Pacific decadal variability (PDV), low-frequency changes in Pacific sea surface temperatures (SSTs), significantly impacts global climate. However, disentangling anthropogenic effects upon PDV is challenging since both vary on similar time scales. Using single-forcing climate model large ensembles, we find that anthropogenic forcing primarily drives a spatially-varying pattern of mean-state change in North Pacific SST that project onto leading PDV patterns, principally the Pacific Decadal Oscillation (PDO) and North Pacific Gyre Oscillation (NPGO). In fact, when the trend is determined by the model ensemble mean, there is no forced change of the PDV modes. However, analysis of single model realizations, where the mean-state trend cannot be cleanly identified, suggests an apparent anthropogenic change in NPGO decadal variability. This suggests that observed PDV responses to anthropogenic forcing may be erroneously convolved with the background trend pattern. Therefore, correctly determining the mean-state trend is a necessary precursor for identifying forced changes to PDV.

The El Niño-Southern Oscillation (ENSO) phenomenon – the dominant source of climate variability on seasonal to multi-year timescales – is predictable a few seasons in advance. Forecast skill at longer multi-year timescales has been found in a few models and forecast systems, but the robustness of this predictability across models has not been firmly established owing to the cost of running dynamical model predictions at longer lead times. In this study, we use a massive collection of multi-model hindcasts performed using model analogs to show that multi-year ENSO predictability is robust across models and arises predominantly due to skillful prediction of multi-year La Niña events following strong El Niño events.

Increasing coastal inundation risk in a warming climate will require accurate and reliable seasonal forecasts of sea level anomalies at fine spatial scale. In this study, we explore statistical downscaling of monthly hindcasts from six current seasonal prediction systems to provide high resolution prediction of sea level anomalies along the North American coast, including at several tide gauge stations. This involves applying a seasonally-invariant downscaling operator, constructing by linearly regressing high-resolution (1/12º) ocean reanalysis data against its coarse-grained (1º) counterpart, to each hindcast ensemble member for the period 1982-2011. The resulting high resolution coastal hindcasts are significantly more skillful than the original hindcasts interpolated onto the high resolution grid. Most of this improvement occurs during summer and fall, without impacting the seasonality of skill noted in previous studies. Analysis of the downscaling operator reveals that it boosts skill by amplifying the most predictable pattern while damping the less predictable pattern.