Spatial Distribution of the Snow Accumulation Rate Along the Ice Flow Lines Between Ridge B and Lake Vostok

Екайкин, А. А.; Lipenkov, V.; Шибаев, Ю. А.

doi:10.15356/2076-6734-2012-4-122-128

Cited by 17 publications

(21 citation statements)

References 1 publication

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“…Colored triangles depict the locations of glaciological boreholes or snow pits; their color represents the inferred surface accumulation rates according to Ekaykin et al . [] in the same color scale as the inverted marker velocities. The star represents Vostok station in the color corresponding to the accumulation rate determined there.…”

Section: Resultsmentioning

confidence: 99%

“…Indeed, the general increase in the downward particle velocity from S to N is consistent with the increase in surface accumulation rate revealed by shallow cores and snow pits [ Ekaykin et al . []; colored triangles in Figure ]. Furthermore, the increased particle velocities along the W shore compared to the lake axis correspond with the notion of a local anomaly of increased accumulation along the shoreline related to the concave surface curvature [ Ekaykin et al ., ].…”

Section: Interpretation and Discussionmentioning

confidence: 99%

“…For instance, the floating ice sheet above the lake offers unique conditions for the determination of rates of surface accumulation, firn densification, basal accretion, and local ice‐mass balance estimates [e.g., Richter et al ., , , ]. Many years of dedicated international glaciological [ Lipenkov et al ., ; Ekaykin et al ., , ] and geophysical fieldwork [ Kapitsa et al ., ; Masolov et al ., , ; Siegert et al ., ; Studinger et al ., ] as well as the drilling activities at Vostok station and ice core interpretation [e.g., Petit et al ., ; Jouzel et al ., , ; Salamatin et al ., ] have made the Lake Vostok region a vital area of research in the middle of the remote central part of East Antarctica.…”

Section: Introductionmentioning

confidence: 99%

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Height changes over subglacial Lake Vostok, East Antarctica: Insights from GNSS observations

et al. 2014

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Height changes of the ice surface above subglacial Lake Vostok, East Antarctica, reflect the integral effect of different processes within the subglacial environment and the ice sheet. Repeated GNSS (Global Navigation Satellite Systems) observations on 56 surface markers in the Lake Vostok region spanning 11 years and continuous GNSS observations at Vostok station over 5 years are used to determine the vertical firn particle movement. Vertical marker velocities are derived with an accuracy of 1 cm/yr or better. Repeated measurements of surface height profiles around Vostok station using kinematic GNSS observations on a snowmobile allow the quantification of surface height changes at 308 crossover points. The height change rate was determined at 1 ± 5 mm/yr, thus indicating a stable ice surface height over the last decade. It is concluded that both the local mass balance of the ice and the lake level of the entire lake have been stable throughout the observation period. The continuous GNSS observations demonstrate that the particle heights vary linearly with time. Nonlinear height changes do not exceed ±1 cm at Vostok station and constrain the magnitude of spatiotemporal lake-level variations. ICESat laser altimetry data confirm that the amplitude of the surface deformations over the lake is restricted to a few centimeters. Assuming the ice sheet to be in steady state over the entire lake, estimates for the surface accumulation, on basal accretion/melt rates and on flux divergence, are derived.

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Section: Resultsmentioning

confidence: 99%

Section: Interpretation and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Height changes over subglacial Lake Vostok, East Antarctica: Insights from GNSS observations

et al. 2014

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“…5). The winter maximum is attributed to clear-sky precipitation, the summer ablation, as well as the increase in moisture transport resulting from the winter weather systems (Bromwich 1988;Ekaykin 2003). However, the performance of the moisture-related atmospheric physics in ERA-Interim may be poor under cold conditions, and result in small snowfall in winter.…”

Section: Discussionmentioning

confidence: 99%

“…Sufficiently long-time series of accumulation observations (at least 5-10 yr) are a prerequisite for the determination of seasonal cycle of snow accumulation over Antarctica (Schlosser 1999;Jouzel et al 2003 (Lazzara et al 2012). Corrected gauge measurements of precipitation at Vostok are used due to its agreement with accumulation from a stake network at Vostok station (Ekaykin et al 2004). Figure 5 shows the averaged seasonal distribution of SMB or precipitation from in situ observations and the climate models (ERA-Interim, JRA-55, MERRA, RACMO2.1, and RACMO2.3) for the respective observation periods.…”

Section: A Comparison Of Multiyear Averaged Modeled and Observed Smbmentioning

confidence: 99%

A Comparison of Antarctic Ice Sheet Surface Mass Balance from Atmospheric Climate Models and In Situ Observations

et al. 2016

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In this study, 3265 multiyear averaged in situ observations and 29 observational records at annual time scale are used to examine the performance of recent reanalysis and regional atmospheric climate model products [ERA-Interim, JRA-55, MERRA, the Polar version of MM5 (PMM5), RACMO2.1, and RACMO2.3] for their spatial and interannual variability of Antarctic surface mass balance (SMB), respectively. Simulated precipitation seasonality is also evaluated using three in situ observations and model intercomparison. All products qualitatively capture the macroscale spatial variability of observed SMB, but it is not possible to rank their relative performance because of the sparse observations at coastal regions with an elevation range from 200 to 1000 m. In terms of the absolute amount of observed snow accumulation in interior Antarctica, RACMO2.3 fits best, while the other models either underestimate (JRA-55, MERRA, ERA-Interim, and RACMO2.1) or overestimate (PMM5) the accumulation. Despite underestimated precipitation by the three reanalyses and RACMO2.1, this feature is clearly improved in JRA-55. However, because of changes in the observing system, especially the dramatically increased satellite observations for data assimilation, JRA-55 presents a marked jump in snow accumulation around 1979 and a large increase after the late 1990s. Although precipitation seasonality over the whole ice sheet is common for all products, ERA-Interim provides an unrealistic estimate of precipitation seasonality on the East Antarctic plateau, with high precipitation strongly peaking in summer. ERA-Interim shows a significant correlation with interannual variability of observed snow accumulation measurements at 28 of 29 locations, whereas fewer than 20 site observations significantly correlate with simulations by the other models. This suggests that ERA-Interim exhibits the highest performance of interannual variability in the observed precipitation.

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Chemistry of snow and ice cores along the ice flow lines at Lake Vostok (Antarctica)

et al. 2020

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Spatial Distribution of the Snow Accumulation Rate Along the Ice Flow Lines Between Ridge B and Lake Vostok

Cited by 17 publications

References 1 publication

Height changes over subglacial Lake Vostok, East Antarctica: Insights from GNSS observations

Height changes over subglacial Lake Vostok, East Antarctica: Insights from GNSS observations

A Comparison of Antarctic Ice Sheet Surface Mass Balance from Atmospheric Climate Models and In Situ Observations

Chemistry of snow and ice cores along the ice flow lines at Lake Vostok (Antarctica)

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