Share This Article:

Assessment of CMIP3-CMIP5 Climate Models Precipitation Projection and Implication of Flood Vulnerability of Bangkok

Abstract Full-Text HTML XML Download Download as PDF (Size:5066KB) PP. 140-162
DOI: 10.4236/ajcc.2015.41011    3,607 Downloads   4,276 Views   Citations
Author(s)    Leave a comment

ABSTRACT

Reliable estimates of precipitation are essential for both research and practical applications. CMIP3 and CMIP5 climate simulations provide both historical simulations and future projections of extreme climate. The 2011 monsoon season was one of case studies with exceptionally heavy and led to extensive and long-lasting flooding in the Chao Phraya river basin, Thailand. Flooding was exacerbated by the rapid expansion of urban areas into flood plains and was the costliest natural disaster in the country’s history, with direct damages estimated at US$45 billion. The present paper focuses on the precipitation downscaling of CMIP3 and CMIP5 models. The majority of CMIP3 and CMIP5 models overestimate the dry spell (in June and July) and underestimate the peak precipitation (in May and September). The interquartile model range for precipitation, which is spanned by the 25th and 75th quantiles, is closer to the observed data for CMIP5 than CMIP3 models. However, overall results suggest that the performance of CMIP5 models cannot be readily distinguished from of CMIP3 models, although there are clear signals of improvements over Bangkok. The correlation coefficient is found between 0.6 - 0.8, implying that most of the models simulate the mean rainfall reasonably well. Both model generations have approximately the same standard deviation as observed, but more spatial variability and more RMS error are found for the future projections. Use of the Multi Model mean shows continuously increased rainfall from the near future to the far future while the Multi Model Median shows increased rainfall only for the far future. These findings in changing precipitation are discussed through the flood behavior in 2011. Results from flood simulation with several adaptation measures reveal that flood cannot be completely avoided. One of the best practices for highflood risk communities is to raise the house with open space in the first floor.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Supharatid, S. (2015) Assessment of CMIP3-CMIP5 Climate Models Precipitation Projection and Implication of Flood Vulnerability of Bangkok. American Journal of Climate Change, 4, 140-162. doi: 10.4236/ajcc.2015.41011.

References

[1] IPCC (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. Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M. and Milller, H.L., Eds., Cambridge University Press, Cambridge, and New York, 996 p.
[2] McGranahan, G., Balk, D. and Anderson, B. (2007) The Rising Tide: Assessing the Risks of Climate Change and Human Settlements in Low Elevation Coastal Zones. Environment and Urbanization, 19, 17-37.
http://dx.doi.org/10.1177/0956247807076960
[3] Nicholls, R.J., Hanson, S., Herweijer, C., Patmore, N., Hallegatte, S., Corfee-Morlot, J., Chateau, J. and Muir-Wood, R. (2008) Ranking Port Cities with High Exposure and Vulnerability to Climate Extremes-Exposure Estimates. Environmental Working Paper No. 1, Organisation for Economic Co-operation and Development (OECD), Paris.
[4] Sivakumar, B. (2011) Global Climate Change and Its Impacts on Water Resources Planning and Management: Assessment and Challenges. Stochastic Environmental Research and Risk Assessment, 25, 583-600.
http://dx.doi.org/10.1007/s00477-010-0423-y
[5] Cayan, D.R., Maurer, E.P., Dettinger, M.D., Tyree, M. and Hayhoe, K. (2008) Climate Change Scenarios for the California Region. Climatic Change, 87, 21-42.
http://dx.doi.org/10.1007/s10584-007-9377-6
[6] Wehner, M. (2013) Methods of Projecting Future Changes in Extremes. In: Extremes in a Changing Climate, Springer, Netherlands, 223-237.
http://dx.doi.org/10.1007/978-94-007-4479-0_8
[7] IPCC (2007) Climate Change 2007: Impacts, Adaptation, and Vulnerability. Exit EPA Disclaimer Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Parry, M.L., et al., Eds., Cambridge University Press, Cambridge.
[8] Schubert, S.D. and Lim, Y.K. (2013) Climate Variability and Weather Extremes: Model-Simulated and Historical Data. In: Extremes in a Changing Climate, Springer, Netherlands, 239-285.
http://dx.doi.org/10.1007/978-94-007-4479-0_9
[9] Delworth, T.L., Broccoli, A.J., Rosati, A., Stouffer, R.J., Balaji, V., Beesley, J.A., et al. (2006) GFDL’s CM2 Global Coupled Climate Models. Part 1: Formulation and Simulation Characteristics. Journal of Climate, 19, 643-674.
http://dx.doi.org/10.1175/JCLI3629.1
[10] Perkins, S.E., Pitman, A.J., Holbrook, N.J. and Mcaneney, J. (2007) Evaluation of the AR4 Climate Models’ Simulated Daily Maximum Temperature, Minimum Temperature, and Precipitation over Australia Using Probability Density Functions. Journal of Climate, 20, 4356-4376.
http://dx.doi.org/10.1175/JCLI4253.1
[11] Johnson, F. and Sharma, A. (2009) Measurement of GCM Skill in Predicting Variables Relevant for Hydroclimatological Assessments. Journal of Climate, 22, 4373-4382.
http://dx.doi.org/10.1175/2009JCLI2681.1
[12] Van Vuuren, D.P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G.C., Kram, T., Krey, V., Lamarque, J.F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S.J. and Rose, S.K. (2011) The Representative Concentration Pathways: An Overview. Climatic Change, 109, 5-31.
http://dx.doi.org/10.1007/s10584-011-0148-z
[13] Meinshausen, M., Smith, S.J., Calvin, K., Daniel, J.S., Kainuma, M.L.T., Lamarque, J.F., et al. (2011) The RCP Greenhouse Gas Concentrations and Their Extensions from 1765 to 2300. Climatic Change, 109, 213-241.
http://dx.doi.org/10.1007/s10584-011-0156-z
[14] Chaturvedi, R.K., Joshi, J., Jayaraman, M., Bala, G. and Ravindranath, N.H. (2012) Multi-Model Climate Change Projections for India under Representative Concentration Pathways. Current Science, 103, 791-802.
[15] Taylor, K.E., Stouffer, R.J. and Meehl, G.A. (2012) An Overview of CMIP5 and the Experimental Design. Bulletin of the American Meteorological Society, 93, 485-498. http://dx.doi.org/10.1175/BAMS-D-11-00094.1
[16] Taylor, K.E., Stouffer, R.J. and Meehl, G.A. (2011) A Summary of the CMIP5 Experiment Design. Program for Climate Model Diagnosis and Intercomparison (PCMDI), 2011.
http://cmip-pcmdi.llnl.gov/cmip5/docs/Taylor_CMIP5_design.pdf
[17] Taylor, K.E., Balaji, V., Hankin, S., Juckes, M., Lawrence, B. and Pascoe, S. (2011) CMIP5 Data Reference Syntax (DRS) and Controlled Vocabularies (Program for Climate Model Diagnosis and Intercomparison (PCMDI).
http://cmip-pcmdi.llnl.gov/cmip5/docs/cmip5_data_reference_syntax.pdf
[18] Meehl, G.A., Goddard, L., Murphy, J., Stouffer, R.J., Boer, G., Danabasoglu, G., et al. (2009) Decadal Prediction: Can It Be Skillful? Bulletin of the American Meteorological Society, 90, 1467-1485.
http://dx.doi.org/10.1175/2009BAMS2778.1
[19] Kug, J.S., Ham, Y.G., Lee, J.Y. and Jin, F.F. (2012) Improved Simulation of Two Types of El Nino inCMIP5 Models. Environmental Research Letters, 7, Article ID: 034002.
http://dx.doi.org/10.1088/1748-9326/7/3/034002
[20] Brands, S., Herrera, S., Fernandez, J. and Gutierrez, J.M. (2013) How Well Do CMIP5 Earth System Models Simulate Present Climate Conditions in Europe and Africa? Climate Dynamics, 41, 803-817.
http://dx.doi.org/10.1007/s00382-013-1742-8
[21] Blazquez, J. and Nunez, M.N. (2013) Analysis of Uncertainties in Future Climate Projections for South America: Comparison of WCRP-CMIP3 and WCRP-CMIP5 Models. Climate Dynamics, 41, 1039-1056.
http://dx.doi.org/10.1007/s00382-012-1489-7
[22] Cattiaux, J., Douville, H. and Peings, Y. (2013) European Temperatures in CMIP5: Origins of Present-Day Biases and Future Uncertainties. Climate Dynamics, 41, 2889-2907.
http://dx.doi.org/10.1007/s00382-013-1731-y
[23] Knutti, R., Abramowitz, G., Collins, M., Eyring, V., Gleckler, P.J., Hewitson, B.M. and Mearns, L. (2010) Good Practice Guidance Paper on Assessing and Combining Multi Model Climate Projections. Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Assessing and Combining Multi Model Climate Projections. IPCC Working Group I Technical Support Unit, University of Bern, Bern.
[24] Mehrotra, R., Sharma, A., Bari, M., Tuteja, N. and Amirthanathan, G. (2014) An Assessment of CMIP5 Multi-Model Decadal Hindcasts over Australia from a Hydrological Viewpoint. Journal of Hydrology, 519, 2932-2951.
http://dx.doi.org/10.1016/j.jhydrol.2014.07.053
[25] Graham, L.P., Andreasson, J. and Carlsson, B. (2007) Assessing Climate Change Impacts on Hydrology from an Ensemble of Regional Climate Models, Model Scales and Linking Methods—A Case Study on the Lule River Basin. Climatic Change, 81, 293-307.
http://dx.doi.org/10.1007/s10584-006-9215-2
[26] Leander, R., Adri Buishand, T., van den Hurk, B.J.J.M. and de Wit, M.J.M. (2008) Estimated Changes in Flood Quantiles of the River Meuse from Resampling of Regional Climate Model Output. Journal of Hydrology, 351, 331-343.
http://dx.doi.org/10.1016/j.jhydrol.2007.12.020
[27] Wilby, R.L., Beven, K.J. and Reynard, N.S. (2008) Climate Change and Fluvial Flood Risk in the UK: More of the Same? Hydrological Processes, 22, 2511-2523.
http://dx.doi.org/10.1002/hyp.6847
[28] Willems, P., Arnbjerg-Nielsen, K., Olsson, J. and Nguyen, V.T.V. (2011) Climate Change Impact Assessment on Urban Rainfall Extremes and Urban Drainage: Methods and Shortcomings. Atmospheric Research, 103, 106-118.
http://dx.doi.org/10.1016/j.atmosres.2011.04.003
[29] Fiseha, B.M., Setegn, S.G., Melesse, A.M., Volpi, E. and Fiori, A. (2014) Impact of Climate Change on the Hydrology of Upper Tiber River Basin Using Bias Corrected Regional Climate Model. Water Resources Management, 28, 1327-1343.
http://dx.doi.org/10.1007/s11269-014-0546-x
[30] World Bank (2011) The World Bank Supports Thailand’s Post-Floods Recovery Effort. The World Bank, Washington DC. http://www.worldbank.org/en/news/2011/12/13/world-
bank-supports-thailands-post-floods-recovery-effort
[31] Vongvisessomjai, S. (2007) Impacts of Typhoon Vae and Linda on Wind Waves in the Upper Gulf of Thailand and East Coast. Songklanakarin Journal of Science and Technology, 29, 1199-1216.
[32] Ziegler, A.D., Lim, H.S., Jachowski, N.R. and Wasson, R.J. (2012) Water Management: Reduce Urban Flood Vulnerability. Nature, 481, 145.
http://dx.doi.org/10.1038/481145b
[33] Stephens, E. and Cloke, H. (2014) Improving Flood Forecasts for Better Flood Preparedness in the UK (and Beyond). Geographical Journal, 180, 310-316.
http://dx.doi.org/10.1111/geoj.12103
[34] Tarolli, P. (2013) Book Review: Natural Hazards in the Asia-Pacific Region: Recent Advances and Emerging Concepts. Natural Hazards and Earth System Sciences, 13, 2551-2552.
http://dx.doi.org/10.5194/nhess-13-2551-2013
[35] Werner, M., van Dijk, M. and Schellekens, J. (2004) Delft-FEWS: An Open Shell Flood Forecasting System. In: Liong, S.Y., Phoon, K.K. and Babovic, V., Eds., Proceedings of the 6th International Conference on Hydroinformatics, World Scientific Publishing Company, Singapore City, 1205-1212.
[36] Wehner, M., Cranston, M., Harrison, T., Whitfield, D. and Schellekens, J. (2009) Recent Developments in Operational Flood Forecasting in England, Wales and Scotland. Meteorological Applications, 16, 13-22.
http://dx.doi.org/10.1002/met.124
[37] World Bank (2012) Thai Flood 2011 Rapid Assessment for Resilient Recovery and Reconstruction Planning. Washington DC, 377 p.
[38] Rogelj, J., Meinshausen, M. and Knutti, R. (2012) Global Warming under Old and New Scenarios Using IPCC Climate Sensitivity Range Estimates. Nature Climate Change, 2, 248-253.
http://dx.doi.org/10.1038/nclimate1385
[39] Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P. M., et al. (2013) Climate Change 2013. The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change—Abstract for Decision-Makers.
[40] Teutschbein, C. and Seibert, J. (2012) Bias Correction of Regional Climate Model Simulations for Hydrological Climate-Change Impact Studies: Review and Evaluation of Different Methods. Journal of Hydrology, 456-457, 12-29.
http://dx.doi.org/10.1016/j.jhydrol.2012.05.052
[41] Reifen, C. and Toumi, R. (2009) Climate Projections: Past Performance No Guarantee of Future Skill? Geophysical Research Letters, 36, L13704.
http://dx.doi.org/10.1029/2009GL038082
[42] Kumar, D., Kodra, E. and Ganguly, A.R. (2014) Regional and Seasonal Intercomparison of CMIP3 and CMIP5 Climate Model Ensembles for Temperature and Precipitation. Climate Dynamics, 43, 2491-2518.
http://dx.doi.org/10.1007/s00382-014-2070-3
[43] IPCC (2013) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., Qin, D., Plattner, G.K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V. and Midgley, P.M., Eds., Cambridge University Press, Cambridge and New York, 1535 p.
[44] Ziegler, A.D., Sheffield, J., Maurer, E.P., Nijssen, B., Wood, E.F. and Lettenmaier, D.P. (2003) Detection of Intensification of Continental-Scale Hydrological Cycles: Temporal Scale of Evaluation. Journal of Climate, 16, 535-547.
http://dx.doi.org/10.1175/1520-0442(2003)016<0535:DOIIGA>2.0.CO;2
[45] World Bank (2010) Climate Risks and Adaptation in Asian Coastal Megacities: A Synthesis Report. Washington DC, 120 p.
[46] Kundzewicz, Z.W., Kanae, S., Seneviratne, S.I., Handmer, J., Nicholls, N., Peduzzi, P., Mechler, R., Bouwer, L.M., Arnell, N., Mach, K., Muir-Wood, R., Brakenridge, G.R., Kron, W., Benito, G., Honda, Y., Takahashi, K. and Sherstyukov, B. (2014) Flood Risk and Climate Change: Global and Regional Perspectives. Hydrological Sciences Journal, 59, 1-28.
http://dx.doi.org/10.1080/02626667.2013.857411
[47] De Graaf, R., van de Giesen, N. and Van de Ven, F. (2007) The Closed City as a Strategy to Reduce Vulnerability of Urban Areas for Climate Change. Water Science & Technology, 56, 165-173.
http://dx.doi.org/10.2166/wst.2007.548
[48] World Bank (2009) Climate Change Impact and Adaptation Study for Bangkok Metropolitan. Main Report, Washington DC, 85 p.
[49] IPCC (2000) Emissions Scenarios, Special Report of the Intergovernmental Panel on Climate Change. Nakicenovic, N. and Swat, R., Eds., Cambridge University Press, Cambridge, 570 p.
[50] Refsgaard, J.C., Arnbjerg-Nielsen, K., Drews, M., Halsnæs, K., Jeppesen, E., Madsen, H., Markandya, A., Olesen, J.E., Porter, J.R. and Christensen, J.H. (2013) The Role of Uncertainty in Climate Change Adaptation Strategies—A Danish Water Management Example. Mitigation and Adaptation Strategies for Global Change, 18, 337-359.
http://dx.doi.org/10.1007/s11027-012-9366-6

  
comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.