The Mountain Glacier forecast framework

山地冰川预报框架

• 批准号: 431767937
• 负责人: Dr. Johannes Fürst
• 金额: --
• 依托单位: Institut für Geographie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/431767937?language=en
• 关键词: Mountain; Glacier; forecast; framework

General glacier retreat around the globe has become iconic to illustrate the effects of global warming and we anticipate that this demise will continue and significantly add to global sea-level rise. Ice loss can also induce shifts in regional water resources, putting some places at high risk of year-around freshwater availability. Better projections of glacier retreat are therefore essential in terms of coastal impact, water management and hydro-glaciological risks.The overall aim of the project is to provide a framework to improve past and future glacier volume projections. The process-based framework will be based on the open-source ice-flow model Elmer/Ice and will allow to reduce three potentially important sources of uncertainty. Anticipating the continuously growing body of information from satellite remote sensing, we will improve and develop robust data assimilation methods to infer the basal topography beneath the ice and calibrate ice-flow parameters for a better representation of the initial state and ice dynamics. The flow model will be coupled to an enhanced surface mass balance model that exhibits a stable melt relation over relevant multi-decadal timescales. Based on ensemble simulations, the framework will allow to better quantify the interaction and propagation of these uncertainties.As a proof of concept, the framework will be applied to hindcast and forecast glacier evolutions in the French Alps and in Cordillera Darwin (Chile). These sites have been chosen because for both glacier sites ice-flow is considered important and because of the availability of in-situ and remotely sensed observations. The two sites will further serve as challenging test setups to scrutinise the applicability, performance and robustness of the modelling framework under different climatic conditions and dynamic settings.

地球仪周围的冰川普遍退缩已成为说明全球变暖影响的标志性现象，我们预计这种消亡将继续下去，并大大加剧全球海平面上升。冰的损失也会导致区域水资源的变化，使一些地方面临全年淡水供应的高风险。因此，更好地预测冰川退缩对沿海地区的影响、水管理和水文冰川风险至关重要，该项目的总体目标是提供一个框架，以改进对过去和未来冰川量的预测。基于流程的框架将基于开源的冰流模型Elmer/Ice，并将减少三个潜在的重要不确定性来源。预计来自卫星遥感的信息将不断增加，我们将改进和开发强有力的数据同化方法，以推断冰下的基本地形，并校准冰流参数，以便更好地表示初始状态和冰动态。流动模型将与增强的表面质量平衡模型耦合，该模型在相关的数十年时间尺度上显示出稳定的融化关系。基于集合模拟，该框架将允许更好地量化这些不确定性的相互作用和传播。作为概念的证明，该框架将被应用于法国阿尔卑斯山和科迪勒拉达尔文（智利）的冰川演变的后报和预测。之所以选择这些地点，是因为这两个冰川地点的冰流被认为很重要，而且可以进行现场和遥感观测。这两个地点将进一步作为具有挑战性的测试设置，以审查模型框架在不同气候条件和动态设置下的适用性，性能和鲁棒性。