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
Authors: Viceto Carolina , Gorodetskaya Irina V. , Rinke Annette , Maturilli Marion , Rocha Alfredo , Crewell Susanne 
Published: January 2022

Abstract. 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.