Toward a Realistic Estimate of Sea-Level Rise and Global Water Balance Due to Glacier Wastage

海平面上升对环境、社会和经济的潜在影响是严重的。可能导致海平面上升的世界海洋的一个主要潜在水源是世界冰川的融化。这项研究项目将研究除南极和格陵兰冰盖外，世界上所有冰川融化的水在最近的过去和未来的潜在贡献。研究人员将使用观测数据、重复的大地测量和测绘以及航空照片对不同冰川到20世纪中叶的物质平衡时间序列的结果进行建模。统计分析将有助于确定冰川区域之间冰川体积变化的遥相关，并将这些与气候的遥相关联系起来。冰川物质平衡对气温和降水变化的静态敏感性将使用长期记录和按山脉平均的冰川学数据来计算。随机标度和渗流理论将用于估计冰川的数量和大小，以及冰川面积、体积和响应时间之间的关系。该项目的预期成果包括极大地改进了对影响海平面和全球水平衡的冰川数量变化的信息和了解，改进了关于大规模气候模式的信息，以及冰川揭示的气候变化的证据。项目成果将用于几个世界气象项目，包括全球气候观测系统(GCOS)，以及联合国环境规划署(UNEP)、全球环境监测系统(GEMS)、世界冰川监测局(WGMS)和位于博尔德市的国家冰雪数据中心。提高预测未来海平面上升的能力有重要的社会和经济原因。世界上很大一部分人口和世界上最有价值的房地产都位于海平面几米以内的高度。当地的影响，如大河泥沙排放量的减少和从地下抽水或抽油造成的下沉，都会因海平面上升而加剧。因此，在更好地理解冰川融化对全球海量的贡献的基础上，改进对海平面上升的估计，对于土地利用规划、海岸带管理以及其他面临风险的社会、政治和经济进程将是重要的。这项研究将大大改进对影响海平面和陆地与海洋之间水量平衡的冰川冰量变化的估计；关于冰川-气候相互作用的区域和时间模式的重要新信息，以及过去在气候和冰川研究中推广这些结果的算法；关于那些径流主要来源于冰川浪费的地区，例如南美洲和中亚的封闭流域，预计由于全球变暖而发生的径流变化的区域信息；关于冰川揭示的气候和气候变化的大规模模式的更好信息，以及在不完整数据集的数学处理方面的新的科学进展。

The potential environmental, social, and economic impacts of rising sea levels are serious. A major potential source of water to the world's oceans that would result in sea-level rise is from melting of the world's glaciers. This research project will examine the contributions of meltwater from all of the world's glaciers except the Antarctic and Greenland ice sheets in the recent past and potential contributions in the future. The investigators will model results from mass-balance time series for different glaciers to the middle of the 20th century using observational data, repeated geodetic and mapping surveys, and aerial photographs. Statistical analyses will help determine teleconnections in glacier volume change between glaciated regions and relate these to teleconnections in climate. Static sensitivities of glacier mass balances to air temperature and precipitation change will be calculated using long-term records and glaciological data averaged by mountain ranges. Stochastic scaling and percolation theory will be used to estimate the number and size of glaciers and relationships among glacier area, volume, and response times. Expected outcomes of the project include greatly improved information and understanding of changes in glacier volume, which affect sea level and the global water balance, improved information on large-scale patterns of climate, and evidence of climate change as revealed by glaciers. Project results will be used by several world meteorological programs, including the Global Climate Observation System (GCOS), and also the United Nations Environmental Program (UNEP), Global Environmental Monitoring System (GEMS), World Glacier Monitoring Service (WGMS), and the National Snow and Ice Data Center in Boulder.There are significant social and economic reasons for improving capabilities to project future sea-level rise. A major fraction of the world's population and the world's most valuable real estate are at elevations within a few meters of sea level. Local effects like reductions in the sediment discharge of large rivers and subsidence caused by pumping water or oil out of the ground are exacerbated by sea-level rise. Improved estimates of sea-level rise based on better understandings of the contributions of melting glaciers to global ocean volume therefore will be important for land-use planning, coastal-zone management, and other social, political, and economic processes that are at risk. This study will produce a greatly improved estimate of changes in glacier ice volume which affects sea level and the water balance between land and ocean; significant new information on regional and temporal patterns of glacier-climate interactions, and algorithms for extending these results in studies of climate and glaciers in the past; regional information on changes in runoff to be expected as a result of global warming in those areas where the runoff is largely derived from glacier wastage, such as closed drainage basins in South America and Central Asia; improved information on large-scale patterns of climate and climate change as revealed by glaciers, and new scientific advances on the mathematical treatment of incomplete data sets.