This work documents how the midlatitude, eddy-driven jets respond to climate change using model output from phase 5 of the Coupled Model

15The Arctic lower atmosphere has warmed more rapidly than that of the globe as a whole, and this 16 has been accompanied by unprecedented sea ice melt.

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Recent work on atmospheric rivers (ARs) has led to a characterization of these impactful features as primarily cold-season phenomena. Here, an all-season analysis of AR incidence in the North Pacific basin is performed for the period spanning 1979–2014 using the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis dataset. An occurrence-based detection algorithm is developed and employed to identify and characterize ARs in instantaneous fields of anomalous vertically integrated water vapor transport. The all-season climatology and variability of AR frequencies due to the seasonal cycle, the El Niño–Southern Oscillation (ENSO), the Madden–Julian oscillation (MJO), and their interactions are presented based on composites of the detected features. The results highlight that ARs exist throughout the year over the North Pacific, although their preferred locations shift substantially throughout the year. This seasonal cycle manifests itself as northward and westward displacement of ARs during the Northern Hemisphere warm seasons, rather than an absolute change in the number of ARs within the domain. It is also shown that changes to the North Pacific mean-state due to ENSO and the MJO may enhance or completely offset the seasonal cycle of AR activity, but that such influences on AR frequencies vary greatly based on location.

Previous studies have suggested that Arctic amplification has caused planetary‐scale waves to elongate meridionally and slow down, resulting in more frequent blocking patterns and extreme weather. Here trends in the meridional extent of atmospheric waves over North America and the North Atlantic are investigated in three reanalyses, and it is demonstrated that previously reported positive trends are likely an artifact of the methodology. No significant decrease in planetary‐scale wave phase speeds are found except in October‐November‐December, but this trend is sensitive to the analysis parameters. Moreover, the frequency of blocking occurrence exhibits no significant increase in any season in any of the three reanalyses, further supporting the lack of trends in wave speed and meridional extent. This work highlights that observed trends in midlatitude weather patterns are complex and likely not simply understood in terms of Arctic amplification alone.

Neural networks have become increasingly prevalent within the geosciences, although a common limitation of their usage has been a lack of methods to interpret what the networks learn and how they make decisions. As such, neural networks have often been used within the geosciences to most accurately identify a desired output given a set of inputs, with the interpretation of what the network learns used as a secondary metric to ensure the network is making the right decision for the right reason. Neural network interpretation techniques have become more advanced in recent years, however, and we therefore propose that the ultimate objective of using a neural network can also be the interpretation of what the network has learned rather than the output itself. We show that the interpretation of neural networks can enable the discovery of scientifically meaningful connections within geoscientific data. In particular, we use two methods for neural network interpretation called backward optimization and layerwise relevance propagation, both of which project the decision pathways of a network back onto the original input dimensions. To the best of our knowledge, LRP has not yet been applied to geoscientific research, and we believe it has great potential in this area. We show how these interpretation techniques can be used to reliably infer scientifically meaningful information from neural networks by applying them to common climate patterns. These results suggest that combining interpretable neural networks with novel scientific hypotheses will open the door to many new avenues in neural network‐related geoscience research.

The initiation of mitosis requires the activation of M-phase promoting factor (MPF). MPF activation and its subcellular localization are dependent on the phosphorylation state of its components, cdc2 and cyclin B1. In a two-hybrid screen using a bait protein to mimic phosphorylated cyclin B1, we identi®ed a novel interaction between cyclin B1 and patched1 (ptc1), a tumor suppressor associated with basal cell carcinoma (BCC). Ptc1 interacted speci®cally with constitutively phosphorylated cyclin B1 derivatives and was able to alter their normal subcellular localization. Furthermore, addition of the ptc1 ligand, sonic hedgehog (shh), disrupts this interaction and allows cyclin B1 to localize to the nucleus. Expression of ptc1 in 293T cells was inhibitory to cell proliferation; this inhibition could be relieved by coexpression of a cyclin B1 derivative that constitutively localizes to the nucleus and that could not interact with ptc1 due to phosphorylation-site mutations to Ala. In addition, we demonstrate that endogenous ptc1 and endogenous cyclin B1 interact in vivo. The ®ndings reported here demonstrate that ptc1 participates in determining the subcellular localization of cyclin B1 and suggest a link between the tumor suppressor activity of ptc1 and the regulation of cell division. Thus, we propose that ptc1 participates in a G 2 /M checkpoint by regulating the localization of MPF. Keywords: basal cell carcinoma/cytoplasmic retention signal/G 2 /M checkpoint/M-phase promoting factor/ nevoid basal cell carcinoma syndrome

Upon landfall, atmospheric rivers (ARs)-plumes of intense water vapor transport-often trigger weather and hydrologic extremes. Presently, no guidance is available to alert decision makers to anomalous AR activity within the subseasonal time scale (~2-5 weeks). Here, we construct and evaluate an empirical prediction scheme for anomalous AR activity based solely on the initial state of two prominent modes of tropical variability: the Madden-Julian oscillation (MJO) and the quasi-biennial oscillation (QBO). The MJO-the dominant mode of intraseasonal variability in the tropical troposphere-modulates landfalling AR activity along the west coast of North America by exciting large-scale circulation anomalies over the North Pacific. In light of emerging science regarding the modulation of the MJO by the QBO-the dominant mode of interannual variability in the tropical stratosphere-we demonstrate that the MJO-AR relationship is further influenced by the QBO. Evaluating the prediction scheme over 36 boreal winter seasons, we find skillful subseasonal "forecasts of opportunity" when knowledge of the MJO and the QBO can be leveraged to predict periods of increased or decreased AR activity. Certain MJO and QBO phase combinations provide empirical subseasonal predictive skill for anomalous AR activity that exceeds that of a state-of-the-art numerical weather prediction model. Given the wide-ranging impacts associated with landfalling ARs, even modest gains in the subseasonal prediction of anomalous AR activity may support decision making and benefit numerous sectors of society.