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 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 GrSMBMIP: intercomparison of the modelled 1980–2012 surface mass balance over the Greenland Ice Sheet

2020 ◽  
Vol 14 (11) ◽  
pp. 3935-3958 ◽  
Author(s):  
Xavier Fettweis ◽  
Stefan Hofer ◽  
Uta Krebs-Kanzow ◽  
Charles Amory ◽  
Teruo Aoki ◽  
...  
Keyword(s):  
Mass Balance ◽  
Climate Models ◽  
Computing Time ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Ice Cores ◽  
Horizontal Resolution ◽  
Surface Mass Balance ◽  
Surface Mass

Abstract. Observations and models agree that the Greenland Ice Sheet (GrIS) surface mass balance (SMB) has decreased since the end of the 1990s due to an increase in meltwater runoff and that this trend will accelerate in the future. However, large uncertainties remain, partly due to different approaches for modelling the GrIS SMB, which have to weigh physical complexity or low computing time, different spatial and temporal resolutions, different forcing fields, and different ice sheet topographies and extents, which collectively make an inter-comparison difficult. Our GrIS SMB model intercomparison project (GrSMBMIP) aims to refine these uncertainties by intercomparing 13 models of four types which were forced with the same ERA-Interim reanalysis forcing fields, except for two global models. We interpolate all modelled SMB fields onto a common ice sheet mask at 1 km horizontal resolution for the period 1980–2012 and score the outputs against (1) SMB estimates from a combination of gravimetric remote sensing data from GRACE and measured ice discharge; (2) ice cores, snow pits and in situ SMB observations; and (3) remotely sensed bare ice extent from MODerate-resolution Imaging Spectroradiometer (MODIS). Spatially, the largest spread among models can be found around the margins of the ice sheet, highlighting model deficiencies in an accurate representation of the GrIS ablation zone extent and processes related to surface melt and runoff. 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 the same order as RCMs compared with observations and therefore remain useful tools for long-term simulations or coupling with ice sheet models. 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 and that this ensemble estimate can be used as a reference for current climate when carrying out future model developments. However, a higher density of in situ SMB observations is required, especially in the south-east accumulation zone, where the model spread can reach 2 m w.e. yr−1 due to large discrepancies in modelled snowfall accumulation.

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 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 Coupling of climate models and ice sheet models by surface mass balance gradients: application to the Greenland Ice Sheet

2012 ◽  
Vol 6 (2) ◽  
pp. 255-272 ◽  
Author(s):  
M. M. Helsen ◽  
R. S. W. van de Wal ◽  
M. R. van den Broeke ◽  
W. J. van de Berg ◽  
J. Oerlemans
Keyword(s):  
Mass Balance ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Lapse Rate ◽  
Surface Mass Balance ◽  
Surface Mass ◽  
Temperature Lapse Rate

Abstract. It is notoriously difficult to couple surface mass balance (SMB) results from climate models to the changing geometry of an ice sheet model. This problem is traditionally avoided by using only accumulation from a climate model, and parameterizing the meltwater run-off as a function of temperature, which is often related to surface elevation (Hs). In this study, we propose a new strategy to calculate SMB, to allow a direct adjustment of SMB to a change in ice sheet topography and/or a change in climate forcing. This method is based on elevational gradients in the SMB field as computed by a regional climate model. Separate linear relations are derived for ablation and accumulation, using pairs of Hs and SMB within a minimum search radius. The continuously adjusting SMB forcing is consistent with climate model forcing fields, also for initially non-glaciated areas in the peripheral areas of an ice sheet. When applied to an asynchronous coupled ice sheet – climate model setup, this method circumvents traditional temperature lapse rate assumptions. Here we apply it to the Greenland Ice Sheet (GrIS). Experiments using both steady-state forcing and glacial-interglacial forcing result in realistic ice sheet reconstructions.

scholarly journals High resolution (1 km) positive degree-day modelling of Greenland ice sheet surface mass balance, 1870–2012 using reanalysis data

2016 ◽  
Vol 63 (237) ◽  
pp. 176-193 ◽  
Author(s):  
DAVID J. WILTON ◽  
AMY JOWETT ◽  
EDWARD HANNA ◽  
GRANT R. BIGG ◽  
MICHIEL R. VAN DEN BROEKE ◽  
...  
Keyword(s):  
Mass Balance ◽  
Climate Models ◽  
Local Level ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Reanalysis Data ◽  
Surface Mass Balance ◽  
Surface Mass ◽  
Degree Day ◽  
Positive Degree

ABSTRACTWe show results from a positive degree-day (PDD) model of Greenland ice sheet (GrIS) surface mass balance (SMB), 1870–2012, forced with reanalysis data. The model includes an improved daily temperature parameterization as compared with a previous version and is run at 1 km rather than 5 km resolution. The improvements lead overall to higher SMB with the same forcing data. We also compare our model with results from two regional climate models (RCMs). While there is good qualitative agreement between our PDD model and the RCMs, it usually results in lower precipitation and lower runoff but approximately equivalent SMB: mean 1979–2012 SMB (± standard deviation), in Gt a−1, is 382 ± 78 in the PDD model, compared with 379 ± 101 and 425 ± 90 for the RCMs. Comparison with in situ SMB observations suggests that the RCMs may be more accurate than PDD at local level, in some areas, although the latter generally compares well. Dividing the GrIS into seven drainage basins we show that SMB has decreased sharply in all regions since 2000. Finally we show correlation between runoff close to two calving glaciers and either calving front retreat or calving flux, this being most noticeable from the mid-1990s.

scholarly journals Greenland ice sheet surface mass balance: evaluating simulations and making projections with regional climate models

2012 ◽  
Vol 6 (6) ◽  
pp. 1275-1294 ◽  
Author(s):  
J. G. L. Rae ◽  
G. Aðalgeirsdóttir ◽  
T. L. Edwards ◽  
X. Fettweis ◽  
J. M. Gregory ◽  
...  
Keyword(s):  
Mass Balance ◽  
Sea Level ◽  
Climate Models ◽  
Regional Climate ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Regional Climate Models ◽  
Surface Mass Balance ◽  
Surface Mass ◽  
Sheet Surface

Abstract. Four high-resolution regional climate models (RCMs) have been set up for the area of Greenland, with the aim of providing future projections of Greenland ice sheet surface mass balance (SMB), and its contribution to sea level rise, with greater accuracy than is possible from coarser-resolution general circulation models (GCMs). This is the first time an intercomparison has been carried out of RCM results for Greenland climate and SMB. Output from RCM simulations for the recent past with the four RCMs is evaluated against available observations. The evaluation highlights the importance of using a detailed snow physics scheme, especially regarding the representations of albedo and meltwater refreezing. Simulations with three of the RCMs for the 21st century using SRES scenario A1B from two GCMs produce trends of between −5.5 and −1.1 Gt yr−2 in SMB (equivalent to +0.015 and +0.003 mm sea level equivalent yr−2), with trends of smaller magnitude for scenario E1, in which emissions are mitigated. Results from one of the RCMs whose present-day simulation is most realistic indicate that an annual mean near-surface air temperature increase over Greenland of ~ 2°C would be required for the mass loss to increase such that it exceeds accumulation, thereby causing the SMB to become negative, which has been suggested as a threshold beyond which the ice sheet would eventually be eliminated.

scholarly journals Annual Greenland accumulation rates (2009–2012) from airborne snow radar

2016 ◽  
Vol 10 (4) ◽  
pp. 1739-1752 ◽  
Author(s):  
Lora S. Koenig ◽  
Alvaro Ivanoff ◽  
Patrick M. Alexander ◽  
Joseph A. MacGregor ◽  
Xavier Fettweis ◽  
...  
Keyword(s):  
Mass Balance ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Ice Cores ◽  
Snow Accumulation ◽  
Ultra Wideband ◽  
The Arctic ◽  
Surface Mass Balance ◽  
Accumulation Rates ◽  
Surface Mass

Abstract. Contemporary climate warming over the Arctic is accelerating mass loss from the Greenland Ice Sheet through increasing surface melt, emphasizing the need to closely monitor its surface mass balance in order to improve sea-level rise predictions. Snow accumulation is the largest component of the ice sheet's surface mass balance, but in situ observations thereof are inherently sparse and models are difficult to evaluate at large scales. Here, we quantify recent Greenland accumulation rates using ultra-wideband (2–6.5 GHz) airborne snow radar data collected as part of NASA's Operation IceBridge between 2009 and 2012. We use a semiautomated method to trace the observed radiostratigraphy and then derive annual net accumulation rates for 2009–2012. The uncertainty in these radar-derived accumulation rates is on average 14 %. A comparison of the radar-derived accumulation rates and contemporaneous ice cores shows that snow radar captures both the annual and long-term mean accumulation rate accurately. A comparison with outputs from a regional climate model (MAR) shows that this model matches radar-derived accumulation rates in the ice sheet interior but produces higher values over southeastern Greenland. Our results demonstrate that snow radar can efficiently and accurately map patterns of snow accumulation across an ice sheet and that it is valuable for evaluating the accuracy of surface mass balance models.

Sources of Uncertainty in Greenland Surface Mass Balance in the 21st century.

2021 ◽  
Author(s):  
Katharina Meike Holube ◽  
Tobias Zolles ◽  
Andreas Born
Keyword(s):  
Mass Balance ◽  
Greenhouse Gas ◽  
21St Century ◽  
Climate Models ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Surface Mass Balance ◽  
Surface Mass ◽  
Sources Of Uncertainty ◽  
Ensemble Of Simulations

<p>The surface mass balance (SMB) of the Greenland Ice Sheet is subject to considerable uncertainties that complicate predictions of sea-level rise caused by climate change.<br>We examine the SMB of the Greenland Ice Sheet and its uncertainty in the 21st century using a wide ensemble of simulations with the surface energy and mass balance model "BEr<em>ge</em>n Snow SImulator" (BESSI). We conduct simulations for four greenhouse gas emission scenarios using the output of 26 climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to force BESSI. In addition, the uncertainty of the SMB simulation is estimated by using 16 different parameter sets in our SMB model. The median SMB across climate models, integrated over the ice sheet, decreases for every emission scenario and every parameter set. As expected, the decrease in SMB is stronger for higher greenhouse gas emissions. The uncertainty range in SMB is considerably greater in our ensemble than in other studies that used fewer climate models as forcing. An analysis of the different sources of uncertainty shows that the differences between climate models are the main reason for SMB uncertainty, exceeding even the uncertainty due to the choice of climate scenario. In comparison, the uncertainty caused by the snow model parameters is negligible. The differences between the climate models are most pronounced in the north of Greenland and in the area around the equilibrium line, whereas the ensemble of simulations agrees that the SMB decrease is greatest in the west of Greenland. </p>

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