Routes to long‐term atmospheric predictability in reduced‐order coupled ocean–atmosphere systems: Impact of the ocean basin boundary conditions

Abstract

The predictability of the atmosphere at short and long time-scales associated with coupling to the ocean is explored in a new version of the Modular Arbitrary-Order Ocean-Atmosphere Model (MAOOAM), based on a two-layer quasigeostrophic atmosphere and a one-layer reduced-gravity quasigeostrophic ocean. This version features a new ocean basin geometry with periodic boundary conditions in the zonal direction. The analysis presented in this article considers a low-order version of the model with 40 dynamical variables. First, the increase of surface friction (and the associated heat flux) due to the ocean can either induce chaos when the aspect ratio between the meridional and zonal directions of the domain of integration is small or suppress chaos when it is large. This reflects the potentially counterintuitive role that the ocean can play in the coupled dynamics. Second, and perhaps more importantly, the emergence of long-term predictability within the atmosphere for specific values of the friction coefficient occurs through intermittent excursions in the vicinity of a (long-period) unstable periodic solution. Once close to this solution, the system is predictable for long times, i.e. a few years. The intermittent transition close to this orbit is, however, erratic and probably hard to predict. This new route to long-term predictability contrasts with the one found in the closed ocean-basin low-order version of MAOOAM, in which the chaotic solution is permanently wandering in the vicinity of an unstable periodic orbit for specific values of the friction coefficient. The model solution is thus influenced by the unstable periodic orbit at any time, and inherits from its long-term predictability. K E Y W O R D Schaos, low-order models, ocean-atmosphere coupling, predictability, surface friction 1 Q J R Meteorol Soc. 2019;145:2791-2805.wileyonlinelibrary.com/journal/qj

Multistability in a coupled ocean–atmosphere reduced‐order model: Nonlinear temperature equations

“…For more details on calculating Lyapunov properties in coupled ocean–atmosphere models, see Vannitsem and Lucarini (2016); Vannitsem (2017); Vannitsem et al . (2019); Vannitsem and Duan (2020).…”

“…To analyse the stability properties of the attractors that were found, we calculated their Lyapunov properties, focusing on values of C identified in the previous section where multiple attractors exist. For more details on calculating Lyapunov properties in coupled ocean-atmosphere models, see Vannitsem and Lucarini (2016); Vannitsem (2017); Vannitsem et al (2019); Vannitsem and Duan (2020). We present the largest Lyapunov exponents (LLE) in Figure 3a, as a function of C, where again there is a multistability present in the two intervals identified.…”

Multistability in a Coupled Ocean-Atmosphere Reduced Order Model: Non-linear Temperature Equations

“…To analyse the stability properties of the attractors that were found we calculated the Lyapunov properties of the stable attractors, focusing on values of C identified in the previous section where multiple stable attractors exist. For more details on calculating Lyapunov properties in coupled ocean-atmosphere models see Vannitsem and Lucarini (2016); Vannitsem (2017); Vannitsem et al (2019); Vannitsem and Duan (2020).…”

qgs: A flexible Python framework of reduced-order multiscale climate models

“…This coupling consists in both mechanical and heat exchange interactions between the two components. The model has already been used in different contexts, in particular for data assimilation (Penny et al, 2019;Tondeur et al, 2020), and predictability studies (Vannitsem et al, 2019;Vannitsem & Duan, 2020) • In the case of a land surface coupling, it emulates the model proposed in Reinhold & Pierrehumbert (1982) and Cehelsky & Tung (1987) with a simple thermal relaxation toward a climatological temperature and a mechanical coupling due to the friction between the land and the atmosphere. It can also emulate the model proposed in Li et al (2018), with mechanical coupling and heat exchange.…”