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Climate Control of Terrestrial Carbon Sequestration

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

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Climate Control of Terrestrial Carbon Sequestration

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  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. 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 little carbon is here? Why so much carbon is here? Too dry!!! Abundant rain and energy! http://www.ncdsnet.net/~kinney/library/biome2.gif

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

  11. FLUXNET DATA • 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. Forthe 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 http://iopscience.iop.org/1748-9326/5/3/034007/media/erl10_3_034007_supp.pdf

  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. 0949637. http://iopscience.iop.org/1748-9326/5/3/034007/fulltext http://environmentalresearchweb.org/cws/article/news/43529

  31. These PPTs are posted on my homepage. You are welcome to use them in your presentations! http://qcpages.qc.cuny.edu/~cyi/research-control.htm

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

  33. Map of C3 and C4 grasses: All forests are C3 0-30% C4 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) = Annual sum of potential evaporation (mm yr-1) = 2.5 MJ kg-1 , the enthalpy of vaporization = Annual sum of precipitation (mm yr-1)

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

  36. Dryness Index what does this mean? what does this mean?

  37. GeobotanicZonality 100 80 Tropical M.I. Budyko (1920-2001) 60 R (kcal cm-2 yr-1) Wet Savanna 40 Savanna Subtropical Steppe 20 Forest Semidesert Desert Steppe, prairie Deciduous 2.0 2.5 3.5 0 0.5 1.0 1.5 3.0 Needle Dryness index Tundra

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