Inverse relationship between present-day tropical precipitation and its sensitivity to greenhouse warming

Abstract

Future changes in rainfall have serious impacts on human adaptation to climate change, but quantification of these changes is subject to large uncertainties in climate model projections. To narrow these uncertainties, significant efforts have been made to understand the intermodel differences in future rainfall changes. Here, we show a strong inverse relationship between present-day precipitation and its future change to possibly calibrate future precipitation change by removing the present-day bias in climate models. The results of the models with less tropical (40° S–40° N) present-day precipitation are closely linked to the dryness over the equatorial central-eastern Pacific, and project weaker regional precipitation increase due to the anthropogenic greenhouse forcing1,2,3,4,5,6 with stronger zonal Walker circulation. This induces Indo-western Pacific warming through Bjerknes feedback, which reduces relative humidity by the enhanced atmospheric boundary-layer mixing in the future projection. This increases the air–sea humidity difference to enhance tropical evaporation and the resultant precipitation. Our estimation of the sensitivity of the tropical precipitation per 1 K warming, after removing a common bias in the present-day simulation, is about 50% greater than the original future multi-model projection.

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Fig. 1: Greater precipitation sensitivity in dry models in CMIP5 archives.
Fig. 2: Decomposition of precipitation sensitivity using the bulk formula.
Fig. 3: The spatial distribution of the difference in climatological change.
Fig. 4: Relationship between SST change and relative humidity change due to greenhouse warming.

References

  1. 1.

    Neelin, J. D., Münnich, M., Su, H., Meyerson, J. E. & Holloway, C. E. Tropical drying trends in global warming models and observations. Proc. Natl Acad. Sci. USA 103, 6110–6115 (2006).

    CAS  Article  Google Scholar 

  2. 2.

    Liu, C. & Allan, R. P. Observed and simulated precipitation responses in wet and dry regions 1850–2100. Environ. Res. Lett. 8, 034002 (2013).

    Article  Google Scholar 

  3. 3.

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

    Article  Google Scholar 

  4. 4.

    Mitchell, J. F., Wilson, C. A. & Cunnington, W. M. On CO2 climate sensitivity and model dependence of results. Q. J. Royal Meteorol. Soc. 113, 293–322 (1987).

    CAS  Article  Google Scholar 

  5. 5.

    Chou, C. et al. Increase in the range between wet and dry season precipitation. Nat. Geosci. 6, 263–267 (2013).

    CAS  Article  Google Scholar 

  6. 6.

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

    CAS  Article  Google Scholar 

  7. 7.

    Soden, B. J. & Held, I. M. An assessment of climate feedbacks in coupled ocean-atmosphere models. J. Clim. 19, 3354–3360 (2006).

    Article  Google Scholar 

  8. 8.

    Webb, M. J. et al. On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles. Clim. Dyn. 27, 17–38 (2006).

    Article  Google Scholar 

  9. 9.

    Gettelman, A., Kay, J. E. & Shell, K. M. The evolution of climate sensitivity and climate feedbacks in the community atmosphere model. J. Clim. 25, 1453–1469 (2012).

    Article  Google Scholar 

  10. 10.

    Sherwood, S. C., Bony, S. & Dufresne, J. L. Spread in model climate sensitivity traced to atmospheric convective mixing. Nature 505, 37–42 (2014).

    Article  Google Scholar 

  11. 11.

    Collins, M. et al. Quantifying future climate change. Nat. Clim. Change 2, 403–409 (2012).

    Article  Google Scholar 

  12. 12.

    Huang, P. & Ying, J. A multimodel ensemble pattern regression method to correct the tropical Pacific SST change patterns under global warming. J. Clim. 28, 4706–4723 (2015).

    Article  Google Scholar 

  13. 13.

    Ham, Y. G. & Kug, J.-S. Present-day constraint for tropical Pacific precipitation changes due to global warming in CMIP5 models. Asia-Pacific J. Atmos. Sci. 52, 459–466 (2016).

    Article  Google Scholar 

  14. 14.

    Lu, J. & Cai, M. Stabilization of the atmospheric boundary layer and the muted global hydrological cycle response to global warming. J. Hydrometeorol. 10, 347–352 (2009).

    Article  Google Scholar 

  15. 15.

    Richter, I. & Xie, S. P. Muted precipitation increase in global warming simulations: A surface evaporation perspective. J. Geophys. Res. 113, D24118 (2008).

  16. 16.

    Trenberth, K. E. Changes in precipitation with climate change. Clim. Res. 47, 123–138 (2011).

    Article  Google Scholar 

  17. 17.

    Westra, S., Alexander, L. V. & Zwiers, F. W. Global increasing trends in annual maximum daily precipitation. J. Clim. 26, 3904–3918 (2013).

    Article  Google Scholar 

  18. 18.

    Jin, F. F. An equatorial ocean recharge paradigm for ENSO. Part I: Conceptual model. J. Atmos. Sci. 54, 811–829 (1997).

    Article  Google Scholar 

  19. 19.

    Kug, J. S., Kang, I. S. & Jhun, J. G. Western Pacific SST prediction with an intermediate El Niño prediction model. Mon. Weather Rev. 133, 1343–1352 (2005).

    Article  Google Scholar 

  20. 20.

    Ham, Y. G. & Kug, J. S. How well do current climate models simulate two types of El Nino? Clim. Dyn. 39, 383–398 (2012).

    Article  Google Scholar 

  21. 21.

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

    Article  Google Scholar 

  22. 22.

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

    Article  Google Scholar 

  23. 23.

    Hayes, S. P., McPhaden, M. J. & Wallace, J. M. The influence of sea-surface temperature on surface wind in the eastern equatorial Pacific: Weekly to monthly variability. J. Clim. 2, 1500–1506 (1989).

    Article  Google Scholar 

  24. 24.

    Wallace, J. M., Mitchell, T. P. & Deser, C. The influence of sea-surface temperature on surface wind in the eastern equatorial Pacific: Seasonal and interannual variability. J. Clim. 2, 1492–1499 (1989).

    Article  Google Scholar 

  25. 25.

    Samelson, R. M. et al. On the coupling of wind stress and sea surface temperature. J. Clim. 19, 1557–1566 (2006).

    Article  Google Scholar 

  26. 26.

    Collins, M. El Niño- or La Niña-like climate change? Clim. Dyn. 24, 89–104 (2005).

    Article  Google Scholar 

  27. 27.

    An, S. I., Kug, J. S., Ham, Y. G. & Kang, I. S. Successive modulation of ENSO to the future greenhouse warming. J. Clim. 21, 3–21 (2008).

    Article  Google Scholar 

  28. 28.

    Liu, J. et al. What drives the global summer monsoon over the past millennium? Clim. Dyn. 39, 1063–1072 (2012).

    Article  Google Scholar 

  29. 29.

    Liu, J., Wang, B., Cane, M. A., Yim, S. Y. & Lee, J. Y. Divergent global precipitation changes induced by natural versus anthropogenic forcing. Nature 493, 656–659 (2013).

    CAS  Article  Google Scholar 

  30. 30.

    Allen, M. R. & Ingram, W. J. Constraints on future changes in climate and the hydrological cycle. Nature 419, 224–232 (2002).

    CAS  Article  Google Scholar 

  31. 31.

    Ham, Y. G. & Kug, J. S. Improvement of ENSO simulation based on intermodel diversity. J. Clim. 28, 998–1015 (2015).

    Article  Google Scholar 

  32. 32.

    Adler, R. F. et al. The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present). J. Hydrometeorol. 4, 1147–1167 (2003).

    Article  Google Scholar 

  33. 33.

    Xie, P. & Arkin, P. A. Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Am. Meteor. Soc. 78, 2539–2558 (1997).

    Article  Google Scholar 

  34. 34.

    Rienecker, M. M. et al. MERRA - NASA’s modern-era retrospective analysis for research and applications. J. Clim. 24, 3624–3648 (2011).

    Article  Google Scholar 

  35. 35.

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

    Article  Google Scholar 

  36. 36.

    Marsh, K. N. (ed.) Recommended Reference Materials for the Realization of Physicochemical Properties (Blackwell, Oxford, 1987).

    Google Scholar 

  37. 37.

    Smith, T. M., Reynolds, R. W., Peterson, T. C., & Lawrimore, J. Improvements to NOAA’s historical merged land–ocean surface temperature analysis (1880–2006). J. Climate 21, 2283–2296 (2008).

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Acknowledgements

Y.-G.H. was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1A6A1A03012647). J.-Y.C. was supported by the Korean Meteorological Administration Research and Development Program under grant KMIPA2015–6170. J.-S.K. is supported by the National Research Foundation of Korea (NRF-2017R1A2B3011511).

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Y.-G.H. and J.-S.K. designed the research, conducted analyses and wrote the majority of the manuscript. M.W. and F.-F.J. conducted the analysis and report-writing tasks. J.-Y.C. performed the model experiments. All of the authors discussed the study results and reviewed the manuscript.

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Correspondence to Jong-Seong Kug.

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Supplementary Table 1 and Supplementary Figures 1–15.

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Ham, YG., Kug, JS., Choi, JY. et al. Inverse relationship between present-day tropical precipitation and its sensitivity to greenhouse warming. Nature Clim Change 8, 64–69 (2018). https://doi.org/10.1038/s41558-017-0033-5

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