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This study quantifies the contribution of atmospheric rivers (ARs) to annual and extreme river runoff and evaluates the relationships between watershed characteristics and AR-related maximum river runoff across British Columbia and southeastern Alaska (BCSAK). Datasets used include gauged runoff from 168 unregulated watersheds, topographic characteristics of those watersheds, a regionalARcatalog, and integrated vapor transport fields for water years (WYs) 1979-2016. ARs contribute ;22% of annual river runoff along the Coast and Insular Mountains watersheds, which decreases inland to ;11% in the watersheds of the Interior Mountains and Plateau. Average association between ARs and annual maximum river runoff attains .80%, .50%, and ,50% along the watersheds of the western flanks of the Coast Mountains, the Interior Mountains, and Interior Plateau, respectively. There is no significant change in AR-related extreme annual maximum runoff across BCSAK during 1979-2016. AR conditions occur during 25 out of 32 of the flood-related natural disasters in British Columbia duringWYs 1979-2016. AR-related annual maximum runoff magnitude is significantly higher than non-AR-related annual maximum runoff for 30% of the watersheds studied. Smaller and steeper watersheds closer to the coast are more susceptible to AR-related annual maximum runoff than their inland counterparts. These results illustrate the importance ofARactivity as a major control for the distribution of peak runoff in BCSAK. This work provides insights on the hydrological response of watersheds of northwestern North America to landfalling ARs that may improve flood risk assessment and disaster management in this region. [ABSTRACT FROM AUTHOR]

Intrusions of warm, moist air into the Arctic during winter have emerged as important contributors to Arctic surface warming. Previous studies indicate that temperature, moisture, and hydrometeor enhancements during intrusions all make contributions to surface warming via emission of radiation down to the surface. Here, datasets from instrumentation at the Atmospheric Radiation Measurement User Facility in Utqiaġvik (formerly Barrow) for the six months from November through April for the six winter seasons of 2013/14–2018/19 were used to quantify the atmospheric state. These datasets subsequently served as inputs to compute surface downwelling longwave irradiances via radiative transfer computations at 1-min intervals with different combinations of constituents over the six winter seasons. The computed six winter average irradiance with all constituents included was 205.0 W m−2, close to the average measured irradiance of 206.7 W m−2, a difference of −0.8%. During this period, water vapor was the most important contributor to the irradiance. The computed average irradiance with dry gas was 71.9 W m−2. Separately adding water vapor, liquid, or ice to the dry atmosphere led to average increases of 2.4, 1.8, and 1.6 times the dry atmosphere irradiance, respectively. During the analysis period, 15 episodes of warm, moist air intrusions were identified. During the intrusions, individual contributions from elevated temperature, water vapor, liquid water, and ice water were found to be comparable to each other. These findings indicate that all properties of the atmospheric state must be known in order to quantify the radiation coming down to the Arctic surface during winter. [ABSTRACT FROM AUTHOR]

The rapid increase in open-water surface area in the Arctic, resulting from sea ice melting during the summer likely as a result of global warming, may lead to an increase in fog [defined as a cloud with a base height below 1000 ft (~304 m)], which may imperil ships and small aircraft transportation in the region. There is a need for monitoring fog formation over the Arctic. Given that ground-based observations of fog over Arctic open water are very sparse, satellite observations may become the most effective way for Arctic fog monitoring. We developed a fog detection algorithm using the temperature difference between the cloud top and the surface, called ∂T in this work. A fog event is said to be detected if ∂T is greater than a threshold, which is typically between −6 and −12 K, depending on the time of the day (day or night) and the surface types (open water or sea ice). We applied this method to the coastal regions of Chukchi Sea and Beaufort Sea near Barrow, Alaska (now known as Utqiaġvik), during the months of March–October. Training with satellite observations between 2007 and 2014 over this region, the ∂T method can detect Arctic fog with an optimal probability of detection (POD) between 74% and 90% and false alarm rate (FAR) between 5% and 17%. These statistics are validated with data between 2015 and 2016 and are shown to be robust from one subperiod to another. [ABSTRACT FROM AUTHOR]

Testing the often-made assumption that ice particle aggregates (snowflakes) are well represented by oblate spheroids, ellipsoid fits are applied to aggregate images. An algorithm to retrieve both the ellipsoidal parameters and the orientations of the fitted ellipsoids is applied to Multi-Angle Snowflake Camera measurements of ice particle aggregates observed in Alaska. The resulting ellipsoids have shapes closer to prolate spheroids than the oft-assumed oblate spheroids. A robust linear relationship exists between the two characteristic aspect ratios of the ellipsoids. The most probable orientation of the maximum dimension of the retrieved ellipsoids is not in the horizontal plane, and the rotational angles of the maximum dimensions in the horizontal plane are not uniform, but instead display some correlation with the wind direction at the times of the measurements. The retrieval results can be used to improve the representation of aggregates in microphysics and/or electromagnetic radiation scattering models applicable to radar and satellite measurements. [ABSTRACT FROM AUTHOR]

The fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) is being used for forecasting the atmospheric layers of aircraft condensation trail (contrail) formation. Contrail forecasts are based on a conventional algorithm describing the adiabatic mixing of aircraft exhaust with environmental air. Algorithm input data are MM5-forecasted temperature and humidity values at defined pressure or sigma levels, and an aircraft-relevant contrail factor that is derived statistically from a contrail observation database. For comparison purposes a mean overlap (MO), which is a parameter quantifying the overlap between forecasted contrail layers and contrail layers derived from radiosonde measurements, is introduced. Mean overlap values are used to test for the altitude and thickness of forecasted contrail layers. Contrail layers from Arctic MM5 and Air Force Weather Agency (AFWA) MM5 models agree well with contrail layers derived from corresponding radiosonde measurements for certain forecast periods; a steady decrease of the MO shows a decrease of contrail forecast accuracy with the increasing forecast period. Mean overlaps around 82% indicate reasonable results for the 48-h forecasts. Verification of MM5 with actual contrail observations shows a slightly better performance of Arctic MM5. A possible dry bias might occur in humidity measurements at low temperature levels due to temperature-dependence errors of the humidity sensor polymer, which might also affect forecasts of humidity of the upper troposphere or lower stratosphere. Despite this fact, this contrail verification study shows hit rates higher than 82% within forecast periods up to 36 h using Arctic MM5. [ABSTRACT FROM AUTHOR]