Snow insulation effects across the Arctic : evaluating a revised snow module in LPJ-GUESS

The effect of future changes in temperature and precipitation patterns on arctic ecosystem functioning is often assessed using state-of-the-art ecosystem models. Many models however lack detailed representation of wintertime processes, as pointed out by recent studies (Wang et al. 2016, Slater and Lawrence 2013). This bias may influence the derived outputs, such as soil temperature, permafrost extent and global carbon budget estimations. In this project, the dynamic vegetation model LPJ-GUESS was applied with different complexity snow schemes, with the aim of assessing whether the developments in snow dynamics enhance the performance of the model in relation to air-soil temperature relationships (snow insulation effect). We hypothesise that refinement of the snow scheme can provide higher agreement between modelled and observational entities. The single site analysis showed that a newly developed Advanced multi-layer, intermediate complexity scheme is best suited to simulate internal snow dynamics, and the derived snow depth and soil temperature outputs are comparable to measured entities. The regional multi-site analysis showed that the Advanced multi-layer scheme can best capture the air-soil temperature variability, but the insulation effect is smaller than observed. The effect of using different snow schemes is evident from the simulated Arctic active layer depth and permafrost extent. Based on these results, the quantification of the snow insulation effect on soil properties and permafrost extent may prompt developments in the model's structural scheme. These updates could help to simulate physical and biogeochemical processes with reduced uncertainty at high latitudes. References: Slater, Andrew G. and David M. Lawrence (2013). “Diagnosing Present and Future Permafrost from Climate Models”. In: Journal of Climate 26.15, pp. 5608–5623. DOI: 10.1175/JCLI-D-12-00341.1. Wang, Wenli et al. (2016). “Evaluation of air-soil temperature relationships simulated by land surface models during winter across the permafrost region”. English. In: Cryosphere 10.4, pp. 1721–1737. ISSN: 1994-0416. DOI: 10.5194/tc-10-1721-2016.The effect of future changes in temperature and precipitation patterns on arctic ecosystem functioning is often assessed using state-of-the-art ecosystem models. Many models however lack detailed representation of wintertime processes, as pointed out by recent studies. This bias may influence the derived outputs, such as soil temperature, permafrost extent and global carbon budget estimations. In this project, the dynamic vegetation model LPJ-GUESS was applied with different complexity snow schemes, with the aim of assessing whether the developments in snow dynamics enhance the performance of the model in relation to air-soil temperature relationships (snow insulation effect). We hypothesise that refinement of the snow scheme can provide higher agreement between modelled and observational entities. The single site analysis showed that a newly developed intermediate complexity scheme is best suited to simulate internal snow dynamics, and the derived snow depth and soil temperature outputs are comparable to measured entities. The regional multi-site analysis showed that the new scheme can best capture the air-soil temperature variability, but the insulation effect is smaller than observed. The effect of using different snow schemes is evident from the simulated Arctic active layer depth and permafrost extent. Based on these results, the quantification of the snow insulation effect on soil properties and permafrost extent may prompt developments in the model's structural scheme. These updates could help to simulate physical and biogeochemical processes with reduced uncertainty at high latitudes