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2 results for “Parallel Ice Sheet Model”
Data and code for publication "The role of history and strength of the oceanic forcing in sea level projections from Antarctica with the Parallel Ice Sheet Model"
<p>Data and code underlying the publication <a href="https://tc.copernicus.org/preprints/tc-2019-330/">"The role of history and strength of the oceanic forcing in sea level projections from Antarctica with the Parallel Ice Sheet Model"</a>.</p> <p>Journal: The Cryosphere</p> <p>Authors: <em>Ronja Reese<sup>1*</sup></em><em>, Anders Levermann</em><sup><em>1,2,3</em></sup><em>, Torsten Albrecht</em><sup><em>1</em></sup><em>, Hélène Seroussi<sup>4</sup></em><em>, Ricarda Winkelmann<sup>1,2 </sup></em></p> <p>(1) Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, D-14412 Potsdam, Germany</p> <p>(2) Institute of Physics and Astronomy, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany</p> <p>(3) LDEO, Columbia University, New York, USA</p> <p>(4) Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA</p> <p>(*) email ronja.reese@pik-potsdam.de</p> <p>Abstract:<br> Mass loss from the Antarctic Ice Sheet constitutes the largest uncertainty in projections of future sea level rise. Ocean-driven melting underneath the floating ice shelves and subsequent acceleration of the inland ice streams is the major reason for currently observed mass loss from Antarctica and is expected to become more important in the future. Here we show that for projections of future mass loss from the Antarctic Ice Sheet, it is essential (1) to better constrain the sensitivity of sub-shelf melt rates to ocean warming and (2) to include the historic trajectory of the ice sheet. In particular, we find that while the ice sheet response in simulations using the Parallel Ice Sheet Model is comparable to the median response of models in three Antarctic Ice Sheet Intercomparison projects – initMIP, LARMIP-2 and ISMIP6 – conducted with a range of ice sheet models, the projected 21st century sea level contribution differs significantly depending on these two factors. For the highest emission scenario RCP8.5, this leads to projected ice loss ranging from 1.4 to 4.0 cm of sea level equivalent in the ISMIP6 simulations where the sub-shelf melt sensitivity is comparably low, opposed to a likely range of 9.2 to 35.9 cm using the exact same initial setup, but emulated from the LARMIP-2 experiments with a higher melt sensitivity based on oceanographic studies. Furthermore, using two initial states, one with and one without a previous historic simulation from 1850 to 2014, we show that while differences between the ice sheet configurations in 2015 are marginal, the historic simulation increases the susceptibility of the ice sheet to ocean warming, thereby increasing mass loss from 2015 to 2100 by about 50 %. Our results emphasize that the uncertainty that arises from the forcing is of the same order of magnitude as the ice dynamic response for future sea level projections.</p> <p>Large zip files contain data, small zip file python notebooks for data analysis and PISM code. Please contact ronja.reese@pik-potsdam.de if you have any further questions.</p>
An iterative process for efficient optimisation of parameters in geoscientific models: a demonstration using the Parallel Ice Sheet Model (PISM) version 0.7.3
<p>Physical processes within geoscientific models are sometimes described by simplified schemes known as parameterisations. The values of the parameters within these schemes can be poorly constrained by theory or observation. Uncertainty in the parameter values translates into uncertainty in the outputs of the models. Proper quantification of the uncertainty in model predictions therefore requires a systematic approach for sampling parameter space. In this study, we develop a simple and efficient approach to identify regions of multi-dimensional parameter space that are consistent with observations. Using the Parallel Ice Sheet Model to simulate the present-day state of the Antarctic Ice Sheet, we find that co-dependencies between parameters preclude the identification of a single optimal set of parameter values. Approaches such as large ensemble modelling are therefore required in order to generate model predictions that incorporate proper quantification of the uncertainty arising from the parameterisation of physical processes.</p>
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