Long-term growth in global gross primary production based on atmospheric carbonyl sulfide

1. Long-term growth in global gross primary production based on atmospheric carbonyl sulfide Elliott Campbell, Assistant Professor Sierra Nevada Research Institute & College of Engineering UC Merced LLNL, August 11, 2011

2. Roadmap • Motivation • Background • Global atmospheric COS model • Model vs. observations • Other activities

3. Motivation

5. CO2 Fertilization Critical Uncertainty in Climate Forecasts • Current measurement techniques • Limited to small spatial and temporal scales • Wide range of results… no consensus

6. Background

7. Carbonyl Sulfide (COS, OCS, CSO) • Source for stratospheric sulfate aerosol. • Important role in stratospheric ozone. • A novel tracer of terrestrial photosynthesis?

8. Global Budget (Montzka et al., JGR, 2008)

9. Leaf Scale • Leaf-scale relative uptake: 1.0 – 3.8 • Simple COS Plant Uptake (Sandoval-Soto, et al., Biogeosciences, 2008)

10. Plot Scale • Relaxed eddy accumulation (Xu et al., 2002) • Canopy gradient approach (Blonquist et al., 2011)

11. Experiment • Airborne Measurements • Regional Atmospheric Modeling

12. Atmospheric Analysis

13. Regional Scale (Campbell et al., Science, 2008)

14. Geoengineering via Surface COS Emissions (Mills, EGU, 2008)

15. Global Scale • Post-industrial [COS] rise is unprecedented in 2000 year record • Trends are similar to SO2 emissions suggesting that anthropogenic source may be a driver • Relationship to GPP? (Montzka et al., 2004; Aydin et al., 2008, Smith et al., 2010)

16. GPP-CO2 Sensitivity from Global COS C4MIP

17. MODEL

18. Global Box Model • Simple model useful for: • Long life-times • Uncertainty and sensitivity analysis • Transparent approach

19. Sources • Oceans • Magnitude assuming steady state at 1850 • Trend assuming decline since 1980 • Anthropogenic • Magnitude by minimizing COS error • Trend based on SO2 inventories

20. [COS]: Simulated (Previous time step) • [CO2]: Observations • GPP: C4MIP or scaled relative to [CO2] • VCOSCO2: Range of top-down and bottom-up VCOSCO2 C4MIP

21. Uncertainty

22. COS Optimization

23. MODEL vs. Observations

24. Global GPP Growth Consistent with Atmospheric COS

25. Large GPP Growth Results in Optimal Fit

26. Results Not Sensitive to Other Source/Sinks

27. Drawdown ~ GPP Growth

28. Conclusions

29. Summary • The atmospheric COS simulations, driven by a wide range of source and sink estimates, only successfully captured the observed COS trends for simulations that included the quantitative relationship between COS plant uptake and GPP • The COS observations were most consistent with the COS simulations that were driven by high GPP-CO2 sensitivity, rather than low GPP-CO2 sensitivity • Since the assumption of low GPP-CO2 sensitivity in carbon-climate models results in anomalous temperature forecasts, the COS constraint may help improve climate forecasts

30. Other activities

31. Sustainable Domain for Bioenergy (Campbell, Lobell, & Field, ES&T, 2008)

32. Transportation per Cropland Area a) Ethanol b) Bioelectricity (Campbell, Lobell, & Field, Science, 2009)

34. Energy Security is not independent of climate change Volatility = 15% Volatility = 30% (Campbell, Sloan, Snyder, et al., In Prep)

35. Optical Properties of Emmissions (Campbell et al., In Prep)

36. Acknowledgements Support • NSF/CAREER: CBET • DOE/NICCR • DOE/BER Students:XianyuYang, Mohamad Abu-Naser Collaborators • DOE/LBL: Margaret Torn • Stanford/Carnegie: Joe Berry, Chris Field, David Lobell • NOAA: Steve Montzka

37. Thank you! Elliott Campbell, Assistant Professor Sierra Nevada Research Institute & College of Engineering ecampbell3@ucmerced.edu, 209.631.9312, skype: elliott.campbell

38. Junge Plot of Lifetime (Campbell et al., Science, 2008)

39. COS Lifetime • Previous studies: 1.5 – 8.9 y from variety of methods • Recent budget estimates: 1.5 – 3 y (Montzka et al., JGR, 2007) • Junge budget estimates : 0.8 – 2.3 y (Campbell et al., Science, 2008)

40. Experiment – TC4

41. Mean Global Distribution (NOAA/ESRL)