Interannual Variability in the North Atlantic Ocean’s Temperature Field and Its Association with the Wind Stress Forcing

Abstract

Spectral analyses of the North Atlantic temperature field in the Simple Ocean Data Analysis (SODA) reanalysis identify prominent and statistically significant interannual oscillations along the Gulf Stream front and in large regions of the North Atlantic. A 7-8-yr oscillatory mode is characterized by a basin-wide southwest-to-northeast-oriented propagation pattern in the sea surface temperature (SST) field. This pattern is found to be linked to a seesaw in the meridional-dipole structure of the zonal wind stress forcing (TAUX). In the subpolar gyre, the SST and TAUX fields of this mode are shown to be in phase opposition, which suggests a cooling effect of the wind stress on the upper ocean layer. Over all, this mode's temperature field is characterized by a strong equivalent-barotropic component, as shown by covariations in SSTs and sea surface heights, and by phase-coherent behavior of temperature layers at depth with the SST field. Recent improvements of multivariate singular spectrum analysis (M-SSA) help separate spatio-temporal patterns. This methodology is developed further and applied to studying the ocean's response to variability in the atmospheric forcing. Statistical evidence is shown to exist for other mechanisms generating oceanic variability of similar 7-8-yr periodicity in the Gulf Stream region; the latter variability is likewise characterized by a strongly equivalent-barotropic component. Two other modes of biennial variability in the Gulf Stream region are also identified, and it is shown that interannual variability in this region cannot be explained by the ocean's response to similar variability in the atmospheric forcing alone.

A Century of Nonlinearity in the Geosciences

“…Overall, the line of work outlined in the preceding paragraphs has provided fairly convincing evidence that intrinsic oceanic LFV, even in the absence of variable atmospheric forcing, is an important source of interannual climate variability. Detailed confrontation of model results with recent reanalysis data for both atmosphere and oceans supports these ideas, at least in the case of the North Atlantic basin (Groth et al, ), where this mechanism also provides a possible explanation of the North Atlantic Oscillation (NAO) and of its approximate 7‐ to 8‐year periodicity. The situation for time‐dependent wind forcing will be discussed in section .…”

On semi‐empirical decomposition of multidecadal climate variability into forced and internally generated components

“…Separating the forced climate signal from internal climate variability using purely statistical methods generally relies on the assumption that these two types of variations possess their own distinct spatiotemporal signatures. These methods include the standard empirical orthogonal function analysis (EOF: Preisendorfer, ; Monahan et al, ), singular spectrum analysis (SSA; Ghil and Vautard, ; Elsner and Tsonis, ) and its multivariate extension M‐SSA (Moron et al, ; Ghil et al ., ; Jamison and Kravtsov, ; Wyatt et al ., ; Kravtsov et al ., ; Groth and Ghil, ; Groth et al ., ), multi‐taper spectral domain approach (Mann and Park, ), empirical mode decomposition (Huang and Wu, ; Wu et al ., ); discriminant analysis (Schneider and Held, ; DelSole and Tippett, ); optimal persistence analysis (DelSole ), among others. Comparison of the observed and simulated space/time patterns detected by these methods serves to assess the models' performance in simulating the observed climate signals and provides clues about dynamical sources of the observed climate variability.…”

Spectral Empirical Orthogonal Function Analysis of Weather and Climate Data

“…Another important step is the incorporation of a null hypothesis based on a mean background spectrum, possibly red noise, into the analysis. Monte Carlo techniques as introduced by Allen and Smith (1996) [see also Groth and Ghil (2015)] for the closely related SSA method seem to offer a viable way forward in this respect. Alternatively, the background spectrum could be estimated from the data by filtering in the frequency domain and subsequently be removed from the Fourier realizations prior to the formation of the CSD matrix.…”

“…Closely related to the present work is the extension of SSA to multidimensional data proposed by Plaut and Vautard (1994), which leads to a space-time formulation referred to as multichannel singular spectrum analysis (M-SSA). For a recent application of M-SSA to Simple Ocean Data Analysis (SODA) reanalysis data with many technical details on the method, the reader is referred to Groth et al (2017). If M-SSA were conducted in the frequency domain, it would correspond to SEOF with n ovlp 5 n fft 2 1.…”