Forcing Single‐Column Models Using High‐Resolution Model Simulations

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.

Constraining stochastic parametrisation schemes using high‐resolution simulations

“…To combine the coarse‐graining procedure with the low‐resolution forecast model, we adapt the methodology described in Christensen et al . (). Instead of considering forecasts made with the global IFS, the IFS SCM is used to integrate forward the equations of motion in each coarse‐scale grid column.…”

“…The required geostrophic wind forcing and vertical velocity forcing are also evaluated using the coarse‐grained fields: see Christensen et al . () for more details.…”

“…This is a typical resolution used in a global ensemble prediction system, and is substantially finer than considered in previous coarse-graining studies. To combine the coarse-graining procedure with the low-resolution forecast model, we adapt the methodology described in Christensen et al (2018b). Instead of considering forecasts made with the global IFS, the IFS SCM is used to integrate forward the equations of motion in each coarse-scale grid column.…”

“…We then assess whether the deterministic schemes could be augmented by a stochastic term to represent uncertainty in the parametrised tendencies. We follow the technique proposed in Christensen et al (2018b), whereby existing high-resolution simulations are coarse-grained to provide the input for a low-resolution single-column model (SCM), which provides the parametrised tendencies. As a case study, we use this coarse-graining methodology to assess the widely used "stochastically perturbed parametrisation tendencies" (SPPT) scheme.…”

Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model

“…The fields have horizontal correlations of 500, 1000 and 2000 km and temporal decorrelations of 6 h, 3 days and 30 days respectively, with associated standard deviations of 0.52, 0.18 and 0.06. The magnitude of the perturbation has been motivated through coarse-graining high-resolution model simulations (Shutts and Pallarès, 2014), and a recent coarsegraining study has also provided justification for the noise temporal and spatial correlation scales (Christensen et al, 2017b). While the smallest scale (500 km and 6 h) dominates on weather forecasting timescales, it is expected that the larger scales will also be important on climate timescales.…”