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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-83
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-83
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Methods for assessment of models 16 May 2019

Methods for assessment of models | 16 May 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Machine dependence as a source of uncertainty in climate models: The HadGEM3-GC3.1 CMIP Preindustrial simulation

Maria-Vittoria Guarino1, Louise C. Sime1, David Schroeder2, Grenville M. S. Lister3, and Rosalyn Hatcher3 Maria-Vittoria Guarino et al.
  • 1British Antarctic Survey, Cambridge, UK
  • 2Department of Meteorology, University of Reading, Reading, UK
  • 3National Centre for Atmospheric Science, University of Reading, Reading, UK

Abstract. When the same weather or climate simulation is run on different High Performance Computing (HPC) platforms, model outputs may not be identical for a given initial condition. While the role of HPC platforms in delivering better climate projections is often discussed in literature, attention is mainly focused on scalability and performance rather than on the impact of machine-dependent processes on the numerical solution. At the same time, machine dependence is an overlooked source of uncertainty when it comes to discussing the model spread observed within the Coupled Model Intercomparison Projects (CMIP).

Here we investigate the impact of machine dependence on model results and quantify, for a selected case study, the magnitude of the uncertainty. We consider the Preindustrial (PI) simulation prepared by the UK Met Office for the forthcoming CMIP6.

We compare key climate variables between PI control simulations run on the UK Met Office supercomputer and the ARCHER HPC platform. Discrepancies strongly depend on the timescale. Decadal means show substantial differences of up to 0.2 °C for global mean air temperature, 1 W/m2 for TOA outgoing longwave flux and 1.2 million km2 for Southern Hemisphere sea ice area. However, on multi-centennial timescales the differences are not significant and the long-term statistics of the two runs are similar.

Differences between the two simulations can be linked to variations in the strongest modes of climate variability. In the Southern Hemisphere, this results in large SST anomalies where ENSO teleconnection patterns are expected that can reach 0.6 °C (and SNR > 1) even on centennial timescales.

Maria-Vittoria Guarino et al.
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Maria-Vittoria Guarino et al.
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In this paper, two versions of the UK Preindustrial control simulation prepared for the forthcoming CMIP6 are used to discuss the machine-dependent nature of coupled climate model simulations. We used the HadGEM3-GC3.1 model, the PI control simulation was run on the UK Met Office supercomputer and on the ARCHER supercomputer. Here we investigate the impact of machine dependence on model results and quantify, for a selected case study, the magnitude of the uncertainty.
In this paper, two versions of the UK Preindustrial control simulation prepared for the...
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