Simulating decadal variability in the north atlantic ocean

Observations and climate models suggest significant decadal variability within the North Atlantic subpolar gyre (NA SPG), though observations are sparse and models disagree on the details of this variability. Therefore, it is important to understand 1) the mechanisms of simulated decadal variability, 2) which parts of simulated variability are more faithful representations of reality, and 3) the implications for climate predictions. Here, we investigate the decadal variability in the NA SPG in the state-of-the-art, high resolution (0.25◦ ocean resolution), climate model ‘HadGEM3’. We find a decadal mode with a period of 17 years that explains 30% of the annual variance in related indices. The mode arises due to the advection of heat content anomalies, and shows asymmetries in the timescale of phase reversal between positive and negative phases. A negative feedback from temperature-driven density anomalies in the Labrador Sea (LS) allows for the phase reversal. The North Atlantic Oscillation (NAO), which exhibits the same periodicity, amplifies the mode. The atmosphere-ocean coupling is stronger during positive rather than negative NAO states, explaining the asymmetry. Within the NA SPG, there is potential predictability arising partly from this mode for up to 5 years. There are important similarities between observed and simulated variability, such as the apparent role for the propagation of heat content anomalies. However, observations suggest interannual LS density anomalies are salinity-driven. Salinity control of density would change the temperature feedback to the south, possibly limiting real-world predictive skill in the southern NA SPG with this model. Finally, to understand the diversity of behaviours, we analyse 42 present-generation climate models. Temperature and salinity biases are found to systematically influence the driver of density variability in the LS. Resolution is a good predictor of the biases. The dependence of variability on the background state has important implications for decadal predictions.