Measurement and modelling of carbon emissions in snow-covered Arctic tundra and taiga biomes

寒冷季节的碳排放对北极环境的年度碳预算做出了重大贡献。由于北部高纬度地区的气温上升速度几乎是地球其他地区的四倍，量化这些环境中的冷季碳通量对于了解它们对未来全球气温的影响至关重要。然而，在北极收集冬季数据在后勤和技术上都具有挑战性，而且在收集碳通量数据的少数几个地点，人们对雪对这些通量的作用仍然知之甚少。因此，目前的陆地生物圈模式（tbm）由于缺乏强迫数据和北极积雪对陆地-雪-大气相互作用影响的不充分表征，无法充分模拟冬季碳通量。该项目旨在确定北极积雪的导热性和结构对冬季生态系统和陆地表面碳交换的作用，在伊努维克（加拿大西北地区）附近的两个研究地点。为了在多个空间尺度上量化积雪对碳通量的影响，将开发低成本传感器，沿着景观和生态系统尺度碳通量测量足迹内的垂直剖面测量积雪CO2和CH4浓度、温度和密度（Baldocchi 2003）。有了这个新的数据集，TBM（例如，社区土地模型- CLM5.0）的不确定性将受到改进的基于过程的冬季碳通量表示的约束。项目成果将通过改进地球系统过程数据和模型，有助于提高气候变化预测的准确性。

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.