Regionally strong feedbacks between the atmosphere and terrestrial biosphere

Journal name:
Nature Geoscience
Volume:
10,
Pages:
410–414
Year published:
DOI:
doi:10.1038/ngeo2957
Received
Accepted
Published online

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.

At a glance

Figures

  1. Atmospheric forcings and biospheric forcings.
    Figure 1: Atmospheric forcings and biospheric forcings.

    ad, X right arrow Y represents the fraction of variance of Y explained by X, for the atmospheric forcing (atmosphere right arrow biosphere) (a,c), and biospheric forcing (biosphere right arrow atmosphere) (b,d). The signs of the fractions in a and b show whether the atmospheric variable increases (positive) or decreases (negative) the biosphere flux, whereas in c and d they show whether the biosphere increases or decreases the atmospheric response. Oceans and regions where SIF partial correlations are less than 0.1 are shown in white. Pixels without significance are shown in grey (p-value < 0.1).

  2. Hotspots of terrestrial biosphere-atmosphere feedbacks.
    Figure 2: Hotspots of terrestrial biosphere–atmosphere feedbacks.

    a,b, The fraction of biosphere–atmosphere coupling variance explained for the full-feedback loop: precipitation right arrow SIF right arrow precipitation (a) and PAR right arrow SIF right arrow PAR (b). The sign of the fraction shows whether the feedback is positive or negative. Oceans and regions where SIF partial correlations are less than 0.1 are shown in white. Pixels without significance are shown in grey (p-value < 0.1).

  3. Comparison of observational and Earth system model results.
    Figure 3: Comparison of observational and Earth system model results.

    a,b, Boxplots showing the distributions of significant observational and model results for the fractions of variance explained for the feedbacks of precipitation right arrow biosphere right arrow precipitation (a) and PAR right arrow biosphere right arrow PAR (b). Boxes are defined by the upper quartile, median and lower quartile of the data while whiskers are defined by the outliers. Only significant pixels are represented (p-value < 0.1).

References

  1. Bateni, S. M. & Entekhabi, D. Relative efficiency of land surface energy balance components. Wat. Resour. Res. 48, 18 (2012).
  2. Koster, R. D., Suarez, M. J. & Heiser, M. Variance and predictability of precipitation at seasonal-to-interannual timescales. J. Hydrometeorol. 1, 2646 (2000).
  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, 5.15.9 (2003).
  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, 47444749 (2012).
  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, 805822 (2011).
  6. Zeng, N., Neelin, J., Lau, K. & Tucker, C. Enhancement of interdecadal climate variability in the Sahel by vegetation interaction. Science 286, 15371540 (1999).
  7. Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600603 (2014).
  8. Koster, R. D. et al. On the nature of soil moisture in land surface models. J. Clim. 22, 43224335 (2009).
  9. Porcar-Castell, A. et al. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. J. Exp. Bot. 65, 40654095 (2014).
  10. Guanter, L. et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc. Natl Acad. Sci. USA 111, E1327E1333 (2014).
  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, 154169 (2016).
  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).
  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, 20812094 (2012).
  14. Wood, J. D. et al. Multiscale analyses of solar-induced florescence and gross primary production. Geophys. Res. Lett. 44, 533541 (2017).
  15. Schlesinger, W. H. & Jasechko, S. Transpiration in the global water cycle. Agric. For. Meteorol. 189–190, 115117 (2014).
  16. Ahlström, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895899 (2015).
  17. Beer, C. et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834838 (2010).
  18. Nemani, R. R. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 15601563 (2003).
  19. Sugihara, G. et al. Detecting causality in complex ecosystems. Science 338, 496500 (2012).
  20. Tuttle, S. & Salvucci, G. Empirical evidence of contrasting soil moisture–precipitation feedbacks across the United States. Science 352, 825828 (2016).
  21. Barnett, L. & Seth, A. K. The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference. J. Neurosci. Methods 223, 5068 (2014).
  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, 38833930 (2013).
  23. Huffman, G. J. et al. Global precipitation at one-degree daily resolution from multisatellite observations. J. Hydrometeorol. 2, 3650 (2001).
  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, 853868 (1996).
  25. Dee, D. P. et al. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553597 (2011).
  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, 6-16-14 (2003).
  27. Ghannoum, O. C4 photosynthesis and water stress. Ann. Bot. 103, 635644 (2009).
  28. Guillod, B. P. et al. Reconciling spatial and temporal soil moisture effects on afternoon rainfall. Nat. Commun. 6, 6443 (2015).
  29. Charney, J. G. Dynamics of deserts and drought in the Sahel. Q. J. R. Meteorol. Soc. 101, 193202 (1975).
  30. Anber, U., Gentine, P., Wang, S. & Sobel, A. H. Fog and rain in the Amazon. Proc. Natl Acad. Sci. USA 112, 1147311477 (2015).
  31. Brando, P. M. et al. Seasonal and interannual variability of climate and vegetation indices across the Amazon. Proc. Natl Acad. Sci. USA 107, 1468514690 (2010).
  32. Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: a review. Earth Sci. Rev. 99, 125161 (2010).
  33. Seneviratne, S. I., Lüthi, D., Litschi, M. & Schär, C. Land–atmosphere coupling and climate change in Europe. Nature 443, 205209 (2006).
  34. Dirmeyer, P. A. The terrestrial segment of soil moisture–climate coupling. Geophys. Res. Lett. 38, L16702 (2011).
  35. Koster, R. D. & Suarez, M. J. Impact of land surface initialization on seasonal precipitation and temperature prediction. J. Hydrometeorol. 4, 408423 (2003).
  36. Koster, R. D. et al. GLACE: the global land–atmosphere coupling experiment. Part I: overview. J. Hydrometeorol. 7, 611625 (2006).
  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, 434439 (2011).
  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, 92779297 (2015).
  39. Levine, N. M. et al. Ecosystem heterogeneity determines the ecological resilience of the Amazon to climate change. Proc. Natl Acad. Sci. USA 113, 793797 (2015).
  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, 16151635 (2015).
  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, 204214 (2013).
  42. Bony, S. et al. Clouds, circulation and climate sensitivity. Nat. Geosci. 8, 261268 (2015).
  43. Zhao, M. et al. Uncertainty in model climate sensitivity traced to representations of cumulus precipitation microphysics. J. Clim. 29, 543560 (2016).
  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, 34753490 (2001).
  45. Stammes, P. et al. Effective cloud fractions from the ozone monitoring instrument: theoretical framework and validation. J. Geophys. Res. 113, 112 (2008).
  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, 375391 (2014).
  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, 809829 (2012).
  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).
  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, 7289 (2016).
  50. Guanter, L. et al. Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements. Remote Sens. Environ. 121, 236251 (2012).
  51. Guanter, L. et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc. Natl Acad. Sci. USA 111, E1327E1333 (2014).
  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, 29772987 (2015).
  53. Anav, A. et al. Spatiotemporal patterns of terrestrial gross primary production: a review. Rev. Geophys. 53, 785818 (2015).
  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).
  55. Xu, L. et al. Satellite observation of tropical forest seasonality: spatial patterns of carbon exchange in Amazonia. Environ. Res. Lett. 10, 84005 (2015).
  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, 28292833 (2013).
  57. Granger, C. W. J. Testing for causality. A personal viewpoint. J. Econ. Dyn. Control 2, 329352 (1980).
  58. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485498 (2012).

Download references

Author information

Affiliations

  1. Department of Earth and Environmental Engineering, Columbia University, New York, New York 10027, USA

    • Julia K. Green,
    • Alexandra G. Konings,
    • Seyed Hamed Alemohammad &
    • Pierre Gentine
  2. Department of Earth System Science, Stanford University, Stanford, California 94305, USA

    • Alexandra G. Konings
  3. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

    • Seyed Hamed Alemohammad &
    • Dara Entekhabi
  4. Department of Global Ecology, Carnegie Institution of Washington, Stanford, California 94305, USA

    • Joseph Berry
  5. Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

    • Dara Entekhabi
  6. Universities Space Research Association, Columbia, Maryland 21046, USA

    • Jana Kolassa
  7. Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA

    • Jana Kolassa
  8. Department of Earth, Environment and Planetary Sciences, Brown University, Providence, Rhode Island 02912, USA

    • Jung-Eun Lee
  9. The Earth Institute, Columbia University, New York, New York 10027, USA

    • Pierre Gentine

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.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

Author details

Supplementary information

PDF files

  1. Supplementary Information (1,969 KB)

    Supplementary Information

Additional data