Association between mean and interannual equatorial Indian Ocean subsurface temperature bias in a coupled model
In the present study the association between mean and interannual subsurface temperature bias over the equatorial Indian Ocean (EIO) is investigated during boreal summer (June through September; JJAS) in the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFSv2) hindcast. Anomalously high subsurface warm bias (greater than 3 °C) over the eastern EIO (EEIO) region is noted in CFSv2 during summer, which is higher compared to other parts of the tropical Indian Ocean. Prominent eastward current bias in the upper 100 m over the EIO region induced by anomalous westerly winds is primarily responsible for subsurface temperature bias. The eastward currents transport warm water to the EEIO and is pushed down to subsurface due to downwelling. Thus biases in both horizontal and vertical currents over the EIO region support subsurface warm bias. The evolution of systematic subsurface warm bias in the model shows strong interannual variability. These maximum subsurface warming episodes over the EEIO are mainly associated with La Niña like forcing. Strong convergence of low level winds over the EEIO and Maritime continent enhanced the westerly wind bias over the EIO during maximum warming years. This low level convergence of wind is induced by the bias in the gradient in the mean sea level pressure with positive bias over western EIO and negative bias over EEIO and parts of western Pacific. Consequently, changes in the atmospheric circulation associated with La Niña like conditions affected the ocean dynamics by modulating the current bias thereby enhancing the subsurface warm bias over the EEIO. It is identified that EEIO subsurface warming is stronger when La Niña co-occurred with negative Indian Ocean Dipole events as compared to La Niña only years in the model. Ocean general circulation model (OGCM) experiments forced with CFSv2 winds clearly support our hypothesis that ocean dynamics influenced by westerly winds bias is primarily responsible for the strong subsurface warm bias over the EEIO. This study advocates the importance of understanding the ability of the models in representing the large scale air–sea interactions over the tropics and their impact on ocean biases for better monsoon forecast.
KeywordsSubsurface temperature Coupled model Indian Ocean La Niña SST
We thank Director, ESSO-IITM for support. We have used (Hadley EN4.1.1) temperature data from http://www.metoffice.gov.uk/hadobs/en4/download-en4-1-1.html, ECCO data from http://www.ecco-group.org, and ERA interim data from http://www.ecmwf.int/en/research/climate-reanalysis/era-interim. We sincerely thank the anonymous reviewers for their valuable comments that helped us to improve the manuscript. Figures are prepared in PyFerret.
- Griffies SM (2012) Elements of the modular ocean model (MOM): 2012 release (GFDL Ocean group technical report no. 7. GFDL Ocean group technical report no. 7. NOAA/Geophysical Fluid Dynamics Laboratory, PrincetonGoogle Scholar
- Griffies S, Harrison MJ, Pacanowski RC, Anthony R. (2004) A technical guide to MOM4, GFDL Ocean group, technical report no. 5, NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, p 342Google Scholar
- Kirtman B, Vecchi GA (2011) Why climate modelers should worry about atmospheric and oceanic weather. In: Chang C-P, Ding Y, Lau N-C, Johnson RH, Wang B, Yasunari T (eds) The global monsoon system: research and forecast, 2nd edn. World scientific series on Asia-Pacific weather and climate, vol 5. World Scientific Publication Company, Singapore, pp 511–524Google Scholar
- Krishnamurthy V, Kinter JL (2003) The Indian monsoon and its relation to global climate variability. Global climate. Rodó X, Comín FA (eds) Springer, Berlin, 186–236Google Scholar
- Levitus S, Boyer TP, Conkright ME, O’ Brien T, Antonov J, Stephens C, Stathoplos L, Johnson D, Gelfeld R (1998) NOAA Atlas NESDIS 18,World Ocean Database 1998: volume 1: introduction. US Gov. Printing Office, Washington, DC, p 346Google Scholar
- Pant GB, Rupa Kumar K (1997) Climates of South Asia. Wiley, Chichester, p 320Google Scholar
- Ramu DA, Sabeerali CT, Chattopadhyay R, Rao DN, George G, Dhakate AR, Salunke K, Srivastava A, Rao SA (2016) Indian summer monsoon rainfall simulation and prediction skill in the CFSv2 coupled model: impact of atmospheric horizontal resolution. J Geophys Res Atmos 121:2205–2221. doi: 10.1002/2015JD024629 CrossRefGoogle Scholar
- Saji NH, Goswami BN, Vinayachandran PN, Yamagata T (1999) A dipole mode in the tropical Indian Ocean. Nature 401:360–363Google Scholar
- Wunsch C, Heimbach P (2013) Dynamically and kinematically consistent global ocean circulation and ice state estimates. In: Sielder G, Griffies SM, Gould J, Church JA (eds) Ocean circulation and climate: a 21st century perspective. International Geophysics Series, vol 103. Academic Press, Oxford, pp 553–579. doi: 10.1016/b978-0-12-391851-2.00021-0
- Yamagata T, Behera SK, Luo JJ, Masson S, Jury MR, Rao SA (2004) Coupled ocean-atmosphere variability in the tropical Indian Ocean. In: Wang C, Xie SP, Carton A (eds) Earth’s climate: the ocean–atmosphere interaction. Geophysical monograph, vol 147. American Geophysical Union, Washington DC, pp 189–211Google Scholar