Identifying and removing structural biases in climate models with history matching

• Published in: Climate Dynamics
• Main Authors: Williamson, Daniel, Blaker, Adam T., Hampton, Charlotte, Salter, James
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2015
• Subjects: Antarctic; Southern Ocean; The Antarctic; Antarc*; North Atlantic Deep Water; North Atlantic
• Online Access: http://nora.nerc.ac.uk/id/eprint/509284/; https://nora.nerc.ac.uk/id/eprint/509284/1/Williamson_et_al_StructErrorPaper.pdf; https://doi.org/10.1007/s00382-014-2378-z

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.