A GCM ASSESSMENT OF ATMOSPHERIC SEASONAL PREDICTABILITY ASSOCIATED WITH SOIL-MOISTURE ANOMALIES OVER NORTH-AMERICA

The impact of soil moisture anomalies on the seasonal mean atmospheric predictability over North America is investigated on the basis of a 1 00-year simulation by an atmospheric general circulation model (AGCM). It is shown that soil moisture anomalies have the smallest persistenc e during the late spring and summer months, yet the associated near-su rface atmospheric climate anomalies are the largest. The causes for th is seasonality are traced to the seasonal variation of the surface eva poration and the modulating control of the atmospheric dynamic variabi lity. It is also shown that the dominant spatial modes of interannual soil moisture variability in late spring can have different degrees of interaction with the surface climate. To what extent do initial soil moisture anomalies and their subsequent evolution impact the atmospher ic variability? This question is of relevance if the soil moisture ano malies are to be observed and are used as one of the initial condition s in seasonal climate forecast. To investigate this, we carried out ad ditional AGCM experiments for which soil moisture anomalies are initia lized as the extreme states from the control simulation. It is found t hat the relationship between the soil moisture and the near-surface at mospheric anomalies in these additional experiments is similar to thos e obtained in the control simulation. This leads us to the conclusions that (1) there is a certain potential for seasonal predictability of the atmospheric surface climate anomalies due to interannual variation s in the soil moisture, and (2) the effect of the soil moisture anomal ies, however, is mostly confined to the near-surface climate variabili ty with little impact on the dynamic variability in the atmosphere, fo r example, in the upper troposphere. We conclude that atmospheric pote ntial predictability associated with soil moisture anomalies is modest and confined to summer months. Any realization of this predictability depends on correctly observing and initializing large-scale soil mois ture anomalies. These conclusions are based on a particular AGCM and i ts land surface parameterization scheme. More thorough investigations with more sophisticated land surface schemes are certainly warranted.