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...
| Main Authors: | , , , , , , , |
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| Format: | Text |
| Language: | unknown |
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Zenodo
2021
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.5281/zenodo.4540263 https://zenodo.org/record/4540263 |
| Summary: | 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 Sodankylä, 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. |
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