Measuring the Impact of a New Snow Model Using Surface Energy Budget Process Relationships

Energy exchange at the snow-atmosphere interface in winter is important for the evolution of temperature at the surface and within the snow, preconditioning the snowpack for melt during spring. This study illustrates a set of diagnostic tools that are useful for evaluating the energy exchange at the Earth's surface in an Earth System Model, from a process-based perspective, using in situ observations. In particular, a new way to measure model improvement using the response of the surface temperature and other surface energy budget (SEB) terms to radiative forcing is presented. These process-oriented diagnostics also provide a measure of the coupling strength between the incoming radiation and the various terms in the SEB, which can be used to ensure that improvements in predictions of user-relevant properties, such as 2 m temperature, are happening for the right reasons. Correctly capturing such process relationships is a necessary step toward achieving more skilful weather forecasts and climate projections. These diagnostic techniques are applied to assess the impact of a new multi-layer snow scheme in the European Centre for Medium-Range Weather Forecasts'-Integrated Forecast System at two high-Arctic sites (Summit, Greenland and Sodankyla, Finland). A previous study showed that it will enhance 2 m temperature forecast skill across the Northern Hemisphere in boreal winter compared to forecasts with the single layer model, reducing a warm bias. In this study we use the diagnostics to show that the bias is improved for the right reasons.