Soil moisture-vegetation-precipitation feedback at the seasonal time scale over North America: Results from CAM3-CLM3

Over North America, many studies showed that positive soil moisture-precipitation feedback has the potential to perpetuate seasonal climate persistence and also influences inter-annual variability. Less studied however is the role of vegetation feedback at the seasonal time scale in modifying the soil moisture-precipitation interactions, in particular, in modifying the impact of soil moisture on subsequent precipitation. To tackle this issue, a series of ensemble simulations were carried out using the coupled CAM-CLM model. The model includes a predictive vegetation phenology scheme so that the coupled model can represent interactions between soil moisture, vegetation and precipitation at the seasonal timescale. Vegetation feedback is found to enhance or suppress the impact of soil moisture on subsequent precipitation, depending on the timing and direction of initial soil moisture anomalies. The nature of vegetation feedback also depends on the hydrological climatology of the model. For the default CAM3-CLM3 model, during summer, wet soil moisture anomalies lead to increased vegetation growth, and the resulting vegetation anomalies enhance the response of precipitation to the initial soil wetness via increased evapotranspiration and surface heating. Therefore, the vegetation feedback is positive under wet summer soil moisture anomalies. The response of vegetation to dry soil moisture anomalies in the summer months, however, is not significant due to a dry bias in the model, so the resulting vegetation feedback on precipitation is minimal. To soil moisture anomalies in spring, vegetation shows delayed response since vegetation growth is limited by both cold temperature and water availability in that time of the year. During the summer following spring soil moisture anomalies, vegetation feedback is negative, i.e., it tends to suppress the response of precipitation. However, this model behavior changes when the parameterization of surface hydrology is modified to reduce the dry bias. Such dependence of the vegetation feedback on hydrological climatology has implications on model dependence of various land-atmosphere interaction studies.