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Spatio-temporal variations of chlorophyll from satellite derived data and CMIP5 models along Indian coastal regions

Journal of Earth System Science volume 130, Article number: 153 (2021) Cite this article

Abstract

Comparison of chlorophyll data of three sets of CMIP5 models for RCP 4.5 (MPI-ESM-MR, HadGEM2-ES and GFDL-ESM2M) and RCP 6.0 (IPSL-CM5A-LR, HadGEM2-ES and GFDL-ESM2M) were done with satellite derived data (OC-CCI) for the period of 1998–2017 along four Indian coastal regions. The monthly, yearly and zone-wise seasonal comparison between model and satellite data were carried out. Analysis of monthly variations of chlorophyll during 1998–2017 reveals that the satellite data show maximum value of 0.53 mg/m3 in September, whereas all other models show maximum in August. Yearly analysis indicates maximum satellite data in the year 2004, while minimum was observed in 2015. HadGEM2-ES exhibited maximum model value and the lowest was found for IPSL-CM5A-LR. It was observed that the maximum chlorophyll value of 2.56 mg/m3 for satellite data was in the monsoon season and the lowest value of 0.14 mg/m3 was in the pre-monsoon. Seasonal analysis reveals no clear match among model and satellite values in any of the coastal regions. In northwest and northeast regions, the satellite values were found higher than the model values in most of the years, whereas in other regions, the model values were found fluctuating with the satellite values. Owing to the mismatch of the model and the satellite values, the work cautions to apply biases or corrections on usage of RCP model data for regional marine climate change research.

Research Highlights

  • Regional and seasonal chlorophyll variations for Indian coastal regions were elucidated for three decades from CMIP5 models and satellite derived data and compared.

  • The northwest region of India exhibits maximum chlorophyll values and variations compared to the southern coasts.

  • The discrepancies among satellite and model chlorophyll data were evidenced from the respective shift of chlorophyll maximum values from September to August.

  • The research emphasis the need to apply necessary bias corrections in regional marine climate forecasts.

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Acknowledgements

This research was funded by the ICAR sponsored National Innovations in Climate Resilient Agriculture Project (NICRA) carried out at ICAR-Central Marine Fisheries Research Institute Kochi. The authors are grateful to the Director ICAR-CMFRI, Kochi, for providing facilities. CMIP5 data employed in this study are available at the Program Climate Model Diagnosis and Intercomparison (PCMDI) and satellite chlorophyll data from European Space Agency’s Ocean Color-Climate Change Initiative (OC-CCI) and the authors are grateful to the data providers. The authors are also thankful to the research scholars in the NICRA project for the support extended.

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Authors and Affiliations

  1. ICAR-Central Marine Fisheries Research Institute, Kochi, Kerala, India

    Dhanya Joseph, G Rojith, P U Zacharia, V H Sajna, S Akash & Grinson George

Authors
  1. Dhanya Joseph
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  2. G Rojith
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  3. P U Zacharia
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  4. V H Sajna
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  5. S Akash
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  6. Grinson George
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Contributions

Dr Dhanya designed the research, performed the data analysis, result inferences and prepared the initial manuscript. Dr Rojith contributed to the research framework refinement, result inferences and rewrote the manuscript. Dr Zacharia supervised the research, reviewed and approved the manuscript. Sajna and Akash contributed to the data analysis. Dr Grinson provided insights to RCP scenarios and performed the final review of the manuscript.

Corresponding author

Correspondence to P U Zacharia.

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Communicated by Maripi Dileep

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Joseph, D., Rojith, G., Zacharia, P.U. et al. Spatio-temporal variations of chlorophyll from satellite derived data and CMIP5 models along Indian coastal regions. J Earth Syst Sci 130, 153 (2021). https://doi.org/10.1007/s12040-021-01663-6

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  • DOI: https://doi.org/10.1007/s12040-021-01663-6

Keywords

  • Chlorophyll
  • CMIP5
  • Indian Ocean
  • climate change
  • Indian coastal regions
  • RCP scenarios