Inverse Modeling of Carbon Monoxide Emissions

1
Inverse Modeling of Carbon Monoxide Emissions

• Gabrielle Pétron

collaborators Claire Granier, Boris Khattatov,
Valery Yudin, Jean-François Lamarque, Louisa
Emmons, David Edwards, Jean-François Müller,
Guy Brasseur, John Gille, Paul Novelli, MOZART
and MOPITT groups
National Center for Atmospheric Research,
Boulder, Colorado, USA Service d Aéronomie,
CNRS/IPSL/Université Paris 6, Paris, France
E-mail gap_at_ucar.edu
2
Thin Blue Veil
accumulation and transport of pollutants
over 90 of the total mass of the Earth
atmosphere is below 10km, ie in the troposphere
Carbon Monoxide from space, MOPITT instrument
average vertical distribution of various gases in
the Earth atmosphere
3
Role of CO in the troposphere
Houston

• Oxidation capacity of the atmosphere
• Precursor of tropospheric ozone
• Indoor and Urban pollutant

Santiago
http//eces.org/archive/gallery/airgfx
4
Object CO emissions

• known nature of sources
• uncertain
• intensities
• location
• timing, seasonality, interannual variations
• splitting fossil fuel/biofuel....
• tools to study CO budget
• observations, emissions inventories, models

5
Sources Sinks of CO (Atmospheric Chemistry and
Global Change, 1998)
TgCO/yr

• Fossil fuel 300-600 Biomass burning
  300-900
• (forests, savannas, agric. waste burning, fuel
  wood use)
• Vegetation 50-200
• Oceans 6- 30
• Methane oxidation 400-1000
• HCNM oxidation 300-1000
• TOTAL Source 1400 3700 TgCO/yr
• Photochemical sink 1400-2600
• Surface deposition 150-500
• TOTAL Sink 1550 3100 TgCO/yr

LARGE UNCERTAINTIES
6
Observations

• few direct observations of emissions
• ?yet many observations of CO distribution in
  the troposphere
• NOAA/CMDL 40 stations
• N/S gradient, seasonality in sources and
  sinks, seasonality in CO
• high precision
• -- remote regions, mostly NH,representativity?

7
CMDL
8
Observations

• few direct observations of emissions
• ?yet many observations of CO distribution in
  the troposphere
• NOAA/CMDL stations
• MAPS (space shuttle) 4 campaigns
• biomass burning impact
• IMG few days from 8/1996 to 6/1997

9
Remote Sensing of CO

• space shuttle
• MAPS 4 flights
• (12-14/11/1981 5-13/10/1984
  9-19/04/1994 30/09-11/10/1994)
• ?max sensitivity in upper troposphere
• satellite
• - IMG a few days between August 1996 and June
  1997 total CO column (?sensitivity max around 6
  km)

April -October 1994
Connors et al., 1999
spectroscopy around 4,7 ?m
4 days June 1997
Clerbaux et al., 2001
10
Observations

• few direct observations of emissions
• ?yet many observations of CO distribution in
  the troposphere
• NOAA/CMDL stations
• MAPS (space shuttle) 4 campaigns
• biomass burning impact
• MOPITT (satellite) since spring 2000
• CO good tracer of continental air pollution

11
MOPITT Data

• Since spring 2000, the MOPITT instrument
  monitors the tropospheric content of CO, covering
  the global surface of the Earth in a few days.

• Characteristics
• Global coverage in 3 days
• 20km x 20km pixel
• 7 levels
• Lower Precision/in situ
• Filtered for clouds
• CO mixing ratios are Level3 data
• We need to know the averaging Kernel what
  MOPITT sees!!

CO at 500 hPa
12
Chemistry-Transport model

• représentation numérique discrète (x,t) de la
  troposphère globale
• chemistry operator
• transport operator
• advection, turbulence turbulent diffusion
  convection
• initial boundary conditions
• monthly emissions, deposition, exchanges with
  the stratosphere
• resolution ?x 200/500km , ?t 20min/1 h
• model with climatological fields IMAGES (5ox5o)
• model with analyzed wind fields MOZART
  (2,8ox2,8o)

13
Models

• Only tool to track CO origin
• here CO emitted in different regions of the world
  has been tagged. The plots show how the
  dispersion in the atmosphere of the various
  colors of CO

day 2
day 65
14
Question....

• Can observations of CO distribution in the
  troposphere help better constrain CO sources?

15
Method

• Optimal Interpolation
• cf word docs

16
Formulation of the problem

• We are going to optimize
• p monthly source processes vector S
• using n monthly averaged observations vector
  cobs

tagging of the sources
source-regions
17
Observation matrix
The coefficients of H are calculated using the
model with fixed OH concentrations.
matrix containing normalized impacts of all
sources i on all observations j
18
Hypotheses

• Transport model is perfect
• Statistics of errors
• All Errors are gaussian and independent
• Statistics of the observations known (first and
  second moments), ? R is diagonal (no correlation)
• Statistics of the a priori sources known (first
  and second moments), ?B is diagonal (no
  correlation)
• CO sink not optimized.
• Chemistry weakly non linear.
• a posteriori sources close ENOUGH to a priori
  sources
• ? no big change in OH
  
• ? Use linearized version of the model

19
CMDL / IMAGES inversion

• representative of the 1990-1996 period
• 33 monthly source-processes
• 39 monthly averaged observations

20
a priori emissions
a posteriori emissions
fossil fuel biomass burning biofuel
Asian annual emissions are multiplied by 2

21

• Timing of biomass burning in Southern Hemisphere
  changed
• emissions peak in September

a posteriori emissions (open symbols)
tagged contributions of various sources to total
CO at Ascension Island
22
a priori / a posteriori uncertainties
impact of changing the a priori sources
uncertainties increasing the uncertainties
gives more weight to the observations
23
CO budget

• Hyp
• 50 uncertainties on a priori fluxes,
• min 10 errors on observations

24
Improvement at stations
Observed and simulated CO (ppbv) black dots
observations. bold lines with no symbol
standard deviation of the observations. open
diamonds CO simulated using the a priori
emissions open squares CO simulated using the
a posteriori emissions.
25
Obs
IMAGES
New sources
Monthly Average CO (ppbv)
26
Validation/ Tests

• Test1 Inversion with pseudo-data
• Test2 improvement of modeled CO at the
  stations
• 30.47 ppbv with a priori sources
• 17.56 ppbv with a posteriori sources
• Test3 CH4 and MCF lifetimes
• 8.74 yr (8.42 w/ a priori) / Krol et al.
• 4.41 yr (4.27 w/ a priori) / Krol et al.

27
Major Results CMDL /IMAGES

• Large increase in Asian Sources (//Kasibhatla et
  al., 2002)
• Change in timing of biomass burning in Southern
  Africa (//Galanter et al. 2000)
• Uncertainties decrease the most for Northern
  Hemisphere sources
• Not enough data to constrain Southern Hemisphere
  emissions
• Optimization of Chemical Production of CO not
  done!

28
What we need to get better results

• Use assimilated meteorology
  (to reduce bias due to errors in model
  transport)
• More observations (esp. in SH)
• Optimize other compounds emissions

29
MOPITT /MOZART inversion

• Similar inverse technique used
• MOPITT binned on MOZART grid
• use data at 700 hPa
• inversion done for each month of observations
• Ne 3000 obs /month
• 15 continental regions / 4 oceanic biogenic
• chemical production of CO not optimized
• 100 relative error on observations and on prior
  emissions

30
Chemistry Transport MODEL

• MOZART 3D-global
• surface to 2hPa
• 31 vertical levels
• Horizontal resolution 2.8º x 2.8º
• 56 chemical species
• Dynamical Fields NCEP, ECMWF, DAO
• timestep 20 min

31
Method Iterative Inversion
Analysis at month k1

• Observations MOPITT CO at 700 hPa
• 65oN-65oS, ½ of the data, April 2000-March 200
• Modeled CO
• projected on MOPITT 700 hPa level (aver. kernel)
• Diagonal covariance matrices
• Observation error 100
• A priori emissions inventory
• 50 error on technological CO sources
• 100 error on other CO sources

dimensions emissions U 106
observations z 3000
32
MOPITT /MOZART inversion

• April 2000 March 2001

33
Inversion results
Annual CO emissions for various regions (TgCO/yr)
34
Monthly global CO surface sourcesApril 2000-
March 2001
a priori

• shift in biomass burning maximum (august?
  sept)
• biofuel use emissions maximum in winter
  (x 2 / summer time)
• fossil fuel emissions maximum in winter
  (30 / summer time)

a posteriori
a posteriori
35
CO Biomass Burning EmissionsTgCO/yrApril
2000-March 2001
a priori
a posteriori
36
Validation
The agreement between the modeled CO and the
observations improves at 26 stations (out of 33)
when using the optimized sources.
using independent observations CMDL CO dataset
37
(No Transcript)
38
Conclusions (1) CO budget

• First time inversion of CO monthly sources
  using satellite data.
• Large underestimation in current inventories
• of Asian emissions (fossil fuel and biofuel)
• of biomass burning emissions in Africa
• New estimates of CO emissions from biomass
  burning

39
Conclusions (2) inverse tehcnique(s)

• Test
• Impact of the uncertainties assigned to
  observations and to a priori emissions
• Impact of meteorological fields used
• Impact of BL ventilation parameterization
• Future work
• Better description of errors, biases of satellite
  data
• Optimization of chemistry, validation of OH
  distribution
• Integrate information from various platforms
• Implement 4D variational assimilation
  (development of the adjoint model of MOZART in
  progress)

40
Formalism

• System to be solved xr xb xb
• z - yb H(xr - xb ) ?
• ? measurement error and model error eoem
• hyp E(xb)E(?)E(?.xbT)0 E(xbxbT)Pb
  E(?.?T)R

unknown xr