Implementation of multi-layer snow scheme in seasonal forecast system and its impact on model climatological bias
This study explores the influence of implementing a multi-layer snow scheme on the climatological bias within a seasonal forecast system. A single-layer snow schemes in land surface models often inadequately represent the insulating effect of snowpack, resulting in warm and cold biases during winter...
| Main Authors: | , |
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| Format: | Text |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/egusphere-2024-1066 https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1066/ |
| Summary: | This study explores the influence of implementing a multi-layer snow scheme on the climatological bias within a seasonal forecast system. A single-layer snow schemes in land surface models often inadequately represent the insulating effect of snowpack, resulting in warm and cold biases during winter and snow melting seasons, respectively. By contrast, multi-layer snow schemes enable enhanced energy transport between the land and atmosphere. To investigate this impact, two versions of the Global Seasonal Forecast System (GloSea) – GloSea5 with a single-layer snow scheme and GloSea6 with a multi-layer snow scheme – are compared over 24 years (1993–2016). Results shed light on the significance of accurately representing the insulating effect of snow in improving retrospective seasonal forecasts. The GloSea6 shows that the snow melting season shifts two weeks later, accompanied by a significant improvement in surface temperature and permafrost extent. The extended snow cover delays the onset of evaporation in spring season, which slows down the physical process of drying out the soil moisture, resulting in the improvement in its climatology and memory. The abundant soil moisture enhances the partitioning of incoming energy into latent heat flux, allowing for more evaporative cooling at the surface, and constrains water-limited coupling. Such improvements in the land surface processes, especially over the mid-latitudes, mitigate the near-surface warming bias over the entire diurnal period and the oversensitivity of atmospheric conditions to the land surface variability. The model performance in simulating precipitation is also improved with the increase in precipitation occurrence over snow-covered regions, significantly reducing model error in the Great Plains, Europe, and South and East Asia. Above all, this study demonstrates the value of implementing a multi-layer snowpack scheme in seasonal forecast models, not only during the snowmelt season but also for the subsequent summer ... |
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