Reducing Snow-Climate Uncertainty in Earth System modelling (ReSCUES)

减少地球系统建模中的雪气候不确定性 (ReSCUES)

• 批准号: NE/P011926/1
• 负责人: Richard Essery
• 金额: $ 46.12万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FP011926%2F1
• 关键词: Reducing; Snow; Climate; Uncertainty; Earth

Snow is a material with remarkable physical properties that profoundly alters the characteristics of the Earth's surface where it lies. Because snow has a high albedo (the fraction of solar radiation that it reflects rather than absorbs) and a high latent heat of fusion (the energy required to melt it), it delays the warming of the atmosphere and the ground in spring each year. Satellite measurements of Northern Hemisphere snow cover have now been available for 50 years, and a strong decreasing trend correlated with warming has been observed in spring over that period. Less snow accumulates in a warmer climate and melts sooner, increasing the absorption of solar radiation and reinforcing the warming (a strong positive feedback). Snow conducts heat poorly because it contains trapped air and so insulates the ground from cold temperatures in winter; this controls soil freezing and provides protection for short plants, small animals and soil microbes living in snowy regions, with important and complex impacts on the global carbon cycle. For all of these reasons, it is important that climate models should be able to predict snow cover accurately. Unfortunately, the latest climate models still differ greatly in their simulations of how snow cover varies from year to year in the current climate and how it will change in the future. There are many potential sources for this uncertainty, including errors in snowfall and temperature patterns predicted by models, multiple processes that control the rate of snowmelt but may be poorly represented in models, and uncertainty in setting optimal values for model parameters. It has proven very difficult to disentangle these sources of uncertainty and to determine how they can be reduced. In this project, we will use a new modelling system in which a single meteorological variable, model process or parameter value can be varied at a time, allowing highly controlled experiments to precisely determine how they influence simulations. Direct measurements of snow properties at research sites and satellite measurements of snow cover and albedo across the Northern Hemisphere will be used to identify the best simulations. Because snow melts both as the weather warms in spring and as the climate warms, improving the ability of models to simulate the current seasonal cycle and past trends can be expected to improve projections of future conditions, provided that the improvements are obtained for sound physical reasons. Improved predictions and better understanding of the sensitivity of snow to climate change will contribute to reviews of climate science by the Intergovernmental Panel on Climate Change which are essential resources for policymakers. Another important feature of snow is that it stores precipitation that falls in the mountains over winter and releases it in warmer times of year when human demand for water is higher. Many parts of the world are provided with water and threatened by floods from melting snow in upstream mountain regions. Even if the total amount of precipitation does not change in a warming climate, a shift to more falling as rain rather than snow will lead to river flows peaking earlier in the year, requiring major changes in the management of water resources. Global climate models, which cannot resolve processes occurring on scales smaller than a few hundred kilometres, are not adequate tools for informing water management decisions, but national weather services are now beginning to run forecasts for limited areas and short periods with kilometre-scale resolutions. We will use high-resolution meteorological data and the same modelling methods that we applied on the hemispheric scale to make and test predictions for snowmelt in well-instrumented areas of the French and Swiss Alps. Methods developed will be incorporated in a "downscaling toolkit" which will be made available to researchers and water managers by the International Network for Alpine Research Catchment Hydrology.

雪是一种具有显著物理特性的物质，它深刻地改变了它所在的地球表面的特征。因为雪有很高的反射率（反射而不是吸收太阳辐射的部分）和很高的熔化潜热（融化它所需的能量），它推迟了每年春天大气和地面的变暖。卫星对北方积雪的测量已经有50年的历史，在这一时期的春季观察到与变暖相关的强烈减少趋势。在较温暖的气候中，积雪较少，融化得更快，增加了对太阳辐射的吸收，加强了变暖（一种强烈的正反馈）。雪的导热性很差，因为它含有被困的空气，因此在冬季将地面与寒冷的温度隔离开来;这控制了土壤冻结，并为生活在积雪地区的矮植物，小动物和土壤微生物提供了保护，对全球碳循环产生了重要而复杂的影响。由于所有这些原因，气候模型应该能够准确地预测积雪是很重要的。不幸的是，最新的气候模型在模拟积雪在当前气候下每年的变化以及未来的变化方面仍然存在很大差异。这种不确定性有许多潜在的来源，包括模型预测的降雪和温度模式的误差，控制融雪速率但在模型中可能表现不佳的多个过程，以及为模型参数设置最佳值的不确定性。事实证明，很难理清这些不确定性的来源，也很难确定如何减少这些来源。在这个项目中，我们将使用一个新的建模系统，其中一个单一的气象变量，模型过程或参数值可以在同一时间变化，允许高度控制的实验，以精确地确定它们如何影响模拟。在研究地点直接测量雪的特性，以及用卫星测量整个北方半球的积雪和积雪，将用来确定最佳的模拟。由于春季天气变暖和气候变暖都会使积雪融化，因此，提高模型模拟当前季节周期和过去趋势的能力，预计将改善对未来状况的预测，前提是这些改进是出于合理的物理原因。更好地预测和更好地了解雪对气候变化的敏感性，将有助于政府间气候变化专门委员会对气候科学的审查，这是决策者的重要资源。雪的另一个重要特征是，它储存了冬季山区福尔斯的降水，并在一年中人类对水的需求更高的温暖时期释放出来。世界上许多地方都有水供应，但上游山区的积雪融化造成洪水威胁。即使在气候变暖的情况下，降水总量没有变化，但更多的降雨而不是降雪将导致河流流量在今年早些时候达到峰值，这需要对水资源管理进行重大改革。全球气候模型无法解决规模小于几百公里的过程，不足以为水管理决策提供信息，但国家气象部门现在开始以更大规模的分辨率对有限地区和短期进行预报。我们将使用高分辨率的气象数据和我们在半球尺度上应用的相同建模方法，在法国和瑞士阿尔卑斯山的仪器设备齐全的地区进行融雪预测和测试。所制定的方法将被纳入一个“缩小尺度工具包”，由国际高山流域水文研究网络提供给研究人员和水管理人员。

项目成果

Uncertainties in Snowpack Simulations-Assessing the Impact of Model Structure, Parameter Choice, and Forcing Data Error on Point-Scale Energy Balance Snow Model Performance

• DOI: 10.1029/2018wr023403
• 发表时间: 2019-04-01
• 期刊: WATER RESOURCES RESEARCH
• 影响因子: 5.4
• 作者: Guenther, Daniel;Markel, Thomas;Strasser, Ulrich
• 通讯作者: Strasser, Ulrich

Snow cover duration trends observed at sites and predicted by multiple models

现场观察并通过多个模型预测的积雪持续时间趋势

• DOI: 10.5194/tc-14-4687-2020
• 发表时间: 2020
• 期刊: Cryosphere
• 影响因子: 5.2
• 作者: R Essery;H Kim;L Wang;P Bartlett;A Boone;C Brutel-Vuilmet;E Burke;M Cuntz;B Decharme;E Dutra and others (T Nitta 24/32)
• 通讯作者: E Dutra and others (T Nitta 24/32)

ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks

• DOI: 10.5194/gmd-11-5027-2018
• 发表时间: 2018-12-10
• 期刊: GEOSCIENTIFIC MODEL DEVELOPMENT
• 影响因子: 5.1
• 作者: Krinner, Gerhard;Derksen, Chris;Zhu, Dan
• 通讯作者: Zhu, Dan

Firn Model Intercomparison Experiment (FirnMICE)

• DOI: 10.1017/jog.2016.114
• 发表时间: 2017-02
• 期刊: Journal of Glaciology
• 影响因子: 3.4
• 作者: J. Lundin;C. Stevens;R. Arthern;C. Buizert;A. Orsi;S. Ligtenberg;S. Simonsen;E. Cummings;R. Essery;W. Leahy;P. Harris;M. Helsen;E. Waddington

Data Assimilation Improves Estimates of Climate-Sensitive Seasonal Snow

• DOI: 10.1007/s40641-020-00159-7
• 发表时间: 2020-05-15
• 期刊: CURRENT CLIMATE CHANGE REPORTS
• 影响因子: 9.5
• 作者: Girotto, Manuela;Musselman, Keith N.;Essery, Richard L. H.
• 通讯作者: Essery, Richard L. H.