Inherent Predictability of Observed Seasonal Mean Precipitation Variations Over the Continental United States

美国大陆观测到的季节平均降水量变化的固有可预测性

• 批准号: 0958907
• 负责人: Bruce Anderson
• 金额: $ 40.52万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-15 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0958907&HistoricalAwards=false
• 关键词: Inherent; Predictability; Observed; Seasonal; Mean

This project seeks to quantify the inherent predictability of observed precipitation variations over the U.S. on seasonal to multi-decadal timescales. The questions to be addressed include: 1) What amount of observed seasonal-mean precipitation variability is uniquely related to variations in the background state of the observed system during a given time-period? 2) When and where do these inherently predictable variations in seasonal-mean precipitation occur within the U.S.? 3) What is the inherent predictability of short-term (12-months) and long-term (12-month) drought occurrences? 4) Do numerical climate models adequately reproduce the inherent predictability of precipitation variations over the U.S.?To answer these questions, the investigators will (i) construct stochastic weather-generation models based upon observed station-based daily precipitation, which can be used to establish the envelope of variability that arises solely from the random behavior of precipitation events, (ii) identify observed interannual to multi-decadal scale variations in seasonal-mean precipitation across the U.S. that lie outside this envelope produced by chance, (iii) use the observed and stochastically-generated daily-precipitation time-series to detect inherently-predictable variations in the severity of drought, as represented by the Standard Precipitation Index, and (vi) evaluate numerical model capability in reproducing the observed inherent predictability of seasonal-mean precipitation and drought over the U.S., using daily precipitation estimates from numerical model representations of the climate of the 20th century.The broader impacts of this project will include improving our confidence in climate forecasts for many regions of the U.S by identifying "hot-spot" regions of historical predictability that are strongly influenced by climate-change processes. Further it will establish the groundwork for a better understanding of the role that regional- and large-scale atmospheric circulations and ocean- and land-surface forcings play in modifying regional precipitation on interannual to multi-decadal time-scales. It will also guide future investigations into numerical model systems' portrayal of physical mechanisms that give rise to observed predictability. Finally, it will help determine the applicability of using stochastic weather-generation models for identifying and analyzing inherently predictable precipitation variations in other regions of the world.

该项目致力于量化美国观测到的降水变化在季节到数十年的时间尺度上的内在可预测性。要解决的问题包括：1)观测到的季节平均降水变率与给定时间段内观测系统背景状态的变化唯一相关的量是多少？2)这些季节平均降水的固有可预测变化发生在美国境内的何时何地？3)短期(12个月)和长期(12个月)干旱发生的内在可预测性是什么？4)数值气候模式是否充分再现了美国降水变化的内在可预测性？为了回答这些问题，研究人员将(I)基于观测站的日降雨量构建随机天气生成模型，该模型可用于建立仅由降水事件的随机行为引起的可变性的包络，(Ii)识别美国各地季节平均降水量的观测到的年际到数十年尺度的变化，这些变化位于偶然产生的包络之外，(Iii)使用观测和随机生成的日降雨量时间序列来检测由标准降水指数表示的干旱严重程度的内在可预测变化，和(Vi)使用20世纪气候的数值模式表示的每日降水量估计，评估数值模式再现观测到的美国季节性平均降水量和干旱的内在可预测性的能力。该项目的更广泛影响将包括通过确定受气候变化过程强烈影响的历史可预测性的“热点”区域，提高我们对美国许多地区气候预报的信心。此外，它将为更好地了解区域和大尺度大气环流以及海洋和陆地表面强迫在改变年际至数十年时间尺度上的区域降水方面所发挥的作用奠定基础。它还将指导未来对数值模式系统对产生观测到的可预测性的物理机制的描述的研究。最后，它将有助于确定使用随机天气生成模型来识别和分析世界其他地区自然可预测的降水变化的适用性。