Seasonal Variations in Drag Coefficient over a Sastrugi-Covered Snowfield in Coastal East Antarctica

Amory, Charles; Gallée, Hubert; Naaim-Bouvet, Florence; Favier, Vincent; Vignon, Étienne; Picard, Ghislain; Trouvilliez, Alexandre; Piard, Luc; Genthon, Christophe; Bellot, Hervé

doi:10.1007/s10546-017-0242-5

Cited by 39 publications

(45 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The pattern visible in Fig. 7 is common for blowing snow over Antarctica: a seasonal cycle peaking during the Antarctic winter (March-November) and displaying lower values for the rest of the year (Mahesh et al, 2003;Lenaerts et al, 2010;Scarchili et al, 2010;Palm et al, 2011;Amory et al, 2017). The overall blowing snow frequency is computed at PE for the 2010-2016 period and reaches 13 %.…”

Section: Frequency Of Blowing Snowmentioning

confidence: 90%

“…The mean time lag since the last precipitation event at PE (23 h) indicates that these events most likely occur shortly after a storm and that cloudless blowing snow (8 %) is mostly associated with katabatic winds. Apart from these factors, sastrugi might also have an impact on blowing snow (Amory et al, 2017) but are not measured here.…”

Section: Blowing Snow and Meteorological Regimesmentioning

confidence: 99%

See 1 more Smart Citation

Blowing snow detection from ground-based ceilometers: application to East Antarctica

et al. 2017

View full text Add to dashboard Cite

Abstract. Blowing snow impacts Antarctic ice sheet surface mass balance by snow redistribution and sublimation. However, numerical models poorly represent blowing snow processes, while direct observations are limited in space and time. Satellite retrieval of blowing snow is hindered by clouds and only the strongest events are considered. Here, we develop a blowing snow detection (BSD) algorithm for ground-based remote-sensing ceilometers in polar regions and apply it to ceilometers at Neumayer III and Princess Elisabeth (PE) stations, East Antarctica. The algorithm is able to detect (heavy) blowing snow layers reaching 30 m height. Results show that 78 % of the detected events are in agreement with visual observations at Neumayer III station. The BSD algorithm detects heavy blowing snow 36 % of the time at Neumayer (2011Neumayer ( -2015 and 13 % at PE station (2010)(2011)(2012)(2013)(2014)(2015)(2016). Blowing snow occurrence peaks during the austral winter and shows around 5 % interannual variability. The BSD algorithm is capable of detecting blowing snow both lifted from the ground and occurring during precipitation, which is an added value since results indicate that 92 % of the blowing snow is during synoptic events, often combined with precipitation. Analysis of atmospheric meteorological variables shows that blowing snow occurrence strongly depends on fresh snow availability in addition to wind speed. This finding challenges the commonly used parametrizations, where the threshold for snow particles to be lifted is a function of wind speed only. Blowing snow occurs predominantly during storms and overcast conditions, shortly after precipitation events, and can reach up to 1300 m a.g.l. in the case of heavy mixed events (precipitation and blowing snow together). These results suggest that synoptic conditions play an important role in generating blowing snow events and that fresh snow availability should be considered in determining the blowing snow onset.

show abstract

Section: Frequency Of Blowing Snowmentioning

confidence: 90%

Section: Blowing Snow and Meteorological Regimesmentioning

confidence: 99%

Blowing snow detection from ground-based ceilometers: application to East Antarctica

et al. 2017

View full text Add to dashboard Cite

show abstract

“…This low value was tuned in the CMIP3 version to compensate for a deficit of longwave downward radiation at surface. The roughness lengths for momentum z 0 and for heat

z_{0 t}

are set to

10^{- 3}

and

10^{- 4} m

, respectively. These values are close to measurements at Dome C (Vignon et al, ) and in reasonable agreement with measurements over other regions of the Antarctic Plateau (Amory et al, ). The snow thermal inertia is set to

350 J {normalm}^{- 2} {normalK}^{- 1} {normals}^{- 1 / 2}

instead of the original largely overestimated value of

2, 000 J {normalm}^{- 2} {normalK}^{- 1} {normals}^{- 1 / 2}

.…”

Section: Climatological Settings Data and Simulationsmentioning

confidence: 99%

“…Extrapolation to observation levels are also plotted (dotted line, see legend for details). Further information on measurements at D17, D47, and D85 stations can be found in Wendler et al (), Barral et al (), and Amory et al () (see also the webpage http://amrc.ssec.wisc.edu/aws/index.php?region=Adelie\%20Coast&year=2017&mode=uw). Note that while landscapes at Dome C, D47, and D85 are relatively homogeneous—suggesting a representativity of the local measurement at the LMDZ grid scale (

\approx 110 km \times 140 km

)—the topographic and geographic spatial variability near D17 at the 10 km scale makes the simulation‐observation comparison delicate at this site.…”

Section: Climate Simulations With the Free Lmdz Model: Impact Of The mentioning

confidence: 99%

Modeling the Dynamics of the Atmospheric Boundary Layer Over the Antarctic Plateau With a General Circulation Model

Vignon

Hourdin

Genthon

et al. 2018

J Adv Model Earth Syst

Self Cite

View full text Add to dashboard Cite

Observations evidence extremely stable boundary layers (SBL) over the Antarctic Plateau and sharp regime transitions between weakly and very stable conditions. Representing such features is a challenge for climate models. This study assesses the modeling of the dynamics of the boundary layer over the Antarctic Plateau in the LMDZ general circulation model. It uses 1 year simulations with a stretched‐grid over Dome C. The model is nudged with reanalyses outside of the Dome C region such as simulations can be directly compared to in situ observations. We underline the critical role of the downward longwave radiation for modeling the surface temperature. LMDZ reasonably represents the near‐surface seasonal profiles of wind and temperature but strong temperature inversions are degraded by enhanced turbulent mixing formulations. Unlike ERA‐Interim reanalyses, LMDZ reproduces two SBL regimes and the regime transition, with a sudden increase in the near‐surface inversion with decreasing wind speed. The sharpness of the transition depends on the stability function used for calculating the surface drag coefficient. Moreover, using a refined vertical grid leads to a better reversed “S‐shaped” relationship between the inversion and the wind. Sudden warming events associated to synoptic advections of warm and moist air are also well reproduced. Near‐surface supersaturation with respect to ice is not allowed in LMDZ but the impact on the SBL structure is moderate. Finally, climate simulations with the free model show that the recommended configuration leads to stronger inversions and winds over the ice‐sheet. However, the near‐surface wind remains underestimated over the slopes of East‐Antarctica.

show abstract

“…Jackson and Carroll (1978) and Inoue (1989) observed that the greatest surface roughnesses in Antarctica occur over sastrugi fields. Amory et al (2016Amory et al ( , 2017 observed sastrugi as they adjust to changing wind direction and measured the seasonal correlation between wind drag and sastrugi size. Andreas and Claffey (1995) observed atmospheric drag change by 30% in 12 hr in response to snow redistribution.…”

Section: Introductionmentioning

confidence: 99%

Statistical Classification of Self‐Organized Snow Surfaces

Kochanski

Anderson

Tucker

2018

Geophysical Research Letters

View full text Add to dashboard Cite

Wind‐swept snow self‐organizes into bedforms. These bedforms affect local and global energy fluxes but have not been incorporated into Earth system models because the conditions governing their development are not well understood. To address this difficulty, we created statistical classifiers, drawn from 736 hr of time‐lapse footage in the Colorado Front Range, that predict bedform presence as a function of wind speed and time since snowfall. These classifiers provide the first quantitative predictions of bedform and sastrugi presence in varying weather conditions. We find that the likelihood that a snow surface is covered by bedforms increases with time since snowfall and with wind speed and that the likelihood that a surface is covered by sastrugi increases with time and with the highest wind speeds. Our observations will be useful to Earth system modelers and represent a new step toward understanding self‐organized processes that ornament 8% of the surface of the planet.

show abstract

Seasonal Variations in Drag Coefficient over a Sastrugi-Covered Snowfield in Coastal East Antarctica

Cited by 39 publications

References 60 publications

Blowing snow detection from ground-based ceilometers: application to East Antarctica

Blowing snow detection from ground-based ceilometers: application to East Antarctica

Modeling the Dynamics of the Atmospheric Boundary Layer Over the Antarctic Plateau With a General Circulation Model

Statistical Classification of Self‐Organized Snow Surfaces

Contact Info

Product

Resources

About