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Forcing single-column models using high-resolution model simulations

Christensen, H. M., Dawson, A. and Holloway, C. E. ORCID: (2018) Forcing single-column models using high-resolution model simulations. Journal of Advances in Modeling Earth Systems, 10 (8). pp. 1833-1857. ISSN 1942-2466

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To link to this item DOI: 10.1029/2017ms001189


To use single column models (SCMs) as a research tool for parametrisation development and process studies, the SCM must be supplied with realistic initial profiles, forcing fields and boundary conditions. We propose a new technique for deriving these required profiles, motivated by the increase in number and scale of high-resolution convection-permitting simulations. We suggest that these high-resolution simulations be coarse-grained to the required resolution of an SCM, and thereby be used as a proxy for the ‘true’ atmosphere. This paper describes the implementation of such a technique. We test the proposed methodology using high-resolution data from the UK Met Office’s Unified Model (MetUM), with a resolution of 4 km, covering a large tropical domain. This data is coarse grained and used to drive the European Centre for Medium-Range Weather Forecast’s (ECMWF) Integrated Forecasting 26 System (IFS) SCM. The proposed method is evaluated by deriving IFS SCM forcing profiles from a consistent T639 IFS simulation. The SCM simulations track the global model, indicating a consistency between the estimated forcing fields and the ‘true’ dynamical forcing in the global model. We demonstrate the benefits of selecting SCM forcing profiles from across a large-domain, namely robust statistics, and the ability to test the SCM over a range of boundary conditions. We also compare driving the SCM with the coarse-grained datase to driving it using the ECMWF operational analysis. We conclude by highlighting the importance of understanding biases in the high-resolution dataset, and suggest that our approach be used in combination with observationally derived forcing datasets.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:77806
Publisher:American Geophysical Union


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