On the Importance of Representing Snow Over Sea-Ice for Simulating the Arctic Boundary Layer

Correctly representing the snow on sea-ice has great potential to improve cryosphere-atmosphere coupling in forecasting and monitoring (e.g., reanalysis) applications, via improved modeling of surface temperature, albedo and emissivity. This can also enhance the all-weather all-surface coupled data assimilation for atmospheric satellite radiances. Using wintertime observations from two Arctic field campaigns, SHEBA and N-ICE2015, and satellite data, we explore the merits of different approaches to represent the snow over sea-ice in a set of 5-day coupled forecasts. Results show that representing the snow insulation effects is essential for capturing the wintertime surface temperature variability over sea-ice and its response to changes in the atmospheric forcing. Modeling the snow over sea-ice improves the representation of strong cooling events, reduces surface temperature biases in clear-sky conditions and improves the simulation of surface-based temperature inversions. In clear-sky conditions, when using a multi-layer snow scheme the root-mean-squared error in the surface temperature is reduced by about 60% for both N-ICE2015 and SHEBA. This study also highlights the role of compensating errors in different components of the surface energy budget in the Arctic boundary layer. During warm air intrusions, errors in the surface temperature increase when cloud phase and cloud radiative processes are misrepresented in the model, inducing large errors in the net radiative energy at the surface. This work indicates that numerical weather prediction systems can fully benefit from a better representation of snow over sea-ice, for example, with multi-layer snow schemes, combined with improvements to other boundary layer processes including mixed phase clouds.

Automated summary

Correctly representing the snow on sea-ice is crucial for improving cryosphere-atmosphere coupling and enhancing weather forecasting and monitoring applications. Different approaches to simulate snow on sea-ice have shown to improve the representation of temperature variability, reduce surface temperature biases, and enhance the simulation of temperature inversions.