Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Towards predictive understanding of regional climate change

Abstract

Regional information on climate change is urgently needed but often deemed unreliable. To achieve credible regional climate projections, it is essential to understand underlying physical processes, reduce model biases and evaluate their impact on projections, and adequately account for internal variability. In the tropics, where atmospheric internal variability is small compared with the forced change, advancing our understanding of the coupling between long-term changes in upper-ocean temperature and the atmospheric circulation will help most to narrow the uncertainty. In the extratropics, relatively large internal variability introduces substantial uncertainty, while exacerbating risks associated with extreme events. Large ensemble simulations are essential to estimate the probabilistic distribution of climate change on regional scales. Regional models inherit atmospheric circulation uncertainty from global models and do not automatically solve the problem of regional climate change. We conclude that the current priority is to understand and reduce uncertainties on scales greater than 100 km to aid assessments at finer scales.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: CMIP5 multimodel mean changes.
Figure 2: Effect of ocean warming pattern on precipitation change.
Figure 3: Intermodel spread of tropical precipitation change.
Figure 4: Probabilistic representation of regional climate change at a grid box near Vienna, Austria (48.5° N, 16.2° E).
Figure 5

References

  1. 1

    Hall, A. Projecting regional change. Science 346, 1461–1462 (2014).

    Article  CAS  Google Scholar 

  2. 2

    IPCC Summary for Policymakers. Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 1–29 (Cambridge University Press, 2013).

  3. 3

    Hawkins, E. & Sutton, R. The potential to narrow uncertainty in regional climate predictions. Bull. Am. Meteorol. Soc. 90, 1095–1107 (2009).

    Article  Google Scholar 

  4. 4

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

    Google Scholar 

  5. 5

    Webb, M. J., Lambert, F. H. & Gregory, J. M. Origins of differences in climate sensitivity, forcing and feedback in climate models. Clim. Dynam. 40, 677–707 (2013).

    Article  Google Scholar 

  6. 6

    Pithan, F. & Mauritsen, T. Arctic amplification dominated by temperature feedbacks in contemporary climate models. Nature Geosci. 7, 181–184 (2014).

    Article  CAS  Google Scholar 

  7. 7

    Screen, J. A. & Simmonds, I. The central role of diminishing sea ice in recent Arctic temperature amplification. Nature 464, 1334–1337 (2010).

    Article  CAS  Google Scholar 

  8. 8

    Joshi, M. M., Turner, A. G. & Hope, C. The use of the land-sea warming contrast under climate change to improve impact metrics. Clim. Change 117, 951–960 (2013).

    Article  Google Scholar 

  9. 9

    Manabe, S. & Stouffer, R. J. Role of ocean in global warming. J. Meteorol. Soc. Jpn 85B, 385–403 (2007).

    Article  Google Scholar 

  10. 10

    Marshall, J. et al. The ocean's role in polar climate change: Asymmetric Arctic and Antarctic responses to greenhouse gas and ozone forcing. Phil. Trans. R. Soc. A 372, 20130040 (2014).

    Article  CAS  Google Scholar 

  11. 11

    Harris, G. R., Sexton, D. M. H., Booth, B. B. B., Collins, M. & Murphy, J. M. Probabilistic projections of transient climate change. Clim. Dynam. 40, 2937–2972 (2013).

    Article  Google Scholar 

  12. 12

    Chou, C. & Neelin, J. D. Mechanisms of global warming impacts on regional tropical precipitation. J. Clim. 17, 2688–2701 (2004).

    Article  Google Scholar 

  13. 13

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

    Article  Google Scholar 

  14. 14

    Christensen, J. H. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 1217–1308 (IPCC, Cambridge Univ. Press, 2013).

    Google Scholar 

  15. 15

    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 

  16. 16

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

    Article  Google Scholar 

  17. 17

    Widlansky, M. J. et al. Changes in South Pacific rainfall bands in a warming climate. Nature Clim. Change 3, 417–423 (2013).

    Article  Google Scholar 

  18. 18

    Liu, Z. Y., Vavrus, S., He, F., Wen, N. & Zhong, Y. F. Rethinking tropical ocean response to global warming: The enhanced equatorial warming. J. Clim. 18, 4684–4700 (2005).

    Article  Google Scholar 

  19. 19

    Kosaka, Y. & Xie, S. P. Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature 501, 403–407 (2013).

    Article  CAS  Google Scholar 

  20. 20

    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 

  21. 21

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

    Article  CAS  Google Scholar 

  22. 22

    Biasutti, M. & Sobel, A. H. Delayed Sahel rainfall and global seasonal cycle in a warmer climate. Geophys. Res. Lett. 36, L23707 (2009).

    Article  Google Scholar 

  23. 23

    Neelin, J. D., Chou, C. & Su, H. Tropical drought regions in global warming and El Nino teleconnections. Geophys. Res. Lett. 30, 2275 (2003).

    Article  Google Scholar 

  24. 24

    Seth, A. et al. CMIP5 projected changes in the annual cycle of precipitation in monsoon regions. J. Clim. 26, 7328–7351 (2013).

    Article  Google Scholar 

  25. 25

    Anderson, B. T. et al. Sensitivity of terrestrial precipitation trends to the structural evolution of sea surface temperatures. Geophys. Res. Lett. 42, 1190–1196 (2015).

    Article  Google Scholar 

  26. 26

    Giannini, A. et al. A unifying view of climate change in the Sahel linking intra-seasonal, interannual and longer time scales. Environ. Res. Lett. 8, 024010 (2013).

    Article  Google Scholar 

  27. 27

    Ma, J. & Xie, S.-P. Regional patterns of sea surface temperature change: A source of uncertainty in future projections of precipitation and atmospheric circulation. J. Clim. 26, 2482–2501 (2013).

    Article  Google Scholar 

  28. 28

    Li, W. H., 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. Atmos. 111, D02111 (2006).

    Article  Google Scholar 

  29. 29

    Harris, P. P., Huntingford, C. & Cox, P. M. Amazon Basin climate under global warming: The role of the sea surface temperature. Phil. Trans. R. Soc. B. 363, 1753–1759 (2008).

    Article  Google Scholar 

  30. 30

    Vecchi, G. A. & Soden, B. J. Global warming and the weakening of the tropical circulation. J. Clim. 20, 4316–4340 (2007).

    Article  Google Scholar 

  31. 31

    Lu, J., Vecchi, G. A. & Reichler, T. Expansion of the Hadley cell under global warming. Geophys. Res. Lett. 34, L06805 (2007).

    Google Scholar 

  32. 32

    Scheff, J. & Frierson, D. Twenty-first-century multimodel subtropical precipitation declines are mostly midlatitude shifts. J. Clim. 25, 4330–4347 (2012).

    Article  Google Scholar 

  33. 33

    Delworth, T. L. & Zeng, F. R. Regional rainfall decline in Australia attributed to anthropogenic greenhouse gases and ozone levels. Nature Geosci. 7, 583–587 (2014).

    Article  CAS  Google Scholar 

  34. 34

    Li, W. H., Li, L. F., Ting, M. F. & Liu, Y. M. Intensification of Northern Hemisphere subtropical highs in a warming climate. Nature Geosci. 5, 830–834 (2012).

    Article  CAS  Google Scholar 

  35. 35

    Shindell, D. T., Voulgarakis, A., Faluvegi, G. & Milly, G. Precipitation response to regional radiative forcing. Atmos. Chem. Phys. 12, 6969–6982 (2012).

    Article  CAS  Google Scholar 

  36. 36

    Ming, Y. & Ramaswamy, V. A model investigation of aerosol-induced changes in tropical circulation. J. Clim. 24, 5125–5133 (2011).

    Article  Google Scholar 

  37. 37

    Rotstayn, L. D. & Lohmann, U. Tropical rainfall trends and the indirect aerosol effect. J. Clim. 15, 2103–2116 (2002).

    Article  Google Scholar 

  38. 38

    Chang, C. Y., Chiang, J. C. H., Wehner, M. F., Friedman, A. R. & Ruedy, R. Sulfate aerosol control of tropical Atlantic climate over the twentieth century. J. Clim. 24, 2540–2555 (2011).

    Article  Google Scholar 

  39. 39

    Li, X., Ting, M., Li, C. & Henderson, N. Mechanisms of Asian Summer Monsoon changes in response to anthropogenic forcing in CMIP5 models. J. Clim. 28, 4107–4125 (2015).

    Article  Google Scholar 

  40. 40

    Xie, S. P., Lu, B. & Xiang, B. Q. Similar spatial patterns of climate responses to aerosol and greenhouse gas changes. Nature Geosci. 6, 828–832 (2013).

    Article  CAS  Google Scholar 

  41. 41

    Rosenfeld, D., Sherwood, S., Wood, R. & Donner, L. Climate effects of aerosol-cloud interactions. Science 343, 379–380 (2014).

    Article  CAS  Google Scholar 

  42. 42

    Yin, J. H. A consistent poleward shift of the storm tracks in simulations of 21st century climate. Geophys. Res. Lett. 32, L18701 (2005).

    Article  CAS  Google Scholar 

  43. 43

    Barnes, E. A. & Polvani, L. Response of the midlatitude jets, and of their variability, to increased greenhouse gases in the CMIP5 models. J. Clim. 26, 7117–7135 (2013).

    Article  Google Scholar 

  44. 44

    Kang, S. M., Polvani, L. M., Fyfe, J. C. & Sigmond, M. Impact of polar ozone depletion on subtropical precipitation. Science 332, 951–954 (2011).

    Article  CAS  Google Scholar 

  45. 45

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

    Article  CAS  Google Scholar 

  46. 46

    van Oldenborgh, G. J., Philip, S. Y. & Collins, M. El Nino in a changing climate: a multi-model study. Ocean. Sci. 1, 81–95 (2005).

    Article  Google Scholar 

  47. 47

    Watanabe, M. et al. Uncertainty in the ENSO amplitude change from the past to the future. Geophys. Res. Lett. 39, L20703 (2012).

    Google Scholar 

  48. 48

    Bellenger, H., Guilyardi, E., Leloup, J., Lengaigne, M. & Vialard, J. ENSO representation in climate models: From CMIP3 to CMIP5. Clim. Dynam. 42, 1999–2018 (2014).

    Article  Google Scholar 

  49. 49

    Collins, M. et al. The impact of global warming on the tropical Pacific ocean and El Nino. Nature Geosci. 3, 391–397 (2010).

    Article  CAS  Google Scholar 

  50. 50

    DiNezio, P. N. et al. Mean climate controls on the simulated response of ENSO to increasing greenhouse gases. J. Clim. 25, 7399–7420 (2012).

    Article  Google Scholar 

  51. 51

    Wittenberg, A. T. Are historical records sufficient to constrain ENSO simulations? Geophys. Res. Lett. 36, L12702 (2009).

    Article  Google Scholar 

  52. 52

    Power, S., Delage, F., Chung, C., Kociuba, G. & Keay, K. Robust twenty-first-century projections of El Nino and related precipitation variability. Nature 502, 541–545 (2013).

    Article  CAS  Google Scholar 

  53. 53

    Cai, W. J. et al. Increasing frequency of extreme El Nino events due to greenhouse warming. Nature Clim. Change 4, 111–116 (2014).

    Article  CAS  Google Scholar 

  54. 54

    Griffiths, G. M. et al. Change in mean temperature as a predictor of extreme temperature change in the Asia-Pacific region. Int. J. Climatol. 25, 1301–1330 (2005).

    Article  Google Scholar 

  55. 55

    Seneviratne, S. I. et al. in Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. (eds Field, C. B. et al.) 109–230 (Cambridge Univ. Press, 2013).

    Google Scholar 

  56. 56

    Huntingford, C., Jones, P. D., Livina, V. N., Lenton, T. M. & Cox, P. M. No increase in global temperature variability despite changing regional patterns. Nature 500, 327–330 (2013).

    Article  CAS  Google Scholar 

  57. 57

    Screen, J. A. Arctic amplification decreases temperature variance in northern mid- to high-latitudes. Nature Clim. Change 4, 577–582 (2014).

    Article  Google Scholar 

  58. 58

    Seneviratne, S. I., Luthi, D., Litschi, M. & Schar, C. Land–atmosphere coupling and climate change in Europe. Nature 443, 205–209 (2006).

    Article  CAS  Google Scholar 

  59. 59

    Meehl, G. A., Arblaster, J. M. & Tebaldi, C. Understanding future patterns of increased precipitation intensity in climate model simulations. Geophys. Res. Lett. 32, L18719 (2005).

    Article  Google Scholar 

  60. 60

    Walsh, K. J. E. et al. Hurricanes and climate: The U. S. CLIVAR working group on hurricanes. Bull. Am. Meteorol. Soc. 96, 997–1017 (2015).

    Article  Google Scholar 

  61. 61

    Knutson, T. R. et al. Tropical cyclones and climate change. Nature Geosci. 3, 157–163 (2010).

    Article  CAS  Google Scholar 

  62. 62

    Vecchi, G. A., Swanson, K. L. & Soden, B. J. Climate change whither hurricane activity? Science 322, 687–689 (2008).

    Article  CAS  Google Scholar 

  63. 63

    Zhao, M. & Held, I. M. TC-permitting GCM simulations of hurricane frequency response to sea surface temperature anomalies projected for the late-twenty-first century. J. Clim. 25, 2995–3009 (2012).

    Article  Google Scholar 

  64. 64

    Murakami, H., Wang, B., Li, T. & Kitoh, A. Projected increase in tropical cyclones near Hawaii. Nature Clim. Change 3, 749–754 (2013).

    Article  Google Scholar 

  65. 65

    Goddard, L. et al. A verification framework for interannual-to-decadal predictions experiments. Clim. Dyn. 40, 245–272 (2013).

    Article  Google Scholar 

  66. 66

    Deser, C., Phillips, A. S. & Alexander, M. A. Twentieth century tropical sea surface temperature trends revisited. Geophys. Res. Lett. 37, L10701 (2010).

    Article  Google Scholar 

  67. 67

    Tokinaga, H., Xie, S. P., Deser, C., Kosaka, Y. & Okumura, Y. M. Slowdown of the Walker circulation driven by tropical Indo-Pacific warming. Nature 491, 439–443 (2012).

    CAS  Google Scholar 

  68. 68

    Schneider, D. P., Deser, C., Fasullo, J. & Trenberth, K. E. Climate data guide spurs discovery and understanding. EOS, Trans. Am. Geophys. Un. 94, 121–122 (2013).

    Article  Google Scholar 

  69. 69

    Phillips, A. S., Deser, C. & Fasullo, J. The NCAR Climate Variability Diagnostics Package with relevance to model evaluation. EOS, Trans. Am. Geophys. Un. 95, 453–455 (2014).

    Article  Google Scholar 

  70. 70

    Bracegirdle, T. J. & Stephenson, D. B. On the robustness of emergent constraints used in multimodel climate change projections of Arctic warming. J. Clim. 26, 669–678 (2013).

    Article  Google Scholar 

  71. 71

    Brown, J. N. et al. Implications of CMIP3 model biases and uncertainties for climate projections in the western tropical Pacific. Clim. Change 119, 147–161 (2013).

    Article  Google Scholar 

  72. 72

    Hung, M. P. et al. MJO and convectively coupled equatorial waves simulated by CMIP5 climate models. J. Clim. 26, 6185–6214 (2013).

    Article  Google Scholar 

  73. 73

    Clement, A. C., Seager, R., Cane, M. A. & Zebiak, S. E. An ocean dynamical thermostat. J. Clim. 9, 2190–2196 (1996).

    Article  Google Scholar 

  74. 74

    Hwang, Y. T. & Frierson, D. M. W. Link between the double-Intertropical Convergence Zone problem and cloud biases over the Southern Ocean. Proc. Natl Acad. Sci. USA 110, 4935–4940 (2013).

    Article  Google Scholar 

  75. 75

    Wang, C. Z., Zhang, L. P., Lee, S. K., Wu, L. X. & Mechoso, C. R. A global perspective on CMIP5 climate model biases. Nature Clim. Change 4, 201–205 (2014).

    Article  Google Scholar 

  76. 76

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

    Article  Google Scholar 

  77. 77

    Rougier, J. Probabilistic inference for future climate using an ensemble of climate model evaluations. Clim. Change 81, 247–264 (2007).

    Article  Google Scholar 

  78. 78

    Boe, J. L., Hall, A. & Qu, X. September sea-ice cover in the Arctic Ocean projected to vanish by 2100. Nature Geosci. 2, 341–343 (2009).

    Article  CAS  Google Scholar 

  79. 79

    Deser, C., Phillips, A., Bourdette, V. & Teng, H. Y. Uncertainty in climate change projections: The role of internal variability. Clim. Dynam. 38, 527–546 (2012).

    Article  Google Scholar 

  80. 80

    Wallace, J. M., Deser, C., Smoliak, B. V. & Phillips, A. S. in Climate Change: Multidecadal and Beyond. World Scientific Series on Asia-Pacific Weather and Climate Vol. 6 (eds Chang, C. P. et al.) (in the press).

  81. 81

    Deser, C., Phillips, A. S., Alexander, M. A. & Smoliak, B. V. Projecting North American climate over the next 50 years: Uncertainty due to internal variability. J. Clim. 27, 2271–2296 (2014).

    Article  Google Scholar 

  82. 82

    Ricke, K. L. & Caldeira, K. Natural climate variability and future climate policy. Nature Clim. Change 4, 333–338 (2014).

    Article  Google Scholar 

  83. 83

    Raisanen, J. & Palmer, T. N. A probability and decision-model analysis of a multimodel ensemble of climate change simulations. J. Clim. 14, 3212–3226 (2001).

    Article  Google Scholar 

  84. 84

    Tebaldi, C. & Knutti, R. The use of the multi-model ensemble in probabilistic climate projections. Phil. Trans. R. Soc. A. 365, 2053–2075 (2007).

    Article  Google Scholar 

  85. 85

    van Oldenborgh, G. J., Reyes, F. J. D., Drijfhout, S. S. & Hawkins, E. Reliability of regional climate model trends. Environ. Res. Lett. 8, 014055 (2013).

    Article  Google Scholar 

  86. 86

    Yokohata, T. et al. Reliability of multi-model and structurally different single-model ensembles. Clim. Dynam. 39, 599–616 (2012).

    Article  Google Scholar 

  87. 87

    Partin, J. W. et al. Multidecadal rainfall variability in South Pacific Convergence Zone as revealed by stalagmite geochemistry. Geology 41, 1143–1146 (2013).

    Article  CAS  Google Scholar 

  88. 88

    Dirmeyer, P. A., Jin, Y., Singh, B. & Yan, X. Q. Trends in land–atmosphere interactions from CMIP5 simulations. J. Hydrometeorol. 14, 829–849 (2013).

    Article  Google Scholar 

  89. 89

    Sherwood, S. & Fu, Q. A drier future? Science 343, 737–739 (2014).

    Article  CAS  Google Scholar 

  90. 90

    Hall, A., Qu, X. & Neelin, J. D. Improving predictions of summer climate change in the United States. Geophys. Res. Lett. 35, L01702 (2008).

    Article  Google Scholar 

  91. 91

    Vecchi, G. A. et al. On the seasonal forecasting of regional tropical cyclone activity. J. Clim. 27, 7994–8016 (2014).

    Article  Google Scholar 

  92. 92

    Peterson, T. C. et al. Explaining extreme events of 2011 from a climate perspective. Bull. Am. Meteorol. Soc. 93, 1041–1067 (2012).

    Article  Google Scholar 

  93. 93

    Zhang, R. & Delworth, T. L. Impact of Atlantic multidecadal oscillations on India/Sahel rainfall and Atlantic hurricanes. Geophys. Res. Lett. 33, L17712 (2006).

    Article  Google Scholar 

  94. 94

    England, M. H. et al. Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. Nature Clim. Change 4, 222–227 (2014).

    Article  Google Scholar 

  95. 95

    Walton, D., Sun, F., Hall, A. & Capps, S. C. A hybrid dynamical–statistical downscaling technique, part I: Development and validation of the technique. J. Clim. 28, 4597–4617 (2015).

    Article  Google Scholar 

  96. 96

    Taschetto, A. S. & England, M. H. Estimating ensemble size requirements of AGCM simulations. Meteorol. Atmos. Phys. 100, 23–36 (2008).

    Article  Google Scholar 

  97. 97

    Delworth, T. L. et al. Simulated climate and climate change in the GFDL CM2.5 high-resolution coupled climate model. J. Clim. 25, 2755–2781 (2012).

    Article  Google Scholar 

  98. 98

    Berckmans, J., Woollings, T., Demory, M. E., Vidale, P. L. & Roberts, M. Atmospheric blocking in a high resolution climate model: Influences of mean state, orography and eddy forcing. Atmos. Sci. Lett. 14, 34–40 (2013).

    Article  Google Scholar 

  99. 99

    Dawson, A. & Palmer, T. Simulating weather regimes: Impact of model resolution and stochastic parameterization. Clim. Dynam. 44, 2177–2193 (2015).

    Article  Google Scholar 

  100. 100

    Leung, L. R. et al. Mid-century ensemble regional climate change scenarios for the western United States. Clim. Change 62, 75–113 (2004).

    Article  Google Scholar 

Download references

Acknowledgements

S.M. Long drew Figures 2 and 3. S.P.X. is supported by the National Science Foundation (NSF) and National Oceanic and Atmospheric Administration (NOAA); and M.C. by NERC NE/I022841/1. NCAR is supported by the NSF.

Author information

Affiliations

Authors

Contributions

S.P.X., C.D. and M.C. led the writing of the paper. All authors contributed to the development and writing of the paper.

Corresponding author

Correspondence to Shang-Ping Xie.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Information (PDF 830 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Xie, SP., Deser, C., Vecchi, G. et al. Towards predictive understanding of regional climate change. Nature Clim Change 5, 921–930 (2015). https://doi.org/10.1038/nclimate2689

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing