Subannual to Interannual Variabilities of SST in the North Atlantic Ocean

Abstract

Based on observational data, this work examines the multi-time-scale feature of the sea surface temperature (SST) variability averaged in the whole North Atlantic Ocean (to be referred to as NASST), as well as its time-scale-dependent connections with El Niño–Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). Traditionally, the NASST index is used to characterize the SST trend and multidecadal variability in the North Atlantic. This study found that superimposed on a prominent long-term trend, NASST is nonnegligible at subannual and interannual time scales, compared with that at decadal to multidecadal time scales. Spatially, the interannual variation of NASST is characterized by a horseshoe-like pattern of the SST anomaly (SSTA) in the North Atlantic. It is mainly a lagged response to ENSO through the atmospheric bridge, and NAO plays a secondary role. At the subannual time scale, both ENSO and NAO play a role in generating the fluctuations of NASST and a horseshoe-like pattern in the North Atlantic. Nevertheless, both the ENSO- and NAO-driven variations only explain a small fraction of the variances in both the interannual and subannual time scales. Thus, other factors unrelated to ENSO or NAO may play a more important role. The associated thermodynamical processes are similar at the two time scales; however, the dynamical processes have a significant contribution to the subannual component, but not to the interannual component. Thus, the SSTA averaged in the North Atlantic as a whole varies at different time scales and is associated with different mechanisms.

Evolution and Prediction of Two Extremely Strong Atlantic Niños in 2019–2021: Impact of Benguela Warming

“…Variability across a range of time scales is a basic feature of the North Atlantic SST variability (e.g., Li et al., 2020). We speculate that a multi‐time scale amplification leads to the extremely strong Atlantic Niños in 2019–2021.…”

“…Also, in addition to model biases (e.g., Huang et al., 2007), the low prediction skill in the tropical Atlantic may be because compared with ENSO, the signal‐to‐noise ratio (SNR) is smaller for Atlantic Niño. The low SNR/predictability is linked to the fact that there is no dominant time scale variation (Li et al., 2020) like ENSO in the tropical Pacific.…”