Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Regionally strong feedbacks between the atmosphere and terrestrial biosphere

Abstract

The terrestrial biosphere and atmosphere interact through a series of feedback loops. Variability in terrestrial vegetation growth and phenology can modulate fluxes of water and energy to the atmosphere, and thus affect the climatic conditions that in turn regulate vegetation dynamics. Here we analyse satellite observations of solar-induced fluorescence, precipitation, and radiation using a multivariate statistical technique. We find that biosphere–atmosphere feedbacks are globally widespread and regionally strong: they explain up to 30% of precipitation and surface radiation variance in regions where feedbacks occur. Substantial biosphere–precipitation feedbacks are often found in regions that are transitional between energy and water limitation, such as semi-arid or monsoonal regions. Substantial biosphere–radiation feedbacks are often present in several moderately wet regions and in the Mediterranean, where precipitation and radiation increase vegetation growth. Enhancement of latent and sensible heat transfer from vegetation accompanies this growth, which increases boundary layer height and convection, affecting cloudiness, and consequently incident surface radiation. Enhanced evapotranspiration can increase moist convection, leading to increased precipitation. Earth system models underestimate these precipitation and radiation feedbacks mainly because they underestimate the biosphere response to radiation and water availability. We conclude that biosphere–atmosphere feedbacks cluster in specific climatic regions that help determine the net CO2 balance of the biosphere.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Atmospheric forcings and biospheric forcings.
Figure 2: Hotspots of terrestrial biosphere–atmosphere feedbacks.
Figure 3: Comparison of observational and Earth system model results.

References

  1. 1

    Bateni, S. M. & Entekhabi, D. Relative efficiency of land surface energy balance components. Wat. Resour. Res. 48, 1–8 (2012).

    Google Scholar 

  2. 2

    Koster, R. D., Suarez, M. J. & Heiser, M. Variance and predictability of precipitation at seasonal-to-interannual timescales. J. Hydrometeorol. 1, 26–46 (2000).

    Article  Google Scholar 

  3. 3

    van den Hurk, B. J. J. M., Viterbo, P. & Los, S. O. Impact of leaf area index seasonality on the annual land surface evaporation in a global circulation model. J. Geophys. Res. 108, 5.1–5.9 (2003).

    Article  Google Scholar 

  4. 4

    Guo, Z., Dirmeyer, P. A., Delsole, T. & Koster, R. D. Rebound in atmospheric predictability and the role of the land surface. J. Clim. 25, 4744–4749 (2012).

    Article  Google Scholar 

  5. 5

    Koster, R. D. et al. The second phase of the global land–atmosphere coupling experiment: soil moisture contributions to subseasonal forecast skill. J. Hydrometeorol. 12, 805–822 (2011).

    Article  Google Scholar 

  6. 6

    Zeng, N., Neelin, J., Lau, K. & Tucker, C. Enhancement of interdecadal climate variability in the Sahel by vegetation interaction. Science 286, 1537–1540 (1999).

    Article  Google Scholar 

  7. 7

    Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014).

    Article  Google Scholar 

  8. 8

    Koster, R. D. et al. On the nature of soil moisture in land surface models. J. Clim. 22, 4322–4335 (2009).

    Article  Google Scholar 

  9. 9

    Porcar-Castell, A. et al. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. J. Exp. Bot. 65, 4065–4095 (2014).

    Article  Google Scholar 

  10. 10

    Guanter, L. et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc. Natl Acad. Sci. USA 111, E1327–E1333 (2014).

    Article  Google Scholar 

  11. 11

    Zhang, Y. et al. Remote sensing of environment consistency between sun-induced chlorophyll fluorescence and gross primary production of vegetation in North America. Remote Sens. Environ. 183, 154–169 (2016).

    Article  Google Scholar 

  12. 12

    Frankenberg, C. et al. New global observations of the terrestrial carbon cycle from GOSAT: patterns of plant fluorescence with gross primary productivity. Geophys. Res. Lett. 38, L17706 (2011).

    Article  Google Scholar 

  13. 13

    Frankenberg, C., O’Dell, C., Guanter, L. & McDuffie, J. Remote sensing of near-infrared chlorophyll fluorescence from space in scattering atmospheres: implications for its retrieval and interferences with atmospheric CO2 retrievals. Atmos. Meas. Tech. 5, 2081–2094 (2012).

    Article  Google Scholar 

  14. 14

    Wood, J. D. et al. Multiscale analyses of solar-induced florescence and gross primary production. Geophys. Res. Lett. 44, 533–541 (2017).

    Article  Google Scholar 

  15. 15

    Schlesinger, W. H. & Jasechko, S. Transpiration in the global water cycle. Agric. For. Meteorol. 189–190, 115–117 (2014).

    Article  Google Scholar 

  16. 16

    Ahlström, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–899 (2015).

    Article  Google Scholar 

  17. 17

    Beer, C. et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834–838 (2010).

    Article  Google Scholar 

  18. 18

    Nemani, R. R. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563 (2003).

    Article  Google Scholar 

  19. 19

    Sugihara, G. et al. Detecting causality in complex ecosystems. Science 338, 496–500 (2012).

    Article  Google Scholar 

  20. 20

    Tuttle, S. & Salvucci, G. Empirical evidence of contrasting soil moisture–precipitation feedbacks across the United States. Science 352, 825–828 (2016).

    Article  Google Scholar 

  21. 21

    Barnett, L. & Seth, A. K. The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference. J. Neurosci. Methods 223, 50–68 (2014).

    Article  Google Scholar 

  22. 22

    Joiner, J. et al. Global monitoring of terrestrial chlorophyll fluorescence from moderate spectral resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2. Atmos. Meas. Tech. Discuss. 6, 3883–3930 (2013).

    Article  Google Scholar 

  23. 23

    Huffman, G. J. et al. Global precipitation at one-degree daily resolution from multisatellite observations. J. Hydrometeorol. 2, 36–50 (2001).

    Article  Google Scholar 

  24. 24

    Wielicki, B. A. et al. Clouds and the Earth’s radiant energy system (CERES): an Earth observing system experiment. Bull. Amer. Meteorol. Soc. 77, 853–868 (1996).

    Article  Google Scholar 

  25. 25

    Dee, D. P. et al. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597 (2011).

    Article  Google Scholar 

  26. 26

    Still, C. J., Berry, J. A., Collatz, G. J. & DeFries, R. S. Global distribution of C3 and C4 vegetation: carbon cycle implications. Glob. Biogeochem. Cycles 17, 6-1–6-14 (2003).

    Article  Google Scholar 

  27. 27

    Ghannoum, O. C4 photosynthesis and water stress. Ann. Bot. 103, 635–644 (2009).

    Article  Google Scholar 

  28. 28

    Guillod, B. P. et al. Reconciling spatial and temporal soil moisture effects on afternoon rainfall. Nat. Commun. 6, 6443 (2015).

    Article  Google Scholar 

  29. 29

    Charney, J. G. Dynamics of deserts and drought in the Sahel. Q. J. R. Meteorol. Soc. 101, 193–202 (1975).

    Article  Google Scholar 

  30. 30

    Anber, U., Gentine, P., Wang, S. & Sobel, A. H. Fog and rain in the Amazon. Proc. Natl Acad. Sci. USA 112, 11473–11477 (2015).

    Article  Google Scholar 

  31. 31

    Brando, P. M. et al. Seasonal and interannual variability of climate and vegetation indices across the Amazon. Proc. Natl Acad. Sci. USA 107, 14685–14690 (2010).

    Article  Google Scholar 

  32. 32

    Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: a review. Earth Sci. Rev. 99, 125–161 (2010).

    Article  Google Scholar 

  33. 33

    Seneviratne, S. I., Lüthi, D., Litschi, M. & Schär, C. Land–atmosphere coupling and climate change in Europe. Nature 443, 205–209 (2006).

    Article  Google Scholar 

  34. 34

    Dirmeyer, P. A. The terrestrial segment of soil moisture–climate coupling. Geophys. Res. Lett. 38, L16702 (2011).

    Article  Google Scholar 

  35. 35

    Koster, R. D. & Suarez, M. J. Impact of land surface initialization on seasonal precipitation and temperature prediction. J. Hydrometeorol. 4, 408–423 (2003).

    Article  Google Scholar 

  36. 36

    Koster, R. D. et al. GLACE: the global land–atmosphere coupling experiment. Part I: overview. J. Hydrometeorol. 7, 611–625 (2006).

    Article  Google Scholar 

  37. 37

    Findell, K. L., Gentine, P., Lintner, B. R. & Kerr, C. Probability of afternoon precipitation in eastern United States and Mexico enhanced by high evaporation. Nat. Geosci. 4, 434–439 (2011).

    Article  Google Scholar 

  38. 38

    Storer, R. L., Zhang, G. J. & Song, X. Effects of convective microphysics parameterization on large-scale cloud hydrological cycle and radiative budget in tropical and midlatitude convective regions. J. Clim. 28, 9277–9297 (2015).

    Article  Google Scholar 

  39. 39

    Levine, N. M. et al. Ecosystem heterogeneity determines the ecological resilience of the Amazon to climate change. Proc. Natl Acad. Sci. USA 113, 793–797 (2015).

    Article  Google Scholar 

  40. 40

    Findell, K. L., Gentine, P., Lintner, B. R. & Guillod, B. P. Data length requirements for observational estimates of land–atmosphere coupling strength. J. Hydrometeorol. 16, 1615–1635 (2015).

    Article  Google Scholar 

  41. 41

    Zhou, S., Duursma, R. A., Medlyn, B. E., Kelly, J. W. G. & Prentice, I. C. How should we model plant responses to drought? An analysis of stomatal and non-stomatal responses to water stress. Agric. For. Meteorol. 182–183, 204–214 (2013).

    Article  Google Scholar 

  42. 42

    Bony, S. et al. Clouds, circulation and climate sensitivity. Nat. Geosci. 8, 261–268 (2015).

    Article  Google Scholar 

  43. 43

    Zhao, M. et al. Uncertainty in model climate sensitivity traced to representations of cumulus precipitation microphysics. J. Clim. 29, 543–560 (2016).

    Article  Google Scholar 

  44. 44

    Koelemeijer, R. B. A., Stammes, P., Hovenier, J. W. & de Haan, J. F. A fast method for retrieval of cloud parameters using oxygen A band measurements from the Global Ozone Monitoring Experiment. J. Geophys. Res. 106, 3475–3490 (2001).

    Article  Google Scholar 

  45. 45

    Stammes, P. et al. Effective cloud fractions from the ozone monitoring instrument: theoretical framework and validation. J. Geophys. Res. 113, 1–12 (2008).

    Article  Google Scholar 

  46. 46

    Joiner, J. et al. The seasonal cycle of satellite chlorophyll fluorescence observations and its relationship to vegetation phenology and ecosystem atmosphere carbon exchange. Remote Sens. Environ. 152, 375–391 (2014).

    Article  Google Scholar 

  47. 47

    Joiner, J. et al. Filling-in of near-infrared solar lines by terrestrial fluorescence and other geophysical effects: simulations and space-based observations from SCIAMACHY and GOSAT. Atmos. Meas. Tech. 5, 809–829 (2012).

    Article  Google Scholar 

  48. 48

    Lee, J. et al. Forest productivity and water stress in Amazonia: observations from GOSAT chlorophyll fluorescence Forest productivity and water stress in Amazonia: observations from GOSAT chlorophyll fluorescence. Proc. R. Soc. B 280, 20130171 (2013).

    Article  Google Scholar 

  49. 49

    Duveiller, G. & Cescatti, A. Spatially downscaling sun-induced chlorophyll fluorescence leads to an improved temporal correlation with gross primary productivity. Remote Sens. Environ. 182, 72–89 (2016).

    Article  Google Scholar 

  50. 50

    Guanter, L. et al. Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements. Remote Sens. Environ. 121, 236–251 (2012).

    Article  Google Scholar 

  51. 51

    Guanter, L. et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc. Natl Acad. Sci. USA 111, E1327–E1333 (2014).

    Article  Google Scholar 

  52. 52

    Yang, X., Tang, J., Mustard, J. F., Lee, J. & Rossini, M. Solar-induced chlorophyll fluorescence correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest. Geophys. Res. Lett. 42, 2977–2987 (2015).

    Article  Google Scholar 

  53. 53

    Anav, A. et al. Spatiotemporal patterns of terrestrial gross primary production: a review. Rev. Geophys. 53, 785–818 (2015).

    Article  Google Scholar 

  54. 54

    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).

    Article  Google Scholar 

  55. 55

    Xu, L. et al. Satellite observation of tropical forest seasonality: spatial patterns of carbon exchange in Amazonia. Environ. Res. Lett. 10, 84005 (2015).

    Article  Google Scholar 

  56. 56

    Parazoo, N. C. et al. Interpreting seasonal changes in the carbon balance of southern Amazonia using measurements of XCO2 and chlorophyll fluorescence from GOSAT. Geophys. Res. Lett. 40, 2829–2833 (2013).

    Article  Google Scholar 

  57. 57

    Granger, C. W. J. Testing for causality. A personal viewpoint. J. Econ. Dyn. Control 2, 329–352 (1980).

    Article  Google Scholar 

  58. 58

    Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank G. Salvucci and U. Lall for discussion on the Granger causality, R. Koster for initial discussion of the paper, and J. Joiner for providing GOME-2 data. This project was supported by both a NASA Earth Science and Space Fellowship as well as a DOE GOAmazon grant. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Supplementary Table 1 of this paper) for producing and making available their model output. For CMIP the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.

Author information

Affiliations

Authors

Contributions

J.K.G., A.G.K. and P.G. wrote the main manuscript text. J.K.G., P.G. and S.H.A. prepared figures. S.H.A. processed the CMIP5 simulations. J.K.G., P.G. and A.G.K. designed the study. All authors reviewed and edited the manuscript.

Corresponding author

Correspondence to Julia K. Green.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Information (PDF 1923 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Green, J., Konings, A., Alemohammad, S. et al. Regionally strong feedbacks between the atmosphere and terrestrial biosphere. Nature Geosci 10, 410–414 (2017). https://doi.org/10.1038/ngeo2957

Download citation

Further reading

Search

Quick links