New insights into CO 2 fluxes from space? Michael Barkley, Rhian Evans, Alan Hewitt, Hartmut Boesch, Gennaro Cappelutti and Paul Monks EOS Group, University of Leicester

Mol Props Nov07

The FSI algorithm: Overview How do we measure atmospheric CO 2 ? –WFM-DOAS retrieval technique (Buchwitz et al., JGR, 2000) designed to retrieve the total columns of CH 4,CO, CO2, H 2 O and N 2 O 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 CO 2 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 CO 2 using Full Spectral Initiation (FSI) WFM-DOAS”, Barkley et al., ACP, 6, ,2006 –Computationally expensive  SCIAMACHY, on ENVISAT, is a passive hyper-spectral grating spectrometer covering in 8 channels the spectral range nm at a resolution of nm Typical pixel size = 60 x 30 km 2 SCIAMACHY

SCIATRAN (Courtesy of IUP/IFE Bremen) LBL mode, HITRAN 2004 Calibration Non-linearity, dark current, ppg & etlaon SCIAMACHY Spectrum (I/I 0 ) Reference Spectrum + weighting functions (CO 2, H 2 O and temperature) CO 2 Column (Normalise with ECMWF Surface Pressure) Accept only: Errors <5%, Range: ppmv Raw Spectra WFM-DOAS fit I - Calibrated Spectra I 0 – Frerick (ESA) ‘A priori’ Data CO 2 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

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 (r 2 > ) –Best at 1.5 km Surface Sites - monthly averages –Time series comparisons (inc. aircraft) –Out of 17, 11 have r 2 > 0.7

Mol Props Nov07 Can we learn anything? Greater CO 2 uptake by forests compared to crops & grass plains? Identification of sub-continental CO 2 sources/sinks ?

Vegetation Indices vs. CO 2

GPP (or Carbon Tracker/NAEI/JULES/ ECOSSE

ΔCO 2 over target area CO 2 mole fractions ending up into the release domain. These concentrations describe the overall CO 2 collected by the air from the Earth's surface through its way to the release areas.

CO 2 over target area absolute CO 2 concentration of the release area + exchanged CO 2 concentration = data comparable with those from satellite

Release Location Land type A Land type B Land type C Background CO2 (A) Background CO2 (C) CO 2 at release location is obtained from weighted background CO 2 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 CO 2.

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 CO 2. Adding these to the weighted background, gives an atmospheric concentration, that is compared to the satellite.

Monthly mean (left) + Standard Deviation (right) of CO 2 fluxes from Carbon Tracker, over North America July 2003.

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

Retrieved FSI CO 2 columns over North America on the 2 nd 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 CO 2 (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 CO 2 –Comparison of vegetation indices vs. CO 2 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’ CO 2 with a non-ideal instrument & ‘primitive’ algorithm

AT2 Sep08 Thanks also to… John Burrows and V. Rozanov IUP/IFE University of Bremen Alastair Manning and Claire Witham Met 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: Units: kgC/m 2 /s.