Measurement and Modelling of Snow in Arctic Tundra and Taiga Biomes

已经发现陆地表面模型（LSM）对北极积雪特性的模拟很差，导致过程表示（例如变质）、地面状况模拟和表面能量预算中的大误差（Domine等人，2019年）。水文和天气预报在很大程度上依赖于LSM来准确描绘这些关键的地球系统过程，以生成可靠的预报，从而为大量人口提供水安全，为水资源管理提供信息并帮助决策（Liston和Elder，2006年）。评估LSM模拟积雪特性的能力将提高输出的可靠性，并产生对人类生活有意义影响的预测。LSM缺陷可以识别和评估使用比较技术，积雪特性的实地测量。已知积雪特性在空间尺度上不同，并涉及比表面积（SSA），密度，深度，导热性和雪水当量（SWE）。空间异质性是LSM目前没有考虑的一个方面，使得预测的应用变得困难，特别是在不同尺度上（Bokhorst等人，2016).该项目旨在限制LSM模拟的不确定性，北极积雪的性质在不同的空间尺度（如泛北极，区域，本地，点）的北极加拿大苔原和针叶林生物群落（特雷尔谷溪和剑桥湾）。利用改进的积雪特性空间分布现场测量与统计比较技术相结合（Leonardini等人，2020年），可以评估当前LSM模拟（例如加拿大业务土壤，植被和雪（SVS）LSM）的准确性。这一评价将被用来确定模型的缺陷，可用于解决目前水文和天气预报的可靠性的不确定性。项目产出将有助于关键的一个星球的研究主题的发展，通过提高我们的能力，模拟地球系统的过程，是非常容易受到快速的气候变化的理解。这项研究的总体目标是限制目前的LSM模拟的不确定性积雪微观结构性能在北极苔原和针叶林生物群落。更具体的研究目标如下：-1通过严格约束的实地测量收集积雪微观结构特性，生成一个综合数据集，反映北极苔原和针叶林生物群落的空间变异性（北方加拿大-特雷尔谷溪和剑桥湾）。2通过使用统计指标比较测量结果，识别和检测LSM模拟积雪特性的能力不足。3确定模型不足对过程表示的影响，这对全球天气和水文预报具有重要意义。

Land surface models (LSMs) have been found to poorly simulate Arctic snowpack properties leading to large errors in process representation (e.g. metamorphism), simulation of the ground surface regime and surface energy budgets (Domine et al., 2019). Hydrological and weather forecasts rely heavily on LSMs to accurately portray these key earth system processes to generate reliable forecasts in order to provide water security to large populations, inform water resource management and aid decision making (Liston and Elder, 2006). Evaluating the capacity of LSMs to simulate snowpack properties will improve the reliability of outputs and produce forecasts that will make meaningful impacts to human life. LSM deficiencies can be identified and evaluated using comparison techniques to field measurements of snowpack properties. Snowpack properties are known to differ over spatial scales and refer to the Specific Surface Area (SSA), density, depth, thermal conductivity and snow water equivalent (SWE). Spatial heterogeneity is an aspect that LSMs do not currently consider, making application of forecasts difficult especially over differing scales (Bokhorst et al., 2016).This project aims to constrain the uncertainty in LSM simulations of Arctic snowpack properties across the differing spatial scales (e.g. pan-arctic, regional, local, point) of Arctic Canada tundra and taiga biomes (Trail Valley Creek and Cambridge Bay). Using improved spatially distributed field measurements of snowpack properties combined with statistical comparison techniques (Leonardini et al., 2020), the accuracy of current LSM simulations (e.g. Canadian operational Soil, Vegetation and Snow (SVS) LSM) can be evaluated. This evaluation will be used to identify model deficiencies which can be used to address uncertainties in the reliability of current hydrological and weather forecasts. Project outputs will aid development of key ONE Planet research themes through an improved understanding of our capacity to model earth system processes that are highly vulnerable to rapid climate change.The overall objective of this research is to constrain the uncertainty in current LSM simulations of snowpack microstructure properties across Arctic tundra and taiga biomes. More specific research objectives are as follows:-1 Produce a comprehensive dataset of snowpack microstructure properties gathered through well-constrained field measurements that reflect spatial variability across Arctic tundra and taiga biomes (Northern Canada - Trail Valley Creek and Cambridge Bay).2 Identify and detect deficiencies in the capability of LSMs to simulate snowpack properties through comparison of measured results using statistical indicators.3 Determine the impact of model deficiencies on process representation, which have important implications for global weather and hydrological forecasting.