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CMIP5 ensemble-based spatial rainfall projection over homogeneous zones of India

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Abstract

Performances of the state-of-the-art CMIP5 models in reproducing the spatial rainfall patterns over seven homogeneous rainfall zones of India viz. North Mountainous India (NMI), Northwest India (NWI), North Central India (NCI), Northeast India (NEI), West Peninsular India (WPI), East Peninsular India (EPI) and South Peninsular India (SPI) have been assessed using different conventional performance metrics namely spatial correlation (R), index of agreement (d-index), Nash–Sutcliffe efficiency (NSE), Ratio of RMSE to the standard deviation of the observations (RSR) and mean bias (MB). The results based on these indices revealed that majority of the models are unable to reproduce finer-scaled spatial patterns over most of the zones. Thereafter, four bias correction methods i.e. Scaling, Standardized Reconstruction, Empirical Quantile Mapping and Gamma Quantile Mapping have been applied on GCM simulations to enhance the skills of the GCM projections. It has been found that scaling method compared to other three methods shown its better skill in capturing mean spatial patterns. Multi-model ensemble (MME) comprising 25 numbers of better performing bias corrected (Scaled) GCMs, have been considered for developing future rainfall patterns over seven zones. Models’ spread from ensemble mean (uncertainty) has been found to be larger in RCP 8.5 than RCP4.5 ensemble. In general, future rainfall projections from RCP 4.5 and RCP 8.5 revealed an increasing rainfall over seven zones during 2020s, 2050s, and 2080s. The maximum increase has been found over southwestern part of NWI (12–30%), northwestern part of WPI (3–30%), southeastern part of NEI (5–18%) and northern and eastern part of SPI (6–24%). However, the contiguous region comprising by the southeastern part of NCI and northeastern part of EPI, may experience slight decreasing rainfall (about 3%) during 2020s whereas the western part of NMI may also receive around 3% reduction in rainfall during both 2050s and 2080s.

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Acknowledgements

We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. Authors would like to express their sincere gratitude to Indian Meteorological Department (IMD), Pune for providing high resolution gridded rainfall data. Authors are thankful to Dr. Rasmus Benestad and Dr. Abdelkader Mezghani, from the Norwegian Meteorological Institute, Norway for “ESD” package  and developers of the packages “hydroGOF”, “fields”, “hyfo” in R. The first author (JA) likes to acknowledge to the Department of Science and Technology (DST), Govt. of India for providing financial support through INSPIRE fellowship (IF 150304) and the Department of Agricultural Meteorology and Physics, BCKV for providing necessary facilities to carry out this research work.

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  1. Department of Physics, Jadavpur University, Kolkata, 700032, India

    Javed Akhter & Argha Deb

  2. Department of Agricultural Meteorology and Physics, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, WB, 741252, India

    Lalu Das

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  1. Javed Akhter
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Akhter, J., Das, L. & Deb, A. CMIP5 ensemble-based spatial rainfall projection over homogeneous zones of India. Clim Dyn 49, 1885–1916 (2017). https://doi.org/10.1007/s00382-016-3409-8

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