Note. T and P refer to air temperature and precipitation. The monthly
anomalies were calculated for November to March based on periods of
2071-2100 and 1985-2014.
3 Results and discussion
3.1 Sensitivity analyses and model calibration
The Sobol indices showed that the dens_min (minimum snow density) and
compaction_rate (the rate at which snow is compacted over time), and
their interactions, mainly influence the modelled seasonal snow depth,
snow density, snow temperature, and GT (Figure D1-3).
The parameter values yielding the lowest measured-modelled differences
in seasonal snow depth and GT are 50, 60, and 90 for dens_min, and 0.5,
0.8, and 0.9, for compaction_rate, at the birch forest, peat plateau,
and fen sites, respectively (Figure D4-8).
For the tundra site, we applied parameter values of 90 and 0.8 for
minimum snow density and compaction rate, as these showed the best
agreement with the available growing-season observational data.
3.2 Evaluation of physical and biogeochemical variables in the
historical period
3.2.1 Evaluation of physical
variables
The seasonal patterns of snow cover were captured in the model at all
sites. However, snowpack depth was considerably underestimated at the
birch forest and the low-elevated fen site, but overestimated in low
vegetation settings such as the peat plateau (Figure E1), likely due to
the model lacking lateral transport and trapping/deposition of
wind-blown snow. This mismatch affected the snow insulating capacity and
caused substantial cold and warm biases in winter GT in the birch forest
and peat plateau, respectively, but not in the fen (Figure E2) where the
modelled snowpack depth exceeds the depth of the maximum insulating
effect (40 cm; Zhang, 2005). The growing season GT was well captured at
the birch forest and fen sites, but was underestimated in the peat
plateau (Figure E2a,c,d).
The model was able to simulate short-term fluctuations in snow depth and
GT during WWEs at all sites, but not their magnitudes (Figure E3). At
the birch forest and peat plateau, larger than measured fluctuations in
GT were modeled during WWEs and these differences were much smaller in
the late winters. The modelled differences were largely linked to the
simulated insulating capacity of snowpacks. Noticeably, stronger modeled
reductions in snow depth occurred during early- and mid-winter WWEs,
when the energy needed to warm-up and subsequently melt the thin and
fresh (less dense) snowpacks is smaller. The modeled and measured GTs at
the fen site remained around 0 °C throughout the winter due to the
strong insulation of the thick snowpack, but the model captured the
observed snowpack responses to WWEs.
3.2.2 Evaluation of biogeochemical variables
The modeled maximum LAI of 1.6 and 1.5 fell within the observed ranges
at the birch forest (Heliasz, 2012) and tundra sites (Simin et al.,
2021). Following the biases in GT, winter CO2heterotrophic respiration (Rh) was underestimated at the
birch forest and overestimated at the peat plateau (Figure E4). We
speculate an overestimation of winter Rh at the tundra
–it presents similar winter Rh values than the fen and
peat plateau, and 6-fold larger than the birch forest– due to the
overestimated soil C pool at this site. The model underestimated the
growing-season ecosystem respiration (Reco) and gross
primary production (GPP) in the tundra (Figure E5c,d). At the birch
forest, the model predicted a strong annual C sink in 2007-2010, but
measurements indicated a fairly neutral C balance (Figure E4a,b). At the
peat plateau and fen sites, the model captured the annual fluctuations
and magnitudes of CO2 fluxes (Figure E4c-f). Across four
sites the model was unable to capture the observed strong C source in
September.
The differences in CH4 fluxes between the peat plateau
and fen sites were well captured by the model. However, the model
underestimated by 55% the winter CH4 fluxes at the peat
plateau, likely due to the colder bias in modeled GT (Figure E6a,b).
3.3 Effects of enhanced WWEs
3.3.1 Impacts on physical variables
The WWE experiments (S1-S3) caused an overall reduction in winter GT (up
to 2 °C) in all ecosystem types, except for the runs driven by WWEs from
CanESM5 SSP585 at the tundra and the peat plateau (Figure 1a-d). The
modeled snow depth also decreased substantially under all WWE
experiments. This might suggest that the major driver of the modelled
WWE effects on winter GT is snow insulation, which decreases as a
combination of the snowpack depth reduction, and the increased thermal
diffusivity (caused by higher thermal conductivity and lower heat
capacity after freeze-thaw processes), facilitating the heat exchange
between atmosphere and soil (Figure 3). However, a “tipping point” may
be crossed above a certain WWE magnitude, when the strong cooling
effects of the reduced snow insulating capacity are counteracted by the
stronger warming effects of longer and more extreme WWEs. This point is
reached faster under shallow snowpacks where snow insulation is weak and
its further reduction has a smaller effect on GT compared to the overall
warmer conditions.
In contrast to WWEs, altered future winter climatologies (S4) increased
the modeled winter GT at all sites, from less than 1 °C to as much as 4
°C at the birch forest, tundra, and peat plateau in the warmest
scenario, i.e., CanESM5 SSP585, despite snowpack reductions of
>80%. The modeled GT warming effect diminishes under thick
snowpacks, due to the snow insulation-related process described above:
at the fen site (thickest snowpack) we can only see a GT warming under
CanESM5 SSP585.
Our results agree with the modelling results by Beer et al., (2018), who
suggested that WWEs may cause GT cooling mainly by reducing the snowpack
depth. However, there is increasing observational evidence that intense
ROS events cause substantial and long-lasting GT warming in winter
(e.g., Hansen et al., 2014) through the latent heat released from
refreezing of infiltrated water at the bottom of thick snowpacks
(Westermann et al., 2011; Pascual et al., 2022). This long-lasting GT
warming cannot be captured in LPJ-GUESS due to the lack of essential
processes describing the energy and water exchanges between the
atmosphere, snowpack, and soil (Figure 3).
In non-winter, the impacts of the manipulation experiments on GT
followed those seen for winter GT, although smaller in magnitude (Figure
1e-h). The WWE impacts on non-winter GWC (0-50 cm depth) were marginal
except for the fen site (decrease of up to 0.3 m-3m-3)(Figure F1). GWC affects the thermal properties of
soils, and its reduction likely contributed to the larger non-winter GT
cooling modeled at the fen. The reduced albedo following an earlier snow
cover disappearance can contribute to faster GT warming in spring, but
this process is not represented in the current version of LPJ-GUESS.