Climate Analysis and Extended Range Seasonal Prediction

摘要ATM-9417563 Gray，William M. 科罗拉多州立大学题目：气候分析和扩展范围季节性预测 该项目涉及分析和进一步发展与下列方面有关的基本科学概念：大西洋盆地季节性飓风活动、非洲萨赫勒季节性降雨、厄尔尼诺/南方涛动变化、亚洲某些区域季风降雨以及中纬度地区季节性可预测性的潜力的大范围定量气候预测计划。 沿着关于基本机制的基础科学，这项工作寻求9-14个月预测周转时间的准确性，足以允许制定和实施有意义的政策和缓解行动。 将对潜在气候前兆信号的频谱进行详尽的探索。 潜在的可预测性将以后报方式进行研究，主要是利用过去35至45年（主要是1950年以来）的全球数据集，但也对更早的数据进行一些扩展。 同样，将广泛探讨使用最小绝对偏差和普通最小二乘法程序组合预测信号的统计回归方法。 虽然令人惊讶的熟练的季节后报测试结果已经获得，这些结果表明，更大的预测技能是可能的，通过进一步分析与气候变量的其他组合。 在这项研究中，同样强调的是针对诊断工作，以更好地了解负责远程前兆信号的基本大气和海洋过程。 这项研究很重要，因为它旨在提高我们预测具有重大经济和人类安全重要性的气候变化的能力。

Abstract ATM-9417563 Gray, William M. Colorado State University Title: Climate Analysis and Extended Range Seasonal Prediction This project involves the analysis and further development of basic scientific concepts related to extended range quantitative climatic forecast schemes for Atlantic basin seasonal hurricane activity, African Sahel seasonal rainfall, ENSO variations, selective regional Asian monsoon rainfall, and the potential for seasonal predictability in the middle latitudes. Along with the basic science concerning underlying mechanisms, this work seeks accuracy at 9-14 month forecast lead times, sufficient to allow the development and implementation of meaningful policy and mitigating actions. The spectrum of potential climate precursor signals will be exhaustively explored. Potential predictability will be studied in the hindcast mode, principally from global data sets for the last 35 to 45 years (primarily since 1950), but with some extensions to much earlier data. Similarly, statistical regression methodology for combining predictor signals using both Least Absolute Deviations and Ordinary Least Squares procedures will be extensively explored. Although surprisingly skillful seasonal hindcast test results have been obtained, these results suggest that greater forecast skill is possible through further analyses with additional combinations of climate variables. Equal emphasis in this research is directed toward diagnostic work for greater physical understanding of the underlying atmospheric and oceanic processes responsible for long range precursor signals. This research is important because it seeks to improve our ability to predict climate variations of major economic and human safety importance.