Atmospheric Atmospheric inversion of COinversion of CO22

sources and sinks sources and sinks Northern Northern

Hemisphere sinkHemisphere sinkJay S. GreggJay S. Gregg

GoalGoal

Inverse modeling identifies carbon Inverse modeling identifies carbon sources and sinks, and coupled with a sources and sinks, and coupled with a planetary transport model, generates planetary transport model, generates predicted COpredicted CO22 concentrations. concentrations.

Ideally, the model is adjusted so that the Ideally, the model is adjusted so that the predicted flux measurements best predicted flux measurements best match those measured at various match those measured at various locations around the globe. locations around the globe.

Paraphrased from Gurney, 2006, http://www.purdue.edu/eas/carbon/inverse_modeling.html

ComponentsComponents

Observed Atmospheric Observed Atmospheric Concentrations of COConcentrations of CO22 Spatiotemporal concentrations (ppm) of Spatiotemporal concentrations (ppm) of

COCO22

Observed Sea Surface Observed Sea Surface Concentrations of COConcentrations of CO22

Partial Pressure of COPartial Pressure of CO22

General Circulation ModelGeneral Circulation Model

Sources and Sinks Sources and Sinks InvolvedInvolved

Fossil-Fuel-Based Emissions (Confidence: Fossil-Fuel-Based Emissions (Confidence: High)High)

Land Use Change (Confidence: Low)Land Use Change (Confidence: Low)

Terrestrial Ecosystem Response to Elevated Terrestrial Ecosystem Response to Elevated COCO22 (Confidence: Low) (Confidence: Low)

Terrestrial Sink (Confidence: Low)Terrestrial Sink (Confidence: Low)

Ocean Sink (Confidence: Low)Ocean Sink (Confidence: Low)

*Confidence refers to amount, temporal *Confidence refers to amount, temporal pattern, and spatial locationpattern, and spatial location

Atmospheric COAtmospheric CO22 ObservationsObservations

Geophysical Monitoring for Climate Geophysical Monitoring for Climate Change (GMCC) NetworkChange (GMCC) Network

Based on flask measurementsBased on flask measurements

20 cites since 198020 cites since 1980

Atmospheric COAtmospheric CO22 Sampling Sampling SitesSites

Mountainous Sites (e.g., Mauna Loa) were not used due to difficulty in elevation for the transport models

ppm +300

Tans et al., 1990

Atmospheric COAtmospheric CO22 ConcentrationConcentration

predicted concentrations from known sources and sinks (b, c, d)

Tans et al., 1990

observed concentrations

Evidence formissing northernhemisphere sink

Oceanic ObservationsOceanic Observations

Observed Observed ppCOCO22 difference between surface difference between surface ocean and atmosphereocean and atmosphere

Transect Sampling, some data gaps in Indian Transect Sampling, some data gaps in Indian and Southern Ocean- extrapolation based and Southern Ocean- extrapolation based on Sea Surface Temperatureson Sea Surface Temperatures

Oceans divided into 2Oceans divided into 2o o x 2x 2oo grids, and mean grids, and mean ppCOCO22 is calculated for the periods is calculated for the periods (January through April) and (July through (January through April) and (July through October)October)

Oceanic COOceanic CO22 Calculations Calculations

Working Formula for F (COWorking Formula for F (CO22 flux across air-sea interface): flux across air-sea interface):

EE: gas transfer coefficient, depends on wind speed: gas transfer coefficient, depends on wind speed

VVpp: gas transfer piston velocity, depends on : gas transfer piston velocity, depends on turbulence, atmospheric and oceanicturbulence, atmospheric and oceanic

SS: solubility of CO: solubility of CO22 in seawater in seawater

ppCOCO22: Sea surface – Atmosphere : Sea surface – Atmosphere

(>0 is a ocean sink, <0 is an ocean source)(>0 is a ocean sink, <0 is an ocean source)Tans et al., 1990

Oceanic COOceanic CO22 Calculations Calculations

Transect Samples as of 1972

Tans et al., 1990

Oceanic COOceanic CO22 Fluxes Fluxes

Largest positive fluxes (sinks) are in the equatorial oceans

Largest negative fluxes (sources) are in the Southern gyres

Tans et al., 1990

Oceanic Oceanic COCO22

FluxesFluxes

Jan-Apr

Jul-Oct

Tans et al., 1990

Transport ModelTransport Model

3-D General Circulation Model 3-D General Circulation Model (GCM) from Goddard Space Flight (GCM) from Goddard Space Flight Center, NASACenter, NASA

Seasonal, diurnalSeasonal, diurnal

Transport Model (vs. Transport Model (vs. Observed)Observed)

Scandinavia Bass Strait

Tans et al., 1990

observedmodeled

Modeled Atmospheric COModeled Atmospheric CO22 ConcentrationsConcentrations

Tans et al., 1990

Rel

ativ

e to

Glo

bal M

ean

Con

cent

ratio

n

observedmodeled

Modeled Modeled FluxesFluxes

(C. Roedenbeck et al., 2002)

Modeled FluxesModeled Fluxes

(C. Roedenbeck et al., 2002)

(C. Roedenbeck et al., 2002)

(C. Roedenbeck et al., 2002)

Modeled NPPModeled NPP

(C. Roedenbeck et al., 2002)

arbitrary units(linear)

Modeled COModeled CO22 Sources and Sources and SinksSinks

Atmospheric COAtmospheric CO22 increases about 3 Gt increases about 3 Gt C/yrC/yr

Sinks are larger in northern hemisphere Sinks are larger in northern hemisphere than southern than southern ocean sink is largest at equatorocean sink is largest at equator must be a larger northern terrestrial sinkmust be a larger northern terrestrial sink

El Nino and La Nina cycles changes El Nino and La Nina cycles changes fluxesfluxes

Still a lot of uncertainty in global carbon Still a lot of uncertainty in global carbon cyclecycle

Which Transport Model Which Transport Model to Use?to Use?

Many different transport models can Many different transport models can give different resultsgive different results

Underscores uncertainty in inverse Underscores uncertainty in inverse model resultsmodel results

Transcom 3 Project (Gurney, 2002) Transcom 3 Project (Gurney, 2002) seeks to compare the outcome from seeks to compare the outcome from various modelsvarious models

Which Which Transport Transport Model to Model to

Use?Use?

Comparison of two transport models, confidence range for all models are in boxes

(Gurney et al., 2002)

Which Transport Model Which Transport Model to Use?to Use?

Confidence range for all models based on latitude (Gurney et al., 2002)

Factors in COFactors in CO22 Flux Flux VariabilityVariability

El Nino and La Nina (increased El Nino and La Nina (increased biomass burning), changes in NPPbiomass burning), changes in NPP

Volcanic Eruptions (e.g., Pinatubo- Volcanic Eruptions (e.g., Pinatubo- changes in NPP from sunlight changes in NPP from sunlight limitations)limitations)

Temperature and humidity affect Temperature and humidity affect microbial respiration (soil microbial respiration (soil respiration increases at higher respiration increases at higher temperatures)temperatures)

(C. Roedenbeck et al., 2002)

ReferencesReferencesI.G. Enting, C.M. Trudinger, R..J.A. Francey (1995) A synthesis I.G. Enting, C.M. Trudinger, R..J.A. Francey (1995) A synthesis

inversion of the concentration of inversion of the concentration of 1313C of atmospheric COC of atmospheric CO22. . Tellus BTellus B 4747, 35-52., 35-52.

S. Fan, et al., (1998) A large terrestrial carbon sink in North America S. Fan, et al., (1998) A large terrestrial carbon sink in North America implied by atmospheric and oceanic carbon dioxide data and implied by atmospheric and oceanic carbon dioxide data and models. Science models. Science 282282, 442-446., 442-446.

K. R. Gurney et al., Towards robust regional estimates of COK. R. Gurney et al., Towards robust regional estimates of CO22 sources sources and sinks using atmospheric transport models, and sinks using atmospheric transport models, Nature Nature 415415, 626 , 626 (2002).(2002).

C. Roedenbeck, S. Houweling, M. Gloor, and M. Heimann (2003) COC. Roedenbeck, S. Houweling, M. Gloor, and M. Heimann (2003) CO22 flux history 1982–2001 inferred from atmospheric data using a flux history 1982–2001 inferred from atmospheric data using a global inversion of atmospheric transport, global inversion of atmospheric transport, Atmos. Chem. PhysAtmos. Chem. Phys., ., 33, , 1919–1964.1919–1964.

P. P. Tans, I. Y. Fung, T. Takahashi, (1990) Observational Constraints P. P. Tans, I. Y. Fung, T. Takahashi, (1990) Observational Constraints on the Global Atmospheric COon the Global Atmospheric CO22 Budget, Budget, Science Science 247247, 1431-1438., 1431-1438.