Liu Glenn

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Last Name
Liu
First Name
Glenn
ORCID
0000-0003-0352-3774

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  • Article
    Understanding the Drivers of Atlantic Multidecadal Variability Using a Stochastic Model Hierarchy
    (American Meteorological Society, 2023-01-23) Liu, Glenn ; Kwon, Young-Oh ; Frankignoul, Claude ; Lu, Jian
    The relative importance of ocean and atmospheric dynamics in generating Atlantic multidecadal variability (AMV) remains an open question. Comparisons between climate models with a slab ocean (SLAB) and fully dynamic ocean components (FULL) are often used to explore this question, but cannot reveal how individual ocean processes generate these differences. We build a hierarchy of physically interpretable stochastic models to investigate the contribution of two upper-ocean processes to AMV: the role of seasonal variation and mixed-layer entrainment. This interpretability arises from the stochastic model’s simplified representation of sea surface temperature (SST), considering only the local upper-ocean response to white-noise atmospheric forcing and its impact on surface heat exchange. We focus on understanding differences between SLAB and FULL non-eddy-resolving preindustrial control simulations of the Community Earth System Model 1 (CESM), and estimate the stochastic model parameters from each respective simulation. Despite its simplicity, the stochastic model reproduces temporal characteristics of SST variability in the SPG, including reemergence, seasonal-to-interannual persistence, and power spectra. Furthermore, the 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, suggesting a role for ocean dynamics beyond entrainment.
  • Article
    Physical insights from the multidecadal prediction of North Atlantic Sea surface temperature variability using explainable neural networks
    (American Geophysical Union, 2023-12-19) Liu, Glenn ; Wang, Peidong ; Kwon, Young-Oh
    North Atlantic sea surface temperatures (NASST), particularly in the subpolar region, are among the most predictable in the world's oceans. However, the relative importance of atmospheric and oceanic controls on their variability at multidecadal timescales remain uncertain. Neural networks (NNs) are trained to examine the relative importance of oceanic and atmospheric predictors in predicting the NASST state in the Community Earth System Model 1 (CESM1). In the presence of external forcings, oceanic predictors outperform atmospheric predictors, persistence, and random chance baselines out to 25-year leadtimes. Layer-wise relevance propagation is used to unveil the sources of predictability, and reveal that NNs consistently rely upon the Gulf Stream-North Atlantic Current region for accurate predictions. Additionally, CESM1-trained NNs successfully predict the phasing of multidecadal variability in an observational data set, suggesting consistency in physical processes driving NASST variability between CESM1 and observations.