Preprints
https://doi.org/10.5194/egusphere-2023-338
https://doi.org/10.5194/egusphere-2023-338
08 Mar 2023
 | 08 Mar 2023

Modelling snowpack dynamics and surface energy budget in boreal and subarctic peatlands and forests

Jari-Pekka Nousu, Matthieu Lafaysse, Giulia Mazzotti, Pertti Ala-aho, Hannu Marttila, Bertrand Cluzet, Mika Aurela, Annalea Lohila, Pasi Kolari, Aaron Boone, Mathieu Fructus, and Samuli Launiainen

Abstract. The snowpack has a major influence on the land surface energy budget. Accurate simulation of the snowpack energy budget is challenging due to e.g. vegetation and topography that complicate the radiation budget, and limitations in theoretical understanding of turbulent transfer in the stable boundary layer. Studies that evaluate snow, hydrology and land surface models (LSMs) against detailed observations of all surface energy components at high latitudes are scarce. In this study, we compared different configurations of SURFEX LSM model against surface energy flux, snow depth and soil temperature observations from four eddy covariance stations in Finland. The sites cover two different climate and snow conditions, representing the southern and northern subarctic zones, and the contrasting forest and peatland ecosystems typical for the boreal landscape. We tested the sensitivity of surface energy fluxes to different process parameterizations implemented in the Crocus snowpack model. In addition, we examined common alternative approaches to conceptualize soil and vegetation, and assess their performance in simulating surface energy fluxes, snow conditions and soil thermal regime. Our results show that using a stability correction function that increases the turbulent exchange under stable atmospheric conditions is imperative to simulate sensible and latent heat fluxes over snow. For accurate simulations of surface heat fluxes and snow/soil conditions in forests, an explicit vegetation representation is necessary. Moreover, we found the peat soil temperature profile simulations to be greatly improved with realistic soil texture (soil organic carbon) parameterization. Although we focused on models within the SURFEX LSM platform, the results have broader implications for choosing suitable turbulent flux parameterization and model structures depending on the potential use cases.

Jari-Pekka Nousu et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-338', Anonymous Referee #1, 24 Apr 2023
    • AC1: 'Reply on RC1', Jari-Pekka Nousu, 30 Jun 2023
  • RC2: 'Comment on egusphere-2023-338', Anonymous Referee #2, 16 May 2023
    • AC2: 'Reply on RC2', Jari-Pekka Nousu, 30 Jun 2023

Jari-Pekka Nousu et al.

Jari-Pekka Nousu et al.

Viewed

Total article views: 735 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
453 258 24 735 15 11
  • HTML: 453
  • PDF: 258
  • XML: 24
  • Total: 735
  • BibTeX: 15
  • EndNote: 11
Views and downloads (calculated since 08 Mar 2023)
Cumulative views and downloads (calculated since 08 Mar 2023)

Viewed (geographical distribution)

Total article views: 736 (including HTML, PDF, and XML) Thereof 736 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 01 Oct 2023
Download
Short summary
The snowpack has a large impact on the land surface energy budget. Accurate calculation of the snowpack energy budget is difficult, and studies that evaluate models against energy budget observations are rare. We compared predictions from well-known models with observations of energy budgets, snow depths and soil temperatures in Finland. Our study identified contrasting strengths and limitations for the models. These results can be used for choosing the right models depending on the use cases.