Forcing single column models using high-resolution model simulations
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 n...
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| Fformat: | Journal article |
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Wiley
2018
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| _version_ | 1826288324360273920 |
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| author | Christensen, H Dawson, A Holloway, C |
| author_facet | Christensen, H Dawson, A Holloway, C |
| author_sort | Christensen, H |
| collection | OXFORD |
| description | 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 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 dataset 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. |
| first_indexed | 2024-03-07T02:11:59Z |
| format | Journal article |
| id | oxford-uuid:a0f74b07-dedd-48d5-b960-d6359b0e758f |
| institution | University of Oxford |
| last_indexed | 2024-03-07T02:11:59Z |
| publishDate | 2018 |
| publisher | Wiley |
| record_format | dspace |
| spelling | oxford-uuid:a0f74b07-dedd-48d5-b960-d6359b0e758f2022-03-27T02:09:33ZForcing single column models using high-resolution model simulationsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:a0f74b07-dedd-48d5-b960-d6359b0e758fSymplectic Elements at OxfordWiley2018Christensen, HDawson, AHolloway, CTo 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 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 dataset 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. |
| spellingShingle | Christensen, H Dawson, A Holloway, C Forcing single column models using high-resolution model simulations |
| title | Forcing single column models using high-resolution model simulations |
| title_full | Forcing single column models using high-resolution model simulations |
| title_fullStr | Forcing single column models using high-resolution model simulations |
| title_full_unstemmed | Forcing single column models using high-resolution model simulations |
| title_short | Forcing single column models using high-resolution model simulations |
| title_sort | forcing single column models using high resolution model simulations |
| work_keys_str_mv | AT christensenh forcingsinglecolumnmodelsusinghighresolutionmodelsimulations AT dawsona forcingsinglecolumnmodelsusinghighresolutionmodelsimulations AT hollowayc forcingsinglecolumnmodelsusinghighresolutionmodelsimulations |