Article

Forest response to rising CO2 drives zonally asymmetric rainfall change over tropical land

  • Nature Climate Changevolume 8pages434440 (2018)
  • doi:10.1038/s41558-018-0144-7
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Abstract

Understanding how anthropogenic CO2 emissions will influence future precipitation is critical for sustainably managing ecosystems, particularly for drought-sensitive tropical forests. Although tropical precipitation change remains uncertain, nearly all models from the Coupled Model Intercomparison Project Phase 5 predict a strengthening zonal precipitation asymmetry by 2100, with relative increases over Asian and African tropical forests and decreases over South American forests. Here we show that the plant physiological response to increasing CO2 is a primary mechanism responsible for this pattern. Applying a simulation design in the Community Earth System Model in which CO2 increases are isolated over individual continents, we demonstrate that different circulation, moisture and stability changes arise over each continent due to declines in stomatal conductance and transpiration. The sum of local atmospheric responses over individual continents explains the pan-tropical precipitation asymmetry. Our analysis suggests that South American forests may be more vulnerable to rising CO2 than Asian or African forests.

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References

  1. 1.

    Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 259, 660–684 (2010).

  2. 2.

    Phillips, O. L. et al. Drought–mortality relationships for tropical forests. New Phytol. 187, 631–646 (2010).

  3. 3.

    Swann, A. L. S., Hoffman, F. M., Koven, C. D. & Randerson, J. T. Plant responses to increasing CO2 reduce estimates of climate impacts on drought severity. Proc. Natl Acad. Sci. USA 113, 10019–10024 (2016).

  4. 4.

    Diffenbaugh, N. S. & Giorgi, F. Climate change hotspots in the CMIP5 global climate model ensemble. Climatic Change 114, 813–822 (2012).

  5. 5.

    Cox, P. et al. Amazonian forest die back under climate–carbon cycle projections for the 21st century. Theor. Appl. Climatol. 78, 137–156 (2004).

  6. 6.

    Mittermeier, R. A. et al. Wilderness and biodiversity conservation. Proc. Natl Acad. Sci. USA 100, 10309–10313 (2003).

  7. 7.

    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).

  8. 8.

    IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  9. 9.

    Yin, L., Fu, R., Shevliakova, E. & Dickinson, R. E. How well can CMIP5 simulate precipitation and its controlling processes over tropical South America? Clim. Dynam. 41, 3127–3143 (2013).

  10. 10.

    Arora, V. K. et al. Carbon–concentration and carbon–climate feedbacks in CMIP5 Earth system models. J. Clim. 26, 5289–5314 (2013).

  11. 11.

    Sun, Y., Solomon, S., Dai, A. & Portmann, R. W. How often does it rain? J. Clim. 19, 916–934 (2006).

  12. 12.

    Randall, D., Khairoutdinov, M., Arakawa, A. & Grabowski, W. Breaking the cloud parameterization deadlock. Bull. Am. Meteorol. Soc. 84, 1547–1564 (2003).

  13. 13.

    Kooperman, G. J., Pritchard, M. S., Burt, M. A., Branson, M. D. & Randall, D. A. Robust effects of cloud superparameterization on simulated daily rainfall intensity statistics across multiple versions of the Community Earth System Model. J. Adv. Model. Earth Syst. 8, 1–26 (2016).

  14. 14.

    Swann, A. L. S., Fung, I. Y. & Chiang, J. C. H. Mid-latitude afforestation shifts general circulation and tropical precipitation. Proc. Natl Acad. Sci. USA 109, 712–716 (2012).

  15. 15.

    Vecchi, G. A. & Harrison, M. J. Weakening of tropical Pacific atmospheric circulation due to anthropogenic forcing. Nature 441, 73–76 (2006).

  16. 16.

    Kang, S. M., Held, I. M., Frierson, D. M. W. & Zhao, M. The response of the ITCZ to extratropical thermal forcing: idealized slab-ocean experiments with a GCM. J. Clim. 21, 3521–3532 (2008).

  17. 17.

    Held, I. M. & Soden, B. J. Robust responses of the hydrological cycle to global warming. J. Clim. 19, 5686–5699 (2006).

  18. 18.

    Xie, S.-P. et al. Global warming pattern formation: sea surface temperature and rainfall. J. Clim. 23, 966–986 (2010).

  19. 19.

    Byrne, M. P. & O’Gorman, P. A. The response of precipitation minus evapotranspiration to climate warming: why the “wet-get-wetter, dry-get-drier” scaling does not hold over land. J. Clim. 28, 8078–8092 (2015).

  20. 20.

    Boos, W. R. & Korty, R. L. Regional energy budget control of the intertropical convergence zone and application to mid-Holocene rainfall. Nat. Geosci. 9, 892–897 (2016).

  21. 21.

    van der Ent, R. J. & Savenije, H. H. G. Oceanic sources of continental precipitation and the correlation with sea surface temperature. Water Resour. Res. 49, 3993–4004 (2013).

  22. 22.

    Cook, K. H. & Vizy, E. K. Effects of twenty-first-century climate change on the Amazon rainforest. J. Clim. 21, 542–560 (2008).

  23. 23.

    Insel, N., Poulsen, C. J. & Ehlers, T. A. Influence of the Andes Mountains on South American moisture transport, convection, and precipitation. Clim. Dynam. 35, 1477–1492 (2010).

  24. 24.

    Fu, R. et al. Increased dry-season length over southern Amazonia in recent decades and its implication for future climate projection. Proc. Natl Acad. Sci. USA 110, 18110–18115 (2013).

  25. 25.

    Arnold, N. P., Branson, M., Kuang, Z., Randall, D. & Tziperman, E. MJO intensification with warming in the superparameterized CESM. J. Clim. 28, 2706–2724 (2015).

  26. 26.

    Turner, A. G. & Annamalai, H. Climate change and the South Asian summer monsoon. Nat. Clim. Change 2, 587–595 (2012).

  27. 27.

    Pu, B. & Dickinson, R. E. Hydrological changes in the climate system from leaf responses to increasing CO2. Clim. Dynam. 42, 1905–1923 (2014).

  28. 28.

    Sellers, P. J. et al. Comparison of radiative and physiological effects of doubled atmospheric CO2 on climate. Science 271, 1402–1406 (1996).

  29. 29.

    Cowan, I. R. Stomatal behaviour and environment. Adv. Bot. Res. 4, 117–228 (1977).

  30. 30.

    Field, C. B., Jackson, R. B. & Mooney, H. A. Stomatal responses to increased CO2: implications from the plant to the global scale. Plant Cell Environ. 18, 1214–1225 (1995).

  31. 31.

    Ball, J. T., Woodrow, I. E. & Berry, J. A. in Progress in Photosynthesis Research (ed. Biggins, J.) 221–224 (Springer, Dordrecht, 1987).

  32. 32.

    Medlyn, B. E. et al. Stomatal conductance of forest species after long-term exposure to elevated CO2 concentration: a synthesis. New Phytol. 149, 247–264 (2001).

  33. 33.

    Medlyn, B. E. et al. Reconciling the optimal and empirical approaches to modeling stomatal conductance. Glob. Change Biol. 17, 2134–2144 (2011).

  34. 34.

    De Kauwe, M. G. et al. Forest water use and water use efficiency at elevated CO2: a model-data intercomparison at two contrasting temperate forest FACE sites. Glob. Change Biol. 19, 1759–1779 (2013).

  35. 35.

    van der Sleen, P. et al. No growth stimulation of tropical trees by 150 years of CO2 fertilization but water-use efficiency increased. Nat. Geosci. 8, 24–28 (2015).

  36. 36.

    Gedney, N. et al. Detection of a direct carbon dioxide effect in continental river runoff records. Nature 439, 835–838 (2006).

  37. 37.

    Coe, M. T., Costa, M. H. & Soares-Filho, B. S. The influence of historical and potential future deforestation on the stream flow of the Amazon River–land surface processes and atmospheric feedbacks. J. Hydrol. 369, 165–174 (2009).

  38. 38.

    Lindsay, K. et al. Pre-industrial-control and twentieth-century carbon cycle experiments with the Earth system model CESM1(BGC). J. Clim. 27, 8981–9005 (2014).

  39. 39.

    Gill, A. E. Some simple solutions for heat-induced tropical circulation. Q. J. R. Meteorol. Soc. 106, 447–462 (1980).

  40. 40.

    Cook, K. H., Hsieh, J.-S. & Hagos, S. M. The Africa–South America intercontinental teleconnection. J. Clim. 17, 2851–2865 (2004).

  41. 41.

    Spracklen, D. V., Arnold, S. R. & Taylor, C. M. Observations of increased tropical rainfall preceded by air passage over forests. Nature 489, 282–285 (2012).

  42. 42.

    Inoue, K. & Back, L. Column-integrated moist static energy budget analysis on various time scales during TOGA COARE. J. Atmos. Sci. 72, 1856–1871 (2015).

  43. 43.

    Raymond, D. J., Sessions, S. L., Sobel, A. H. & Fuchs, Z. The mechanics of gross moist stability. J. Adv. Model. Earth Syst. 1, 1–20 (2009).

  44. 44.

    Vizy, E. K. & Cook, K. H. Relationship between Amazon and high Andes rainfall. J. Geophys Res. 112, 1–14 (2007).

  45. 45.

    Malhi, Y. et al. Exploring the likelihood and mechanism of a climate-change-induced die back of the Amazon rainforest. Proc. Natl Acad. Sci. USA 106, 20610–20615 (2009).

  46. 88.

    Lintner, B. R. et al. Characterizing CMIP5 model spread in simulated rainfall in the Pacific Intertropical Convergence and South Pacific Convergence zones. J. Geophys. Res. Atmos. 121, 590–607 (2016).

  47. 46.

    Bi, D. et al. The ACCESS coupled model: description, control climate and evaluation. Aust. Meteorol. Oceanogr. J. 63, 41–64 (2013).

  48. 47.

    Dix, M. et al. The ACCESS coupled model: documentation of core CMIP5 simulations and initial results. Aust. Meteorol. Oceanogr. J. 63, 83–99 (2013).

  49. 48.

    Gent, P. R. et al. The Community Climate System Model Version 4. J. Clim. 24, 4973–4991 (2011).

  50. 49.

    Long, M. C., Lindsay, K., Peacock, S., Moore, J. K. & Doney, S. C. Twentieth-century oceanic carbon uptake and storage in CESM1(BGC). J. Clim. 26, 6775–6800 (2012).

  51. 50.

    Hurrell, J. et al. The Community Earth System Model: a framework for collaborative research. Bull. Am. Meteorol. Soc. 94, 1339–1360 (2013).

  52. 51.

    Fogli, P. G. et al. INGV-CMCC Carbon (ICC): A Carbon Cycle Earth System Model (Euro-Mediterranean Center on Climate Change, 2009).

  53. 52.

    Vichi, M. et al. Global and regional ocean carbon uptake and climate change: sensitivity to a substantial mitigation scenario. Clim. Dynam. 37, 1929–1947 (2011).

  54. 53.

    Scoccimarro, E. et al. Effects of tropical cyclones on ocean heat transport in a high resolution coupled general circulation model. J. Clim. 24, 4368–4384 (2011).

  55. 54.

    Voldoire, A. et al. The CNRM-CM5.1 global climate model: description and basic evaluation. Clim. Dynam. 40, 2091–2121 (2013).

  56. 55.

    Rotstayn, L. D. et al. Aerosol- and greenhouse gas-induced changes in summer rainfall and circulation in the Australasian region: a study using single-forcing climate simulations. Atmos. Chem. Phys. 12, 6377–6404 (2012).

  57. 56.

    Arora, V. K. et al. Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases. Geophys. Res. Lett. 38, L05805 (2011).

  58. 57.

    von Salzen, K. et al. The Canadian Fourth Generation Atmospheric Global Climate Model (CanAM4). Part I: representation of physical processes. Atmos. Ocean 51, 104–125 (2013).

  59. 58.

    Hazeleger, W. et al. EC-Earth V2.2: description and validation of a new seamless Earth system prediction model. Clim. Dynam. 39, 2611–2629 (2012).

  60. 59.

    Qiao, F. et al. Development and evaluation of an Earth system model with surface gravity waves. J. Geophys. Res. Oceans 118, 4514–4524 (2013).

  61. 60.

    Delworth, T. L. et al. GFDL’s CM2 global coupled climate models. Part I: formulation and simulation characteristics. J. Clim. 19, 643–674 (2006).

  62. 61.

    Donner, L. J. et al. The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3. J. Clim. 24, 3484–3519 (2011).

  63. 62.

    Dunne, J. P. et al. GFDL’s ESM2 global coupled climate–carbon Earth system models. Part I: physical formulation and baseline simulation characteristics. J. Clim. 25, 6646–6665 (2012).

  64. 63.

    Dunne, J. P. et al. GFDL’s ESM2 global coupled climate–carbon Earth system models Part II: carbon system formulation and baseline simulation characteristics. J. Clim. 26, 2247–2267 (2013).

  65. 64.

    Schmidt, G. A. et al. Present day atmospheric simulations using GISS Model: comparison to in-situ, satellite and reanalysis data. J. Clim. 19, 153–192 (2006).

  66. 65.

    Collins, W. J. et al. Development and evaluation of an Earth-system model HadGEM2. Geosci. Model Dev. 4, 1051–1075 (2011).

  67. 66.

    Martin, G. M. et al. The HadGEM2 family of Met Office unified model climate configurations. Geophys. Model Dev. 4, 723–757 (2011).

  68. 67.

    Jones, C. D. et al. The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci. Model Dev. 4, 543–570 (2011).

  69. 68.

    Dufresne, J.-L. et al. Climate Change projections using the IPSL-CM5 Earth system model: from CMIP3 to CMIP5. Clim. Dynam. 40, 2123–2165 (2013).

  70. 69.

    Watanabe, M., Chikira, M., Imada, Y. & Kimoto, M. Convective control of ENSO simulated in MIROC. J. Clim. 24, 543–562 (2011).

  71. 70.

    Watanabe, M. et al. Improved climate simulation by MIROC5: mean states, variability, and climate sensitivity. J. Clim. 23, 6312–6335 (2010).

  72. 71.

    Giorgetta, M. A. et al. Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project Phase 5. J. Adv. Model. Earth Syst. 5, 572–597 (2013).

  73. 72.

    Yukimoto, S. et al. Meteorological Research Institute-Earth System Model v1 (MRI-ESM1) Model Description (MRI, 2011) .

  74. 73.

    Yukimoto, S. et al. A new global climate model of the Meteorological Research Institute: MRI-CGCM3–model description and basic performance. J. Meteorol. Soc. Jpn. 90A, 23–64 (2012).

  75. 74.

    Adachi, Y. et al. Basic performance of a new Earth system model of the Meteorological Research Institute (MRI-ESM1). Pap. Meteorol. Geophys. 64, 1–19 (2013).

  76. 75.

    Tjiputra, J. F. et al. Evaluation of the carbon cycle components in the Norwegian Earth System Model (NorESM). Geophys. Model Dev. 6, 301–325 (2013).

  77. 76.

    Iversen, T. et al. The Norwegian Earth System Model, NorESM1–M. Part 2: climate response and scenario projections. Geosci. Model Dev. 6, 1–27 (2013).

  78. 77.

    Wu, T. A mass-flux cumulus parameterization scheme for large-scale models: description and test with observations. Clim. Dynam. 38, 725–744 (2012).

  79. 78.

    Xin, X. et al. How well does BCC_CSM1.1 reproduce the 20th century climate change over China? Atmos. Ocean Sci. Lett. 6, 21–26 (2012).

  80. 79.

    Xin, X., Zhang, L., Zhang, J., Wu, T. & Fang, Y. Climate change projections over East Asia with BCC_CSM1.1 climate model under RCP scenarios. J. Meteorol. Soc. Jpn 91, 413–429 (2013).

  81. 80.

    Volodin, E. M., Dianskii, N. A. & Gusev, A. V. Simulating present-day climate with the INMCM4.0 coupled model of the atmospheric and oceanic general circulations. Izv. Atmos. Ocean Phys. 46, 414–431 (2010).

  82. 81.

    Stan, C. & Xu, L. Climate simulations and projections with a superparameterized climate model. Environ. Model. Softw. 60, 134–152 (2014).

  83. 82.

    Moss, R. H. et al. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756 (2010).

  84. 83.

    Neale, R. B. et al. NCAR Technical Note: Description of the NCAR Community Atmosphere Model (CAM 4.0) (National Center for Atmospheric Research, 2010).

  85. 84.

    Lawrence, D. M. et al. Parameterization improvements and functional and structural advances in version 4 of the Community Land Model. J. Adv. Model. Earth Syst. 3, 1–27 (2011).

  86. 85.

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

  87. 86.

    Miralles, D. G. et al. Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst. Sci. 15, 453–469 (2011).

  88. 87.

    Benedict, J. J., Maloney, E. D., Sobel, A. H. & Frierson, D. M. Gross moist stability and MJO simulation skill in three full-physics GCMs. J. Atmos. Sci. 71, 3327–3349 (2014).

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Acknowledgements

G.J.K., Y.C. and J.T.R. acknowledge support from the Gordon and Betty Moore Foundation (GBMF3269). C.D.K., F.M.H., M.S.P. and J.T.R. acknowledge support from the US Department of Energy (DOE) Office of Science Biological and Environmental Research programmes. The DOE support includes funding from the Regional and Global Climate Modeling programme to the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation (RUBISCO) Scientific Focus Area, from the Terrestrial Ecosystem Sciences programme to the Next Generation Ecosystem Experiments — Tropics, and from the Early Career programme (DE-SC0012152). K.L. acknowledges support from the National Center for Atmospheric Research (NCAR), which is sponsored by the US National Science Foundation (NSF). A.L.S.S. acknowledges support from the NSF (AGS-1321745 and AGS-1553715). CESM development is led by NCAR and supported by the NSF and DOE. CESM simulations were run at the NSF NCAR Computational and Information Systems Laboratory on Yellowstone (P36271028).

Author information

Affiliations

  1. Department of Earth System Science, University of California, Irvine, Irvine, CA, USA

    • Gabriel J. Kooperman
    • , Yang Chen
    • , Michael S. Pritchard
    •  & James T. Randerson
  2. Department of Geography, University of Georgia, Athens, GA, USA

    • Gabriel J. Kooperman
  3. Computational Earth Sciences Group and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    • Forrest M. Hoffman
  4. Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN, USA

    • Forrest M. Hoffman
  5. Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    • Charles D. Koven
  6. Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, CO, USA

    • Keith Lindsay
  7. Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA

    • Abigail L. S. Swann
  8. Department of Biology, University of Washington, Seattle, WA, USA

    • Abigail L. S. Swann

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Contributions

All authors contributed to designing the experiment, interpreting the results and editing the manuscript. G.J.K. performed the simulations, carried out the analysis and drafted the manuscript. M.S.P. conducted the moist stability analysis.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Gabriel J. Kooperman.

Supplementary information

  1. Supplementary Information

    Supplementary Figures 1–7, Supplementary Tables 1–5 and Supplementary Methods