Evaluating regional impacts of spring snow conditions on early warm season precipitation in central Canada

The feedback of spring snowpack on subsequent warm season precipitation could be important for investigating land-atmosphere interaction. The snow-precipitation linkage would, if established physically from modeling or observational studies, offer substantial “land memory” enhancing atmosphere predictability at intra-seasonal to seasonal scale. However, literature mainly focused on using numerical models to assess possible climatic effects of anomalous snow cover (or mass), and most of these studies were addressing the role of continental scale snowpack in modulating the model simulated atmosphere general circulation. In a large sense, the revealed snow effects are specific to model settings (e.g., model parameterization, atmosphere boundary condition). The goal of this research is to evaluate the physical linkage between spring snowpack (April average snow depth) and lagged precipitation with observational datasets, and from a regional-scale perspective. The analyzed domain is central Canada, with prolonged snow season extending to May. To better understanding the role of spatial heterogeneity of landscape, we divided the research area into several regions, and took a multi-scale (local&regional) approach to extract relations and patterns from observation. Results showed that both the moisture and energy effects of spring snowpack could be important for regulating early warm season precipitation. Both high-impacts and neutral areas are found in terms of the snow feedback intensity. Meanwhile the role of atmosphere low-frequency variability is investigated. In addition, in those regions with high spring snow impacts, the physical mechanisms (including vegetation effects) through which snow anomaly can lead to precipitation anomaly are evaluated.