Single Column Modeling, GCM Parameterizations and ARM Data

Sam Iacobellis, Dana Lane, and Richard Somerville


Our presentation is based on the work since the last SCM Workshop meeting in April 1996 and consists of four topics:
  1. Study of relaxation techniques
  2. Examination of model results with prognostic cloud liquid water
  3. Evaluation of model cloud height results
  4. Sensitivity of model results to the specification of the surface latent heat flux.
We have studied the above topics using forcing data from six SCM IOPs at the ARM SGP CART site (Table 1).

Spring 1994 MSRC-SUNYSB 17 Days
Summer 1994 ARM Web Site 16 Days
Fall 1994 CSU Web Site 19 Days
Spring 1995 ARM Web Site 18 Days
Spring 1995 CSU Web Site 17 Days
Fall 1995 CSU Web Site 24 Days
Table 1: SCM IOP Data Sets.

Relaxation Techniques

After running our SCM for each of the six IOPs, we often found large differences between model temperature and humidity and observations. The temperature errors can be as high as 20K towards the end of a model run. Other SCM modeling groups participating in ARM have also found similar errors (see Notes from the LLNL SCM Workshop, April 1996). The introduction of a simple relaxation correction (time constant = 24 hrs) on the model temperature and humidity profiles drastically reduces these errors. Model temperature results from the Summer 1995 IOP are shown in Figure 1 (no relaxation) and Figure 2 (with relaxation). Fall 1994 IOP SCM temperature results are shown in Figure 3 and Figure 4. The temperature error in these two model runs were reduced between 50-80%.

Analysis of the temporal mean temperature correction for each of the six IOPs tested (Figure 5) revealed some interesting patterns. Namely, below about 800 mb, the SCM was producing temperatures higher than observed in all six IOPs. Between 800 and 300 mb, there seemed to be no trend, while above 300 mb the SCM again produced temperatures that were too high compared to the sounding measurements. These areas where the model is consistently producing excessively warm temperatures may indicate either a deficiency in the model physics or problems in the production of the forcing terms. We suggest that an intermodel comparison between the SCM groups be conducted to help determine the source of these errors.

Prognostic Cloud Liquid Water

The model precipitation results from each of the six IOPs in Table 1 are shown in Figure 6a and Figure 6b. These model runs were performed using a relaxation correction (Tau=24 hours) to the model temperature and humidity profiles. The SCM precipitation compares very well with surface measurements from the Oklahoma Mesonet in 5 of the 6 IOPs. These encouraging results allowed us to begin a preliminary investigation of whether the inclusion of cloud liquid water as a prognostic variable improves the model results when compared to ARM measurements. We tested 5 different model configurations (shown in Table 2) which differed only in the specification of cloud liquid water, cloud optical thickness and effective droplet radius.

Cloud Liquid Water Cloud Optical Thickness Effective Droplet Radius
CCM2 None Specified=f(T,P) Not Applicable
CW Explicit Specified=f(T,P) Not Applicable
CWRF Explicit Calculated Fixed (10 microns)
CWRV Explicit Calculated Varying (warm clouds only)
CWRI Explicit Calculated Varying (all clouds)
Table 2: SCM Configurations.

Our results show that total cloud fraction (Figure 7), downwelling surface shortwave (Figure 8) and outgoing longwave (Figure 9) are much better approximated (compared to surface and satellite measurements) using model configurations that include cloud liquid water as a prognostic variable.

Model Cloud Height Results

A detailed analysis of the model cloud fractions from the Spring 1994 IOP indicate that the SCM is underestimating the amount of low clouds (surface to 700 mb) and overestimating the amount of high clouds (above 400 mb) compared to satellite estimates (see Figure 10). The observed low cloud amount reaches a maximum on April 22 and is concurrent with a reduction in the observed surface downwelling solar radiation (Figure 11). However, the observed reduction in downwelling solar on April 22 is relatively modest, suggesting that the low clouds may be optically and geometrically thin clouds. Reruning the model with a much finer vertical resolution (53 layers compared to 16 layers) resulted in model cloud fields closer to the satellite estimates (Figure 12). The model downwelling solar from the high resolution run is also closer to surface observations (Figure 13). A major implication from this result is that the forcing data should be supplied on a vertical grid fine enough to support these "high" resolution model runs.

Sensitivity to Surface Latent Heat Flux

Several SCM experiments were conducted to investigate the sensitivity of model precipitation to changes in the magnitude of the surface latent heat flux. A sensitivity experiment was performed for each of the six IOP data sets listed in Table 1. A flow chart illustrating the structure of each sensitivity experiment is shown in Figure 14. Each sensitivity experiment consisted of three individual SCM runs. The SCM is first run (Control) using 24-hour relaxation with the correction terms being saved. In the next two runs (Experiment 1 and 2) the correction terms from the control run are used in place of relaxation. The latent heat flux in Experiment 1 is 90% of that used in the Control run, while the latent heat flux in Experiment 2 is 110% of that used in the Control run.

To help analyze the results of these experiments we have defined the factor A_r, where

A_r = LN [ ( {Prec_110} - {Prec_090} ) / ( {FQ_110} - {FQ_090} ) ].

The { } brackets indicate averages over Experiment 1 (090) or Experiment 2 (110). If A_r > 0, the increase in precipitation exceeds the increase in the surface moisture flux. If A_r < 0, the increase in precipitation is less than the increase in the surface moisture flux. A_r for each IOP is listed below.

Spring 1994 +0.608
Summer 1994 -0.350
Fall 1994 +0.506
Spring 1995 +0.347
Summer 1995 +0.195
Fall 1995 -0.649
Table 3: A_r for each SCM IOP experiment.

The results from the six sets of experiments varied considerably. In some experiments the change in precipitation exceeded the changed in the surface latent heat flux, while in other cases the opposite held true. Figure 15 shows the temporal mean vertical velocity profile from each of the six IOPs. The mean vertical velocity is predominantly upwards in 5 of the 6 IOPs. However, there is considerable variability between the 6 profiles. We found that the term A_r is closely related to the vertical velocity in the lower troposphere. Figure 16 shows the term A_r vs. the mean vertical velocity in the region 900-600 mb for each of the six IOPs. These experiments illustrate, that under certain conditions, an error in the surface latent heat flux = D, may result in an error in the model precipitation of up to 2D.

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