Recent increases in terrestrial carbon uptake at little cost to the water cycle

Nature Communications

Volumn 8, Issue 1, 2017, Pages

  Cheng, Lei ^a   Zhang, Lu ^a   Wang, Ying Ping ^a   Canadell, Josep G ^a   Chiew, Francis H S ^a   Beringer, Jason ^b   Li, Longhui ^c   Miralles, Diego G ^d   Piao, Shilong ^e,f,g   Zhang, Yongqiang ^a  

^a CSIRO   (Australia)

^b UNIVERSITY OF WESTERN AUSTRALIA   (Australia)

^c UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^d GHENT UNIVERSITY   (Belgium)

^e PEKING UNIVERSITY   (China)

^f INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^g CAS CENTER FOR EXCELLENCE IN TIBETAN PLATEAU EARTH SCIENCES   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CARBON;

CARBON CYCLE; CARBON DIOXIDE; CLIMATE CHANGE; CONCENTRATION (COMPOSITION); GLOBAL WARMING; GROUND-BASED MEASUREMENT; HYDROLOGICAL CYCLE; LEAF AREA INDEX; NET PRIMARY PRODUCTION; OBSERVATIONAL METHOD; VAPOR PRESSURE; WATER ECONOMICS; WATER USE EFFICIENCY;

ARTICLE; ASIA; ATMOSPHERE; CARBON CYCLE; CHINA; ECOSYSTEM; EDDY COVARIANCE; EUROPE; LEAF AREA; MONGOLIA; NORTH AMERICA; RUSSIAN FEDERATION; SOUTH AMERICA; TAIGA; TROPICAL RAIN FOREST; VALIDITY; VAPOR PRESSURE; WATER CYCLE;

EID: 85026386650     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/s41467-017-00114-5     Document Type: Article

References (70)

1. Huntington, T. G. Evidence for intensification of the global water cycle: review and synthesis. J. Hydrol. 319, 83-95 (2006).
2. Friedlingstein, P. et al. Positive feedback between future climate change and the carbon cycle. Geophys. Res. Lett. 28, 1543-1546 (2001).
3. Arora, V. K. et al. Carbon-concentration and carbon-climate feedbacks in cmip5 earth system models. J. Clim. 26, 5289-5314 (2013).
4. Körner, C., Morgan, J., & Norby, R. in Terrestrial Ecosystems in a Changing World (eds Canadell, J. G. et al.), pp. 9-21 (Springer, 2007).
5. 2. New Phytol. 165, 351-372 (2005).
6. Keenan, T. F. et al. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499, 324-327 (2013).
7. 2 fertilization but water-use efficiency increased. Nat. Geosci. 8, 24-28 (2015).
8. Frank, D. C. et al. Water-use efficiency and transpiration across European forests during the Anthropocene. Nat. Clim. Change 5, 579-583 (2015).
9. Gedney, N. et al. Detection of a direct carbon dioxide effect in continental river runoff records. Nature 439, 835-838 (2006).
10. 2: implications from the plant to the global scale. Plant Cell Environ. 18, 1214-1225 (1995).
11. 2-induced suppression of transpiration cannot explain increasing runoff. Hydrol. Process. 22, 311-314 (2008).
12. Le Quere, C. et al. Trends in the sources and sinks of carbon dioxide. Nat. Geosci. 2, 831-836 (2009).
13. Gregory, J. M., Jones, C. D., Cadule, P. & Friedlingstein, P. Quantifying carbon cycle feedbacks. J. Clim. 22, 5232-5250 (2009).
14. Medlyn, B. E. et al. Using ecosystem experiments to improve vegetation models. Nat. Clim. Change 5, 528-534 (2015).
15. 2 on global river runoff trends. Proc. Natl Acad. Sci. USA 104, 15242-15247 (2007).
16. Ben, B. B. B. et al. High sensitivity of future global warming to land carbon cycle processes. Environ. Res. Lett. 7, 024002 (2012).
17. 2 effects on vegetation. Nat. Clim. Change 6, 75-78 (2016).
18. Canadell, J. G., & Schulze, E. D. Global potential of biospheric carbon management for climate mitigation, Nat. Commun. 5, 5282 (2014).
19. Jackson, R. B. et al. Trading water for carbon with biological carbon sequestration. Science 310, 1944-1947 (2005).
20. Beer, C. et al. Temporal and among-site variability of inherent water use efficiency at the ecosystem level. Global Biogeochem. Cycles 23, GB2018 (2009).
21. Wong, S. C., Cowan, I. R. & Farquhar, G. D. Stomatal conductance correlates with photosynthetic capacity. Nature 282, 424-426 (1979).
22. Medlyn, B. E. et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Global Change Biol. 17, 2134-2144 (2011).
23. Huang, M. et al. Change in terrestrial ecosystem water-use efficiency over the last three decades. Global Change Biol. 21, 2366-2378 (2015).
24. Dekker, S. C., Groenendijk, M., Booth, B. B. B., Huntingford, C. & Cox, P. M. Spatial and temporal variations in plant water-use efficiency inferred from tree-ring, eddy covariance and atmospheric observations. Earth Syst. Dynam. 7, 525-533 (2016).
25. Reichstein, M., Bahn, M., Mahecha, M. D., Kattge, J. & Baldocchi, D. D. Linking plant and ecosystem functional biogeography. Proc. Natl Acad. Sci. USA 111, 13697-13702 (2014).
26. Friedlingstein, P. et al. Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Clim. 27, 511-526 (2013).
27. Beer, C. et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834-838 (2010).
28. Anav, A. et al. Spatiotemporal patterns of terrestrial gross primary production: a review. Rev. Geophys. 53, 785-818 (2015).
29. Wang, Y. P. et al. Correlations among leaf traits provide a significant constraint on the estimate of global gross primary production. Geophys. Res. Lett. 39, L19405 (2012).
30. Lin, Y. et al. Optimal stomatal behaviour around the world. Nat. Clim. Change 5, 459-464 (2015).
31. Jung, M. et al. Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. J. Geophys. Res. 116, G00J07 (2011).
32. 2 driven by El Nino. Nature 477, 579-582 (2011).
33. Anav, A. et al. Evaluating the land and ocean components of the global carbon cycle in the CMIP5 earth system models. J. Clim. 26, 6801-6843 (2013).
34. Jiang, C. & Ryu, Y. Multi-scale evaluation of global gross primary productivity and evapotranspiration products derived from Breathing Earth System Simulator (BESS). Remote Sens. Environ. 186, 528-547 (2016).
35. Jung, M. et al. Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature 467, 951-954 (2010).
36. Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. BioScience 54, 547-560 (2004).
37. Beer, C., Reichstein, M., Ciais, P., Farquhar, G. D. & Papale, D. Mean annual GPP of Europe derived from its water balance. Geophys. Res. Lett. 34, L05401 (2007).
38. Turner, D. P. et al. Evaluation of MODIS NPP and GPP products across multiple biomes. Remote Sens. Environ. 102, 282-292 (2006).
39. Canadell, J. G. & Raupach, M. R. Managing forests for climate change mitigation. Science 320, 1456-1457 (2008).
40. 2 is conserved across a broad range of productivity. Proc. Natl Acad. Sci. USA 102, 18052-18056 (2005).
41. 2 enhancement of forest productivity constrained by limited nitrogen availability. Proc. Natl Acad. Sci. USA 107, 19368-19373 (2010).
42. Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791-795 (2016).
43. 2 fertilisation has increased maximum foliage cover across the globe's warm, arid environments. Geophys. Res. Lett. 12, 3031-3035 (2013).
44. Zhang, L., Dawes, W. R. & Walker, G. R. Response of mean annual evapotranspiration to vegetation changes at catchment scale. Water Resour. Res. 37, 701-708 (2001).
45. Zhang, Y. et al. Multi-decadal trends in global terrestrial evapotranspiration and its components. Sci. Rep. 6, 19124 (2016).
46. Wang, L., Good, S. P. & Caylor, K. K. Global synthesis of vegetation control on evapotranspiration partitioning. Geophys. Res. Lett. 41, 6753-6757 (2014).
47. Jarvis, P. G., & McNaughton, K. G. Stomatal control of transpiration: scaling up from leaf to region. Adv. Ecol. Res. 15, 1-49 (1986).
48. Cowling, S. A. & Field, C. B. Environmental control of leaf area production: implications for vegetation and land-surface modeling. Global Biogeochem. Cycles 17, 1007 (2003).
49. Sivapalan, M., Blöschl, G., Zhang, L. & Vertessy, R. Downward approach to hydrological prediction. Hydrol. Process. 17, 2099 (2003).
50. Jarvis, P. G. in Scaling Physiological Processes: Leaf to Globe (eds Ehleringer, J. R. and Field, C. B.) (Academic Press, 1993).
51. Houlton, B. Z., Wang, Y., Vitousek, P. M. & Field, C. B. A unifying framework for dinitrogen fixation in the terrestrial biosphere. Nature 454, 327-330 (2008).
52. 2, light and humidity responses. Funct. Plant Biol. 14, 619-632 (1987).
53. 2] on instantaneous transpiration efficiency at leaf and canopy scales in Eucalyptus saligna. Global Change Biol. 18, 585-595 (2012).
54. Ball, J. T., Woodrow, I. E., and Berry, J. A. A Model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions. In Progress in Photosynthesis Research (Ed. Biggins, J.) 221-224 (Springer 1987).
55. Schulze, E. D., Kelliher, F. M., Korner, C., Jon, L. & Leuning, R. Relationships among maximum stomatal conductance, ecosystem surface conductance, carbon assimilation rate, and plant nitrogen nutrition: a global ecology scaling exercise. Ann. Rev. Ecol. Syst. 25, 629-660 (1994).
56. Kelliher, F. M., Leuning, R., Raupach, M. R. & Schulze, E. D. Maximum conductances for evaporation from global vegetation types. Agr. Forest Meteorol. 73, 1-16 (1995).
57. Jung, M., Henkel, K., Herold, M. & Churkina, G. Exploiting synergies of global land cover products for carbon cycle modeling. Remote Sens. Environ. 101, 534-553 (2006).
58. Keeling, C. D. et al. Atmospheric carbon dioxide variations at Mauna Loa Observatory. Hawaii, Tellus 28, 538-551 (1976).
59. New, M., Hulme, M. & Jones, P. Representing twentieth-century space-time climate variability. Part I: development of a 1961-90 mean monthly terrestrial climatology. J. Clim. 12, 829-856 (1999).
60. Sheffield, J., Goteti, G. & Wood, E. F. Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Clim. 19, 3088-3111 (2006).
61. Weedon, G. P. et al. The WFDEI meteorological forcing data set: WATCH forcing data methodology applied to ERA-Interim reanalysis data. Water Resour. Res. 50, 7505-7514 (2014).
62. Zhu, Z. et al. Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011. Remote Sens. 5, 927 (2013).
63. Liang, S., & Xiao, Z. Global Land Surface Products: Leaf Area Index Product Data Collection (1985-2010) (Beijing Normal University, 2012).
64. Miralles, D. G., De Jeu, R. A. M., Gash, J. H., Holmes, T. R. H. & Dolman, A. J. Magnitude and variability of land evaporation and its components at the global scale. Hydrol. Earth Syst. Sci. 15, 967-981 (2011).
65. Pinzon, J. E. & Tucker, C. J. A non-stationary 1981-2012 AVHRR NDVI3g time series. Remote Sens. 6, 6929-6960 (2014).
66. Zeng, Z. et al. A worldwide analysis of spatiotemporal changes in water balance-based evapotranspiration from 1982 to 2009. J. Geophys. Res. Atmos 119, 1186-1202 (2014).
67. Rienecker, M. M. et al. MERRA: NASA's Modern-Era retrospective analysis for research and applications. J. Clim. 24, 3624-3648 (2011).
68. Reichle, R. H. et al. Assessment and enhancement of MERRA land surface hydrology estimates. J. Clim. 24, 6322-6338 (2011).
69. Dee, D. P. et al. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. Roy. Meteor. Soc. 137, 553-597 (2011).
70. Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide,. Biogeosciences 12, 653-679 (2015).