Reassessment of the Antarctic Surface Mass Balance Using Calibrated Output of a Regional Atmospheric Climate Model

Berg, Willem Jan van de; Broeke, M. R. van den; Reijmer, C. H.; Meijgaard, E. van

doi:10.1002/9781118782033.ch32

Cited by 9 publications

(9 citation statements)

References 35 publications

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“…PISM solves the nonsliding shallow ice approximation (SIA) and the shallow shelf approximation (SSA) for grounded ice, where the SSA solution acts as a sliding law, and only the SSA for floating ice. New ice thickness data derived for this study (methods described in section 2.2.2 were used for different simulations with varying data sets for boundary conditions as follows: surface temperature (Comiso, ; Fortuin & Oerlemans, ; van Wessem et al, ), surface mass balance (Arthern et al, ; van de Berg et al, ; van Wessem et al, ), geothermal flux (Fox Maule et al, ; Shapiro & Ritzwoller, )), and the update from Purucker () based on the method of Fox Maule et al (). The original data set of Fox Maule et al () was capped at a value of 0.07 W/m 2 according to the recommendation for the SeaRISE‐Antarctica setup (Bindschadler et al, ).…”

Section: Methodsmentioning

confidence: 99%

Missing Evidence of Widespread Subglacial Lakes at Recovery Glacier, Antarctica

et al. 2018

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Recovery Glacier reaches far into the East Antarctic Ice Sheet. Recent projections point out that its dynamic behavior has a considerable impact on future Antarctic ice loss (Golledge et al., 2017, https://doi.org/10.1002/2016GL072422). Subglacial lakes are thought to play a major role in the initiation of the rapid ice flow (Bell et al., 2007, https://doi.org/10.1038/nature05554). Satellite altimetry observations have even suggested several actively filling and draining subglacial lakes beneath the main trunk (B. E. Smith et al., 2009, https://doi.org/10.3189/002214309789470879). We present new data of the geometry of this glacier and investigate its basal properties employing radio‐echo sounding. Using ice sheet modeling, we were able to constrain estimates of radar absorption in the ice, but uncertainties remain large. The magnitude of the basal reflection coefficient is thus still poorly known. However, its spatial variability, in conjunction with additional indicators, can be used to infer the presence of subglacial water. We find no clear evidence of water at most of the previously proposed lake sites. Especially, locations, where altimetry detected active lakes, do not exhibit lake characteristics in radio‐echo sounding. We argue that lakes far upstream the main trunk are not triggering enhanced ice flow, which is also supported by modeled subglacial hydrology.

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Section: Methodsmentioning

confidence: 99%

Missing Evidence of Widespread Subglacial Lakes at Recovery Glacier, Antarctica

et al. 2018

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“…For SMB, we use the monthly products calculated from a 1979–2013 climate simulation of RACMO2.3 [ Ligtenberg et al , ; van Wessem et al , ]. Field data have been used to estimate the RACMO absolute precision [ van de Berg et al , ]. In the Antarctic, the uncertainty (1 σ ) in SMB over grounded ice averages 7% or 144 Gt/yr [ Lenaerts et al , ].…”

Section: Methodsmentioning

confidence: 99%

Mass loss of the Amundsen Sea Embayment of West Antarctica from four independent techniques

Sutterley

Velicogna

Rignot

et al. 2014

Geophysical Research Letters

100

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We compare four independent estimates of the mass balance of the Amundsen Sea Embayment of West Antarctica, an area experiencing rapid retreat and mass loss to the sea. We use ICESat and Operation IceBridge laser altimetry, Envisat radar altimetry, GRACE time-variable gravity, RACMO2.3 surface mass balance, ice velocity from imaging radars, and ice thickness from radar sounders. The four methods agree in terms of mass loss and acceleration in loss at the regional scale. Over 1992-2013, the mass loss is 83 ± 5 Gt/yr with an acceleration of 6.1 ± 0.7 Gt/yr 2 . During the common period 2003-2009, the mass loss is 84 ± 10 Gt/yr with an acceleration of 16.3 ± 5.6 Gt/yr 2 , nearly 3 times the acceleration over 1992 . Over 2003, the mass loss is 102 ± 10 Gt/yr with an acceleration of 15.7 ± 4.0 Gt/yr 2 . The results reconcile independent mass balance estimates in a setting dominated by change in ice dynamics with significant variability in surface mass balance.

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“…The physical properties of surface snow influence the energy balance of the Antarctic ice sheet [ van de Berg et al , ; Brun et al , ]. They determine snow albedo [ Scambos et al , ], whose slight changes can have a large impact on the Antarctic climate [ Hansen , ; Picard et al , ].…”

Section: Introductionmentioning

confidence: 99%

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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Reassessment of the Antarctic Surface Mass Balance Using Calibrated Output of a Regional Atmospheric Climate Model

Cited by 9 publications

References 35 publications

Missing Evidence of Widespread Subglacial Lakes at Recovery Glacier, Antarctica

Missing Evidence of Widespread Subglacial Lakes at Recovery Glacier, Antarctica

Mass loss of the Amundsen Sea Embayment of West Antarctica from four independent techniques

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

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