Detecting change in precipitation indices using observed (1977–2016) and modeled future climate data in Portland, Oregon, USA

This study addresses how regional changes to precipitation may be identified by exploring the effect of temporal resolution on trend detection. Climate indices that summarize precipitation characteristics are used with Mann–Kendall monotonic testing to investigate precipitation trends in Portland, Oregon (OR) from 1977 to 2016. Observational records from rain gages are compared with downscaled global climate models to determine trends for the historic (1977–2005) and future (2006–2100) periods. Standard indices created by the Expert Team on Climate Change Detection and Indices (ETCCDI) are deployed. ETCCDI indices that summarize conditions at the annual level are generated alongside a limited number of ETCCDI indices summarized at the monthly level. For the future climate, the indices summarized at the annual level demonstrate trends indicative of an intensifying hydrologic cycle. The historical record depicted by annual indices does not show trends. The historical record is viewed differently by changing the indices to monthly summaries, which causes trend detection to increase and hallmark indicators of an intensifying hydrologic cycle to become apparent. HIGHLIGHTS We used the Expert Team on Climate Change Detection and Indices (ETCCDI) precipitation indices and the Mann–Kendall test to detect trends in precipitation.; We compared observed precipitation records (1977–2005) with future (2006–2100) projections from five downscaled global climate models for the Portland area.; While four of five climate models projected an intensifying hydrologic cycle from 2006 to 2100, trends were not detected in the 1977–2016 observational record.; When data were disaggregated from annual to monthly, many of the hallmarks of an intensified hydrologic cycle were observed in the 1977–2016 Portland record in spring, winter, and fall months.; Trend detection of increasing precipitation intensity was detected more at a finer temporal scale (i.e., hourly data), indicating a finer temporal analysis is critical for urban flood risk management.