1. Light Aircraft CO 2 Observations and the Global Carbon Cycle 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. Expected from fossil fuel emissions Motivation: Atmospheric CO 2 increase Climate change

3. Annual-mean CO 2 exchange (PgCyr -1 ) from atmospheric O 2 Surface Observations TransCom1 fossil-fuel gradients Global and hemispheric constraints on the carbon cycle

4. [courtesy of Scott Denning]

5. Seasonal vertical mixing

6. Transcom3 neutral biosphere flux response Latitude ppm

7. Gurney et al, Nature, 2002 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, GBC, 2004

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

9. ModelModel Name Northern Total Flux (1  ) Tropical Total Flux (1  ) Northern Land Flux (1  ) Tropical Land Flux (1  ) 1CSU-4.4 (0.2)3.7 (0.6)-3.6 (0.3)3.3 (0.7) 2GCTM-3.4 (0.2)2.3 (0.7)-2.0 (0.3)2.7 (0.8) 3UCB-4.4 (0.3)3.7 (0.6)-3.1 (0.3)4.0 (0.7) 4UCI-2.6 (0.3)0.5 (0.7)-1.5 (0.3)-0.1 (0.8) 5JMA-1.4 (0.3)-0.5 (0.8)-0.9 (0.4)-0.5 (0.9) 6MATCH.CCM3-3.0 (0.2)2.2 (0.6)-2.1 (0.3)2.3 (0.7) 7MATCH.NCEP-4.0 (0.2)3.2 (0.5)-4.0 (0.3)3.4 (0.7) 8MATCH.MACCM2-3.7 (0.3)3.1 (0.8)-3.0 (0.3)2.5 (0.9) 9NIES-4.0 (0.3)2.2 (0.6)-3.5 (0.3)2.7 (0.8) ANIRE-4.5 (0.3)1.6 (0.7)-2.8 (0.3)1.2 (0.8) BTM2-1.6 (0.3)-1.4 (0.7)-0.5 (0.3)-1.0 (0.8) CTM3-2.4 (0.2)1.4 (0.6)-2.2 (0.3)1.0 (0.8) fluxes in PgCyr -1 = GtCyr -1 = “billions of tons of C per year” Transcom 3 Level 2 annual-mean model fluxes @ $3 - $30 / ton, 3 PgCyr -1 ~ $10 - $100 billion / year

10. TransCom3 predicted seasonal effects explain most of the variability in estimated fluxes. Response to neutral biosphere flux Impact on predicted fluxes

11. ppm pressure NSNSNSNS Transcom3 neutral biosphere flux response

12. 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). Map of airborne flask sampling locations

13. Airborne flask sampling data

14. Altitude-time CO 2 contour plots for all sampling locations 20 -15 10 -10 10 -10 0 -5

15. Northern Hemisphere average CO 2 contour plot from observations

16. Model-predicted NH Average CO 2 Contour Plots Observed NH Average CO 2 Contour Plot

17. Vertical CO 2 profiles for different seasonal intervals

18. Observed and predicted NH average profiles

19. 3 models that most closely reproduce the observed annual-mean vertical CO 2 gradients (4, 5, and C): northern land uptake = -1.5 ± 0.6 PgCyr -1 tropical land emission = +0.1 ± 0.8 PgCyr -1 All model average: northern land uptake = -2.4 ± 1.1 PgCyr -1 tropical land emission = +1.8 ± 1.7 PgCyr -1 Estimated fluxes versus predicted 1 km – 4 km gradients Observed value

20. Interlaboratory calibration offsets and measurement errors Diurnal biases Interannual variations and long-term trends Flight-day weather bias Spatial and Temporal Representativeness Observational and modeling biases evaluated: All were found to be small or in the wrong direction to explain the observed annual-mean discrepancies

21. Estimated fluxes versus predicted 1 km – 4 km gradients for different seasonal intervals Observed values

22. Should annual-mean or seasonal gradients be used to evaluate models? Annual-mean fluxes are of most interest because they are relevant to annual ecosystem budgeting, to policy makers, and to projections of future greenhouse gas levels No model does well at all times of year. Models that do well in summer do poorly in other seasons. Errors in seasonal timing of fluxes make selection of seasonal criteria problematic Seasonal effects are inherently cumulative, such that a model with large seasonal errors that offset will do better in annual- mean that one with small seasonal errors that compound.

23. Models with large tropical sources and large northern uptake are inconsistent with observed annual-mean vertical gradients. A global budget with less tropical  north carbon transfer is also more consistent with bottom-up estimates and does not conflict with independent global 13 C and O 2 constraints. 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 Conclusions:

24. What is the role for heavier and lighter aircraft? Continuous instrumentation Multiple species Long-range operations Boundary-layer intensives Low-altitude flux legs

25. CO 2 [ppm]CO [ppb] CO 2 [ppm]CO [ppb] a)b) c)d) North South CO 2 CO COBRA-NA 2000

26. First-Order Regional CO 2 Flux Estimates Transport Predictions and CO 2 Profiles for July 29, 2004 Airborne Carbon in the Mountains Experiment (ACME-04) All flights:

27. HIPPO (PIs: Harvard, NCAR, Scripps, and NOAA): A global and seasonal survey of CO 2, O 2, CH 4, CO, N 2 O, H 2, SF 6, COS, CFCs, HCFCs, O 3, H 2 O, and hydrocarbons HIAPER Pole-to-Pole Observations of Atmospheric Tracers Fossil fuel CO 2 gradients over the Pacific UCIUCIs JMAMATCH.CCM3 ppm pressure SNSNSN NS NS NS NS

28. TransCom3 Modelers: Kevin Robert 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 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