F-gas emissions monitoring throughatmospheric observation
Observations: Jgor Arduini, Tim Arnold, Andreas Engel, Paul Fraser, ArnoudFrumau, Chris Harth, Paul Krummel, Michela Maione, Steve Montzka, Jens
Mühle, Simon O’Doherty, Joe Pitt, Chris Rennick, Peter Salameh, Tanja Schuck,
Gerry Spain, Kieran Stanley, Martin Vollmer, Ray Weiss, Angelina Wenger,
Dickon Young
Modelling: Stephan Henne, Lei Hu, Alistair Manning, Alison Redington, Stefan
Reimann, Cathy Trudinger

Outline• Global F-gas concentrations are virtually all rising• GHG inventory compilers need to estimate
national emissions of a wide range of F-gases• Atmospheric observations can significantly help
inventory compilers understand emissions• Confirm magnitudes of emission• Identify significant emission sources• Give an early indication of national emissions

Global Concentration: HFC-134a(mobile air-conditioner)

Consistently growing by more than 5 ppt per year

Inversion Systems
To understand the recent
history of the air arriving at
measurement stations

Atmospheric Transport
ModelPrior
Knowledge

With EstimatedUncertainties
To estimate the surfaceemissions that bestdescribe the observations

Inverse Modelling System

Estimate of surfaceemissions
Uncertainties
Estimated

Atmospheric
Observations of GHG

Emission Distributions
PFC-218

SF6Identify significant hot spots of emission
F-gas Observation Stations

UK SF6 emissions• Significant mis-matches identified• Good agreement confirms inventory• High temporal resolution helps understand emissions

Total UK HFC emissions
2020 inventory

F-gas inventory
reviewed following
comparison with topdown estimates
Significant fall in UKemissions 2018-2019
Inventory estimates amuch slower decline
Early indication ofemissions in 2021and 2022

Paper published in 2021
(Manning et. al. ACP 2021)

UK HFCemissionsby gasMost significant HFCs
Emissions in Tg CO2-eq
Observed fall in 20182019 much greater thanreported in Inventory

Total UK PFC emissions
Inventory mostly
lower than InTEM
estimates but
uncertainties overlap
Potential sharp rise in2021 and 2022

Swiss NIR – HFC-134a example
Empa
Inventory
higherthan topdown
estimateAustralian NIR – HFC-134a example
CSIRO
Inventory
higherthan topdown
estimate

USA NIR – HFC-134a example
NOAA
Inventory
higherthan topdown
estimate

Conclusions• Global F-gas concentrations are virtually all rising• Atmospheric observations can significantly help
inventory compilers understand emissions• Confirm magnitudes of emission• Identify significant emission sources• Give an early indication of national emissions• 4 countries currently include ‘top-down’ inversion
estimates from atmospheric observations in their
NIRs – others are planning to do so• New EU project PARIS has major focus on F-gases

USA NIR – HFC examples
NOAA

Estimating emissions using Atmospheric Observations

InTEM

Inversion Technique forEmission Modelling
Generate regionalemission estimatesfrom ‘polluted’ (abovebaseline) observations.

UK DECC(Deriving Emissions relatedto Climate Change)

Network ofobservations

MHD from 1987
TAC & RGL from 2012
HFD & BSD from 2014

UK HFCemissions

UK PFCemissionsby gasEmissions in Tg CO2-eq

InTEM (Inversion Technique for Emission Modelling)
Uncertainty Matrices

M’ = M [t x n] | P [t x p] | F [t x f]e’ = e [n] | b [p] | q [f]M = Dilution matrix
P = Baseline Adj + Outer Region
F = Location Biast1

e = Surface
Emissionst2t3• Air history maps from transport model• Time-series of relative contributions ofeach surface source to each observation

y [t]Times series
observations

e’p = ep | bp | qp
ep = Prior
Emission
Assume UK prior
Very uncertain

B [n’ x n’] : Prior uncertainty
R [t x t] : Model-Obs uncertainty
Uncertainty Analysis Matrix

Baseline Prior adjusted within InTEM• Depending on where air enters regional domain• Each station has unique baseline
1.9 ppmCH4Monthly & Annual Trend

Now also solve for
6 Outer Regions

Baselinemodifiedbased onwhere theair comes
fromBASELINE