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Abstract. The Greenland Ice Sheet (GrIS) rapid mass loss is primarily driven by an increase in meltwater runoff, which highlights the importance of understanding the formation, evolution, and impact of meltwater features on the ice sheet. Buried lakes are meltwater features that contain liquid water and exist under layers of snow, firn, and/or ice. These lakes are invisible in optical imagery, challenging the analysis of their evolution and implication for larger GrIS dynamics and mass change. Here, we present a method that uses a convolutional neural network, a deep learning method, to automatically detect buried lakes across the GrIS. For the years 2018 and 2019 (which represent low- and high-melt years, respectively), we compare total areal extent of both buried and surface lakes across six regions, and we use a regional climate model to explain the spatial and temporal differences. We find that the total buried lake extent after the 2019 melt season is 56 % larger than after the 2018 melt season across the entire ice sheet. Northern Greenland has the largest increase in buried lake extent after the 2019 melt season, which we attribute to late-summer surface melt and high autumn temperatures. We also provide evidence that different processes are responsible for buried lake formation in different regions of the ice sheet. For example, in southwest Greenland, buried lakes often appear on the surface during the previous melt season, indicating that these meltwater features form when surface lakes partially freeze and become insulated as snowfall buries them. Conversely, in southeast Greenland, most buried lakes never appear on the surface, indicating that these features may form due to downward percolation of meltwater and/or subsurface penetration of shortwave radiation. We provide support for these processes via the use of a physics-based snow model. This study provides additional perspective on the potential role of meltwater on GrIS dynamics and mass loss.
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Unprecedented atmospheric conditions (1948–2019) drive the 2019 exceptional melting season over the Greenland ice sheet
Abstract. Understanding the role of atmospheric circulation anomalies on the surfacemass balance of the Greenland ice sheet (GrIS) is fundamental for improvingestimates of its current and future contributions to sea level rise. Here,we show, using a combination of remote sensing observations, regionalclimate model outputs, reanalysis data, and artificial neural networks, thatunprecedented atmospheric conditions (1948–2019) occurring in the summerof 2019 over Greenland promoted new record or close-to-record values ofsurfacemass balance (SMB), runoff, and snowfall. Specifically, runoff in 2019 ranked second withinthe 1948–2019 period (after 2012) and first in terms of surface massbalance negative anomaly for the hydrological year 1 September 2018–31 August 2019. The summer of 2019 was characterized by an exceptionalpersistence of anticyclonic conditions that, in conjunction with low albedoassociated with reduced snowfall in summer, enhanced the melt–albedofeedback by promoting the absorption of solar radiation and favoredadvection of warm, moist air along the western portion of the ice sheettowards the north, where the surface melt has been the highest since 1948.The analysis of the frequency of daily 500 hPa geopotential heights obtainedfrom artificial neural networks shows that the total number of days with thefive most frequent atmospheric patterns that characterized the summer of2019 was 5 standard deviations above the 1981–2010 mean, confirming theexceptional nature of the 2019 season over Greenland.
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- Award ID(s):
- 1901603
- PAR ID:
- 10163936
- Date Published:
- Journal Name:
- The Cryosphere
- Volume:
- 14
- Issue:
- 4
- ISSN:
- 1994-0424
- Page Range / eLocation ID:
- 1209 to 1223
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract The increase in Greenland Ice Sheet (GrIS) surface runoff since the turn of the century has been linked to a rise in Greenland blocking frequency. However, a range of synoptic patterns can be considered blocked flow and efforts that summarize all blocking types indiscriminately likely fail to capture consequential differences in GrIS response. To account for these differences, we employ ERA5 reanalysis to identify summer blocking using two independent blocking metrics: the Greenland Blocking Index (GBI) and the blocking index of Pelly and Hoskins (2003,https://doi.org/10.1175/1520-0469(2003)060<0743:ANPOB>2.0.CO;2). We then conduct a self‐organizing map analysis to objectively classify synoptic conditions during Greenland blocking episodes and identify three primary blocking types: (a) a high‐amplitude Omega block, (b) a lower‐amplitude, stationary summer ridge, and (c) a cyclonic wave breaking pattern. Using Modèle Atmosphérique Régional output, we document the spatiotemporal progression of the surface energy and mass balance for each blocking type. Relative to all blocking episodes, summer ridge patterns produce more melt over the southern ice sheet, Omega blocks produce more melt across the northern ice sheet, and cyclonic wave breaking patterns produce more melt in northeast Greenland. Our results indicate that the recent trend in summer Greenland blocking was largely driven by an increase in Omega patterns and suggest that Omega blocks have played a central role in the recent acceleration of GrIS mass loss. Furthermore, the GBI exhibited a relative bias toward Omega patterns, which may help explain why it has measured stronger trends in summer Greenland blocking than other blocking metrics.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/tc-14-1209-2020
