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Abstract We investigate coupled climate model initialized predictions of the Pacific Decadal Oscillation (PDO) prediction skill in the Community Earth System Model (CESM) Seasonal to Multi Year Large Ensemble (SMYLE). The PDO is predictable up to a year in advance in SMYLE; however, the predictability depends on verification month, with skill degrading most rapidly in boreal spring for all initializations. To examine the role of teleconnections from El Niño–Southern Oscillation (ENSO) in the prediction skill of the PDO, we use a multi‐linear regression model. The linear model shows that initial value persistence explains most of the PDO prediction skill in SMYLE. In addition, the PDO prediction skill's seasonal dependence is fully reproduced only when ENSO is included as a predictor. These results suggest that ENSO has a strong influence on the seasonality of PDO predictions. 

Abstract In this paper we summarize improvements in climate model simulation of eastern boundary upwelling systems (EBUS) when changing the forcing dataset from the Coordinated Ocean-Ice Reference Experiments (CORE; ∼2° winds) to the higher-resolution Japanese 55-year Atmospheric Reanalysis for driving ocean–sea ice models (JRA55-do, ∼0.5°) and also due to refining ocean grid spacing from 1° to 0.1°. The focus is on sea surface temperature (SST), a key variable for climate studies, and which is typically too warm in climate model representation of EBUS. The change in forcing leads to a better-defined atmospheric low-level coastal jet, leading to more equatorward ocean flow and coastal upwelling, both in turn acting to reduce SST over the upwelling regions off the west coast of North America, Peru, and Chile. The refinement of ocean resolution then leads to narrower and stronger alongshore ocean flow and coastal upwelling, and the emergence of strong across-shore temperature gradients not seen with the coarse ocean model. Off northwest Africa the SST bias mainly improves with ocean resolution but not with forcing, while in the Benguela, JRA55-do with high-resolution ocean leads to lower SST but a substantial bias relative to observations remains. Reasons for the Benguela bias are discussed in the context of companion regional ocean model simulations. Finally, we address to what extent improvements in mean state lead to changes to the monthly to interannual variability. It is found that large-scale SST variability in EBUS on monthly and longer time scales is largely governed by teleconnections from climate modes and less sensitive to model resolution and forcing than the mean state. 

Interannual variations in marine net primary production (NPP) contribute to the variability of available living marine resources, as well as influence critical carbon cycle processes. Here we provide a global overview of near‐term (1 to 10 years) potential predictability of marine NPP using a novel set of initialized retrospective decadal forecasts from an Earth System Model. Interannual variations in marine NPP are potentially predictable in many areas of the ocean 1 to 3 years in advance, from temperate waters to the tropics, showing a substantial improvement over a simple persistence forecast. However, some regions, such as the subpolar Southern Ocean, show low potential predictability. We analyze how bottom‐up drivers of marine NPP (nutrients, light, and temperature) contribute to its predictability. Regions where NPP is primarily driven by the physical supply of nutrients (e.g., subtropics) retain higher potential predictability than high‐latitude regions where NPP is controlled by light and/or temperature (e.g., the Southern Ocean). We further examine NPP predictability in the world's Large Marine Ecosystems. With a few exceptions, we show that initialized forecasts improve potential predictability of NPP in Large Marine Ecosystems over a persistence forecast and may aid to manage living marine resources.