Open Access Open Access  Restricted Access Subscription Access

Study of Sea Surface Salinity Due to River Fluxes Using the CMIP6 Models for the Bay of Bengal Region


Affiliations
1 Department of Ocean Engineering and Naval Architecture, Indian Institute of Technology, Kharagpur 721 302, India., India
 

The large influx of freshwater and mixing of different water masses make simulating salinity challenging for the Bay of Bengal (BoB) region. This study analyses the variability of the simulated sea surface salinity (SSS) using models present in the Coupled Modal Intercomparison Project Phase 6 (CMIP6). We collected data for 37 models from CMIP6 and validated them against the Argo (2005–14) and Aquarius (2011–14) data. Based on the skill scores, we narrowed down our search to one CMIP6 model, viz. CIESM. This model was used to study the freshwater spread (FWS) in BoB during different seasons. We found that the correlation between pH and FWS was appreciable. The CIESM model was then used to project the future trends for 10 years for the tier-1 scenario. The trend analysis of future projections revealed a positive trend in SSP1-2.6, with a decrea­sing trend in SSP2-4.5 and SSP5-8.5.

Keywords

Climate Models, Freshwater Spread, River Fluxes, Skill Score, Trend Analysis.
User
Notifications
Font Size

  • Arias, P. A. et al., Technical summary. Climate Change, 2021, 51, 221–227.
  • Vinayachandran, P. et al., A summer monsoon pump to keep the Bay of Bengal salty. Geophys. Res. Lett., 2013, 40(9), 1777–1782.
  • Shetye, S. et al., Hydrography and circulation in the western Bay of Bengal during the northeast monsoon. J. Geophys. Res.: Oceans, 1996, 101(C6), 14011–14025.
  • Vinayachandran, P., Murty, V. and Ramesh Babu, V., Observations of barrier layer formation in the Bay of Bengal during summer monsoon. J. Geophys. Res.: Oceans, 2002, 107(C12), SRF–19.
  • Thadathil, P. et al., Surface layer temperature inversion in the Bay of Bengal: main characteristics and related mechanisms. J. Geophys. Res.: Oceans, 2016, 121(8), 5682–5696.
  • Valsala, V., Singh, S. and Balasubramanian, S., A modeling study of interannual variability of Bay of Bengal mixing and barrier layer formation. J. Geophys. Res.: Oceans, 2018, 123(6), 3962–3981.
  • Prasanna Kumar, S. et al., Why is the Bay of Bengal less produc-tive during summer monsoon compared to the Arabian Sea? Geophys. Res. Lett., 2002, 29(24), 88–101.
  • Jana, S., Gangopadhyay, A. and Chakraborty, A., Impact of seasonal river input on the Bay of Bengal simulation. Continent. Shelf Res., 2015, 104, 45–62.
  • Behara, A. and Vinayachandran, P., An OGCM study of the impact of rain and river water forcing on the Bay of Bengal. J. Geophys. Res.: Oceans, 2016, 121(4), 2425–2446.
  • UNESCO, Discharge of Selected Rivers of the World: A Contribu-tion to the International Hydrological Decade, 1969.
  • Feely, R. A., Doney, S. C. and Cooley, S. R., Ocean acidification: pre-sent conditions and future changes in a high CO2. Oceanography, 2009, 22(4), 36–47.
  • Kumar, V., Joshi, A. and Warrior, H., Assessment of the CMIP6 mod-els to study interseasonal SST variabilities in the BoB. ISH J. Hy-draul. Eng., 2022, 43, 1–9.
  • Sumangala, D. and Warrior, H., Coastal modelling incorporating artificial neural networks for improved velocity prediction. ISH J. Hydraul. Eng., 2022, 28(Suppl. 1), 261–271.
  • Warrior, H. and Carder, K., An optical model for heat and salt budget estimation for shallow seas. J. Geophys. Res.: Oceans, 2007, 112(C12).
  • Lin, X., Qiu, Y., Cha, J. and Guo, X., Assessment of Aquarius sea surface salinity with Argo in the Bay of Bengal. Int. J. Remote Sensing, 2019, 40(22), 8547–8565.
  • Momin, I. M., Mitra, A. K., Prakash, S., Mahapatra, D., Gera, A. and Rajagopal, E., Variability of sea surface salinity in the tropical Indian Ocean as inferred from Aquarius and in situ datasets. Int. J. Remote Sensing, 2015, 36(7), 1907–1920.
  • Du, Y., Zhang, Y. and Shi, J., Relationship between sea surface salinity and ocean circulation and climate change. Sci. China Earth Sci., 2019, 62(5), 771–782.
  • Sreeush, M. G., Rajendran, S., Valsala, V., Pentakota, S., Prasad, K. and Murtugudde, R., Variability, trend and controlling factors of Ocean acidification over western Arabian Sea upwelling region. Mar. Chem., 2019, 209, 14–24.
  • Sridevi, B. and Sarma, V., Role of river discharge and warming on ocean acidification and pCO2 levels in the Bay of Bengal. Tellus B, 2021, 73(1), 1–20.
  • Madkaiker, K., Valsala, V., Sreeush, M. G., Mallissery, A., Chakraborty, K. and Deshpande, A., Understanding the seasonality, trends and controlling factors of Indian Ocean acidification over distinctive bio-provinces. J. Geophys. Res., 2023, 128; https://doi.org/ 10.1029/2022JG006926.
  • Chakraborty, K., Valsala, V., Bhattacharya, T. and Ghosh, J., Sea-sonal cycle of surface ocean pCO2 and pH in the northern Indian Ocean and their controlling factors. Prog. Oceanogr., 2021, 198, 102683.
  • Gidden, M. J. et al., Global emissions pathways under different socio-economic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century. Geosci. Model Dev., 2019, 12(4), 1443–1475.
  • Joshi, A. and Warrior, H., Comprehending the role of different mechanisms and drivers affecting the sea-surface pCO2 and the air– sea CO2 fluxes in the Bay of Bengal: a modeling study. Mar. Chem., 2022, 83, 245–251.
  • Sarma, V., Krishna, M., Paul, Y. and Murty, V., Observed changes in ocean acidity and carbon dioxide exchange in the coastal Bay of Bengal – a link to air pollution. Tellus B, 2015, 67(1), 24638.

Abstract Views: 196

PDF Views: 129




  • Study of Sea Surface Salinity Due to River Fluxes Using the CMIP6 Models for the Bay of Bengal Region

Abstract Views: 196  |  PDF Views: 129

Authors

V. Kumar
Department of Ocean Engineering and Naval Architecture, Indian Institute of Technology, Kharagpur 721 302, India., India
A. P. Joshi
Department of Ocean Engineering and Naval Architecture, Indian Institute of Technology, Kharagpur 721 302, India., India
H. V. Warrior
Department of Ocean Engineering and Naval Architecture, Indian Institute of Technology, Kharagpur 721 302, India., India

Abstract


The large influx of freshwater and mixing of different water masses make simulating salinity challenging for the Bay of Bengal (BoB) region. This study analyses the variability of the simulated sea surface salinity (SSS) using models present in the Coupled Modal Intercomparison Project Phase 6 (CMIP6). We collected data for 37 models from CMIP6 and validated them against the Argo (2005–14) and Aquarius (2011–14) data. Based on the skill scores, we narrowed down our search to one CMIP6 model, viz. CIESM. This model was used to study the freshwater spread (FWS) in BoB during different seasons. We found that the correlation between pH and FWS was appreciable. The CIESM model was then used to project the future trends for 10 years for the tier-1 scenario. The trend analysis of future projections revealed a positive trend in SSP1-2.6, with a decrea­sing trend in SSP2-4.5 and SSP5-8.5.

Keywords


Climate Models, Freshwater Spread, River Fluxes, Skill Score, Trend Analysis.

References





DOI: https://doi.org/10.18520/cs%2Fv124%2Fi11%2F1290-1299