Estimating Recent Climatic Change via Historical Air-Temperature Frequency Distributions

Indiana University, Bloomington IN

Investigators

Abstract

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.