Imprint of chaotic ocean variability on transports in the Southwest Pacific at interannual timescales

Abstract

Abstract. The Southwest Pacific Ocean sits at a bifurcation where southern subtropical waters are redistributed equatorward and poleward by different ocean currents. The processes governing the interannual variability of these currents are not completely understood. This issue is investigated using a probabilistic modeling strategy that allows disentangling the atmospherically-forced deterministic ocean variability and the chaotic intrinsic ocean variability. A large ensemble of 50 simulations performed with the same ocean general circulation model (OGCM) driven by the same realistic atmospheric forcing that only differ by a small initial perturbation is analyzed over 1980–2015. Our results show that, in the Southwest Pacific, the interannual variability of the transports is strongly dominated by chaotic ocean variability south of 20° S. In the tropics, while the interannual variability of transports and eddy kinetic energy modulation is largely deterministic and explained by El Nino Southern Oscillation (ENSO), ocean nonlinear processes still explain 10 to 20 % of their interannual variance at large-scale. Regions of strong chaotic variance generally coincide with regions of high mesoscale activity, suggesting that a spontaneous inverse cascade is at work from mesoscale toward lower frequencies and larger scales. The spatiotemporal features of the low-frequency oceanic chaotic variability are complex but spatially coherent within certain regions. In the Subtropical Countercurrent area, they appear as interannually-varying, zonally elongated alternating current structures, while in the EAC region, they are eddy-shaped. Given this strong imprint of large-scale chaotic oceanic fluctuations, our results question the attribution of interannual variability to the atmospheric forcing in the region from point-wise observations and one-member simulations.

Imprints of Ocean Chaotic Intrinsic Variability on Bottom Pressure and Implications for Data and Model Analyses

“…Although associated with mesoscale and smaller scale dynamics, $${p}_{b}^{i}$$ variability on larger spatial and longer temporal scales can also result through nonlinear interactions (e.g., Arbic et al., 2012; Charney, 1971; Scott & Arbic, 2007; Venaille et al., 2011). In fact, the importance of such chaotic intrinsic variability has been found in sea level (e.g., Close et al., 2021; Llovel et al., 2018; Penduff et al., 2011, 2014, 2019; Sérazin et al., 2015) and other dynamical variables (e.g., Cravatte et al., 2020; Leroux et al., 2018; Penduff et al., 2018), but its impact on p b remains to be studied in detail.…”