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

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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Fig. 1: Multi-model mean annual mean precipitation change and tropical precipitation asymmetry index.
Fig. 2: Annual mean precipitation change from the CESM.
Fig. 3: Seasonal and interannual precipitation variability.
Fig. 4: Changes in evapotranspiration and specific humidity.
Fig. 5: Annual mean moisture budget (precipitation, evapotranspiration and moisture convergence) and normalized gross moist stability changes.

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).

    Article  Google Scholar 

  2. 2.

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

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  4. 4.

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  6. 6.

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

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  10. 10.

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

    Article  Google Scholar 

  11. 11.

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

    Article  Google Scholar 

  12. 12.

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  15. 15.

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

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

  17. 17.

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

    Article  Google Scholar 

  18. 18.

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

  26. 26.

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  28. 28.

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

    CAS  Article  Google Scholar 

  29. 29.

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  33. 33.

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  36. 36.

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

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  39. 39.

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

    Article  Google Scholar 

  40. 40.

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

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  44. 44.

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

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

  47. 46.

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  49. 48.

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  51. 50.

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  55. 54.

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

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  66. 65.

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

    Article  Google Scholar 

  67. 66.

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

    Article  Google Scholar 

  68. 67.

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  70. 69.

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

    Article  Google Scholar 

  71. 70.

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  82. 81.

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

    Article  Google Scholar 

  83. 82.

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

    CAS  Article  Google Scholar 

  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).

    Google Scholar 

  86. 85.

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

    Article  Google Scholar 

  87. 86.

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

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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).

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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.

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Correspondence to Gabriel J. Kooperman.

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Kooperman, G.J., Chen, Y., Hoffman, F.M. et al. Forest response to rising CO2 drives zonally asymmetric rainfall change over tropical land. Nature Clim Change 8, 434–440 (2018). https://doi.org/10.1038/s41558-018-0144-7

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