1. Discussion with NAS ITAP Committee (arlene.fiore@noaa.gov)
Arlene M. Fiore
“characterizing uncertainties in transport / photochemistry models”
Acknowledgments: D. Reidmiller, I. Bey, F. Dentener, M. Evans, I. Held, D. Jaffe,
T. Keating, M. Schultz, R. Stouffer + the TF HTAP modeling team
August 26, 2008

2. Characterizing uncertainty: IPCC AR-4 suggests approx
Characterizing uncertainty: IPCC AR-4 suggests approx. equal contributions from scenarios and models
Figure SPM.5, Summary for Policymakers
Estimates with CTMs generally include the same uncertainties as in climate models, plus chemistry (deposition, etc), plus ??

3. Possible considerations for the panel: Characterizing uncertainty in CTMs
Characterizing uncertainty: Need for new approaches?
Evaluation with observations: Screen out “less useful” models?
Role of individual models vs. ensembles?
What information is most useful (in the policy process)?

4. Approaches in sister communities: “Grading” models, highlighting the value of multi-model mean
Reichler and Kim, BAMS, 2008 : IPCC AR-4 models (vs. earlier intercomparisons)
Model ensemble mean
best
Index of agreement with observations
worst
Waugh and Eyring, ACPD, 2008: CCMVal Models – process-oriented
Stated AC&C goal – apply similar approach to CTMs for the troposphere
Model grade
Individual Models
All evaluations
Transport
Polar dynamics
1= best
0= worst

5. A measure of uncertainty in SR relationships (sfc o3): Model spread can be > factor of 2
To generate “more useful” information:
Identify processes responsible for this spread
(emissions/transport/chemistry)
Evaluate which models most accurately
represent those processes
…begin by comparing with surface obs

6. Simulated vs. observed monthly mean surface O3
EASTERN USA
CENTRAL EUROPE
OBS (EMEP)
MODELS
MODEL ENS. MEAN
Surface Ozone (ppb)
OBS (CASTNet)
Month of 2001
Does the EUS bias
influence the SR
estimates in summer?

7. EUS Bias not correlated with EU response to NA...
...can we identify observational constraints on the model spread
in SR relationships?
Work in progress

8. A first step: Contributions to inter-model spread in
Utility of idealized tracer simulations to quantify model differences due to transport (vs. emissions and chemistry) for ozone and aerosol?
A first step: Contributions to inter-model spread in
annual mean Arctic CO concentrations
Surface
250 hPa
Source region
Source region
Adapted from Figure 8 of Shindell et al., ACPD
Role of processes isolated using both idealized tracers + full chemistry simulations
Role for other (less complex) models to estimate uncertainty in
representations of specific processes in CTMs

9. HTAP Event Simulations: Moving towards process-based evaluation
I. Bey, M. Evans, K. Law, R. Park, E. Real, S. Turquety
1. chemical signatures of air masses,
2. chemical evolution in background vs. polluted plume ensembles,
3. export efficiencies,
4. injection heights on biomass burning plumes
Preliminary results from the French model MOCAGE,
courtesy of N. Bousserez and J.-L- Attié, Laboratoire d’aérologie Toulouse, France
observations
model
“clean” lower trop.
biomass burning influenced air masses
middle-upper troposphere
“polluted” lower trop.
from Isabelle Bey’s presentation at DC June 2008 HTAP meeting

10. Wide model range in “AQ-relevant metrics”: MAM average MDA8 surface O3 over the USA (HTAP models)
Work in progress…

11. VOY
WST
LAV
ROM
GRC
SND
• 6 CASTNet sites have been analyzed to date; potential to extend to “regional average”
• Varying degrees of model skill in capturing observed max daily 8-hr avg (MDA8) O3
c/o David Reidmiller, U Washington, work in progress

12. Model Evaluation: Spring (MAM)
Multi-model mean
Multi-model mean
c/o David Reidmiller, U Washington, work in progress

13. Model Evaluation: Spring (MAM)
c/o David Reidmiller, U Washington, work in progress

14. Foreign Contribution through O3 distribution
Sum of influence from ALL 3 foreign source regions
c/o David Reidmiller, U Washington, work in progress

15. 10-model mean response of MDA8 ozone to 20% reductions of foreign emissions: MAM average
Ensemble mean O3 decrease from
-20% (EA + EU + SA)
Ensemble stddev in O3 decrease from
-20% (EA + EU + SA)
Work in progress…

16. Possible considerations for the panel
Characterizing uncertainty in CTMs: Need for new approaches?
HTAP (SR + TP1x + ES simulations) – transport, emissions, chemistry
Adjoint / Sensitivity techniques ( 1 model vs. ensemble)
Parameter perturbation approaches? e.g., climateprediction.net, QUMP
“Simpler” models
Evaluation with observations: Screen out “less useful” models?
Pros/cons of summary statistics to rapidly communicate model evaluation
Which obs best differentiate the most useful models for our application?
Role of individual models vs. ensembles?
What information is most useful (in the policy process)?

17. Distinction between “sensitivity” and “contribution”
SENSITIVITY ( TF HTAP approach)
CONTRIBUTION
What is the response of surface
ozone to a 20% reduction in EA anthrop. emissions?
What ozone concentrations would
exist if EA anthrop.
emissions were turned off?
Response to (20% decrease in emissions * 5) < Response to “zeroing out”
Non-linearity well known [e.g., Liu et al., 1987, Lin et al., 1988, NRC, 1991]
Distinction driven mainly by non-linear O3 response to NOx
 Melding of the two approaches in the ITAP literature
Care is needed in defining question being asked (and appropriate approach)