The Impact of a Low Bias in SWE Initialization on CFS Seasonal Forecasts

Snow is a key component of land processes in the land-atmosphere interactions that strongly affect weather and climate prediction. Based on our recent study (Broxton et al. 2017, J. Climate, accepted), we have used the ensemble seasonal forecasting archives from the NCEP Climate Forecast System (CFS) to address the impact of a low bias in SWE initialization on CFS seasonal forecasts.

Across much of the Northern Hemisphere, the CFS forecasts made earlier in the winter (e.g., on 1 January) are found to have more snow water equivalent (SWE) in April-June than forecasts made later (e.g., on 1 April), and further, later forecasts tend to predict earlier snowmelt than earlier forecasts. As a result, other forecasted model quantities (e.g., soil moisture in April-June) show systematic differences dependent on the forecast lead time. Notably, earlier forecasts predict much colder near-surface air temperatures in April-June than later forecasts.

Although the later forecasts of temperature are more accurate based on our global hourly surface air temperature dataset (Wang and Zeng 2013), earlier forecasts of SWE are more realistic based on our daily SWE product over continental U.S. (Broxton et al. 2016), suggesting that the improvement in temperature forecasts occurs for the wrong reasons. Thus, this study highlights the need to improve atmospheric processes in the model (e.g., radiative transfer, turbulence) that would cause cold biases when a more realistic amount of snow is on the ground.

Furthermore, SWE differences in earlier vs. later forecasts are found to much more strongly affect April-June temperature forecasts than the sea surface temperature differences over different regions, suggesting the major role of snowpack in seasonal prediction during the spring-summer transition over snowy regions.