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This page describes the experimental protocol for the ISMIP6 projections that target the upcoming IPCC AR6 assessment. Due to the delay in CMIP6 climate simulations, the initial set of ISMIP6 simulations are based on CMIP5 projections. As CMIP6 model output become available, ISMIP6 will include simulations based on these models.

The experimental framework was revised in September 2018 during an ISMIP6 workshop held in Sassenheim (NL). The protocol, summarized in Fig 1, allows for:

• Sampling CMIP scenarios: main focus is on the high emission RCP8.5, but ice sheet evolution in response to low emission RCP2.6 is also investigated.

• Sampling CMIP models: 6 AOGCMs have been selected from the CMIP5 model ensemble. The AOGCMs were identified based on the following steps: 1) present plausible climates near Greenland (evaluated by model biases over the historical period), 2) have the data at the temporal resolution needed for RCM downscaling, 3) sample a diversity of forcing (evaluated by differences in projections and code similarities).

• Sampling ice sheet model uncertainty: "standard" and "open" experiments. The "standard" experiments are based on parameterizations developed by the ocean and atmospheric focus groups, while "open" experiments utilize the parameterizations already in use by respective ice sheet models.

• Sampling forcing uncertainty: the standard experiments include "high", "mid" and "low" parameters.

• Experiment ranking: This experimental framework results in 12 to 72 experiments, and a control run. Not every ice sheet model will be able to carry out the full set of experiments. The experiments are therefore ranked, groups are encouraged to work shown the list and complete as many experiments as possible. This approach is based on Shannon et al. (2013): it ensures that all groups do a subset of identical experiments, while it also allows faster models to explore the experiment space more fully.

Greenland exp design.png

Figure 1: Overview of the Greenland experimental framework

Initial state and experiment duration

Groups can reuse their initMIP initial state, or start from a new initial state. To facilitate analysis of the sea level projections resulting from the ISMIP6 suite of ice sheet model simulations, ISMIP6 uses January 1995 to December 2014 as reference period. The experiments start on January 2015 and end in December 2100. The cut off date is constrained by the availability of forcing.

Control Run

The control run is needed to evaluate model drift. As in the initMIP setup, the control run is obtained by running the model forward without any anomaly forcing, such that whatever surface mass balance (SMB) was used in the initialization technique would continue unchanged.

Atmospheric forcing: SMB and temperature anomalies

ISMIP6 will provide surface forcing datasets for the Greenland ice sheet (GrIS) based on CMIP global climate model (GCM) simulations. Two basic approaches are possible: using GCM output directly, or re-interpreting the GCM climates through higher-resolution regional climate models (RCMs). The later allows to capture narrow regions at the periphery of the Greenland ice sheet with large surface mass balance (SMB) gradients, which are not captured by CMIP5 GCMs. As a further complication, experience with initMIP shows that there is a wide variation in ice-sheet profiles, extents and forcing requirements across the ISMs that will be used. For the forcing datasets to be usable, they must be able to be used flexibly, and not be tied to a single ice-sheet shape that may have existed in a GCM or RCM. The revised experimental framework described here takes into account the wide variation in ice-sheet profiles.

For CMIP6, many of the GCMs that have indicated participation in ISMIP6 now use multiple elevation classes to downscale SMB to finer grid resolution. Once these models have completed the CMIP6 projections, our goal is to include additional ISMIP6 projections using SMB downscaled via elevation class.

For the ISMIP6 projections based on CMIP5 GCMs, the surface forcing datasets were prepared by Xavier Fettweis, using the MAR regional climate model. RCM downscaling can take account of modest future topography/extent changes, and thus cope with the fact that individual ISM runs may not use exactly the same profile. All RCM runs use a fixed topography, but vertical SMB gradients for each grid cell can be derived, often using the local gradients to neigbouring cells at different elevations. It should be noted that such statistical downscaling techniques are less effective for the AIS, where elevation gradients are smaller than on GrIS. In addition, MAR calculates a potential SMB term for areas that are outside its actual ice sheet extent, allowing application to ISMs with ice lying outside the MAR ice-sheet mask.

The atmospheric forcings consist of annual anomalies of SMB and surface temperature, along with some fields to represent the dependence of SMB and surface temperature on elevation. SMB is needed by ISMs to compute mass changes at the surface, and surface temperature (i.e., the ice temperature at the base of the snow, as distinct from the 2-m air temperature or skin temperature) is used by many ISMs as an upper boundary condition. SMB anomalies will be given in units of kg m-2 s-1, and surface temperature in units of deg K. The units of SMB_anomaly are (meter ice equivalent/year) with an assumed density of 910 kg/m^3 and 31556926 s/yr. The following remarks refer mostly to SMB, but the same comments would generally apply to surface temperature as well.

To do: same comments as for Antarctica: decide on units. GMD paper say kg m-2 s-1, and surface temperature in units of deg K. For initMIP, The units of SMB_anomaly are (meter ice equivalent/year) with an assumed density of 910 kg/m^3 and 31556926 s/yr.

The SMB and its anomalies will be provided on the ISMIP6 standard ice sheet grid for Greenland. ISMs will then horizontally interpolate the anomaly forcing from the standard grid to their native grids. The anomaly forcing will not simply be a function of horizontal position (x,y), but will include dependence on the vertical coordinate z, so that ISMs can simulate SMB-elevation feedbacks. Thus, two ISMs with different topography would see different anomalies, consistent with climate conditions (e.g., net accumulation or ablation) at each location. Elevation feedbacks have been shown to be important in century-scale simulations. For example, Le clec’h et al. (2017) considered differences between no coupling (SMB independent of z), one-way coupling (i.e., correcting SMB outputs for ISM topography changes), and two-way coupling (allowing ice-sheet topographic changes to feed back on the RCM simulation.) Their results suggest that one-way coupling is sufficient to represent elevation feedbacks until the end of the 21st century. For large topographic changes on longer timescales, it might be necessary to incorporate two-way feedbacks, which are beyond the scope of the standalone ISM effort (but will be considered in the ISMIP6 coupled GCM-ISM experiments).

Before applying SMB anomalies, ISMs will need to be initialized by applying a baseline SMB (either a time series or a climatology). ISMIP6 will provide SMB climatologies for the reference period (January 1995 to December 2014) from the same models computing the anomalies. ISMs can use these these climatologies for spin-up, if desired, but are free to use their own preferred SMB forcing. Let SMB_ref(x,y) denote the SMB used to initialize the ISM, and let h_ref(x,y) denote the ice sheet surface elevation at the end of the initialization. If a time-dependent SMB is used for spin-up, then SMB_ref(x,y) is the average over the reference period.

Here we propose two different methods for implementation of atmospheric forcing, depending on how close the ice sheet surface elevation at the end of the initialization (h_ref(x,y)) is to the ice sheet surface elevation assumed by the RCM (h_rcm(x,y)).

Method 1: when h_ref(x,y) is similar to h_rcm(x,y)

This will be the case for ice sheet models that typically use data assimilation in their initialization. For those models, ISMIP6 provides aSMB(x,y,t) at h_rcm(x,y), along with dSMBdz(x,y,t). Here, aSMB is the time-dependent SMB anomaly in a changing climate, computed in an RCM with fixed surface topography h_rcm, and dSMBdz(x,y,t) is the time-dependent vertical gradient of SMB. aSMB will be provided on an annual basis, while dSMBdz on annual or 10yrs???

The regional model MAR already provides data in this format. MAR runs at a resolution of ~15 km for Greenland. The resulting SMB (or its anomaly) is then downscaled to a finer grid using the method described by Franco et al. (2012). At each location on the fine grid, the vertical gradient is SMB is found by summing and averaging pairwise differences in the nearest neighbor cells on the coarse grid. This vertical gradient is used to downscale the SMB to the finer grid, allowing resolution of steep topography that is not represented accurately on the coarse grid.

Given aSMB(x,y,t) and dSMBdz(x,y) on the standard grid, the ISM will horizontally interpolate these fields to its local grid. Then during runtime, the SMB at a given time and location is computed as

SMB(x,y,t) = SMB_ref(x,y) + aSMB(x,y,t) + dSMBdz(x,y) * [h(x,y,t) - h_ref(x,y)],

where h(x,y,t) is the time-dependent surface elevation. ISMs will likely need to implement code changes to handle the lapse-rate correction. The models will not need h_rcm(x,y) to compute SMB, but it is provided for reference. Since dSMBdz is computed at h_rcm and is not given as a function of z, this approach may be inaccurate if h_ref is significantly different from h_rcm.

The datasets can be obtained via the ISMIP6 ftp server (email to obtain the login information).


Modeling groups should use the 1km version to conservatively interpolate to their model native grid (see Appendix 1, below). Files of lower resolution (5km, 10km, and 20km) are provided for groups using the output grid as “native grid”. Note that the output grid is slightly different than that used in initMIP-Greenland, which had been set to Bamber et al. (2001) projection. The output grid for the Greenland ice sheet is now EPSG:3413, because it is the projection used by observational datasets that are key input for ice sheet models using data assimilation to obtain their initial states.

Method 2: when h_ref is significantly different from h_rcm

Note: google doc says that the text below needs to be altered to include dSMBdz_ltbl and that the lapse rate needs to be included. The google doc is

This will be the case for ice sheet models that typically use inter-glacial spinup in their initialization. For those models, ISMIP6 will generate a time-dependent SMB anomaly, aSMB_ltbl(b,h,t), where b is a basin number (e.g., Greenland might be divided into ~20 discrete ice-flow basins analogous to watersheds), and h is surface elevation. aSMB_ltbl(b,h,t) takes the form of a lookup table, which is constructed as described by Goelzer et al. (2018, in prep). ISMIP6 will generate this dataset for participating ice sheet models.

Forcing is provided in this format to address the deficiencies of method (1) when h_ref(x,y) differs significantly from h_rcm(x,y). For example, consider a near-coastal Greenland grid cell with h_rcm = 100 m. Suppose the spun-up ISM has a relatively advanced ice margin, with h_ref = 500 m in this cell. Then, instead of using aSMB from the RCM in this location, it is more appropriate to use aSMB from a location in the same basin, but at higher elevation. The lookup table provides this information.

There are several ways an ISM might apply the lookup table. First, consider the case that elevation changes are modest on the time scale of interest, so that at a given location, h(x,y,t) is not too different from h_ref(x,y). Then, given aSMB_ltbl(b,h,t), the ISM would loop through each grid cell, identify the basin, and interpolate between adjacent h values in the table to compute the local SMB anomaly aSMB(x,y,t) at h_ref(x,y). (Near the margin between basins; values from two or more basins might be included in a weighted sum; see Goelzer et al. 2018 for details.) During the run, SMB is computed as

SMB(x,y,t) = SMB_ref(x,y) + aSMB(x,y,t).

This is formally the same as (1), but with the lapse rate correction omitted because elevation dependence has been incorporated in aSMB. Runtime code changes would not be needed for ISMs that already can run initMIP-Greenland.

question Sophie to Heiko, Bill and Jonathan: Can we find a way to better define how one would decide if 'h(x,y,t) is close to h_ref(x,y)' versus 'h(x,y,t) is not deemed to be sufficiently close to h_ref(x,y)' so that clearer to reader which case they have to apply? Also, is the implementation needed for the second case clear enough?

Next, consider the case that elevation changes during the run are significant, such that h(x,y,t) is not deemed to be sufficiently close to h_ref(x,y). Then the ISM would need to interpolate from the lookup table to the time-varying elevation h(x,y,t). In this case, runtime code changes would be needed.

To obtain the datasets modelers simply need to provide ISMIP6 with h_ref(x,y). ISMIP6 would then compute aSMB for you. Please send an email at when you are ready to upload your h_ref(x,y), along with ice mask ANYTHING ELSE THAT MODELERS NEED TO SEND???

Oceanic forcing: Calving and frontal melt

ISMIP6 provides dataset of BLABLA for models that have their own methods for implementing oceanic induced retreat. In addition, modeling groups are encouraged to participate with the ISMIP6 Standard approach described below. The later is a simple retreat intended to be easily implemented by the majority of ISM taking part in ISMIP6. In addition, for models that wish to implement a more complex oceanic forcing, the ISMIP6 Greenland ocean focus group has developed a second methodology, described in ISMIP6 high resolution ocean melt rate approach.

ISMIP6 Standard approach imposes an empirically-derived, sector averaged retreat (Fig 2) as a function of climate forcing. This method was developed for ISMIP6 as a result of the ocean forcing focus group and is described in greater details in Slater et al. (in prep), and in the webinar:

Retreat rate.png

Fig 2: Example of the empirically-derived retreat scenarios for the 7 sectors of the Greenland ice sheet, obtained with MIROC5, RCP8.5. TO DO: MODIFY FIG so that it shows the 7 ISMIP6 Basins

As described in the webinar, retreat will be imposed when the ice sheet geometry and ice front retreat scenario indicates that the land_ice_area_fraction_retreat mask is ice free for a given year. (Note the name was chosen so that it is closely related to the standard name land_ice_area_fraction -corresponding to variable name sftgif- in the ISMIP6 data request. For ease of communication, we use the longer standard name). Implementation for a specific ISM (also illustrated in Fig 3) requires the following steps:

1. Identify ice prone to outlet glacier retreat, by interpolating initial ice mask conservatively to 1 km ISMIP6 diagnostic grid
 to obtain a mask for ice fraction:

land_ice_area_fraction(x,y) = [0.0, …, 1.0] with 0.0 for ice free and 1.0 for ice covered

2. Calculate the distance to nearest ocean grid cell, or "distance_map", based on model ice fraction and observed mask of ice with potential ocean contact -> distance_map(x,y)

3. Calculate land ice fraction retreat based on distance map and retreat scenario: 

land_ice_area_fraction_retreat(x,y,t) = [0.0, …, 1.0] with 0.0 for ice free and 1.0 for ice covered

4. Interpolate land_ice_area_fraction_retreat mask from diagnostic grid to model grid (conservatively)

5. Apply retreat in forward experiments (sub-grid implementation may be required for models with coarse resolutions to allow for partial retreat):

if land_ice_area_fraction_retreat(x,y,t) = 0.0, apply full retreat

if 0.0 < land_ice_area_fraction_retreat(x,y,t) < 1.0 apply partial retreat

Greenland implementation.png

Fig 3: Illustration of the steps required for the implementation of the oceanic retreat.

ISMIP6 will generate the land_ice_area_fraction_retreat masks (steps 2-3) for each model. As retreat is provided as a series of ice fraction masks, ice sheet models with coarse resolution should use a sub-grid approach. A suggestion is to apply an land_ice_area_fraction_retreat that is relative to the reference thickness. Models may have a different strategy for this sub-grid implementation.

question: do we want to give an eq for sub-grid implementation?"

To obtain the land_ice_area_fraction_retreat masks appropriate for your model, please submit your land_ice_area_fraction mask (step 1) to ISMIP6 on the 1km ISMIP6 grid (EPSG:3413) as a netcdf file called This file should be generated by conservative regridding. If your native grid is regular and on EPSG:3413, please provide your original modelled ice mask with x,y information instead. Upload your file to the directory ISMIP6/Ocean_Forcing/Greenland/Retreat_Implementation/MODELFILES/MODELNAME, where MODELNAME is the name of your model, and let us know via email that your file is uploaded.

ISMIP6 high resolution ocean melt rate approach was developed by the Greenland ocean focus group.


Requirements for the standard experiments

• Participants can and are encouraged to contribute with different models and/or initialisation methods

• Models have to be able to prescribe a given SMB anomaly

• Adjustment of SMB due to geometric changes in forward experiments is encouraged.

• Bedrock adjustment in forward experiment is allowed.

• The choice of model input data is unconstrained to allow participants the use of their preferred model setup without modification. Modelers without preferred data set choice can have a look at the ISMIP6 Datasets page for possible options.

• To allow for analysis, any modeling choice needs to be well documented. Please follow the guidance for model output described in XXXX

Requirements for the open experiments


Experiment Ranking

With the help of the atmosphere and ocean focus groups, 6 CMIP5 AOGCMs have been selected for ISMIP6 standalone ice sheet model projections. The table below lists the initial number of experiments based on the first three AOGCMs: GIVE NAMES. This table is the minimum contribution expected from ISMIP6 models. Groups that have their own methods for implementing ocean forcing, are encouraged to do the suite with "open" experiments. All groups are encouraged to contribute to the "standard" experiments. Depending on the results of experiments 3 and 7, which consider RCP2.6, additional AOGCMs may be suggested with RCP2.6 for models that are able to do many simulations, but these would be a lower priority than the completing the set with the 6 AOGCMs with RCP8.5.

Note: As of Jan 21, MAR has completed MIROC5 and NORESM for both RCP8.5 and RCP2.6. Greenland model selection team is actively working on final selection

Expt RCP AOGCM Std/open Forcing Unc Note
0 N/A N/A Control N/A Model drift evaluation
1 8.5 AOGCM1 Open Medium Expected high SLR
2 8.5 AOGCM2 Open Medium Expected low SLR
3* 2.6 AOGCM2 Open Medium Expected high SLR
4 8.5 AOGCM3 Open Medium Expected mid SLR
5 8.5 AOGCM1 Standard Medium Expected high SLR
6 8.5 AOGCM2 Standard Medium Expected low SLR
7* 2.6 AOGCM2 Standard Medium Expected high SLR
8 8.5 AOGCM3 Standard Medium Expected mid SLR
9 8.5 AOGCM1 Standard High Forcing Uncertainty
10 8.5 AOGCM1 Standard Low Forcing Uncertainty


Franco et al. (2012) FINIR

Goelzer, H., Nowicki, S., Edwards, T., Beckley, M., Abe-Ouchi, A., Aschwanden, A., Calov, R., Gagliardini, O., Gillet-Chaulet, F., Golledge, N. R., Gregory, J., Greve, R., Humbert, A., Huybrechts, P., Kennedy, J. H., Larour, E., Lipscomb, W. H., Le clec'h, S., Lee, V., Morlighem, M., Pattyn, F., Payne, A. J., Rodehacke, C., Rückamp, M., Saito, F., Schlegel, N., Seroussi, H., Shepherd, A., Sun, S., van de Wal, R., and Ziemen, F. A. (2018). Design and results of the ice sheet model initialisation experiments initMIP-Greenland: an ISMIP6 intercomparison, The Cryosphere, 12, 1433-1460, doi:10.5194/tc-12-1433-2018.

Le clec’h et al. (2017) FINIR

Shannon, S.R., Payne A.J., Bartholomew I.D., Van Den Broeke M.R., Edwards T.L., Fettweis X., Gagliardini O., Gillet-Chaulet F., Goelzer H., Hoffman M.J., Huybrechts P. (2013) Enhanced basal lubrication and the contribution of the Greenland ice sheet to future sea-level rise, Proceedings of the National Academy of Sciences, 110(35):14156-61.

Appendix 1 – Output grid definition and interpolation

All 2D data is requested on a regular grid with the following description. Polar stereo-graphic projection with standard parallel at 70° N and a central meridian of 45° W (315° E) on datum WGS84 (EPSG3413 projection). The lower left cell center is at (-720000m,-3450000m) with nx=1681 and ny=2881 cells in x and y-direction at full km positions (xmin = -720 km, xmax = +960 km, ymin = -3450 km, ymax = -570 km). The output should be submitted on a resolution adapted to the resolution of the model and can be 20 km, 10 km, 5 km, 2 km or 1 km. The data will be conservatively interpolated to 1 km resolution for archiving and 5 km resolution for diagnostic processing by ISMIP6.

If interpolation is required in order to transform the SMB forcing to your native grid, and transform your model variables to the ISMIP6 output grid (20 km, 10 km, 5 km, 2 km, 1 km), it is required that conservative interpolation is used. The motivation for using a common method for all models is to minimize model to model differences due to the choice of interpolation method.

Note: The previously requested regular grid was in polar stereo-graphic projection with standard parallel at 71° N and a central meridian of 39° W (321° E) on datum WGS84. The lower left corner is at (-800000 m, -3400000 m) and the upper right at (700000 m, -600000 m). This is the same grid (Bamber et al., 2001) used to provide the SMB anomaly forcing previously. This grid was changed to the EPSG3413 projection described above.

A1.1 Regridding Tools and Tips

  • An overview of the regridding process can be found on the Regridding page.
  • Regridding_with_CDO contains tools and tips that have been used by ISMIP6 members

Appendix 2 – Naming conventions, upload and model output data.

Please provide:

• one variable per file for all 2D fields

• all variables in one file for the scalar variables

• a completed readme file

A2.1 File name convention



File name convention for scalar variables:


File name convention for readme file:



<variable> = netcdf variable name (e.g. lithk)

<IS> = ice sheet (AIS or GIS)

<GROUP> = group acronym (all upper case or numbers, no special characters)

<MODEL> = model acronym (all upper case or numbers, no special characters)

<EXP> = experiment name (init, ctrl or asmb)

For example, a file containing the scalar variables for the Greenland ice sheet, submitted by group “JPL” with model “ISSM” for experiment “ctrl” would be called:

If JPL repeats the experiments with a different version of the model (for example, by changing the sliding law), it could be named ISSM2, and so forth.

A2.2 Uploading your model output

Please upload your model output on the FTP server, and email for the user name and latest password. Note sftp does not work!


After log in, go to the ISMIP6/initMIP/output directory via:

ftp> cd /ISMIP6/initMIP/output

and create a directory named <GROUP> with the following sub-directory structure:

initMIP output/ <GROUP>/ <MODEL>/ init/ ctrl/ asmb/

Create additional <MODEL> directories when participating with more than one model or model version.

An example of model output files can be found in /ISMIP6/initMIP/output/ISMIP6/REF.

A2.3 Model output variables and README file

The README file is an important contribution to the ISMIP6 submission. It may be obtained here or requested by email to

The variables requested in the table below serve to evaluate and compare the different models and initialization techniques. Some of the variables may not be applicable for your model, in which case they are to be omitted (with explanation in the README file). Also, specify missing values in your netcdf file where needed, and fields should be undefined outside of the ice mask.

We distinguish between state variables (e.g. ice thickness, temperatures and velocities) and flux variables (e.g. SMB). Flux variables are defined as positive when the process adds mass to the ice sheet and negative otherwise. Note the different treatment for state variables (snapshots) and fluxes (time average). The standard should be averaging over all native time steps for yearly scalar output and for 5 year periods for 2D fields. Please specify how your reported flux data has been averaged over time in the README file.

Example model output files can be found in /ISMIP6/initMIP/output/ISMIP6/REF on the ftp server.

Variable Dim Type Variable Name Standard Name Units Comment
2D variables requested every five years, starting at t=0, snapshots for type ST and as five year average for type FL
Ice thickness x,y,t ST lithk land_ice_thickness m The thickness of the ice sheet
Surface elevation x,y,t ST orog surface_altitude m The altitude or surface elevation of the ice sheet
Bedrock elevation x,y,t ST topg bedrock_altitude m The bedrock topography (unchanged in forward exps.)
Geothermal heat flux x,y C hfgeoubed upward_geothermal_heat_flux_at_ground_level W m-2 Geothermal Heat flux (unchanged in forward exps.)
Surface mass balance flux x,y,t FL acabf land_ice_surface_specific_mass_balance_flux kg m-2 s-1 Surface Mass Balance flux (for areas covered by ice only)
Basal mass balance flux x,y,t FL libmassbf land_ice_basal_specific_mass_balance_flux kg m-2 s-1 Basal mass balance flux (for areas covered by ice only)
Ice thickness imbalance x,y,t FL dlithkdt tendency_of_land_ice_thickness m s-1 dHdt
Surface velocity in x x,y,t ST uvelsurf land_ice_surface_x_velocity m s-1 u-velocity at land ice surface
Surface velocity in y x,y,t ST vvelsurf land_ice_surface_y_velocity m s-1 v-velocity at land ice surface
Surface velocity in z x,y,t ST wvelsurf land_ice_surface_upward_velocity m s-1 w-velocity at land ice surface
Basal velocity in x x,y,t ST uvelbase land_ice_basal_x_velocity m s-1 u-velocity at land ice base
Basal velocity in y x,y,t ST vvelbase land_ice_basal_y_velocity m s-1 v-velocity at land ice base
Basal velocity in z x,y,t ST wvelbase land_ice_basal_upward_velocity m s-1 w-velocity at land ice base
Mean velocity in x x,y,t ST uvelmean land_ice_vertical_mean_x_velocity m s-1 The vertical mean land ice velocity is the average from the bedrock to the surface of the ice
Mean velocity in y x,y,t ST vvelmean land_ice_vertical_mean_y_velocity m s-1 The vertical mean land ice velocity is the average from the bedrock to the surface of the ice
Surface temperature x,y,t ST litempsnic temperature_at_ground_level_in_snow_or_firn K Ice temperature at surface
Basal temperature x,y,t ST litempbot land_ice_basal_temperature K Ice temperature at base
Basal drag x,y,t ST strbasemag magnitude_of_land_ice_basal_drag Pa Magnitude of basal drag
Calving flux x,y,t FL licalvf land_ice_specific_mass_flux_due_to_calving kg m-2 s-1 Loss of ice mass resulting from iceberg calving. Only for grid cells in contact with ocean
Land ice area fraction x,y,t ST sftgif land_ice_area_fraction 1 Fraction of grid cell covered by land ice (ice sheet, ice shelf, ice cap, glacier)
Grounded ice sheet area fraction x,y,t ST sftgrf grounded_ice_sheet_area_fraction 1 Fraction of grid cell covered by grounded ice sheet, where grounded indicates that the quantity correspond to the ice sheet that flows over bedrock
Floating ice sheet area fraction x,y,t ST sftflf floating_ice_sheet_area_fraction 1 Fraction of grid cell covered by ice sheet flowing over seawater
Scalar outputs requested every full year, as snapshots for type ST as 1 year averages for type FL. The t=0 value should contain the data of the initialization.
Total ice mass t ST lim land_ice_mass kg spatial integration, volume times density
Mass above floatation t ST limnsw land_ice_mass_not_displacing_sea_water kg spatial integration, volume times density
Grounded ice area t ST iareag grounded_land_ice_area m^2 spatial integration
Floating ice area t ST iareaf floating_ice_shelf_area m^2 spatial integration
Total SMB flux t FL tendacabf tendency_of_land_ice_mass_due_to_surface_mass_balance kg s-1 spatial integration
Total BMB flux t FL tendlibmassbf tendency_of_land_ice_mass_due_to_basal_mass_balance kg s-1 spatial integration
Total calving flux t FL tendlicalvf tendency_of_land_ice_mass_due_to_calving kg s-1 spatial integration

Appendix 3 – Participating Models and Characteristics

Greenland Standalone Ice Sheet Modeling

Contributors Model Group ID Group

Model Characteristics

Model ID Numerics Ice Flow Initialization Initial Year Initial SMB Velocity Bed Surface GHF Res min Res max