Plain Language Summary
In the Arctic, winter warming events (WWEs) are episodes of
exceptionally warm weather that last from hours to a few days and often
occur in combination with rainfall. The combined effect of multiple
processes triggered by WWEs (involving, for example, changes in snow
depth and snow properties, and heat exchanges between air, rain and
meltwater, and soils) results in profound changes in ground temperatures
and water fluxes, which many ecosystem processes depend on (vegetation
dynamics, organic matter decomposition by microbes, etc). However,
large-scale ecosystem models, which are used to study how arctic
ecosystems will change in the future, often overlook the effects of WWEs
because they oversimplify some complex physical processes and operate at
longer temporal scales than WWEs. WWEs will likely become more frequent
and intense in the future as the climate continues to warm. This study
used a widely used ecosystem model, LPJ-GUESS, to investigate how the
ecosystems will respond to more frequent and intense WWEs. The ecosystem
responses that we observed were notable but in the opposite direction as
observed in field measurements. We identified the processes that are
lacking in the model and are causing this mismatch which, if implemented
in the model, would significantly improve the predictions of future
ecosystem changes in response to climate change.
1 Introduction
The Arctic is warming three times faster than the global average, with
the strongest warming occuring in autumn and winter (AMAP 2021). The
occurrence and impacts of winter warming events (WWEs), i.e.
short-lasting extraordinarily warm spells, often accompanied by rainfall
(rain on snow; ROS), are increasing rapidly and expanding geographically
(e.g. Bartsch et al., 2010; Vikhamar-Schuler et al., 2016; Pan et al.,
2018). Climate models predict a further increase in the coming decades.
Despite their short duration, these extreme events could cause societal
and environmental impacts that can override the impacts of long-term
climatic trends (e.g., Phoenix & Bjerke, 2016).
In the winter, the impacts of WWEs are currently considered a research
priority for better understanding future ecosystem dynamics in the
subarctic (Pascual et al., 2020). WWEs can affect ground temperatures
(GT) in multiple ways, mostly through: (1) direct heat transfer from the
air, (2) latent heat release from refreezing melt and rainwater, and (3)
changing the snowpack properties, such as depth and density, which
influence the energy exchanges between the atmosphere, snow, and soil.
These altered winter processes can further influence ground albedo and
groundwater content (GWC), which has impacts lasting to the growing
seasons (Pascual & Johansson, 2022). These WWE-associated environmental
changes can further alter microbial activity, greenhouse gas (GHG)
emissions (e.g., Natali et al., 2019), and permafrost and vegetation
dynamics (Bruhwiler et al., 2021), ultimately altering the arctic carbon
budget.
These interlinked processes and feedbacks related to WWEs are difficult
to disentangle in observational data and thus challenging to implement
in models. Moreover, large-scale ecosystem models which run with monthly
climate data do not explicitly account for the impacts of such
stochastic climate extremes (e.g. Tang et al., 2015).
In this study, we investigate the potential effects of predicted WWE
scenarios on winter and growing-season physical and biogeochemical
variables using the latest version of a widely used dynamic ecosystem
model, LPJ-GUESS. We aim to evaluate the model’s performance and
identify model gaps in representing ecosystem responses to future WWEs.
2 Materials and Methods
2.1 Study sites
The Torneträsk area, in northern Sweden, is a topographically
heterogeneous area that aligns along a strong west-east
oceanic-continental climatic gradient, with precipitation and winter
temperature decreasing eastwards due to the increasing continentality
and the rain shadow effect caused by the Scandes Mountains. The area has
experienced rapid climate warming (Callaghan et al., 2010) and a
substantial increase in the frequency and intensity of WWEs (Pascual &
Johansson, 2022).
Vegetation in the area varies following its climatic and altitudinal
gradients. Birch forests occur below c. 600 and 800 m.a.s.l (Van
Bogaert, 2010). Tundra species dominate above the tree-line, while in
the lowlands, birch forests alternate with peat plateaus underlain by
permafrost, and non-permafrost fens.
In this modelling study, we selected four sites representing these
dominant ecosystem types in the Torneträsk area, including (1) a birch
forest (~370 m.a.s.l) located <10 km east of
the Abisko Station (ANS) (Heliasz et al., 2012); (2) a tundra site
(~410 m.a.s.l), located <1 km to the southeast
of the ANS (Michelsen et al., 2012 and references therein); (3) a peat
plateau (~380 m.a.s.l) known as Storflaket, located c. 6
km east of the ANS (Johansson et al., 2013), and (4) a fen
(~515 m.a.s.l) located c. 25 km west of the ANS, near
the Katterjokk Station (SMHI). The dominant vegetation species at these
sites are found in Appendix A.
2.2 Model description and simulation setup
2.2.1 Model description
The Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) is a
process-based dynamic ecosystem model widely used on regional and global
scale studies (Smith et al., 2001; 2014). The model simulates vegetation
dynamics (including vegetation establishment, mortality and competition,
etc.), water, carbon and nitrogen cycles, and soil biogeochemistry. This
study used the latest version of LPJ-GUESS (version 4.1, Smith et al.,
2014), with the recently-developed dynamic, intermediate complexity snow
scheme enabling the simulation of climate-snow-soil interaction. The
model can simulate up to five snow layers, their physical and thermal
properties, and their development throughout the cold season. Based on
the individual snow layer properties (e.g., temperature, density,
thermal conductivity), freeze-thaw processes in snow layers, and heat
transport through the snowpack between the atmosphere and soil can be
simulated, and ROS events can be accounted for (Pongracz et al., 2021).
LPJ-GUESS includes detailed representations of permafrost and wetland
processes, including peatland hydrology, peatland-specific PFTs, and
CH4 emissions (see Wania et al., 2009a, 2009b, 2010).
2.2.2 Model setup
Daily climate data provided by the ANS (ANS 2020) and Katterjokk Station
(SMHI), including air temperature, air temperature daily range, and
precipitation, together with shortwave radiation (1913-1984, Sheffield
et al., 2006; 1984-2018, ANS) and annual CO2concentrations obtained from the Global Monitoring Laboratory
(https://gml.noaa.gov/ccgg/trends/), were used to drive the model from
1913 to 2018. Soil property data was extracted from the WISE5min, V1.2
Soil Property Database (Batjes 2012). More details about the setup and
input data are found in Appendix B.
Representative plant functional types (PFTs) were selected for each site
(Table A1). We enabled high-latitude and wetland-specific plant
functional types (PFTs) in the simulations to better capture
site-specific vegetation conditions (see Wania et al., 2009 for more
details). The PFT parameters at each site followed previous studies
(e.g., Tang et al., 2015; Gustafson et al., 2021) (Table A2).
2.2.3 Model calibration and evaluation
Sobol sensitivity analyses (SA) were conducted to explore the influence
of different parameters and parameter interactions on the estimated
seasonal snow density, snow depth, snow temperature, and GT at the study
sites (except for the tundra site due to lack of observational data for
calibration and evaluation) (Appendix D). A sampling of eight relevant
parameters, using ranges based on literature values, and a certain
percentage of changes from the original values (Table D1), was
conducted. Among the most influential parameters, we selected the
parameter values that minimized the absolute differences between the
measured and the modeled seasonal snow depth and GT at each site
(2006-2012 at the peat plateau, 2001-2010 elsewhere) (Figures D4-8). The
model was subsequently evaluated with independent observational data
(2011-2018) when possible (Appendix E).
2.2.4 Model simulations with future WWEs
We generated manipulation experiments with different levels of WWE
frequencies and intensities imposed on the observation-based climate
inputs (HISTORICAL dataset) to assess the responses of different
ecosystem processes to these extreme events. The applied frequencies and
intensities were based on different climate scenarios in the Coupled
Model Intercomparison Project Phase 6 (CMIP6) (Eyring et al., 2016). We
selected six climate scenarios from two general circulation models
(GCMs) with different climate sensitivities, i.e., CanESM5 and
GFDL-ESM4, and three shared socioeconomic pathways representing three
levels of varying GHG projections, i.e., SSP119, SSP270, and SSP585. For
each scenario (n=6), daily meteorological data (1950-2100) for the
gridcell near the Torneträsk area was extracted, and then bias-corrected
at a daily scale. Detailed descriptions of the CMIP6 scenarios and the
bias-correction method are found in Appendix C.
The bias-corrected GCM’s outputs were used to create the manipulation
experiments (Table 1). In addition to the HISTORICAL runs (S0), we
designed three additional experiments in which the future monthly
anomalies in the frequency and intensity of melt days (S1), ROS (S2),
and both (S3) in the GCM’s outputs (Table C2) were added to the
HISTORICAL climate inputs, maintaining the long-term climate means as
unchanged as possible. These anomalies were calculated based on four
indices modified from Vikhamar et al. (2016) (Table C1).
The effects of altered WWEs and those from the altered long-term climate
trends were compared by using an additional experiment (S4) in which the
future winter monthly anomalies (Table C3) were directly added to the
historical daily air temperature and precipitation.
Table 1 . Description of the HISTORICAL and MANIPULATION
runs.