Relative contribution of soil moisture and snow mass to seasonal climate predictability: a pilot study

Land surface hydrology (LSH) is a potential source of long-range atmospheric predictability that has received less attention than sea surface temperature (SST). In this study, we carry out ensemble atmospheric simulations driven by observed or climatological SST in which the LSH is either interactive or nudged towards a global monthly re-analysis. The main objective is to evaluate the impact of soil moisture or snow mass anomalies on seasonal climate variability and predictability over the 1986-1995 period. We first analyse the annual cycle of zonal mean potential (perfect model approach) and effective (simulated vs. observed climate) predictability in order to identify the seasons and latitudes where land surface initialization is potentially relevant. Results highlight the influence of soil moisture boundary conditions in the summer mid-latitudes and the role of snow boundary conditions in the northern high latitudes. Then, we focus on the Eurasian continent and we contrast seasons with opposite land surface anomalies. In addition to the nudged experiments, we conduct ensembles of seasonal hindcasts in which the relaxation is switched off at the end of spring or winter in order to evaluate the impact of soil moisture or snow mass initialization. LSH appears as an effective source of surface air temperature and precipitation predictability over Eurasia (as well as North America), at least as important as SST in spring and summer. Cloud feedbacks and large-scale dynamics contribute to amplify the regional temperature response, which is however, mainly found at the lowest model levels and only represents a small fraction of the observed variability in the upper troposphere.