Measurement and Modelling of Arctic Snow across the Forest-Tundra Ecotone

Abstract

Seasonal snow cover patterns across the Arctic are changing in response to climate warming. Some of the most significant impacts are observed across the forest-tundra ecotone, where boreal forest transitions into Arctic tundra. Changes in snow cover impact the surface energy balance, regional hydrology and ground thermal regime. To better understand these impacts, improved methods are required to characterise and simulate changes in snow properties. Significant variation between measured and simulated snowpack properties in Arctic environments has been found, due to omission of key Arctic processes. Improving process representations in snowpack models has the potential to enhance future projections. Spaceborne radar missions could provide new continental-scale snow observations but depend on accurate estimates of snowpack microstructure only possible from models.
The default configuration of the snow model Crocus within the Soil, Vegetation and Snow version 2 (SVS2-Crocus) land surface model was found to be inappropriate for simulating profiles of snow density and specific surface area (SSA) across the forest-tundra ecotone. Accurate simulation of snow properties required incorporating missing Arctic processes – wind-induced compaction, the impact of basal vegetation on compaction and metamorphism and alternative thermal conductivity formulations – into an ensemble version of SVS2-Crocus, creating Arctic SVS2-Crocus. Top performing Arctic SVS2-Crocus modifications that raise wind speeds to increase compaction in snow surface layers and prevent snowdrift and increase viscosity in basal layers, improved simulation of snow density profiles across the forest-tundra ecotone, which is crucial for many applications.
The impact of an ensemble of simulated snow microstructure properties on Ku-band (13.5 GHz) snow backscatter was evaluated using the Snow Microwave Radiative Transfer (SMRT) model. Uncertainties in simulation of SSA by SVS2-Crocus led to large errors in simulation of snow backscatter, which is used to retrieve snow water equivalent (SWE). Scaling the scattering effects of simulated snowpacks reduced errors in snow backscatter simulations; implementing a minimum simulated SSA value reduced errors even further. Improvements in simulating snow SSA and Ku-band backscatter enhance our potential to retrieve SWE from satellites, which will be crucial for understanding the impact of future climate change in seasonally snow-covered environments.

Date of Award	26 Jun 2025
Original language	English
Awarding Institution	Northumbria University
Supervisor	Nick Rutter (Supervisor), Leanne Wake (Supervisor) & Vincent Vionnet (Supervisor)