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UNCERTAINTIES IN ATMOSPHERIC MODELLING. Dick Derwent rdscientific, Newbury, United Kingdom Joint TFEIP/TFMM Workshop Dublin 22 nd October 2007 This work was supported by the United Kingdom DEFRA through contract number AQ 03508. WHY DO WE NEED MODELS?. - PowerPoint PPT Presentation
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UNCERTAINTIES IN ATMOSPHERIC MODELLING
Dick Derwent
rdscientific, Newbury, United Kingdom
Joint TFEIP/TFMM Workshop
Dublin
22nd October 2007
This work was supported by the United Kingdom DEFRA through contract number AQ 03508
WHY DO WE NEED MODELS?
Observational networks tell us what is happening to air quality but not why it is happening.
Models provide a framework to link together information connecting a disparate range of issues:
•emissions, •meteorological data, •atmospheric chemistry, •air quality data
Much of the presentation refers to ozone rather than PM.
WHY MODEL OZONE ?
To provide a vehicle for exploring ignorance and answering questions.
• Where have the elevated ozone levels come from ?
• How important is long-range transport of ozone?
• Which VOCs are the most important to control ?
DAILY SOURCE ATTRIBUTION OF GROUND-LEVEL OZONE AT A RURAL LOCATION AT HARWELL, OXFORDSHIRE DURING 2006
0
10
20
30
40
50
60
01/0
1/20
06
01/0
2/20
06
01/0
3/20
06
01/0
4/20
06
01/0
5/20
06
01/0
6/20
06
01/0
7/20
06
01/0
8/20
06
01/0
9/20
06
01/1
0/20
06
01/1
1/20
06
01/1
2/20
06
O3,
pp
b
Europe-regional
North America
Asia
Europe-intercontinental
Extra-continental
Stratosphere
EMEP site GB36R
WHY MODEL OZONE ?
To develop a means of prediction for policy-makers.
• What would happen of this source of precursor emissions is controlled ?
• Which sources are best to control ?
• What will happen if nothing is done to control emissions ?
IMPACT OF THE CAFÉ THEMATIC STRATEGY ON SUMMERTIME OZONE
Results from the Unified EMEP Eulerian Model (Tarrason et al., 2005)
MODELS ARE INHERENTLY SIMPLIFICATIONS OF THE REAL-WORLD
When models are built, they are always simpler than the world they represent. This simplification is achieved by:
• Generalisation
• Distortion
• Deletion
• Neglect
THE PURSUIT OF COMPLEXITY
Models are always incomplete and efforts to make them more complete can be problematic:
• adding new features and processes may introduce more uncertain parameters
• complex models may contain more parameters than can be calibrated with the available observations
• scientific advances will never make it possible to build the perfect model
MAIN AREAS OF UNCERTAINTY
The simplifications inherent in models introduce uncertainties. There are four main areas of uncertainty:
• theoretical aspects – not fully understood• empirical aspects – difficult to measure• parametrical aspects – simplified concepts • temporal aspects – not stable in time
UNCERTAINTIES - THEORETICAL ASPECTS
• How to cope with atmospheric dispersion?
• How to cope with the range of spatial scales involved?
• Are we dealing with long range transport of ozone or with the formation of ozone on the long range transport scale?
UNCERTAINTIES – EMPIRICAL ASPECTS
• How to handle the 100s of VOCs, the 1000s of RO2 radicals, the 10,000s of reactive intermediates?
• How to measure them, how to describe ozone formation using them?
• How to construct a chemical mechanism from smog chamber data?
UNCERTAINTIES – PARAMETRICAL ASPECTS
Simplified concepts need parameters to describe them.
• flux-based dry deposition schemes need data on vegetation and soil status, precipitation, phenology
• natural biogenic emission schemes need data on radiation, temperature, plant species, phenology
• solar photolysis rates need data on aerosol, cloud and stratospheric ozone column
UNCERTAINTIES – TEMPORAL ASPECTS
Some parameters are not stable in time and present problems when working down from annual values.
• some emission processes have a large stochastic element
• some emissions are strongly event-based, accidental or just random
HOW TO HANDLE UNCERTAINTIES ?
A wide range of possibilities exist for handling uncertainties:
Probabilistic uncertainty analysis• represent all model uncertainties probabilistically• compute distribution of output of interest
Scenario assessment or sensitivity analysis• consider ‘pessimistic’, ‘neutral’ or ‘optimistic’ scenarios for parameters
WHAT ARE THE MAJOR UNCERTAINTIES ?
• missing processes
• weather conditions
• chemical mechanism
• emissions from human activities
• emissions from natural processes
UNCERTAINTY ANALYSIS IN A PHOTOCHEMICAL TRAJECTORY MODEL
• VOC emissions• NOx emissions• SO2 emissions• CO emissions• methane emissions• isoprene emissions• deposition velocities• initial concentrations• boundary conditions• x,y,z trajectory position• air parcel temperature• air parcel pressure• air parcel humidity• boundary layer depth
UK PTM MODEL PERFORMANCE DURING JULY 2006
0
20
40
60
80
100
120
01/0
7/2
006
03/0
7/2
006
05/0
7/2
006
07/0
7/2
006
09/0
7/2
006
11/0
7/2
006
13/0
7/2
006
15/0
7/2
006
17/0
7/2
006
19/0
7/2
006
21/0
7/2
006
23/0
7/2
006
25/0
7/2
006
27/0
7/2
006
29/0
7/2
006
31/0
7/2
006
Ozo
ne,
pp
b
Obs
84%-ile
97%-ile
30 mesoscale trajectories per 15:00z
Output from the HYSPLIT Trajectory Model (NOAA ARL)
There are difficulties selecting a single air parcel trajectory.
UNCERTAINTY ANALYSIS FOR 18TH JULY 2006
Subjective uncertainty ranges adopted for:
ammonia emissionsVOC emissionsNOx emissionsSO2 emissionsCO emissionsCH4 emissionsisoprene emissionsPAR speciationXYL speciationTOL speciationFORM speciationALD2 speciationOLE speciationETH speciationO3 dry depositionOther species dry depositionInitial conditionsBoundary layer depthJ valuesTemperaturesLongitude of air parcelLatitude of air parcel
+ 1000 3-D trajectories from the mesoscale NWP model
MONTE CARLO ANALYSIS FOR 1ST JULY 2007
100,000 model runs
Observations 82 ± 8 ppb
233 ‘acceptable’ model runs in this range
WHAT ARE FEATURES OF 233 ACCEPTABLE PARAMETER SETS?
0 0.5 1 1.5 2
PAR fraction
XYL fraction
TOL fraction
FORM fraction
ALD2 fraction
OLE fraction
ETH fraction
other species deposition
NH3 emissions
VOC emissions
NOx emissions
SO2 emissions
CO emissions
CH4 emissions
isoprene emissions
O3 deposition velocity
initial concentrations
boundary layer depth
J-values
temperature
Multiplier
Central value
OZONE RESPONSES TO 30% NOx REDUCTION IN THE 233 MODEL RUNS WITH ACCEPTABLE PARAMETER SETS
0
5
10
15
20
25
30
35
40
45
50
-20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20
Delta O3, ppb
Nu
mb
er o
f m
od
el r
un
s
ozone decrease
47 parameter sets
ozone increase
186 parameter sets
Harwell, Oxfordshire 1st July 2006
Mean ± 1sd
+2.5 ppb
EQUIFINALITY PROBLEM
• Many different parameter sets within a chosen model structure may be acceptable for reproducing observations
• It may not be possible to find a single optimal representation in a complex model of a given set of observations
CONCLUSIONS (largely for ozone and policy applications)
• We have to face up to model input data being uncertain
• uncertainty propagation is model and output dependent
• uncertainties in emission inventories are no longer my main concern (except in gridding and biogenic sources)
• process descriptions and parameterisations are a major concern
• non-inventoried emissions such as forest fires, agricultural burning and industrial fires are a major cause of ozone and PM episodes