Collaborative Research: Quantifying Feedbacks Affecting High Altitude Climate Change

合作研究：量化影响高海拔气候变化的反馈

• 批准号: 1064326
• 负责人: James Miller
• 金额: $ 30.04万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-15 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1064326&HistoricalAwards=false
• 关键词: Collaborative; Research; Quantifying; Feedbacks; Affecting

In many mountain regions there is evidence that temperatures are changing at different rates than the global average. Three questions arise: Are temperatures in mountain regions increasing faster than the global average? Within mountain regions are warming rates dependent on elevation? And if the answers to the above are yes, why do such differences occur? Several different feedbacks can contribute, including those related to snow-albedo, atmospheric water vapor, cloud cover, and cloud properties. These feedbacks are difficult to quantify because the relationship between two climate variables is invariably interconnected with other variables as well. Also, the sparsity of observations in high-altitude regions exacerbates this difficulty.This project will combine surface-based and satellite observations with climate model simulations and a neural network analysis scheme to (1) quantify some of the principal relationships that contribute to feedbacks on temperature in high altitude regions, and (2) investigate how these relationships and feedbacks might change through the 21st century in response to increasing atmospheric greenhouse gases. The focus will be on the Tibetan Plateau and the Rocky Mountains in southwestern Colorado. The neural network analysis calculates partial derivatives between pairs of climate variables (e.g., downward longwave radiation and cloud cover) so that the strength of the various links in a feedback loop can be determined. Broader impacts of this work include: (1) The neural network can be applied in other regions and can enable researchers to quantify important feedbacks in the climate system and analyze non-linear processes; (2) By combining surface-based and satellite observations, a new spatially and temporally expanded observational data base will be available to the research community; (3) A better understanding of climate change in mountain regions will benefit the public by improving management practices that affect the future of water resources, agriculture, tourism, and ecosystems in high altitude regions; (4) A high-school teacher will be supported to work with the investigators to help develop and implement podcasts on mountains and climate change; (5) There will be training for a postdoctoral fellow and undergraduates; and (6) Educational materials will be developed in collaboration with the Mountain Studies Institute in Colorado.

有证据表明，在许多山区，气温变化的速度与全球平均水平不同。由此产生了三个问题：山区的气温上升速度是否快于全球平均水平？在山区，变暖的速度取决于海拔吗？如果上述问题的答案是肯定的，那么为什么会出现这种差异呢？ 几种不同的反馈可以做出贡献，包括与雪量、大气水蒸气、云量和云特性相关的反馈。这些反馈很难量化，因为两个气候变量之间的关系总是与其他变量相互关联。本项目将把联合收割机地面观测和卫星观测与气候模式模拟和神经网络分析方案相结合，以便（1）量化有助于高海拔地区温度反馈的一些主要关系，以及（2）调查这些关系和反馈在整个世纪如何响应大气温室气体的增加而变化。重点将放在西藏高原和科罗拉多西南部的落基山脉。神经网络分析计算气候变量对之间的偏导数（例如，向下的长波辐射和云量），从而可以确定反馈回路中各个环节的强度。这项工作的更广泛影响包括：（1）神经网络可应用于其他区域，使研究人员能够量化气候系统中的重要反馈并分析非线性过程;（2）通过结合地面观测和卫星观测，将为研究界提供一个新的空间和时间扩展观测数据库;（3）更好地了解山区的气候变化，将通过改善影响高海拔地区水资源、农业、旅游业和生态系统未来的管理做法，使公众受益;（4）将支持一名高中教师与调查人员合作，帮助开发和实施关于山区和气候变化的播客;（5）将为一名博士后研究员和本科生提供培训;（6）将与科罗拉多的山区研究所合作编制教育材料。