Blowing Snow at McMurdo Station, Antarctica During the AWARE Field Campaign: Surface and Ceilometer Observations

Abstract

Blowing snow (BLSN) is an impactful process in cold climates, affecting regional thermodynamics, radiation properties, and the surface mass balance of snow. Though it has significant climatic impacts, the process is still poorly understood and not widely included in weather and climate models. In 2016, the AWARE Field Campaign saw the deployment of a large suite of in situ and remote sensing instruments to McMurdo Station, Antarctica allowing for investigation of BLSN. A ceilometer‐based BLSN detection algorithm used elsewhere in Antarctica is applied to data from AWARE, yielding a BLSN frequency of 14.1% compared to 8.2% as detected by human observers. To increase confidence in detections, the algorithm is updated to have shorter temporal averaging and to include a variety of meteorological thresholds to limit false detections due to fog. Efforts to incorporate a laser disdrometer into the algorithm were unsuccessful. An unphysical dependence of particle size distributions on wind speed is found suggesting observations are problematic at wind speeds greater than 10 m s−1. The revised algorithm detected a BLSN frequency of 7.4%, increasing agreement with human observations and confidence that the process is actively occurring at the observation site. These observations are put into context of a climatology of human observations of BLSN at McMurdo station from 2002–2018. An annual average of 8.0%–14.0% is estimated, with a total annual range of 3.4%–21.3%. Regardless of whether BLSN is observed by humans or instrument, the majority of cases at this location are associated with ongoing precipitation.

Blowing Snow at McMurdo Station, Antarctica During the AWARE Field Campaign: Multi‐Instrument Observations of Blowing Snow

“…To demonstrate the MI BLSN depth algorithm, two case studies are presented that represent the variety of BLSN events McMurdo station experiences. The selected case days are the same as those presented in Loeb and Kennedy (2021), which readers are referred to for more in-depth descriptions of meteorological background and BLSN detection. An additional case study for 10 February 2016 is shown in Supporting Information S1 (Figure S6).…”

Using Machine Learning Algorithm to Detect Blowing Snow and Fog in Antarctica Based on Ceilometer and Surface Meteorology Systems

“…The site deployed various instruments, such as a Vaisala CL31 ceilometer, surface meteorology systems and particle size and velocity disdrometer (Parsivel 2 ; manufactured by Ott Hydromet GmbH at Kempten, Germany). In addition, the Antarctic Meteorological Research Center (AMRC) at the University of Wisconsin-Madison has provided the three-hour periods observation results by human observers at McMurdo Station since 1999 [10].…”

“…When there are no particles, the voltage signal of the photodiode is usually large. If the particles pass through the field of view, the output of the photodiode will be reduced to measure the shadow of the particles [10]. Parsivel 2 can directly measure the particle size and particle number and obtain the derived parameters, such as radar reflectivity and particle number density.…”

“…The sublimation of blowing snow is an important source of water vapor in Antarctica [9]. Blowing snow changes the nature of surface snowpacks by sublimation and transmission [10], affecting surface energy balance. According to the statistics, these processes remove from 50% to 80% of the snow cover in coastal Antarctica [11].…”

“…However, advection fog with wind speed above 3 m/s in summer is common at McMurdo Station. For this reason, Loeb and Kennedy thought that an additional 90% relative humidity threshold can be used to separate fog from blowing snow [10]. Therefore, they introduced the data of surface meteorology systems on the basis of Gossart to roughly distinguish blowing snow and fog by setting wind speed, humidity and visibility thresholds.…”