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Aircraft CO 2 Observations and Global Carbon Budgeting

This study examines the global carbon budget and the role of aircraft observations in understanding CO2 fluxes. It highlights the importance of accurate carbon cycle modeling and improved transport for more precise flux estimates. The results suggest that intact tropical forests play a significant role in offsetting carbon emissions.

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Aircraft CO 2 Observations and Global Carbon Budgeting

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  1. Aircraft CO2 Observations and Global Carbon Budgeting Britton Stephens, NCAR EOL and TIIMES Collaborating Institutions: USA: NOAA GMD, CSU, France: LSCE, Japan: Tohoku Univ., NIES, Nagoya Univ., Russia: CAO, SIF, England: Univ. of Leeds, Germany: MPIB, Australia: CSIRO MAR

  2. Carbon cycle science as a field began with the careful observational work of Dave Keeling IGY started 50 years ago this month Keeling, C.D., Rewards and penalties of monitoring the earth, Annu. Rev. Energy Environ., 23, 25-82, 1998.

  3. Global and hemispheric constraints on the carbon cycle Annual-mean CO2 exchange (PgCyr-1) from atmospheric O2 TransCom1 fossil-fuel gradients 0.3 ± 0.9 5.4 ± 0.3 1.8 ± 0.8 Surface Observations 1.0 ± 0.6 6.4 ± 0.4 2.2 ± 0.4 IPCC, 2007

  4. [courtesy of Scott Denning]

  5. Seasonal vertical mixing [figure courtesy of Scott Denning]

  6. Transcom3 neutral biosphere flux response “Rectifier Effect” ppm Latitude

  7. TransCom3 model results based on surface data imply a large transfer of carbon from tropical to northern land regions. Level 1 (annual mean) Level 2 (seasonal) Gurney et al, Nature, 2002 Gurney et al, GBC, 2004

  8. Bottom-up estimates have generally failed to find large uptake in northern ecosystems and large net sources in the tropics

  9. TransCom 3 Level 2 annual-mean model fluxes (PgCyr-1) Comparison to other studies fluxes in PgCyr-1 = GtCyr-1 = “billions of tons of C per year” @ $3 - $30 / ton, 3 PgCyr-1 ~ $10 - $100 billion / year

  10. Impact on predicted fluxes TransCom3 predicted rectifier explains most of the variability in estimated fluxes

  11. Transcom3 neutral biosphere flux response pressure N S N S N S N S ppm

  12. Map of airborne flask sampling locations Northern Hemisphere sites include Briggsdale, Colorado, USA (CAR); Estevan Point, British Columbia, Canada (ESP); Molokai Island, Hawaii, USA (HAA); Harvard Forest, Massachusetts, USA (HFM); Park Falls, Wisconsin, USA (LEF); Poker Flat, Alaska, USA (PFA); Orleans, France (ORL); Sendai/Fukuoka, Japan (SEN); Surgut, Russia (SUR); and Zotino, Russia (ZOT). Southern Hemisphere sites include Rarotonga, Cook Islands (RTA) and Bass Strait/Cape Grim, Australia (AIA).

  13. Airborne flask sampling data

  14. 20 -15 10 -10 10 -10 0 -5 Altitude-time CO2 contour plots for all sampling locations

  15. Model-predicted NH Average CO2 Contour Plots Observed NH Average CO2 Contour Plot

  16. Vertical CO2 profiles for different seasonal intervals

  17. Observed and predicted NH average profiles

  18. Estimated fluxes versus predicted 1 km – 4 km gradients • 3 models that most closely reproduce the observed annual-mean vertical CO2 gradients (4, 5, and C): • Northern Land = • -1.5 ± 0.6 PgCyr-1 • Tropical Land = • +0.1 ± 0.8 PgCyr-1 • All model average: • Northern Land = • -2.4 ± 1.1 PgCyr-1 • Tropical Land = • +1.8 ± 1.7 PgCyr-1 Observed value

  19. Observational and modeling biases evaluated: • Interlaboratory calibration offsets and measurement errors • Diurnal biases • Interannual variations and long-term trends • Flight-day weather bias • Spatial and Temporal Representativeness WLEF Diurnal Cycle Observations All were found to be small or in the wrong direction to explain the observed annual-mean discrepancies [Schulz et al., Environ. Sci. Technol. 2004, 38, 3683-3688]

  20. Estimated fluxes versus predicted 1 km – 4 km gradientsfor different seasonal intervals Observed values

  21. Conclusions: • Models with large tropical sources and large northern uptake are inconsistent with observed annual-mean vertical gradients. • A global budget with less tropical-to-north carbon transfer is more consistent with bottom-up estimates and does not conflict with independent global 13C and O2 constraints. • The mean of systematically varying model results is meaningless. • Simply adding airborne data into the inversions will not necessarily lead to more accurate flux estimates • Models’ seasonal vertical mixing must be improved to produce flux estimates with high confidence • There is value in leaving some data out of the inversions to look for systematic biases • Improved carbon flux estimates will come from models with improved transport, assimilation of discrete samples, and more comprehensive atmospheric observations.

  22. S S S N N N HIAPER Pole-to-Pole Observations of Atmospheric Tracers HIPPO (PIs: Harvard, NCAR, Scripps, and NOAA): A global and seasonal survey of CO2, O2, CH4, CO, N2O, H2, SF6, COS, CFCs, HCFCs, O3, H2O, and hydrocarbons Fossil fuel CO2 gradients over the Pacific UCI UCIs pressure N S N S JMA MATCH.CCM3 pressure N S N S ppm

  23. 1 1Faraday, 1855 • Airborne CO2 measurements indicate: • Northern forests, including U.S. and Europe, are taking up much less CO2 than previously thought • Intact tropical forests are strong carbon sinks and are playing a major role in offsetting carbon emissions Science, June 22, 2007 • Implications of this work: • Helps to resolve a major environmental mystery of the past two decades •  Northern “missing carbon sink” has not been found because it is not there • Improved understanding of processes responsible for carbon uptake will improve predictions of climate change and assessment of mitigation strategies graphic from NCAR communications (Steve Deyo)

  24. Aircraft Data Providers: Pieter P. Tans, Colm Sweeney, Philippe Ciais, Michel Ramonet, Takakiyo Nakazawa, Shuji Aoki, Toshinobu Machida, Gen Inoue, Nikolay Vinnichenko, Jon Lloyd, Armin Jordan, Martin Heimann, Olga Shibistova, Ray L. Langenfelds, L. Paul Steele, Roger J. Francey TransCom3 Modelers: Kevin R. Gurney, Rachel M. Law, Scott Denning, Peter J. Rayner, David Baker, Philippe Bousquet, Lori Bruhwiler, Yu-Han Chen, Philippe Ciais, Inez Y. Fung, Martin Heimann, Jasmin John, Takashi Maki, Shamil Maksyutov, Philippe Peylin, Michael Prather, Bernard C. Pak, Shoichi Taguchi Additional Modeling: Wouter Peters, Philippe Ciais, Philippe Bousquet, Lori Bruhwiler

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