Using a Daily Homogenized Temperature Product to Assess Long-Term Trends in Extreme Heat Events and Associated Health Impacts in the United States

Land surface air temperature products have been essential for monitoring the evolution of the climate system. Before a temperature dataset is included in such reports, it is important that non-climatic influences be removed or changed so the dataset is considered homogenous. These inhomogeneities include changes in station location, instrumentation and observing practices. While many homogenized products exist on the monthly time scale, few daily products exist, due to the complication of removing breakpoints that are truly inhomogeneous rather than solely by chance (for example, sharp changes due to synoptic conditions). Recently, a sub monthly homogenized dataset has been developed using data and software provided by NOAA’s National Centers for Environmental Information (NCEI).

Homogeneous daily data are useful for identification and attribution of extreme heat events over a period of time. Projections of increasing temperatures are expected to result in corresponding increases in the frequency, duration, and intensity of extreme heat events. It is also established that extreme heat events can have significant public health impacts, including short-term increases in mortality and morbidity. In addition, it can exacerbate chronic health conditions in vulnerable populations, including renal and cardiovascular issues. To understand how heat events impact a specific population, it will be important to connect observations on the duration and intensity of extreme heat events with health impacts data including insurance claims and hospital admissions data. Developing a robust understanding of the health impacts of heat events will allow public health officials to develop adaptation measures to increase the resilience of vulnerable populations.

The goal of this presentation is to a) describe the methodology to identify extreme heat events using daily homogeneous temperature data, b) provide a climatology of heat event onset, length and severity, and c) explore a case study of an anomalous heat event with available health data.