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Earth Systems and Environment

, Volume 3, Issue 3, pp 399–417 | Cite as

The Use of a CMIP5 Climate Model to Assess Regional Temperature and Precipitation Variation due to Climate Change: A Case Study of Dhaka Megacity, Bangladesh

  • Md. Masudur RahmanEmail author
  • Md. Abdur Rob
Original Article
  • 31 Downloads

Abstract

The Dhaka megacity is highly vulnerable to anthropogenic climate change. In addition to the risks associated with high population density and unplanned infrastructures, temperature and precipitation changes are two environmental factors which have the greatest potential to negatively impact the residential population, both now and into the future. This study uses historical climate data recorded in the Dhaka area for the 1995–2014 period, as well as a multi-model dataset, to understand existing climate variability and possible future climate change scenarios. Future climate scenarios and predictions for this area have been carried out with CMIP5 40 GCMs using the three new representative concentration pathways (RCP 4.5, RCP 6.0 and RCP 8.5) adopted by the IPCC. Climate model projections suggest that the average temperature would increase approximately 2.56 °C by the end of the twenty-first century and future monsoonal rainfall events would also substantially increase in frequency, particularly in the month of July. The results indicate that the long, hot and humid (pre-monsoon) and humid and wet (monsoon) season will persist over Dhaka for an increased length of time. A multi-model ensemble projection clearly showed that the risks associated with the modeled climate change parameters could increase Dhaka’s vulnerability to climate change by the end of the twenty-first century. It also indicated that issues associated with waterlogging, public health, transport system, and water supply would impact many areas within the Dhaka megacity. This study provides information, which can be used to assist in the development of measures to support the sustainable growth of Dhaka.

Keywords

Climate change Climate variability Projections Future climate RCPs 

Notes

Acknowledgements

The authors wish to thank the ICT Division, Ministry of Posts, Telecommunications and Information Technology and the Government of the People’s Republic of Bangladesh, for providing a Master of Philosophy (M. Phil.) Research Fellowship for this work. The authors are also grateful to the International Global Change Institute (IGCI) and the University of Waikato, Hamilton, New Zealand, for software sponsorship (SimCLIM 2013), the provision of licensed software and the CMIP5 AR5 Global and Bangladesh spatial dataset used for future climate change projections.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflicts of interest.

References

  1. Abbas F, Ahmad A, Safeeq M, Ali S, Saleem F, Hammad HM, Farhad W (2014) Changes in precipitation extremes over arid to semiarid and subhumid Punjab, Pakistan. Theor Appl Climatol 116:671–680CrossRefGoogle Scholar
  2. Ahmed R, Kim IK (2003) Patterns of daily rainfall in Bangladesh during the summer monsoon season: case studies at three stations. Phys Geogr 24(4):295–318.  https://doi.org/10.2747/0272-3646.24.4.295 CrossRefGoogle Scholar
  3. Amin A, Nasim W, Mubeen M, Sarwar S, Urich P, Ahmad A, Wajid A, Khaliq T, Rasul F, Hammad HM, Rehmani MIA, Mubarak H, Mirza N, Wahid A, Ahamd S, Fahad S, Ullah A, Khan MN, Ameen A, Amanullah Shahzad B, Saud S, Alharby H, Karim STA-U, Adnan M, Islam F, Ali QS (2016) Regional climate assessment of precipitation and temperature in Southern Punjab (Pakistan) using SimCLIM climate model for different temporal scales. Theor Appl Climatol 131:121–131.  https://doi.org/10.1007/s00704-016-1960-1 CrossRefGoogle Scholar
  4. Annamalai H, Hamilton K, Sperber KR (2007) South Asian summer monsoon and its relationship with ENSO in the IPCC AR4 simulations. J Clim 20:1071–1083CrossRefGoogle Scholar
  5. Banglapedia contributors (2018) Climate. In Banglapedia, Asiatic Society of Bangladesh. Retrieved 23:05, http://en.banglapedia.org/index.php?title=Climate Accessed 4 Sept 2018
  6. Bao Q (2012) Projected changes in Asian summer monsoon in RCP scenarios of CMIP5. Atmos Ocean Sci Lett 5(1):43–48CrossRefGoogle Scholar
  7. Bao Y, Hoogenboom G, McClendon R, Urich P (2015) Soybean production in 2025 and 2050 in the southeastern USA based on the SimCLIM and the CSM-CROPGRO-Soybean models. Clim Res 63:73–89.  https://doi.org/10.3354/cr01281 CrossRefGoogle Scholar
  8. Basha G, Kishore P, Ratnam MV, Jayaraman A, Kouchak AA, Ouarda TBMJ, Velicogna I (2017) Historical and projected surface temperature over India during the 20th and 21st century. Sci Rep 7:2987CrossRefGoogle Scholar
  9. BBS (2015) Population projection of Bangladesh: dynamics and trends 2011–2061. Bangladesh Bureau of Statistics, Ministry of Planning, Dhaka. ISBN 978-984-33-9960-1Google Scholar
  10. Bombardi RJ, Carvalho LMV (2009) IPCC global coupled model simulations of the South America monsoon system. Clim Dyn 33:893–916CrossRefGoogle Scholar
  11. Castro CL, Pielke RA, Leoncini G (2005) Dynamical downscaling: assessment of value retained and added using the Regional Atmospheric Modeling System (RAMS). J Geophys Res 110:D05108.  https://doi.org/10.1029/2004JD004721 CrossRefGoogle Scholar
  12. Christensen JH, Hewitson B, Busuioc A, Chen A, Gao X, Held I, Jones R, Kolli RK, Kwon W-T, Laprise R, Magaña Rueda V, Mearns L, Menéndez CG, Räisänen J, Rinke A, Sarr A, Whetton P (2007) Regional climate projections. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate change 2007: the physical science basis contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on climate change, 11th edn. Cambridge University Press, Cambridge, pp 847–940Google Scholar
  13. Cook KH, Vizy EK (2006) Coupled model simulations of the West African monsoon system: twentieth-century simulations and twenty-first-century predictions. J Clim 19:3681–3703CrossRefGoogle Scholar
  14. David F (2009) Dhaka tops risk table in Asia climate threat study. Reuters, London (2009-11-12) Google Scholar
  15. DelSole T, Shukla J (2012) Climate models produce skillful predictions of Indian summer monsoon rainfall. Geophys Res Lett 39:L09703CrossRefGoogle Scholar
  16. Dewan A (2013) Floods in a megacity: geospatial techniques in assessing hazards, risk and vulnerability. Springer, Dordrecht, pp 119–156CrossRefGoogle Scholar
  17. Dewan AM, Yamaguchi Y (2009) Land use and land cover change in Greater Dhaka, Bangladesh: using remote sensing to promote sustainable urbanization. Appl Geogr 29(3):390–401CrossRefGoogle Scholar
  18. Dewan AM, Kabir MH, Nahar K, Rahman MZ (2012) Urbanisation and environmental degradation in Dhaka metropolitan area of Bangladesh. Int J Environ Sustain Dev 11(2):118–147CrossRefGoogle Scholar
  19. Diaconescu EP, Laprise R (2013) Can added value be expected in RCM-simulated large scales? Clim Dyn 41(7–8):1769–1800CrossRefGoogle Scholar
  20. Easterling DR, Horton B, Jones PD, Peterson TC, Karl TR, Parker DE, Salinger MJ, Razuvayev V, Plummer N, Jamason P, Folland CK (1997) Maximum and minimum temperature trends for the globe. Science 277:364–367.  https://doi.org/10.1126/science.277.5324.364 CrossRefGoogle Scholar
  21. Feser F (2006) Enhanced detectability of added value in limited-area model results separated into different spatial scales. Mon Weather Rev 134(8):2180–2190CrossRefGoogle Scholar
  22. Gleckler PJ, Taylor KE, Doutriaux C (2008) Performance metrics for climate models. J Geophys Res 113:D06104CrossRefGoogle Scholar
  23. Harrison MSJ, Palmer TN, Richardson DS, Buizza R, Petroliagis T (1995) Joint ensembles from the UKMO and ECMWF models. In: ECMWF Seminar Proceedings: predictability, Vol 2, ECMWF, Reading, UK, p 61–120Google Scholar
  24. Hasan ABMSU, Rahman MZ (2013) Change in temperature over Bangladesh associated with degrees of global warming. Asian J Appl Sci Eng 2:2307–9584 (No 2/2013, ISSN 2305–915X) Google Scholar
  25. Hayat H, Akbar TA, Tahir AA, Hassan QK, Dewan A, Irshad M (2019) Simulating current and future river-flows in the Karakoram and Himalayan regions of Pakistan using snowmelt-runoff model and RCP scenarios. Water 11(4):761.  https://doi.org/10.3390/w11040761 CrossRefGoogle Scholar
  26. Hulme M, Wigley T, Barrow E, Raper S, Centella A, Smith S, Chipanshi A (2000) Using a climate scenario generator for vulnerability and adaptation assessments: MAGICC and SCENGEN Version 2.4 Workbook, Climatic Research Unit, Norwich, UK, pp 52Google Scholar
  27. IPCC (2013) Summary for policymakers. Climate change 2013: the physical science basis. contribution of Working Group I to the Fifth Assessment Report of the Inter-Governmental Panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  28. IPCC (2014) Summary for policymakers. Climate change: impacts, adaptation, and vulnerability, contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on climate change. Cambridge University Press, Cambridge, pp 1–32Google Scholar
  29. Islam MN, Rafiuddin M, Ahmed AU, Kolli RK (2008) Calibration of PRECIS in employing future scenarios in Bangladesh. Int J Climatol 28(5):617–628.  https://doi.org/10.1002/joc.1559 CrossRefGoogle Scholar
  30. Jang J, Hong S-Y (2016) Comparison of simulated precipitation over East Asia in two regional models with hydrostatic and non-hydrostatic dynamical cores. Mon Weather Rev 144:3579–3590CrossRefGoogle Scholar
  31. Janjic ZI, Gerrity JP Jr, Nickovic S (2001) An alternative approach to nonhydrostatic modeling. Mon Weather Rev 129:1164–1178.  https://doi.org/10.1175/1520-0493(2001)129%3c1164:AAATNM%3e2.0.CO;2 CrossRefGoogle Scholar
  32. Kanarska Y, Shchepetkin A, McWilliams JC (2007) Algorithm for non-hydrostatic dynamics in the Regional Oceanic Modeling System. Ocean Model 18(3–4):143–174.  https://doi.org/10.1016/j.ocemod.2007.04.001 CrossRefGoogle Scholar
  33. Kenny GJ, Warrick RA, Mitchell ND, Mullan AB, Salinger MJ (1995) CLIMPACTS: an integrated model for assessment of the effects of climate change on the New Zealand environment. J Biogeogr 22:883–895CrossRefGoogle Scholar
  34. Khatun MA, Rashid MB, Hygen HO (2016) MET report: climate of Bangladesh. report no. 08/2016. ISSN 2387–4201. http://bmd.gov.bd/p/Climate-Report/. Accessed 12 July 2019
  35. Kripalani RH, Oh JH, Kulkarni A, Sabade SS, Chaudhari HS (2007) South Asian summer monsoon precipitation variability: coupled climate model simulations and projections under IPCCAR4. Theor Appl Climatol 90:133–159.  https://doi.org/10.1007/s00704-006-0282-0 CrossRefGoogle Scholar
  36. Krishnamurti TN, Kishtawal CM, Zhan Z, Timothy L, David B, Eric W (2000) Multimodel ensemble forecasts for weather and seasonal climate. J Clim 13:4196–4216CrossRefGoogle Scholar
  37. Kundzewicz ZW, Somlyódy L (1997) Climatic change impact on water resources in a systems perspective. Water Resour Manag 11(6):407–435.  https://doi.org/10.1023/A:1007984001105 CrossRefGoogle Scholar
  38. Kundzewicz ZW, Mata LJ, Arnell NW, Doll P, Kabat P, Jimenez B, Miller KA, Oki T, Sen Z, Shiklomanov IA (2007) Freshwater resources and their management. In: Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (eds) Climate Change, 2007: impacts, adaptation and vulnerability. contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on climate change. Cambridge University Press, Cambridge, pp 173–210Google Scholar
  39. Lal M, Harasawa H (2000) Comparison of the present-day climate simulation over Asia in selected coupled atmosphere-ocean global climate models. J Meteorol Soc Jpn 78(6):871–879CrossRefGoogle Scholar
  40. Luca AD, Elia Rd, Laprise R (2012) Potential for added value in precipitation simulated by high-resolution nested regional climate models and observations. Clim Dyn 38(5–6):1229–1247CrossRefGoogle Scholar
  41. Lucas-Picher P, Wulff-Nielsen M, Christensen JH, Adalgeirsdottir G, Mottram R, Simonsen SB (2012) Very high-resolution regional climate model simulations over Greenland: identifying added value. J Geophys Res 117:D02108.  https://doi.org/10.1029/2011JD016267 CrossRefGoogle Scholar
  42. Maity R, Aggarwal A, Chanda K (2016) Do CMIP5 models hint at a warmer and wetter India in the 21st century? J water clim change 07:2.  https://doi.org/10.2166/wcc.2015.126 CrossRefGoogle Scholar
  43. Meehl GA, Bony S (2011) Introduction to CMIP5. CLIVAR Exch no 56 16:2Google Scholar
  44. Mishra SK, Sahany S, Salunke P, Kang IS, Jain S (2018) Fidelity of CMIP5 multi-model mean in assessing Indian monsoon simulations. npj Clim Atmos Sci 1:39.  https://doi.org/10.1038/s41612-018-0049-1 CrossRefGoogle Scholar
  45. Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, van Vuuren DP, Carter TR, Emori S, Kainuma M, Kram T, Meehl GA, Mitchell JFB, Nakicenovic N, Riahi K, Smith SJ, Stouffer RJ, Thomson AM, Weyant JP, Wilbanks TJ (2010) The next generation of scenarios for climate change research and assessment. Nature 463:747–756.  https://doi.org/10.1038/nature08823 CrossRefGoogle Scholar
  46. Mullick MRA, Nur RM, Alam MJ, Islam KMA (2019) Observed trends in temperature and rainfall in Bangladesh using pre-whitening approach. Glob Planet Change 172:104–113.  https://doi.org/10.1016/j.gloplacha.2018.10.001 CrossRefGoogle Scholar
  47. Peel MC, Finlayson BL, Mcmahon TA (2007) Updated world map of the Köppen-Geiger climate classification. Hydrol Earth Sys Sci Discuss 11(5):1633–1644 (European Geosciences Union, ffhal-00305098) CrossRefGoogle Scholar
  48. Pierce DW, Barnett TP, Santer BD, Gleckler PJ (2009) Selecting global climate models for regional climate change studies. Proc Natl Acad Sci USA 106:8441–8446CrossRefGoogle Scholar
  49. Porter JR, Xie L, Challinor AJ, Cochrane K, Howden SM, Iqbal MM, Lobell DB, Travasso MI (2014) Food security and food production systems. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds) Climate change 2014: impacts, adaptation, and vulnerability. Part a: global and sectoral aspects. contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on climate change. Cambridge University Press, Cambridge, pp 485–533Google Scholar
  50. Preethi B, Kripalani R, Krishna KK (2010) Indian summer monsoon rainfall variability in global coupled ocean-atmospheric models. Clim Dyn 35:1521–1539CrossRefGoogle Scholar
  51. Prömmel K, Geyer B, Jones JM, Widmann M (2010) Evaluation of the skill and added value of a reanalysis-driven regional simulation for Alpine temperature. Int J Climatol 30:760–773Google Scholar
  52. Qi F, Fei J, Ma Z, Chen J, Huang X, Cheng X (2018) Comparison of simulated tropical cyclone intensity and structures using the WRF with hydrostatic and nonhydrostatic dynamical cores. Atmosphere 9:483.  https://doi.org/10.3390/atmos9120483 CrossRefGoogle Scholar
  53. Rabbani G, Rahman AA, Islam N (2011) Climate change implications for Dhaka City: a need for immediate measures to reduce vulnerability. In: Otto-Zimmermann K (ed) Resilient Cities. Local Sustainability, vol 1. Springer, DordrechtGoogle Scholar
  54. Rahman MM, Islam MN, Ahmed AU, Georgi F (2012a) Rainfall and temperature scenarios for Bangladesh for the middle of 21st century using RegCM. J Earth Syst Sci 121(2):287–295.  https://doi.org/10.1007/s12040-012-0159-9 CrossRefGoogle Scholar
  55. Rahman MM, Rajib MA, Hassan MM, Iskander SM, Khondoker MTH, Rakib ZB, Ankur AK (2012b) Application of RCM-based climate change indices in assessing future climate: part II–precipitation concentration. In: World environmental and water resources congress 2012: crossing boundaries, pp 1787–1793.  https://doi.org/10.1061/9780784412312.178
  56. Rai A, Joshi MK, Pandey AC (2012) Variations in diurnal temperature range over India: under global warming scenario. J Geophys Res 117:D02114.  https://doi.org/10.1029/2011JD016697 CrossRefGoogle Scholar
  57. Rajib MA, Rahman MM, Islam AKMS (2011) McBean EA (2011) Analyzing the future monthly precipitation pattern in Bangladesh from multi-model projections using both GCM and RCM. World Environ Water Resour Congr.  https://doi.org/10.1061/9780784412312.177 CrossRefGoogle Scholar
  58. Reichler T, Kim J (2008) How well do coupled models simulate today’s climate. Bull Am Meteorol Soc 89:303–311CrossRefGoogle Scholar
  59. Sabeerali CT, Dandi AR, Dhakate A, Salunke K, Mahapatra S, Rao SA (2013) Simulation of boreal summer intraseasonal oscillations in the latest CMIP5 coupled GCMs. J Geophys Res 118:4401–4420Google Scholar
  60. Shahid S (2010a) Recent trends in the climate of Bangladesh. Clim Res 42:185–193CrossRefGoogle Scholar
  61. Shahid S (2010b) Rainfall variability and the trends of wet and dry periods in Bangladesh. Int J Climatol 30(15):2299–2313.  https://doi.org/10.1002/joc.2053 CrossRefGoogle Scholar
  62. Shahid S (2011) Trends in extreme rainfall events of Bangladesh. Theor Appl Climatol 104(3–4):489–499CrossRefGoogle Scholar
  63. Shahid S, Khairulmaini OS (2009) Spatio-temporal variability of rainfall over Bangladesh during the time period 1969–2003. Asia Pac J Atmos Sci 45(3):375–389Google Scholar
  64. Shahid S, Harun SB, Katimon A (2012) Changes in diurnal temperature range in Bangladesh during the time period 1961–2008. Atmos Res 118:260–270.  https://doi.org/10.1016/j.atmosres.2012.07.008 CrossRefGoogle Scholar
  65. Shahid S, Wang X-J, Harun SB, Shamsudin SB, Ismail B, Minhans M (2016) Climate variability and changes in the major cities of Bangladesh: observations, possible impacts and adaptation. Reg Environ Change 16:459.  https://doi.org/10.1007/s10113-015-0757-6 CrossRefGoogle Scholar
  66. Shirazi SA, Zahid F, Bokhari MH (2006) Impact of spatio-temporal trends and anomalies of surface air temperature over Punjab. Geogr Pap 1:22–32Google Scholar
  67. Siew JH, Tangang FT, Juneng L (2014) Evaluation of CMIP5 coupled atmosphere-ocean general circulation models and projection of the Southeast Asian winter monsoon in the 21st century. Int J Climatol 34:2872–2884Google Scholar
  68. SimCLIM (2017) SimCLIM. Retrieved 23:05, https://www.climsystems.com/simclim/ Accessed 4 Sept 2018
  69. Sohail MG, Burke F (2013) Climate change and precipitation in Pakistan -a meteorological prospect. Int J Econ Environ Geol 4:10–15Google Scholar
  70. Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) (2007) Climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  71. Stoelinga MT, Warner TT (1999) Nohydrostatic, mesobeta-scale model simulations of cloud ceiling and visibility for an east coast winter precipitation event. J Appl Meteorol 38:385–404.  https://doi.org/10.1175/1520-0450(1999)038%3c0385:NMSMSO%3e2.0.CO;2 CrossRefGoogle Scholar
  72. Taylor KE, Stouffer RJ, Meehl GA (2012) An Overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:1–33CrossRefGoogle Scholar
  73. Tebaldi C, Knutti R (2007) The use of the multi-model ensemble in probabilistic climate projections. Philos Trans R Soc A 365:2053–2075.  https://doi.org/10.1098/rsta.2007.2076 CrossRefGoogle Scholar
  74. Warrick RA (2009) Using SimCLIM for modelling the impacts of climate extremes in a changing climate: a preliminary case study of household water harvesting in Southeast Queensland. 18th World IMACS/MODSIM Congress, Cairns, Australia, pp 2583–2589Google Scholar
  75. World Population Review (2019) Bangladesh population 2019 report. http://worldpopulationreview.com/countries/bangladesh-population/. Accessed 12 July 2019
  76. Yin C, Li Y, Urich P (2013) SimCLIM 2013 data manual. CLIMsystems Ltd., Flagstaff, Hamilton, New Zealand. www.climsystems.com. Accessed 31 Mar 2017
  77. Zijlema M, Stelling G, Smit P (2011) SWASH: an operational public domain code for simulating wave fields and rapidly varied flows in coastal waters. Coast Eng 58(10):992–1012.  https://doi.org/10.1016/j.coastaleng.2011.05.015 CrossRefGoogle Scholar
  78. Zou L, Zhou T (2013) Near future (2016–40) summer precipitation changes over China as projected by a Regional Climate Model (RCM) under the RCP8.5 emissions scenario: comparison between RCM downscaling and the driving GCM. Adv Atmos Sci 30(3):806–818CrossRefGoogle Scholar

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© King Abdulaziz University and Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Geography and EnvironmentUniversity of DhakaDhakaBangladesh

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