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
- NSF-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. -
The treatment of surface melt, runoff, and the snow-firn-ice transition in ice-sheet models (ISMs) is becoming increasingly important, as mobile liquid on Greenland and Antarctic flanks increases due to climate warming in the next century and beyond. Simple Positive Degree Day (PDD)-based box models used in some ISMs crudely capture liquid storage and refreezing, but need to be extended to include vertical structure through the whole firn-ice column, as in some regional climate models (RCMs). This is a necessary prelude to modeling the flow of mobile meltwater in channel-river-moulin systems, and routing to the base and/or margins of the ice sheet. More detailed column models of snow and firn exist, that include compaction, grain size, and other processes. Some focus on dry-snow zones, and have fine vertical resolution spanning the entire firn column with Lagrangian tracking of annual snow layers (e.g., FirnMICE: Lundin et al., J. Glac., 2017). However, they are mostly too computationally expensive for ISM applications, and are not designed for ablation zones with meltwater and bare ice in summer. More general models are used in some RCMs that include similar physics but with fewer layers, and are applicable both to accumulation and ablation zones. Here we formulate a new snow-firn model, similar to those in RCMs, for use within an ice-sheet model. A limited number of vertical layers is used (∼10), with Lagrangian tracking of layers, grain size evolution, compaction, ice lenses, liquid melting, storage, percolation and runoff. Surface melting is computed from linearized net atmospheric energy fluxes, not from PDDs. The model is tested using the FirnMICE experiments, and using gridded RACMO2 modern climate input over Greenland, seeking to balance model performance with computational efficiency.more » « less
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Abstract. Snowfall is the major source of mass for the Greenland ice sheet (GrIS) but the spatial and temporalvariability of snowfall and the connections between snowfall and mass balance have so far been inadequatelyquantified. By characterizing local atmospheric circulation and utilizing CloudSat spaceborne radarobservations of snowfall, we provide a detailed spatial analysis of snowfall variability and its relationshipto Greenland mass balance, presenting first-of-their-kind maps of daily spatial variability in snowfallfrom observations across Greenland. For identified regional atmospheric circulation patterns, we show that thespatial distribution and net mass input of snowfall vary significantly with the position and strength ofsurface cyclones. Cyclones west of Greenland driving southerly flow contribute significantly more snowfall thanany other circulation regime, with each daily occurrence of the most extreme southerly circulation patterncontributing an average of 1.66 Gt of snow to the Greenland ice sheet. While cyclones east of Greenland,patterns with the least snowfall, contribute as little as 0.58 Gt each day. Above 2 km on the ice sheet wheresnowfall is inconsistent, extreme southerly patterns are the most significant mass contributors, with up to1.20 Gt of snowfall above this elevation. This analysis demonstrates that snowfall over the interior ofGreenland varies by up to a factor of 5 depending on regional circulation conditions. Using independentobservations of mass changes made by the Gravity Recovery and Climate Experiment (GRACE), we verify that thelargest mass increases are tied to the southerly regime with cyclones west of Greenland. For occurrences of thestrongest southerly pattern, GRACE indicates a net mass increase of 1.29 Gt in the ice sheet accumulation zone(above 2 km elevation) compared to the 1.20 Gt of snowfall observed by CloudSat. This overall agreementsuggests that the analytical approach presented here can be used to directly quantify snowfall masscontributions and their most significant drivers spatially across the GrIS. While previous research hasimplicated this same southerly regime in ablation processes during summer, this paper shows that ablation massloss in this circulation regime is nearly an order of magnitude larger than the mass gain from associatedsnowfall. For daily occurrences of the southerly circulation regime, a mass loss of approximately 11 Gt isobserved across the ice sheet despite snowfall mass input exceeding 1 Gt. By analyzing the spatialvariability of snowfall and mass changes, this research provides new insight into connections between regionalatmospheric circulation and GrIS mass balance.more » « less
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Abstract Substantial marine, terrestrial, and atmospheric changes have occurred over the Greenland region during the last century. Several studies have documented record‐levels of Greenland Ice Sheet (GrIS) summer melt extent during the 2000s and 2010s, but relatively little work has been carried out to assess regional climatic changes in other seasons. Here, we focus on the less studied cold‐season (i.e., autumn and winter) climate, tracing the long‐term (1873–2013) variability of Greenland's air temperatures through analyses of coastal observations and model‐derived outlet glacier series and their linkages with North Atlantic sea ice, sea surface temperature (SST), and atmospheric circulation indices. Through a statistical framework, large amounts of west and south Greenland temperature variance (up to
r 2 ~ 50%) can be explained by the seasonally‐contemporaneous combination of the Greenland Blocking Index (GBI) and the North Atlantic Oscillation (NAO; hereafter the combination of GBI and NAO is termed GBI). Lagged and concomitant regional sea‐ice concentration (SIC) and the Atlantic Multidecadal Oscillation (AMO) seasonal indices account for small amounts of residual air temperature variance (r 2 < ~10%) relative to the GBI. The correlations between GBI and cold‐season temperatures are predominantly positive and statistically‐significant through time, while regional SIC conditions emerge as a significant covariate from the mid‐20th century through the conclusion of the study period. The inclusion of the cold‐season Pacific Decadal Oscillation (PDO) in multivariate analyses bolsters the air temperature variance explained by the North Atlantic regional predictors, suggesting the remote, background climate state is important to long‐term Greenland temperature variability. These findings imply that large‐scale tropospheric circulation has a strong control on surface temperature over Greenland through dynamic and thermodynamic impacts and stress the importance of understanding the evolving two‐way linkages between the North Atlantic marine and atmospheric environment in order to more accurately predict Greenland seasonal climate variability and change through the 21st century.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/tc-14-1209-2020
