Methods to estimate uncertaintiesEU Workshop on uncertainties in greenhouse gas inventories

5 to 6th September 2005, Helsinki, Finland.

1National Environmental Technology Centre - Netcen - Harwell Science Park, Didcot, Oxfordshire, OX11 0QJ, UK.2The Met Office, FitzRoy Road, Exeter, Devon, EX1 3PB, UK.3Institute of Grassland and Environmental Research (IGER), North Wyke Research Station, Okehampton, Devon, EX20 2SB, UK.

John Watterson1, Justin Goodwin1, Melissa Downes1, Alistair Manning2, andLorna Brown3

With thanks to John Abbott1 and Neil Passant1

What is in this presentation

Topics

Overview of methods and guidance – initial thoughts Estimation of uncertainties in activity data (AD) Use of IPCC default uncertainties in national inventories Estimation of uncertainty in national emission factors (EFs) Verification of emission data: How can comparison of different

models/methods be used to estimate uncertainties? Estimation of uncertainties in models Combining uncertainties Treatment of correlations

Some general problems with uncertainty analysis

Strictly uncertainties in inventories cannot be exactly quantified

Unknown sources Gaps in understanding of existing sources Measurement for emission factors are

inadequate to quantify uncertainties Emission factors may be inappropriate for

specific sources Expert elicitation has a role – workshops later

this afternoon

However

We need to understand the likely magnitude of uncertainties and their impacts

But there is hope!! We do have some knowledge and

understanding of uncertainties We need to identify major uncertainties to

direct improvements in GHG inventories

Overview of methods and guidance ‘Approach 1’

emission sources aggregated up to level similar to IPCC Summary Table 7A

uncertainties then estimated for these categories uncertainties calculated based on error propagation

equations Provides basis for Key Source analysis

‘Approach 2’ corresponds to Monte Carlo approach Can use software such as @RISK and MS excel

spreadsheets – or write your own MC code

Recommend reading the 2006 IPCC Guidelines – Volume 1 Chapter 3 “Uncertainties”

Estimation of uncertainties in Activity Data

Estimation of uncertainties in activity data (AD) - examples

Energy

Agriculture

Digest of UK Energy Statistics(UK Department for Trade and Industry)Energy statistics for the UK (imports, exports, production, consumption, demand) of liquid, solid and gaseous fuelsCalorific values of fuels and conversion factors

UK Defra - Institute of Grassland and Environmental Research (IGER)

Estimation of uncertainties in activity data (AD)

IndustrialProcesses

Pollution Inventory(Environment Agency)Scottish Environmental Protection AgencyUnited Kingdom Petroleum Industry AssociationUnited Kingdom Offshore Operators AssociationIron and Steel Statistics Bureau

Etc.

Uncertainties in UK fuel activity data

Fuel activity data taken from Digest of UK Energy Statistics

Uncertainties used for the fuel activity data estimated from the statistical difference between supply and demand for each fuel

Effectively the residuals when a mass balance is performed on the production, imports, exports and consumption of fuels

For solid and liquid fuels both positive and negative results are obtained indicating that these are uncertainties rather than losses

Uncertainties in UK fuel activity data

Quoted uncertainty refers to the total fuel consumption rather than the consumption by a particular sector, e.g. residential coal

To avoid underestimating uncertainties, it was necessary to correlate the uncertainties used for the same fuel in different sectors

Uncertainties in UK fuel activity data

For gaseous fuels uncertainties include losses and tended to be negative. For natural gas, a correction was made to take account of leakage from the gas transmission system but for other gases this was not possible.

The uncertainties in activity data for minor fuels (colliery methane, orimulsion, SSF, petroleum coke) and non-fuels (limestone, dolomite and clinker) were estimated based on judgement comparing their relative uncertainty with that of the known fuels.

Time series of variability in the supply and demand of coal

Difference between supply and demand of coal

-2000

-1500

-1000

-500

0

500

1000

1500

2000

1980 1985 1990 1995 2000 2005

Year

Diff

eren

ce (k

tonn

es)

Coal

Difference in supply and demand of coal

Trend suggests improvement in accuracy of estimates of supply and demand over time?

Difference in supply and demand of natural gas

Difference between supply and demand of gas

0

5000

10000

15000

20000

25000

30000

1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004

Year

Diffe

renc

e (G

Wh)

Gas

Supply greater than demand – is this all due to losses (fugitive emissions) in the gas transmission system?

Could apply a correction if estimated fugitive emissions are known

Decline in difference reflects measures implemented in the UK to reduce fugitive emissions in the gas transmission system

Uncertainty in coal activity data

Time series of uncertainties as 95% Confidence Intervals expressed as a percentage of the central estimate for UK coal supply and demand

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

1980 1985 1990 1995 2000 2005

Year

2 S

D /

mea

n

Yearly valuesRolling 5 year mean

Some general comments on using statistical differences derived from energy balance data

Uncertainties in the fuel combustion data for specific sectors or applications, are probably higher than the uncertainty suggested by the statistical difference between supply and demand

Warning - if a statistical difference is zero it is likely that the data are of uncertain quality and this does not imply zero uncertainty. In these instances, the data quality should be examined for QA/QC purposes and the relevant statistical agencies should investigate

Use of IPCC default uncertainties in national inventories

Using IPCC default uncertainties

Where possible, uncertainty data for EFs should be derived from published country specific studies

estimate values of uncertainties you may be able to derive the PDF from available

data

If such data are unavailable, then use default values from guidelines

suggest it will be best to refer to the 2006 IPCC guidelines which should be available in early 2006

unless you have other evidence, assume PDF normal

before using defaults, try using expert judgement / elicitation to produce more applicable data

Typical data available in 2006 IPCC guidelines

Data above taken from Stationary Combustion Chapter in 2006 Guidelines

Derived from EMEP/CORINAIR Guidebook Very limited sector specific information and wide

range of uncertainty quoted

TABLE 2.12 DEFAULT UNCERTAINTY ESTIMATES FOR STATIONARY COMBUSTION EMISSION FACTORS

Sector CH4 N2O Public Power, co-generation and district heating Commercial, Institutional & Residential combustion Industrial combustion

50-150% 50-150% 50-150%

Order of magnitude* Order of magnitude Order of magnitude

*i.e. having an uncertainty range from one-tenth of the mean value to ten times the mean value. Source: IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories(2000)

GLs suggest an overall uncertainty value of 7 % for the CO2

emission factors of Energy

Using uncertainties in the IPCC guidelines

50 150

Probability Distribution

100

2 = 50

95% CI = 2 / E= 50 /

100= 50 %

e.g. Uncertainties in CH4 emissions from Table 2.12 in previous slide

= 1 standard deviation of the mean, E

Using uncertainties in the IPCC guidelines

e.g. uncertainties in N2O emissions from Table 2.12These are order of magnitude uncertainties – need to use Approach 2 (i.e. MC simulation) and define a suitable PDF

Estimation of uncertainty in national emission factors (EFs)

Example from data to support UK review of carbon emission factors (CEF)

Large number of samples used to estimate CEF

Checks to see if a weighted mean approach produces a more accurate CEF estimate

Uncertainty values can be used directly from this report

Verification of emission data: how can comparison of

different models/methods be used to estimate uncertainties?

Initial considerations

One a very basic level comparisons using different models/methods can be used to assess uncertainties by

(a) The closeness of the estimates gives a feel for potential gross errors. It depends on how independent the methods are and the potential errors in each method ‑ both estimation and modeling approaches could have problems, but for different reasons.

(b) By comparison across a wide number of pollutants a qualitative feel for the uncertainty for any particular pollutant can be gauged.

Verification of the UK GHG inventory

The approach uses the Lagrangian dispersion model NAME (Numerical Atmospheric dispersion Modelling Environment)

Sorts the observations made at Mace Head into those that represent Northern Hemisphere baseline air masses and those that represent regionally-polluted air masses arriving from Europe. The Mace Head observations and the hourly air origin maps are applied in an inversion algorithm to estimate the magnitude and spatial distribution of the European emissions that best support the observations

The technique has been applied to 2-yearly rolling subsets of the data and used to estimate longer term averages

Verification of the UK GHG inventory

The inversion (best-fit) technique, simulated annealing, is used to fit the model emissions to the observations.

It assumes that the emissions from each grid box are uniform in both time and space over the duration of the data. This in turn implies that the release

is assumed independent of meteorological factors such as temperature and diurnal or annual cycles, and

that, in so far as the emission relates to industrial production or other anthropogenic activity, use there is no definite cycle or intermittency.

The estimated releases will include any natural release as well as anthropogenic emissions.

Baseline analysis

Based on meteorological analyses

NAME model derived air origin maps

Darker shade – Greater contribution from area

All possible surface sources over previous 10 days

Maps generated for each hour 1995-2004

Mace Head

Inverse modelling Aim: generate emission estimates from ‘polluted’ observations (above baseline) Use NAME to predict concentration time series at Mace Head from each source Scale emissions to obtain best match between model and observations

Simulated Annealing Iterative technique No prior information

Apply to all monitored species Independent verification of emissions

Equation: A e = m

Minimise: m - A e

A: the dilution matrixm: observed concentrations (- baseline)e: emissions

NAME model predictions of emissions of N2O across Europe

Nitrous oxide – comparison of GHG inventory estimates and model estimates

0

20

40

60

80

100

120

140

160

180

200

1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03 2003-04

Nitr

ous

Oxi

de E

mis

sion

s (k

t/yr)

GHGINAME - inc part North SeaNAME

Thermal oxidiser

abatement system fitted to

adipic acid plant

Quality of agreement between UK GHGI estimates and model

Reasonable agreement between modelled and measured which gives confidence of the inventory estimates

But fitment of abatement to adipic acid plant not reflected in NAME model trend

This problem investigated with representatives from the adipic acid plant and the meteorological office

Where was the problem – GHG inventory or model?

Answer – probably mostly the model, but check the GHG inventory also

The NAME model assumes no definite cycle or intermittency in emissions – this was not the case – periods were the adipic acid plant was shut down and periods where abatement not operating

So, make adjustments to the model

The oxidised nitrogen from wastewater is not currently included in the GHG – this (small) source could be added to improve the accuracy of the N2O estimate

So, make checks on the inventory

Estimation of uncertainties in models

Initial considerations

Model is a representation of a ‘real world’ system – but can never exactly mimic the ‘real world’

Key considerations in model uncertainty Has the correct ‘real world’ been identified –

for example, the ‘real world’ in a GHG inventory would be a complete and unbiased inventory

Is the model an accurate representation of this ‘real world’?

Example using N2O from agriculture in the UK GHG inventory

Recent detailed study into the uncertainty of the model used to estimate emissions from the UK GHG inventory

An inventory of nitrous oxide emissions from agriculture using the IPCC methodology: emission estimate, uncertainty and sensitivity analysis (2001). Brown, L., Amstrong Brown, S., Jarvis, S.C., Syed, B., Goulding, K.W.T., Philips, V.R., Sneath, R.W. and Pain, B.F. Atmos Environ., 35, 1439-1449.

Approach

Monte Carlo approach used to estimate the uncertainty in the model

26 parameters were included For some parameters, a beta pert distribution

used derived from IPCC minima, maxima and most likely (default) values. No information in IPCC GLs to suggest alternative distribution.

Sensitivity analysis performed using multivariate stepwise regression using @RISK software

Results

N2O emissions from UK agriculture were estimated to be 87 Gg N2O-N for both 1990 and 1995 using the IPCC default EFs

Total estimate shown to have high overall uncertainty of 62%

Comparisons of results from this study and other UK-derived inventories suggests the default IPCC inventory may overestimate emissions

Uncertainty in individual components determined This has identified the components of the model

where improvements could be made since emissions are a significant fraction of the total and the associated uncertainties are high

Uncertainty associated with parameters

Parameter % of total N2O from UK agriculture

Parameter / Uncertainty

Direct sector - soil 54% EF1 (direct emission from soil) 31%

EF3PRP (emission from pasture range and paddock)+ 11% to –17%

Indirect sector – leached N and deposited ammonia

29% 126%

Treatment of correlations

Correlations

When to use a correlation

Activity Data are calculated via mass balance Supply and demand of fuels in energy statistics

Emission Factors are shared across activities Natural gas or gas/diesel oil used by different sources

Emission Factors are calculated or extrapolated across a time series

Methane from livestock

Correlations (ii)

How to use a correlation

Can be used in combination Activity and EF’s correlated

If correlations occur, the easiest and most effective method is to use a Monte Carlo Simulation

NB: Correlations may not have an effect. Will only affect areas where the inventory is sensitive and/or the dependencies are very strong

Combining uncertainties

Combining uncertainties

For Tier 2 analysis - a  Monte Carlo approach is necessary. Uncertainties are set and the correlations marked. The software is

then set up and run and automatically takes these into account.

For component uncertainties <60%, a sum of squares approach can be used.

UT = (UE2 + UA

2)

For component uncertainties >60% all that is possible is to combine limiting values to define an overall range

U% = (E+A+E*A/100) and L% = (E+A-E*A/100)  

U=Uncertainty, T=total, E=Emission Factor, A=Activity Data, U%=upper limit, L%= lower limit

Example Monte Carlo model

Calculating uncertainties

Frequency

ValueMin Max

Fuel/Activity Uncertainty

Emission Factor UncertaintyEmission Uncertainty

Uniform Triangular

Range

Probability Distribution

Distribution Types:normal Lognormal

Tier 2 - Monte-Carlo Method

0

2

4

6

8

10

S1

0

2

4

6

8

10

S1

Model0

2

4

6

8

10

S1

Input 2

Input 1

Result

Tier 2 - Monte-Carlo Method Step 1: Assess component uncertainties

Expert Judgement & Data• Maximum, Minimum• Distribution type

Step2: Run the analysis up to 20,000 iterations

Step 3: Results 5th - 95th percentile = Range as % of the mean

Min Max Min Max

Factors Activity

Emission

Min Max Min Max

Factors Activity

Emission

Comments

Correlations do affect the overall uncertainty result – suggest approach is to start identifying inputs that are correlated, rather than setting up the model with the input level at the lowest level of aggregation and examining the correlations in each parameter individually

It is easy to produce MC output that superficially looks credible – but carefully check underlying assumptions

You can write a programme to complete a MC analysis – you do not need to use an expensive commercial package

Demonstration of MC model

UK has set up MC model to illustrate certain key points

Suggested layout of a simple MC model Defining non-correlated PDFs Considering correlations How to deal with emissions and associated

uncertainty where individual EF and AD uncertainties are unknown

Example output table

Final thoughts

Guidance Data Review Implement

- Read the IPCC guidance

- Consider comments made by Expert Reviewers and in Peer Reviews

- Gather country specific information on EFs and AD

- Use IPCC defaults only if sufficient information cannot be found

- Careful with Monte Carlo analysis – easy to produce poor quality work

- Get the help of a statistician

Background reading

first!

Gather sufficient

information

Follow IPPC Good

Practice

Try to be open to

criticism!

- Ask for peer review- Reflect on output of the

uncertainty analysis – is it sensible?

Acknowledgements

The UK GHG inventory is funded by UK Defra and the Devolved Administrations

UK Defra Jim Penman – Head Response Strategies Susan Donaldson – GHG Science Advisor Joanne Halliday – GHG Science Advisor Steve Cornellius - GHG Science Advisor