Search from over 60,000 research works

Advanced Search

Forcing single-column models using high-resolution model simulations

[thumbnail of CHRISTENSEN_CoarseGraining_5.0.pdf]
CHRISTENSEN_CoarseGraining_5.0.pdf - Accepted Version (11MB)
Restricted to Repository staff only
Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Christensen, H. M., Dawson, A. and Holloway, C. E. orcid id iconORCID: https://orcid.org/0000-0001-9903-8989 (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 doi: 10.1029/2017ms001189

Abstract/Summary

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.

Altmetric Badge

Item Type Article
URI https://reading-clone.eprints-hosting.org/id/eprint/77806
Item Type Article
Refereed Yes
Divisions Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher American Geophysical Union
Download/View statistics View download statistics for this item

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Search Google Scholar