Single-Column Modeling, GCM Parameterizations and ARM Data
Somerville, R. C., Scripps Institution of Oceanography
General Circulation and Single Column Models/Parameterizations
Cloud Modeling
Randall, D.A., K.-M. Xu, R.C.J. Somerville, and S. Iacobellis, 1996: "Single-Column Models and Cloud Ensemble Models as Links between Observations and Climate Models," J. Climate 9(8)1683-1697.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
We have developed a Single-Column Model (SCM) to validate GCM cloud-radiation parameterizations against ARM observational data. The SCM is a computationally efficient one-dimensional representation of the atmospheric column overlying a single GCM grid cell. The SCM is integrated in time from observed initial states, and is constrained with observational estimates of horizontal flux convergences. The surface latent and sensible heat fluxes were specified from ARM EBBR observations. The model output is a complete atmospheric heat and water budget, including temperature and moisture profiles, clouds and their radiative properties, diabatic heating terms, surface energy balance components, and hydrologic cycle elements, all specified as functions of time.
The time-dependent advection of heat, water and momentum (i.e., forcing data) are specified from ARM observations taken during Intensive Observation Periods (IOPs) at the ARM SGP site. Forcing data from a total of 6 IOPs have been used to operate the SCM and are produced from soundings of T, q, u, and v using objective analysis techniques. To prevent large model errors from developing, the SCM temperature and humidity profiles are relaxed towards observed values using a relaxation time scale of 24 hours. The SCM was run using the forcing data from the six IOPs and the model precipitation results are shown Figure 1. The SCM precipitation compares very well with surface measurements from the Oklahoma Mesonet in 5 of the 6 IOPs.
The encouraging SCM precipitation results shown in Figure 1 suggest that model heat and moisture budgets are realistically balanced when simple 24-hour relaxation is applied. This allowed us to begin a preliminary investigation of whether the inclusion of cloud liquid water as a prognostic variable improves the model cloud-radiative results when compared to ARM measurements. We tested 5 different model configurations (shown below) which differed only in the specification of cloud liquid water, cloud optical thickness and effective droplet radius.
| NAME | Cloud Liquid Water | Cloud Optical Thickness | Cloud 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) |
| CWRI | Explicit | Calculated | Varying (all clouds) |
The SCM cloud fraction, downwelling shortwave radiation, and outgoing longwave radiation, were averaged over the length of each model run and then compared to the corresponding observed means.
We present the results from the Spring 1994 and Fall 1994 IOP in Figures 2 and 3, respectively.
These results show that total cloud fraction, downwelling surface shortwave and outgoing longwave are much better estimated (compared to surface and satellite measurements) using model configurations that include cloud liquid water as a prognostic variable.
Figure 4 shows the total cloud fraction, low cloud fraction, high cloud fraction and downwelling solar from the CCM2 and CWRF model runs during the Spring 1994 IOP. While the CWRF model configuration produced total cloud amount close to observations, the SCM underestimates the amount of low clouds (surface to 700 mb) and overestimates the amount of high clouds (above 400 mb). The observed low cloud amount reaches a maximum on April 22 and is concurrent with a reduction in the observed surface downwelling solar radiation. However, the observed reduction in downwelling solar on April 22 is relatively modest, suggesting that the low clouds may be optically and geometrically thin.
Rerunning the CWRF model configuration with a much finer vertical resolution (53 layers vs. 16 layers) resulted in model cloud fields closer to the satellite estimates (Figure 5). The model downwelling solar from the high resolution run is also closer to surface observations. These results indicate that the forcing data should be supplied on a vertical grid fine enough to support these "high" resolution SCM runs.
We reached the following conclusions:
- Inclusion of cloud liquid water as a prognostic variable improves the realism of model cloud and radiative results.
- Experiments with varying the SCM vertical resolution indicate that it may be important to maintain high vertical resolution (~10-20 mb) for accurate cloud modeling.
Recent publications from this project are listed below.
Byrne, R. N., R. C. J. Somerville and B. Subasilar, 1996: Broken-cloud enhancement of solar radiation absorption. J. Atmos. Sci., 53, 878-886.
Lee, W.-H., S. F. Iacobellis and R. C. J. Somerville, 1997: Cloud-radiation forcings and feedbacks: General circulation model tests and observational validation. J. Climate, in press.
Lee, W.-H., and R. C. J. Somerville, 1996: Effects of alternative cloud radiation parameterizations in a general circulation model. Ann. Geophysicae, 14, 107-114.
Randall, D. A., K.-M. Xu, R. C. J. Somerville and S. Iacobellis, 1996: Single-column models and cloud ensemble models as links between observations and climate models. J. Climate, 9, 1683-1697.
Somerville, R. C. J., S. F. Iacobellis, and W.-H. Lee, 1996: Effects of cloud-radiation schemes on climate model results. World Resource Review, 8, 321-333.
