Using Space-based Observations to Better Constrain Emissions of Precursors of Tropospheric Ozone

Dylan B. A. Jones, Paul I. Palmer, Daniel J. JacobDivision of Engineering and Applied Sciences

and Department of Earth and Planetary SciencesHarvard University, Cambridge, MA

Kevin W. Bowman, John WordenCalifornia Institute of Technology

Jet Propulsion Laboratory, Pasadena, CA

Ross N. HoffmanAtmospheric and Environmental Research, Inc.

Lexington, MA

Randall V. Martin, Kelly V. ChanceHarvard-Smithsonian Center for Astrophysics

Cambridge, MA

Rise in Tropospheric Ozone over the 20th Century

Observations at mountain sites in Europe [Marenco et al., 1994]

Concentrations of O3 have increased dramatically due to human activity

Impact of Human Activity on Background O3

NOx

Hydrocarbons (RH)NOx

Hydrocarbons (RH)

CONTINENT 2 OCEAN

O3

Boundary layer

(0-2.5 km)

Free Troposphere

CONTINENT 1

OH RO2, HO2

RH, CO

NONO2

hO3

O3

Global Background O3

Direct Intercontinental Transport

greenhouse gas

air pollution (smog) air pollution (smog)

“Bottom-up” Emission Inventories

Biofuel

FossilFuel

Biomass Burning

Bottom-up emissions E = (fuel burned) x (emission factors)

Must extrapolate in space and timeusing population and economic data

Emissions are highly uncertain

How Uncertain are Bottom-up Inventories?

Source Tg CH4/yr

Uncertainty

Energy 91 48 - 143

Animals 89 45 - 135

Rice 39 20 - 80

Biomass burning 27 10 - 50

Landfills 56 30 - 80

Wetlands 180 100 - 250

Termites 20 0 - 100

Other (hydrates, etc..) 30 15 - 65

Total 532 268 - 903

EDGAR 1995 CH4 inventory

[Olivier, 2002]

How Uncertain are Bottom-up Inventories?

Source Tg CH4/yr

Uncertainty

Energy 91 48 - 143

Animals 89 45 - 135

Rice 39 20 - 80

Biomass burning 27 10 - 50

Landfills 56 30 - 80

Wetlands 180 100 - 250

Termites 20 0 - 100

Other (hydrates, etc..) 30 15 - 65

Total 532 268 - 903

EDGAR 1995 CH4 inventory

[Olivier, 2002] Range of uncertainty for 1995 estimate

[IPCC, 2001]

Error based on 15% uncertainty in globally averaged OH

Inverse Modeling

-1 -1/ 2

ε a2 2 2 2 2 2ε a ε a ε a

E E1 1 1 1ˆ ˆE = + + σ = +σ σ σ σ σ σ

with

a priori “bottom-up” estimate Ea a

Monitoring site measures concentration C

Atmospheric “forward” model gives C = kE

“top-down” estimateE

Best “a posteriori” estimate:

Inverse model E = k-1C

“observational” error

includes terms from

• instrument • representativeness • model parameters

Platform multiple ERS-2 Terra ENVISAT Space station

Aura TBD TBD

Sensor TOMS GOME MOPITT MODIS/MISR

SCIAMACHY MIPAS SAGE-3 TES OMI MLS CALIPSO OCO

Launch 1979 1995 1999 1999 2002 2002 2004 2004 2004 2004 2004 2005

O3 N N/L L L N/L N L

CO N N/L L N/L

CO2 N/L N

NO L

NO2 N N/L N

HNO3 L L

CH4 N N/L N

HCHO N N/L N

SO2 N N/L N

BrO N N/L N

HCN L

aerosol N N N L N N

Increasing spatial resolutionN = NadirL = Limb

Satellite Observations of Tropospheric Chemistry

Global Ozone Monitoring Experiment (GOME)

•Launched April 1995

•Nadir-viewing solar backscatter instrument (237-794 nm)

•Polar sun-synchronous orbit

•Complete global coverage in 3 days

•Spatial resolution 320x40 km2, three cross-track scenes

GEOS-CHEM Global 3-D Model

• Driven by assimilated meteorology from the NASA Data Assimilation Office

• Horizontal resolution of 2º latitude x 2.5º longitude

• O3-NOx-hydrocarbon chemistry

• Emission Inventories:

Isoprene: Global Emission Inventory Activity (GEIA) [Benkovitz et al., 1996]

CO: biofuels (Yevich and Logan, 2003), fossil fuels and biomass burning (Duncan et al., 2003)

GOME HCHO Columns for July 1996

Hot spots reflect high hydrocarbon emissions from fires and biosphere

0

0.5

1.0

1.5

2.0

2.5[1016 molec cm-2]

Dominant hydrocarbon sources: CH4

Isoprene HCHOOH

CO

GEOS-CHEM Relationship Between HCHO Columns and Isoprene Emissions in N. America

Palmer et al. [2003]

GEOS-CHEMModel, July 1996(25-50ºN, 65-130ºW)

NW NE

SESW

Isoprene emission [1013 atomC cm-2 s-1]

Mod

el H

CH

O C

olum

n [1

016 m

olec

cm

-2]

Cont. from other sources

Isoprene lifetime < 1 hour Isoprene emissions correlated with HCHO columns

Isoprene Emissions for July 1996 by Scaling of GOME Formaldehyde Columns

GOME

(official EPA inventory)

Use linear relationship between isopreneemissions and HCHO columns from model to map GOME HCHO column to top-down isoprene emissions

[Palmer et al., 2003]

(a priori inventory in GEOS-CHEM)

Top-down emissions provide a better fit to in situobservations of HCHO over US

0 1 3 5[1012 atom C cm-2 s-1]

The Next Generation: Tropospheric Emission Spectrometer

• Will be launched in 2004

• Infrared, high resolution Fourier spectrometer(3.3 - 15.4 m)

• Polar, sun-synchronous orbit

• Makes 14.56 orbits per day and repeats its orbit every 16 days

• Has 2 viewing modes: a nadir view and a limb view

• Spatial resolution of nadir measurement is 8 x 5 km2

Assume that we know the true sources of CO

Use GEOS-CHEM 3-D model to simulate “true” pseudo-atmosphere

Sample pseudo-atmosphere along orbit of TES and simulate retrievals of CO

Make a prioriestimate of CO sources

by applying errorsto the “true” source

Use an optimal estimation

inverse method

Obtain a posteriori sources and errors;

How successful are we at finding the true source and reducing the error?

APPROACH:

OBJECTIVE: Determine whether nadir observations of CO from TES will have enough information to reduce uncertainties in our estimates of continental sources of CO

Inversion Analysis State Vector

CHEM

NAFF

RWFF RWBB

AFBB

EUFF

SABB

ASFFASBB

NAFF: 121.3 SABB: 96.5EUFF: 131.1 RWBB: 98.0ASFF: 258.3 RWFF: 149.8ASBB: 96.0 CHEM: 1125.0AFBB: 193.9

“True” Emissions (Tg/yr)

Total = 2270 Tg/yr

• FF = Fossil Fuel + Biofuel• All sources include contributions from oxidation of VOCs• OH is specified• Use a “tagged CO” method to estimate contribution from each source• Use inverse method to solve for annually-averaged emissions

Modeled COLevel 13 (6 km)15 Mar. 2001, 0 GMT

North Americanfossil fuel CO tracer

• Sample the synthetic atmosphere along the orbit of TES

• Assume that the TES nadir footprint of 8 x 5 km is representative of the entire GEOS-CHEM 2º x 2.5º grid box

• We consider data only between the equator and 60ºN

• Assume cloud-free conditions

CO at 510.9 hPa for March 15, 2001

Simulating the TES Data

Generating the Nadir Retrievals of CO

The retrieved profile is:

A = averaging kernels

ya = a priori profileyt = true profile from GEOS-CHEM

trueprofile

retrieved

a priori

ˆ ta ay = y + A(y - y ) + retrieval noise

= retrieved profilex = estimate of the state vector (the CO sources)xa = a priori estimate of the CO sources

(generated by perturbing true state) Sa = error covariance of the a priori

(assume a priori error of 50%)F(x) = forward model simulation of xS = error covariance of observations = instrument error + model error + representativeness error

• Unlike isoprene, CO has a long lifetime (weeks – months) so transport is important need a more formal inversion analysis

• Minimize a maximum a posteriori cost function

Inversion Methodology

ˆ ˆT -1 T -1ε a a aJ(x) = (y - F(x)) S (y - F(x)) + (x - x ) S (x - x )

y

The forward Model (GEOS-CHEM) must account for the verticalsensitivity of TES and the a priori information used in the retrievals

The Forward Model

ta aˆ ( ( ) ) i y y A H x y ε

a m r a( ) ( ( ) ) F x y A H x ε ε y

i = Retrieval noise (< 10%, based on spectral noise of instrument)

Must add noise to forward model to account for errors in GEOS-CHEM simulation of CO

r = representativeness error m = model error

H(x) = GEOS-CHEM model

• root mean square error based on the difference of 89 pairs of 48-hr and 24-hr forecasts of CO during Feb-April 2001

Model Level 16 (8 km)

CO Mixing Ratio (ppb)

The “NMC method”• assume that the difference between successive forecasts, which are

valid at the same time, is representative of the forecast error structure

Constructing the Covariance Matrix for the Model Error

• Compare model with observations of CO from TRACE-P and estimate the relative error • Assume mean bias in relative error is due to emission errors• Remove mean bias and assume the residual relative error (RRE) is due to transport• Use RRE to scale forecast error structure

Characterizing the Model Error

• Representativeness error = 5%, based on sub-grid variability of TRACE-P data

Mean forecast error

RRE

Model Error (Feb-April, 2001)

Inversion Results: 4 Days of Pseudo-Data

• With unbiased Gaussian error statistics, TES is extremely powerful for constraining continental sources of CO

Conclusions

• Current space-based observations provide valuable information to help quantify emissions of O3 precursors

• Observations from TES and future high resolution satellite instruments have the potential to significantly enhance our ability to constrain regional source estimates of O3 precursors

• Realization of this potential rests on proper error characterization, particularly for the model transport error

Will require an integrated approach – in situ (surface and aircraft) and space-based observations

THE FUTURE: Multi-species chemical data assimilation

• Integrate observations from surface sites, aircraft, and satellites (low Earth orbit and geostationary) to construct an optimized 4-D view of the chemical state of the atmosphere

EXTRA SLIDES

Model Transport Error

Model Level 16 (8 km)

CO Mixing Ratio (ppb)

RMS error based on the difference of 89 pairs of 48-hr and 24-hr forecasts of CO during TRACE-P

RMS error b/w model and MOPITT CO column during TRACE-P

x1018 molec/cm2

(Colette Heald)

The forward Model (GEOS-CHEM) must account for the verticalsensitivity of TES and the a priori information used in the retrievals

The Forward Model

ta aˆ ( ( ) ) σ y y A H x y G na m r a( ) ( ( ) ) F x y A H x ε ε y

Gn = Retrieval noiseG = gain matrix associated with A = standard deviation of spectral noise Si = G(I)GT

n = Gaussian white noise with unity variance

Must add noise to forward model to account for errors in GEOS-CHEM simulation of COr = representativeness errorm = model error

H(x) = GEOS-CHEM model

CO Source Distribution in GEOS-CHEMFossil Fuels Biofuels

Biomass Burning

• Other Sources: oxidation of methane and other VOCs• Main sink: reaction with atmospheric OH