Influence of meter-scale wind-formed features on the variability of the microwave brightness temperature around Dome C in Antarctica

Picard, Ghislain; Royer, A.; Arnaud, Laurent; Fily, Michel

doi:10.5194/tc-8-1105-2014

Cited by 53 publications

(84 citation statements)

References 61 publications

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“…Hence they are more likely to be representative of the average snow SSA in the topmost 2 cm at Dome C, even though larger-scale spatial variability exists (e.g. Picard et al, 2014). This probably explains the success of Crocus to simulate the SSA variations derived from spectral albedo measurements (Fig.…”

Section: Metamorphism Snowfall and Wind-driven Ssa Variationsmentioning

confidence: 85%

Summertime evolution of snow specific surface area close to the surface on the Antarctic Plateau

et al. 2015

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Abstract. On the Antarctic Plateau, snow specific surface area (SSA) close to the surface shows complex variations at daily to seasonal scales which affect the surface albedo and in turn the surface energy budget of the ice sheet. While snow metamorphism, precipitation and strong wind events are known to drive SSA variations, usually in opposite ways, their relative contributions remain unclear. Here, a comprehensive set of SSA observations at Dome C is analysed with respect to meteorological conditions to assess the respective roles of these factors. The results show an average 2-to-3-fold SSA decrease from October to February in the topmost 10 cm in response to the increase of air temperature and absorption of solar radiation in the snowpack during spring and summer. Surface SSA is also characterized by significant daily to weekly variations due to the deposition of small crystals with SSA up to 100 m 2 kg −1 onto the surface during snowfall and blowing snow events. To complement these field observations, the detailed snowpack model Crocus is used to simulate SSA, with the intent to further investigate the previously found correlation between interannual variability of summer SSA decrease and summer precipitation amount. To this end, some Crocus parameterizations have been adapted to Dome C conditions, and the model was forced by ERAInterim reanalysis. It successfully matches the observations at daily to seasonal timescales, except for the few cases when snowfalls are not captured by the reanalysis. On the contrary, the interannual variability of summer SSA decrease is poorly simulated when compared to 14 years of microwave satellite data sensitive to the near-surface SSA. A simulation with disabled summer precipitation confirms the weak influence in the model of the precipitation on metamorphism, with only 6 % enhancement. However, we found that disabling strong wind events in the model is sufficient to reconciliate the simulations with the observations. This suggests that Crocus reproduces well the contributions of metamorphism and precipitation on surface SSA, but snow compaction by the wind might be overestimated in the model.

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Section: Metamorphism Snowfall and Wind-driven Ssa Variationsmentioning

confidence: 85%

Summertime evolution of snow specific surface area close to the surface on the Antarctic Plateau

et al. 2015

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“…SNTHERM simulates a grain size that is closer in concept to the visual estimates of grain diameter than the other two models. The large spread when coupling snowpack evolution and microwave models, due to the differences in the modelling of snow microstructure, is consistent with the wide range of studies that have investigated how to link snowpack observations of microstructure to the microstructure parameter required in electromagnetic models (e.g Kendra et al, 1998;Du et al, 2005;Liang et al, 2008;The Cryosphere, 11, 229-246, 2017 www.the-cryosphere.net/11/229/2017/ Durand et al, 2008;Brucker et al, 2011;Xu et al, 2012;Montpetit et al, 2013;Roy et al, 2013;Rutter et al, 2014;Picard et al, 2014). Nevertheless there are differences between microwave emission models for a particular microstructure evolution model and even differences within the same family of emission models.…”

Section: Discussionmentioning

confidence: 70%

“…This is highlighted by the treatment of field observations of SSA to derive optical diameter as even these require some form of scaling (e.g. Montpetit et al, 2013;Picard et al, 2014;Rutter et al, 2014). Use of scale factors can improve brightness temperature accuracy at some frequency and polarisations but may decrease the accuracy at others.…”

Section: Discussionmentioning

confidence: 99%

“…In an attempt to put these results into context, there are a number of studies that have quantified brightness temperature simulation errors for these models. These fall into different categories, depending on sensor characteristics, the source of the evaluation data (ground-based, airborne, satellite) and presence of ice lenses , the treatment of the snow microstructure (Picard et al, 2014), snow type, observation angle, and the specific electromagnetic model , and the underlying substrate (Lemmetyinen et al, 2009;Derksen et al, 2014). Examples of unscaled field observations of microstructure compared with ground-based observations include the HUT simulations of , who found an RMSE of 10-34 K, and Rutter et al (2014), who found a bias of 34-68 K that was reduced to < 0.6 K upon application of grain scale factors of 2.6-5.3.…”

Section: Discussionmentioning

confidence: 99%

“…Examples of unscaled field observations of microstructure compared with ground-based observations include the HUT simulations of , who found an RMSE of 10-34 K, and Rutter et al (2014), who found a bias of 34-68 K that was reduced to < 0.6 K upon application of grain scale factors of 2.6-5.3. Scaling, or best-fit, relationships were used by Durand et al (2008) (mean absolute error 3.1 K at V-pol and 9.3 K at H-pol), Montpetit et al (2013) , Brucker et al (2011) (RMSE 1.5 K), Picard et al (2014) (RMSE 1-11 K), and Roy et al (2013) . However, in some cases the frequencydependent results have been combined and in others kept separate.…”

Section: Discussionmentioning

confidence: 99%

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Microstructure representation of snow in coupled snowpack and microwave emission models

Sandells¹,

Essery

Rutter

et al. 2017

The Cryosphere

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Abstract. This is the first study to encompass a wide range of coupled snow evolution and microwave emission models in a common modelling framework in order to generalise the link between snowpack microstructure predicted by the snow evolution models and microstructure required to reproduce observations of brightness temperature as simulated by snow emission models. Brightness temperatures at 18.7 and 36.5 GHz were simulated by 1323 ensemble members, formed from 63 Jules Investigation Model snowpack simulations, three microstructure evolution functions, and seven microwave emission model configurations. Two years of meteorological data from the Sodankylä Arctic Research Centre, Finland, were used to drive the model over the 2011-2012 and 2012-2013 winter periods. Comparisons between simulated snow grain diameters and field measurements with an IceCube instrument showed that the evolution functions from SNTHERM simulated snow grain diameters that were too large (mean error 0.12 to 0.16 mm), whereas MOSES and SNICAR microstructure evolution functions simulated grain diameters that were too small (mean error −0.16 to −0.24 mm for MOSES and −0.14 to −0.18 mm for SNICAR). No model (HUT, MEMLS, or DMRT-ML) provided a consistently good fit across all frequencies and polarisations. The smallest absolute values of mean bias in brightness temperature over a season for a particular frequency and polarisation ranged from 0.7 to 6.9 K.Optimal scaling factors for the snow microstructure were presented to compare compatibility between snowpack model microstructure and emission model microstructure. Scale factors ranged between 0.3 for the SNTHERMempirical MEMLS model combination (2011)(2012) and 3.3 for DMRT-ML in conjunction with MOSES microstructure (2012)(2013). Differences in scale factors between microstructure models were generally greater than the differences between microwave emission models, suggesting that more accurate simulations in coupled snowpack-microwave model systems will be achieved primarily through improvements in the snowpack microstructure representation, followed by improvements in the emission models. Other snowpack parameterisations in the snowpack model, mainly densification, led to a mean brightness temperature difference of 11 K at 36.5 GHz H-pol and 18 K at V-pol when the Jules Investigation Model ensemble was applied to the MOSES microstructure and empirical MEMLS emission model for the 2011-2012 season. The impact of snowpack parameterisation increases as the microwave scattering increases. Consistency between snowpack microstructure and microwave emission models, and the choice of snowpack densification algorithms should be considered in the design of snow mass retrieval systems and microwave data assimilation systems.

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Modeling the impact of snow drift on the decameter-scale variability of snow properties on the Antarctic Plateau

Libois

Picard

Arnaud

et al. 2014

J. Geophys. Res. Atmos.

133

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The annual accumulation and the physical properties of snow close to the surface on the Antarctic Plateau are characterized by a large decameter‐scale variability resulting from snow drift that is not simulated by one‐dimensional snow evolution models. Here, the detailed snowpack model Crocus was adapted to Antarctic conditions and then modified to account for this drift‐induced variability using a stochastic snow redistribution scheme. For this, 50 simulations were run in parallel and were allowed to exchange snow mass according to rules driven by wind speed. These simple rules were developed and calibrated based on in situ pictures of the snow surface recorded for two years. The simulation performed with this new model shows three substantial improvements with respect to standard Crocus simulations. First, significant and rapid variations of snow height observed in hourly measurements are well reproduced, highlighting the crucial role of snow drift in snow accumulation. Second, the statistics of annual accumulation is also simulated successfully, including the years with net ablation which are as frequent as 15% in the observations and 11% in the simulation. Last, the simulated vertical profiles of snow density and specific surface area down to 50 cm depth were compared to 98 profiles measured at Dome C during the summer 2012–2013. The observed spatial variability is partly reproduced by the new model, especially close to the surface. The erosion/deposition processes explain why layers with density lower than 250 kg m−3 or specific surface area larger than 30 m2 kg−1 can be found deeper than 10 cm.

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Influence of meter-scale wind-formed features on the variability of the microwave brightness temperature around Dome C in Antarctica

Cited by 53 publications

References 61 publications

Summertime evolution of snow specific surface area close to the surface on the Antarctic Plateau

Summertime evolution of snow specific surface area close to the surface on the Antarctic Plateau

Microstructure representation of snow in coupled snowpack and microwave emission models

Modeling the impact of snow drift on the decameter-scale variability of snow properties on the Antarctic Plateau

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