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Performance evaluation of CMIP6 climate models for selecting a suitable GCM for future precipitation at different places of Tamil Nadu

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Abstract

Climate change refers to long-term variations in climate parameters. Future climate information can be projected using a GCM (General Circulation Model). Identifying a particular GCM is crucial for climate impact studies. Researchers are perplexed about selecting a suitable GCM for downscaling to predict future climate parameters. Recent updates to CMIP6 global climate models have included shared socioeconomic pathways based on the IPCC (Intergovernmental Panel on Climate Change) Sixth Assessment Report (AR6). The performance of 24 CMIP6 GCMs in precipitation with a multi-model ensemble filter was compared to IMD (India Meteorological Department) 0.25 × 0.25 degrees rainfall data in Tamil Nadu. The performance was evaluated with the help of Compromise Programming (CP), which involves metrics such as R2 (Pearson correlation co-efficient), PBIAS (Percentage Bias), NRMSE (Normalized Root Mean Square Error), and NSE (Nash–Sutcliffe Efficiency). The GCM ranking was performed through Compromise programming by comparing the IMD data and GCM data. The results of the CP analyses of the statistical metrics suggest that the suitable GCMs for the North-East monsoon are CESM2 for Chennai, CAN-ESM5 for Vellore, MIROC6 for Salem, BCC-CSM2-MR for Thiruvannamalai, MPI-ESM-1–2-HAM for Erode, MPI-ESM1-2-LR for Tiruppur, MPI-ESM1-2-LR for Trichy, MPI-ESM1-2-LR for Pondicherry, MPI-ESM1-2-LR for Dindigul, CNRM-CM6-HR for Thanjavur, MPI-ESM1-2-LR for Thirunelveli and UKESM1-0-LL for Thoothukudi. The appropriate suitable GCMs for South-West monsoon as CESM2 is appropriate for Chennai, IPSL-CM6A-LR for Vellore, CESM2-WACCM-FV2 for Salem, CAMS-CSM1-0 for Thiruvannamalai, MPI-ESM-1–2-HR for Erode, MPI-ESM-1–2-HR for Tiruppur, EC- EARTH3 for Trichy, EC- EARTH3 for Pondicherry, MPI-ESM-1–2-HR for Dindigul, CESM2-FV2 for Thanjavur, ACCESS-CM2 for Thirunelveli and ACCESS-CM2 for Thoothukudi respectively. This study emphasizes the importance of selecting an appropriate GCM. Selecting a suitable GCM will be useful in climate change impact studies and there by suggesting necessary adaptation and mitigation strategies.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  • Abbasian, M., Moghim, S., & Abrishamchi, A. (2019). Performance of the general circulation models in simulating temperature and precipitation over Iran. Theoretical and Applied Climatology, 135(3–4), 1465–1483. https://doi.org/10.1007/s00704-018-2456-y

    Article  Google Scholar 

  • Abbaspour, K. C., Faramarzi, M., Ghasemi, S. S., & Yang, H. (2009). Assessing the impact of climate change on water resources in Iran. Water Resources Research, 45(10), 1–16. https://doi.org/10.1029/2008WR007615

    Article  Google Scholar 

  • Ahmed, K., Sachindra, D. A., Shahid, S., Demirel, M. C., & Chung, E. S. (2019). Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics. Hydrology and Earth System Sciences, 23(11), 4803–4824. https://doi.org/10.5194/hess-23-4803-2019

    Article  Google Scholar 

  • Alexander, L. V., & Arblaster, J. M. (2017). Historical and projected trends in temperature and precipitation extremes in Australia in observations and CMIP5. Weather and Climate Extremes, 15(October 2016), 34–56. https://doi.org/10.1016/j.wace.2017.02.001

    Article  Google Scholar 

  • Alexandrov, V. A., & Hoogenboom, G. (2000). The impact of climate variability and change on crop yield in Bulgaria. Agricultural and Forest Meteorology, 104(4), 315–327. https://doi.org/10.1016/S0168-1923(00)00166-0

    Article  Google Scholar 

  • Almazroui, M., Saeed, F., Saeed, S., Nazrul Islam, M., Ismail, M., Klutse, N. A. B., & Siddiqui, M. H. (2020a). Projected Change in Temperature and Precipitation Over Africa from CMIP6. Earth Systems and Environment, 4(3), 455–475. https://doi.org/10.1007/s41748-020-00161-x

    Article  Google Scholar 

  • Almazroui, M., Saeed, S., Saeed, F., Islam, M. N., & Ismail, M. (2020b). Projections of Precipitation and Temperature over the South Asian Countries in CMIP6. Earth Systems and Environment, 4(2), 297–320. https://doi.org/10.1007/s41748-020-00157-7

    Article  Google Scholar 

  • Anil, S., Manikanta, V., & Pallakury, A. R. (2021). Unravelling the influence of subjectivity on ranking of CMIP6 based climate models: A case study. International Journal of Climatology, 41(13), 5998–6016. https://doi.org/10.1002/joc.7164

    Article  Google Scholar 

  • Azhdari, Z., Rafeie Sardooi, E., Bazrafshan, O., Zamani, H., Singh, V. P., Mohseni Saravi, M., & Ramezani, M. (2020). Impact of climate change on net primary production (NPP) in south Iran. Environmental Monitoring and Assessment, 192(6), 1–16. https://doi.org/10.1007/s10661-020-08389-w

    Article  CAS  Google Scholar 

  • Babaousmail, H., Hou, R., Ayugi, B., Ojara, M., Ngoma, H., Karim, R., Rajasekar, A., & Ongoma, V. (2021). Evaluation of the performance of cmip6 models in reproducing rainfall patterns over north africa. Atmosphere, 12(4), 1–25. https://doi.org/10.3390/atmos12040475

    Article  Google Scholar 

  • Chang, C.-C. (2002). The potential impact of climate change on Taiwan’s agriculture. Agricultural Economics, 27(1), 51–64. https://doi.org/10.1111/j.1574-0862.2002.tb00104.x

    Article  Google Scholar 

  • Diadovski, I. K., Atanassova, M. P., & Ivanov, I. S. I. S. (2007). Integral assessment of climate impact on the transboundary Mesta River flow formation in Bulgaria. Environmental Monitoring and Assessment, 127(1–3), 383–388. https://doi.org/10.1007/s10661-006-9287-5

    Article  Google Scholar 

  • Emiru, N. C., Recha, J. W., Thompson, J. R., Belay, A., Aynekulu, E., Manyevere, A., Demissie, T. D., Osano, P. M., Hussein, J., Molla, M. B., Mengistu, G. M., & Solomon, D. (2021). Impact of Climate Change on the Hydrology of the Upper Awash River Basin, Ethiopia. Hydrology, 9(1), 3. https://doi.org/10.3390/hydrology9010003

    Article  Google Scholar 

  • Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937–1958. https://doi.org/10.5194/gmd-9-1937-2016

    Article  Google Scholar 

  • Gregory, J. M., Stott, P. A., Cresswell, D. J., Rayner, N. A., Gordon, C., & Sexton, D. M. H. (2002). Recent and future changes in Arctic sea ice simulated by the HadCM3 AOGCM. Geophysical Research Letters, 29(24), 1999–2002. https://doi.org/10.1029/2001GL014575

    Article  Google Scholar 

  • Gupta, H. V., Sorooshian, S., & Yapo, P. O. (1999). Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. Journal of hydrologic engineering, 4(2), 135–143.

    Article  Google Scholar 

  • Gwoździej-Mazur, J., Jadwiszczak, P., Kaźmierczak, B., Kózka, K., Struk-Sokołowska, J., Wartalska, K., & Wdowikowski, M. (2022). The impact of climate change on rainwater harvesting in households in Poland. Applied Water Science, 12(2), 1–15. https://doi.org/10.1007/s13201-021-01491-5

    Article  CAS  Google Scholar 

  • Homsi, R., Shiru, M. S., Shahid, S., Ismail, T., Harun, S. B., Al-Ansari, N., Chau, K. W., & Yaseen, Z. M. (2020). Precipitation projection using a CMIP5 GCM ensemble model: A regional investigation of Syria. Engineering Applications of Computational Fluid Mechanics, 14(1), 90–106. https://doi.org/10.1080/19942060.2019.1683076

    Article  Google Scholar 

  • Iqbal, Z., Shahid, S., Ahmed, K., Ismail, T., Ziarh, G. F., Chung, E. S., & Wang, X. (2021). Evaluation of CMIP6 GCM rainfall in mainland Southeast Asia. Atmospheric Research, 254(February). https://doi.org/10.1016/j.atmosres.2021.105525

  • Javanmard, S., Yatagai, A., Nodzu, M. I., Bodaghjamali, J., & Kawamoto, H. (2010). Comparing high-resolution gridded precipitation data with satellite rainfall estimates of TRMM-3B42 over Iran. Advances in Geosciences, 25, 119–125. https://doi.org/10.5194/adgeo-25-119-2010

    Article  Google Scholar 

  • Loganathan, P., & Mahindrakar, A. B. (2020). Assessment and ranking of CMIP5 GCMs performance based on observed statistics over Cauvery river basin – Peninsular India. Arabian Journal of Geosciences, 13(22). https://doi.org/10.1007/s12517-020-06217-6

  • Malhi, G. S., Kaur, M., & Kaushik, P. (2021). Impact of climate change on agriculture and its mitigation strategies: A review. Sustainability (switzerland), 13(3), 1–21. https://doi.org/10.3390/su13031318

    Article  CAS  Google Scholar 

  • Mendelsohn, R. (2014). The impact of climate change on agriculture in Asia. Journal of Integrative Agriculture, 13(4), 660–665. https://doi.org/10.1016/S2095-3119(13)60701-7

    Article  Google Scholar 

  • Mohamed, M. A., El Afandi, G. S., & El-Mahdy, M. E. S. (2022). Impact of climate change on rainfall variability in the Blue Nile basin. Alexandria Engineering Journal, 61(4), 3265–3275. https://doi.org/10.1016/j.aej.2021.08.056

    Article  Google Scholar 

  • Mooij, W. M., Hülsmann, S., De Senerpont Domis, L. N., Nolet, B. A., Bodelier, P. L. E., Boers, P. C. M., Dionisio Pires, L. M., Gons, H. J., Ibelings, B. W., Noordhuis, R., Portielje, R., Wolfstein, K., & Lammens, E. H. R. R. (2005). The impact of climate change on lakes in the Netherlands: A review. Aquatic Ecology, 39(4), 381–400. https://doi.org/10.1007/s10452-005-9008-0

    Article  CAS  Google Scholar 

  • Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology, 10(3), 282–290.

    Article  Google Scholar 

  • Nunes, L. J. R., Meireles, C. I. R., Gomes, C. J. P., & Ribeiro, N. M. C. A. (2022). The impact of climate change on forest development: A sustainable approach to management models applied to mediterranean-type climate regions. Plants, 11(1). https://doi.org/10.3390/plants11010069

  • Pai D.S., Latha Sridhar, Rajeevan M., Sreejith O.P., Satbhai N.S. & Mukhopadhyay B. (2014). Development of a new high spatial resolution (0.25° X 0.25°)Long period (1901-2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. MAUSAM, 65(1), 1-18.

  • Ringuest, J. L., & Ringuest, J. L. (1992). Compromise programming. Multiobjective optimization: behavioral and computational considerations, 51-59.

  • Riahi, K., Vuuren, D. P. Van, Kriegler, E., Edmonds, J., Neill, B. C. O., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Crespo, J., Kc, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J., … Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy , land use , and greenhouse gas emissions implications : An overview. 42, 153–168. https://doi.org/10.1016/j.gloenvcha.2016.05.009

  • Salman, S. A., Shahid, S., Ismail, T., Al-Abadi, A. M., Wang, X. J., & Chung, E. S. (2019). Selection of gridded precipitation data for Iraq using compromise programming. Measurement: Journal of the International Measurement Confederation, 132, 87–98. https://doi.org/10.1016/j.measurement.2018.09.047

    Article  Google Scholar 

  • Shen, Z., Duan, A., Li, D., & Li, J. (2021). Assessment and ranking of climate models in Arctic Sea ice cover simulation: From CMIP5 to CMIP6. Journal of Climate, 34(9), 3609–3627. https://doi.org/10.1175/JCLI-D-20-0294.1

    Article  Google Scholar 

  • Shiru, M. S., & Chung, E. S. (2021). Performance evaluation of CMIP6 global climate models for selecting models for climate projection over Nigeria. Theoretical and Applied Climatology, 146(1–2), 599–615. https://doi.org/10.1007/s00704-021-03746-2

    Article  Google Scholar 

  • Singh, L., & Saravanan, S. (2020). Impact of climate change on hydrology components using CORDEX South Asia climate model in Wunna, Bharathpuzha, and Mahanadi, India. Environmental Monitoring and Assessment, 192(11). https://doi.org/10.1007/s10661-020-08637-z

  • Sreelatha, K., & Anand Raj, P. (2021). Ranking of CMIP5-based global climate models using standard performance metrics for Telangana region in the southern part of India. ISH Journal of Hydraulic Engineering, 27(S1), 556–565. https://doi.org/10.1080/09715010.2019.1634648

    Article  Google Scholar 

  • Srinivasa Raju, K., Sonali, P., & Nagesh Kumar, D. (2017). Ranking of CMIP5-based global climate models for India using compromise programming. Theoretical and Applied Climatology, 128(3–4), 563–574. https://doi.org/10.1007/s00704-015-1721-6

    Article  Google Scholar 

  • Thapa, S., Li, H., Li, B., Fu, D., Shi, X., Yabo, S., Lu, L., Qi, H., & Zhang, W. (2021). Impact of climate change on snowmelt runoff in a Himalayan basin, Nepal. Environmental Monitoring and Assessment, 193(7), 1–17. https://doi.org/10.1007/s10661-021-09197-6

    Article  Google Scholar 

  • Zhai, J., Mondal, S. K., Fischer, T., Wang, Y., Su, B., Huang, J., Tao, H., Wang, G., Ullah, W., & Uddin, M. J. (2020). Future drought characteristics through a multi-model ensemble from CMIP6 over South Asia. Atmospheric Research, 246(May), 105111. https://doi.org/10.1016/j.atmosres.2020.105111

    Article  Google Scholar 

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Acknowledgements

The authors are very grateful to Climate Research and Services, Indian Meteorological Department, Pune, India-411005 for helping as with the data for this study. We extend our special thanks to the reviewers and the editorial team, for their time and effort to review the manuscript. We appreciate all valuable comments and suggestions which helped us improve the quality of this manuscript.

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  1. Department of Environmental and Water Resources Engineering, School of Civil Engineering, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India

    Hemanandhini S & Vignesh Rajkumar L

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  1. Hemanandhini S
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S, H., L, V.R. Performance evaluation of CMIP6 climate models for selecting a suitable GCM for future precipitation at different places of Tamil Nadu. Environ Monit Assess 195, 928 (2023). https://doi.org/10.1007/s10661-023-11454-9

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