scholarly journals Dynamic Mass Loss from Greenland's Peripheral Glaciers

2021 ◽  
Author(s):  
Katherine E. Bollen
Keyword(s):  
Mass Loss ◽  
Mass Balance ◽  
Ice Sheet ◽  
Glacier Mass Balance ◽  
Atmospheric Models ◽  
Ice Caps ◽  
Dynamic Mass ◽  
Meltwater Runoff ◽  
Glacier Dynamics ◽  
Flux Gate

While global glacier mass balance has decreased rapidly over the last two decades, mass loss has been greatest in regions with marine-terminating glaciers. In Greenland, peripheral glaciers and ice caps (GICs) cover only ~5% of Greenland’s area but contributed ~14-20% of the island’s ice mass loss between 2003-2008. Although Greenland GIC’s mass loss due to surface meltwater runoff have been estimated using atmospheric models, mass loss due to changes in ice discharge into surrounding ocean basins (i.e., dynamic mass loss) remains unquantified. Here, we use the flux gate method to estimate discharge from Greenland’s 594 marine-terminating peripheral glaciers between 1985 – 2018, and compute dynamic mass loss as the discharge anomaly relative to the 1985-1998 period. Greenland GIC discharge averages 2.14 Gt/yr from 1985-1998 and abruptly increases to an average of 3.87 Gt/yr from 1999-2018, indicating a -1.72 Gt/yr mass anomaly. This mass loss is driven by synchronous widespread acceleration around Greenland and, like the ice sheet, is primarily caused by changes in discharge from a small number of glaciers with larger discharge. These estimates indicate that although Greenland GICs are small, they are sensitive to changes in climate and should not be overlooked in future analyses of glacier dynamics and mass loss.

scholarly journals Constraining GRACE-derived cryosphere-attributed signal to irregularly shaped ice-covered areas

2013 ◽  
Vol 7 (6) ◽  
pp. 1901-1914 ◽  
Author(s):  
W. Colgan ◽  
S. Luthcke ◽  
W. Abdalati ◽  
M. Citterio
Keyword(s):  
Monte Carlo ◽  
Mass Loss ◽  
Mass Balance ◽  
Spherical Harmonic ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Ice Caps ◽  
Ice Coverage ◽  
Ice Sheet Mass Balance ◽  
Harmonic Representation

Abstract. We use a Monte Carlo approach to invert a spherical harmonic representation of cryosphere-attributed mass change in order to infer the most likely underlying mass changes within irregularly shaped ice-covered areas at nominal 26 km resolution. By inverting a spherical harmonic representation through the incorporation of additional fractional ice coverage information, this approach seeks to eliminate signal leakage between non-ice-covered and ice-covered areas. The spherical harmonic representation suggests a Greenland mass loss of 251 ± 25 Gt a−1 over the December 2003 to December 2010 period. The inversion suggests 218 ± 20 Gt a−1 was due to the ice sheet proper, and 34 ± 5 Gt a−1 (or ~14%) was due to Greenland peripheral glaciers and ice caps (GrPGICs). This mass loss from GrPGICs exceeds that inferred from all ice masses on both Ellesmere and Devon islands combined. This partition therefore highlights that GRACE-derived "Greenland" mass loss cannot be taken as synonymous with "Greenland ice sheet" mass loss when making comparisons with estimates of ice sheet mass balance derived from techniques that sample only the ice sheet proper.

scholarly journals GrSMBMIP: Intercomparison of the modelled 1980-2012 surface mass balance over the Greenland Ice sheet

2020 ◽  
Author(s):  
Xavier Fettweis ◽  
Keyword(s):  
Mass Loss ◽  
Mass Balance ◽  
Climate Models ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Ice Cores ◽  
Surface Mass Balance ◽  
Surface Mass ◽  
Meltwater Runoff

<p>The Greenland Ice Sheet (GrIS) mass loss has been accelerating at a rate of about 20 +/- 10 Gt/yr<sup>2</sup> since the end of the 1990's, with around 60% of this mass loss directly attributed to enhanced surface meltwater runoff. However, in the climate and glaciology communities, different approaches exist on how to model the different surface mass balance (SMB) components using: (1) complex physically-based climate models which are computationally expensive; (2) intermediate complexity energy balance models; (3) simple and fast positive degree day models which base their inferences on statistical principles and are computationally highly efficient. Additionally, many of these models compute the SMB components based on different spatial and temporal resolutions, with different forcing fields as well as different ice sheet topographies and extents, making inter-comparison difficult. In the GrIS SMB model intercomparison project (GrSMBMIP) we address these issues by forcing each model with the same data (i.e., the ERA-Interim reanalysis) except for two global models for which this forcing is limited to the oceanic conditions, and at the same time by interpolating all modelled results onto a common ice sheet mask at 1 km horizontal resolution for the common period 1980-2012. The SMB outputs from 13 models are then compared over the GrIS to (1) SMB estimates using a combination of gravimetric remote sensing data from GRACE and measured ice discharge, (2) ice cores, snow pits, in-situ SMB observations, and (3) remotely sensed bare ice extent from MODerate-resolution Imaging Spectroradiometer (MODIS). Our results reveal that the mean GrIS SMB of all 13 models has been positive between 1980 and 2012 with an average of 340 +/- 112 Gt/yr, but has decreased at an average rate of -7.3 Gt/yr<sup>2</sup> (with a significance of 96%), mainly driven by an increase of 8.0 Gt/yr<sup>2</sup> (with a significance of 98%) in meltwater runoff. Spatially, the largest spread among models can be found around the margins of the ice sheet, highlighting the need for accurate representation of the GrIS ablation zone extent and processes driving the surface melt. In addition, a higher density of in-situ SMB observations is required, especially in the south-east accumulation zone, where the model spread can reach 2 mWE/yr due to large discrepancies in modelled snowfall accumulation. Overall, polar regional climate models (RCMs) perform the best compared to observations, in particular for simulating precipitation patterns. However, other simpler and faster models have biases of same order than RCMs with observations and remain then useful tools for long-term simulations. It is also interesting to note that the ensemble mean of the 13 models produces the best estimate of the present day SMB relative to observations, suggesting that biases are not systematic among models. Finally, results from MAR forced by ERA5 will be added in this intercomparison to evaluate the added value of using this new reanalysis as forcing vs the former ERA-Interim reanalysis (used in SMBMIP). </p>

scholarly journals Constraining GRACE-derived cryosphere-attributed signal to irregularly shaped ice-covered areas

2013 ◽  
Vol 7 (4) ◽  
pp. 3417-3447 ◽  
Author(s):  
W. Colgan ◽  
S. Luthcke ◽  
W. Abdalati ◽  
M. Citterio
Keyword(s):  
Monte Carlo ◽  
Mass Loss ◽  
Mass Balance ◽  
Spherical Harmonic ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Ice Caps ◽  
Ice Coverage ◽  
Ice Sheet Mass Balance ◽  
Harmonic Representation

Abstract. We use a Monte Carlo approach to invert a spherical harmonic representation of cryosphere-attributed mass change in order to infer the most likely underlying mass changes within irregularly shaped ice-covered areas at nominal 26 km resolution. By inverting a spherical harmonic representation through the incorporation of additional fractional ice coverage information, this approach seeks to eliminate signal leakage between non- and ice-covered areas. The spherical harmonic representation suggests a Greenland mass loss of 251 ± 25 Gt yr−1 over the December 2003 to December 2010 period. The inversion suggests 218 ± 20 Gt yr−1 was due to the ice sheet proper, and 34 ± 5 Gt yr−1 (or ~ 14%) was due to Greenland peripheral glaciers and ice caps (GrPGIC). This mass loss from GrPGIC exceeds that inferred from all ice masses on both Ellesmere and Devon Islands combined. This partition therefore highlights that GRACE-derived "Greenland" mass loss cannot be taken as synonymous with "Greenland ice sheet" mass loss when making comparisons with estimates of ice sheet mass balance derived from techniques that only sample the ice sheet proper.

scholarly journals Greenland ice sheet mass balance from 1840 through next week

2021 ◽  
Author(s):  
Kenneth D. Mankoff ◽  
Xavier Fettweis ◽  
Peter L. Langen ◽  
Martin Stendel ◽  
Kristian K. Kjledsen ◽  
...  
Keyword(s):  
Mass Loss ◽  
Mass Balance ◽  
Climate Models ◽  
Regional Climate ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Regional Climate Models ◽  
Mass Gain ◽  
Surface Mass ◽  
Meltwater Runoff

Abstract. The mass of the Greenland ice sheet is declining as mass gain from snowfall is exceeded by mass loss from surface meltwater runoff, marine-terminating glacier calving and submarine melting, and basal melting. Here we use the input/output (IO) method to estimate mass change from 1840 through next week. Mass gains come from three regional climate models (RCMs; HIRHAM/HARMONIE, MAR, and RACMO) and a semi-empirical surface mass balance (SMB) model. Mass losses come from the RCMs, a statistical SMB model, ice discharge at marine terminating glaciers, and ice melted at the base of the ice sheet. From these products we provide an annual estimate of GIS mass balance from 1840 through 1985 and a daily estimate at sector and region scale from 1986 through next week. Compared to other mass balance estimates, this product updates daily, has higher temporal resolution, and is the first IO product to include the basal mass balance which is a source of an additional ~8 % mass loss. Our results demonstrate an accelerating GIS-scale mass loss and general agreement among six other products. Results from this study are available at https://dataverse01.geus.dk/privateurl.xhtml?token=d09976c4-4f89-43ef-8f91-173d269806a4 (Mankoff et al., 2021).

scholarly journals GrSMBMIP: Intercomparison of the modelled 1980–2012 surface mass balance over the Greenland Ice sheet

2020 ◽  
Author(s):  
Xavier Fettweis ◽  
Stefan Hofer ◽  
Uta Krebs-Kanzow ◽  
Charles Amory ◽  
Teruo Aoki ◽  
...  
Keyword(s):  
Mass Loss ◽  
Mass Balance ◽  
Climate Models ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Ice Cores ◽  
Surface Mass Balance ◽  
Surface Mass ◽  
Meltwater Runoff

Abstract. The Greenland Ice Sheet (GrIS) mass loss has been accelerating at a rate of about 20 ± 10 Gt/yr2 since the end of the 1990's, with around 60 % of this mass loss directly attributed to enhanced surface meltwater runoff. However, in the climate and glaciology communities, different approaches exist on how to model the different surface mass balance (SMB) components using: (1) complex physically-based climate models which are computationally expensive; (2) intermediate complexity energy balance models; (3) simple and fast positive degree day models which base their inferences on statistical principles and are computationally highly efficient. Additionally, many of these models compute the SMB components based on different spatial and temporal resolutions, with different forcing fields as well as different ice sheet topographies and extents, making inter-comparison difficult. In the GrIS SMB model intercomparison project (GrSMBMIP) we address these issues by forcing each model with the same data (i.e., the ERA-Interim reanalysis) except for two global models for which this forcing is limited to the oceanic conditions, and at the same time by interpolating all modelled results onto a common ice sheet mask at 1 km horizontal resolution for the common period 1980–2012. The SMB outputs from 13 models are then compared over the GrIS to (1) SMB estimates using a combination of gravimetric remote sensing data from GRACE and measured ice discharge, (2) ice cores, snow pits, in-situ SMB observations, and (3) remotely sensed bare ice extent from MODerate-resolution Imaging Spectroradiometer (MODIS). Our results reveal that the mean GrIS SMB of all 13 models has been positive between 1980 and 2012 with an average of 340 ± Gt/yr, but has decreased at an average rate of −7.3 Gt/yr2 (with a significance of 96 %), mainly driven by an increase of 8.0 Gt/yr2 (with a significance of 98 %) in meltwater runoff. Spatially, the largest spread among models can be found around the margins of the ice sheet, highlighting the need for accurate representation of the GrIS ablation zone extent and processes driving the surface melt. In addition, a higher density of in-situ SMB observations is required, especially in the south-east accumulation zone, where the model spread can reach 2 mWE/yr due to large discrepancies in modelled snowfall accumulation. Overall, polar regional climate models (RCMs) perform the best compared to observations, in particular for simulating precipitation patterns. However, other simpler and faster models have biases of same order than RCMs with observations and remain then useful tools for long-term simulations. Finally, it is interesting to note that the ensemble mean of the 13 models produces the best estimate of the present day SMB relative to observations, suggesting that biases are not systematic among models.

scholarly journals Monitoring southwest Greenland’s ice sheet melt with ambient seismic noise

2016 ◽  
Vol 2 (5) ◽  
pp. e1501538 ◽  
Author(s):  
Aurélien Mordret ◽  
T. Dylan Mikesell ◽  
Christopher Harig ◽  
Bradley P. Lipovsky ◽  
Germán A. Prieto
Keyword(s):  
Mass Loss ◽  
Mass Balance ◽  
Sea Level ◽  
Seismic Noise ◽  
Seismic Velocity ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Sea Level Changes ◽  
Ice Sheet Mass Balance ◽  
Velocity Changes

The Greenland ice sheet presently accounts for ~70% of global ice sheet mass loss. Because this mass loss is associated with sea-level rise at a rate of 0.7 mm/year, the development of improved monitoring techniques to observe ongoing changes in ice sheet mass balance is of paramount concern. Spaceborne mass balance techniques are commonly used; however, they are inadequate for many purposes because of their low spatial and/or temporal resolution. We demonstrate that small variations in seismic wave speed in Earth’s crust, as measured with the correlation of seismic noise, may be used to infer seasonal ice sheet mass balance. Seasonal loading and unloading of glacial mass induces strain in the crust, and these strains then result in seismic velocity changes due to poroelastic processes. Our method provides a new and independent way of monitoring (in near real time) ice sheet mass balance, yielding new constraints on ice sheet evolution and its contribution to global sea-level changes. An increased number of seismic stations in the vicinity of ice sheets will enhance our ability to create detailed space-time records of ice mass variations.

scholarly journals Seasonal mass variations show timing and magnitude of meltwater storage in the Greenland Ice Sheet

2018 ◽  
Vol 12 (9) ◽  
pp. 2981-2999 ◽  
Author(s):  
Jiangjun Ran ◽  
Miren Vizcaino ◽  
Pavel Ditmar ◽  
Michiel R. van den Broeke ◽  
Twila Moon ◽  
...  
Keyword(s):  
Mass Balance ◽  
Climate Model ◽  
Spatial Scales ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Surface Mass Balance ◽  
High Accumulation ◽  
Outlet Glacier ◽  
Surface Mass ◽  
Meltwater Runoff

Abstract. The Greenland Ice Sheet (GrIS) is currently losing ice mass. In order to accurately predict future sea level rise, the mechanisms driving the observed mass loss must be better understood. Here, we combine data from the satellite gravimetry mission Gravity Recovery and Climate Experiment (GRACE), surface mass balance (SMB) output of the Regional Atmospheric Climate Model v. 2 (RACMO2), and ice discharge estimates to analyze the mass budget of Greenland at various temporal and spatial scales. We find that the mean rate of mass variations in Greenland observed by GRACE was between −277 and −269 Gt yr−1 in 2003–2012. This estimate is consistent with the sum (i.e., -304±126 Gt yr−1) of individual contributions – surface mass balance (SMB, 216±122 Gt yr−1) and ice discharge (520±31 Gt yr−1) – and with previous studies. We further identify a seasonal mass anomaly throughout the GRACE record that peaks in July at 80–120 Gt and which we interpret to be due to a combination of englacial and subglacial water storage generated by summer surface melting. The robustness of this estimate is demonstrated by using both different GRACE-based solutions and different meltwater runoff estimates (namely, RACMO2.3, SNOWPACK, and MAR3.9). Meltwater storage in the ice sheet occurs primarily due to storage in the high-accumulation regions of the southeast and northwest parts of Greenland. Analysis of seasonal variations in outlet glacier discharge shows that the contribution of ice discharge to the observed signal is minor (at the level of only a few gigatonnes) and does not explain the seasonal differences between the total mass and SMB signals. With the improved quantification of meltwater storage at the seasonal scale, we highlight its importance for understanding glacio-hydrological processes and their contributions to the ice sheet mass variability.

scholarly journals On the recent contribution of the Greenland ice sheet to sea level change

2016 ◽  
Vol 10 (5) ◽  
pp. 1933-1946 ◽  
Author(s):  
Michiel R. van den Broeke ◽  
Ellyn M. Enderlin ◽  
Ian M. Howat ◽  
Peter Kuipers Munneke ◽  
Brice P. Y. Noël ◽  
...  
Keyword(s):  
Mass Loss ◽  
Mass Balance ◽  
Sea Level ◽  
Pore Space ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Sea Level Change ◽  
Recent Contribution ◽  
Surface Mass ◽  
Level Change

Abstract. We assess the recent contribution of the Greenland ice sheet (GrIS) to sea level change. We use the mass budget method, which quantifies ice sheet mass balance (MB) as the difference between surface mass balance (SMB) and solid ice discharge across the grounding line (D). A comparison with independent gravity change observations from GRACE shows good agreement for the overlapping period 2002–2015, giving confidence in the partitioning of recent GrIS mass changes. The estimated 1995 value of D and the 1958–1995 average value of SMB are similar at 411 and 418 Gt yr−1, respectively, suggesting that ice flow in the mid-1990s was well adjusted to the average annual mass input, reminiscent of an ice sheet in approximate balance. Starting in the early to mid-1990s, SMB decreased while D increased, leading to quasi-persistent negative MB. About 60 % of the associated mass loss since 1991 is caused by changes in SMB and the remainder by D. The decrease in SMB is fully driven by an increase in surface melt and subsequent meltwater runoff, which is slightly compensated by a small ( <  3 %) increase in snowfall. The excess runoff originates from low-lying ( <  2000 m a.s.l.) parts of the ice sheet; higher up, increased refreezing prevents runoff of meltwater from occurring, at the expense of increased firn temperatures and depleted pore space. With a 1991–2015 average annual mass loss of  ∼  0.47 ± 0.23 mm sea level equivalent (SLE) and a peak contribution of 1.2 mm SLE in 2012, the GrIS has recently become a major source of global mean sea level rise.

scholarly journals Geodetic Mass Balance of Haxilegen Glacier No. 51, Eastern Tien Shan, from 1964 to 2018

2022 ◽  
Vol 14 (2) ◽  
pp. 272
Author(s):  
Chunhai Xu ◽  
Zhongqin Li ◽  
Feiteng Wang ◽  
Jianxin Mu ◽  
Xin Zhang
Keyword(s):  
Mass Loss ◽  
Mass Balance ◽  
Laser Scanning ◽  
Mean Value ◽  
Tien Shan ◽  
Glacier Mass Balance ◽  
Topographic Map ◽  
Glacier Surface ◽  
Elevation Changes ◽  
Geodetic Mass Balance

The eastern Tien Shan hosts substantial mid-latitude glaciers, but in situ glacier mass balance records are extremely sparse. Haxilegen Glacier No. 51 (eastern Tien Shan, China) is one of the very few well-measured glaciers, and comprehensive glaciological measurements were implemented from 1999 to 2011 and re-established in 2017. Mass balance of Haxilegen Glacier No. 51 (1999–2015) has recently been reported, but the mass balance record has not extended to the period before 1999. Here, we used a 1:50,000-scale topographic map and long-range terrestrial laser scanning (TLS) data to calculate the area, volume, and mass changes for Haxilegen Glacier No. 51 from 1964 to 2018. Haxilegen Glacier No. 51 lost 0.34 km2 (at a rate of 0.006 km2 a−1 or 0.42% a−1) of its area during the period 1964–2018. The glacier experienced clearly negative surface elevation changes and geodetic mass balance. Thinning occurred almost across the entire glacier surface, with a mean value of −0.43 ± 0.12 m a−1. The calculated average geodetic mass balance was −0.36 ± 0.12 m w.e. a−1. Without considering the error bounds of mass balance estimates, glacier mass loss over the past 50 years was in line with the observed and modeled mass balance (−0.37 ± 0.22 m w.e. a−1) that was published for short time intervals since 1999 but was slightly less negative than glacier mass loss in the entire eastern Tien Shan. Our results indicate that Riegl VZ®-6000 TLS can be widely used for mass balance measurements of unmonitored individual glaciers.

scholarly journals Longest time series of glacier mass changes in the Himalaya based on stereo imagery

2010 ◽  
Vol 4 (4) ◽  
pp. 2593-2613 ◽  
Author(s):  
T. Bolch ◽  
T. Pieczonka ◽  
D. I. Benn
Keyword(s):  
Time Series ◽  
Mass Loss ◽  
Mass Balance ◽  
Aerial Images ◽  
Glacier Mass Balance ◽  
Glacier Changes ◽  
Debris Cover ◽  
Stereo Imagery ◽  
Glacier Velocity ◽  
The Himalaya

Abstract. Mass loss of Himalayan glaciers has wide-ranging consequences such as declining water resources, sea level rise and an increasing risk of glacial lake outburst floods (GLOFs). The assessment of the regional and global impact of glacier changes in the Himalaya is, however, hampered by a lack of mass balance data for most of the range. Multi-temporal digital terrain models (DTMs) allow glacier mass balance to be calculated since the availability of stereo imagery. Here we present the longest time series of mass changes in the Himalaya and show the high value of early stereo spy imagery such as Corona (years 1962 and 1970) aerial images and recent high resolution satellite data (Cartosat-1) to calculate a time series of glacier changes south of Mt. Everest, Nepal. We reveal that the glaciers are significantly losing mass with an increasing rate since at least ~1970, despite thick debris cover. The specific mass loss is 0.32 ± 0.08 m w.e. a−1, however, not higher than the global average. The spatial patterns of surface lowering can be explained by variations in debris-cover thickness, glacier velocity, and ice melt due to exposed ice cliffs and ponds.

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