April 21, 2023
Journal Article

Understanding the Drivers of Atlantic Multidecadal Variability using a Stochastic Model Hierarchy


The relative importance of ocean and atmospheric dynamics in generating Atlantic Multidecadal Variability (AMV) remains an open question. Comparison between climate models with slab and fully-dynamic ocean components are often used to address this question, but such comparison is less informative for understanding contributions from individual upper-ocean processes. We investigate the role of seasonal variation and mixed-layer entrainment in AMV using a hierarchy of stochastic models that solely consider the local upper ocean response to stochastic atmospheric forcing and its impact on the surface heat exchanges. To better understand the difference between pre-industrial control simulations using the Community Earth System Model 1 (CESM1) coupled with fully-dynamic and slab ocean models, we estimate the stochastic model parameters, including heat flux feedback, mixed-layer depth, and stochastic forcing amplitude, from each respective CESM1 simulation. Despite its simplicity, the stochastic model reproduces temporal characteristics of sea surface temperature (SST) variability in the subpolar gyre, including reemergence, seasonal-to-interannual persistence and power spectra. Furthermore, unrealistically persistent SST of the CESM-SLAB ocean simulation is reproduced in slab ocean-like configurations when the mixed-layer depth is constant. Our stochastic model also reveals that vertical entrainment primarily damps SST variability, thus explaining why the slab ocean simulation exhibits larger SST variance than the full ocean case. In terms of the spatial pattern, 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, suggesting a role for ocean dynamics beyond entrainment.

Published: April 21, 2023


Liu G., K. Young-Oh, C.J. Frankignoul, and J. Lu. 2023. Understanding the Drivers of Atlantic Multidecadal Variability using a Stochastic Model Hierarchy. Journal of Climate 36, no. 4:1043–1058. PNNL-SA-172895. doi:10.1175/JCLI-D-22-0309.1

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