Advertisement

Journal of Meteorological Research

, Volume 32, Issue 3, pp 337–350 | Cite as

Near-Term Projections of Global and Regional Land Mean Temperature Changes Considering Both the Secular Trend and Multidecadal Variability

  • Yajie Qi
  • Zhongwei Yan
  • Cheng Qian
  • Ying Sun
Article
  • 17 Downloads

Abstract

Near-term climate projections are needed by policymakers; however, these projections are difficult because internally generated climate variations need to be considered. In this study, temperature change scenarios in the near-term period 2017–35 are projected at global and regional scales based on a refined multi-model ensemble approach that considers both the secular trend (ST) and multidecadal variability (MDV) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations. The ST and MDV components are adaptively extracted from each model simulation by using the ensemble empirical mode decomposition (EEMD) filter, reconstructed via the Bayesian model averaging (BMA) method for the historical period 1901–2005, and validated for 2006–16. In the simulations of the “medium” representative concentration pathways scenario during 2017–35, the MDV-modulated temperature change projected via the refined approach displays an increase of 0.44°C (90% uncertainty range from 0.30 to 0.58°C) for global land, 0.48°C (90% uncertainty range from 0.29 to 0.67°C) for the Northern Hemispheric land (NL), and 0.29°C (90% uncertainty range from 0.23 to 0.35°C) for the Southern Hemispheric land (SL). These increases are smaller than those projected by the conventional arithmetic mean approach. The MDV enhances the ST in 13 of 21 regions across the world. The largest MDV-modulated warming effect (46%) exists in central America. In contrast, the MDV counteracts the ST in NL, SL, and eight other regions, with the largest cooling effect (220%) in Alaska.

Key words

near-term projection multidecadal variability multi-model ensemble method ensemble empirical mode decomposition (EEMD) Bayesian model averaging (BMA) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgments

The authors thank the reviewers and the Editor for their comments and suggestions to help improve the manuscript. We also thank the Met Office Hadley Center and Climate Research Unit for providing the observed HadCRUT4 data used in this work and the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the model data.

References

  1. Bieniek, P. A., J. E. Walsh, R. L., Thoman, et al., 2014: Using climate divisions to analyze variations and trends in Alaska temperature and precipitation. J. Climate, 27, 2800–2818, doi: 10.1175/JCLI-D-13-00342.1.CrossRefGoogle Scholar
  2. Bindoff N. L., P. A. Stott, K. M. AchutaRao, et al., 2013: Detection and attribution of climate change: From global to regional. Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker T. F., et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 867–952, doi: 10.1017/CBO9781107415324.022.Google Scholar
  3. Booth B. B. B., N. J. Dunstone, P. R. Halloran, et al., 2012: Aerosols implicated as a prime driver of twentieth-century North Atlantic climate variability. Nature, 484, 228–232, doi: 10. 1038/nature10946.CrossRefGoogle Scholar
  4. Chandler R. E., 2013: Exploiting strength, discounting weakness: Combining information from multiple climate simulators. Philos. Trans. Roy. Soc. A Math. Phys. Eng. Sci., 371, 1–19, doi: 10.1098/rsta.2012.0388.CrossRefGoogle Scholar
  5. Chen W. L., Z. H. Jiang, and L. R. Li, 2011: Probabilistic projections of climate change over China under the SRES A1B scenario using 28 AOGCMs. J. Climate, 24, 4741–4756, doi: 10.1175/2011JCLI4102.1.CrossRefGoogle Scholar
  6. Chikamoto Y., A. Timmermann, J. J. Luo, et al., 2015: Skilful multi-year predictions of tropical trans-basin climate variability. Nat. Commun., 6, 6869, doi: 10.1038/ncomms7869.CrossRefGoogle Scholar
  7. Dai A. G., 2013: The influence of the Inter-decadal Pacific Oscillation on US precipitation during 1923–2010. Climate Dyn., 41, 633–646, doi: 10.1007/s00382-012-1446-5.CrossRefGoogle Scholar
  8. DelSole T., M. K. Tippett, and J. Shukla, 2011: A significant component of unforced multidecadal variability in the recent acceleration of global warming. J. Climate, 24, 909–926, doi: 10.1175/2010JCLI3659.1.CrossRefGoogle Scholar
  9. Delworth T. L., and M. E. Mann, 2000: Observed and simulated multidecadal variability in the Northern Hemisphere. Climate Dyn., 16, 661–676, doi: 10.1007/s003820000075.CrossRefGoogle Scholar
  10. Enfield D. B., A. M. Mestas-Nuñez, and P. J. Trimble, 2001: The Atlantic Multidecadal Oscillation and its relation to rainfall and river flows in the continental U.S. Geophys. Res. Lett., 28, 2077–2080, doi: 10.1029/2000GL012745.CrossRefGoogle Scholar
  11. England M. H., S. McGregor, P. Spence, et al., 2014: Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. Nature Climate Change, 4, 222–227, doi: 10.1038/nclimate2106.CrossRefGoogle Scholar
  12. Flato G., J. Marotzke, B. Abiodun, et al., 2013: Evaluation of climate models. Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker T. F., et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 741–866, doi: 10.1017/CBO9781107415324.020.Google Scholar
  13. Fraley C., A. E. Raftery, and T. Gneiting, 2010: Calibrating multimodel forecast ensembles with exchangeable and missing members using Bayesian model averaging. Mon. Wea. Rev., 138, 190–202, doi: 10.1175/2009MWR3046.1.CrossRefGoogle Scholar
  14. Fu C. B., C. Qian, and Z. H. Wu, 2011: Projection of global mean surface air temperature changes in next 40 years: Uncertainties of climate models and an alternative approach. Sci. China Earth Sci., 54, 1400–1406, doi: 10.1007/s11430-011-4235-9.CrossRefGoogle Scholar
  15. Furrer R., S. R. Sain, D. Nychka, et al., 2007: Multivariate Bayesian analysis of atmosphere–ocean general circulation models. Environ. Ecol. Stat., 14, 249–266, doi: 10.1007/s 10651-007-0018-z.CrossRefGoogle Scholar
  16. Gao L. H., Z. W. Yan, and X. W. Quan, 2015: Observed and SSTforced multidecadal variability in global land surface air temperature. Climate Dyn., 44, 359–369, doi: 10.1007/s00382-014-2121-9.CrossRefGoogle Scholar
  17. Ge Q. S., H. L. Liu, X. Ma, et al., 2017: Characteristics of temperature change in China over the last 2000 years and spatial patterns of dryness/wetness during cold and warm periods. Adv. Atmos. Sci., 34, 941–951, doi: 10.1007/s00376-017-6238-8.CrossRefGoogle Scholar
  18. Giorgi F., and R. Francisco, 2000: Uncertainties in regional climate change prediction: A regional analysis of ensemble simulations with the HADCM2 coupled AOGCM. Climate Dyn., 16, 169–182, doi: 10.1007/PL00013733.CrossRefGoogle Scholar
  19. Giorgi F., and L. O. Mearns, 2002: Calculation of average, uncertainty range, and reliability of regional climate changes from AOGCM simulations via the “reliability ensemble averaging” (REA) method. J. Climate, 15, 1141–1158, doi: 10.1175/1520-0442(2002)015<1141:COAURA>2.0.CO;2.CrossRefGoogle Scholar
  20. Greene A. M., L. Goddard, and U. Lall, 2006: Probabilistic multimodel regional temperature change projections. J. Climate, 19, 4326–4343, doi: 10.1175/JCLI3864.1.CrossRefGoogle Scholar
  21. Hartmann B., and G. Wendler, 2005: The significance of the 1976 Pacific climate shift in the climatology of Alaska. J. Climate, 18, 4824–4839, doi: 10.1175/JCLI3532.1.CrossRefGoogle Scholar
  22. Hawkins E., R. S. Smith, J. M. Gregory, et al., 2016: Irreducible uncertainty in near-term climate projections. Climate Dyn., 46, 3807–3819, doi: 10.1007/s00382-015-2806-8.CrossRefGoogle Scholar
  23. Hu H. F., X. F. Zhi, H. H. Guo, et al., 2016: Bayesian Model Averaging prediction of summer circulation over East Asia based on CMIP5 data. J. Meteor. Sci., 36, 340–348. (in Chinese)Google Scholar
  24. Huang N. E., and Z. H. Wu, 2008: A review on Hilbert–Huang transform: Method and its applications to geophysical studies. Rev. Geophys., 46, RG2006, doi: 10.1029/2007RG000228.CrossRefGoogle Scholar
  25. IPCC, 2013: Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker T. F., et al., Eds. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp, doi: 10.1017/CBO9781107415324.Google Scholar
  26. Ji F., Z. H. Wu, J. P. Huang, et al., 2014: Evolution of land surface air temperature trend. Nature Climate Change, 4, 462–466, doi: 10.1038/nclimate2223.CrossRefGoogle Scholar
  27. Kirtman B., S. B. Power, J. A. Adedoyin, et al., 2013: Near-term climate change: Projections and predictability. Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker T. F., et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 953–1028, doi: 10.1017/CBO 9781107415324.023.Google Scholar
  28. Knight J. R., R. J. Allan, C. K. Folland, et al., 2005: A signature of persistent natural thermohaline circulation cycles in observed climate. Geophys. Res. Lett., 32, L20708, doi: 10.1029/2005GL024233.CrossRefGoogle Scholar
  29. Kosaka Y., and S. P. Xie, 2013: Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature, 501, 403–407, doi: 10.1038/nature12534.CrossRefGoogle Scholar
  30. Kosaka Y., and S. P. Xie, 2016: The tropical Pacific as a key pacemaker of the variable rates of global warming. Nat. Geosci., 9, 669–673, doi: 10.1038/ngeo2770.CrossRefGoogle Scholar
  31. Li X. C., S. P. Xie, S. T. Gille, et al., 2016: Atlantic-induced pantropical climate change over the past three decades. Nature Climate Change, 6, 275–279, doi: 10.1038/NCLIMATE2840.CrossRefGoogle Scholar
  32. Luo J. J., W. Sasaki, and Y. Masumoto, 2012: Indian Ocean warming modulates Pacific climate change. Proc. Natl. Acad. Sci. USA, 109, 18701–18706, doi: 10.1073/pnas.1210239109.CrossRefGoogle Scholar
  33. Luo J. J., G. Wang, and D. Dommenget, 2018: May common model biases reduce CMIP5’s ability to simulate the recent Pacific La Niña-like cooling? Climate Dyn., 50, 1335–1351, doi: 10.1007/s00382-017-3688-8.CrossRefGoogle Scholar
  34. Masson D., and R. Knutti, 2011: Spatial-scale dependence of climate model performance in the CMIP3 ensemble. J. Climate, 24, 2680–2692, doi: 10.1175/2011JCLI3513.1.CrossRefGoogle Scholar
  35. McGregor S., A. Timmermann, M. F. Stuecker, et al., 2014: Recent Walker circulation strengthening and Pacific cooling amplified by Atlantic warming. Nature Climate Change, 4, 888–892, doi: 10.1038/nclimate2330.CrossRefGoogle Scholar
  36. Meehl G. A., L. Goddard, J. Murphy, et al., 2009: Decadal prediction: Can it be skillful? Bull. Amer. Meteor. Soc., 90, 1467–1485, doi: 10.1175/2009BAMS2778.1.CrossRefGoogle Scholar
  37. Meehl G. A., and H. Y. Teng, 2012: Case studies for initialized decadal hindcasts and predictions for the Pacific region. Geophys. Res. Lett., 39, L22705, doi: 10.1029/2012GL053423.CrossRefGoogle Scholar
  38. Morice C. P., J. J. Kennedy, N. A. Rayner, et al., 2012: Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set. J. Geophys. Res., 117, D08101, doi: 10.1029/2011JD017187.CrossRefGoogle Scholar
  39. Newman M., 2013: An empirical benchmark for decadal forecasts of global surface temperature anomalies. J. Climate, 26, 5260–5269, doi: 10.1175/JCLI-D-12-00590.1.CrossRefGoogle Scholar
  40. Qi Y. J., C. Qian, and Z. W. Yan, 2017: An alternative multi-model ensemble mean approach for near-term projection. Int. J. Climatol., 37, 109–122, doi: 10.1002/joc.4690.CrossRefGoogle Scholar
  41. Qian C., 2016: Disentangling the urbanization effect, multidecadal variability, and secular trend in temperature in eastern China during 1909–2010. Atmos. Sci. Lett., 17, 177–182, doi: 10.1002/asl.640.CrossRefGoogle Scholar
  42. Qian C., Z. H. Wu, C. B. Fu, et al., 2011: On changing El Niño: A view from time-varying annual cycle, interannual variability, and mean state. J. Climate, 24, 6486–6500, doi: 10.1175/JCLI-D-10-05012.1.CrossRefGoogle Scholar
  43. Qian C., and T. J. Zhou, 2014: Multidecadal variability of North China aridity and its relationship to PDO during 1900–2010. J. Climate, 27, 1210–1222, doi: 10.1175/JCLI-D-13-00235.1.CrossRefGoogle Scholar
  44. Raftery A. E., T. Gneiting, F. Balabdaoui, et al., 2005: Using Bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev., 133, 1155–1174, doi: 10.1175/MWR2906.1.CrossRefGoogle Scholar
  45. Räisänen J., and J. S. Ylhäisi, 2011: How much should climate model output be smoothed in space? J. Climate, 24, 867–880, doi: 10.1175/2010JCLI3872.1.CrossRefGoogle Scholar
  46. Schlesinger M. E., and N. Ramankutty, 1994: An oscillation in the global climate system of period 65–70 years. Nature, 367, 723–726, doi: 10.1038/367723a0.CrossRefGoogle Scholar
  47. Schmittner A., M. Latif, and B. Schneider, 2005: Model projections of the North Atlantic thermohaline circulation for the 21st century assessed by observations. Geophys. Res. Lett., 32, L23710, doi: 10.1029/2005GL024368.CrossRefGoogle Scholar
  48. Semenov V. A., M. Latif, D. Dommenget, et al., 2010: The impact of North Atlantic–Arctic multidecadal variability on Northern Hemisphere surface air temperature. J. Climate, 23, 5668–5677, doi: 10.1175/2010JCLI3347.1.CrossRefGoogle Scholar
  49. Sutton R. T., and D. L. Hodson, 2005: Atlantic Ocean forcing of North American and European summer climate. Science, 309, 115–118, doi: 10.1126/science.1109496.CrossRefGoogle Scholar
  50. Taylor K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485–498, doi: 10.1175/BAMS-D-11-00094.1.CrossRefGoogle Scholar
  51. Tebaldi C., L. O. Mearns, D. Nychka, et al., 2004: Regional probabilities of precipitation change: A Bayesian analysis of multi-model simulations. Geophys. Res. Lett., 31, L24213, doi: 10.1029/2004GL021276.CrossRefGoogle Scholar
  52. Tung K. K., and J. S. Zhou, 2013: Using data to attribute episodes of warming and cooling in instrumental records. Proc. Natl. Acad. Sci. USA, 110, 2058–2063, doi: 10.1073/pnas.1212471110.CrossRefGoogle Scholar
  53. van Oldenborgh G. J., F. J. D. Reyes, S. S. Drijfhout, et al., 2013: Reliability of regional climate model trends. Environ. Res. Lett., 8, 014055, doi: 10.1088/1748-9326/8/1/014055.CrossRefGoogle Scholar
  54. Wei M., F. L. Qiao, and J. Deng, 2015: A quantitative definition of global warming hiatus and 50-year prediction of globalmean surface temperature. J. Atmos. Sci., 72, 3281–3289, doi: 10.1175/JAS-D-14-0296.1.CrossRefGoogle Scholar
  55. Wilcox L. J., E. J. Highwood, and N. J. Dunstone, 2013: The influence of anthropogenic aerosol on multidecadal variations of historical global climate. Environ. Res. Lett., 8, 024033, doi: 10.1088/1748-9326/8/2/024033.CrossRefGoogle Scholar
  56. Wu B., X. L. Chen, F. F. Song, et al., 2015: Initialized decadal predictions by LASG/IAP climate system model FGOALSs2: Evaluations of strengths and weaknesses. Adv. Meteor., 2015, 904826, doi: 10.1155/2015/904826.Google Scholar
  57. Wu K. J., and W. L. Qian, 2015: Secular non-linear trends and multi-timescale oscillations of regional surface air temperature in eastern China. Climate Res., 63, 19–30, doi: 10.3354/cr01284.CrossRefGoogle Scholar
  58. Wu Z. H., and N. E. Huang, 2009: Ensemble empirical mode decomposition: A noise-assisted data analysis method. Adv. Adapt. Data Anal., 1, 1–41, doi: 10.1142/S1793536909000047.CrossRefGoogle Scholar
  59. Wu Z., N. E. Huang, J. M. Wallace, et al., 2011: On the timevarying trend in global-mean surface temperature. Climate Dyn., 37, 759–773, doi: 10.1007/s00382-011-1128-8.CrossRefGoogle Scholar
  60. Xin X. G., F. Gao, M. Wei, et al., 2018: Decadal prediction skill of BCC_CSM1.1 climate model in East Asia. Int. J. Climatol., 38, 584–592, doi: 10.1002/joc.5195.CrossRefGoogle Scholar
  61. Yang C., Z. W. Yan, and Y. H. Shao, 2012: Probabilistic precipitation forecasting based on ensemble output using generalized additive models and Bayesian model averaging. Acta Meteor. Sinica, 26, 1–12, doi: 10.1007/s13351-012-0101-8.CrossRefGoogle Scholar
  62. Yao S. L., J. J. Luo, G. Huang, et al., 2017: Distinct global warming rates tied to multiple ocean surface temperature changes. Nature Climate Change, 7, 486–491, doi: 10.1038/NCLIMATE3304.CrossRefGoogle Scholar
  63. Zhang R., T. L. Delworth, and I. M. Held, 2007: Can the Atlantic Ocean drive the observed multidecadal variability in Northern Hemisphere mean temperature? Geophys. Res. Lett., 34, L02709, doi: 10.1029/2006gl028683.Google Scholar
  64. Zhang R., T. L. Delworth, R. Sutton, et al., 2013: Have aerosols caused the observed Atlantic multidecadal variability? J. Atmos. Sci., 70, 1135–1144, doi: 10.1175/JAS-D-12-0331.1.CrossRefGoogle Scholar
  65. Zhang X. L., and X. D. Yan, 2014: A novel method to improve temperature simulations of general circulation models based on ensemble empirical mode decomposition and its application to multi-model ensembles. Tellus A, 66, 24,846, doi: 10.3402/tellusa.v66.24846.CrossRefGoogle Scholar
  66. Zheng J. Y., Y. Liu, and Z. X. Hao, 2015: Annual temperature reconstruction by signal decomposition and synthesis from multi-proxies in Xinjiang, China, from 1850 to 2001. PLoS One, 10, e0144210, doi: 10.1371/journal.pone.0144210.CrossRefGoogle Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Yajie Qi
    • 1
    • 2
  • Zhongwei Yan
    • 1
    • 3
  • Cheng Qian
    • 1
    • 3
  • Ying Sun
    • 4
    • 5
  1. 1.CAS Key Laboratory of Regional Climate–Environment for Temperate East Asia, Institute of Atmospheric PhysicsChinese Academy of Sciences (CAS)BeijingChina
  2. 2.Institute of Urban MeteorologyChina Meteorological AdministrationBeijingChina
  3. 3.University of Chinese Academy of SciencesBeijingChina
  4. 4.Laboratory for Climate Studies, National Climate CenterChina Meteorological AdministrationBeijingChina
  5. 5.Joint Center for Global Change StudiesBeijing Normal UniversityBeijingChina

Personalised recommendations