Comparison of the land-surface interaction in the ECMWF reanalysis model with the 1987 FIFE data.

Data from the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment for the summer season of 1987 are used to assess the land-surface interaction of the ECMWF reanalysis. In comparison with an earlier study, using the 1992 ECMWF operational model, the land-surface interaction is greatly improved. The bias in the incoming solar radiation has been removed, although there seems to be a small low bias in the incoming longwave, which is significant at night. The four-layer soil moisture model depicts the seasonal cycle well, and the root zone is recharged satisfactorily after major rain events. Consequently, the evaporative fraction (EF) over the season is now generally quite good. There is, however, a low bias in EF in June and high bias in October, which is probably due to the absence of a seasonal cycle in the model vegetation. The evaporative fraction also appears too high in the model just after rainfall. It also appears that the model lacks a realistic seasonal control on the soil heat flux. The surface diurnal thermodynamic cycle has two noticeable errors. The temperature minimum at sunrise is too low, because the surface uncouples too much at night under the stable boundary layer, and the incoming longwave radiation is biased low. There is also an unrealistic diurnal cycle of mixing ratio, q, with too strong a midmorning peak, and too large a fall during the day to a late afternoon minimum that is biased low. These errors in the diurnal cycle of q may feed back on the diurnal cycle of precipitation. The morning peak is partly related to the too-strong inversion at sunrise, which slows the deepening of the boundary layer. The late afternoon minimum of mixing ratio (below that of the model analysis) leads to a positive nudging of soil moisture in the analysis cycle. The model summer mixing ratio has a small high bias of 0.5 g kg−1.

FIFE surface DATA

Related Topics

• FIFE,
• Hydrology,
• Land-surface,
• Parameterization,
• Reanalysis&FX