Automated parameter tuning applied to sea ice in a global climate model



Raw data used in study by Roach et al. PP data, is a format used by the Met Office for its weather and climate model data output. Data can be read by the iris python module from conda-forge. See for documentaton on the package.

## Access ##
This dataset is held in the Edinburgh DataVault, directly accessible only to authorised University of Edinburgh users. External users are very welcome to request access to a copy of the data by contacting the Principal Investigator, Contact Person or Data Manager named on this page. University of Edinburgh users who wish to have direct access should consult the information about retrieving data from the DataVault at: .


This study investigates the hypothesis that a significant portion of spread in climate model projections of sea ice is due to poorly-constrained model parameters. New automated methods for optimization are applied to historical
sea ice in a global coupled climate model (HadCM3) in order to calculate the combination of parameters required to reduce the difference between simulation and observations to within the range of model noise. The optimized parameters
result in a simulated sea-ice time series which is more consistent with Arctic observations throughout the satellite record (1980-present), particularly in the September minimum, than the standard configuration of HadCM3. Divergence
from observed Antarctic trends and mean regional sea ice distribution reflects broader structural uncertainty in the climate model. We also find that the optimized parameters do not cause adverse effects on the model climatology.
This simple approach provides evidence for the contribution of parameter uncertainty to spread in sea ice extent trends and could be customized to investigate uncertainties in other climate variables.

Data Citation

Tett, Simon "Automated parameter tuning applied to sea ice in a global climate model" (2019) [dataset] Edinburgh DataVault
Date made available1 Sep 2019
PublisherEdinburgh DataVault
Date of data production1 Jan 2015 - 1 Oct 2017
Geographical coverageGlobal

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