Constraining a System of Interacting Parameterizations through Multiple-Parameter Evaluation: Tracing a Compensating Error between Cloud Vertical Structure and Cloud Overlap

R A J Neggers,A P Siebesma

doi:10.1175/jcli-d-12-00779.1

Abstract

Abstract This study explores the opportunities created by subjecting a system of interacting fast-acting parameterizations to long-term single-column model evaluation against multiple independent measurements at a permanent meteorological site. It is argued that constraining the system at multiple key points facilitates the tracing and identification of compensating errors between individual parametric components. The extended time range of the evaluation helps to enhance the statistical significance and representativeness of the single-column model result, which facilitates the attribution of model behavior as diagnosed in a general circulation model to its subgrid parameterizations. At the same time, the high model transparency and computational efficiency typical of single-column modeling is preserved. The method is illustrated by investigating the impact of a model change in the Regional Atmospheric Climate Model (RACMO) on the representation of the coupled boundary layer–soil system at the Cabauw meteorological site in the Netherlands. A set of 12 relevant variables is defined that covers all involved processes, including cloud structure and amplitude, radiative transfer, the surface energy budget, and the thermodynamic state of the soil and various heights of the lower atmosphere. These variables are either routinely measured at the Cabauw site or are obtained from continuous large-eddy simulation at that site. This 12-point check proves effective in revealing the existence of a compensating error between cloud structure and radiative transfer, residing in the cloud overlap assumption. In this exercise, the application of conditional sampling proves a valuable tool in establishing which cloud regime exhibits the biggest impact.

Highlights

Clouds significantly affect the earth’s climate, for a large part because of their impact on the transfer of solar and thermal radiation (Ramanathan 1987)
Clouds are often generated by processes that act on spatial and temporal scales that are much smaller than the scales of discretization in general circulation models (GCMs), and as a consequence their impact has to be represented through parameterization
In the example documented in this study, we applied this method to evaluate the impact of the implementation of a new boundary layer scheme in the Regional Atmospheric Climate Model (RACMO) on the cloud-radiative model climate at Cabauw

Summary

Introduction

Clouds significantly affect the earth’s climate, for a large part because of their impact on the transfer of solar and thermal radiation (Ramanathan 1987). The complexity of parameterization schemes has hampered progress in recent decades (e.g., Randall et al 2003a) To address this issue, the evaluation of parameterization schemes against relevant measurements has been an active field of research (GEWEX Cloud System Science Team 1993; Randall et al 2003b), as testified by the numerous model intercomparison studies at process level (e.g., Stevens et al 2001; Brown et al 2002; Siebesma et al 2003; van Zanten et al 2011). The evaluation of parameterization schemes against relevant measurements has been an active field of research (GEWEX Cloud System Science Team 1993; Randall et al 2003b), as testified by the numerous model intercomparison studies at process level (e.g., Stevens et al 2001; Brown et al 2002; Siebesma et al 2003; van Zanten et al 2011) These initiatives have been successful in providing the modeling community with benchmark results for certain controlled situations or regimes that have to be reproduced by any model, acting as a ‘‘testing ground’’ for new or existing parameterizations. In reality this has proven hard to realize because either the required multitude of independent measurements was not available or the measurements did not cover sufficiently long time periods to ensure statistical significance in the evaluation result

Methods

Results

Conclusion