1. THERE’S A RECTIFIER IN MY CLOSET: Vertical CO 2 Transport and Latitudinal Flux Partitioning Britton Stephens, National Center for Atmospheric Research TransCom Meeting, Purdue 2007 [illustrations from There’s a Nightmare in my Closet by Mercer Mayer]

2. 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 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 Additional Modeling: Wouter Peters, Philippe Ciais, Philippe Bousquet, Lori Bruhwiler

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

4. Annual mean accumulation near surface and depletion aloft [Denning et al., Nature, 1995] Observed

5. Transcom3 neutral biosphere flux response Latitude ppm

6. Gurney et al, Nature, 2002 TransCom3 model results show a large transfer of carbon from tropical to northern land regions. Level 1 (annual mean) Level 2 (seasonal) Gurney et al, GBC, 2004

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

8. 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) TransCom 3 Level 2 annual-mean model fluxes (PgCyr -1 ) StudyN. TotalT. TotalN. LandT. Land Jacobson et al., 2006 ('92-'96)-3.95.0-2.94.2 Baker et al., 2006 ('91-'00)-3.72.7-2.61.9 Gurney et al., 2004 ('92-'96)-3.31.8-2.41.8 CarbonTracker, 2007 ('01-'05)-2.81.1-1.80.1 Rödenbeck et al., 2003 ('92-'96)-2.3-0.1-0.7 Rödenbeck et al., 2003 ('96-'99)-2.10.3-0.4-0.8 Comparison to other studies

9. TransCom3 predicted rectifier explains most of the variability in estimated fluxes Impact on predicted fluxes ModelModel Name 1CSU 2GCTM 3UCB 4UCI 5JMA 6MATCH.CCM3 7MATCH.NCEP 8MATCH.MACCM2 9NIES ANIRE BTM2 CTM3

10. ppm pressure NSNSNSNS Transcom3 neutral biosphere flux response

11. 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). Airborne flask sampling locations 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

12. Airborne flask sampling data

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

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

15. Vertical CO 2 profiles for different seasonal intervals

16. Observed and predicted NH average profiles

17. 3 models that most closely reproduce the observed annual-mean vertical CO 2 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 Estimated fluxes versus predicted 1 km – 4 km gradients Observed value ModelModel Name 1CSU 2GCTM 3UCB 4UCI 5JMA 6MATCH.CCM3 7MATCH.NCEP 8MATCH.MACCM2 9NIES ANIRE BTM2 CTM3

18. 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 [Schulz et al., Environ. Sci. Technol. 2004, 38, 3683-3688] WLEF Diurnal Cycle Observations

19. Estimated fluxes versus predicted 1 km – 4 km gradients for different seasonal intervals Observed values ModelModel Name 1CSU 2GCTM 3UCB 4UCI 5JMA 6MATCH.CCM3 7MATCH.NCEP 8MATCH.MACCM2 9NIES ANIRE BTM2 CTM3

20. Should annual-mean or seasonal gradients be used to evaluate model fluxes? 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, but do not want to reject all models. Errors in seasonal timing of fluxes make selection of seasonal criteria problematic. Seasonal (rectifier) 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.

21. HIPPO ’08-’11 (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

22. 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 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: And of course, watch out for the next monster....

23. Representativeness