Atmospheric Cold Pools and Their Influence on Sea Surface Temperature in the Bay of Bengal

Abstract

Recent observations show that atmospheric cold pool (ACP) events are plentiful in the Bay of Bengal (BoB) during summer (May–September) and fall (October–November) and that these events can significantly modify local air‐sea interaction processes on sub‐daily time scales. In this study, we examine whether the magnitude of sea surface temperature (SST) drop associated with ACP events shows any diurnal variability during summer and fall. For this purpose, we use moored buoy data with a 10‐min temporal resolution at 8°, 12°, and 15°N along 90°E and a one‐dimensional mixed layer (ML) model. The analysis shows a reduction in SST (ΔSST) due to ACPs in the BoB during summer and fall, with a maximum magnitude of ΔSST during the afternoon (1200–1600 LST). However, the maximum magnitude of ΔSST during the afternoon is a factor of two higher during fall (∼−0.14°C) than summer (∼−0.07°C). Analysis based on observations and ACP sensitivity experiments indicates that the shallow daytime thermocline and associated thin surface ML is the primary factor regulating the day to night difference in ΔSST associated with ACPs. The presence of this shallow daytime thermocline and thin ML amplifies the effects on SST of net surface heat loss and entrainment of cold sub‐surface water associated with enhanced ACP wind speeds.

Seasonal Variation of the Land Breeze System in the Southwestern Bay of Bengal and Its Influence on Air‐Sea Interactions

“…The mean sunrise time in the study region is around 0600 IST; hence, a 24‐hr interval starting from 0600 IST defines 1 day in this study. This approach is similar to previous studies of sub‐daily variability in the BoB (Jofia et al., 2021; Girishkumar et al., 2021) and the western equatorial Indian Ocean (Seo et al., 2014). The analysis is not sensitive to the precise definition of what constitutes a day, for example, using 0000–2300 IST instead of 0600–0500 IST does not significantly affect our results.…”

“…Downwelling longwave radiation data from the mooring were in error during the study period, so that downwelling longwave radiation data from Clouds and the Earth's Radiant Energy System (CERES) with 1° × 1° spatial resolution and one‐hour temporal resolution (Wielicki et al., 1996) were used instead to estimate LHF from the bulk algorithm. A comparison of downwelling longwave radiation data from CERES with measurements from RAMA buoys at 15°N, 90°E in the central BoB and 5°N, 95°E in the eastern equatorial Indian Ocean demonstrated that CERES data were a reasonable replacement for missing in‐situ data at BD11 (A. R. Aparna & Girishkumar, 2022; Girishkumar et al., 2021; Joseph et al., 2021). BD11 also measures sub‐surface temperature and salinity though these data are not used in the present study.…”

“…(2021) and Girishkumar et al. (2021) examined sub‐daily variability of atmospheric cold pool events and their effects on surface heat fluxes and SST. In addition, Turk et al.…”

Dynamical systems persistence parameter of sea surface temperature and its associations with regional averaged index over the tropical Pacific

“…As one of the most important components in the climate system, sea surface temperature (SST) field plays a critical role in determining climate variations and variability, especially in air–sea interactions for growth and amplification of anomalies in both the ocean and atmosphere. SST varies across a wide range of temporal and spatial scales (Deser and Blackmon, 1993; Kushnir, 1994; Wu and Liu, 2005; Fan and Schneider, 2012; Strobach et al ., 2020; Girishkumar et al ., 2021), which has been confirmed by the observed spectra for tropical SST exhibiting dominated structures from intraseasonal to interdecadal time scales (Kug et al ., 2009). The strongest variability in the Tropics is related to the El Niño–Southern Oscillation (ENSO), which is the dominant variability on seasonal‐interannual time scales with coherent spatial pattern.…”

“…Due to the complicated space–time SST variability (Fan and Schneider, 2012; Strobach et al ., 2020; Girishkumar et al ., 2021) and ENSO's complexity (Capotondi et al ., 2015; Paek et al ., 2017; Hu and Fedorov, 2018; Timmermann et al ., 2018; Hu et al ., 2020). Only using the regional averaged SSTA to define the traditional Niño indices is not enough to characterize the typical SST events, more aspects from SST field (such as mean state of SST, local extreme SSTA patterns, see Williams and Patricola, 2018) or more information from other fields (Wolter and Timlin, 2011; Wiedermann et al ., 2016) are required.…”