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Chennai Extreme Rainfall Event of 2015 under Future Climate Projections Using the Pseudo Global Warming Dynamic Downscaling Method


Affiliations
1 Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600 036, India
 

Here we report results of a detailed numerical study on the effect of climate change on the characteristics of a very severe rainfall event that occurred in the coastal city of Chennai, Tamil Nadu, India in December 2015. The pseudo global warming (PGW) method was used to obtain the initial and boundary conditions of the future climate and projections were done for the far future, i.e. the year 2075 using the representative concentration pathway scenario of 8.5. The Weather Research and Forecasting (WRF) model was used for simulations with perturbed initial and boundary conditions by the PGW method in a dynamic downscaling framework. The sensitivities of Microphysics and cumulus parameterization schemes in WRF were first studied. The warm rain microphysics (Kessler) scheme and Kain–Fritsch (KF) cumulus scheme showed good agreement with the observed data. Once the best schemes were identified for such an extreme event and for the specific region under consideration, simulations were carried out for future and current climate conditions. Results show that the bulk Richardson number, energy helicity index, K-index, moisture convergence, vertical temperature and mixing ratio all increase significantly in future climate conditions, thereby leading to heavy precipitation. The precipitation in Chennai region increased by 17.37% on the peak rainy day (1 December 2015) in future compared to current. The key takeaway though is that on succeeding days, the amount of precipitation was seen to increase dramatically by 183.5%, 233.9% and 70.8%. This is bound to lead to severe flood events that are likely to continue for more days in the future, thereby posing further risk and potential for damage.

Keywords

Climate Change, Extreme Rainfall Events, Pseudo Global Warming Method, Weather Research And Forecasting.
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  • Eckstein, D., Hutfils, M. and Winges, M., Global climate risk index 2019. Germanwatch; https://germanwatch.org/en/16046 (accessed on 23 August 2019).

  • Wasson, R. J. et al., Riverine flood hazard: disaster risk reduction in India. Proc. Indian Natl. Sci. Acad., 2018, 99, 65–76.

  • Mukherjee, S., Aadhar, S., Stone, D. and Mishra, V., Increase in extreme precipitation events under anthropogenic warming in India. Weather Climate Extrem., 2018, 20, 45–53.

  • Goswami, B. N., Venugopal, V., Sangupta, D., Madhusoodanan, M. S. and Xavier, P. K., Increasing trend of extreme rain events over India in a warming environment. Science, 2006, 314, 1442– 1445.

  • Mishra, A. K., Quantifying the impact of global warming on precipitation patterns in India. Meteorol. Appl., 2019, 26, 153– 160.

  • Prakash, S., Mahesh, C., Sathiyamoorthy, V. and Gairola, R. M., Increasing trend of northeast monsoon rainfall over the equatorial Indian Ocean and peninsular India. Theor. Appl. Climatol., 2013, 112, 185–191.

  • Mishra, A. K. and Nagaraju, V., Space-based monitoring of severe flooding of a southern state in India during south-west monsoon season of 2018. Nat. Hazards, 2019, 97, 949–953.

  • Mishra, V. and Shah, H. L., Hydroclimatological perspective of the Kerala flood of 2018. J. Geol. Soc. India, 2018, 92, 645–650.

  • Kerala rains: 1038 villages declared flood-hit, The Hindu, 25 August 2019, p. 2.

  • Boyaj, A., Ashok, K., Ghosh, S., Devanand, A. and Dandu, G., The Chennai extreme rainfall event in 2015: the Bay of Bengal connection. Climate Dyn., 2018, 50, 2867–2879.

  • Reshmi Mohan, P., Srinivas, C. V., Yesubabu, V., Baskaran, R. and Venkatraman, B., Simulation of a heavy rainfall event over Chennai in southeast India using WRF: sensitivity to microphysics parameterization. Atmos. Res., 2018, 210, 83–99.

  • Intergovernmental Panel on Climate Change, Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Pachauri, R. K. and Meyer, L. A.), IPCC, Geneva, Switzerland, 2015.

  • Willett, K. M., Gillett, N. P., Jones, P. D. and Thorne, P. W., Attribution of observed surface humidity changes to human influence. Nature, 2007, 449, 710–712.

  • Min, S. K., Zhang, X., Zwiers, F. W. and Hegerl, G. C., Human contribution to more intense precipitation extremes. Nature, 2011, 470, 378–381.

  • Prein, A. F., Rasmussen, R. M., Ikeda, K., Liu, C., Clark, M. P. and Holland, G. J., The future intensification of hourly precipitation extremes. Nature Climate Change, 2017, 7, 48–52.

  • Trenberth, K. E., Dai, A., Rasmussen, R. M. and Parsons, D. B., The changing character of precipitation. Bull. Am. Meteorol. Soc., 2003, 84, 1205–1217 + 1167.

  • Singh, K. S., Bonthu, S., Purvaja, R., Robin, R. S., Kannan, B. A. M. and Ramesh, R., Prediction of heavy rainfall over Chennai Metropolitan City, Tamil Nadu, India: impact of microphysical parameterization schemes. Atmos. Res., 2018, 202, 219–234.

  • Srinivas, C. V., Yesubabu, V., Prasad, D. H., Prasad, K. H., Greeshma, M. M., Baskaran, R. and Venkatraman, B., Simulation of an extreme heavy rainfall event over Chennai, India using WRF: sensitivity to grid resolution and boundary layer physics. Atmos. Res., 2018, 210, 66–82.

  • Sanap, S. D., Priya, P., Sawaisarje, G. K. and Hosalikar, K. S., Heavy rainfall events over southeast peninsular India during northeast monsoon: role of El Niño and easterly wave activity. Int. J. Climatol., 2019, 39, 1954–1968.

  • Schär, C., Frei, C., Lüthi, D. and Davies, H. C., Surrogate climatechange scenarios for regional climate models. Geophys. Res. Lett., 1996, 23, 669–672.

  • Taniguchi, K. and Sho, K., Application of the pseudo global warming dynamic downscaling method to the Tokai heavy rain in 2000. J. Meteorol. Soc. Jpn., 2015, 93, 551–570.

  • Scalzitti, J., Strong, C. and Kochanski, A., Climate change impact on the roles of temperature and precipitation in western US snowpack variability. Geophys. Res. Lett., 2016, 43, 5361–5369.

  • Expósito, F. J., González, A., Pérez, J. C., Díaz, J. P. and Taima, D., High-resolution future projections of temperature and precipitation in the Canary Islands. J. Climate, 2015, 28, 7846–7856.

  • Skamarock, W. C. et al., A Description of the Advanced Research WRF Version 3, NCAR Technical Note, NCAR/TN-468+STR, 2008.

  • Kedia, S., Vellore, R. K., Islam, S. and Kaginalkar, A., A study of Himalayan extreme rainfall events using WRF-Chem. Meteorol. Atmos. Phys., 2019, 131, 1133–1143.

  • Hong, S. Y. and Lee, J. W., Assessment of the WRF model in reproducing a flash-flood heavy rainfall event over Korea. Atmos. Res., 2009, 93, 818–831.

  • Mugume, I. et al., Assessing the performance of WRF model in simulating rainfall over western Uganda. J. Climatol. Weather Forecast., 2017, 5, 1–9.

  • Mohanty, U. C., Osuri, K. K., Routray, A., Mohapatra, M. and Pattanayak, S., Simulation of Bay of Bengal tropical cyclones with WRF model: impact of initial and boundary conditions. Mar. Geod., 2010, 33, 294–314.

  • Sandeep, C. P. R., Krishnamoorthy, C. and Balaji, C., Impact of cloud parameterization schemes on the simulation of cyclone Vardah using the WRF model. Curr. Sci., 2018, 115, 1143–1153.

  • Powers, J. G. et al., The Weather Research and Forecasting model: overview, system efforts, and future directions. Bull. Am. Meteorol. Soc., 2017, 98, 1717–1737.

  • National Centers for Environmental Prediction/National Weather Service/NOAA/US. Department of Commerce. NCEP GFS 0.25 degree global forecast grids historical archive. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, 2015 (updated daily); https://doi.org/10.5065/D65D8PWK (accessed on 10 June 2019).

  • Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S. and Schlax, M. G., Daily high-resolution-blended analyses for sea surface temperature. J. Climate, 2007, 20, 5473–5496.

  • Monaghan, A. J., Steinhoff, D. F., Bruyere, C. L. and Yates, D., NCAR CESM global bias-corrected CMIP5 output to support WRF/MPAS research. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, 2014; https://doi.org/10.5065/D6DJ5CN4 (accessed on 10 June 2019).

  • Bruyère, C. L., Done, J. M., Holland, G. J. and Fredrick, S., Bias corrections of global models for regional climate simulations of high-impact weather. Climate Dyn., 2013, 43, 1847–1856.

  • Jayasankar, C. B., Surendran, S. and Rajendran, K., Robust signals of future projections of Indian summer monsoon rainfall by IPCC AR5 climate models: role of seasonal cycle and interannual variability. Geophys. Res. Lett., 2015, 42, 3513–3520.

  • Rasmussen, R. et al., High-resolution coupled climate runoff simulations of seasonal snowfall over Colorado: a process study of current and warmer climate. J. Climate, 2011, 24, 3015–3048.

  • Mesoscale and Microscale Meteorology Division, and National Center for Atmospheric Research. ARW Version 3 Modelling System User’s Guide, January 2016; In Book http://www2.mmm.ucar.edu/wrf/users/docs/user_guide_V3.6/contents.html.

  • Huffman, G., TRMM (TMPA-RT) near real-time precipitation L3 3 hour 0.25° × 0.25° V7, Goddard Earth Sciences Data and Information Services Center (GES DISC), Greenbelt, MD, USA, 2016; https://disc.gsfc.nasa.gov/datacollection/TRMM_3B42RT_7.html (accessed on 10 June 2019).

  • Copernicus Climate Change Service (C3S): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS), 2017; https://cds.climate.copernicus.eu/cdsapp#!/home (accessed on 15 June 2019).

  • Phadtare, J., Role of Eastern Ghats orography and cold pool in an extreme rainfall event over Chennai on 1 December 2015. Mon. Weather Rev., 2018, 146, 943–965.

  • Zhao, J., Guo, Z. H., Su, Z. Y., Zhao, Z. Y., Xiao, X. and Liu, F., An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed. Appl. Energ., 2016, 162, 808–826.

  • Ulmer, F.-G. and Balss, U., Spin-up time research on the Weather Research and Forecasting model for atmospheric delay mitigations of electromagnetic waves. J. Appl. Remote Sensing, 2016, 10, 016027.

  • The NCAR Command Language (Version 6.4.0), UCAR/NCAR/CISL/TDD, Boulder, Colorado, USA, 2017; http://dx.doi.org/10.5065/D6WD3XH5.

  • Stensrud, D. J., Cortinas, J. V. and Brooks, H. E., Discriminating between tornadic and non tornadic thunderstorms using mesoscale model output. Weather Forecast., 1997, 12, 613–632.

  • Rasmussen, E. N., Refined supercell and tornado forecast parameters. Weather Forecast., 2003, 18, 530–535.

  • da Silva, F. P., Rotunno Filho, O. C., Sampaio, R. J., Dragaud, I. C., de Araújo, A. A., da Silva, M. G. and Pires, G. D., Evaluation of atmospheric thermodynamics and dynamics during heavyrainfall and no-rainfall events in the metropolitan area of Rio de Janeiro, Brazil. Meteorol. Atmos. Phys., 2019, 131, 299–311.

  • Kanase, R. D. and Salvekar, P. S., Effect of physical parameterization schemes on track and intensity of cyclone LAILA using WRF model. Asia-Pac. J. Atmos. Sci., 2015, 51, 205–227.

  • Wei, J., Su, H. and Yang, Z. L., Impact of moisture flux convergence and soil moisture on precipitation: a case study for the southern United States with implications for the globe. Climate Dyn., 2016, 46, 467–481.

  • Mittal, R., Tewari, M., Radhakrishnan, C., Ray, P., Singh, T. and Nickerson, A. K., Response of tropical cyclone Phailin (2013) in the Bay of Bengal to climate perturbations. Climate Dyn., 2019, 53, 2013–2030.


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  • Chennai Extreme Rainfall Event of 2015 under Future Climate Projections Using the Pseudo Global Warming Dynamic Downscaling Method

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Authors

P. Jyoteeshkumar
Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600 036, India
P. V. Kiran
Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600 036, India
C. Balaji
Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600 036, India

Abstract


Here we report results of a detailed numerical study on the effect of climate change on the characteristics of a very severe rainfall event that occurred in the coastal city of Chennai, Tamil Nadu, India in December 2015. The pseudo global warming (PGW) method was used to obtain the initial and boundary conditions of the future climate and projections were done for the far future, i.e. the year 2075 using the representative concentration pathway scenario of 8.5. The Weather Research and Forecasting (WRF) model was used for simulations with perturbed initial and boundary conditions by the PGW method in a dynamic downscaling framework. The sensitivities of Microphysics and cumulus parameterization schemes in WRF were first studied. The warm rain microphysics (Kessler) scheme and Kain–Fritsch (KF) cumulus scheme showed good agreement with the observed data. Once the best schemes were identified for such an extreme event and for the specific region under consideration, simulations were carried out for future and current climate conditions. Results show that the bulk Richardson number, energy helicity index, K-index, moisture convergence, vertical temperature and mixing ratio all increase significantly in future climate conditions, thereby leading to heavy precipitation. The precipitation in Chennai region increased by 17.37% on the peak rainy day (1 December 2015) in future compared to current. The key takeaway though is that on succeeding days, the amount of precipitation was seen to increase dramatically by 183.5%, 233.9% and 70.8%. This is bound to lead to severe flood events that are likely to continue for more days in the future, thereby posing further risk and potential for damage.

Keywords


Climate Change, Extreme Rainfall Events, Pseudo Global Warming Method, Weather Research And Forecasting.

References





DOI: https://doi.org/10.18520/cs%2Fv118%2Fi12%2F1968-1979