Extreme heat waves beset western North America during 2021, including a 46.7°C (116°F) observation in Portland, Oregon, an astonishing 5°C above the previous record. Using Portland as an example we provide evidence for a latent risk of extreme heat waves in the Pacific Northwest (PNW) and along the west coast of the United States where a maritime climate and its intrinsic variations yield a positive skewness in summertime daily maximum temperatures. A generalized Pareto extreme value analysis yields a heavy tailed distribution with a return period of 300-1000 years, indicating that, while rare, the event was possible, contrary to prior claims that the event was “virtually impossible”. We demonstrate that the extreme temperatures can be explained by the coincident extreme values of geopotential heights, and that the relationship between heights and extreme temperatures has not materially changed over the observational record. The dynamical nature of the event along with recent developments in stochastic theory justifies the use of skewed and heavy-tailed distributions which may provide the basis for a more proactive approach to managing the risk of future events.

The Niwot Ridge and Green Lakes Valley (NWT) long-term ecological research (LTER) site collects environmental observations spanning both alpine and subalpine regimes. The first observations began in 1952 and have since expanded to nearly 300 available datasets over an area of 99 km2 within the north-central Colorado Rocky Mountains that include hydrological (n = 101), biological (n = 79), biogeochemical (n = 62), and geographical (n = 56) observations. The NWT LTER database is well suited to support hydrologic investigations that require long-term and interdisciplinary data sets. Experimentation and data collection at the NWT LTER are designed to characterize ecological responses of high-mountain environments to changes in climate, nutrients, and water availability. In addition to the continuation of the many legacy NWT datasets, expansion of the breadth and utility of the NWT LTER database is driven by new initiatives including (a) a catchment-scale sensor network of soil moisture, temperature, humidity, and snow-depth observations to understand hydrologic connectivity and (b) snow-albedo alteration experiments using black carbon to evaluate the effects of snow-disappearance on ecosystems. Together, these observational and experimental datasets provide a substantial foundation for hydrologic studies seeking to understand and predict changes to catchment and local-scale process interactions.