Peer Review10.5194/tc-2023-65-ac1
Reply on RC1
Marie G. P. Cavitte
- 09 Aug 2023
TL;DR: The spatial representativeness of SMB derived from point data over the Antarctic Ice Sheet is highly variable and depends on the spatial scale of the data and the topography of the surface.
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Abstract: <strong class="journal-contentHeaderColor">Abstract.</strong> Surface mass balance (SMB) over the Antarctic Ice Sheet must be better understood to document current Antarctic contribution to sea-level rise. Field point data using snow stakes and ice cores are often used to evaluate the state of the ice sheet's mass balance as well as to validate SMB derived from regional climate models, which are then used to produce future climate projections. However, spatial representativeness of individual point data remains largely unknown, particularly in the coastal regions of Antarctica with highly variable terrains. Here, we compare ice core data collected at the summit of eight ice rises along the coast of Dronning Maud Land, as well as at the Dome Fuji site, and shallow ice-penetrating radar data over these regions. Shallow radar data has the advantage of being spatially extensive with a temporal resolution that varies between annual and sub-decadal resolution from which we can derive a SMB record over the entire radar survey. This comparison allows us therefore to evaluate the spatial variability of SMB and the spatial representativeness of ice-core derived SMB. We found that ice core mean SMB is very local and the difference with radar-derived SMB increases in a logarithmic-fashion as the surface covered by the radar data increases, with for most ice rises a plateau ~1–2 km away from the ice crest where there are strong wind-topography interactions, and ~10 km where the ice shelves begin. The relative uncertainty in measuring SMB also increases rapidly as we move away from the ice core sites. Five of our ice rise sites show a strong spatial representativeness in terms of temporal variability, while the other three sites show it is limited to a surface areas between 20–120 km<sup>2</sup>. The Dome Fuji site on the other hand shows a small difference between pointwise and area mean SMB, as well as a strong spatial representativeness in terms of temporal variability. We found no simple parameterization that could represent the spatial variability observed at all the sites. However, these data clearly indicate that local spatial SMB variability must be considered when assessing mass balance as well as comparing modeled SMB values to point field data.
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Figures
Figure 3. Ice core SMB uncertainty due to (a) density-related SMB error, (b) depth-related SMB error, (c) combined total SMB uncertainty, each study site is portrayed by a different color. SMB uncertainty decreases with depth due to the improved fit of the exponential density profile away from the surface. Abbreviations are the same as in Fig. 1. 
Figure 2. Radar-derived SMB uncertainty due to (a) density-related mass error, (b) density-related twtt error, (c) measured IRH twtt thickness, (d) combined SMB uncertainty. Each studied region is portrayed by a different color. SMB uncertainty increases with depth as SMB is calculated with respect to a cumulative mass between time markers. Note that the density-related mass error and the density-related twtt error is the same for Le and Dj and so the two curves overlay each other. Abbreviations are the same as in Fig. 1. 
Figure 5. Ice core (blue) vs regional (radar spatial average, red) SMB history, with the calculated SMB uncertainties as blue and red bands, respectively (legend is provided on the top left-most panel). Sites are labeled at the top of each panel and are ordered left to right from the western most coastal site to the eastern most coastal site, and the Dome Fuji site is last. On top of each panel, ∆µt indicates the difference in the mean SMB between the two SMB series in cm w.e. yr−1, with difference given as a percentage of the ice core mean SMB in brackets. ∆obst indicates the uncertainty in measuring SMB locally between two SMB series in cm w.e. yr−1, with relative uncertainty as a percentage value in brackets (normalized to the standard deviation of the ice core SMB anomaly around the temporal mean). 
Table 2. IRH data set sources and main characteristics. Same abbreviations as in Fig. 1. 
Table 1. Radar system characteristics and key references for all radar surveys in this study. See Fig. 1 for site name abbreviations. 
Figure 4. Spatial distribution of SMB through time at Blȧskimen Island. Each panel represents a different time interval, going from most recent at the top left, to the oldest at the bottom right, the time intervals are provided in the top left corner of each frame. Contours are REMA v2.0 elevation contours, with a 30 m interval, gray arrows show the mean wind direction (RACMO2.3 5.5 km simulations over 1979–2017, Lenaerts et al. (2017); Van Wessem et al. (2018)). The wind magnitude scale is shown on top of the first panel. Numbers in lower right corner of each panel are radar area average SMB followed by ice core SMB in cm w.e. yr−1.
References
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Jan Melchior van Wessem,Willem Jan van de Berg,Brice Noël,Erik van Meijgaard,Charles Amory,Gerit Birnbaum,Constantijn L. Jakobs,Konstantin Krüger,Jan T. M. Lenaerts,Stef Lhermitte,Stefan R. M. Ligtenberg,Brooke Medley,Carleen Reijmer,Kristof Van Tricht,Luke D. Trusel,Lambertus H. van Ulft,Bert Wouters,Jan Wuite,Michiel R. van den Broeke +18 more
TL;DR: In this article, the authors evaluate modelled Antarctic ice sheet (AIS) near-surface climate, surfacemass balance (SMB) and surface energy balance (SEB) from the updated polar version of the regional atmospheric climate model, RACMO2 (1979-2016).
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Quantarctica, an integrated mapping environment for Antarctica, the Southern Ocean, and sub-Antarctic islands
Kenichi Matsuoka,Anders Skoglund,George Roth,Jean de Pomereu,Huw J. Griffiths,R. K. Headland,Brad Herried,Katsuro Katsumata,Anne M. Le Brocq,Kathy J. Licht,Fraser Morgan,Fraser Morgan,Peter Neff,Catherine Ritz,Mirko Scheinert,Takeshi Tamura,Anton Van de Putte,Michiel R. van den Broeke,Angela von Deschwanden,César Deschamps-Berger,Brice Van Liefferinge,Stein Tronstad,Yngve Melvær +22 more
TL;DR: Quantarctica provides an integrated environment to view and analyse multiple Antarctic datasets together conveniently and with a low entry barrier.
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Increased snowfall over the Antarctic Ice Sheet mitigated twentieth-century sea-level rise
TL;DR: In this article, the authors reconstruct 200 years of Antarctic-wide snow accumulation by synthesizing a newly compiled database of ice core records using reanalysis-derived spatial coherence patterns.
New estimations of precipitation and surface sublimation in East Antarctica from snow accumulation measurements
Massimo Frezzotti,M. Pourchet,O. Flora,Stefano Gandolfi,Stefano Urbini,Christian Vincent,Silvia Becagli,R. Gragnani,Marco Proposito,Mirko Severi,Rita Traversi,Roberto Udisti,Michel Fily +12 more
TL;DR: In this paper, different methods were used, compared and integrated (stake farms, ice cores, snow radar, surface morphology, remote sensing) at eight sites along a transect from Terra Nova Bay (TNB) to Dome C (DC) (East Antarctica), to provide detailed information on the SMB.
Airborne‐radar and ice‐core observations of annual snow accumulation over Thwaites Glacier, West Antarctica confirm the spatiotemporal variability of global and regional atmospheric models
Brooke Medley,Ian Joughin,Sarah B. Das,Eric J. Steig,Howard Conway,Sivaprasad Gogineni,A. S. Criscitiello,Joseph R. McConnell,Ben Smith,M. R. van den Broeke,Jan T. M. Lenaerts,David H. Bromwich,Julien P. Nicolas +12 more
TL;DR: This paper used an airborne-radar method, verified with ice-core accumulation records, to determine the spatiotemporal variations of snow accumulation over Thwaites Glacier, West Antarctica between 1980 and 2009.