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Abstract We document the propagation of annular modes—zonally symmetric patterns of variability—in Mars's atmosphere using a reanalysis dataset. Mars's Northern Annular Mode (MNAM) sees anomalies of zonal‐mean zonal wind emerge near the subtropics and migrate poleward with a period of 150 days, similarly to Earth's Southern Annular Mode. The mechanism of propagation involves the interaction of the two leading empirical orthogonal functions that define the MNAM. Moreover, the propagation encourages alternating bands of surface wind stress to migrate polewards with a 150‐day period. In addition, a 150‐day periodicity in anomalous column dust optical depth most likely emerges in response to extrema of the MNAM. The combination of the impact of the MNAM's internally forced periodicity on the surface wind stress and the seasonal cycle may contribute to the inter‐annual variability of global dust events, as suggested by a Monte Carlo estimate that correctly approximates the observed incidence of global dust events. 

Abstract The variability of the Southern Hemisphere (SH) extratropical large‐scale circulation is dominated by the Southern Annular Mode (SAM), whose timescale is extensively used as a key metric in evaluating state‐of‐the‐art climate models. Past observational and theoretical studies suggest that the SAM lacks any internally generated (intrinsic) periodicity. Here, we show, using observations and a climate model hierarchy, that the SAM has an intrinsic 150‐day periodicity. This periodicity is robustly detectable in the power spectra and principal oscillation patterns (aka dynamical mode decomposition) of the zonal‐mean circulation, and in hemispheric‐scale precipitation and ocean surface wind stress. The 150‐day period is consistent with the predictions of a new reduced‐order model for the SAM, which suggests that this periodicity is associated with a complex interaction of turbulent eddies and zonal wind anomalies, as the latter propagate from low to high latitudes. These findings present a rare example of periodic oscillations arising from the internal dynamics of the extratropical turbulent circulations. Based on these findings, we further propose a new metric for evaluating climate models, and show that some of the previously reported shortcomings and improvements in simulating SAM's variability connect to the models' ability in reproducing this periodicity. We argue that this periodicity should be considered in evaluating climate models and understanding the past, current, and projected Southern Hemisphere climate variability. 

Abstract The forecast skill of numerical weather prediction (NWP) models and the intrinsic predictability can be different among weather regimes. Here, we examine the predictability of distinct Pacific‐North American weather regimes during extended boreal summer. The four identified weather regimes include Pacific trough, Arctic low, Arctic high, and Alaskan ridge. The medium range forecast skill of these regimes is quantified in the ECMWF and the National Centers for Environmental Prediction models from the TIGGE project. Based on anomaly correlation coefficient, persistence, and transition frequency, the highest forecast skill is consistently found for the Arctic high regime. Based on the instantaneous local dimension and persistence from a dynamical systems analysis, the Arctic high regime has the highest intrinsic predictability. The analysis also suggests that overall, the Pacific trough regime has the lowest intrinsic predictability. These findings are consistent with the forecast skills of the NWP models, and highlight the link between prediction skill and intrinsic predictability.