Model Bias Reduction and the Limits of Oceanic Decadal Predictability: Importance of the Deep Ocean

Abstract

cean general circulation models (GCMs), as part of comprehensive climate models, are extensively used for experimental decadal climate prediction. Understanding the limits of decadal ocean predictability is critical for making progress in these efforts. However, when forced with observed fields at the surface, ocean models develop biases in temperature and salinity. Here, the authors ask two complementary questions related to both decadal prediction and model bias: 1) Can the bias be temporarily reduced and the prediction improved by perturbing the initial conditions? 2) How fast will such initial perturbations grow? To answer these questions, the authors use a realistic ocean GCM and compute temperature and salinity perturbations that reduce the model bias most efficiently during a given time interval. The authors find that to reduce this bias, especially pronounced in the upper ocean above 1000 m, initial perturbations should be imposed in the deep ocean (specifically, in the Southern Ocean). Over 14 yr, a 0.1-K perturbation in the deep ocean can induce a temperature anomaly of several kelvins in the upper ocean, partially reducing the bias. A corollary of these results is that small initialization errors in the deep ocean can produce large errors in the upper-ocean temperature on decadal time scales, which can be interpreted as a decadal predictability barrier associated with ocean dynamics. To study the mechanisms of the perturbation growth, the authors formulate an idealized model describing temperature anomalies in the Southern Ocean. The results indicate that the strong mean meridional temperature gradient in this region enhances the sensitivity of the upper ocean to deep-ocean perturbations through nonnormal dynamics generating pronounced stationary-wave patterns.