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| Funder | Natural Environment Research Council |
|---|---|
| Recipient Organization | Northumbria University |
| Country | United Kingdom |
| Start Date | Sep 30, 2023 |
| End Date | Mar 30, 2027 |
| Duration | 1,277 days |
| Number of Grantees | 2 |
| Roles | Student; Supervisor |
| Data Source | UKRI Gateway to Research |
| Grant ID | 2880812 |
Cold season carbon emissions make a significant contribution to annual carbon budgets in Arctic environments. With air temperature in northern high latitudes increasing at rates nearly four times faster than the rest of the Earth, quantifying cold season carbon fluxes from these environments is crucially important for understanding their impact on future global air temperatures.
However, winter data collection in the Arctic is logistically and technically challenging, and at the few sites where carbon flux data is collected the role of snow on these fluxes is still poorly understood. Consequently, current Terrestrial Biosphere Models (TBMs) are unable to adequately simulate wintertime carbon fluxes, due to both a lack of forcing data and inadequate representation of the effect of Arctic snow cover on land-snow-atmosphere interactions.
This project aims to determine the role of Arctic snowpack thermal conductivity and structure on wintertime ecosystem and land-surface carbon exchanges across the northern boreal forest - tundra ecotone at two research sites near Inuvik (Northwest Territories, Canada). To quantify the effect of snow on carbon fluxes at multiple spatial scales low-cost sensors will be developed to measure snowpack CO2 and CH4 concentrations, temperature, and density along vertical profiles within the footprint of landscape- and ecosystem-scale carbon flux measurements made with eddy covariance (Baldocchi 2003).
With this novel dataset, TBM (e.g., Community Land Model - CLM5.0) uncertainties will be constrained by improved process-based representations winter carbon fluxes. Project outcomes will contribute to improving accuracy in climate change predictions through better earth system process data and models.
Northumbria University
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