Searching for Abrupt Climate Change Precursors Using Ultra High Resolution Ice Core Analysis

使用超高分辨率冰芯分析寻找气候突变的前兆

• 批准号: 1203640
• 负责人: Andrei Kurbatov
• 金额: $ 79.76万
• 依托单位: University of Maine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1203640&HistoricalAwards=false
• 关键词: Searching; Abrupt; Climate; Change; Precursors

The project will undertake an ultra high resolution, multi-parameter investigation of past climate to develop predictors for future abrupt climate change through identification of the "tipping points" which occur prior to an abrupt climate change events. Ultra-high resolution (up to 4 micron) ice core records will be produced -- well beyond the ~1cm resolution currently available -- using continuous flow injection into both a Thermo Element 2 ICP-MS and a Picarro Inc. L-2130-I H2O isotope analyzer sampled using a newly developed state-of-the-art cryocell chamber integrated with digital image recording and annotating software. The PI will look for precursors that reveal evidence of changes in, for example, the timing and magnitude of temperature, precipitation and atmospheric circulation from multi-decadal down to hundreds of sampling levels per year. The project will train students to be well versed in advanced laboratory and numerical modeling methods. Students will learn data reduction and visualization algorithms while also helping to provide a data stream that has broad applicability to climate. Cyberinfrastructure developed under NSF funding to the Climate Change Institute will be used to store, process, and visualize this high volume of ice core data while also making the information available to the public with high transparency for climate change use and attribution.

该项目将对过去的气候进行超高分辨率、多参数调查，通过确定气候突变事件发生之前的“临界点”，为未来的气候突变制定预测指标。 超高分辨率（高达4微米）的冰芯记录将产生-远远超过目前可用的~ 1厘米分辨率-使用连续流动注射到Thermo Element 2 ICP-MS和Picarro Inc. L-2130-I H2O同位素分析仪使用新开发的最先进的低温室采样，并集成了数字图像记录和注释软件。 PI将寻找揭示温度、降水和大气环流的时间和幅度变化的证据的前兆，从几十年到每年数百次采样。 该项目将培养学生精通先进的实验室和数值模拟方法。 学生将学习数据简化和可视化算法，同时还有助于提供对气候具有广泛适用性的数据流。 在NSF资助下开发的网络基础设施将用于存储，处理和可视化这些大量的冰芯数据，同时还将以高透明度向公众提供气候变化使用和归因的信息。