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Comparative Analysis of CMIP5-Based Monsoon Season Rainfall Against Satellite-Based Estimations over India

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Abstract

The present study is designed to assess the rainfall pattern from Climate Model Inter-comparison Project Phase 5 (CMIP5) based on the satellite-derived rainfall products, Tropical Rainfall Measuring Mission (TRMM) over the Indian Region utilising daily as well as monthly rainfall data during monsoon season, ranging from 1st June to 30th September (JJAS). In this context, five best defined global climate models (GCMs) that participated in CMIP5 archive along with its multi-model mean (MMM) have been analysed to investigate the rainfall pattern during JJAS in terms of spatial map and time series under the forcing scenarios i.e., Representative Concentration Pathway 4.5 (RCP 4.5) and Representative Concentration Pathway 8.5 (RCP 8.5) from 2006 to 2018 over Indian region. On the other hand, spatial maps and time series have also been generated using TRMM rainfall data at daily (TRMM 3B42v7) and monthly (TRMM 3B43v7) scales during the reference time period. Thereafter, comparative study of the JJAS rainfall pattern between CMIP5 models and TRMM products has been carried out, whether the GCMs are able to simulate rainfall data reasonably well compared to satellite-derived estimates or not under various forcing scenarios over this region? Based on the assessment, it is noted that CMIP5 models have the ability to simulate daily mean monsoon season rainfall; however, it underestimates the rainfall intensity at daily scale over the north-east and south-west parts of India. Moreover, statistical analysis indicated more biases in the western coast and the north-eastern parts of India where it receives the highest amount of rainfall during JJAS. The outcomes presented here may be useful for assessing the reliability of CMIP5 models to project the rainfall pattern in near future under the various warming scenarios over the Indian Region.

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References

  1. Rao YP (1976) Southwest monsoon. In: Indian Meteorological Department https://imetsociety.org/wp-content/pdf/docs/swmonsoon_yprao.pdf

    Google Scholar 

  2. Goswami BN, Venugopal V, Sengupta D, Madhusoodanan MS, Xavier PK (2006) Increasing trend of extreme rain events over India in a warming environment. Science 314(5804):1442–1445. https://doi.org/10.1126/science.1132027

    Article  Google Scholar 

  3. Meher JK, Das L, Akhter J, Benestad RE, Mezghani A (2017) Performance of CMIP3 and CMIP5 GCMs to simulate observed rainfall characteristics over the Western Himalayan Region. J Climate 30(19):7777–7799. https://doi.org/10.1175/JCLI-D-16-0774.1

    Article  Google Scholar 

  4. Menon A, Levermann A, Schewe J, Lehmann J, Frieler K (2013) Consistent increase in Indian monsoon rainfall and its variability across CMIP-5 models. Earth Syst Dynam 4(2):287–300. https://doi.org/10.5194/esd-4-287-2013

    Article  Google Scholar 

  5. Singh RB, Mal S (2014) Trends and variability of monsoon and other rainfall seasons in Western Himalaya, India. Atmos Sci Lett 15(3):218–226. https://doi.org/10.1002/asl2.494

    Article  Google Scholar 

  6. Ray M, Doshi N, Alag N, Sreedhar R (2011) Climate vulnerability in North Western Himalayas. In: Environics Trust http://www.ced.org.in/docs/inecc/member_reports/Climatevulnerabilityrs2508.pdf

    Google Scholar 

  7. Sharmila S, Joseph S, Sahai AK, Abhilash S, Chattopadhyay R (2015) Future projection of Indian summer monsoon variability under climate change scenario: an assessment from CMIP5 climate models. Global Planet Change 124:62–78. https://doi.org/10.1016/j.gloplacha.2014.11.004

    Article  Google Scholar 

  8. Kundu SK, Singh C (2020) Rainfall pattern over the North-West Himalayan region: historical time period vs. future warming scenarios. Theor Appl Climatol 141(1–2):257–269. https://doi.org/10.1007/s00704-020-03210-7

    Article  Google Scholar 

  9. Bharti V, Singh C (2015) Evaluation of error in TRMM 3B42V7 precipitation estimates over the Himalayan region. J Geophys Res Atmos 120(24):12458–12473. https://doi.org/10.1002/2015JD023779

    Article  Google Scholar 

  10. Yeggina S, Teegavarapu RSV, Muddu S (2020) Evaluation and bias corrections of gridded precipitation data for hydrologic modelling support in Kabini River basin, India. Theor Appl Climatol 140(3–4):1495–1513. https://doi.org/10.1007/s00704-020-03175-7

    Article  Google Scholar 

  11. Wu J, Xu Y, Gao XJ (2017) Projected changes in mean and extreme climates over Hindu Kush Himalayan region by 21 CMIP5 models. Adv Clim Change Res 8(3):176–184. https://doi.org/10.1016/j.accre.2017.03.001

    Article  Google Scholar 

  12. Choudhary A, Dimri AP (2017) Assessment of CORDEX-South Asia experiments for monsoonal precipitation over Himalayan region for future climate. Climate Dynam 50:3009–3030. https://doi.org/10.1007/s00382-017-3789-4

    Article  Google Scholar 

  13. Sabeerali CT, Ramu Dandi A, Dhakate A, Salunke K, Mahapatra S, Rao SA (2013) Simulation of boreal summer intraseasonal oscillations in the latest CMIP5 coupled GCMs. J Geophys Res Atmos 118(10):4401–4420. https://doi.org/10.1002/jgrd.50403

    Article  Google Scholar 

  14. Kundu SK, Singh C, Chauhan P (2022) Assessment of regional and global climate models for the investigation of monsoon rainfall variability over the North-West Himalayan region. Int J Climatol 42(9):4580–4600. https://doi.org/10.1002/joc.7491

    Article  Google Scholar 

  15. Xu R, Tian F, Yang L, Hu H, Lu H, Hou A (2017) Ground validation of GPM IMERG and TRMM 3B42V7 rainfall products over Southern Tibetan plateau based on a high-density rain gauge network. J Geophys Res 122:910–924. https://doi.org/10.1002/2016JD025418

    Article  Google Scholar 

  16. Saikranthi K, Narayana Rao T, Radhakrishna B, Rao SVB (2014) Morphology of the vertical structure of precipitation over India and adjoining oceans based on long-term measurements of TRMM PR. J Geophys Res Atmos 119(13):8433–8449. https://doi.org/10.1002/2014JD021774

    Article  Google Scholar 

  17. Sunilkumar K, Narayana Rao T, Saikranthi K, Purnachandra Rao M (2015) Comprehensive evaluation of multisatellite precipitation estimates over India using gridded rainfall data. J Geophys Res Atmos 120(17):8987–9005. https://doi.org/10.1002/2015JD023437

    Article  Google Scholar 

  18. Bharti V, Singh C, Ettema J, Turkington TAR (2016) Spatiotemporal characteristics of extreme rainfall events over the Northwest Himalaya using satellite data. Int J Climatol 36(12):3949–3962. https://doi.org/10.1002/joc.4605

    Article  Google Scholar 

  19. Huffman GJ, Bolvin DT, Nelkin EJ, Wolff DB, Adler RF, Gu G et al (2007) The TRMM Multisatellite Precipitation Analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8(1):38–55. https://doi.org/10.1175/JHM560.1

    Article  Google Scholar 

  20. Rahman SH, Sengupta D, Ravichandran M (2009) Variability of Indian summer monsoon rainfall in daily data from gauge and satellite. J Geophys Res 114(D17):D17113. https://doi.org/10.1029/2008JD011694

    Article  Google Scholar 

  21. Prakash S, Mitra AK, Momin IM, Rajagopal EN, Basu S, Collins M et al (2015) Seasonal intercomparison of observational rainfall datasets over India during the southwest monsoon season. Int J Climatol 35(9):2326–2338. https://doi.org/10.1002/joc.4129

    Article  Google Scholar 

  22. Liu Z, Mehran A, Phillips T, AghaKouchak A (2014) Seasonal and regional biases in CMIP5 precipitation simulations. Climate Res 60(1):35–50. https://doi.org/10.3354/cr01221

    Article  Google Scholar 

  23. Stanfield RE, Jiang JH, Dong X, Xi B, Su H, Donner L et al (2016) A quantitative assessment of precipitation associated with the ITCZ in the CMIP5 GCM simulations. Climate Dynam 47(5–6):1863–1880. https://doi.org/10.1007/s00382-015-2937-y

    Article  Google Scholar 

  24. Raghavan SV, Liu J, Nguyen NS, Vu MT, Liong S-Y (2018) Assessment of CMIP5 historical simulations of rainfall over Southeast Asia. Theor Appl Climatol 132(3–4):989–1002. https://doi.org/10.1007/s00704-017-2111-z

    Article  Google Scholar 

  25. NASA (2023) Tropical Rainfall Measuring Mission Project: TRMM (TMPA) rainfall estimate L3 3 hour 0.25 degree × 0.25 degree V7 (TRMM_3B42). https://doi.org/10.5067/TRMM/TMPA/3H/7

  26. Abro MI, Wei M, Zhu D, Elahi E, Ali G, Khaskheli MA et al (2020) Hydrological evaluation of satellite and reanalysis precipitation products in the glacier-fed river basin (Gilgit). Arab J Geosci 13(14):631. https://doi.org/10.1007/s12517-020-05621-2

    Article  Google Scholar 

  27. Abro MI, Zhu D, Wei M, Majidano AA, Khaskheli MA, Ul Abideen Z, Memon MS (2019) Hydrological appraisal of rainfall estimates from radar, satellite, raingauge and satellite–gauge combination on the Qinhuai River Basin, China. Hydrol Sci J 64(16):1957–1971. https://doi.org/10.1080/02626667.2018.1557335

    Article  Google Scholar 

  28. Bharti, V. (2015). Investigation of extreme rainfall events over the Northwest Himalaya Region using satellite data (University of Twente). https://www.iirs.gov.in/iirs/sites/default/files/StudentThesis/Vidhi_NHDRM_2013-15.pdf

  29. Tawde SA, Singh C (2015) Investigation of orographic features influencing spatial distribution of rainfall over the Western Ghats of India using satellite data. Int J Climatol 35(9):2280–2293. https://doi.org/10.1002/joc.4146

    Article  Google Scholar 

  30. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93(4):485–498. https://doi.org/10.1175/BAMS-D-11-00094.1

    Article  Google Scholar 

  31. Akpovi BA, Zhu D, Abro MI, Lawin AE, Houngnibo M, Bessou J (2022) Hydrological appraisal using multi-source rainfall data in PDM model over the Qinhuai River basin in China. Arab J Geosci 15(3):236. https://doi.org/10.1007/s12517-022-09545-x

    Article  Google Scholar 

  32. Emori, S., Taylor, K., Hewitson, B., Zermoglio, F., Juckes, M., Lautenschlager, M., … Stockhause, M. (2016). CMIP5 data provided at the IPCC Data Distribution Centre. 1–8.

  33. NASA. (2020). Global precipitation measurement: the Tropical Rainfall Measuring Mission (TRMM). October 2020, from https://gpm.nasa.gov/missions/trmm

  34. NCAR-UCAR. (2020). Climate data guide, TRMM: Tropical Rainfall Measuring Mission. October 2020, from The National Center for Atmospheric Research website: https://climatedataguide.ucar.edu/climate-data/trmm-tropical-rainfall-measuring-mission#:~:text=The Tropical Rainfall Measuring Mission,the associated release of energy.

  35. Theon JS (1994) The tropical rainfall measuring mission (TRMM). Adv Space Res 14(3):159–165. https://doi.org/10.1016/0273-1177(94)90210-0

    Article  Google Scholar 

  36. Chakraborty A, Agrawal S (2017) Role of west Asian surface pressure in summer monsoon onset over central India. Environ Res Lett 12(7). https://doi.org/10.1088/1748-9326/aa76ca

  37. Flato G, Marotzke J, Abiodun B, Braconnot P, Chou SC, Collins W 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 741–866. https://doi.org/10.1017/CBO9781107415324

    Chapter  Google Scholar 

  38. Farber, D. (2007). Climate models: a user’s guide. UC Berkeley, Public Law and Legal Theory Research Paper Series, 901(1030607), 1–46. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1030607#%23

    Google Scholar 

  39. Sooraj KP, Terray P, Xavier P (2016) Sub-seasonal behaviour of Asian summer monsoon under a changing climate: assessments using CMIP5 models. Climate Dynam 46(11–12):4003–4025. https://doi.org/10.1007/s00382-015-2817-5

    Article  Google Scholar 

  40. Kundu SK, Singh C (2019) A comparative study of regional climate models and global coupled models over Uttarakhand. Int J Mar Environ Sci 13(4):185–188

    Google Scholar 

  41. Liu C, Allan RP, Huffman GJ (2012) Co-variation of temperature and precipitation in CMIP5 models and satellite observations. Geophys Res Lett 39(13):n/a-n/a. https://doi.org/10.1029/2012GL052093

    Article  Google Scholar 

  42. Mehran A, AghaKouchak A, Phillips TJ (2014) Evaluation of CMIP5 continental precipitation simulations relative to satellite-based gauge-adjusted observations. J Geophys Res Atmos 119(4):1695–1707. https://doi.org/10.1002/2013JD021152

    Article  Google Scholar 

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Acknowledgements

Authors are thankful to IIRS for support. CMIP5 data is obtained from https://esgf-data.dkrz.de/search/esgf-dkrz/ and TRMM data is obtained from https://giovanni.gsfc.nasa.gov/giovanni/. Analyses and visualizations used in this paper were produced with the Giovanni online data system, developed and maintained by the NASA GES DISC.

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SKK wrote the main manuscript and prepared figures. CS helped to formulate the methodology. SKK and CS both reviewed the manuscript.

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Correspondence to Charu Singh.

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Kundu, S.K., Singh, C. Comparative Analysis of CMIP5-Based Monsoon Season Rainfall Against Satellite-Based Estimations over India. Remote Sens Earth Syst Sci 6, 297–308 (2023). https://doi.org/10.1007/s41976-023-00096-7

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