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Projected strengthening of Amazonian dry season by constrained climate model simulations


The vulnerability of Amazonian rainforest, and the ecological services it provides, depends on an adequate supply of dry-season water, either as precipitation or stored soil moisture. How the rain-bearing South American monsoon will evolve across the twenty-first century is thus a question of major interest. Extensive savanization, with its loss of forest carbon stock and uptake capacity, is an extreme although very uncertain scenario1,2,3,4,5,6. We show that the contrasting rainfall projections simulated for Amazonia by 36 global climate models (GCMs) can be reproduced with empirical precipitation models, calibrated with historical GCM data as functions of the large-scale circulation. A set of these simple models was therefore calibrated with observations and used to constrain the GCM simulations. In agreement with the current hydrologic trends7,8, the resulting projection towards the end of the twenty-first century is for a strengthening of the monsoon seasonal cycle, and a dry-season lengthening in southern Amazonia. With this approach, the increase in the area subjected to lengthy—savannah-prone—dry seasons is substantially larger than the GCM-simulated one. Our results confirm the dominant picture shown by the state-of-the-art GCMs, but suggest that the ‘model democracy’ view of these impacts can be significantly underestimated.

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Figure 1: Simulated changes in Amazonian precipitation.
Figure 2: Amazonian precipitation climatology, trends and long-term changes.
Figure 3: Simulated and constrained projections.
Figure 4: Projected changes in Amazonian dry season and potential impacts on rainforest.


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

    Article  Google Scholar 

  2. Hirota, M., Holmgren, M., Nes, E. H. V. & Scheffer, M. Global resilience of tropical forest and savanna to critical transitions. Science 334, 232–235 (2011).

    Article  CAS  Google Scholar 

  3. Zeng, Z. et al. Committed changes in tropical tree cover under the projected 21st century climate change. Sci. Rep. 3, 1951 (2013).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  5. Good, P., Jones, C., Lowe, J., Betts, R. & Gedney, N. Comparing tropical forest projections from two generations of Hadley Centre Earth System Models, HadGEM2-ES and HadCM3LC. J. Clim. 26, 495–511 (2013).

    Article  Google Scholar 

  6. Huntingford, C. et al. Simulated resilience of tropical rainforests to CO2-induced climate change. Nature Geosci. 6, 268–273 (2013).

    Article  CAS  Google Scholar 

  7. Marengo, J. A., Tomasella, J., Alves, L. M., Soares, W. R. & Rodriguez, D. A. The drought of 2010 in the context of historical droughts in the Amazon region. Geophys. Res. Lett. 38, L12703 (2011).

    Article  Google Scholar 

  8. 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).

    Article  CAS  Google Scholar 

  9. Collins, M. et al. in IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 12, 1029–1136 (IPCC, Cambridge Univ. Press, 2013).

    Google Scholar 

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

    Article  Google Scholar 

  11. Chou, C., Neelin, J. D., Chen, C-A. & Tu, J-Y. Evaluating the Rich-Get-Richer mechanism in tropical precipitation change under global warming. J. Clim. 22, 1982–2005 (2009).

    Article  Google Scholar 

  12. Seager, R., Naik, N. & Vecchi, G. A. Thermodynamic and dynamic mechanisms for large-scale changes in the hydrological cycle in response to global warming. J. Clim. 23, 4651–4668 (2010).

    Article  Google Scholar 

  13. Muller, C. J. & O’Gorman, P. A. An energetic perspective on the regional response of precipitation to climate change. Nature Clim. Change 1, 266–271 (2011).

    Article  Google Scholar 

  14. Huang, P., Xie, S-P., Hu, K., Huang, G. & Huang, R. Patterns of the seasonal response of tropical rainfall to global warming. Nature Geosci. 6, 357–361 (2013).

    Article  CAS  Google Scholar 

  15. Chadwick, R., Boutle, I. & Martin, G. Spatial patterns of precipitation change in CMIP5: Why the rich do not get richer in the tropics. J. Clim. 26, 3803–3822 (2013).

    Article  Google Scholar 

  16. Bony, S. et al. Robust direct effect of carbon dioxide on tropical circulation and regional precipitation. Nature Geosci. 6, 447–451 (2013).

    Article  CAS  Google Scholar 

  17. Knutti, R. & Sedlek, J. Robustness and uncertainties in the new CMIP5 climate model projections. Nature Clim. Change 3, 369–373 (2013).

    Article  Google Scholar 

  18. Vera, C. et al. Toward a unified view of the American monsoon systems. J. Clim. 19, 4977–5000 (2006).

    Article  Google Scholar 

  19. Cox, P. M. et al. Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature 494, 341–344 (2013).

    Article  CAS  Google Scholar 

  20. 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 

  21. Joetzjer, E., Douville, H., Delire, C. & Ciais, P. Present-day and future Amazonian precipitation in global climate models: CMIP5 versus CMIP3. Clim. Dynam. 41, 2921–2936 (2013).

    Article  Google Scholar 

  22. Li, W., Fu, R. & Dickinson, R. E. Rainfall and its seasonality over the Amazon in the 21st century as assessed by the coupled models for the IPCC AR4. J. Geophys. Res. 111, D02111 (2006).

    Article  Google Scholar 

  23. Seth, A., Rojas, M. & Rauscher, S. A. CMIP3 projected changes in the annual cycle of the South American Monsoon. Climatic Change 98, 331–357 (2010).

    Article  Google Scholar 

  24. Stevens, B. & Bony, S. What are climate models missing? Science 340, 1053–1054 (2013).

    Article  CAS  Google Scholar 

  25. Shepherd, T. G. Atmospheric circulation as a source of uncertainty in climate change projections. Nature Geosci. 7, 703–708 (2014).

    Article  CAS  Google Scholar 

  26. Brovkin, V. et al. Effect of anthropogenic land-use and land-cover changes on climate and land carbon storage in CMIP5 projections for the twenty-first century. J. Clim. 26, 6859–6881 (2013).

    Article  Google Scholar 

  27. Hall, A. & Qu, X. Using the current seasonal cycle to constrain snow albedo feedback in future climate change. Geophys. Res. Lett. 33, L03502 (2006).

    Google Scholar 

  28. Shiogama, H. et al. Observational constraints indicate risk of drying in the Amazon basin. Nature Commun. 2, 253 (2011).

    Article  Google Scholar 

  29. Friedl, M. A. et al. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010).

    Article  Google Scholar 

  30. Allan, R. & Ansell, T. A new globally complete monthly historical gridded mean sea level pressure dataset (HadSLP2): 1850–2004. J. Clim. 19, 5816–5842 (2006).

    Article  Google Scholar 

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This research received financial support from the European Union Seventh Framework Programme, under the project AMAZALERT (Grant Agreement No. 282664). J.P.B. is grateful for support from the CONICYT (Chile) grants FONDAP (Center for Climate and Resilience Research, No. 15110009) and FONDECYT (No. 3150492). 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. The precipitation data used in this paper were extracted from publicly available sites and are described, with references, in the supporting material. The MODIS MCD12C1 product was obtained through the online Data Pool at the NASA Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota (

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J.P.B. designed the study and conducted the analysis. All authors advised on the approach followed, interpreted the results and contributed to the manuscript preparation.

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Correspondence to Juan P. Boisier.

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Boisier, J., Ciais, P., Ducharne, A. et al. Projected strengthening of Amazonian dry season by constrained climate model simulations. Nature Clim Change 5, 656–660 (2015).

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