Atmospheric rivers and associated precipitation patterns during the ACLOUD and PASCAL campaigns near Svalbard (May–June 2017): case studies using observations, reanalyses, and a regional climate model

Recently, a significant increase in the atmospheric moisture content has been documented over the Arctic, where both local contributions and poleward moisture transport from lower latitudes can play a role. This study focuses on the anomalous moisture transport events confined to long and narrow corridors, known as atmospheric rivers (ARs), which are expected to have a strong influence on Arctic moisture amounts, precipitation, and the energy budget. During two concerted intensive measurement campaigns – Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) and the Physical feedbacks of Arctic planetary boundary layer, Sea ice, Cloud and AerosoL (PASCAL) – that took place at and near Svalbard, three high-water-vapour-transport events were identified as ARs, based on two tracking algorithms: the 30 May event, the 6 June event, and the 9 June 2017 event. We explore the temporal and spatial evolution of the events identified as ARs and the associated precipitation patterns in detail using measurements from the French (Polar Institute Paul Emile Victor) and German (Alfred Wegener Institute for Polar and Marine Research) Arctic Research Base (AWIPEV) in Ny-Ålesund, satellite-borne measurements, several reanalysis products (the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA) Interim (ERA-Interim); the ERA5 reanalysis; the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2); the Climate Forecast System version 2 (CFSv2); and the Japanese 55-Year Reanalysis (JRA-55)), and the HIRHAM regional climate model version 5 (HIRHAM5). Results show that the tracking algorithms detected the events differently, which is partly due to differences in the spatial and temporal resolution as well as differences in the criteria used in the tracking algorithms. The first event extended from western Siberia to Svalbard, caused mixed-phase precipitation, and was associated with a retreat of the sea-ice edge. The second event, 1 week later, had a similar trajectory, and most precipitation occurred as rain, although mixed-phase precipitation or only snowfall occurred in some areas, mainly over the coast of north-eastern Greenland and the north-east of Iceland, and no differences were noted in the sea-ice edge. The third event showed a different pathway extending from the north-eastern Atlantic towards Greenland before turning south-eastward and reaching Svalbard. This last AR caused high precipitation amounts on the east coast of Greenland in the form of rain and snow and showed no precipitation in the Svalbard region. The vertical profiles of specific humidity show layers of enhanced moisture that were concurrent with dry layers during the first two events and that were not captured by all of the reanalysis datasets, whereas the HIRHAM5 model misrepresented humidity at all vertical levels. There was an increase in wind speed with height during the first and last events, whereas there were no major changes in the wind speed during the second event. The accuracy of the representation of wind speed by the reanalyses and the model depended on the event. The objective of this paper was to build knowledge from detailed AR case studies, with the purpose of performing long-term analysis. Thus, we adapted a regional AR detection algorithm to the Arctic and analysed how well it identified ARs, we used different datasets (observational, reanalyses, and model) and identified the most suitable dataset, and we analysed the evolution of the ARs and their impacts in terms of precipitation. This study shows the importance of the Atlantic and Siberian pathways of ARs during spring and beginning of summer in the Arctic; the significance of the AR-associated strong heat increase, moisture increase, and precipitation phase transition; and the requirement for high-spatio-temporal-resolution datasets when studying these intense short-duration events.

Aerosol Atmospheric Rivers: Climatology, Event Characteristics, and Detection Algorithm Sensitivities

Leveraging the concept of atmospheric rivers (ARs), a detection technique based on a widely utilized global algorithm to detect ARs (Guan et al., 2018; Guan and Waliser, 2015, 2019) was recently developed to detect aerosol atmospheric rivers (AARs) using the Modern-Era Retrospective analysis for Research and Applications, Version 2 reanalysis (Chakraborty et al., 2021a). The current study further characterizes and quantifies various details of AARs that were not provided in that study, such as AARs’ seasonality, event characteristics, vertical profiles of aerosol mass mixing ratio and wind speed, and the fraction of total annual aerosol transport conducted by AARs. Analysis is also performed to quantify the sensitivity of AAR detection to the criteria and thresholds used by the algorithm. AARs occur more frequently over, and typically extend from, regions with higher aerosol emission. For a number of planetary-scale pathways that exhibit large climatological aerosol transport, AARs contribute 40–80 % to the total annual transport. DU AARs are more frequent in boreal spring, SS AARs are often more frequent during the boreal winter (summer) in the Northern (Southern) Hemisphere, CA AARs are more frequent during dry seasons and often originate from the global rainforests and industrial areas, and SU AARs are present in the Northern Hemisphere during all seasons. For most aerosol types, the mass mixing ratio within AARs is highest near the surface and decreases monotonically with altitude. However, DU and CA AARs over or near the African continent exhibit peaks in their aerosol mixing ratio profiles around 700 hPa. AAR event characteristics are mostly independent of species with mean length, width, and length/width ratio around 4000 km, 600 km, and 8, respectively.

Atmospheric Rivers Spur High-Tide Floods on U.S. West Coast

Researchers analyzed 36 years of data to understand how atmospheric rivers and other factors drive chronic coastal flooding.