Forcing Single-Column Models Using High-Resolution Model Simulations

Area of Science:

• Atmospheric Science
• Climate Modeling
• Computational Meteorology

Background:

• Single-column models (SCMs) require realistic initial profiles, forcing fields, and boundary conditions for accurate parameterization development and process studies.
• The increasing scale and number of high-resolution convection-permitting simulations necessitate improved methods for deriving these SCM inputs.

Purpose of the Study:

• To propose and implement a novel technique for deriving realistic SCM input profiles from high-resolution atmospheric simulations.
• To evaluate the effectiveness of this technique using data from the UK Met Office's Unified Model and the European Centre for Medium-Range Weather Forecasts' Integrated Forecasting System (IFS) SCM.

Main Methods:

• Coarse-graining high-resolution (4 km) convection-permitting simulation data to match SCM resolution.
• Utilizing these coarse-grained data as a proxy for the true atmosphere to drive the IFS SCM.
• Comparing SCM performance driven by coarse-grained data versus operational analysis data.

Main Results:

• SCM simulations driven by coarse-grained data closely tracked the global model, demonstrating consistency between derived forcing fields and true dynamical forcing.
• Selecting SCM forcing profiles from large domains yielded robust statistics and allowed testing across diverse boundary conditions.
• The proposed method showed comparable or improved results when compared to using operational analysis data.

Conclusions:

• The proposed technique effectively derives realistic forcing profiles for SCMs from high-resolution simulations.
• This method offers a valuable tool for parameterization development and process studies, enhancing consistency with global models.
• Understanding biases in high-resolution data is crucial, and this approach can be combined with observational data for further refinement.