Estimating Recent Climatic Change via Historical Air-Temperature Frequency Distributions

年和月平均气温是用来估计最近气候变化的标准参数。传统的关注平均气温的方法忽略了可以从历史气温数据中获得的额外概率信息。最近的气候变化可以通过集中趋势、方差和其他形状测量的各种变化在频率分布中表现出来。该项目将制定一个全面的程序，用于同时检测气温频率分布的所有部分的趋势。该项目将利用48个相邻州的1062个站点的广泛的每日气温数据，开发时变百分比和其他参数的使用，以创新的方式估计最近的气候变化。将开发灵活的程序来估计移动窗口内的百分位数和参数，并评估这些程序的稳健性。温度数据的变化将与湿度和云量的同时变化以及气温日循环形状的变化有关。监测这些协同作用将提供一个气候变化视角，这是对集中趋势的传统关注的实质性延伸。该项目将开发方法来检测仅检测平均温度时不明显的趋势。例如，气温的趋势可能显示平均上升，而频率分布的下半部分显示明显变暖，而上半部分没有变化。这些变化不仅表明气候变暖，而且表明极端温暖的空气温度变化不大，可变性减少。作为一种一般的方法，这项研究将开发新的方法来检测频率分布的变化，这些变化可以应用于在相对较高的时间分辨率下观察到的任何环境变量。考虑到生态、经济和社会系统对变率变化和极端环境事件更为敏感，本研究具有广泛的适用性。所确定的变化将对评估气候变化的经济、社会和生态层面具有价值。特别是对时变百分位数的研究，为环境变化研究提供了一种灵活而有力的方法。

Annual and monthly mean air temperatures are the standard parameters used to estimate recent climatic change. The traditional focus on mean air temperature has ignored the additional probabilistic information that can be derived from historical data on daily temperatures. Recent climatic change can be manifest in frequency distributions through a variety of changes to central tendency, variance, and other shape measures. This project will develop a comprehensive procedure for the simultaneous detection of trends in all portions of air-temperature frequency distributions. The project will use spatially extensive daily air-temperature data for 1,062 stations over the 48 contiguous states to develop the use of time-varying percentiles and other parameters that can estimate recent climatic change in innovative ways. Flexible procedures that estimate percentiles and parameters within moving windows will be developed, and the robustness of these procedures will be evaluated. Changes detected in the temperature data will be related to simultaneous changes in humidity and cloud cover as well as change in the shape of the diurnal cycle of air temperature. Monitoring these synergistic interactions will provide a climate-change perspective that is a substantial extension of the traditional focus on central tendency.This project will develop methods to detect trends that are not apparent when one examines only mean temperatures. For example, trends in air temperature may show an increase in the mean while the lower portion of the frequency distribution shows substantial warming and the upper portion shows no change. These changes would indicate not only a warming climate but also one where there is reduced variability with little change in extremely warm air temperatures. As a general approach, this research will develop new methods for detecting changes in frequency distributions that can be applied to any environmental variable that is observed at relatively high temporal resolution. Considering that ecological, economic, and social systems tend to be more sensitive to changes in variability and extreme environmental events, this research has wide applicability. The changes identified will be of value for evaluating the economic, social, ecological dimensions of climate change. The examination of time-varying percentiles, in particular, provides a flexible and robust approach for environmental change research.