Climate model forecast biases assessed with a perturbed physics ensemble

Mulholland, D. P., Haines, K. ORCID: https://orcid.org/0000-0003-2768-2374, Sparrow, S. N. and Wallom, D. (2017) Climate model forecast biases assessed with a perturbed physics ensemble. Climate Dynamics, 49 (5-6). pp. 1729-1746. ISSN 0930-7575 doi: 10.1007/s00382-016-3407-x

Abstract/Summary

Perturbed physics ensembles have often been used to analyse long-timescale climate model behaviour, but have been used less often to study model processes on shorter timescales. We combine a transient perturbed physics ensemble with a set of initialised forecasts to deduce regional process errors present in the standard HadCM3 model, which cause the model to drift in the early stages of the forecast. First, it is shown that the transient drifts in the perturbed physics ensembles can be used to recover quantitatively the parameters that were perturbed. The parameters which exert most influence on the drifts vary regionally, but upper ocean mixing and atmospheric convective processes are particularly important on the 1-month timescale. Drifts in the initialised forecasts are then used to recover the ‘equivalent parameter perturbations’, which allow identification of the physical processes that may be at fault in the HadCM3 representation of the real world. Most parameters show positive and negative adjustments in different regions, indicating that standard HadCM3 values represent a global compromise. The method is verified by correcting an unusually widespread positive bias in the strength of wind-driven ocean mixing, with forecast drifts reduced in a large number of areas as a result. This method could therefore be used to improve the skill of initialised climate model forecasts by reducing model biases through regional adjustments to physical processes, either by tuning or targeted parametrisation refinement. Further, such regionally tuned models might also significantly outperform standard climate models, with global parameter configurations, in longer-term climate studies.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/67590
Identification Number/DOI	10.1007/s00382-016-3407-x
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO) Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Springer
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