1. INTRODUCTION

In this study, observations from the Atmospheric Radiation Measurement (ARM) Program are used to estimate the sensitivity of cloud-radiation model results produced by various cloud parameterizations within a single-column model (SCM). The cloud parameterizations differ with regard to the inclusion of cloud liquid water, the specification of the effective cloud droplet/ice particle radius (Reff), and the parameterization of the cloud optical properties (both shortwave and longwave).

2. MODEL

The SCM is a diagnostic model resembling a single vertical column of a 3-dimensional general circulation model (GCM). The one-dimensional SCM is forced with horizontal advection terms derived from either observations or numerical weather prediction analyses. In this paper the horizontal advective terms were derived from observations taken during the Summer 1995 (July 18 - August 2) Intensive Observing Period (IOP) at the ARM Southern Great Plains (SGP) site.

2.1 Clouds

Tiedtke (1993): This scheme includes two prognostic equations for cloud liquid water/ice and cloud amount. Terms representing the formation of clouds and cloud water/ice due to convection, boundary layer turbulence and stratiform condensation processes are included in these equations. Cloud water/ice is removed (and clouds are dissipated) through evaporation and conversion of cloud droplets and ice to precipitation. When this scheme is active, shortwave cloud optical properties are calculated as a function of the cloud water path (CWP) and Reff using the parameterizations of Slingo (1989) for liquid water clouds and Ebert and Curry (1992) for ice clouds. The longwave cloud emissivity is calculated as a function of the CWP and Reff using the scheme of Ebert and Curry (1992).

CCM3: In this scheme the stratiform cloud amount depends upon the large-scale relative humidity, vertical velocity and static stability, while convective cloud amount is parameterized as a function of convective mass flux. This cloud parameterization is closely related to that of Slingo (1987). Cloud optical thickness is computed as a function of temperature and pressure (McFarlane et al, 1992) while the cloud emissivity is calculated as e=1.0 - e-0.75t, where t is the cloud optical thickness.

2.2 Effective Cloud Droplet/Ice Particle Radius (Reff)

Model realizations were performed using six different schemes to parameterize Reff. These schemes are summarized below in Table 1:


IRE Liquid water clouds Ice water clouds
0 Reff = 10 microns Reff = 10 microns
1 Reff = func(LWC) (Bower et al, 1994) Reff = 10 microns
2 Reff = func(LWC) (Bower et al, 1994) Reff = func(T) (Suzuki et al, 1993)
3 Reff = func(LWC) (Bower et al, 1994) Reff = func(IWC) (McFarlane et al, 1992)
4 Reff = func(LWC) (Bower et al, 1994) Reff = func(T) (Ou and Liou, 1995)
5 Reff = func(LWC) (Bower et al, 1994) Reff = func(T,IWC) (Wyser, 1998)
Table 1. Parameterizations of Reff available in the SCM.

2.3 Control Version

3. RESULTS

3.1 Control Run

The dominant components of the model heat budget at 930, 500 and 300 hPa are shown in Figure 1. The strength of the relaxation correction terms are usually a minor component of the heat and moisture budget. The relative strength of the relaxation correction is greatest within the boundary layer and generally decreases with height, albeit it rarely becomes insignificant. The structure of the moisture relaxation term is similar and not shown.

The effects of the relaxation on the model cloud-radiation variables can be seen in Figure 2. Here we plot the results of the control run (red curves), an identical model run except without relaxation (blue curves) and observations (black curves). In general, the model cloud and radiative flux results are closer to observed when relaxation is employed. Figure 3 displays contour plots of cloud fraction as a function of pressure and time from the control run with and without relaxation. Also shown in this figure are 10-minute averages of cloudbase height derived from micropulse lidar near the center of the ARM SGP site. It should be noted that direct comparison of this measured cloud base height to model cloud fraction is difficult because i) the cloud base height represents a point measurement while the model data represents an average over the SGP site (appr. 200x300km); and ii) the micropulse lidar measurements can be contaminated by precipitation. Despite these difficulties, the measured cloudbase heights appear to show that during several periods the model run without relaxation does not produce enough clouds at lower levels. While the results from the model run with relaxation also have this deficiency, there does appear to be some improvement (e.g. July 22, Aug 1-2).

The atmospheric conditions were predominantly convective during the time period used in these model runs. In the Tiedtke cloud scheme used in the control run, the production of convective clouds is related to the detrainment of mass and cloud water in cumulus updrafts. Figure 4 shows the mean detrained cloud mass and liquid water produced in the control run. The lack of convective clouds at lower levels appears to be due to relatively low amounts of detrained liquid water. Future work is planned to investigate whether this is a prevalent feature of the cumulus convection package used or due to other model deficiencies.

3.2 Sensitivity to Cloud Parameterization

Another model run was performed using the cloud parameterization from CCM3. Time-averaged results from this run and the control run with the Tiedtke cloud scheme are shown in Table 2 (both of these runs utilized relaxation correction). Contour plots of cloud fraction as a function of pressure and time from these two runs are shown in Figure 5. Comparison of the model data and observations in Table 2 and Figure 5indicates that the CCM3 cloud scheme overall produces to much cloudiness. However, there are times when it appears that the CCM3 cloud scheme produces more realistic low clouds than the Tiedtke scheme (e.g., July 19 and August 1-2).

Experiment Cloud
Amount
(%)
Downwelling
Surface SW
(W m-2)
Downwelling
Surface LW
(W m-2)
CFSW
(W m-2)
CFLW
(W m-2)
OLR
(W m-2)
Tiedtke Clouds 56 253 401 -48 47 249
CCM3 Clouds 73 160 421 -121 69 227
OBSERVATIONS 52 238 405 -61 46 252
Table 2. Sensitivity of model results to cloud parameterization.

3.3 Sensitivity to Parameterization of Reff

The SCM was run using each of the six parameterizations for effective cloud droplet/ice particle radius shown in Table 1. Note that the run using the parameterization denoted by IRE=5 is the control run. The different parameterizations result in a wide range of mean Reff between 600 and 100 hPa as shown in Figure 6. The different values Reff result in significant variations in the model calculated cloud optical properties. Figure 7 shows the mean model cloud fraction, cloud water content, cloud extinction, and cloud emissivity. Contour plots of the model shortwave and longwave heat rates for the cases of IRE=0 and IRE=5 (control) are shown in Figure 8 and Figure 9. The mean vertical profile of shortwave and longwave heating rates for all six values of IRE are shown Figure 10. The different parameterizations of the Reff result in variations in the model radiative heating rate of up to 0.3 C day-1. Additional sensitivity tests with the SCM indicate that nearly all of the variations in the radiative heating rates are due to differences in the calculated cloud optical properties. Time-averaged results from these six sensitivity runs are shown in Table 3. The most striking results are that the downwelling surface shortwave flux and the OLR can vary by up to 35 W m-2 and 18 W m-2, respectively, depending on which parameterization of Reff is employed. Similar variations are found in the shortwave and longwave cloud forcing terms.

Experiment Cloud
Amount
(%)
Downwelling
Surface SW
(W m-2)
Downwelling
Surface LW
(W m-2)
CFSW
(W m-2)
CFLW
(W m-2)
OLR
(W m-2)
IRE=0 56 220 403 -80 63 233
IRE=1 56 220 403 -80 62 233
IRE=2 55 246 402 -55 50 245
IRE=3 59 227 403 -73 59 237
IRE=4 57 255 401 -47 44 251
IRE=5 56 253 401 -48 47 249
Table 3.Sensitivity of model results to effective cloud droplet radius parameterization.

4. CONCLUSIONS

5. FUTURE WORK

6. REFERENCES

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