1. Climate Control of Terrestrial Carbon Sequestration
Chuixiang Yi1, Daniel Ricciuto2,
and 150 more co-authors
1School of Earth and Environmental Sciences
Queens College, City University of New York
2Oak Ridge National Laboratory, Oak Ridge, TN

2. Carbon-Climate Feedback
positive coupling
Carbon
CO2
Climate T
We know that global warming is caused by increasing of atmospheric CO2.

3. Carbon-Climate Feedback
positive coupling
Carbon
CO2
Climate T
Positive or
negative?
We do not know how climate affects atmosphere-biosphere CO2 exchanges on annual or longer time scale.

4. Warming in the 21st Century
The temperature at northern-high latitudes will be increased much more than others near the end of 21th century, as predicted by many climate models (IPCC, 2007)
High latitudes

5. Drying in the 21st Century
Low latitudes
Projected decreases in precipitation are likely in most subtropical land regions near the end of 21th century, as predicted by many climate models (IPCC, 2007)

6. We need find evidence from observations!!!
Do these climate changes affect the net ecosystem exchanges of CO2 (NEE)?
positive coupling
Carbon
CO2
Climate T
Positive or
negative?
We need find evidence from observations!!!

7. Eddy-Flux Tower

8. Does Climate Control NEE?

9. What are drivers of the vegetation distribution?
Why so much carbon is here?
Why so little carbon is here?
Too dry!!!
Abundant rain and energy!

10. Hypothesis
Climate factors are the dominant factor in NEE variability globally as represented within FLUXNET.

11. FLUXNET DATA Site-year average was used in our analysis
125 sites over 6 continents with a total of 559 site-years.
NEE were gap-filled by the LaThuile project or by PIs themselves.
Meteorological data (T, P, Rn) were gap-filled via a program developed by Dr. Ricciuto.
Site-year average was used in our analysis

12. Test relationship between NEE and climate controls
For the entire datasets (125 NEE data points for 125 sites)

13. NEE VS T for Entire Dataset

14. NEE VS Dryness for Entire Dataset

15. For the entire datasets
The correlations between NEE and each of climate controls (T, P, Rn, dryness) are poor!
All R2 < 0.26

16. Segregate the entire datasets into three groups:
T-group Temperature-limited;
D-group Dryness-limited;
B-group Limited by both T and D.
Grouping method is described on the web

17. Temperature-limited group

18. Dryness-limited group

19. B-group is limited by both T and D.

20. B-group is limited by both T and D.

21. Contour lines of entire data
Function of T
Function of D

22. Contour lines of T-group NEE
Not a Function of D

23. Contour lines of D-group NEE
Not a Function of T

24. Contour lines of B-group NEE A Function of both T and D

25. Findings
NEE (CO2 Flux) is highly limited: (1) by mean annual temperature at mid- and high-latitudes; (2) by dryness at mid- and low latitudes; and (3) by both temperature and dryness around mid-latitudes.
The sensitivity of NEE to mean annual temperature breaks down at 16oC, above which dryness influence overrules temperature influence.

26. Our findings suggest:
The most likely climate-changes in the 21st Century would strongly intensify terrestrial carbon dioxide uptake in high latitudes and weaken uptake in low latitudes.

27. What is the segregation method?
It is secret (just joking)!
It is simple!
But it is objective not subjective.

28. The segregation method
Establish prototype subgroups: We first employed a mixture regression model to determine posterior probability belonging to T-Group and to D-Group. The prototype subgroups include only sites with more than 99% posterior probability. As a result, there are 26 sites in the prototype TG and 21 sites in the prototype DG.

29. The segregation method
2. Calculating Residual Index for a site that is not within the two prototype subgroups.
Grouping by RI

30. Acknowledgements
This work was financially supported by the U.S. National Science Foundation DEB under Grant No

31. These PPTs are posted on my homepage
These PPTs are posted on my homepage. You are welcome to use them in your presentations!

32. T-dependence of light-use efficiencies of C3/C4 plants
Supporting evidence
T-dependence of light-use efficiencies of C3/C4 plants C3
T-dependent
Most located above 45oN C4
T-independent
Tropical/subtropical
J. Ehleringer

33. Map of C3 and C4 grasses: All forests are C3
60-100% of grasses is T-independent C4
(Arctic and alpine tundra are 100% C3)

34. Dryness Index M.I. Budyko (1920-2001)
= Annual sum of net radiation (MJ m-2 yr-1)
= 2.5 MJ kg-1 , the enthalpy of vaporization
= Annual sum of precipitation (mm yr-1)
= Annual sum of potential evaporation (mm yr-1)
lecture-2-

35. Dryness Index Potential evapotranspiration EP M.I. Budyko (1920-2001)
Maximum evapotranspiration, or capacity of evapotranspiration by available energy
lecture-2-

36. Dryness Index
what does this mean?
what does this mean?
lecture-2-

37. Geobotanic Zonality 20 40 60 80 100 R (kcal cm-2 yr-1) Steppe Forest
M.I. Budyko
( ) 20 40 60 80
100
R (kcal cm-2 yr-1)
Tropical
Wet Savanna
Savanna
Subtropical
Steppe
Forest
Semidesert
Desert
Steppe, prairie
Deciduous
Needle
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Dryness index
Tundra