Diagnosis of Community Atmospheric Model 2 (CAM2) in numerical weather forecast configuration at Atmospheric Radiation Measurement sites

Abstract

The Community Atmospheric Model 2 (CAM2) is run as a short‐term (1–5 days) forecast model initialized with reanalysis data. The intent is to reveal model deficiencies before complex interactions obscure the root error sources. Integrations are carried out for three Atmospheric Radiation Measurement (ARM) Program intensive operational periods (IOPs): June/July 1997, April 1997, and March 2000. The ARM data are used to validate the model in detail for the Southern Great Plains (SGP) site for all the periods and in the tropical west Pacific for the March 2000 period. The model errors establish themselves quickly, and within 3 days the model has evolved into a state distinctly different from the ARM observations. The summer forecasts evince a systematic error in convective rainfall. This error manifests itself in the temperature and moisture profiles after a single diurnal cycle. The same error characteristics are seen in the March 2000 tropical west Pacific forecasts. The model performs well in the spring cases at the SGP. Most of the error is manifested during rainy periods. The ARM cloud radar comparison to the model reveals cloud errors which are consistent with the relative humidity profile errors. The cloud errors are similar to those seen in climatological integrations, but the state variable errors are different. Thus there is the possibility that the some basic parameterization errors are obscured in the climatological integrations. The approach described here will facilitate parameterization experimentation, diagnoses, and validation. One way of reducing cloud feedback uncertainty is to make the physical processes behave in the most realistic manner possible. Paradoxically, perhaps the best way to reduce uncertainty in cloud feedback mechanisms is to evaluate the model processes with realistic forcing before such feedbacks have any significant affect.

Near-surface meteorology during the Arctic Summer Cloud Ocean Study (ASCOS): evaluation of reanalyses and global climate models

“…For one, reanalysis output is sometimes used to force large-scale or local sea ice and ocean models to evaluate their performance against available observations (e.g., Brodeau et al, 2010;Miller et al, 2007). Curry et al (2002) evaluated several data sets used to force sea ice models during the SHEBA time period and noted that substantial differences can be found between the different data sets.…”

“…In this instance, the European Centre for MediumRange Weather Forecasts (ECMWF) Year of Tropical Convection (YOTC) analysis was used to initialize forecasts within CAPT. The analysis data are interpolated from the finer-resolution analysis grid of 0.150 • and 91 levels to the CAM5 grids using procedures outlined in Boyle et al (2005). These procedures use a slightly different interpolation approach for each of the dynamic state variables (i.e., horizontal winds, temperature, specific humidity and surface pressure), along with careful adjustments to account for the difference in representation of the earth's topography between models.…”

Can we trust climate models to realistically represent severe European windstorms?

“…The experimental set-up used in this study builds on the Transpose-AMIP experimental methodology, in which climate models are run in NWP mode (Phillips et al 2004;Boyle et al 2005). The details of the methodology for the latest Transpose-AMIP II experiment are described in Williams et al (2013).…”

“…Non-atmospheric state variables that spin up relatively slowly, e.g. land surface and aerosols, are initiated from a climatology derived from a model's AMIP run or through the nudging method of Boyle et al (2005). Variables that spin up quickly, e.g.…”

Can nudging be used to quantify model sensitivities in precipitation and cloud forcing?

“…The nudging technique, namely the Newtonian relaxation of meteorological conditions toward weather reanalysis or a baseline simulation, has been used as a simple data assimilation technique in studies that aimed at diagnosing model biases under realistically simulated atmospheric states [e.g., Jeuken et al, 1996;Ghan et al, 2001;Boyle et al, 2005;Schmidt et al, 2006;Subramanian and Zhang, 2014;Ma et al, 2015]. It has also been used to help reduce the effect of natural variability thus making it easier to distinguish signal from noise [e.g., Lohmann and Hoose, 2009;Lohmann and Ferrachat, 2010;Kooperman et al, 2012].…”