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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

Experiment Ranking

With the help of the atmosphere and ocean focus groups, six 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: <AOGCM1>, NorESM, MIROC5. This table is the minimum contribution expected from ISMIP6 models and should be worked on in order. Modeling groups that can run many simulations are encouraged to explore the ice sheet response using three additional CMIP5 AOGCMs. For groups that already have their own methods for implementing ocean and atmosphere forcing in place, the suite of "open" experiments (1-4) allows for a direct start. The basic requirement for the "open" experiments is that the forcing is based on the given CMIP5 AOGCM and uses the given duration for the reference period and scenario (see below). All groups are expected to submit results for the "standard" experiments, using forcing methods suggested by ISMIP6. Depending on the results of experiments 3 and 7, which consider RCP2.6, additional AOGCMs may be suggested (exploring RCP2.6 in detail has low priority if the ISM response is very limited). Additional RCP2.6 experiments may be reserved for models that are able to do many simulations, but these would be of lower priority than completing the set with the 6 AOGCMs with RCP8.5. Once all the datasets are prepared for the additional AOGCMs, the next series experiments will be announced.

Note: As of Feb 25, atmospheric dataset for MIROC5 under RCP8.5 and RCP2.6 are ready. Ocean retreat from MIROC5 under RCP8.5 are ready, as well as dataset of thermal forcing and runoff (needed for the high resolution method) from MIROC5 (RCP8.5, RCP2.6) and NorESM (RCP8.5). Greenland model selection team is actively working on final selection

Expt RCP AOGCM Std/open Ocean Forcing Unc Note
0 N/A N/A Control N/A Model drift evaluation
1 8.5 <AOGCM1> Open Medium High atmosphere warming, median ocean warming
2 8.5 NorESM Open Medium Low atmosphere change, low ocean warming
3 2.6 <AOGCM1> Open Medium High atmosphere warming, median ocean warming
4 8.5 MIROC5 Open Medium Median atmosphere warming, median ocean warming
5 8.5 <AOGCM1> Standard Medium High atmosphere warming, median ocean warming
6 8.5 NorESM Standard Medium Low atmosphere changes, low ocean warming
7 2.6 <AOGCM1> Standard Medium High atmosphere warming, median ocean warming
8 8.5 MIROC5 Standard Medium Median atmosphere warming, median ocean warming
9 8.5 MIROC5 Standard High Ocean Forcing Uncertainty
10 8.5 MIROC5 Standard Low Ocean Forcing Uncertainty

Experiment duration and initial state

The projections start on January 2015 and end in December 2100. The start date follows the CMIP6 protocol, while the cutoff date is constrained by the availability of forcing data. The "start ice sheet configuration" for the projections will in many cases be distinct from the "ice sheet initial state". Depending on the date that a modeler assigns to its "initial state", modelers will need to do a short historical run to bring their models to January 2015. Datasets for atmospheric and oceanic forcing are provided, based on the AOGCM used for the projections, but groups can choose their own datasets to reach the "start date".

Groups can reuse their initMIP initial state. However, if the assigned date of the initial state is earlier than January 2015, modelers have to find a way to prepare an updated starting state for end 2014. If modellers chose to produce a new initial state, rerunning the initMIP-Greenland experiments ('ctrl', 'asmb') is required to place results with this new model version into context.

Control Run and schematic forcing

The control run ('ctrl') is needed to evaluate model drift. As in the initMIP-Greenland setup (Goelzer et al., 2018), the control run is obtained by running the model forward without any anomaly forcing, such that whatever surface mass balance (SMB) and ocean forcing was used in the initialization technique would continue unchanged. The second initMIP experiment ('asmb') that also needs to be repeated for each new model version is forcing with a schematic SMB anomaly.

The ISMIP6 standard grid has a horizontal resolution of 1 km and uses the projection EPSG:3413. Note that this grid is different than that used in initMIP-Greenland. We have changed to the new grid because it's projection is used by many observational datasets that are key input for ice sheet models. The revised datasets for 'asmb' forcing for the new projections can be obtained from:

Atmospheric forcing: SMB and temperature anomalies


ISMIP6 provides anomalies of SMB and temperature, along with associated climatology, as it allows for an experimental framework that is applicable to a diverse set of ice sheet models.

Before applying SMB anomalies, ISMs need to be initialized by applying a baseline SMB (either a time series or a climatology). ISMIP6 provides SMB climatologies for the reference period (January 1960 to December 1989) from the same models computing the anomalies. This reference period for Greenland SMB was chosen because the ice sheet was in steady state with the surrounding climate. ISMs can use these climatologies for spin-up, if desired, but are free to use their own preferred SMB forcing.

Elevation feedbacks have been shown to be important in century-scale simulations. For example, Le clec’h et al. (2019) 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).

Provided forcing data sets

ISMIP6 provides surface forcing datasets for the Greenland ice sheet (GrIS) based on CMIP AOGCM simulations. The AOGCM output is re-interpreted 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 AOGCMs.

For CMIP6, many of the AOGCMs 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 AOGCMs, 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 geometry. All RCM runs use a fixed topography, but vertical SMB gradients for each grid cell are derived using the method described by Franco et al. (2012). At each location the vertical gradient in SMB is found by summing and averaging pairwise differences in the nearest neighbor cells. This vertical gradient is used to downscale the SMB (15 km) to a finer grid (1 km), allowing resolution of steep topography that is not represented accurately on the coarse grid. The same information is used to parameterise the SMB-height feedback in the projections. In addition, MAR calculates a potential SMB term for areas that are outside of the observed ice sheet extent, allowing application to ISMs with ice lying outside the MAR ice-sheet mask. However, experience with initMIP has shown that large variations in ice-sheet extent can lead to considerable bias in the projections. We therefore propose a method (see below) for models with large difference from the observed ice sheet extent, to remap the SMB anomaly to the individual modelled ice sheet geometry.

The atmospheric forcings consist of annual anomalies of SMB and surface temperature, along with a fields to represent the dependence of SMB and surface temperature on elevation (dSMBdz, dTdz). 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 in the ice temperature calculations.

The SMB anomaly aSMB is given in units [kg m-2 s-1] in yearly values, one year per file. It should be applied constant over a full year and step change at the beginning of a new year. To convert to units [m yr-1] typically used in an ice sheet model, multiply the netcdf variable by 31556926 s/yr, 1/1000 m3/kg and by the density ratio rhow/rhoi:

aSMB [m yr-1] = aSMB [kg m-2 s-1] * 31556926 / 1000 * (1000/rhoi), where rhoi is your specific ice density (typically 917.0 or similar).

The SMB and its anomalies are provided on the ISMIP6 1 km standard ice sheet grid for Greenland. ISMs then horizontally interpolate the anomaly forcing conservatively from the standard grid to their native grids.

The SMB change with surface elevation dSMBdz is given in units [kg m-2 s-1 m-1] in yearly values, one year per file. To parameterise the SMB-height feedback, the SMB has to be corrected by dSMBdz * h-h_ref, updated every full year, where h and h_ref are the modelled surface elevation the initial modelled ice sheet surface elevation, respectively.


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 mask at the end of the initialization is to the observed ice sheet mask, the one assumed by the RCM.

Method 1: when the ice sheet mask is similar to the observed

This is typically the case for ice sheet models that use data assimilation in their initialization, but could be the case for other modelling approaches. 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 and dSMBdz are provided on an annual basis and should be updated every full year.

Given aSMB(x,y,t) and dSMBdz(x,y,t) on the standard grid, the ISM 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 do 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 of for SMB (aSMB, dSMBdz), and surface temperature (aST, dSTdz) from 1950 to 2100, along with the climatology (1960-1989) for MIROC5-rcp8.5, MIROC5-rcp2.6, and NorESM1-rcp8.5 can be obtained via the ISMIP6 ftp server (email to obtain the login information) at

Unless advised otherwise, always use the latest version of the forcing files (v1, on Feb 25, 2019)

Method 2: when the ice sheet mask is very different from the observed

This is typically the case for ice sheet models that use a glacial-interglacial spinup in their initialization, but could also be the case e.g. for other models that fully relax to a suboptimal SMB. For those models, ISMIP6 generates a time-dependent SMB anomaly, aSMB(x,y,t) and dSMBdz(x,y,t) that are applied as described above. However, the main difference is that the forcing files are specific for the geometry of your modelled initial state.

In order to make the forcing applicable for different ice sheet geometries, we first translate a given SMB anomaly field as a function of absolute location, to a function of surface elevation for 25 regional drainage basins. This step exploits the strong elevation dependence of aSMB. We can then remap aSMB (and dSMBdz) to different modelled geometries. This preserves the overall aSMB patterns and reduces unphysical biases. The procedure to generate the remapped aSMB and dSMBdz is described in Goelzer et al. (2019, in prep).

To obtain the datasets, modelers simply need to provide ISMIP6 with their modelled initial surface elevation h_ref(x,y) and ice mask sftgif(x,y). ISMIP6 will then compute aSMB for you. Please send an email to when you are ready to upload your initial state (h_ref, sftgif).

An example data set produced for the observed geometry as h_ref(x,y) is available for MAR3.9-MIROC5-RCP85 at

The decision on which forcing method to use depends on the expected biases inherent to both approaches. Given the initial state (h_ref and sftgif) we can estimate the biases with a simple integration of the SMB anomaly for a static case (i.e. no ice dynamics). If you are not sure which forcing strategy is best suited for you model, please contact us by email to

Oceanic forcing: Calving and frontal melt

ISMIP6 provides dataset of runoff and ocean thermal forcing for models that have their own methods for implementing oceanic induced retreat. In addition, modeling groups are expected to participate with one of the two ISMIP6 approaches described below. The ISMIP6 Standard approach is a simple retreat intended to be easily implemented by the majority of ISM taking part in ISMIP6. Alternatively, 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:

Oceans overview greenland.png 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.

As described in the webinar, retreat is 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.

Note on the retreat dataset: the retreat rate dataset was calibrated using grounding line position of glaciers that do not have ice shelves. Although the retreat dataset is therefore not optimum for glaciers that have a floating tongue, it is suggested that retreat is imposed at the ice front. Modeling groups that have the capability of computing ice tongue basal melt may use the provided dataset of thermal forcing per basin (high resolution dataset).

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/Projections/GrIS/Ocean_Forcing/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 allows ice sheet models to specify terminus retreat for each individual marine-terminating outlet glacier, thus allowing glaciers to retreat at different rates. This is accomplished by specifying a glacier terminus melt rate, calculated as a function of subglacial discharge (approximated as surface runoff from each glacier catchment) and ocean thermal forcing (Fig. 4). In addition to the melt rate, a calving rate must be specified to obtain the total frontal ablation rate at each terminus. Additional details can be found in the webinar and the updated presentation slides:

Dataset for thermal forcing and runoff from 1950 to 2100 are available for MIROC5-rcp2.6, MIROC5-rcp8.5, and NorESM1-rcp8.5 at: As the dataset were created for an ice sheet geometry corresponding to present day observations from GIMP and BedMachine3, ISM that have differ substantially from this initial geometry should not use this dataset. (ie: if you are using Method 2 for the atmospheric forcing/SMB remapping, then the dataset is not applicable for your model, as the observed drainage basins used to generate the runoff field may not work with your model. You could create a dataset that is appropriate for your model from the remapped runoff and a water routing consistent with your geometry).

GrIS Melt Parameterization flowchart.png

Fig 4: High-resolution melt-rate approach flow chart. Ocean thermal forcing and runoff fields are provided to the ice sheet modelers by the ISMIP6 ocean forcing working group. All parameters in the recommended melt-rate parameterization (Xu et al., 2013; Rignot et al., 2016) are provided, as well. Each ice sheet model must implement the approach and specify their own calving parameterization to calculate a frontal ablation rate for each outlet glacier.

ISMIP6 open approach is used sample a larger variety of oceanic forcing parameterizations, as it remains an active field of research. Models are free to continue applying the ocean forcing parameterization they used during the model initialization or their preferred method, but should still rely on the ocean forcing datasets provided by ISMIP6 to simulate future ocean conditions.

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

• Models have to be able to prescribe a given ice front retreat

• 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. A README file needs to be submitted along the outputs as an integral part of the contribution to the ISMIP6. It may be obtained here (need to update the readme file) or requested by email to

Requirements for the open 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

• Models can choose the ocean parameterization of their choice but this parameterization should use the ocean forcing provided

• 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. A README file needs to be submitted along the outputs as an integral part of the contribution to the ISMIP6. It may be obtained here (need to update the readme file) or requested by email to


Franco, B., Fettweis, X., Lang, C., and Erpicum, M.: Impact of spatial resolution on the modelling of the Greenland ice sheet surface mass balance between 1990–2010, using the regional climate model MAR, The Cryosphere, 6, 695-711,, 2012.

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.

Goelzer, H. et al. (2019) Remapping of Greenland ice sheet surface mass balance anomalies for large ensemble sea-level change projections, in prep.

Le clec'h, S., Charbit, S., Quiquet, A., Fettweis, X., Dumas, C., Kageyama, M., Wyard, C., and Ritz, C.: Assessment of the Greenland ice sheet–atmosphere feedbacks for the next century with a regional atmospheric model coupled to an ice sheet model, The Cryosphere, 13, 373-395,, 2019.

Rignot, E., Xu, Y., Menemenlis, D., Mouginot, J., Scheuchl, B., Li, X., et al. (2016). Modeling of ocean-induced ice melt rates of five west Greenland glaciers over the past two decades. Geophysical Research Letters, 43(12).

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.

Xu, Y., Rignot, E., Fenty, I., Menemenlis, D., & Flexas, M. M. (2013). Subaqueous melting of Store Glacier, west Greenland from three-dimensional, high-resolution numerical modeling and ocean observations. Geophysical Research Letters, 40(17).


The experimental protocol and datasets for the ISMIP6-Projections-Greenland standalone ice sheet simulations would not have been possible without the effort of many scientists that have given their time and expertise, and have run models to convert the CMIP5 models output into datasets that standalone ice sheet models can use. ISMIP6 would like to thank the ocean focus group under the leadership of Fiamma Straneo, the atmospheric focus group under the leadership of Bill Lipscomb and Robin Smith, and the CMIP5 model evaluation focus group under the leadership of Alice Barthel. Donald Slater, Denis Felixson, Mathieu Morlinghem and Heiko Goelzer have been instrumental in the development of the ice front retreat and melt parameterization and associated dataset. Xavier Fettweis, Patrick Alexander and Heiko Goelzer prepared the atmospheric dataset. Alice Barthel, Chris Little, Cecile Agosta, and Jamie Holte provided a rigorous analysis of the CMIP5 models against historical data, which allowed the CMIP5 model evaluation group and the ISMIP6 steering committee to select the CMIP5 models used in this effort. Finally, we thank the ISMIP6 ice sheet modelers for their feedback on the design of the protocol and their willingness to participate in ISMIP6.

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_ice_sheet_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
Martin Rückamp, Angelika Humbert ISSM AWI Alfred Wegener Institute for Polar and Marine Research, DE /University of Bremen, DE
Victoria Lee, Tony Payne BISICLES BGC University of Bristol, Bristol, UK
Isabel Nias, Sophie Nowicki, Denis Felikson ISSM GSFC NASA Goddard Space Flight Center, Greenbelt, USA
Fabien Gillet-Chaulet Elmer IGE Laboratoire de Glaciologie et Géophysique de l'Environnement, FR
Ralf Greve, Reinhard Calov SICOPOLIS ILTS-PIK Institute of Low Temperature Science, Hokkaido University, Sapporo, JP /

Potsdam Institute for Climate Impact Research, Potsdam, DE

Heiko Goelzer IMAUICE IMAU Utrecht University, Institute for Marine and Atmospheric Research (IMAU), Utrecht, NL
Helene Seroussi, Nicole Schlegel ISSM JPL NASA Jet Propulsion Laboratory, Pasadena, USA
William Lipscomb, Gunter Leguy CISM NCAR National Center for Atmospheric Research, Boulder, CO, USA
Andy Aschwanden PISM UAF Geophysical Institute, University of Alaska Fairbanks, USA
Youngmin Choi, Helene Seroussi, Mathieu Morlighem ISSM UCIJPL NASA Jet Propulsion Laboratory, Pasadena, USA / University of California Irvine, Irvine, USA
Sainan Sun and Frank Pattyn FETISH ULB Laboratoire de Glaciologie, Université Libre de Bruxelles, Brussels, BE
Sébastien Le Clec’h GISM VUB Vrije Universiteit Brussel, Brussels, BE

Model Characteristics

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