New insights into CO2 fluxes from space?

New insights into CO2 fluxes from space?

Michael Barkley, Rhian Evans, Alan Hewitt, Hartmut Boesch, Gennaro Cappelutti and

Paul MonksEOS Group, University of Leicester

Mol Props Nov07

Mol Props Nov07

The FSI algorithm: Overview

• How do we measure atmospheric CO2?– WFM-DOAS retrieval technique (Buchwitz et

al., JGR, 2000) designed to retrieve the total columns of CH4,CO, CO2, H2O and N2O from spectral measurements in NIR made by SCIAMACHY

• Least squares fit of model spectrum + ‘weighting functions’ to observed sun-normalised radiance

– We use WFM-DOAS to derive CO2 total columns from absorption at ~1.56 μm

• Key difference to Buchwitz’s approach:– No look-up table – Calculate a reference spectrum for every

single SCIAMACHY observation i.e. to obtain ‘best’ linearization point – no iterations

• See “Measuring atmospheric CO2 using Full Spectral Initiation (FSI) WFM-DOAS” , Barkley et al., ACP, 6, 3517-

3534 ,2006– Computationally expensive

SCIAMACHY, on ENVISAT, is a passive hyper-spectral grating spectrometer covering in 8 channels the spectral range 240-2040 nm at a resolution of 0.2-1.4 nm

Typical pixel size = 60 x 30 km2

SCIAMACHY

SCIATRAN(Courtesy of IUP/IFE Bremen)(Courtesy of IUP/IFE Bremen)

LBL mode, HITRAN 2004

CalibrationNon-linearity, dark current, ppg & etlaon

SCIAMACHY Spectrum

(I/I0)

Reference Spectrum + weighting

functions(CO2, H2O and temperature)

CO2 Column(Normalise with ECMWF Surface Pressure)

Accept only: Errors <5%, Range:340-400 ppmv

Raw Spectra

WFM-DOAS fit

I - Calibrated Spectra

I0 – Frerick (ESA)

‘A priori’ DataCO2 profiles taken from climatology (Remedios, ULeic)

ECMWF: temperature, pressure and water vapour profiles

‘A priori’ albedo - inferred from SCIAMACHY radiance as a f(SZA)

‘A priori’ aerosol (maritime/rural/urban)

SCIAMACHY

Spectra, geolocation, viewing geometry, time

Process only if : cloud free, forward scan, SZA ‹75

Cloud Filter SPICI (SRON)

(Krijger et al, ACP, 2005)

Note: No scaling of FSI data

FSI Spectral fits

• Good fit residuals• Retrievals errors:

– Over land 1-4%– Over oceans 1-25%

Mol Props Nov07

Mol Props Nov07

Mol Props Nov07

North-South Gradients

NH vs. SH

Validation Summary

• FTIR– Park Falls ~ -2%– Egbert ~ -4%

• TM3– Bias ~ -2%– SCIAMACHY overestimates seasonal cycle by factor 2-3

with respect to the TM3 – reason?– Bias of TM3 w.r.t Egbert FTIR data ~ -2%

• Aircraft – collocated observations in time & space– Sites over Siberia (r2 > 0.72-0.9)– Best at 1.5 km

• Surface Sites - monthly averages– Time series comparisons (inc. aircraft)– Out of 17, 11 have r2 > 0.7

Mol Props Nov07

Can we learn anything?

• Greater CO2 uptake by forests compared to crops & grass plains?

• Identification of sub-continental CO2 sources/sinks ?

Vegetation Indices vs. CO2

GPP (or Carbon Tracker/NAEI/JULES/ ECOSSE

ΔCO2 over target area

CO2 mole fractions ending up into the release domain. These concentrations describe the overall CO2 collected by the air from

the Earth's surface through its way to the release areas.

CO2 over target area

absolute CO2 concentration of the release area

+

exchanged CO2 concentration

=data comparable with those from satellite

ReleaseLocatio

n

Land type A

Land type B

Land type C

Background CO2 (A)

Background CO2 (C)

CO2 at release location is obtained from weighted background CO2 plus a weighted flux contribution.In simplified diagram, the thick arrow from direction C represents greatest surface contact time, and the greatest contribution of the background CO2.

Compare to

satellite

Strong absorber

Weak emitter

Weak absorber

Land area is divided into a small grid.For each grid square the surface influence is multiplied by the strength of flux (from e.g. carbon tracker).Summing up all of the grid squares produces a delta CO2.Adding these to the weighted background, gives an atmospheric concentration, that is compared to the satellite.

Monthly mean (left) + Standard Deviation (right) of CO2 fluxes from Carbon Tracker, over North America July 2003.

Surface influence (time spent at lowest altitude grid) of air released at 40-50N, 90-100W on 14th July 2003.

Retrieved FSI CO2 columns over North America on the 2nd July 2003.

Mol Props Nov07

Summary• Encouraging first results from the FSI algorithm

– Good agreement with FT stations at Egbert & Park Falls (bias -2 to -4%)– Good agreement with TM3 model (more uptake in summer)– Good agreement with aircraft data over Siberia– Good agreement with AIRS CO2 (accounting for different vertical sensitivity)– Good agreement with surface network– Good correlation with vegetation spatial distribution

• Key point: SCIAMACHY can track changes in near surface CO2

– Comparison of vegetation indices vs. CO2 indicate SCIAMACHY can track biological signal at regional level (though at limit of sensitivity)

• Can we measure fluxes:– It is clear that there is information in the SCIAMACHY data on

the biospheric fluxes– Preliminary estimates seem to show a quantitative correlation

with carbon tracker when weighted for surface contact time – Further work is required and underway

• Outlook - Very encouraging for GOSAT and OCO– ‘Measuring’ CO2 with a non-ideal instrument & ‘primitive’ algorithm

AT2 Sep08

Thanks also to…

John Burrows and V. RozanovIUP/IFE University of Bremen

Alastair Manning and Claire WithamMet Office, UK

Flux datasets

• Carbon Tracker: biosphere, sea, fossil fuel, fire and overall

• JULES: vegetation (Joerg Kaduk, Geography, Leicester)

• ECOSSE: soil (Jo Smith, Aberdeen)• NAEI: anthropogenic emissions

JULES Gross Primary

Production (GPP): rate at which an

ecosystem produces,

captures and stores chemical

energy as biomass. Average

over 5 years: 1999-2003. Units:

kgC/m2/s.