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Uncertainty about sea-level rise is dominated by uncertainty about iceberg calving, mass loss from glaciers or ice sheets by fracturing. Review of the rapidly growing calving literature leads to a few overarching hypotheses. Almost all calving occurs near or just downglacier of a location where ice flows into an environment more favorable for calving, so the calving rate is controlled primarily by flow to the ice margin rather than by fracturing. Calving can be classified into five regimes, which tend to be persistent, predictable, and insensitive to small perturbations in flow velocity, ice characteristics, or environmental forcing; these regimes can be studied instrumentally. Sufficiently large perturbations may cause sometimes-rapid transitions between regimes or between calving and noncalving behavior, during which fracturing may control the rate of calving. Regime transitions underlie the largest uncertainties in sea-level rise projections, but with few, important exceptions, have not been observed instrumentally. This is especially true of the most important regime transitions for sea-level rise. Process-based models informed by studies of ongoing calving, and assimilation of deep-time paleoclimatic data, may help reduce uncertainties about regime transitions. Failure to include calving accurately in predictive models could lead to large underestimates of warming-induced sea-level rise. ▪ Icebergmore »calving, the breakage of ice from glaciers and ice sheets, affects sea level and many other environmental issues. ▪ Modern rates of iceberg calving usually are controlled by the rate of ice flow past restraining points, not by the brittle calving processes. ▪ Calving can be classified into five regimes, which are persistent, predictable, and insensitive to small perturbations. ▪ Transitions between calving regimes are especially important and with warming might cause faster sea-level rise than generally projected. Expected final online publication date for the Annual Review of Earth and Planetary Sciences, Volume 51 is May 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.« less

Summertime air quality is a growing public health concern in the populated region of Northern Utah. Whereas winter air pollution is highly linked with local atmospheric temperature inversions associated with upper atmospheric high-pressure and radiational cooling in valleys, the relationship between climate factors and the frequency of poor air quality during summer is still unknown. Analyzing the last 20 years of data, we demonstrated that summertime unhealthy days (as defined by PM2.5 air quality index level) in Northern Utah highly correlate with the number of dry-hot days, wildfire size, and an upper atmospheric ridge over the Northwestern United States. The persistent atmospheric ridge enhances lightning-caused fire burned areas in northwestern states and then transports the wildfire smoke toward Northern Utah. Similarly, climate model simulations confirm observational findings, such as an increasing trend of the upper atmospheric ridge and summertime dry days in the northwestern states. Such metrics developed in this study could be used to establish longer-term monitoring and seasonal forecasting for air quality and its compounding factors, which is currently limited to forecasting products for only several days.

Vehicles can utilize their sensors or receive messages from other vehicles to acquire information about the surrounding environments. However, the information may be inaccurate, faulty, or maliciously compromised due to sensor failures, communication faults, or security attacks. The goal of this work is to detect if a lane-changing decision and the sensed or received information are anomalous. We develop three anomaly detection approaches based on deep learning: a classifier approach, a predictor approach, and a hybrid approach combining the classifier and the predictor. All of them do not need anomalous data nor lateral features so that they can generally consider lane-changing decisions before the vehicles start moving along the lateral axis. They achieve at least 82% and up to 93% F1 scores against anomaly on data from Simulation of Urban MObility (SUMO) and HighD. We also examine system properties and verify that the detected anomaly includes more dangerous scenarios.

Pairs of interacting transcription factors (TFs) have previously been shown to bind to enhancers and promoters and contribute to their physical interactions. However, to date, we have limited knowledge about such TF pairs. To fill this void, we systematically studied the co-occurrence of TF-binding motifs in interacting enhancer–promoter (EP) pairs in seven human cell lines. We discovered 423 motif pairs that significantly co-occur in enhancers and promoters of interacting EP pairs. We demonstrated that these motif pairs are biologically meaningful and significantly enriched with motif pairs of known interacting TF pairs. We also showed that the identified motif pairs facilitated the discovery of the interacting EP pairs. The developed pipeline, EPmotifPair, together with the predicted motifs and motif pairs, is available at https://doi.org/10.6084/m9.figshare.14192000. Our study provides a comprehensive list of motif pairs that may contribute to EP physical interactions, which facilitate generating meaningful hypotheses for experimental validation.