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

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.

METEOROLOGICAL DATA.zip contains meteorological data csv-files for each site. Data come from the Finnish Meteorological Institute (FMI) open database (FMI, 2021) (Station IDs: Lompolojänkkä: 778135, Kenttärova: 101317, Hyytiälä: 101987, https://www.ilmatieteenlaitos.fi/havaintoasemat). Meteorological data at Siikaneva come from the SMEAR database (Alekseychik et al. 2022a) (https://smear.avaa.csc.fi/). At Siikaneva and Hyytiälä, the shortwave and longwave radiation were obtained from the SMEAR database, while at Lompolojänkkä and Kenttärova data from FMI stations were used. The meteorological data gaps were first filled by the contiguous sites and the remaining gaps by other nearby meteorological stations (IDs: Sodankylä: 101932, Ähtäri: 101520). The missing radiation observations were first filled by the contiguous sites, and the remaining gaps by ERA5 reanalysis data (Hersbach et al. 2020). Meteorological data is accompanied with metadata (METEO_metadata) and summary of flag counts (METEO_flags).

EVALUATION DATA.zip contains evaluation data csv-files for each site. The surface energy fluxes of Lompolojänkkä and Kenttärova come from FMI while data of Hyytiälä and Siikaneva were downloaded from SMEAR open database. These data have been detailed in original site and data publications by Aurela et al. (2015) (Lompolojänkkä and Kenttärova) and Mammarella et al. (2016, 2019); Alekseychik et al. (2022b) (Siikaneva and Hyytiälä). Automated height of snow observations come from FMI and SMEAR open databases. Manual snow measurements at Lompolojänkkä, Kenttärova and Hyytiälä are described by Marttila et al. (2021) and Aalto et al. (2022). Soil temperature measurements are detailed in Aurela et al. (2015) and Aalto et al. (2022). Evaluation data is accompanied with metadata (EVALUATION_metadata).

FORCING DATA.zip and NAMELIST FILES.zip contain SURFEX model specific forcing nc-files and parameter txt-files. These are needed for a SURFEX simulation. Documentation can be found at http://www.umr-cnrm.fr/surfex/spip.php?rubrique88

Aalto, J., Aalto, P., Keronen, P., Kolari, P., Rantala, P., Taipale, R., Kajos, M., Patokoski, J., Rinne, J., Ruuskanen, T., and others: SMEAR II Hyytiälä forest meteorology, greenhouse gases, air quality and soil, \url{https://doi.org/10.23729/62f7ad2c-7fe0-4f66-b0a4-8d57c80524ec}, 2022

Alekseychik, P., Kolari, P., Rinne, J., Haapanala, S., Laakso, H., Taipale, R., Matilainen, T., Salminen, T., Levula, J., Tuittila, E.-S., and others: SMEAR II Siikaneva 1 wetland meteorology and soil, \url{https://doi.org/10.23729/7d205559-3ef9-4f34-8e08-ea24316f50c8}, 2022a

Alekseychik, P., Peltola, O., Li, X., Aurela, M., Hatakka, J., Pihlatie, M., Rinne, J., Haapanala, S., Laakso, H., Taipale, R., and others: SMEAR II Siikaneva 1 wetland eddy covariance, \url{https://doi.org/10.23729/f6455f02-905b-4bf7-a870-743bd3788bf6}, 2022b

Aurela, M., Lohila, A., Tuovinen, J. P., Hatakka, J., Penttilä, T., and Laurila, T.: Carbon dioxide and energy flux measurements in four northern-boreal ecosystems at Pallas, Boreal Environment Research, 20, 455–473, 2015.

FMI: Finnish Meteorological Institute past weather observations, available at: https://en.ilmatieteenlaitos.fi/download-observations, 2021.

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Sim-mons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren,P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J.,Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5 global reanalysis, Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.

Mammarella, I., Peltola, O., Nordbo, A., and Järvi, L.: Quantifying the uncertainty of eddy covariance fluxes due to the use of different software packages and combinations of processing steps in two contrasting ecosystems, Atmospheric Measurement Techniques, 9, 4915–4933, https://doi.org/10.5194/amt-9-4915-2016, 2016.

Mammarella, I., Rannik, , Launiainen, S., Alekseychik, P., Peltola, O., Keronen, P., Kolari, P., Laakso, H., Matilainen, T., Salminen, T., andothers: SMEAR II Hyytiälä forest eddy covariance, \url{https://doi.org/10.23729/40f64739-11d1-4e5f-8dc2-da931512c91c}, 2019.

Marttila, H., Lohila, A., Ala-Aho, P., Noor, K., Welker, J. M., Croghan, D., Mustonen, K., Meriö, L., Autio, A., Muhic, F., Bailey, H., Aurela, M., Vuorenmaa, J., Penttilä, T., Hyöky, V., Klein, E., Kuzmin, A., Korpelainen, P., Kumpula, T., Rauhala, A., and Kløve, B.: Subarctic catchment water storage and carbon cycling – Leading the way for future studies using integrated datasets at Pallas, Finland, Hydrological Processes, 35, 1–19, https://doi.org/10.1002/hyp.14350, 2021.