Weather Systems Connecting Modes of Climate Variability to Regional Hydroclimate Extremes

Weather system clustering provides a high-level summary of regional meteorological conditions. Most quantitative clustering schemes focus on precipitation alone, which does not sufficiently describe the meteorological conditions driving hydroclimate variability. This study presents the Weather Anomaly Clustering (WAC-hydro), which extends the existing capability of predicting weather systems to predicting hydroclimate variability. Focusing on both precipitation and temperature predictions, WAC-hydro identifies 12 clusters of daily weather anomaly modes in the US Pacific Northwest Puget Sound region during 1981-2020. The influence of El Nino-Southern Oscillation and Madden-Julian Oscillation on regional precipitation can be well approximated by their modulation on the weather clusters. Within each weather cluster, local factors such as topography only play a secondary role in the hydrologic variability. The weather clusters highlight two types of flood-inducing regional weather conditions, one causing floods by inducing positive precipitation anomalies and the other causing floods through combined precipitation and temperature-induced rain-on-snow effect. Weather systems clustering (WSC) provides a high-level summary of regional weather conditions, and they have wide applications in weather forecasting, climate analysis, disaster preparation and travel planning. Although many WSC algorithms have been developed, most focus on either phenomenological tagging (i.e., tagging each day as sunny/rainy/snow/fog/etc. type) or predicting a single meteorological variable (usually precipitation P) when optimized toward surface meteorological conditions. Given the importance of both P and temperature (T2) in driving land hydroclimatic variability and extreme, a WSC is developed and optimized for concurrent P and T2 prediction. The system (Weather Anomaly Clustering or WAC-hydro) is demonstrated in a US Pacific Northwest watershed and identified 12 different daily weather clusters during the cold season from 1981 to 2020. These weather clusters feature unique combinations of P/T2 conditions, causing differing regional snowpack and runoff responses: one cluster causes more floods by enhancing P, while another causes floods through a combination of enhanced P and warm temperature induced rain-on-snow. Additionally, the weather clusters can link the regional P to well-known modes of climate variability, suggesting that their modulations on regional hydroclimate variability can be estimated using their modulations on the weather systems. Therefore, WAC-hydro can bridge large-scale climate conditions to regional/local hydroclimatic conditions. A new weather system clustering model is developed specifically for hydroclimate analysis and predictionTwelve weather systems in the Puget Sound region connect the regional hydroclimate to major modes of climate variabilityRegional cold season floods are tied to two weather systems, each highlighting unique flood mechanisms

Automated summary

This study introduces a new method called Weather Anomaly Clustering (WAC-hydro) for predicting both precipitation and temperature, which helps link large-scale climate conditions to regional hydroclimate conditions. By identifying 12 clusters of daily weather anomaly modes in the US Pacific Northwest Puget Sound region, this method provides insights into the flood mechanisms and their connections to climate variability modes.