Forcing Single-Column Models Using High-Resolution Model Simulations.

Hannah M Christensen,Andrew Dawson,Christopher E Holloway

doi:10.1029/2017ms001189

Hannah M Christensen, Andrew Dawson + Show 1 more

Open Access

https://doi.org/10.1029/2017ms001189

Copy DOI

Abstract

To use single‐column models (SCMs) as a research tool for parameterization 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, with a resolution of 4 km, covering a large tropical domain. These data are coarse grained and used to drive the European Centre for Medium‐Range Weather Forecast's 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 data set to driving it using the European Centre for Medium‐Range Weather Forecast operational analysis. We conclude by highlighting the importance of understanding biases in the high‐resolution data set and suggest that our approach be used in combination with observationally derived forcing data sets.

Highlights

Single-column models (SCMs) are widely used to understand physical processes and to inform the development of parameterization schemes
An SCM takes a single atmospheric column from a parent general circulation model (GCM). This column contains the physical parameterization schemes that are used in the GCM to represent unresolved subgrid-scale processes, while the model dynamics is replaced with boundary forcings
It is important to verify that this is a true bias in the Integrated Forecasting System (IFS) and not a bias in the Met Office Unified Model (MetUM) Cascade simulation: the Cascade simulation does not exhibit significant biases in tropospheric humidity compared to ERA Interim reanalysis data, indicating this is a true bias in the IFS

Summary

Introduction

Single-column models (SCMs) are widely used to understand physical processes and to inform the development of parameterization schemes. An SCM takes a single atmospheric column from a parent general circulation model (GCM). This column contains the physical parameterization schemes that are used in the GCM to represent unresolved subgrid-scale processes, while the model dynamics is replaced with boundary forcings. IFS file name time date second lat lon nlev nlevp nlevs pressure_f pressure_h height_f height_h v t q ql qi ps cloud_fraction t_skin q_skin t_soil q_soil snow omega etadotdpdeta ug vg tadv qadv uadv vadv sfc_sens_flx sfc_lat_flx abedo orog anor isor sdfor slor heat_rough.

Objectives

Results

Conclusion