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- ItemThe different stratospheric influence on cold-extremes in Eurasia and North America(London : Springer Nature, 2018) Kretschmer, Marlene; Cohen, Judah; Matthias, Vivien; Runge, Jakob; Coumou, DimThe stratospheric polar vortex can influence the tropospheric circulation and thereby winter weather in the mid-latitudes. Weak vortex states, often associated with sudden stratospheric warmings (SSW), have been shown to increase the risk of cold-spells especially over Eurasia, but its role for North American winters is less clear. Using cluster analysis, we show that there are two dominant patterns of increased polar cap heights in the lower stratosphere. Both patterns represent a weak polar vortex but they are associated with different wave mechanisms and different regional tropospheric impacts. The first pattern is zonally symmetric and associated with absorbed upward-propagating wave activity, leading to a negative phase of the North Atlantic Oscillation (NAO) and cold-air outbreaks over northern Eurasia. This coupling mechanism is well-documented in the literature and is consistent with the downward migration of the northern annular mode (NAM). The second pattern is zonally asymmetric and linked to downward reflected planetary waves over Canada followed by a negative phase of the Western Pacific Oscillation (WPO) and cold-spells in Central Canada and the Great Lakes region. Causal effect network (CEN) analyses confirm the atmospheric pathways associated with this asymmetric pattern. Moreover, our findings suggest the reflective mechanism to be sensitive to the exact region of upward wave-activity fluxes and to be state-dependent on the strength of the vortex. Identifying the causal pathways that operate on weekly to monthly timescales can pave the way for improved sub-seasonal to seasonal forecasting of cold spells in the mid-latitudes.
- ItemS2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts(Malden, MA : Wiley-Blackwell, 2018) Cohen, Judah; Coumou, Dim; Hwang, Jessica; Mackey, Lester; Orenstein, Paulo; Totz, Sonja; Tziperman, EliThe discipline of seasonal climate prediction began as an exercise in simple statistical techniques. However, today the large government forecast centers almost exclusively rely on complex fully coupled dynamical forecast systems for their subseasonal to seasonal (S2S) predictions while statistical techniques are mostly neglected and those techniques still in use have not been updated in decades. In this Opinion Article, we argue that new statistical techniques mostly developed outside the field of climate science, collectively referred to as machine learning, can be adopted by climate forecasters to increase the accuracy of S2S predictions. We present an example of where unsupervised learning demonstrates higher accuracy in a seasonal prediction than the state-of-the-art dynamical systems. We also summarize some relevant machine learning methods that are most applicable to climate prediction. Finally, we show by comparing real-time dynamical model forecasts with observations from winter 2017/2018 that dynamical model forecasts are almost entirely insensitive to polar vortex (PV) variability and the impact on sensible weather. Instead, statistical forecasts more accurately predicted the resultant sensible weather from a mid-winter PV disruption than the dynamical forecasts. The important implication from the poor dynamical forecasts is that if Arctic change influences mid-latitude weather through PV variability, then the ability of dynamical models to demonstrate the existence of such a pathway is compromised. We conclude by suggesting that S2S prediction will be most beneficial to the public by incorporating mixed or a hybrid of dynamical forecasts and updated statistical techniques such as machine learning.