculated within boxMeteorology from a climate model
Validation of present-day climate models
14 models, 58 simulations
mean of models
Observed global mean temperature anomalies
Temperature anomalies relative to 1901-1950 mean
Models allowed to run freely, forced only by observed trends in greenhouse gases and aerosols. What causes spread in model response?
IPCC, 2007
What causes spread in model response?
1. Climate chaos = “Butterfly effect” = noise, interannual variability Starting the very same model with slightly different initial conditions will yield different day-by-day or year-by-year results.
Mickley et al., 2011
Plot shows regional warming due to removal of US aerosol starting in 2010.
Red dotted curves are results with same A1B forcing, but different initial conditions. Green is same but no US aerosol.
Modelers run ensembles of simulations.
Temperature anomalies over eastern US
9-year running means
What causes spread in model response?
2. Differences in parameterizations or model resolutions, which lead to differences in model sensitivity to changing forcing.
Global mean temperature response to 1% a-1 increase in CO2 for ~20 models.
One simulation per model.
Doubled CO2 IPCC, 2001
What causes spread in model response?3. Unknown processes, lack of understanding of basic processes.
E.g. aerosol indirect effect, aerosols provide cloud condensation nuclei.
Range of estimates of aerosol indirect forcing in Wm-2 in present-day atmosphere varies greatly among many models.
IPCC 2007By comparison, CO2 forcing ~ +1.6 W m-2
The observed atmosphere also has “noise.”
14 models, 58 simulations
mean of models
Observed global mean temperature anomalies
Temperature anomalies relative to 1901-1950 mean
Even a “perfect” model cannot capture observed temperatures exactly because of climate chaos.
Hard to tell what is signal and what is noise. How long should noise last? Years? Decades?
IPCC, 2007
Signal or noise?
Another source of uncertainty in future simulations is the path of socio-economic development.
Different scenarios follow different socio-economic paths for developed and developing countries.
IPCC 2007
Global mean surface temperature anomalies
A2 = heavy fossil fuelB1 = alternative fuelsA1B = mix of fossil + alternative fuels
Another source of uncertainty: abrupt climate change
Younger Dryas period= sudden cooling, followed by abrupt warming.
Greenland warmed by 7oC in a few decades.
Earth system hits a tipping point and is thrown into new state. Possible triggers: • Loss of sea ice• Reversal in ocean
currents
Last Ice Age
http://www.ncdc.noaa.gov
Future regional predictions for meteorology in A1B 2100 atmosphere show large variation across North America.
Percent change in 2100 precipitation relative to present-day
Number of models showing increasing precipitation
IPCC 2007
most models
few models
Annual DJF JJA
Exploring the uncertainty in climate models: big field of research
Can we better characterize the spread of uncertainty in one model?
Response of model to abrupt doubling of CO2 shows large spread.
Results from 90K simulations, each simulation with varied parameters for cloud processes. Large number of simulations needed to capture spread.Researchers colonized personal laptops across UK (like SETI project).
Stainforth et al., 2005
Global mean Temperature response to 2x CO2.
For the effect of climate change on air quality, we need to think about changes in episodic phenomena, e.g.:• Stagnation• Heat waves• Wildfires
Probability of ozone exceedance
Northeast/ mid Atlantic in summer
maximum daily temperature (K)
Pro
babi
lity
Reasons for increasing probability of ozone exceedances at higher max temps:• Greater stagnation + clear skies• Faster chemical reactions.• Greater biogenic emissions
Lin et al., 2001
Climate change and air quality
Calculation of maximum temperatures in climate models is sensitive to choice of parameters having to do with land cover/soil.
Lower and upper estimates of JJA maximum temperatures in 2x CO2 atmosphere
Central 80% range of increases for 44 versions of one climate model, with varying land cover parameters. oC
Percent variability in Tmax accounted for by vegetation parameters.
Clark et al., 2010 15
Forest roughness parameter Vegetation root depth
Lower estimate Upper estimate
50%30%6%
0 8
Surface ozone levels are sensitive to cold-front passage.
How will frequency of cold-front passages change in future?
Leibensperger et al., 2008
Multiple linear regression coefficients for total PM2.5 on meteorological variables. Units: μg m-3 D-1 (p-value < 0.05)
Stagnation is also strongly correlated with high PM2.5.
Mean PM2.5 is 2.6 μg m-3 greater on a stagnant day
Tai et al. 2010
Correlations of PM2.5 with key meteorological variables.
1998-2008 meteorology + EPA-AQS observations
Increases in total PM2.5 on a stagnant day vs. a non-stagnant day.
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Principal component (PC) decomposition of eight meteorological variables (xk) to identify dominant meteorological regimes that drive PM2.5 variability:
Time series for dominant PC and deseasonalized PM2.5: Midwest in Jan 2006
r = -0.54
Dominant meteorological modes driving PM2.5 in much of Midwest and East are associated with cyclone passage.
2
10
-1-2
Dominant PC in Midwest consists of low T, low and rising surface pressure, strong NW wind.
Meteorology signals the arrival of a cold front.
Dominant PC in East is cyclone passage, in West is maritime inflow.
6
30
-3-6
PCObserved
PM2.5
(µg m-3)
Jan 28 Jan 3018Tai et al., 2011
PC
PM2.5
Evaluation of present-day meteorological modes in AR4 climate models reveals differences among models.
Modeled (2 IPCC models) and observed (NCEP/NCAR) 1981-2000 time series of frequency of dominant meteorological mode for PM2.5 in U.S. Midwest
Freq
uenc
y (d
-1)
Some models capture both the long-term mean and variability of meteorological mode frequency well.
As a first step, we use only those models that capture present-day mean and variability of frequency to predict future PM2.5
N42° W87.5°
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Observed
2 sample models
We compare the observed and AR4 modeled frequency of those meteorological modes driving PM2.5 variability across US.
Modeled (IPCC) and observed (NCEP/NCAR) 1981-2000 mean of frequency of dominant meteorological modes for PM2.5 in U.S. (western, central, eastern)
Mod
eled
20-
year
mea
n fre
quen
cy (d
-1)
Observed 20-year mean frequency (d-1)
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We choose the 9 models whose frequency of the dominant meteorological modes best agrees with observations.
We apply sensitivity of PM2.5 to changing frequency of dominant meteorological mode in the A1B atmosphere.
Models show increased duration of stagnation, with corresponding increases in annual mean PM2.5.There is huge variation among models.
2000-2050 climate change leads to increases in annual mean PM2.5 across much of the Eastern US.
1981-2065 change in period of dominant meteorological modes for PM2.5 variability
averaged over 9 IPCC models
Corresponding 1981-2065 change in annual mean PM2.5 concentrations (unit: µg m-3)
day
mg m-3
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Another example: wildfires in the Western US in a future climate
Our previous research has shown that area burned depends largely on temperature, precipitation, and relative humidity.
Median change in key variables by 2050s relative to present-day, calculated by 14 AR4 models.
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Models show large variation in changes in key wildfire variables across western US.
Projected changes in key variables by 2050s, relative to present-day, across 6 ecoregions in the Western US.
JJA only.
PNW, Pacific Northwest CCS, California Coastal ShrubDSW, Desert Southwest NMS, Nevada Mountains /Semi-desert RMF, Rocky Mountains ForestERM, Eastern Rocky Mountains /Great Plains.
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Medians for all regions show increases in area burned.
Wildfire in Western United States in 2050s A1B climate.R
atio
of 2
050s
/ pr
esen
t-day
Ratio of 2050s area burned / present-day area burned
Pacific
Nort
hwes
t
Desert
Sou
thwes
t
Nevad
a Mou
ntains
Rocky
Mou
ntatin
s
Easter
n Roc
kies
Califor
nia C
oasta
l Shru
b
median
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Given the uncertainties in climate models, how can atmospheric chemists/ epidemiologists proceed?
1. Compare signal of change to noise (interannual variability).
2. Look for those models that best capture present-day variables of relevance to atmospheric chemistry / health. Then use projections from only that subset of models.
3. Calculate the probabilities of specific changes. Most simply, give equal weight to all models/ensemble members, then calculate the percentage that show a specific effect.
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Three ways to study chemistry-climate interactions.
1950 2000 2010 2050
2. Apply model chemical fields (ozone + aerosols) to climate model
3. Archive meteorology needed to run chemistry model.
1. Implement chemistry scheme inside climate model! But this is computationally expensive. Chemistry can be simplified.
Physics + Parameterized processes +Chemistry
Climate model
