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Time of emergence in regional precipitation changes: an updated assessment using the CMIP5 multi-model ensemble

  • Thuy-Huong Nguyen
  • Seung-Ki Min
  • Seungmok Paik
  • Donghyun Lee
Article

Abstract

This study conducted an updated time of emergence (ToE) analysis of regional precipitation changes over land regions across the globe using multiple climate models from the Coupled Model Intercomparison Project phase 5 (CMIP5). ToEs were estimated for 14 selected hotspots over two seasons of April to September (AS) and October to March (OM) from three RCP scenarios representing low (RCP2.6), medium (RCP4.5), and high (RCP8.5) emissions. Results from the RCP8.5 scenario indicate that ToEs would occur before 2040 over seven hotspots including three northern high-latitude regions (OM wettening), East Africa (OM wettening), South Asia (AS wettening), East Asia (AS wettening) and South Africa (AS drying). The Mediterranean (both OM and AS drying) is expected to experience ToEs in the mid-twenty-first century (2040-2080). In order to measure possible benefits from taking low-emission scenarios, ToE differences were examined between the RCP2.6 scenario and the RCP4.5 and RCP8.5 scenarios. Significant ToE delays from 26 years to longer than 67 years were identified over East Africa (OM wettening), the Mediterranean (both AS and OM drying), South Asia (AS wettening), and South Africa (AS drying). Further, we investigated ToE differences between CMIP3-based and CMIP5-based models using the same number of models for the comparable scenario pairs (SRESA2 vs. RCP8.5, and SRESB1 vs. RCP4.5). Results were largely consistent between two model groups, indicating the robustness of ToE results. Considerable differences in ToEs (larger than 20 years) between two model groups appeared over East Asia and South Asia (AS wettening) and South Africa (AS drying), which were found due to stronger signals in CMIP5 models. Our results provide useful information on the timing of emerging signals in regional and seasonal hydrological changes, having important implications for associated adaptation and mitigation plans.

Keywords

Time of emergence Precipitation CMIP5 RCP scenarios Signal-to-noise ratio 

Notes

Acknowledgements

We thank two anonymous reviewers for their thoughtful comments. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (no. 2017R1A2B2008951). We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Tables 1 and 2 of this paper) for producing and making available their model output.

References

  1. Bador M, Terray L, Boé J (2016) Emergence of human influence on summer record-breaking temperatures over Europe. Geophys Res Lett 43:404–412CrossRefGoogle Scholar
  2. Bindoff NL et al (2013) Detection and attribution of climate change: from global to regional. In: Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change, pp 869–928Google Scholar
  3. Christensen JH et al (2007) Regional climate projections. In: Climate change the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change, pp 849–926Google Scholar
  4. Ciavarella A, Stott P, Lowe J (2017) Early benefits of mitigation in risk of regional climate extremes. Nat Clim Change 7:326–330CrossRefGoogle Scholar
  5. Collins M, Knutti R, Arblaset J et al (2013) Long-term climate change: projections, commitments and irreversibility. In: Stocker TF, Qin D, Plattner GK et al (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  6. Dee DP et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597CrossRefGoogle Scholar
  7. Deser C, Phillips A, Bourdette V, Teng H (2012) Uncertainty in climate change projections: the role of internal variability. Clim Dyn 38:527–547CrossRefGoogle Scholar
  8. Diffenbaugh NS, Scherer M (2011) Observational and model evidence of global emergence of permanent, unprecedented heat in the 20th and 21st centuries. Clim Change 107:615–624CrossRefGoogle Scholar
  9. Diffenbaugh NS, Ashfaq M, Scherer M (2011) Transient regional climate change: analysis of the summer climate response in a high-resolution, century-scale ensemble experiment over the continental United States. J Geophys Res 116:D24111CrossRefGoogle Scholar
  10. Flato G et al (2013) Evaluation of climate models. In: Climate change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change, pp 743–824Google Scholar
  11. Fraser EDG, Simelton E, Termansen M, Gosling SN, South A (2013) “Vulnerability hotspots”: integrating socio-economic and hydrological models to identify where cereal production may decline in the future due to climate change induced drought. Agric For Meteorol 170:195–205CrossRefGoogle Scholar
  12. Giorgi F (2010) Uncertainties in climate change projections, from the global to the regional scale. EPJ Web Conf 9:115–129.  https://doi.org/10.1051/epjconf/201009009CrossRefGoogle Scholar
  13. Giorgi F, Bi X (2009) Time of emergence (TOE) of GHG-forced precipitation change hot-spots. Geophys Res Lett 36:L06709CrossRefGoogle Scholar
  14. Giorgi F, Coppola E (2010) Does the model regional bias affect the projected regional climate change? An analysis of global model projections. Clim Change 100:787–795CrossRefGoogle Scholar
  15. Hawkins E, Sutton R (2011) The potential to narrow uncertainty in projections of regional precipitation change. Clim Dyn 37:407–418CrossRefGoogle Scholar
  16. Hawkins E, Sutton R (2012) Time of emergence of climate signals. Geophys Res Lett 39:L01702CrossRefGoogle Scholar
  17. Hawkins E, Anderson B, Diffenbaugh N, Mahlstein I, Betts R, Hegerl G, Joshi M, Knutti R, McNeall D, Solomon S, Sutton R (2014) Uncertainties in the timing of unprecedented climates. Nature 511:E3–E5CrossRefGoogle Scholar
  18. Herold N, Alexander LV, Donat MG, Contractor S, Becker A (2016) How much does it rain over land? Geophys Res Lett 43:341–348.  https://doi.org/10.1002/2015GL066615CrossRefGoogle Scholar
  19. Hewitson B et al (2014) Regional context. In: Climate change 2014: impacts, adaptation, and vulnerability. Part B: regional aspects. Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change, pp 1133–1197Google Scholar
  20. Kay JE et al (2015) The Community Earth System Model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability. Bull Am Meteorol Soc 96:1333–1349CrossRefGoogle Scholar
  21. King AD, Donat MG, Fischer EM, Hawkins E, Alexander LV, Karoly DJ, Dittus AJ, Lewis SC, Perkins SE (2015) The timing of anthropogenic emergence in simulated climate extremes. Environ Res Lett 10:094015CrossRefGoogle Scholar
  22. King AD, Black MT, Min S-K, Fischer EM, Mitchell DM, Harrington LJ, Perkins-Kirkpatrick SE (2016) Emergence of heat extremes attributable to anthropogenic influences. Geophys Res Lett 43:3438–3443CrossRefGoogle Scholar
  23. Kirtman B et al (2013) Near-term climate change: projections and predictability. In: Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change, pp 955–1008Google Scholar
  24. Kjellström E, Bärring L, Nikulin G, Nilsson C, Persson G, Strandberg G (2016) Production and use of regional climate model projections—a Swedish perspective on building climate services. Clim Ser 2–3:15–29CrossRefGoogle Scholar
  25. Kuhlbrodt T, Gregory JM (2012) Ocean heat uptake and its consequences for the magnitude of sea level rise and climate change. Geophys Res Lett 39:L18608CrossRefGoogle Scholar
  26. Lambert SJ, Boer GJ (2001) CMIP1 evaluation and intercomparison of coupled climate models. Clim Dyn 17:83–106CrossRefGoogle Scholar
  27. Lee JY, Wang B (2014) Future change of global monsoon in the CMIP5. Clim Dyn 42:101–119CrossRefGoogle Scholar
  28. Lee D et al (2016) Time of emergence of anthropogenic warming signals in the Northeast Asia assessed from multi-regional climate models. Asia Pac J Atmos Sci 52:129–137CrossRefGoogle Scholar
  29. Lehner F, Deser C, Sanderson BM (2016) Future risk of record-breaking summer temperatures and its mitigation. Clim Change.  https://doi.org/10.1007/s10584-016-1616-2Google Scholar
  30. Lin L, Gettelman A, Fu Q, Xu Y (2016) Simulated differences in 21st century aridity due to different scenarios of greenhouse gases and aerosols. Clim Change.  https://doi.org/10.1007/s10584-016-1615-3Google Scholar
  31. Livia T, Timothy A (2014) The physical drivers of historical and 21st century global precipitation changes. Environ Res Lett 9:064024CrossRefGoogle Scholar
  32. Maraun D (2013) When will trends in European mean and heavy daily precipitation emerge? Environ Res Lett 8:014004CrossRefGoogle Scholar
  33. Mariotti A, Pan Y, Zeng N, Alessandri A (2015) Long-term climate change in the Mediterranean region in the mist of decadal variability. Clim Dyn 44:1437–1456CrossRefGoogle Scholar
  34. Min S-K, Park E-H, Kwon W-T (2004) Future projections of East Asian climate change from multi-AOGCM ensembles of IPCC SRES scenario simulations. J Meteorol Soc Jpn 82:1187–1211CrossRefGoogle Scholar
  35. Min S-K, Zhang X, Zwiers F (2008) Human-induced Arctic moistening. Science 320:518–520CrossRefGoogle Scholar
  36. Mora C et al (2013) The projected timing of climate departure from recent variability. Nature 502:183–187CrossRefGoogle Scholar
  37. Moss RH et al (2010) The next generation of scenarios for climate change research and assessment. Nature 463:747–756CrossRefGoogle Scholar
  38. Mullan D, Swindles G, Patterson T, Galloway J, Macumber A, Falck H, Crossley L, Chen J, Pisaric M (2016) Climate change and the long-term viability of the world’s busiest heavy haul ice road. Theor Appl Climatol.  https://doi.org/10.1007/s00704-016-1830-xGoogle Scholar
  39. Muthers S et al (2014) The coupled atmosphere–chemistry–ocean model SOCOL-MPIOM. Geosci Model Dev 7:2157–2179CrossRefGoogle Scholar
  40. New M, Hulme M, Jones PD et al (2000) Representing twentieth century space–time climate variability. Part II: development of 1901–1996 monthly grids of terrestrial surface climate. J Clim 13:2217–2238CrossRefGoogle Scholar
  41. Noake K, Polson D, Hegerl G, Zhang X (2012) Changes in seasonal land precipitation during the latter twentieth-century. Geophys Res Lett 39:L03706CrossRefGoogle Scholar
  42. Peel MC, Finlayson BL, McMahon TA (2007) Updated world map of the Köppen–Geiger climate classification. Hydrol Earth Syst Sci 11:1633–1644CrossRefGoogle Scholar
  43. Phillips TJ, Gleckler PJ (2006) Evaluation of continental precipitation in 20th century climate simulations: the utility of multimodel statistics. Water Resour Res 42:W03202.  https://doi.org/10.1029/2005WR004313CrossRefGoogle Scholar
  44. Piontek F et al (2014) Multisectoral climate impact hotspots in a warming world. Proc Natl Acad Sci 111:3233–3238CrossRefGoogle Scholar
  45. Räisänen J (2001) CO2-induced climate change in CMIP2 experiments: quantification of agreement and role of internal variability. J Clim 14:2088–2104CrossRefGoogle Scholar
  46. Rogelj J, Meinshausen M, Knutti R (2012) Global warming under old and new scenarios using IPCC climate sensitivity range estimates. Nat Clim Change 2:248–253CrossRefGoogle Scholar
  47. Sarojini BB, Stott PA, Black E (2016) Detection and attribution of human influence on regional precipitation. Nat Clim Change 6:669–675CrossRefGoogle Scholar
  48. Schleussner CF et al (2016) Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 °C and 2 °C. Earth Syst Dyn 7:327–351CrossRefGoogle Scholar
  49. Sedláček J, Knutti R (2014) Half of the world’s population experience robust changes in the water cycle for a 2 °C warmer world. Environ Res Lett 9:044008CrossRefGoogle Scholar
  50. Stocker TF et al (2013) Technical summary. In: Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change, pp 35–109Google Scholar
  51. Sui Y, Lang X, Jiang D (2014) Time of emergence of climate signals over China under the RCP4.5 scenario. Clim Change 125:265–276CrossRefGoogle Scholar
  52. van Vuuren DP et al (2011) The representative concentration pathways: an overview. Clim Change 109:5–31CrossRefGoogle Scholar
  53. Wan H, Zhang X, Zwiers F, Min S-K (2015) Attributing northern high-latitude precipitation change over the period 1966–2005 to human influence. Clim Dyn 45:1713–1726CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Division of Environmental Science and EngineeringPohang University of Science and TechnologyPohangKorea
  2. 2.REMOSAT LaboratoryUniversity of Science and Technology of HanoiHanoiVietnam

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