Articles | Volume 14, issue 8
https://doi.org/10.5194/gmd-14-5107-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-5107-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
17 Aug 2021
Development and technical paper |
| 17 Aug 2021
| 17 Aug 2021
An iterative process for efficient optimisation of parameters in geoscientific models: a demonstration using the Parallel Ice Sheet Model (PISM) version 0.7.3
Steven J. Phipps
CORRESPONDING AUTHOR
Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australia
School of Geography, Planning, and Spatial Sciences, University of Tasmania, Hobart, Tasmania, Australia
Ikigai Research, Hobart, Tasmania, Australia
Jason L. Roberts
Australian Antarctic Division, Kingston, Tasmania, Australia
Matt A. King
School of Geography, Planning, and Spatial Sciences, University of Tasmania, Hobart, Tasmania, Australia
The Australian Centre for Excellence in Antarctic Science, University of Tasmania, Hobart, Tasmania, Australia
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Accurate estimates of englacial temperature and geothermal heat flux are incredibly important
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Cited articles
Albrecht, T., Martin, M., Haseloff, M., Winkelmann, R., and Levermann, A.: Parameterization for subgrid-scale motion of ice-shelf calving fronts, The Cryosphere, 5, 35–44, https://doi.org/10.5194/tc-5-35-2011, 2011. a
Albrecht, T., Aschwanden, A., Brown, J., Bueler, E., DellaGiustina, D.,
Feldman, J., Fischer, B., Habermann, M., Haseloff, M., Hock, R., Khroulev, C.,
Levermann, A., Lingle, C., Martin, M., Mengel, M., Maxwell, D., van Pelt, W.,
Seguinot, J., Winkelmann, R., and Ziemen, F.: PISM User's Manual, manual date
30 June 2015, based on PISM revision stable v0.7.1-2-g79b8840,
2015. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p
An, M., Wiens, D. A., Zhao, Y., Feng, M., Nyblade, A., Kanao, M., Li, Y., Maggi, A., and Lévêque, J.: Temperature, lithosphere-asthenosphere boundary, and heat flux beneath the Antarctic Plate inferred from seismic velocities, J. Geophys. Res.-Sol. Ea., 120, 8720–8742, https://doi.org/10.1002/2015JB011917, 2015. a
Aschwanden, A. and Blatter, H.: Mathematical modeling and numerical simulation of polythermal glaciers, J. Geophys. Res., 114, F01027, https://doi.org/10.1029/2008JF001028, 2009. a
Aschwanden, A., Bueler, E., Khroulev, C., and Blatter, H.: An enthalpy formulation for glaciers and ice sheets, J. Glaciol., 58, 441–457, https://doi.org/10.3189/2012JoG11J088, 2012. a, b
Aschwanden, A., Aðalgeirsdóttir, G., and Khroulev, C.: Hindcasting to measure ice sheet model sensitivity to initial states, The Cryosphere, 7, 1083–1093, https://doi.org/10.5194/tc-7-1083-2013, 2013. a
Balay, S., Gropp, W. D., McInnes, L. C., and Smith, B. F.: Efficient
Management of Parallelism in Object Oriented Numerical Software Libraries, in:
Modern Software Tools in Scientific Computing, edited by: Arge, E.,
Bruaset, A. M., and Langtangen, H. P., Birkhäuser Press, Boston, Massachusetts, 163–202,
1997. a
Balay, S., Abhyankar, S., Adams, M. F., Brown, J., Brune, P., Buschelman, K., Dalcin, L., Eijkhout, V., Gropp, W. D., Kaushik, D., Knepley, M. G., McInnes, L. C., Rupp, K., Smith, B. F., Zampini, S., and Zhang, H.: PETSc Users Manual, Tech. Rep. ANL-95/11 – Revision 3.6, Argonne National Laboratory, Argonne, Illinois, 2015. a
Bellprat, O., Kotlarski, S., Lüthi, D., and Schär, C.: Objective calibration of regional climate models, J. Geophys. Res., 117, D23115, https://doi.org/10.1029/2012JD018262, 2012. a
Bindschadler, R. A., Nowicki, S., Abe-Ouchi, A., Aschwanden, A., Choi, H., Fastook, J., Granzow, G., Greve, R., Gutowski, G., Herzfeld, U., Jackson, C., Johnson, J., Khroulev, C., Levermann, A., Lipscomb, W. H., Martin, M. A., Morlighem, M., Parizek, B. R., Pollard, D., Price, S. F., Ren, D., Saito, F., Sato, T., Seddik, H., Seroussi, H., Takahashi, K., Walker, R., and Wang, W. L.: Ice-sheet model sensitivities to environmental forcing and their use in projecting future sea level (the SeaRISE project), J. Glaciol., 59, 195–224, https://doi.org/10.3189/2013JoG12J125, 2013. a
Bueler, E. and Brown, J.: Shallow shelf approximation as a ”sliding law” in a thermomechanically coupled ice sheet model, J. Geophys. Res., 114, F03008, https://doi.org/10.1029/2008JF001179, 2009. a, b, c, d
Bueler, E., Brown, J., and Lingle, C.: Exact solutions to the thermomechanically coupled shallow ice approximation: effective tools for verification, J. Glaciol., 53, 499–516, https://doi.org/10.3189/002214307783258396, 2007. a, b
Calov, R. and Greve, R.: A semi-analytical solution for the positive degree-day model with stochastic temperature variations, J. Glaciol., 51, 173–175, https://doi.org/10.3189/172756505781829601, 2005. a
Chang, W., Applegate, P. J., Haran, M., and Keller, K.: Probabilistic calibration of a Greenland Ice Sheet model using spatially resolved synthetic observations: toward projections of ice mass loss with uncertainties, Geosci. Model Dev., 7, 1933–1943, https://doi.org/10.5194/gmd-7-1933-2014, 2014. a, b, c
Clarke, G. K. C.: Subglacial processes, Annu. Rev. Earth Pl. Sc., 33, 247–276, https://doi.org/10.1146/annurev.earth.33.092203.122621, 2005. a
Cuffey, K. and Paterson, W. S. B.: The Physics of Glaciers, Academic Press, Burlington, Massachusetts, 4th edn., 2010. a
DeConto, R. M. and Pollard, D.: Contribution of Antarctica to past and future sea-level rise, Nature, 531, 591–597, https://doi.org/10.1038/nature17145, 2016. a, b, c
DeVries, P. M. R., Thompson, T. B., and Meade, B. J.: Enabling large-scale viscoelastic calculations via neural network acceleration, Geophys. Res. Lett., 44, 2662–2669, https://doi.org/10.1002/2017GL072716, 2017. a
Edwards, T. L., Brandon, M. A., Durand, G., Edwards, N. R., Golledge, N. R., Holden, P. B., Nias, I. J., Payne, A. J., Ritz, C., and Wernecke, A.: Revisiting Antarctic ice loss due to marine ice-cliff instability, Nature, 566, 58–64, https://doi.org/10.1038/s41586-019-0901-4, 2019. a, b, c, d
Errico, R. M.: What Is an Adjoint Model?, B. Am. Meteorol. Soc., 78, 2577–2592, 1997. a
Feldmann, J., Albrecht, T., Khroulev, C., Pattyn, F., and Levermann, A.: Resolution-dependent performance of grounding line motion in a shallow model compared with a full-Stokes model according to the MISMIP3d intercomparison, J. Glaciol., 60, 353–360, https://doi.org/10.3189/2014JoG13J093, 2014. a
Forest, C. E., Stone, P. H., and Sokolov, A. P.: Constraining climate model parameters from observed 20th century changes, Tellus, 60, 911–920, https://doi.org/10.1111/j.1600-0870.2008.00346.x, 2008. a, b
Forget, G., Campin, J.-M., Heimbach, P., Hill, C. N., Ponte, R. M., and Wunsch, C.: ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation, Geosci. Model Dev., 8, 3071–3104, https://doi.org/10.5194/gmd-8-3071-2015, 2015. a
Fretwell, P., Pritchard, H. D., Vaughan, D. G., Bamber, J. L., Barrand, N. E., Bell, R., Bianchi, C., Bingham, R. G., Blankenship, D. D., Casassa, G., Catania, G., Callens, D., Conway, H., Cook, A. J., Corr, H. F. J., Damaske, D., Damm, V., Ferraccioli, F., Forsberg, R., Fujita, S., Gim, Y., Gogineni, P., Griggs, J. A., Hindmarsh, R. C. A., Holmlund, P., Holt, J. W., Jacobel, R. W., Jenkins, A., Jokat, W., Jordan, T., King, E. C., Kohler, J., Krabill, W., Riger-Kusk, M., Langley, K. A., Leitchenkov, G., Leuschen, C., Luyendyk, B. P., Matsuoka, K., Mouginot, J., Nitsche, F. O., Nogi, Y., Nost, O. A., Popov, S. V., Rignot, E., Rippin, D. M., Rivera, A., Roberts, J., Ross, N., Siegert, M. J., Smith, A. M., Steinhage, D., Studinger, M., Sun, B., Tinto, B. K., Welch, B. C., Wilson, D., Young, D. A., Xiangbin, C., and Zirizzotti, A.: Bedmap2: improved ice bed, surface and thickness datasets for Antarctica, The Cryosphere, 7, 375–393, https://doi.org/10.5194/tc-7-375-2013, 2013. a, b, c, d
Gilford, D. M., Ashe, E. L., DeConto, R. M., Kopp, R. E., Pollard, D., and Rovere, A.: Could the Last Interglacial Constrain Projections of Future Antarctic Ice Mass Loss and Sea-Level Rise?, J. Geophys. Res.-Earth, 125, e2019JF005418, https://doi.org/10.1029/2019JF005418, 2020. a, b
Gladstone, R. M., Payne, A. J., and Cornford, S. L.: Parameterising the grounding line in flow-line ice sheet models, The Cryosphere, 4, 605–619, https://doi.org/10.5194/tc-4-605-2010, 2010. a
Gladstone, R. M., Lee, V., Rougier, J., Payne, A. J., Hellmer, H., Brocq, A. L., Shepherd, A., Edwards, T. L., Gregory, J., and Cornford, S. L.: Calibrated prediction of Pine Island Glacier retreat during the 21st and 22nd centuries with a coupled flowline model, Earth Planet. Sc. Lett., 333–334, 191–199, https://doi.org/10.1016/j.epsl.2012.04.022, 2012. a
Golledge, N. R., Kowalewski, D. E., Naish, T. R., Levy, R. H., Fogwill, C. J., and Gasson, E. G. W.: The multi-millennial Antarctic commitment to future sea-level rise, Nature, 526, 421–425, https://doi.org/10.1038/nature15706, 2015. a, b, c
Guillas, S., Rougier, J., Maute, A., Richmond, A. D., and Linkletter, C. D.: Bayesian calibration of the Thermosphere-Ionosphere Electrodynamics General Circulation Model (TIE-GCM), Geosci. Model Dev., 2, 137–144, https://doi.org/10.5194/gmd-2-137-2009, 2009. a
Habermann, M., Truffer, M., and Maxwell, D.: Changing basal conditions during the speed-up of Jakobshavn Isbræ, Greenland, The Cryosphere, 7, 1679–1692, https://doi.org/10.5194/tc-7-1679-2013, 2013. a
Hawkins, E., Smith, R. S., Allison, L. C., Gregory, J. M., Woollings, T. J., Pohlmann, H., and de Cuevas, B.: Bistability of the Atlantic overturning circulation in a global climate model and links to ocean freshwater transport, Geophys. Res. Lett., 38, L10605, https://doi.org/10.1029/2011GL047208, 2011. a
Heimbach, P. and Bugnion, V.: Greenland ice-sheet volume sensitivity to basal, surface and initial conditions derived from an adjoint model, Ann. Glaciol., 50, 67–80, https://doi.org/10.3189/172756409789624256, 2009. a
Hellmer, H. H., Jacobs, S. S., and Jenkins, A.: Oceanic Erosion of a Floating Antarctic Glacier in the Amundsen Sea, vol. 75 of Antarctic Research Series, American Geophysical Union, 83–99, https://doi.org/10.1029/AR075, 1998. a
Helton, J. C. and Davis, F. J.: Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems, Reliab. Eng. Syst. Safe., 81, 23–69, https://doi.org/10.1016/S0951-8320(03)00058-9, 2003. a
Holland, D. M. and Jenkins, A.: Modeling Thermodynamic Ice–Ocean Interactions at the Base of an Ice Shelf, J. Phys. Oceanogr., 29, 1787–1800, https://doi.org/10.1175/1520-0485(1999)029<1787:MTIOIA>2.0.CO;2, 1999. a
Hourdin, F., Mauritsen, T., Gettelman, A., Golaz, J.-C., Balaji, V., Duan, Q., Folini, D., Ji, D., Klocke, D., Qian, Y., Rauser, F., Rio, C., Tomassini, L., Watanabe, M., and Williamson, D.: The Art and Science of Climate Model Tuning, B. Am. Meteorol. Soc., 98, 589–602, https://doi.org/10.1175/BAMS-D-15-00135.1, 2017. a, b
Jackson, C., Sen, M. K., and Stoffa, P. L.: An Efficient Stochastic Bayesian Approach to Optimal Parameter and Uncertainty Estimation for Climate Model Predictions, J. Climate, 17, 2828–2841, https://doi.org/10.1175/1520-0442(2004)017<2828:AESBAT>2.0.CO;2, 2004. a
Järvinen, H., Räisänen, P., Laine, M., Tamminen, J., Ilin, A., Oja, E., Solonen, A., and Haario, H.: Estimation of ECHAM5 climate model closure parameters with adaptive MCMC, Atmos. Chem. Phys., 10, 9993–10002, https://doi.org/10.5194/acp-10-9993-2010, 2010. a
Kim, Y. and Nakata, N.: Geophysical inversion versus machine learning in inverse problems, The Leading Edge, 37, 894–901, https://doi.org/10.1190/tle37120894.1, 2018. a
Kingslake, J., Scherer, R. P., Albrecht, T., Coenen, J., Powell, R. D., Reese, R., Stansell, N. D., Tulaczyk, S., Wearing, M. G., and Whitehouse, P. L.: Extensive retreat and re-advance of the West Antarctic Ice Sheet during the Holocene, Nature, 558, 430–434, https://doi.org/10.1038/s41586-018-0208-x, 2018. a, b, c
Larour, E., Seroussi, H., Morlighem, M., and Rignot, E.: Continental scale, high order, high spatial resolution, ice sheetmodeling using the Ice Sheet System Model (ISSM), J. Geophys. Res., 117, F01022, https://doi.org/10.1029/2011JF002140, 2012. a
Lee, B. S., Haran, M., Fuller, R. W., Pollard, D., and Keller, K.: A fast particle-based approach for calibrating a 3-D model of the Antarctic ice sheet, Ann. Appl. Stat., 14, 605–634, https://doi.org/10.1214/19-AOAS1305, 2020. a, b, c
Lee, L. A., Pringle, K. J., Reddington, C. L., Mann, G. W., Stier, P., Spracklen, D. V., Pierce, J. R., and Carslaw, K. S.: The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei, Atmos. Chem. Phys., 13, 8879–8914, https://doi.org/10.5194/acp-13-8879-2013, 2013. a
Levermann, A., Albrecht, T., Winkelmann, R., Martin, M. A., Haseloff, M., and Joughin, I.: Kinematic first-order calving law implies potential for abrupt ice-shelf retreat, The Cryosphere, 6, 273–286, https://doi.org/10.5194/tc-6-273-2012, 2012. a, b, c, d
Lliboutry, L. and Duval, P.: Various isotropic and anisotropic ices found in glaciers and polar ice caps and their corresponding rheologies, Ann. Geophys., 3, 207–224, 1985. a
Lyu, G., Köhl, A., Matei, I., and Stammer, D.: Adjoint-Based Climate Model Tuning: Application to the Planet Simulator, J. Adv. Model. Earth Sy., 10, 207–222, https://doi.org/10.1002/2017MS001194, 2018. a
MacAyeal, D. R.: Large-scale ice flow over a viscous basal sediment: Theory and application to ice stream B, Antarctica, J. Geophys. Res., 94, 4071–4087, https://doi.org/10.1029/JB094iB04p04071, 1989. a
Martin, M. A., Winkelmann, R., Haseloff, M., Albrecht, T., Bueler, E., Khroulev, C., and Levermann, A.: The Potsdam Parallel Ice Sheet Model (PISM-PIK) – Part 2: Dynamic equilibrium simulation of the Antarctic ice sheet, The Cryosphere, 5, 727–740, https://doi.org/10.5194/tc-5-727-2011, 2011. a, b, c, d
McNeall, D., Williams, J., Booth, B., Betts, R., Challenor, P., Wiltshire, A., and Sexton, D.: The impact of structural error on parameter constraint in a climate model, Earth Syst. Dynam., 7, 917–935, https://doi.org/10.5194/esd-7-917-2016, 2016. a
Neelin, J. D., Bracco, A., Luo, H., McWilliams, J. C., and Meyerson, J. E.: Considerations for parameter optimization and sensitivity in climate models, P. Natl. Acad. Sci. USA, 107, 21349–21354, https://doi.org/10.1073/pnas.1015473107, 2010. a
O'Hagan, A.: Bayesian analysis of computer code outputs: A tutorial, Reliab. Eng. Syst. Safe., 91, 1290–1300, https://doi.org/10.1016/j.ress.2005.11.025, 2006. a
Paterson, W. S. B. and Budd, W. F.: Flow parameters for ice sheet modeling, Cold Reg. Sci. Technol., 6, 175–177, https://doi.org/10.1016/0165-232X(82)90010-6, 1982. a
Payne, A. J., Huybrechts, P., Abe-Ouchi, A., Calov, R., Fastook, J. L., Greve, R., Marshall, S. J., Marsiat, I., Ritz, C., Tarasov, L., and Thomassen, M. P. A.: Results from the EISMINT model intercomparison: the effects of thermomechanical coupling, J. Glaciol., 46, 227–238, https://doi.org/10.3189/172756500781832891, 2000. a
Phipps, S. J., Roberts, J. L., and King, M. A.: An iterative process for efficient optimisation of parameters in geoscientific models: a demonstration using the Parallel Ice Sheet Model (PISM) version 0.7.3, Zenodo [data set], https://doi.org/10.5281/zenodo.4275053, 2020. a
Pollard, D. and DeConto, R. M.: Description of a hybrid ice sheet-shelf model, and application to Antarctica, Geosci. Model Dev., 5, 1273–1295, https://doi.org/10.5194/gmd-5-1273-2012, 2012. a
Pollard, D., Chang, W., Haran, M., Applegate, P., and DeConto, R.: Large ensemble modeling of the last deglacial retreat of the West Antarctic Ice Sheet: comparison of simple and advanced statistical techniques, Geosci. Model Dev., 9, 1697–1723, https://doi.org/10.5194/gmd-9-1697-2016, 2016. a
Rougier, J.: Probabilistic Inference for Future Climate Using an Ensemble of Climate Model Evaluations, Climatic Change, 81, 247–264, https://doi.org/10.1007/s10584-006-9156-9, 2007. a
Schoof, C.: A variational approach to ice stream flow, J. Fluid Mech., 556, 227–251, https://doi.org/10.1017/S0022112006009591, 2006. a, b
Sen Gupta, A., Muir, L. C., Brown, J. N., Phipps, S. J., Durack, P. J., Monselesan, D., and Wijffels, S. E.: Climate drift in the CMIP3 models, J. Climate, 25, 4621–4640, https://doi.org/10.1175/JCLI-D-11-00312.1, 2012. a
Sexton, D. M. H., Murphy, J. M., Collins, M., and Webb, M. J.: Multivariate probabilistic projections using imperfect climate models part I: outline of methodology, Clim. Dynam., 38, 2513–2542, https://doi.org/10.1007/s00382-011-1208-9, 2012. a
Solonen, A., Ollinaho, P., Laine, M., Haario, H., Tamminen, J., and Järvinen, H.: Efficient MCMC for Climate Model Parameter Estimation: Parallel Adaptive Chains and Early Rejection, Bayesian Anal., 7, 715–736, https://doi.org/10.1214/12-BA724, 2012. a, b
Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M.: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK and New York, NY, USA, 2013. a
van Pelt, W. J. J.,
Oerlemans, J., Reijmer, C. H., Pettersson, R., Pohjola, V. A., Isaksson, E.,
and Divine, D.: An iterative inverse method to estimate basal topography and
initialize ice flow models, The Cryosphere, 7, 987–1006,
https://doi.org/10.5194/tc-7-987-2013, 2013. a
Van Wessem, J. M., Reijmer, C. H., Morlighem, M., Mouginot, J., Rignot, E., Medley, B., Joughin, I., Wouters, B., Depoorter, M. A., Bamber, J. L., Lenaerts, J. T. M., Van De Berg, W. J., Van Den Broeke, M., and Van Meijgaard, E.: Improved representation of East Antarctic surface mass balance in a regional atmospheric climate model, J. Glaciol., 60, 761–770, https://doi.org/10.3189/2014JoG14J051, 2014. a
Weis, M., Greve, R., and Hutter, K.: Theory of shallow ice shelves, Continuum Mech. Therm., 11, 15–50, https://doi.org/10.1007/s001610050102, 1999. a, b
Williamson, D. B., Blaker, A. T., and Sinha, B.: Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model, Geosci. Model Dev., 10, 1789–1816, https://doi.org/10.5194/gmd-10-1789-2017, 2017. a
Winkelmann, R., Martin, M. A., Haseloff, M., Albrecht, T., Bueler, E., Khroulev, C., and Levermann, A.: The Potsdam Parallel Ice Sheet Model (PISM-PIK) – Part 1: Model description, The Cryosphere, 5, 715–726, https://doi.org/10.5194/tc-5-715-2011, 2011. a, b, c, d
Short summary
Simplified schemes, known as parameterisations, are sometimes used to describe physical processes within numerical models. However, the values of the parameters are uncertain. This introduces uncertainty into the model outputs. We develop a simple approach to identify plausible ranges for model parameters. Using a model of the Antarctic Ice Sheet, we find that the value of one parameter can depend on the values of others. We conclude that a single optimal set of parameter values does not exist.
Simplified schemes, known as parameterisations, are sometimes used to describe physical...
