Measurement and Modelling of Snow in Arctic Tundra and Taiga Biomes

北极苔原和针叶林生物群落雪的测量和建模

• 批准号: 2597850
• 金额: --
• 依托单位: Northumbria University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2597850
• 关键词: Measurement; Modelling; Snow; Arctic; Tundra

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.

陆面模型(LSM)被发现不能很好地模拟北极的积雪特性，导致在过程表示(例如变质作用)、地表状况模拟和地表能量收支方面存在很大误差(Domine等人，2019年)。水文和天气预报在很大程度上依赖土地管理系统来准确描述这些关键的地球系统过程，以产生可靠的预报，从而为广大人口提供水安全、水资源管理信息和辅助决策(Liston and Elder，2006)。评估LSM模拟积雪特性的能力将提高输出的可靠性，并产生将对人类生活产生有意义影响的预报。可以使用现场积雪特性测量的比较技术来识别和评估LSM的缺陷。众所周知，积雪特性在空间尺度上是不同的，指的是比表面积(SSA)、密度、深度、导热系数和雪水当量(SWE)。空间异质性是LSM目前没有考虑的一个方面，这使得预测的应用变得困难，特别是在不同的尺度上(Bokhorst等人，2016)。该项目旨在限制LSM对不同空间尺度(例如，泛北极、区域、局部、点)的北极冻土带和泰加生物群(步道谷溪和剑桥湾)的北极积雪特性模拟的不确定性。使用改进的积雪特性的空间分布现场测量结合统计比较技术(Leonardini等人，2020)，可以评估当前LSM模拟(例如，加拿大作业土壤、植被和雪(SVS)LSM)的准确性。这项评估将用于确定模型的不足之处，以解决当前水文和天气预报可靠性方面的不确定性。项目成果将通过更好地了解我们模拟极易受到快速气候变化影响的地球系统过程的能力，帮助开发关键的One Planet研究主题。这项研究的总体目标是限制当前北极冻土带和泰加生物群积雪微结构特性的LSM模拟的不确定性。更具体的研究目标如下：-1制作通过严格限制的实地测量收集的积雪微结构特性的综合数据集，反映北极冻土带和泰加生物群(加拿大北部--特雷尔河谷小溪和剑桥湾)的空间变异性。2通过使用统计指标比较测量结果，查明和发现LSM模拟积雪特性的能力方面的不足。