Long-term variability of Sea Surface Temperature in the Tropical Indian Ocean in relation to climate change and variability
Introduction
The Indian ocean warming and its teleconnections play a crucial role in the modulation of the global climate system (e.g., Luo et al., 2012; Chen et al., 2019). Since the 1950s, the tropical Indian Ocean (TIO; 30oN-30oS and 30°E-120°E) has warmed faster than elsewhere in the tropical ocean basins (Han et al., 2014) consistent with the global sea surface temperature (SST) trend. The Indian Ocean SST variability has implications on environmental and associated socioeconomic status of the Indian Ocean rim countries. The intensity and distribution of Indian summer monsoon has changed and is projected to change substantially in the future (Sharmila et al., 2015; Sahany et al., 2019; Bhowmick et al., 2019; Mishra et al., 2019). Considering the regional and remote impacts of TIO warming, it is highly desirable to understand the SST variability and change to improve the monsoon predictability (Kucharski and Abid, 2017).
Long-term variations of SST anomaly patterns resulted from a combination of intrinsic modes of atmospheric and oceanic circulation variabilities and coupled ocean-atmosphere interactions (Deser et al., 2010). The SST variability of TIO shows a centennial warming trend superimposed on interannual and decadal variations as well as a rapid warming trend during 2003–2013 (Li et al., 2017). Previous studies (e.g., Li et al., 2016; Han et al., 2014) speculated that the remote forcing from Atlantic and Pacific basins contributes to upper-ocean warming of TIO. However, based on observation and climate model-based analysis, Li et al. (2017) suggested that the tropical ocean basins are tightly connected through multiple atmospheric teleconnections besides the inter-oceanic connection. Atlantic Ocean regional effect can feedback to the Pacific (Kucharski et al., 2015), which influences the Indian Ocean through atmospheric teleconnections (Li et al., 2016).
Previous studies (e.g., Deser et al., 2010; Dong and Zhou, 2014) linked the warming of Indian Ocean, especially since the 1950s, to El Niño driven air-sea interactions and greenhouse gas forced radiative and heat flux contributions. Dong and Zhou (2014) showed that anthropogenic forcing contributes to basin-wide warming pattern causing frequent Indian Ocean Dipole (IOD) events. Enhanced wind stress curl in the south Indian Ocean also contributed to the basin-wide warming of the Indian Ocean (Cai et al., 2007). Anthropogenic activities and warming in the Indian Ocean have influenced the Indian summer monsoon (e.g., Singh et al., 2019; Roxy et al., 2014). Hence, a rigorous analysis of SST variability of TIO and its quantification has scientific and societal significance. Our understanding regarding the persistent long-term warming revealed by observations, reanalysis and climate models is less sufficient for reliable future projection and impact assessments. The previous study by Dong and McPhaden (2016) suggested weaker warming over the north tropical Indian Ocean compared to the enhanced warming of southern Indian Ocean (south of 10oS) under the influence of Pacific heat transport through an inter-oceanic connection. We limit our study domain to TIO to highlight its warming trend and coherency with global tropical oceans. Also, the tropical Indian Ocean warming/cooling is sensitive to Indian summer monsoon variability and is highly significant to the livelihood of the population across the Indian subcontinent.
In this paper, we examine three gridded reanalysis SST data sets available for the period of 1900–2017 to examine the consistency and causes of TIO warming. The study is aimed at addressing the following questions: Is the previously reported Indian Ocean warming trend also prominent over global tropical oceans and is it robust across different SST products? Is the imprint of natural and anthropogenic warming localized in a specific region across different SST products? The study also attempts to understand how the air-sea fluxes and mixed layer heat content of the tropical Indian Ocean are adjusting in a warming climate. A brief description of datasets and methodologies used in the study is given in Section 2. The results and discussions are presented in Section 3, and the results have been summarised in Section 4.
Section snippets
Data sets
In the present study, five gridded reanalysis SST datasets are considered; the Hadley Center sea ice and SST dataset version 1.1 (HadISST1.1; Rayner et al., 2003), the extended reconstructed SST version 5 (ERSSTv5; Smith and Reynolds, 2004; Smith et al., 2008), and the centennial in situ observation-based estimate of SST (COBE-SST2; Hirahara et al., 2014; Ishii et al., 2005). The HadISST (1o x 1o), ERSST (2o x 2o), and COBE (1o x 1o) SST products use different statistical procedures to fill the
Long-term warming of tropical Indian Ocean
We consider the ensemble mean SST anomaly obtained from three SST data sets to substantiate the extensively discussed (e.g., Roxy et al., 2014; Dong and McPhaden, 2016) TIO warming. The study examines the SST variability over the 118 years from 1900 to 2017. However, we also considered the periods 1948–2017 and 1980–2017 to ensure the robustness and to explore the mechanisms of consistent warming of TIO. Fig. 1 intercompares the spatial patterns of SST trends in each product and ensemble mean
Summary and conclusions
Previous studies suggest SSTs over the tropical Indian Ocean region show interannual, decadal, and multi-decadal variations superimposed on a robust warming trend. However, the quantification and the causes of the long-term SST variability remain unclear. Here, we investigate the SST variability at a long-term scale in the TIO by analysing ensemble mean SST anomalies constructed from three gridded reanalysis products for 118 years (1900–2017) and a CMIP6 climate model historical simulation for
Declaration of Competing Interest
The authors declare no competing interests.
Acknowledgements
This research is partly supported by the DST Centre of Excellence in Climate Modeling at IIT Delhi. The authors thank all the modeling and research centres for making the data available i.e. HadISST, ERSST, COBE, NCEP and GODAS. We thank https://esgfnode.llnl.gov/ and http://archive.ceda.ac.uk/ for making available the CESM2 historical simulations. This is NIO contribution number 6669.
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