U.S.-Australian Cooperative Research: Climate Prediction Design for Agricultural Production

该奖项将支持伊利诺伊大学/州水调查部门的Peter Lamb博士和澳大利亚气象局研究中心的Neville Nicholls博士之间的合作研究。这项研究是为了扩展和扩大伊利诺伊大学/州水调查研究项目，该项目涉及中西部农业生长季节气候预测的设计。在这个合作项目中，美国的努力将扩大到解决澳大利亚东北部高粱生产的气候预测设计问题。这项新的研究将增强美国中西部工作开发的广泛的经济建模能力。它还将尝试利用厄尔尼诺-南方涛动（ENSO）现象对澳大利亚东北部降雨施加的强大控制所固有的大量生长期气候预测技能。该项目将为美国中西部地区（生长季节气候难以预测）的结果提供一个视角，更好地理解当前可实现的气候预测技能的价值，为澳大利亚农业其他部门的气候预测系统的设计和评估提供一种操作模式。对于生长期降雨受ENSO强烈控制的发展中国家也是如此。该项目将澳大利亚大气科学家的专业知识、澳大利亚基于南方涛动（SO）的季节性降雨数据与美国研究团队广泛的经济建模能力结合起来，代表了卓越的合作。这是气候预测设计研究的重要基础工作，也具有提高农业生产力的潜力。

This award will support collaborative research between Dr. Peter Lamb of the University of Illinois/State Water Survey Division and Dr. Neville Nicholls of the Australian Bureau of Meterorology Research Centre. This research is to extend and broaden a University of Illinois/State Water Survey research program concerned with the design of growing season climate predictions for midwestern agriculture. In this cooperative project, the U.S. effort will be extended to address the climate prediction design issue for grain sorghum production in northeastern Australia. This new research will enhance the extensive economic modeling capabilities developed by the U.S. midwestern work. It will also attempt to exploit the substantial growing season climate prediction skill that is inherent in the strong control that the El Nino-Southern Oscillation (ENSO) phenomenon exerts on northeastern Australian rainfall. The project will provide a perspective on results reached for the U.S. Midwest (where the growing season climate is less predictable), furnish a better understanding of the value of currently achievable climate prediction skill, provide a modus operandi for designing and valuing climate prediction systems for other sectors of Australian agriculture, as well as for developing nations whose growing season rainfall is strongly controlled by ENSO. The project represents excellent collaboration in combining Australian atmospheric scientist expertise and Australian data on Southern Oscillation (SO)-based seasonal rainfall with the extensive economic modeling capabilities of the U.S.research team. This is important fundamental work on climate prediction design research, as well as having potential for improving agricultural productivity.