Estimating regional C fluxes by exploiting observed correlations between CO and CO 2 Paul Palmer Division of Engineering and Applied Sciences Harvard University.

Slides:

Advertisements

Similar presentations

Correlation of CO-HCN-C 2 H 2 -C 2 H 6 : Global Constraints on Combustion Sources Biomass burning Biofuels Natural gas/coal C2H6C2H6 ~2 mon CO Fossil fuel.

Advertisements

Exploiting Satellite Observations of Tropospheric Trace Gases Ross N. Hoffman, Thomas Nehrkorn, Mark Cerniglia Atmospheric and Environmental Research,

Improving estimates of CO 2 fluxes through a CO-CO 2 adjoint inversion Monika Kopacz, Daniel J. Jacob, Parvadha Suntharalingam April 12, rd GEOS-Chem.

Evolution of methane concentrations for the period : Interannual variability in sinks and sources J. Drevet, I. Bey, J.O. Kaplan, S. Koumoutsaris,

Constraints on the Production of Nitric Oxide by Lightning as Inferred from Satellite Observations Randall Martin Dalhousie University With contributions.

Evaluating the Impact of the Atmospheric “ Chemical Pump ” on CO 2 Inverse Analyses P. Suntharalingam GEOS-CHEM Meeting, April 4-6, 2005 Acknowledgements.

Offshore China Over Japan Slope (> 840 mb) = 51 R 2 = 0.76 Slope (> 840 mb) = 22 R 2 = 0.45 Suntharalingam et al, 2004 REGIONAL [CO 2 ]:[CO] CORRELATIONS.

Modeling CO 2 and its sources and sinks with GEOS-Chem Ray Nassar 1, Dylan B.A. Jones 1, Susan S. Kulawik 2 & Jing M. Chen 1 1 University of Toronto, 2.

This Week—Tropospheric Chemistry READING: Chapter 11 of text Tropospheric Chemistry Data Set Analysis.

Constraining global isoprene emissions with GOME formaldehyde column measurements Changsub Shim, Yuhang Wang, Yunsoo Choi Georgia Institute of Technology.

Evaluating the Role of the CO 2 Source from CO Oxidation P. Suntharalingam Harvard University TRANSCOM Meeting, Tsukuba June 14-18, 2004 Collaborators.

Effects of Tropical Deforestation on Tropospheric Chemistry: A 10-year Study using GEOS-Chem Prasad Kasibhatla, Duke University James Randerson and Yang.

Impact of Reduced Carbon Oxidation on Atmospheric CO 2 : Implications for Inversions P. Suntharalingam TransCom Meeting, June 13-16, 2005 N. Krakauer,

1 Using satellite data in joint CO 2 -CO inversion to improve CO 2 flux estimates Helen Wang 1,4, Daniel J. Jacob 1, Monika Kopacz 1, Dylan B.A. Jones.

Assimilation of TES ozone into the GEOS-Chem and GFDL AM2 models: implications for chemistry-climate coupling Mark Parrington, Dylan Jones University of.

Intercomparison methods for satellite sensors: application to tropospheric ozone and CO measurements from Aura Daniel J. Jacob, Lin Zhang, Monika Kopacz.

EOS CHEM. EOS CHEM Platform Orbit: Polar: 705 km, sun-synchronous, 98 o inclination, ascending 1:45 PM +/- 15 min. equator crossing time. Launch date.

EOS CHEM. EOS-CHEM Platform Orbit: Polar: 705 km, sun-synchronous, 98 o inclination, ascending 1:45 PM +/- 15 min. equator crossing time. Launch date.

Towards a more integrated approach to tropospheric chemistry Paul Palmer Division of Engineering and Applied Sciences, Harvard University Acknowledgements:

TOP-DOWN CONSTRAINTS ON REGIONAL CARBON FLUXES USING CO 2 :CO CORRELATIONS FROM AIRCRAFT DATA P. Suntharalingam, D. J. Jacob, Q. Li, P. Palmer, J. A. Logan,

Randall Martin Space-based Constraints on Emission Inventories of Nitrogen Oxides Chris Sioris, Kelly Chance (Smithsonian Astrophysical Observatory) Lyatt.

Overview of Techniques for Deriving Emission Inventories from Satellite Observations Frascati, November 2009 Bas Mijling Ronald van der A.

Randall Martin Space-based Constraints on Emissions of Nitrogen Oxides With contributions from: Chris Sioris, Kelly Chance (Smithsonian Astrophysical Observatory)

Exploiting observed CO:CO 2 correlations in Asian outflow to invert simultaneously for emissions of CO and CO 2 Observed correlations between trace gases.

Daily Inventory of Biomass Burning Emissions using Satellite Observations and Using Satellite Observations of CO from MOPITT Colette Heald Advisor: Daniel.

Nitrogen Oxide Emissions Constrained by Space-based Observations of NO 2 Columns University of Houston Amir Souri, Yunsoo Choi, Lijun Diao & Xiangshang.

Development of an EnKF to estimate CO 2 fluxes from realistic distributions of X CO2 Liang Feng, Paul Palmer

INVERSE MODELING OF ATMOSPHERIC COMPOSITION DATA Daniel J. Jacob See my web site under “educational materials” for lectures on inverse modeling atmospheric.

The GEOS-CHEM Simulation of Trace Gases over China Li ZHANG and Hong LIAO Institute of Atmospheric Physics Chinese Academy of Sciences April 24, 2008.

Results Figure 2 Figure 2 shows the time series for the a priori and a posteriori (optimized) emissions. The a posteriori estimate for the CO emitted by.

Determining emissions of environmentally important gases using data from aircraft and satellites with Dorian Abbot, Arlene Fiore, Colette Heald, Daniel.

Emissions of CO from Asia during TRACE-P Paul Palmer, Daniel Jacob, Dylan Jones, Colette Heald David Streets, Glen Sachse, Hanwant Singh

Developing Daily Biomass Burning Inventories from Satellite Observations and MOPITT Observations of CO during TRACE P Colette Heald Advisor: Daniel Jacob.

Asian Sources of Methane and Ethane Y. Xiao, D.J. Jacob, J. Wang, G.W. Sachse, D.R. Blake, D.G. Streets, et al. Atmospheric Chemistry Modeling Group Harvard.

Carbon dioxide from TES Susan Kulawik F. W. Irion Dylan Jones Ray Nassar Kevin Bowman Thanks to Chip Miller, Mark Shephard, Vivienne Payne S. Kulawik –

Inverse modeling in a linear algebra framework State vector x (dimension n) Observation vector y (dimension m) correlations between vector elements Use.

INVERSE MODELING TECHNIQUES Daniel J. Jacob. GENERAL APPROACH FOR COMPLEX SYSTEM ANALYSIS Construct mathematical “forward” model describing system As.

OVERVIEW OF ATMOSPHERIC PROCESSES: Daniel J. Jacob Ozone and particulate matter (PM) with a global change perspective.

Constraints on the Production of Nitric Oxide by Lightning as Inferred from Satellite Observations Randall Martin Dalhousie University With contributions.

1 Examining Seasonal Variation of Space-based Tropospheric NO 2 Columns Lok Lamsal.

Review: Constraining global isoprene emissions with GOME formaldehyde column measurements Shim et al. Luz Teresa Padró Wei-Chun Hsieh Zhijun Zhao.

The Orbiting Carbon Observatory (OCO) Mission: Retrieval Characterisation and Error Analysis H. Bösch 1, B. Connor 2, B. Sen 1, G. C. Toon 1 1 Jet Propulsion.

Error correlation between CO 2 and CO as a constraint for CO 2 flux inversion using satellite data from different instrument configurations Helen Wang.

NASA Carbon Cycle & Ecosystems Joint Science Workshop 28 April - 2 May 2008 Berrien Moore III Climate Central Princeton, NJ & University of New Hampshire.

Terrestrial emissions of isoprene Paul Palmer Division of Engineering and Applied Sciences, Harvard University

RESULTS: CO constraints from an adjoint inversion REFERENCES Streets et al. [2003] JGR doi: /2003GB Heald et al. [2003a] JGR doi: /2002JD

USE OF GEOS-CHEM BY SMITHSONIAN ASTROPHYSICAL OBSERVATORY AND DALHOUSIE UNIVERSITY Randall Martin Mid-July SAO Halifax, Nova Scotia.

Using Space-based Observations to Better Constrain Emissions of Precursors of Tropospheric Ozone Dylan B. A. Jones, Paul I. Palmer, Daniel J. Jacob Division.

Global Characterization of X CO2 as Observed by the OCO (Orbiting Carbon Observatory) Instrument H. Boesch 1, B. Connor 2, B. Sen 1,3, G. C. Toon 1, C.

Comparison of adjoint and analytical approaches for solving atmospheric chemistry inverse problems Monika Kopacz 1, Daniel J. Jacob 1, Daven Henze 2, Colette.

Analysis of Satellite Observations to Estimate Production of Nitrogen Oxides from Lightning Randall Martin Bastien Sauvage Ian Folkins Chris Sioris Chris.

27-28/10/2005IGBP-QUEST Fire Fast Track Initiative Workshop Inverse Modeling of CO Emissions Results for Biomass Burning Gabrielle Pétron National Center.

Potential of Observations from the Tropospheric Emission Spectrometer to Constrain Continental Sources of Carbon Monoxide D. B. A. Jones, P. I. Palmer,

Assimilated Inversion of NO x Emissions over East Asia using OMI NO 2 Column Measurements Chun Zhao and Yuhang Wang School of Earth and Atmospheric Science,

CO2 sources and sinks in China as seen from the global atmosphere

Advisor: Michael McElroy

Spring AGU, Montreal May 20, 2004

Randall Martin Dalhousie University

Paul Palmer, University of Leeds

Monika Kopacz, Daniel Jacob, Jenny Fisher, Meghan Purdy

SUMMER 2004 FIELD STUDIES Modeling support by Harvard University

North American Hydrocarbon Emissions Measured from Space

Towards a more integrated approach to tropospheric chemistry

Continental outflow of ozone pollution as determined by ozone-CO correlations from the TES satellite instrument Lin Zhang Daniel.

What determines column CO2?

Assessment of the Potential of Observations from TES

HARVARD GROUP TRACE-P ACTIVITIES (a planned papers)

Constraints on Asian Carbon Fluxes using TRACE-P CO2/CO Correlations

Paul Palmer, University of Leeds

Hartmut Bösch and Sarah Dance

Presentation transcript:

Estimating regional C fluxes by exploiting observed correlations between CO and CO 2 Paul Palmer Division of Engineering and Applied Sciences Harvard University

IPCC ballpark flux estimates for fast exchange processes (10 9 tonnes C)

IPCC Chinese Government Statistics Shown Downward Trend in Chinese CO 2 Emissions (Streets et al., Science, 294, , 2001) China Energy Databook v6, 2004 China GDP (Billion 1995 yuan constant) Year Large uncertainty

E = A F Bottom-up Emission Inventories are Very Uncertain Emissions (Tg C yr -1 ) Activity Rate (Tg fuel yr -1 ) (amount of fuel burned) Emission Factor (TgC / Tg fuel) Coal-burning cook stoves in Xian, China

RH + OH  …  CO  CO s km Direct & indirect emissions CMDL site Many 100s km 10s km Increasing model transport error Remote data have limitations in estimating regional C budgets

Aircraft data can improve level of disaggregation of continental emissions cold front cold air warm air Main transport processes:  DEEP CONVECTION  OROGRAPHIC LIFTING  FRONTAL LIFTING Feb – April 2001 NASA TRACE-P

Sources of CO from Asia Main sink is the hydroxyl radical (OH)  Lifetime ~1-3 months Product of incomplete combustion BB BF FF + Oxidation of hydrocarbons BF BB FF

Offshore China Over Japan Slope (> 840 mb) = 51 R 2 = 0.76 Slope (> 840 mb) = 22 R 2 = 0.45 Suntharalingam et al, 2004 ATMOSPHERIC CO 2 :CO CORRELATIONS PROVIDE UNIQUE INFORMATION ON SOURCE REGION AND TYPE - CO 2 :CO emission ratios vary with combustion efficiency - Range in regional emission ratios reflect mix of sources and variation in fossil fuel combustion ratio A priori bottom-up Top-down CO CO 2

Observation vector y State vector (Emissions x) Modeling Overview Inverse model x = Fluxes of CO and CO 2 from Asia (Tg C/yr) y = TRACE-P CO and CO 2 concentration data Forward model (GEOS-CHEM) x = x a + (K T S y -1 K + S a -1 ) -1 K T S y -1 (y – Kx a ) ^ y = Kx a +  Jacobian describes CTM

GEOS-CHEM global 3D chemical transport model Driven by NASA GMAO met data (3/6 hr) 2x2.5 o resolution/30 vertical levels O 3 -NO x -VOC-aerosol coupled chemistry Evaluated using ground-based, aircraft, and satellite observations

Consistent CO and CO 2 Emissions Inventories Biomass burning: Variability from observed daily firecount data (AVHRR) Heald et al, 2003 Anthropogenic emissions for 2001: domestic ff, biofuel, transport, industrial ff Streets et al, 2003

Seasonal Cycle of Chinese CO and CO 2 Emissions during TRACE-P TERRESTIAL BIOSPHERE: CASA (Randerson, et al, 1997) OCEAN BIOSPHERE: Takahashi et al, 1999 Gt C yr -1 Fraction of annual emissions CO Annual Mean Streets et al, 2003 TOTAL FOSSIL BIOSPHERE BIOBURN BIOFUEL TOTAL TRACE-P

[OH] from full-chemistry model (CH 3 CCl 3  = 6.3 years) State vector x = emissions from individual countries and individual processes Estimating the Jacobian  [CO]/  CO emission China (CH) Japan (JP) Southeast Asia (SEA) Rest of World (ROW) Global 3D CTM 2x2.5 deg resolution Korea (KR) Boreal Asia (BA) Linear calculation is straightforward: J CHBB = [CO] CHBB CO CHBB/emissions

0-2 km Latitude [deg] CO [ppb] CO 2 [ppm] 4-6 km 2-4 km GEOS-CHEM TRACE-P Observations Remove CO 2 bias using 10 th percentile of [CO 2 ]: ppm

Linear Inverse Model x = x a + (K T S y -1 K + S a -1 ) -1 K T S y -1 (y – Kx a ) S = (K T S y -1 K + S a -1 ) -1 X s = retrieved state vector (the CO sources) X a = a priori estimate of the CO sources S a = error covariance of the a priori K = forward model operator S y = error covariance of observations = instrument error + model error + representativeness error Gain matrix ^ ^

S y Measurement accuracy Representation Model error (most important) GEOS-CHEM Error specification for CO and CO 2 S a Anthropogenic (c/o Streets): China (78%), Japan (17%), Southeast Asia (100%), Korea (42%) – uniform 25% Biomass burning: 50% 30% Chemistry (~CH 4 ): 25% Biosphere: 75% GEOS-CHEM 2x2.5  cell TRACE-P All latitudes (measured-model) /measured Altitude [km] Mean bias RRE CO (y*RRE) 2 ~38ppb (CO) ~1.87ppm (CO 2 ) RRE = total observation error

NUMBER OF EIGENVALUES OF PREWHITENED JACOBIAN  1 = DOF K = S KS ~  -1/21/2 a CO: CH ANTH*, KRJP &, SEA, CH BB, BA ROW CO 2 : CH ANTH*, KRJP &, CH BB $, BA BS, ROW (inc SEA $ ) *Collocated sources; &coarse resolution forces merging; $observed gradients too weak to resolve well resolved Rodgers, 2000

Independent Inversion of CO and CO 2 emissions A priori A posteriori CO 2 emissions [Tg March 2001] CO emissions [Tg yr -1 ] Biospheric CO 2 Anthropogenic CO 2  1 ~ K Results consistent with [CO 2 ]:[CO] analysis Estimated Chinese anthropogenic CO(CO 2 ) sources are currently too low (high). Chinese biospheric CO 2 fluxes are estimated too high.

CO 2 state vector A posteriori correlation matrix illustrates the ambiguity between anthropogenic and biospheric CO 2 emissions Chinese anthropogenic CO 2 Chinese biospheric CO 2 ^ C

Monte Carlo approach to modeling correlations between CO and CO 2 E CO =  (A +  A  A) (F CO +  CO  F CO ) E CO2 =  (A +  A  A) (F CO2 +  CO2  F CO2 ) Carbon Conservation (CO+CO 2 ~ ) Perturbed F N N Unperturbed F  10.9 

r > 0 CO Emissions CO 2 Emissions FF AA CO Emissions CO 2 Emissions FF AA  A >>  F  A <<  F r < 1 Interpretation of correlations

VALUES OF UNCERTAINTY FROM STREETS’ INCONSISTENT WITH DATA ANALYSIS AND LEAD TO SMALL CO 2 :CO CORRELATIONS E = A F  A: CO 5-25%; CO %  F: CO %; CO % Correlations: China ~0 Korea/Japan -0.2 Southeast Asia ~0 Correlations within sectors > lumped sectors

Alternative Correlations Tested… CO 2 :CO Correlation Chinese anthropogenic Korea + Japan Southeast Asia Streets’ Min(  A 25%) Min(  A 50%) Also r = 0.5,…,1.0

A correlation of > 0.7 is needed to start decoupling biospheric and anthropogenic CO 2 A posteriori Uncertainty [unit] Anthropogenic CO 2 Biospheric CO 2 Anthropogenic CO Lowest correlations correspond to those calculated using Monte Carlo method

Future satellite missions The “A Train” MODIS/ CERES IR Properties of Clouds AIRS Temperature and H 2 O Sounding Aqua 1:30 PM Cloudsat PARASOL CALPSO- Aerosol and cloud heights Cloudsat - cloud droplets PARASOL - aerosol and cloud polarization OCO - CO 2 CALIPSO Aura OMI - Cloud heights OMI & HIRLDS – Aerosols MLS& TES - H 2 O & temp profiles MLS & HIRDLS – Cirrus clouds 1:38 PM OCO 1:15 PM OCO - CO 2 column C/o M. Schoeberl

Launch date in Will provide column CO 2 measurements 3 spectrometers that measure CO 2 at 1.61  m and 2.05  m and O 2 at 0.76  m Field of view of spectrometers is 1x1.5 km 2 Sun-synchronous orbit with 16-day repeat cycle and 1:15 pm equator crossing time Orbiting Carbon Observatory (OCO) New Concept: Testing science objectives of satellite instruments before launch Tropospheric Emission Spectrometer (TES) Launched in July 2004 An IR, high resolution Fourier spectrometer Measures spectral range  m Limb and nadir view (footprint is 8x5 km 2 ) Sun-synchronous orbit with 16-day repeat cycle Will measurements of CO and CO 2 from TES and OCO provide accurate constraints on carbon fluxes from different regions in Asia? Jones et al, 2004

Simulation: Constraining Asian Carbon Fluxes from Space Generate pseudo-data from the satellites for March 1-31, 2001 Inverse model with realistic instrument and model errors, and which accounts for data loss due to cloud cover and the vertical sensitivity of the instruments CO 2 column along OCO orbit (1 day) CO (825 mb) along TES orbit (1 day) ppm ppb Jones et al, 2004

Significant reduction in uncertainty in estimates of the dominant Asian biospheric fluxes (China and Boreal Asia) China Fuel JP/KR Fuel SE Asia Fuel India Fuel China BB SE Asia BB India BB Boreal Asia BB China Japan Korea SE Asia India Boreal Asia Rest of world A priori A posteriori China Fuel JP/KR Fuel SE Asia Fuel India Fuel China BB SE Asia BB India BB Boreal Asia BB China Japan Korea SE Asia India Boreal Asia Rest of world A priori A posteriori China Fuel JP/KR Fuel SE Asia Fuel India Fuel China BB SE Asia BB India BB Boreal Asia BB China Japan Korea SE Asia India Boreal Asia Rest of world A priori A posteriori China Fuel JP/KR Fuel SE Asia Fuel India Fuel China BB SE Asia BB India BB Boreal Asia BB China Japan Korea SE Asia India Boreal Asia Rest of world A priori A posteriori China Fuel JP/KR Fuel SE Asia Fuel India Fuel China BB SE Asia BB India BB Boreal Asia BB China Japan Korea SE Asia India Boreal Asia Rest of world A priori A posteriori China Fuel JP/KR Fuel SE Asia Fuel India Fuel China BB SE Asia BB India BB Boreal Asia BB China Japan Korea SE Asia India Boreal Asia Rest of world A priori A posteriori China Fuel JP/KR Fuel SE Asia Fuel India Fuel China BB SE Asia BB India BB Boreal Asia BB China Japan Korea SE Asia India Boreal Asia Rest of world A priori A posteriori CO Sources CO 2 Sources Biospheric CO 2 A Posteriori Error Estimates [%] China Fuel JP/KR Fuel SE Asia Fuel India Fuel China BB SE Asia BB India BB Boreal Asia BB China Fuel JP/KR Fuel SE Asia Fuel India Fuel China BB SE Asia BB India BB Boreal Asia BB Chinese biospheric fluxes weakly coupled to anthropogenic emissions Jones et al, 2004

Closing Remarks Estimated Chinese anthropogenic CO(CO 2 ) sources are currently too low (high). Chinese biospheric CO 2 fluxes are estimated too high but they are coupled to anthropogenic CO 2. Correlations between CO 2 and CO can decouple these signals. Emission correlations summed over sectors are too weak – need r > 0.7, impossible with current inverse model configuration. Work in progress – much still to explore.