PNNL

Abstract

One of the goals of the Atmospheric Radiation Measurement (ARM) program is to provide long-term observations for evaluating and improving cloud and radiation treatment in global climate models. Unfortunately, the traditional parametric approach of diagnosing cloud and radiation properties for gridcells that are tens to hundreds kilometers across from large-scale model fields is not well suited for comparison with time series of ground based observations at selected locations. A recently emerging approach called a multi-scale modeling framework (MMF) has shown promise to bridge the scale gap. The MMF consists of a two-dimensional or small three-dimensional cloud resolving model (CRM) embedded into each grid column of the Community Atmospheric Model (CAM), thereby computing cloud properties at a scale that is more consistent with observations. We present a comparison of data from two ARM sites, one at the Southern Great Plains (SGP) in Oklahoma and one at Nauru Island in the Tropical Western Pacific (TWP) region, with output from both the CAM and MMF. Two sets of one year long simulations are considered: one using climatological sea surface temperatures (SST) and another using 1999 SST. Each set includes a run with the MMF as well as the CAM run with traditional or standard cloud and radiation treatment. Time series of cloud fraction, precipitation intensity, and downwelling solar radiation flux at the surface are statistically analyzed. For the TWP site, nearly all parameters of frequency distributions of these variables from the MMF run are shown to be more consistent with observation than those from the CAM run. This change is attributed to the improved representation of convective clouds in the MMF compared to the conventional climate model. For the SGP, the MMF shows little to no improvement in predicting the same quantities. Possible causes of this lack of improvement are discussed.