Difference between revisions of "ISMIP6-Projections2300-Antarctica"
m (→Atmospheric forcing: SMB and temperature anomalies)
m (→Oceanic forcing: temperature, salinity, thermal forcing and melt rate parameterization)
|Line 263:||Line 263:|
== Oceanic forcing: temperature, salinity, thermal forcing and melt rate parameterization==
== Oceanic forcing: temperature, salinity, thermal forcing and melt rate parameterization==
ISMIP6 provides datasets of extrapolated ocean "ambient" temperature (T), salinity (S) and thermal forcing (TF) from
ISMIP6 provides datasets of extrapolated ocean "ambient" temperature (T), salinity (S) and thermal forcing (TF) from that are appropriate for present and future ice-shelf cavities. These datasets originate from CMIP models and have been extrapolated under ice shelves , using rules that account for sills and troughs (Fig3). The datasets are on the 8km Antarctic grid. information on how the datasets were producedthe presentations and webinar
or at https://github.com/xylar/ismip6-ocean-forcing.
or at https://github.com/xylar/ismip6-ocean-forcing.
|Line 271:||Line 271:|
Figure 3: Bathymetry and IMBIE2 basins (left) used in the sub-ice shelf extrapolation of ocean temperature (right).
Figure 3: Bathymetry and IMBIE2 basins (left) used in the sub-ice shelf extrapolation of ocean temperature (right).
'''Modeling groups are free to use their ice shelf melt rate parameterization of choice'''
'''Modeling groups are free to use their iceshelf melt rate parameterization of choice'''that the parameterization the ocean forcing datasets (T, S, and TF) provided by ISMIP6. The temperature, salinity and thermal forcing data provided for CMIP5 models are the anomalies of each model with respect to its January 2014 average, added to an observational climatology (based on WOA, EN4 and MEOP datasets). Thus, the be directly by models without anomalies or reference observations of your own. However, groups need to compute anomalies (as in one example in Section 3 of sftp://transfer.ccr.buffalo.edu/projects/grid/ghub/ISMIP6/Projections/AIS/Ocean_Forcing/ISMIP6_ocean_protocol.pdf ). In such cases, anomalies should be computed with respect to the January 2014 average, as this was used to anomalize the CMIP5 model input (and slightly different from the time period, , spanned by the observations). '''Groups multiple submissions different melt rate parameterizationsall -1 experiments Table 1, independent initialization and historical .
'''quadratic-dependence melt rate parameterization''' was developed by the Antarctic ocean focus group and was the standard melt parameterization in ISMIP6 Antarctica (Seroussi et al., 2020). This parameterization is described in detail in sftp://transfer.ccr.buffalo.edu/projects/grid/ghub/ISMIP6/Projections/AIS/Ocean_Forcing/parameterizations/melt_parameterization_ISMIP6.pdf, and Favier et al. (2019). Modeling groups are free to use either median, 5th percentile, or 95th percentile values of the and parameters (provided in the /Ocean_Forcing/parameterizations directory), so long as the parameter choice remains consistent. groups to explore the sensitivity of the melt parameterizations to the of and ;, groups submit independent initialization and historical runs for each .
== Antarctic ice shelf fracture ==
== Antarctic ice shelf fracture ==
Revision as of 23:17, 6 January 2022
- 1 Overview
- 2 List of Projections
- 3 Initialization, historical runs, and projection runs
- 4 Atmospheric forcing: SMB and temperature anomalies
- 5 Oceanic forcing: temperature, salinity, thermal forcing and melt rate parameterization
- 6 Antarctic ice shelf fracture
- 7 Requirements for the projections
- 8 References
- 9 Acknowledgements
- 10 Appendix 1 – Output grid definition and interpolation
- 11 Appendix 2 – Naming conventions, upload and model output data
- 11.1 A2.1 File name convention
- 11.2 A2.2 Retrieving dataset and Uploading your model output
- 11.3 A2.3 Model output variables and README file
- 12 Appendix 3 – Participating Models and Characteristics
This page describes the experimental protocol for the ISMIP6 2300 projections that focus on simulations of the Antarctic Ice Sheet (AIS) extended to year 2300. These simulations are based on CMIP5 and CMIP6 climate model outputs, and are a follow-on to the simulations to 2100 described in ISMIP6-Projections-Antarctica and the paper by Seroussi et al. (2020). Some experiments use climate forcing from coupled global models that were run until 2300 under CMIP forcing scenarios, while other experiments use repeated forcing from the 2080-2100 period, sampled randomly between 2100 and 2300.
At a later date, there might be extended ISMIP6 projections for the Greenland Ice Sheet (GrIS), following on the paper by Goelzer et al. (2020) .
List of Projections
Two CMIP5 models (CCSM4 and HadGEM2) and two CMIP6 models (CESM2 and UKESM) were run with extended high CO2 forcing to 2300 (RCP8.5 and ssp5-85, respectively, for CMIP5 and CMIP6) and were selected for long-term projections. No NorESM1-M extension to 2300 is available, so the extended experiments with NorESM forcing use repeat forcing only.
There are 14 experiments in all. Each experiment ID begins with the prefix 'AE' to signify 'Antarctic extension'. Table 1 lists six Tier 1 experiments that we ask each group to run if possible. This selection includes one run for each of the four climate models with extended forcing to 2300; a low-forcing run for comparison to the high-forcing runs; and one run with repeat forcing from the late 21st century forcing for comparison to runs with extended forcing. If a group is unable to run all six Tier 1 experiments, we ask that they choose a subset.
Table 2 lists an additional eight Tier 2 experiments that we encourage each group to run if resources allow. These include three more experiments with repeat late 21st century forcing, for comparison to the runs with extended forcing. There is one experiment with low emissions (ssp1-26) extended to 2300, to complement the RCP2.6 experiment in Tier 1. Finally, there are four experiments with prescribed ice-shelf collapse driven by hydrofracture, to compare to the runs without shelf collapse. We excluded shelf-collapse runs from Tier 1 because not all ice sheet models have this capability.
Modeling groups that can run many simulations are encouraged to further explore the ice sheet response using targeted experiments. For these experiments, groups should repeat the runs in Tables 1 and 2 with either atmospheric or ocean forcing enabled, but not both. This will help us analyze the relative contributions of atmospheric and ocean forcing, especially for the runs with more extreme warming. The experiment numbers are as in Tables 1 and 2, but with a lower-case 'a' or 'o' appended to show which forcing is applied.
For the original ISMIP6 projections, each group was asked to run with a standard sub-shelf melting parameterization based on the method described by Jourdain et al. (2020) , and optionally an open parameterization chosen by the group. For the extended Antarctic projections, we no longer prescribe a standard method. Instead, sub-shelf melting schemes are left to the discretion of each group. This change reflects our desire to fully sample the methods in use. As in the original projections, we ask each group to use the thermal forcing data provided by ISMIP6, so that different ice sheet model responses can be attributed to differences in the models rather than the forcing.
Note: All datasets needed for the Tier 1, Tier 2, and targeted experiments are available on the UB server. The same datasets are used for the additional targeted experiments. (See Table) [Link to be updated]
|Table 1: Tier 1 Experiments|
|AE01||NorESM1-M||RCP2.6||Repeat||No||Low warming scenario|
|AE02||CCSM4||RCP8.5||To 2300||No||Large atmospheric changes and variable regional ocean warming|
|AE03||HadGEM2||RCP8.5||To 2300||No||(?) atmospheric warming and high ocean warming|
|AE04||CESM2||ssp5-85||To 2300||No||Large atmospheric changes and variable regional ocean warming|
|AE05||UKESM||ssp5-85||To 2300||No||(?) atmospheric warming and high ocean warming|
|AE06||UKESM||ssp5-85||Repeat||No||Repeat forcing for comparison to AE05|
|Table 2: Tier 2 Experiments|
|AE07||NorESM1-M||RCP8.5||Repeat||No||Large atmospheric changes and median ocean warming|
|AE08||HadGEM2||RCP8.5||Repeat||No||Repeat forcing for comparison to AE03|
|AE09||CESM2||ssp5-85||Repeat||No||Repeat forcing for comparison to AE04|
|AE10||UKESM||ssp1-26||To 2300||No||Extended low warming scenario|
|AE11||CCSM4||RCP8.5||To 2300||Yes||Collapse experiment for comparison to AE02|
|AE12||HadGEM||RCP8.5||To 2300||Yes||Collapse experiment for comparison to AE03|
|AE13||CESM2||ssp5-85||To 2300||Yes||Collapse experiment for comparison to AE04|
|AE14||UKESM||ssp5-85||To 2300||Yes||Collapse experiment for comparison to AE05|
Initialization, historical runs, and projection runs
All experiments start on 1 January 2015 and end on 31 December 2300. The start date follows the CMIP6 protocol for projections, while the end date is constrained by the availability of forcing. In many cases, a short historical run will be needed to bring the models from the initialization date (say, the end of a spin-up to 20th century conditions) to the projection start date of January 2015. The initialization date (or initial state) is left to each group's discretion and can be any time before January 2015.
Some experiments (#1, 2, 3, 5, 6, 7, 8, 11, 12, and 14 in Tables 1 and 2) use the same forcing for years 2015–2100 as in the original Antarctic projections. However, the CESM2 ssp5-85 forcing (#4, 9, and 13) has changed, coming from a run with a different atmosphere component (WACCM instead of CAM). The low-emission UKESM forcing (#10) was not part of the original projections. For the experiments with identical 21st century forcing, groups that already ran their models through 2100 can simply continue from 1 January 2101, provided their ice sheet models have not changed. If the models have changed, we ask that groups repeat their initialization and historical run, and then start the projections from 2015.
The protocol does not include a control run (i.e., a forward run that holds fixed the surface mass balance and ocean forcing used to initialize the model). Instead, experiments 1 and 10 serve as low-warming counterparts to the 12 experiments with RCP8.5 or ssp5-85 forcing.
Each model configuration should have a single historical run, from which all the projections will branch. Groups are free to choose the forcing for the historical run – for example, using a reanalysis, historical forcing from an RCM or AOGCM, or a combination of multiple datasets. Groups should not carry out a separate historical run for each AOGCM experiment, because this would complicate the forcing strategy and interpretation.
For groups choosing AOGCM forcing for the initialization or historical run, ISMIP6 provides an SMB and surface temperature climatology, along with anomalies, for each AOGCM used to generate a projection dataset. For Antarctica, the SMB and temperature climatology corresponds to 1995–2014, to align with the AR6 reference period. Antarctic SMB and temperature anomalies are available from 1950. For the Southern Ocean, the datasets start from 1850, and the climate model climatology corresponds to 1995–2014. Groups using an Antarctica dataset provided by ISMIP6 are recommended to use the NorESM1-M climatology and anomalies for SMB and surface temperature (in the directory Atmosphere_Forcing/noresm1-m_rcp8.5). [Bill asks: Is this still true?] For the ocean, modelers can use observational climatology (Ocean_Forcing/climatology_from_obs_1995-2017) and/or anomalies (Ocean_Forcing/noresm1-m_rcp8.5/1850-1994).
The climate model ocean climatologies (Ocean_Forcing/noresm1-m_rcp8.5/climatology_1995-2014) are not intended for use by modelers, but are provided so that users can see what was subtracted during the dataset preparation, as the ocean forcing data (Ocean_Forcing/noresm1-m_rcp8.5/1850-1994) is the sum of the observational climatology and model anomalies.
To better sample uncertainties, we encourage groups to submit results using more than one model configuration – for example, from a model run at two or more grid resolutions, or with substantially different physics options. Each configuration would be associated with a separate suite of up to 14 extension experiments. In this case, it is appropriate to do an independent initialization-plus-historical run for each configuration.
Atmospheric forcing: SMB and temperature anomalies
ISMIP6 provides surface forcing datasets for the AIS based on CMIP AOGCM simulations. Two approaches are possible: using AOGCM output directly, or re-interpreting the GCM climates through higher-resolution regional climate models (RCMs). The latter approach, which better captures large surface mass balance (SMB) gradients regions near the periphery of the ice sheet, has been used for ISMIP6 Greenland experiments. For the Antarctic experiments, RCMs are not used, so SMB anomalies based on AOGCM output are applied directly.
For the ISMIP6 projections based on CMIP5 and CMIP6 AOGCMs, the surface forcing consists of anomalies in SMB and surface temperature (illustrated in Fig. 2 for three CMIP5 models). 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. The following remarks refer mostly to SMB, but the same comments would generally apply to surface temperature as well.
ISMIP6 provides yearly averaged surface mass balance anomalies, aSMB(x,y,t), along with its components (precipitation, evaporation and runoff), as well as the SMB climatologies used to compute the anomalies: aSMB_AOGCM(x,y,t) = SMB_AOGCM(x,y,t) - SMB_CLIM_AOGCM(x,y), where SMB_AOGCM is the SMB for a given AOGCM and SMB_CLIM_AOGCM is the climatology for that AOGCM. The SMB_CLIM_AOGCM were computed by taking the mean value of all SMB_AOGCM over the reference period (from January 1995 to December 2014). ISMs can use these climatologies for initialization runs, if desired, but are free to use their preferred SMB forcing for these runs.
During the projection run, modelers need to reintroduce the climatology that best fits their simulations. SMB is computed as:
SMB(x,y,t) = SMB_ref(x,y) + aSMB(x,y,t).
where SMB_ref is the SMB that the ice sheet model would have used over the reference period (from January 1995 to December 2014) and should be the same for all experiments. If a time-dependent SMB is used, then SMB_ref(x,y) is the average over the reference period. If an SMB climatology is used, then SMB_ref(x,y) is simply the climatology. ISMIP6 accepts that existing climatologies (or datasets of SMB averaged over many years) may not align with the AR6 reference period. However, we assume that the differences between climatologies will be less than the interannual variability of the SMB derived from the AOGCMs, and thus changes in aSMB. What is important is that SMB_ref is computed over many years. This assumption allows each ISM to use their preferred SMB.
aSMB(x,y,t) is constant over the entire year and changes stepwise at the beginning of the following year. SMB climatologies and anomalies are given in units of kg m-2 s-1 (water equivalent), and surface temperature in units of deg K. To convert aSMB to units [m yr-1 ice] typically used in an ice sheet model, multiply the netcdf variable by 31556926 s/yr, 1/1000 m3/kg and by the density ratio ρw/ρi:
aSMB [m yr-1] = aSMB [kg m-2 s-1] * 31556926 / 1000 * (1000/ρi),
where ρw = 1000 kg m3 is the density of water, and ρi is your specific ice density (typically 917.0 kg m3 or similar).
The datasets can be obtained via the ISMIP6 ftp server (email email@example.com to obtain the login information) under: sftp://transfer.ccr.buffalo.edu/projects/grid/ghub/ISMIP6/Projections/AIS/Atmosphere_Forcing/
Files are provided for several resolutions (1 km, 2 km, 4 km, 8 km, 16 km, and 32 km). Modeling groups should use the resolution closest to their native grid to conservatively interpolate the data to the model grid (see Appendix 1, below).
Figure 2: SMB and surface temperature anomalies for CCSM4, MIROC-ESM-CHEM, and NorESM1-M under RCP8.5 and 2.6 (top). SMB climatology for the reference period (January 1995-December 2014) for these models under RCP8.5 (middle), along with difference in SMB climatology between RCP8.5 and 2.6 (bottom).
Oceanic forcing: temperature, salinity, thermal forcing and melt rate parameterization
ISMIP6 provides datasets of extrapolated ocean "ambient" temperature (T), salinity (S) and thermal forcing (TF) from 1850—2300 that are appropriate for present and future ice-shelf cavities. These datasets originate from CMIP models and have been extrapolated under ice shelves by Xylar Asay-Davis, using rules that account for sills and troughs (Fig. 3). The datasets are on the ISMIP6 8-km Antarctic grid. For more information on how the datasets were produced, please retrieve the presentations and webinar at sftp://transfer.ccr.buffalo.edu/projects/grid/ghub/ISMIP6/Projections/AIS/Ocean_Forcing/ or at https://github.com/xylar/ismip6-ocean-forcing.
Figure 3: Bathymetry and IMBIE2 basins (left) used in the sub-ice shelf extrapolation of ocean temperature (right).
Modeling groups are free to use their sub-ice-shelf melt rate parameterization of choice, provided that the parameterization uses the ocean forcing datasets (T, S, and TF) provided by ISMIP6. The temperature, salinity and thermal forcing data provided for CMIP5 and CMIP6 models are the anomalies of each model with respect to its January 1995—December 2014 average, added to an observational climatology (based on WOA, EN4 and MEOP datasets). Thus, the datasets can be used directly by models without computing anomalies or selecting reference observations of your own. However, groups might need to compute anomalies (as discussed in one example in Section 3 of sftp://transfer.ccr.buffalo.edu/projects/grid/ghub/ISMIP6/Projections/AIS/Ocean_Forcing/ISMIP6_ocean_protocol.pdf ). In such cases, anomalies should be computed with respect to the January 1995—December 2014 average, as this period was used to anomalize the CMIP5 model input (and is slightly different from the time period, 1995—2017, spanned by the observations). Groups with multiple submissions using different melt rate parameterizations should carry out all the Tier-1 experiments (Table 1), and optionally the Tier-2 experiments (Table 2), for each submission. Each submission will have an independent initialization and historical run.
Groups may use the quadratic-dependence melt rate parameterization that was developed by the Antarctic ocean focus group and was the standard melt parameterization in ISMIP6 Antarctica (Seroussi et al., 2020). This parameterization is described in detail in sftp://transfer.ccr.buffalo.edu/projects/grid/ghub/ISMIP6/Projections/AIS/Ocean_Forcing/parameterizations/melt_parameterization_ISMIP6.pdf, ISMIP6-Projections-Antarctica, and Favier et al. (2019). Modeling groups are free to use either median, 5th percentile, or 95th percentile values of the γ0 and ΔT parameters (provided in the /Ocean_Forcing/parameterizations directory), so long as the parameter choice remains consistent. We encourage groups to explore the sensitivity of the melt parameterizations to the values of γ0 and ΔT. In this case, groups should submit independent initialization and historical runs for each configuration.
Antarctic ice shelf fracture
Surface melting can trigger ice shelf collapse (for example, the Larsen B ice shelf in the Antarctic Peninsula). This mechanism is separate from cliff-collapse, but is a precursor to cliff-collapse. Although the mechanisms for Larsen B-style ice shelf collapse are still poorly understood, ISMIP6 provides dataset for ice shelf collapse in the form of a time dependent mask (Fig 4). These datasets were derived from CMIP5 near surface air temperature (tas) following the method described in Trusel et al. (2015), which results in annual surface melt. For ISMIP6, Luke Trussel prepared the bias corrected annual surface melt, which were used to generate the masks. Ice shelves are assumed to collapse following a 10 year period with annual surface melt above 725 mm (Trusel et al., 2015). Some experiments require modeling ice shelf collapse and the ISMIP6 masks provided should be used in this case. Note that ice shelf collapse is only included in tier-2 experiments and thus, is not a requirement for ISMIP6-2300 participation.
Models are free to decide on the appropriate method to simulate tributary glaciers' behavior following the collapse of ice shelves. As the masks were derived from observations, the observed ice shelf may not always corresponds to an ice shelf in the ISM. In the event that the ice shelf collapse mask corresponds to a region which an ISM considers to be grounded (ice sheet), the collapse should not be imposed. Similarly, in the event that applying the mask results in "iceberg" or regions of floating ice shelf that are now detached from the ice shelf, these floating parts of the ice should be removed as well.
The datasets can be obtained from: sftp://transfer.ccr.buffalo.edu/projects/grid/ghub/ISMIP6/Projections/AIS/Ice_Shelf_Fracture/
Figure 4: Ice shelf collapse mask for CCSM4 under RCP8.5
Requirements for the projections
• 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 use an ocean melt parameterization based on ocean thermal forcing evolving over time, such as one that was proposed for the tier-1 experiments of ISMIP6-Antarctica Projections
• 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 or requested by email to ismip6-at-gmail.com.
Barthel, A., Agosta, C., Little, C.M., Hattermann, T., Jourdain, N.C., Goelzer, H., Nowicki, S., Seroussi, H., Straneo, F. and Bracegirdle, T.J., (2020). CMIP5 model selection for ISMIP6 ice sheet model forcing: Greenland and Antarctica, The Cryosphere, 14(3), 855–879, https://doi.org/10.5194/tc-14-855-2020
Favier, L., Jourdain, N. C., Jenkins, A., Merino, N., Durand, G., Gagliardini, O., Gillet-Chaulet, F., and Mathiot, P.: Assessment of Sub-Shelf Melting Parameterisations Using the Ocean-Ice Sheet Coupled Model NEMO(v3.6)-Elmer/Ice(v8.3), Geosci. Model Dev., https://doi.org/10.5194/gmd-12-2255-2019, 2019.
Jourdain, N.C., Asay-Davis, X., Hattermann, T., Straneo, F., Seroussi, H., Little, C.M. and Nowicki, S., 2020. A protocol for calculating basal melt rates in the ISMIP6 Antarctic ice sheet projections. The Cryosphere, 14(9), 3111-3134. https://doi.org/10.5194/tc-14-3111-2020
Nowicki, S., Goelzer, H., Seroussi, H., Payne, A. J., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Alexander, P., Asay-Davis, X. S., Barthel, A., Bracegirdle, T. J., Cullather, R., Felikson, D., Fettweis, X., Gregory, J. M., Hattermann, T., Jourdain, N. C., Kuipers Munneke, P., Larour, E., Little, C. M., Morlighem, M., Nias, I., Shepherd, A., Simon, E., Slater, D., Smith, R. S., Straneo, F., Trusel, L. D., van den Broeke, M. R., and van de Wal, R. (2020). Experimental protocol for sea level projections from ISMIP6 stand-alone ice sheet models, The Cryosphere, 14, 2331–2368, https://doi.org/10.5194/tc-14-2331-2020
Seroussi, H., Nowicki, S., Simon, E., Abe Ouchi, A., Albrecht, T., Brondex, J., Cornford, S., Dumas, C., Gillet-Chaulet, F., Goelzer, H., Golledge, N. R., Gregory, J. M., Greve, R., Hoffman, M. J., Humbert, A., Huybrechts, P., Kleiner, T., Larour, E., Leguy, G., Lipscomb, W. H., Lowry, D., Mengel, M., Morlighem, M., Pattyn, F., Payne, A. J., Pollard, D., Price, S., Quiquet, A., Reerink, T., Reese, R., Rodehacke, C. B., Schlegel, N.-J., Shepherd, A., Sun, S., Sutter, J., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., and Zhang, T., 2019 initMIP-Antarctica: An ice sheet model initialization experiment of ISMIP6, The Cryosphere., 13, 1441-1471, https://doi.org/10.5194/tc-13-1441-2019
Seroussi, H., Nowicki, S., Payne, A. J., Goelzer, H., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Albrecht, T., Asay-Davis, X., Barthel, A., Calov, R., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Gregory, J. M., Greve, R., Hattermann, T., Hoffman, M. J., Humbert, A., Huybrechts, P., Jourdain, N. C., Kleiner, T., Larour, E., Leguy, G. R., Lowry, D. P., Little, C. M., Morlighem, M., Pattyn, F., Pelle, T., Price, S. F., Quiquet, A., Reese, R., Schlegel, N.-J., Shepherd, A., Simon, E., Smith, R. S., Straneo, F., Sun, S., Trusel, L. D., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., Zhao, C., Zhang, T., and Zwinger, T., 2020 ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century, The Cryosphere, 14, 3033–3070, https://doi.org/10.5194/tc-14-3033-2020
The experimental protocol and datasets for the ISMIP6-Projections2300-Antarctica 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 and CMIP6 model outputs 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. Xylar Asay-Davis, Nicolas Jourdain, Tore Hattermann, Chris Little, Helene Seroussi have been instrumental in the development of the ice shelf basal melt rate parameterization and associated datasets. Erika Simon, Richard Cullather and Sophie Nowicki prepared the atmospheric dataset. Luke Trusel and Helene Seroussi prepared the ice shelf fracture dataset. Alice Barthel, Chris Little, Cecile Agosta, Nicolas Jourdain, and Tore Hattermann 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 71° S and a central meridian of 0° W on datum WGS84. The lower left corner is at (-3040000 m, -3040000 m) and the upper right at (3040000 m, 3040000 m). This is the same grid used to provide the 8km SMB and oceanic forcings. The output should be submitted on a resolution adapted to the resolution of the model and can be 32 km, 16 km, 8 km, 4 km, 2 km or 1 km. The data will be stored on this resolution for archiving and conservatively interpolated on a 8 km resolution for diagnostic processing by ISMIP6. Output should be provided with single precision.
If interpolation is required in order to transform the SMB forcing to your native grid, and transform your model variables to one of the standard ISMIP6 output grid (32 km, 16 km, 8 km, 4 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.
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.
- ISMIP6 is designing tools to help with the regridding.
- If you need help with conservative interpolation, please email ismip6-at-gmail.com.
Appendix 2 – Naming conventions, upload and model output data
COMING SOON: GUIDANCE FOR CMIP6 MODEL REGISTRATION, ADDITIONAL GUIDANCE FOR FILE PREPARATIONS and REVISED FILENAME CONVENTION. HOWEVER, THE CURRENT FILE STRUCTURE AND GUIDANCE BELOW IS DESIGNED TO FACILITATE THE FINAL FILE PREPARATION (NEEDED BEFORE UPLOADING TO THE CMIP6 ARCHIVE). THE EXTRA INFORMATION/FILE RENAMING WILL BE IMPLEMENTED BY SCRIPTS CURRENTLY WRITTEN BY ISMIP6, WHICH WILL ALSO CHECK FOR CF COMPLIANCE ETC. WE WILL PROVIDE HELP TO MODELERS FOR THE FINAL FILE FORMATING PREPARATION, AND MODELERS SHOULD PROCEED WITH SAVING THEIR RESULTS USING THE INFORMATION BELOW
• one variable per file for all 2D fields and scalar variables
• a completed readme file
• single precision should be used for all output
A2.1 File name convention
File name convention for 2D fields and scalar variables:
File name convention for readme file:
<variable> = 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 (from the experiment list, e.g. ctrl or exp01)
For example, a file containing the variable "orog" for the Antarctic ice sheet, submitted by group “JPL” with model “ISSM” for experiment “ctrl” would be called: orog_AIS_JPL_ISSM_ctrl.nc
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 Retrieving dataset and Uploading your model output
We are using GHub via Globus to provide forcing datasets and submit model results.
A2.2.1 How to gain access GHub
All ISMIP6 datasets are stored at the University of Buffalo’s GHub. Information to access GHub datasets can be found on: https://vhub.org/groups/ghub/accessing_data
In order to gain access to the ISMIP6 datasets and upload or download files please send a request to ismip6 [at] gmail.com address with your name, and affiliation for account setup.
TO BE CONTINUED
A2.2.2 Where to upload your results
TO BE CONTINUED
A2.2.3 Reducing the size of files
The size of the model files on higher resolution grid can be largely reduced by file compression which will save space on the storage server. An example command is given below and the results before and after. In the examples that follow we can get a factor of 10 compression and for the masks even more given that contiguous masks are highly compressible because they are repeated data. NetCDF files have been designed with compression in mind. A NetCDF file can be compressed and nothing has to be changed in the way that it is read into Matlab or Python (or any other language that uses standard NetCDF read/write libraries).
The nccopy command copies an input netCDF file to an output netCDF file after compressing the file significantly. The ‘-d’ option stands for the deflation level, from 1 (faster but lower compression) to 9 (slower but more compression) and the ‘-s’ option is the shuffling option to improve compression even more. We recommend using ‘d1’ option since this option seems to accomplish the desired compression.
Example of netcdf compression command:
nccopy -d1 -s sftgif_GIS_JPL_ISSMPALEO_historical.nc sftgif_GIS_JPL_ISSMPALEO_historical_c.nc
Example of compression variant, seems to work better for masks:
nccopy -d1 sftgif_GIS_JPL_ISSMPALEO_historical.nc sftgif_GIS_JPL_ISSMPALEO_historical_c.nc
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 ismip6-at-gmail.com
A2.3.1 General guidelines
The variables requested in Table A1 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).
We distinguish between state variables "ST" (e.g. ice thickness, temperatures and velocities) and flux variables "FL" (e.g. SMB). State variables should be given as snapshot information at the end of one year for both scalars and 2D variables (for initMIP, 2D variables were only requested over five year periods), while flux variables are to be averaged over the respective periods. Please specify in your README file how your reported flux data has been averaged over time. Ideally, the standard would be go average over all native time steps.
Flux variables are defined positive when the process adds mass to the ice sheet and negative otherwise.
All "missing data" must be assigned the single precision floating point value of 1.e20. Fields should be undefined outside of the ice mask.
A2.3.2 How to record time in historical and projection files
In compliance with CMIP6, time should be defined in "days since <basetime>", where <basetime> must be specified by the user, typically in the form year-month-day (e.g., "days since 1800-1-1"). For simulations meant to represent a particular historical period, set the ‘base time’ to the time at the beginning of the simulation. A historical run initialized with forcing for year 2007 would, for example, have units of “days since 2007-1-1”. For the future scenario runs, retain the same <basetime> as used in the historical run from which it was initiated. Note the CF definition for years (section 4.4): a common_year is 365 days, a leap_year is 366 days, a Julian_year is 365.25 days, a Gregorian_year is 365.2425 days, a 360_day has all years with 360 days divided into 30 day months (please see the CF link above for other examples on calendar setting in section 4.4).
To illustrate a time recording for the historical file and projections for a typical state variable (ST, eg thickness) and flux variable (FL, eg SMB), we assume that our <basetime> is January 1st 2013, and that we use a calendar = 360_days. Other calendars can be used, but you need to indicate the calendar used in the netcdf, and of course if you use a different calendar, the time entries will be different. What needs to be recorded is shown in green in the Table below. For state variables,
For state variables, like thickness for the historical:
dimensions: time = UNLIMITED ; // (3 currently) variables: double time(time) ; time:units = "days since 1-1-2013" ; // This date correspond to the example basetime time:calendar = "360_day" ; // Other calendars can be used... change here to relevant calendar time:axis = "T" ; time:long_name = "time" ; time:standard_name = "time" ; data: time = 0, 360, 720; // If you use a different calendar these values will change
and thickness for the projection (note that the full time entries are not shown, only beginning and end) would be:
dimensions: time = UNLIMITED ; variables: double time(time) ; time:units = "days since 1-1-2013" ; // This date correspond to the example basetime time:calendar = "360_day" ; // Other calendars can be used... change here to relevant calendar time:axis = "T" ; time:long_name = "time" ; time:standard_name = "time" ; data: time = 720, 1080, 1440, …, 103320, 103680; // If you use a different calendar these values will change
The flux variable, like SMB, would be recorded as the average over a full year, so for the historical:
dimensions: time = UNLIMITED ; // (2 currently) bnds = 2 ; variables: double time(time) ; time:bounds = "time_bnds" ; time:units = "days since 1-1-2013 " ; // This date correspond to the example basetime time:calendar = "360_day" ; // Other calendars can be used... change here to relevant calendar time:axis = "T" ; time:long_name = "time" ; time:standard_name = "time" ; double time_bnds(time, bnds) ; data: time = 180, 540 ; // If you use a different calendar these values will change. //This is the middle of the time_bnds time_bnds = 0, 360, //If you use a different calendar these values will change. //These are the day since basetime at the beginning and end of the year 360, 720 ;
and the projection (note that the full time entries are not shown, only beginning and end):
dimensions: time = UNLIMITED ; // bnds = 2 ; variables: double time(time) ; time:bounds = "time_bnds" ; time:units = "days since 1-1-2013 " ; // This date correspond to the example basetime time:calendar = "360_day" ; // Other calendars can be used... change here to relevant calendar time:axis = "T" ; time:long_name = "time" ; time:standard_name = "time" ; double time_bnds(time, bnds) ; variables: double time(time) ; time:bounds = "time_bnds" ; data: time = 900, 1260, 1620, ..., 103140, 103500; // If you use a different calendar these values will change. //This is the middle of the time_bnds time_bnds = 720, 1080, //If you use a different calendar these values will change. //These are the day since basetime at the beginning and end of the year 1080, 1440, 1440, 1800, .... 102960, 103320, 103320, 103680;
A2.3.3 Table A1: Variable request for ISMIP6
If your quantity does not change with time, then simply save one time entry. An example is geothermal heat flux, which varies in some models but not others.
| Table A1: Variable request for ISMIP6 projections.
Bold names or "alias" indicate a change compared to initMIP, to align the request with the CMIP6 official MIPtable "IyrAnt" or names in the CF convention. If possible please use the new names, and if not, the name change will occur when your files are checked for CMIP compliance.
The first entry should be that from which the simulation starts. Fields such as surface mass balance flux should be what was applied as boundary conditions.
|Variable||Dim||Type||Variable Name||Standard Name||Units||Comment|
|2D variables requested yearly as snapshots (end of the year) for type ST and as yearly 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|
|Base elevation||x,y,t||ST||base||base_altitude||m||The altitude of the lower ice surface elevation of the ice sheet|
|Bedrock elevation||x,y,t||ST||topg||bedrock_altitude||m||The bedrock topography (may change during the projections)|
|Geothermal heat flux||x,y,t||FL||hfgeoubed||upward_geothermal_heat_flux_in_land_ice alias "upward_geothermal_heat_flux_at_ground_level"||W m-2||Geothermal Heat flux at the land ice interface (only needed beneath the grounded ice). If this quantity does not change with time, then a single entry is sufficient|
|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|
|Basal mass balance flux beneath grounded ice||x,y,t||FL||libmassbfgr alias "libmassbf"||land_ice_basal_specific_mass_balance_flux||kg m-2 s-1||Basal mass balance flux (only beneath grounded ice)|
|Basal mass balance flux beneath floating ice||x,y,t||FL||libmassbffl alias "libmassbf"||land_ice_basal_specific_mass_balance_flux||kg m-2 s-1|| Basal mass balance flux (only beneath floating ice)
|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||xvelsurf alias "uvelsurf"||land_ice_surface_x_velocity||m s-1||u-velocity at land ice surface|
|Surface velocity in y||x,y,t||ST||yvelsurf alias "vvelsurf"||land_ice_surface_y_velocity||m s-1||v-velocity at land ice surface|
|Surface velocity in z||x,y,t||ST||zvelsurf alias "wvelsurf"||land_ice_surface_upward_velocity||m s-1||w-velocity at land ice surface|
|Basal velocity in x||x,y,t||ST||xvelbase alias "uvelbase"||land_ice_basal_x_velocity||m s-1||u-velocity at land ice base|
|Basal velocity in y||x,y,t||ST||yvelbase alias "vvelbase"||land_ice_basal_y_velocity||m s-1||v-velocity at land ice base|
|Basal velocity in z||x,y,t||ST||zvelbase alias "wvelbase"||land_ice_basal_upward_velocity||m s-1||w-velocity at land ice base|
|Mean velocity in x||x,y,t||ST||xvelmean alias "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||yvelmean alias "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||litemptop alias "litempsnic"||temperature_at_top_of_ice_sheet_model alias "temperature_at_ground_level_in_snow_or_firn"||K||Ice temperature at surface|
|Basal temperature beneath grounded ice sheet||x,y,t||ST||litempbotgr alias "litempbot"||temperature_at_base_of_ice_sheet_model alias "land_ice_basal_temperature"||K||Ice temperature at base of grounded ice sheet|
|Basal temperature beneath floating ice shelf||x,y,t||ST||litempbotfl alias "litempbot"||temperature_at_base_of_ice_sheet_model alias "land_ice_basal_temperature"||K||Ice temperature at base of floating ice shelf|
|Basal drag||x,y,t||ST||strbasemag||land_ice_basal_drag alias "magnitude_of_land_ice_basal_drag"||Pa||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|
|Ice front melt and calving flux||x,y,t||FL||lifmassbf||land_ice_specific_mass_flux_due_to_calving_and_ice_front_melting||kg m-2 s-1||Loss of ice mass resulting from ice front melting and calving. Only for grid cells in contact with ocean|
|Grounding line flux||x,y,t||FL||ligroundf||land_ice_specific_mass_flux_at_grounding_line||kg m-2 s-1||Loss of grounded ice mass resulting at grounding line. Only for grid cells in contact with grounding line|
|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_shelf_area_fraction alias "floating_ice_sheet_area_fraction"||1||Fraction of grid cell covered by ice sheet flowing over seawater|
|Scalar outputs requested every full year: snapshots for type ST and 1 year averages for type FL.|
|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||iareagr alias "iareag"||grounded_ice_sheet_area alias "grounded_land_ice_area"||m^2||spatial integration|
|Floating ice area||t||ST||iareafl alias "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 BMB flux beneath floating ice||t||FL||tendlibmassbffl||tendency_of_land_ice_mass_due_to_basal_mass_balance||kg s-1||spatial integration (computed beneath floating ice only)|
|Total calving flux||t||FL||tendlicalvf||tendency_of_land_ice_mass_due_to_calving||kg s-1||spatial integration|
|Total calving and ice front melting flux||t||FL||tendlifmassbf||tendency_of_land_ice_mass_due_to_calving_and_ice_front_melting||kg s-1|| spatial integration
|Total grounding line flux||t||FL||tendligroundf||tendency_of_grounded_ice_mass||kg s-1||spatial integration|
Appendix 3 – Participating Models and Characteristics
Antarctica Standalone Ice Sheet Modeling for 2300 Projections
|Thomas Kleiner, Johannes Sutter, Angelika Humbert||PISM||AWI||Alfred Wegener Institute for Polar and Marine Research, DE /University of Bremen, DE|
|Stephen Price, Matthew Hoffman,Tong Zhang||MALI||DOE||Los Alamos National Laboratory, Los Alamos, USA|
|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, Roderik van de Wal||IMAUICE||IMAU||Utrecht University, Institute for Marine and Atmospheric Research (IMAU), Utrecht, NL & NORCE Norwegian Research Centre, Bergen, NO|
|Helene Seroussi, Nicole Schlegel||ISSM||JPL||NASA Jet Propulsion Laboratory, Pasadena, USA & Dartmouth College, Hanover, NH, USA|
|Aurélien Quiquet, Christophe Dumas||GRISLI||LSCE||Laboratoire des Sciences du Climat et de l’Environnement,Université Paris-Saclay, France|
|William Lipscomb, Gunter Leguy||CISM||NCAR||National Center for Atmospheric Research|
|Ronja Reese, Torsten Albrecht, Matthias Mengel, Ricarda Winkelmann||PISM||PIK||Potsdam Institute for Climate Impact Research, DE|
|Helene Seroussi, Mathieu Morlighem, Tyler Pelle||ISSM||UCIJPL||Dartmouth College, Hanover, NH, USA & University of California San Diego, San Diego, USA|
|Frank Pattyn ,Sainan Sun||f.ETISh||ULB||Laboratoire de Glaciologie, Université Libre de Bruxelles, Brussels, BE|
|Chen Zhao, Rupert Gladstone, Ben Galton-Fenzi||ELMER||UTAS||University of Tasmania, Australia|
|Jonas Van Breedam, Philippe Huybrechts||AISMPALEO||VUB||Vrije Universiteit Brussel, Brussels, BE|
|Nick Golledge, Dan Lowry||PISM||VUW||Antarctic Research Centre, Victoria University of Wellington, NZ|
The Model Characteristics table can be found here.