Greenland 2012 melt event effects on CryoSat‐2 radar altimetry

Nilsson, Johan; Vallelonga, Paul; Simonsen, Sebastian B.; Sørensen, Louise Sandberg; Forsberg, R.; Dahl-Jensen, Dorthe; Hirabayashi, Motohiro; Goto‐Azuma, Kumiko; Hvidberg, Christine S.; Kjær, Helle Astrid; Satow, Kazuhide

doi:10.1002/2015gl063296

Cited by 75 publications

(85 citation statements)

References 19 publications

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“…Following on from this, accumulation can then be estimated by differencing the minimum height in one summer with the early summer peak the following year, although this requires extensive melt across the ice cap for both summers, so would apply only for the winter 2011-12 accumulation on Devon Ice Cap. The influence of changing conditions on the apparent CS2-detected elevation was also observed with the low-resolution mode (LRM) data in Greenland after the extensive 2012 melt (Nilsson et al, 2015). In this work an apparent CS2 height increase was shown to be due to the creation of refrozen melt layers, and not a true surface height increase.…”

Section: Devon Ice Capsupporting

confidence: 61%

CryoSat-2 delivers monthly and inter-annual surface elevation change for Arctic ice caps

et al. 2015

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Abstract. We show that the CryoSat-2 radar altimeter can provide useful estimates of surface elevation change on a variety of Arctic ice caps, on both monthly and yearly timescales. Changing conditions, however, can lead to a varying bias between the elevation estimated from the radar altimeter and the physical surface due to changes in the ratio of subsurface to surface backscatter. Under melting conditions the radar returns are predominantly from the surface so that if surface melt is extensive across the ice cap estimates of summer elevation loss can be made with the frequent coverage provided by CryoSat-2. For example, the average summer elevation decreases on the Barnes Ice Cap, Baffin Island, Canada were 2.05 ± 0.36 m (2011), 2.55 ± 0.32 m (2012), 1.38 ± 0.40 m (2013) and 1.44 ± 0.37 m (2014), losses which were not balanced by the winter snow accumulation. As winter-to-winter conditions were similar, the net elevation losses were 1.0 ± 0.20 m (winter 2010/11 to winter 2011/12), 1.39 ± 0.20 m (2011/12 to 2012/13) and 0.36 ± 0.20 m (2012/13 to 2013/14); for a total surface elevation loss of 2.75 ± 0.20 m over this 3-year period. In contrast, the uncertainty in height change from Devon Ice Cap, Canada, and Austfonna, Svalbard, can be up to twice as large because of the presence of firn and the possibility of a varying bias between the true surface and the detected elevation due to changing year-to-year conditions. Nevertheless, the surface elevation change estimates from CryoSat for both ice caps are consistent with field and meteorological measurements.

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Section: Devon Ice Capsupporting

confidence: 61%

CryoSat-2 delivers monthly and inter-annual surface elevation change for Arctic ice caps

et al. 2015

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“…A prominent example of the outsized importance of short‐lived intense AR events is provided by the extraordinary conditions observed during July 2012, when two extreme ARs resulted in the most extensive GrIS surface melt in the modern record. The lasting effects of these and other ephemeral events include the development of unusually thick buried ice layers in the GrIS percolation zone (De la Peña et al, ; Nilsson et al, ; Steger et al, ) and a substantial rise in the water table of firn aquifers in southeast Greenland (Koenig et al, ; Miège et al, ).…”

Section: Summary and Discussionmentioning

confidence: 99%

Atmospheric River Impacts on Greenland Ice Sheet Surface Mass Balance

2018

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Greenland Ice Sheet (GrIS) mass loss has accelerated since the turn of the twenty‐first century. Several recent episodes of rapid GrIS ablation coincided with intense moisture transport over Greenland by atmospheric rivers (ARs), suggesting that these events influence the evolution of GrIS surface mass balance (SMB). ARs likely provide melt energy through several physical mechanisms, and conversely, may increase SMB through enhanced snow accumulation. In this study, we compile a long‐term (1980–2016) record of moisture transport events using a conventional AR identification algorithm as well as a self‐organizing map classification applied to MERRA‐2 data. We then analyze AR effects on the GrIS using melt data from passive microwave satellite observations and regional climate model output. Results show that anomalously strong moisture transport by ARs clearly contributed to increased GrIS mass loss in recent years. AR activity over Greenland was above normal throughout the 2000s and early 2010s, and recent melting seasons with above‐average GrIS melt feature positive moisture transport anomalies over Greenland. Analysis of individual AR impacts shows a pronounced increase in GrIS surface melt after strong AR events. AR effects on SMB are more complex, as strong summer ARs cause sharp SMB losses in the ablation zone that exceed moderate SMB gains induced by ARs in the accumulation zone during summer and in all areas during other seasons. Our results demonstrate the influence of the strongest ARs in controlling GrIS SMB, and we conclude that projections of future GrIS SMB should accurately capture these rare ephemeral events.

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“…This caused melt/freeze metamorphism with a consequent increase of backscatter levels also over the Greenland dry snow zone [34]. This fact resulted in reduced penetration for CryoSat [35] and possibly also for TanDEM-X. It is therefore important to notice that the data used for the current investigation were acquired before such events, after a quite stable period of several years, documented by the collection of data provided by the C-band scatterometer ASCAT from 2007 onwards [36].…”

Section: Reference Snow Melt Datamentioning

confidence: 99%

Characterization of Snow Facies on the Greenland Ice Sheet Observed by TanDEM-X Interferometric SAR Data

et al. 2017

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This paper presents for the first time a detailed study on information content of X-band single-pass interferometric spaceborne SAR data with respect to snow facies characterization. An approach for classifying different snow facies of the Greenland Ice Sheet by exploiting X-band TanDEM-X interferometric synthetic aperture radar acquisitions is firstly detailed. Large-scale mosaics of radar backscatter and volume correlation factor, derived from quicklook images of the interferometric coherence, represent the starting point for applying an unsupervised classification method based on the c-means fuzzy clustering algorithm. The data was acquired during winter 2010/2011. A partition of four different snow facies was chosen and interpreted using reference melt data, snow density, and in situ measurements. The variations in the stratification and micro-structure of firn, such as the variations of density with depth and the presence of percolation features, are identified as relevant parameters for explaining the significant differences in the observed interferometric signatures among different snow facies. Moreover, a statistical analysis of backscatter and volume correlation factor provided useful parameters for characterizing the snow facies behavior and analyzing their dependency on the acquisition geometry. Finally, knowing the location and characterization of the different snow facies, the two-way X-band penetration depth over the whole Ice Sheet was estimated. The obtained mean values vary from 2.3 m for the outer snow facies up to 4.18 m for the inner one. The presented approach represents a starting point for a long-term monitoring of ice sheet dynamics, by acquiring time-series, and is of high relevance for the design of future SAR missions as well.

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Greenland 2012 melt event effects on CryoSat‐2 radar altimetry

Cited by 75 publications

References 19 publications

CryoSat-2 delivers monthly and inter-annual surface elevation change for Arctic ice caps

CryoSat-2 delivers monthly and inter-annual surface elevation change for Arctic ice caps

Atmospheric River Impacts on Greenland Ice Sheet Surface Mass Balance

Characterization of Snow Facies on the Greenland Ice Sheet Observed by TanDEM-X Interferometric SAR Data

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