Abstract
A collection of scientific analyses, metrics, and visualizations for robust validation of ice sheet models is presented using the Land Ice Verification and Validation toolkit (LIVVkit), version 2.1, and the LIVVkit Extensions repository (LEX), version 0.1. This software collection targets stand-alone ice sheet or coupled Earth system models, and handles datasets and analyses that require high-performance computing and storage. LIVVkit aims to enable efficient and fully reproducible workflows for postprocessing, analysis, and visualization of observational and model-derived datasets in a shareable format, whereby all data, methodologies, and output are distributed to users for evaluation. Extending from the initial LIVVkit software framework, we demonstrate Greenland ice sheet simulation validation metrics using the coupled Community Earth System Model (CESM) as well as an idealized stand-alone high-resolution Community Ice Sheet Model, version 2 (CISM2), coupled to the Albany/FELIX velocity solver (CISM-Albany or CISM-A). As one example of the capability, LIVVkit analyzes the degree to which models capture the surface mass balance (SMB) and identifies potential sources of bias, using recently available in situ and remotely sensed data as comparison. Related fields within atmosphere and land surface models, e.g., surface temperature, radiation, and cloud cover, are also diagnosed. Applied to the CESM1.0, LIVVkit identifies a positive SMB bias that is focused largely around Greenland's southwest region that is due to insufficient ablation.
| Original language | English |
|---|---|
| Pages (from-to) | 1067-1086 |
| Number of pages | 20 |
| Journal | Geoscientific Model Development |
| Volume | 12 |
| Issue number | 3 |
| DOIs | |
| State | Published - Mar 22 2019 |
Funding
Acknowledgements. Support for this work was provided through Scientific Discovery through Advanced Computing (SciDAC) program funded by the US Department of Energy Office of Advanced Scientific Computing Research and Office of Biological and Environmental Research. This paper has been authored by UT-Battelle, LLC and used resources of the National Center for Computational Sciences at Oak Ridge National Laboratory, both of which are supported by the Office of Science of the US Department of Energy under contract no. DE-AC05-00OR22725. Contributions of Charles S. Zender were made possible by support from DOE E3SM DE-SC0012998 and NASA ACCESS NNX14AH55A. Miren Vizcaíno acknowledges support the European Research Council ERC-StG-678145-CoupledIceClim. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this paper, or allow others to do so, for United States Government purposes.
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