Model data for probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under high ice-shelf basal melt conditions

Model data for probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under high ice-shelf basal melt conditions======================================================================

This archive contains ice-sheet model output and statistical models used in the manuscript:"Probabilistic projections of the Amery Ice Shelf catchment, Antarctica, underhigh ice-shelf basal melt conditions" by:Sanket Jantre, Matthew Hoffman, Nathan M. Urban, Trevor Hillebrand, MauroPerego, Stephen Price, John D. Jakeman

OVERVIEW--------

Statistical models and corresponding datasets are archived in the fileAmery_UQ_Study_Statistical_Models.zip.  That file contains a README thatdescribes its contents.Contact for statistical models:Sanket Jantre, Brookhaven National Laboratory, [e-mail]

The remaining files in this archive contain output from ice-sheet model simulations using the MPAS-Albany Land Ice (MALI) model (Hoffman et al. 2018, https://github.com/MALI-Dev/E3SM)applied to a regional domain of the Amery Ice Shelf catchmentof Antarctica with mesh resolution varying from 4 to 20 km.The contents of these files are described below.Contact for MALI simulations:Matthew Hoffman, Los Alamos National Laboratory, [e-mail]

ENSEMBLES---------

The ensembles consist of all or a subset of 200 model runs with values for 6parameters sampled from a Sobol' sequence over a specified range.

Parameters and Sampled Ranges:1. Ice stiffness scaling factor, Cφ: (0.8, 1.2)2. Basal friction scaling factor, Cμ: (0.8, 1.2)3. Basal slip exponent, q: (0.1, 0.333)4. Calving yield stress, σmax: (80, 180) kPa5. Ice-shelf melt coefficient, γ0: (9620, 471000) m yr−16. Ice-shelf basal melt rate, m: (12, 58) Gt yr−1

SCENARIOS---------

The archive contains output from 4 scenarios described in the manuscript:

Historical relaxation (RELX): For each ensemble member, we conducted a 50 yearrelaxation from the initial condition using historical climate forcing tointegrate out fast transient behavior. For surface mass balance, we applied a1995–2017 climatological average from RACMO2.3p1 (Van Wessem et al., 2014; vanden Broeke, 2019). The ocean thermal forcing was the observation-basedclimatology compiled for ISMIP6-Antarctica, which uses data from 1995–2018(Jourdain et al., 2020; Nowicki et al., 2020). The 50 year relaxation durationwas chosen as the most rapid adjustments occur in the first few decades ofintegration, while the long term adjustment to a fully steady state takesthousands of years. Relaxation to full steady state would requiresubstantially more computing resources than our entire set of ensembles andalso leads to the complication of different runs having potentially verydifferent initial states. Future improvements to model initialization thataccount for surface elevation change (Perego et al., 2014) may reduce modeldrift and adjust this requirement. The final model state in each run at theend of RELX was given the nominal date of January 1, 2015, and all threeprojection ensembles were branched from these states.

Control projection (CTRL): The CTRL projection ensemble were an extension ofthe RELX configurations, continuing the same surface mass balance and oceanthermal forcing from January 1, 2015, to January 1, 2300. This ensemble wasused to assess model drift relative to the forced response of the climatescenarios. 

SSP1-2.6 projection (SSP1): Our SSP1 projection used annual surface massbalance and ocean thermal forcing derived from a UKESM SSP1-2.6 climatescenario (expAE10 from Seroussi et al., 2024). Surface mass balance and oceanthermal forcing were applied as anomalies relative to the climatological meanforcings in RELX/CTRL to avoid issues related to climate model bias and abruptchanges in forcing. This ensemble was also run from January 1, 2015, toJanuary 1, 2300.

SSP5-8.5 projection (SSP5) Our SSP5 projection used the UKESM SSP5-8.5projection forcings (expAE05 from Seroussi et al., 2024), again applied asanomalies and from 2015 to 2300.

OUTPUT------

Each ensemble directory contains at the base level:* branch_ensemble.cfg - configuration file for the ensemble_generator test  case (https://mpas-dev.github.io/compass/latest/users_guide/landice/test_groups/ensemble_generator.html)   of COMPASS (Configuration Of Model for Prediction Across Scales Setups, https://github.com/MPAS-Dev/compass)  used to set up the ensemble.* mesh_vars.nc - netCDF file containing the mesh variables.  This file is the  same for all ensembles.  These fields need to be appended to an output file to  visualize in, e.g., Paraview.  This can be done with the command:  ncks -A mesh_vars.nc output.nc

Each ensemble contains subdirectories for each run numbered 000-199.  Each rundirectory contains the files:* globalStats.nc - scalar metrics at every time step* output.nc - spatial fields at 10 year intervals* run_info.cfg - summary of parameter values

Notes:* The SSP1 and SSP5 ensembles contain a subset of the full 200 runs  because some runs were filtered out as being unnecessary during model  calibration (see manuscript).* The RELX ensemble is run for 50 years with an arbitrary start year of 2000.  The CTRL, SSP1, and SSP5 ensembles are started in 2015 from the final state  of the corresponding RELX run (nominally 2050).  Because spatial data has  been saved in this archive at 10 year intervals, the first output for the  three projection ensembles is 2020.  To get the initial condition for the  projection ensembles, use the 2050 state from RELX for the corresponding  model run.* Spatial field output at 1 year intervals, as well as run input, log, and  restart files are available from the corresponding contact.  The complete  run data is about 2 TB.

CITATION--------If you find these data useful, please cite the DOI for this archive (10.5281/zenodo.11166628), as well as our manuscriptsubmitted to The Cryosphere.

This archive is approved by Los Alamos National Laboratoryfor public release under LA-UR-24-24969; distribution is unlimited.

REFERENCES----------

Hoffman, M. J., Perego, M., Price, S. F., Lipscomb, W. H., Zhang, T.,Jacobsen, D., Tezaur, I., Salinger, A. G., Tuminaro, R., and Bertagna, L.:MPAS-Albany Land Ice (MALI): a variable-resolution ice sheet model for Earthsystem modeling using Voronoi grids, Geoscientific Model Development, 11,3747–3780, 2018.

Jourdain, N. C., Asay-Davis, X., Hattermann, T., Straneo, F., Seroussi, H.,Little, C. M., and Nowicki, S.: A protocol for calculating basal melt rates inthe ISMIP6 Antarctic ice sheet projections, The Cryosphere, 14, 3111–3134,https://doi.org/10.5194/tc-14-3111-2020, 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.:Experimental protocol for sea level projections from ISMIP6 stand-alone icesheet models, The Cryosphere, 14, 2331–2368,https://doi.org/10.5194/tc-14-2331-2020, 2020.

Perego, M., Price, S., and Stadler, G.: Optimal Initial Conditions forCoupling Ice Sheet Models to Earth System Models, Journal of GeophysicalResearch: Earth Surface, 119, 1894–1917, https://doi.org/10.1002/2014JF003181,2014.

Seroussi, H., et al. 2024. ISMIP6 Projections 2300 Antarctica Protocol.https://theghub.org/groups/ismip6/wiki/ISMIP6-Projections2300-Antarctica. 

van den Broeke, M.: RACMO2.3p1 annual surface mass balance Antarctica(1979-2014), PANGAEA - Data Publisher for Earth & Environmental Science,https://doi.org/10.1594/PANGAEA.896940, 2019.

van Wessem, J., Reijmer, C., Morlighem, M., Mouginot, J., Rignot, E., Medley,B., Joughin, I., Wouters, B., Depoorter, M., Bamber, J., Lenaerts, J., Van DeBerg, W., Van Den Broeke, M., and Van Meijgaard, E.: Improved Representationof East Antarctic Surface Mass Balance in a Regional Atmospheric ClimateModel, Journal of Glaciology, 60, 761–770,https://doi.org/10.3189/2014JoG14J051, 2014.