Summary And Outlook

This paper describes a new parameterization of SCF in ECHAM4. However, the more general results of the investigation of surface processes are also of interest for other GCMs.

Comparisons with observations revealed that it is beneficial to parameterize the SCF for three surface types:

(i) flat, non-forested lands

The simulated SCF is in best agreement with satellite-based measurements when using a tanh-function to relate SWE to SCF.

(ii) mountainous, non-forested land

When the subgrid orography is included in the calculation according to Douville et al. (1995a), the simulation of surface albedo is improved,

(iii) forests

The use of the CLASS (Canadian Land Surface Scheme for GCMs) submodel for snow albedo combined with a simple interception model, considerably improves the simulation of the snow-covered forest albedo. For example, the surface albedo over the boreal forests in Siberia and Canada decreases by up to 0.1 in winter, which is in better agreement with observations. The subsequent rise in surface temperature over extended parts of Eurasia and North America, due to the increased radiative heating of the surface, is statistically significant and yields a more rapid spring snowmelt and an accelerated retreat of the snow line. This reduces the overestimated snow amount as simulated in CTRL during late spring, a well-known problem in many current GCMs (Foster et al., 1996).

Two remote-sensing data sets (surface albedo from SRB, NOAA/NESDIS for SCF) were instrumental in the development of the new parameterizations in the ECHAM4 surface scheme. In general, remote sensing data products are vital when surface schemes in GCMs are enhanced, as shown by the following examples.

For the validation of simulated SWE, the USAF snow depth climatology is deemed to be the most reliable of the limited data sets available (Foster et al., 1996). However, since models simulate SWE rather than snow depth, a transformation from snow depth to SWE is required, which can be a significant source of error. In addition to visible satellite imagery, passive microwave data from the Scanning Multichannel Microwave Radiometer (SMMR) can also be used to estimate snow cover extent and snow depth (Chang et al., 1987). However, SMMR significantly underestimates snow mass during winter, especially in North America (Foster et al., 1996, Yang et al., 1999). Remote-sensed SWE data therefore need increased accuracy in order to be used for model validation.

However, remote sensing is the most suitable technique for deriving large consistent data sets of surface albedos and snow cover (Hall et al., 1995). High quality surface data at high resolution can be expected from the EOS

moderate resolution imaging spectroradiometer (MODIS) launched in December 1999. Surface albedo, e.g., will be available daily at 250 m resolution (Strahler et al., 1999).

The leaf area index (LAI) in ECHAM4 is constant for each grid square. This deficiency could be easily eliminated by using monthly LAIs retrieved from the International Satellite Land Surface Climatology Project (ISLSCP, Sellers et al., 1996b). Furthermore, better boundary conditions (e.g., fraction of grid square covered with forest and vegetation) could be provided from ISLSCP Initiative I global datasets (Sellers et al., 1996b).

In summary, development and validation of ECHAM4 can benefit from the rapidly growing amount of accurate global remote-sensed surface data.

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