Understanding the ocean’s “memory” and mechanisms controlling the persistence of large-scale sea surface temperature (SST) anomalies is critical for the prediction of interannual and multidecadal climate variability. At decadal and longer timescales, the relative importance of atmosphere versus ocean dynamics for SST variability remains uncertain. In the North Atlantic region, the debate surrounds identifying the key driver of the Atlantic Multidecadal Variability (AMV, or the Atlantic Multidecadal Oscillation). In this study, the stochastic model hierarchy is used to understand SST and AMV behavior in Community Earth System Model simulations using the slab ocean and full ocean (CESM-SLAB and CESM-FULL, respectively).
The simplicity of stochastic model and its hierarchy with sequentially allowing seasonal variation in the atmospheric forcing and mixed layer depth as well as the mixed layer entrainment, provided a powerful tool to quantifying the respective roles of ocean and atmospheric dynamics in driving the AMV.
Despite its simplicity, the stochastic model reproduces temporal characteristics of SST variability in the subpolar North Atlantic, including reemergence, seasonal-to-interannual persistence, and power spectra. Furthermore, unrealistically persistent SST of the CESM-SLAB ocean simulation is reproduced in the equivalent stochastic model configuration where the mixed-layer depth (MLD) is constant. The stochastic model also reveals that vertical entrainment primarily damps SST variability, thus explaining why SLAB exhibits larger SST variance than FULL. The stochastic model driven by temporally stochastic, spatially coherent forcing patterns reproduces the canonical AMV pattern. However, the amplitude of low-frequency variability remains underestimated by ~50%, suggesting a significant role for ocean dynamics beyond entrainment.