6. Snow, Vegetation, and Permafrost Interactions Breakout Group

Welcome to the snow–vegetation–permafrost breakout! Please see the agenda for our breakout session .

• 6.1. Snow, Vegetation, Permafrost (SVP) Breakout Agenda
  • 6.1.1. SVP Breakout January 14, 2026
  • 6.1.2. SVP Breakout Thursday, January 15, 2026
  • 6.1.3. SVP Breakout Friday, January 16, 2026

Snow, shrubs, and permafrost are tightly coupled controls on Arctic ecosystem function, climate feedbacks, and infrastructure stability. Snow regulates winter soil temperatures and spring hydrology by insulating the ground and controlling the timing and magnitude of meltwater, while shrubs modify snow accumulation and redistribution, often deepening snowpacks and enhancing winter insulation. These interactions can warm soils, accelerate permafrost thaw, and alter active layer depth, with cascading effects on soil moisture, nutrient availability, and vegetation composition.

Permafrost stores vast amounts of frozen organic carbon, and its thaw can trigger long-term releases of CO₂ and CH₄, amplifying global climate change. Shrub expansion further modifies surface albedo and energy balance, reinforcing regional warming. Together, changes in snow, shrubs, and permafrost represent a nonlinear, self-reinforcing system that strongly influences Arctic climate feedbacks and ecosystem trajectories.

Under future warming scenarios, some research has indicated that precipitation is likely to increase, a phenomenon referred to as Arctic Amplification (e.g., Bintanja et al. 2018, England et al. 2021, Rantanen et al. 2022).

In the ELM model, just as we changed temperature in TEM to simulate warming, we can also change summer and winter precipitation. In the example below, we walk through shifting both summer and winter precipitation to examine impacts on subsurface conditions.

Tip

Reminder - if you need don’t have the visualization container running, you can launch one with:

cd field-to-model

docker run -it --pull always --rm \
  -p 8888:8888 \
  -v $(pwd):/home/jovyan \
  -v inputdata:/mnt/inputdata \
  -v output:/mnt/output \
  yuanfornl/ngee-arctic-modex26:vis-main-latest

6.2. Variables of Interest in TEM and ELM

The following variables are examined to understand how changes in temperature, precipitation, and vegetation affect snow, soil, and permafrost processes.

Model variables and temporal resolution

Variable	TEM	ELM
Soil active layer depth (m)	ALD, yearly	ALT, daily
Gross primary production (g m⁻² time⁻¹)	GPP, monthly	GPP, daily
Snow pack thickness (m)	SNOWTHICK, monthly	SNOW_DEPTH, daily
Snow water equivalent (kg m⁻²)	SWE, monthly	H2OSNO, daily
Soil layer thickness (m)	LAYERDZ, monthly	levgrnd, static
Temperature by layer (°C)	TLAYER, monthly	TSOI_10CM, daily (K)

6.3. Setting Up Model Simulations

Mean annual temperature is projected to increase by up to +8.5 °C at NGEE-Arctic sites according to downscaled CMIP6 projections using a 15-member ensemble (https://dap.climateinformation.org/dap/).

Seasonal precipitation changes are represented as percent differences from a 1981–2010 climate normal. Temperature perturbations are applied as absolute °C offsets.

Emissions scenario: Median annual or seasonal (SSP245, 2041–2070, difference from 1981–2010)

Site Abbreviation	Location	Lat / Long	T	Pjul	Pjan
beo	Alaska	71.30 / -156.60	4.67	31.74	44.97
council	Alaska	64.85 / -163.71	3.25	21.59	13.08
kougarok	Alaska	65.16 / -164.83	3.43	20.68	15.41
teller	Alaska	64.74 / -165.95	3.56	11.99	17.92
toolik_lake	Alaska	68.63 / -149.59	3.09	36.97	45.97
imnaviat_creek	Alaska	68.60 / -149.30	3.05	39.97	56.93
upper_kuparuk	Alaska	68.61 / -149.31	3.05	39.97	56.93
trail_valley_creek	Canada	68.74 / -133.50	3.82	21.76	14.83
abisko	Sweden	68.35 / 18.82	2.06	21.55	6.74
bayelva	Norway	78.92 / 11.83	3.29	19.53	15.68
samoylov_island	Russia	72.37 / 126.50	3.56	9.49	33.93

The imnaviat_creek site is used as the example below, though any of the available Arctic sites may be selected.

6.4. TEM and ELM Models

In this next few presentations, we will go into more details on ELM and TEM, and show you how to run each of these models and look at model results for different cases for snow, vegetation, and permafrost model outputs.

• 6.4.1. ELM Model
  • Running ELM Cases
  • Looking at ELM Results
  • Advanced ELM Processing and Information
• 6.4.2. TEM Model
  • Running TEM Cases
  • Looking at TEM Results
  • Advanced TEM Processing and Information
  • TEM background for Snow, Vegetation, and Permafrost

6.5. References

Bintanja, R., 2018. The impact of Arctic warming on increased rainfall. Scientific Reports, 8(1), p.16001.

England, M.R., Eisenman, I., Lutsko, N.J. and Wagner, T.J., 2021. The recent emergence of Arctic amplification. Geophysical Research Letters, 48(15), p.e2021GL094086.

Rantanen, M., Karpechko, A.Y., Lipponen, A., Nordling, K., Hyvärinen, O., Ruosteenoja, K., Vihma, T. and Laaksonen, A., 2022. The Arctic has warmed nearly four times faster than the globe since 1979. Communications Earth & Environment, 3(1), p.168.