# Identifying and removing structural biases in climate models with history matching

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## Abstract

We describe the method of history matching, a method currently used to help quantify parametric uncertainty in climate models, and argue for its use in identifying and removing structural biases in climate models at the model development stage. We illustrate the method using an investigation of the potential to improve upon known ocean circulation biases in a coupled non-flux-adjusted climate model (the third Hadley Centre Climate Model; HadCM3). In particular, we use history matching to investigate whether or not the behaviour of the Antarctic Circumpolar Current (ACC), which is known to be too strong in HadCM3, represents a structural bias that could be corrected using the model parameters. We find that it is possible to improve the ACC strength using the parameters and observe that doing this leads to more realistic representations of the sub-polar and sub-tropical gyres, sea surface salinities (both globally and in the North Atlantic), sea surface temperatures in the sinking regions in the North Atlantic and in the Southern Ocean, North Atlantic Deep Water flows, global precipitation, wind fields and sea level pressure. We then use history matching to locate a region of parameter space predicted not to contain structural biases for ACC and SSTs that is around 1 % of the original parameter space. We explore qualitative features of this space and show that certain key ocean and atmosphere parameters must be tuned carefully together in order to locate climates that satisfy our chosen metrics. Our study shows that attempts to tune climate model parameters that vary only a handful of parameters relevant to a given process at a time will not be as successful or as efficient as history matching.

## Keywords

Tuning Ensembles Emulators HadCM3 Climate model## Notes

### Acknowledgments

This research was funded by the NERC RAPID-RAPIT project (NE/G015368/1). Daniel Williamson was also funded by an EPSRC fellowship, grant number EP/K019112/1. We would like to thank the CPDN team for their work on submitting our ensemble to CPDN users. We’d also like to thank the Institute of Advanced Study at Durham University for funding and hosting our workshop on ocean model discrepancy which formed the motivation for these investigations. In addition, we thank the oceanographers who participated in this workshop. We’d like to thank the CPDN users around the world who contributed their spare computing resource as part of the generation of our ensemble. NCEP and CMAP Precipitation data were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their website at http://www.esrl.noaa.gov/psd/.

## Supplementary material

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