Paul Palmer, University of Leeds Space-based measurements of Carbon Compounds Paul Palmer, University of Leeds www.env.leeds.ac.uk/~pip

Importance of VOC emissions in C budget? 61 60 5.5 1.6 + ballpark flux estimates for fast exchange processes (109 tonnes C) Importance of VOC emissions in C budget? Kesselmeier, et al, 2002 What are the relative roles of the oceans and land ecosystems in absorbing CO2? Is there a northern hemisphere land sink? What are the relative roles of North America and Eurasia What controls carbon sinks? How will sinks respond to climate change? IPCC

Carbon-containing compounds from space: platforms, instruments, species ERS-2 Terra Envisat Aqua Aura ??? Launch 1995 1999 2002 2004 2008 Sensor GOME MOPITT SCIAMACHY AIRS TES OMI OCO GOSAT CO2 X CO HCHO CH4

Horizontal spatial scales Developing a self-consistent view of the carbon cycle O(1 km) O(10s km) O(10-100s km) Top-Down Horizontal spatial scales Models Bottom-Up

Global Ozone Monitoring Experiment (GOME) & the Ozone Monitoring Instrument (OMI) Launched in 2004 GOME (European), OMI (Finnish/USA) are nadir SBUV instruments Ground pixel (nadir): 320 x 40 km2 (GOME), 13 x 24 km2 (OMI) 10.30 desc (GOME), 13.45 asc (OMI) cross-equator time GOME: 3 viewing angles  global coverage within 3 days OMI: 60 across-track pixels  daily global coverage O3, NO2, BrO, OClO, SO2, HCHO, H2O, cloud properties

GOME HCHO columns July 2001 Biogenic emissions Biomass burning [1016 molec cm-2] 1 2 0.5 1.5 2.5 * Columns fitted: 337-356nm * Fitting uncertainty < continental signals

Tropospheric O3 is an important climate forcing agent NO HO2 OH NO2 O3 hv HC+OH  HCHO + products NOx, HC, CO Level of Scientific Understanding Natural VOC emissions (50% isoprene) ~ CH4 emissions. IPCC, 2001

May Jun Jul Aug Sep 1996 1997 1998 1999 2000 2001 GOME HCHO column [1016 molec cm-2] 1 2 0.5 1.5 2.5

Relating HCHO Columns to VOC Emissions hours OH h, OH VOC kHCHO EVOC = (kVOCYVOCHCHO) HCHO ___________ Local linear relationship between HCHO and E VOC source Distance downwind WHCHO Isoprene a-pinene propane 100 km EVOC: HCHO from GEOS-CHEM and MCM models

Isoprene emissions July 1996 GEIA EPA BEIS2 7.1 Tg C 2.6 Tg C MEGAN 3.6 Tg C [1012 atom C cm-2 s-1]

Modeling biospheric VOC emissions PAR – direct and diffuse (GMAO) Temperature: Instantaneous (G95) 10-day avg (Petron ‘01) Fixed base emission factors (Guenther 2004) Canopy model (Guenther 1995) Altitude Isoprene emissions do not generally begin until the forest canopy has reached a mature state, 2 weeks after leaves emerge and photosynthesis had begun. Emission April Sep LAI MODIS/AVHRR LAI Emissions

Seasonal Variation of Y2001 Isoprene Emissions MEGAN GOME MEGAN GOME May Jun Aug Sep Jul 3.5 7 1012 atom C cm-2s-1 Good accord for seasonal variation, regional distribution of emissions (differences in hot spot locations – implications for O3 prod/loss). Other biogenic VOCs play a small role in GOME interpretation

GOME Isoprene Emissions: 1996-2001 May Jun Jul Aug Sep 1996 1997 1998 1999 2000 2001 [1012 molecules cm-2s-1] 5 10 Relatively inactive

Surface temperature explains 80% of GOME-observed variation in HCHO NCEP Surface Temperature [K] GOME HCHO Slant Column [1016 molec cm-2] Curves fitted to GOME data Modeled curves Time to revise model parameterizations of isoprene emissions?

Higher spatial resolution with OMI 13x24 km2 (OMI) vs 40x320 km2 (GOME) at nadir Biogenic emissions – isoprene, monoterpenes, mbo 10^12 molec emissions flux (mol/cm2/s) Correlation( r ) 0.63 0.59 0.44 0.46 0.27 North America, July 2005 Africa 24 Sept – 19 Oct 2004 MODIS Fire Counts

The Orbiting Carbon Observatory (OCO) “First global space-based measurements of CO2 with the precision and spatial resolution needed to quantify carbon sources and sinks” Launch in 2008 2-year mission Spectroscopic observations of CO2 and O2 to estimate the column integrated CO2 dry air mole fraction, XCO2 = 0.2095 x (column CO2) / (column O2) Precisions of 1 ppm on regional scales Global coverage in 16 days JPL-based instrument: PI D. Crisp; Deputy PI: C. Miller (Crisp et al, 2004)

Monitoring CO2 from Space High resolution (λ/Δλ=17.5k-21k) spectra of reflected sunlight in near IR: CO2 1.61 m and 2.06 m bands O2 0.765 m A-band Retrieval of column average CO2 dry air mole fraction, XCO2 with BL sensitivity (Also need Ps, albedo, T, water vapour, clouds, and aerosols, provided by OCO) ~0.3% (1ppm) precision to improve on in situ data Nadir Mode Target Mode Glint Mode Why high spectral resolution? Lines must be resolved from the continuum to minimize systematic errors CO2 1.61 m band is well suited for retrieving CO2 column abundances CO2 2.06 m band CO2, cloud, aerosol, water vapor, temperature O2 A-band constrains clouds, aerosols, and surface pressure OCO samples at high spatial resolution Nadir mode: 1 km x 1.5 km footprint Isolates cloud-free scenes Thousand of samples on regional scales Glint Mode: High SNR over oceans Target modes: Calibration

Assessing OCO Performance with OSSEs CO2 fluxes XCO2 map Retrieval End-to-end retrievals of XCO2 from individual simulated nadir soundings at SZAs of 35o and 75o. The simulations include sub-visual cirrus clouds (0.02c 0.05), light to moderate aerosol loadings (0.05a 0.15), over ocean and land surfaces. INSET: Distribution of XCO2 errors (ppm) for each case XCO2 along OCO orbits CO2, O2 spectral radiances; λ/Δλ=21,000 Kuang et al, 2002 (250 Gb/day)

Validation Program Ensures Accuracy and Minimizes Spatially Coherent Biases Ground-based in-situ measurements NOAA CMDL Flask Network + Tower Data TAO/Taurus Buoy Array Uplooking FTS measurements of XCO2 3 funded by OCO 4 upgraded NDSC Aircraft measurements of CO2 profile Wofsy (US), Ciais (CNRS Aerocarb) Laboratory and on-orbit calibration Buoy Network CMDL

Coordinated calibration/validation activities in the A-train PARASOL – polarization data AIRS – T, P, H2O, CO2, CH4 MODIS – cloud/aerosols, albedo CloudSat – cloud climatology CALIPSO – vertical profiles of cloud & aerosol; cirrus particle size **OCO – O2 A-Band Spectra** OCO – XCO2, P(surface), T, H2O, cloud, aerosol TES – T, P, H2O, O3, CH4, CO MLS – O3, H2O, CO HIRDLS – T, O3, H2O, CO2, CH4 OMI – O3, aerosol climatology, HCHO Also exploit physically based correlations between measurements of different carbon compounds, e.g., CO and CO2

Coupled inversions exploit correlations REGIONAL CO2:CO CORRELATIONS PROVIDE UNIQUE INFORMATION ON SOURCE REGION AND TYPE Offshore China Over Japan A priori bottom-up CO2 CO2 CO CO Slope (> 840 mb) = 51 R2 = 0.76 Slope (> 840 mb) = 22 R2 = 0.45 Top-down information from TRACE-P Suntharalingam et al, 2004 Coupled inversions exploit correlations

Use CO-CO2 correlations to “disentangle” biospheric and combustion CO2 sources State vector (Emissions x) Consistent CO and CO2 Emissions Jacobian describes CTM y = Kxa +  Forward model (GEOS-CHEM) Observation vector y Inverse model x = Fluxes of CO and CO2 from Asia (Tg C/yr) y = TRACE-P CO and CO2 concentration data x = xa + (KTSy-1K + Sa-1)-1 KTSy-1(y – Kxa) ^

Quantification of CO:CO2 Correlations Colocated Emissions Atm. Dyn. Processes Emissions (g gas yr-1) Emission Factor (g gas/g fuel) cold air warm air E = A F Coal-burning cook stoves in Xian, China Activity Rate (g fuel yr-1) E.g., frontal system Use A, F, A and F for CO and CO2 from Streets et al, 2003 in a Monte Carlo approach. Correlations <0.1. Correlations implemented in different ways: Sa and Sy [CO]:[CO2] correlations from TRACE-P are typically 0.7-0.8 Sa Sy

CO:CO2 correlations improve CO2 inversions China Anth. CO China Anth. CO2 China Bio. CO2 Tg /Mar 2001 Tg yr -1 Tg yr -1 Sa CO2:CO Correlation Tg /Mar 2001 Tg yr -1 Tg yr -1 Sy CO2:CO Correlation A posteriori Source Estimate A posteriori Source Uncertainty Palmer et al, 2005

Closing Remarks Community faced with the arrival of huge amounts of data! Cal/Val of satellite and in situ data still necessary. How do we use satellite data to learn about the carbon cycle? (Too late to include a CO channel on OCO!) What can we learn?