Using CO observations from space to track long-range transport of pollution

Daniel J. Jacob

with Patrick Kim, Peter Zoogman, Helen Wang

and funding from NASA

110

E

120

E

130

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140

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150

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160

E

Long

itude

0 N

10 N

20 N

30 N

40 N

50 N

Latitude

DC-8

Flights

P-3

B F

lights

MOPITT CO validation during NASA/TRACE-P (Mar-Apr 2001)

DC-8

Asian outflow

MOPITT

Pre

ssu

re,

hP

a

CO, ppbv

DC-8 w/MOPITT AKMOPITT

DC-8 data

MOPITT

sample validation profile

Validation statistics

r2 = 0.98

bias = +6%

0.3 km

12 km

Jacob et al. [JGR 2003]

Application of the GEOS-Chem model adjoint to optimize CO sources using multi-sensor data

Annual mean CO columnMay 2004- April2005

emission

AIRS

MOPITT

TES

SCIAMACHY (Bremen)

bottom-up annual sources

correction factors

Adjointinversion

NEI

EMEP

GFEDEDGAR

Streets

Kopacz et al. [2010]

hPa

MOPITT multispectral retrieval can separate optimization of CO sources and sink

MOPITT NIR+TIRaveraging kernel[Worden et al., 2010]

South America

China

Observations (2006) GEOS-Chem (GEOS-4)GEOS-Chem (GEOS-5) GEOS-Chem (-20% OH)

Below 800 hPa Free troposphereC

O, p

pb

v

150

330

150

80

160

60110

50

200

500

800

Patrick Kim (Harvard) J J D J J D

Interannual variability of Arctic spring pollution from AIRS COARCTAS demonstrated value of AIRS CO for tracking plumes over the Arctic

2003-2008 April mean AIRS CO Interannual anomaly (ENSO Index)

• European sector most polluted, N American sector cleanest

Fisher et al. [2010]

• Transport of Asian pollution to the Arctic is correlated with ENSO through strength of Aleutian Low

2003+ = 2004 = 2005 + =

2006 - = 2007 = 2008 -

ARCTAS

AIRS CO and OMI tropospheric ozone (700-400 hPa, 2008)

ozone, SON

CO, MAM

CO, SON

ozone, MAM

OMIAIRS

NASA A-TrainAIRS and OMI:1. Provide daily coverage2. Observe same scenes at same time of day3. Have similar averaging kernels

Patrick Kim (Harvard)

Ozone-CO correlations in the free troposphere

Slopes of RMA regression lines for O3 vs. CO at 700-400 hPa on 2ox2.5o grid (2008)

OMI ozone and AIRS CO GEOS-Chemozone pollution outflow is correct

stratosphericinfluence is too low

Patrick Kim (Harvard)

GEO-CAPE: geostationary satellite observationof air quality over North America

Ozone CO

UVUV+Vis+TIR

NIR+TIR

• Payload to include ozone and CO measurements with sensitivity in lowest 2 km

• Achieving that sensitivity for ozone likely requires UV+Vis+TIR multispectral observation

• Can model error correlation between ozone and CO enable such sensitivity through data assimilation?

Natraj et al. [2012]

How ozone data assimilation works – and how ozone-CO error correlation can help

ozone

CO

ozone

produce ozonemodel forecast for to

continuous 3-D field

assimilateozone

observations Improved ozone3-D field at to

produceforecastfor to+t

CO CO

assimilateCO observations

CO forecast Improved CO

applyozone-COerror correlations

produce jointforecastfor to+t

Ozone-CO model error correlations in surface air

O3

CO

Afternoon error correlations, Aug 2006

Negative error correlations over eastern US are driven by PBL height:

PBL height Ozone CO

from GEOS-Chem simulations using GEOS-5 vs. GEOS-4 meteorological fields

≡ GEOS-5 – GEOS4

Peter Zoogman, Harvard

7.0813

4.7

675

4.2487476

5.9

Observation system simulation experiment (OSSE)shows benefit of accounting for ozone-CO error correlations

in a data assimilation system for surface ozoneRMSE for daily max 8-h average surface ozone in US in August 2006, and number of misdiagnosed exceedences of air quality standard (75 ppb)

Ozone-CO error correlation enables a UV-only ozone instrument to perform comparably to UV+Vis+TIR instrument if error correlations can be adequately characterized

Root-mean squareerror (RMSE) in ppb

Number of exceedence errors (false positives or negatives)

Peter Zoogman, Harvard

forecast

Using CO2-CO model error correlationsto improve CO2 surface flux inversions from satellite data

GEOS-Chem CO2-CO column error correlations, GEOS-5 vs. GEOS-4 (2006)

January July

• Correlations are positive in growing season, negative for growing season• OSSE suggests that joint CO2-CO inversion can reduce errors in CO2 surface

flux inversions by up to 50% • Joint CO2-CO inversion can also reduce the aggregation error from temporal

and spatial averaging of fire emissions

Wang et al. [2009]

Helen Wang, Harvard-Smithsonian