Natural Variations and Forced Changes in Historical and Future Precipitation and Drought

这个项目的重点是气候变化的检测和归属，这意味着区分强制气候变化和自然发生的气候系统内部变异性的任务。进行这种划分的方法是，获取20世纪和21世纪气候变化模拟的大型集合，并使用其集合平均值作为对强制气候变化的估计。假设集合平均值给出了强迫变化的足够准确的表示，这个估计可以从观测记录中提取出来，以揭示自然的可变性。探测和归属方法被应用于全球和区域空间尺度上的变量，包括地面气温、降水量和自校准的帕尔默干旱严重程度指数(ScPDSI)。将用于本研究的气候模式集成集合来自耦合模式相互比较项目(CMIP5)的第5版。集合平均值中的强迫气候变化信号是使用主成分分析确定的，与趋势分析不同，主成分分析不假设气候变化在时间上线性进行，并且可以捕捉由于强迫气候变化的季节性而导致的年度循环强度的变化等特征。使用包括简单减法、回归和最大协方差分析在内的方法从观测记录中提取模型集合中的强迫信号。将研究强迫变化和自然变化的区域模式(例如美国西南部的干旱)与气候变化的大尺度模式的关系，包括年代际太平洋涛动(IPO)、大西洋年代际振荡(AMO)和厄尔尼诺/南方涛动(ENSO)事件。进一步的工作将试图通过异常大气环流建立海表面温度(SST)模式与陆地变化(特别是干旱)之间的联系。最后，一旦自然变率与强迫气候变化分离，将对自然变率模式在十年时间尺度上统计气候预测的潜在价值进行评估。这项工作旨在区分自然变率和强迫变化在观测气候记录中的相对贡献，这一区分具有重大的社会价值。将强制变化和内部变化分开将非常有助于规划目的，因为自然变化的组成部分可能在某个时候逆转，而强制的长期变化可能是永久性的。该项目还试图确定与气候变化的自然模式有关的预测技能，任何这类技能都将对受气候变化影响的各种利益攸关方有用。此外，国际和平协会就气候和干旱的潜在变化向包括媒体、K-12学生和教师以及农民在内的受众开展了各种外联工作，并将根据这一奖项继续这样做。该奖项将支持和培训两名研究生，从而为这一研究领域的未来劳动力提供支持。

AbstractThis project is focused on the detection and attribution of climate change, meaning the task of distinguishing forced climate change from the naturally occurring internal variability of the climate system. The method for making this separation is to take a large ensemble of climate change simulations for the 20th and 21st centuries and use its ensemble mean as an estimate of forced climate change. Assuming that the ensemble mean gives a sufficiently accurate representation of the forced change, this estimate can be factored out of the observed record to reveal the natural variability. The detection and attribution methodology is applied to variables including surface air temperature, precipitation, and the self-calibrated Palmer Drought Severity Index (scPDSI), on both global and regional spatial scales. The ensemble of climate model integrations to be used for the study comes from version 5 of the Coupled Model Intercomparison Project (CMIP5). The forced climate change signal in the ensemble mean is determined using principle component analysis, which unlike trend analysis does not assume that climate change proceeds linearly in time, and can capture features such as changes in the strength of the annual cycle due to the seasonality of forced climate change. The forced signal from the model ensemble would be factored out of the observed record using methods including simple subtraction, regression, and maximum covariance analysis. Regional patterns of forced change and natural variability (drought in the Southwest US, for one) would be examined for relationships with large-scale modes of climate variability including the Interdecadal Pacific Oscillation (IPO), the Atlantic Multidecadal Oscillation (AMO), and El Nino/Southern Oscillation (ENSO) events. Further work would attempt to establish mechanisms relating sea surface temperature (SST) patterns to changes over land (drought, in particular) through anomalous atmospheric circulations. Finally, once the natural variability is separated from the forced climate change, an assessment would be made of the potential value of the natural variability modes for statistical climate prediction on decadal timescales.The work seeks to distinguish the relative contributions of natural variability and forced change in the observed climate record, a distinction which is of great societal value. A separation of forced change and internal variability would be quite helpful for planning purposes, as the natural variability component is likely to reverse at some point, while the forced secular changes will likely be permanent. The project also seeks to identify the predictive skill associated with natural modes of climate variability, and any such skill would be useful for a variety of stakeholders affected by variations in climate. In addition, the PI has conducted a variety of outreach efforts to audiences including the media, K-12 students and teachers, and farmers, regarding potential changes in climate and drought, and will continue to do so under this award. The award will support and train two graduate students, thereby providing for the future workforce in this research area.