Subseasonal Heatwave Prediction
Summertime heat wave activity has been on the rise over previous decades (Gershunov et al. 2009, Gershunov and
Guirguis 2012) with impacts on California's resources and economy via their effects on energy, agriculture, and public
health. Although heat waves have received most of the attention in summertime, when they occur on top of seasonally
warmest temperatures, the strongest regional long-term warming has occurred in spring over the mountainous
Western U.S., including California (e.g. Cayan et al. 2001, Cayan et al. 2013). This springtime warming has been
linked with dwindling snowpack (e.g. Knowles et al. 2006) and, although springtime heat waves have not received
scientific attention, we expect that springtime warming has come hand-in-hand with accentuated heat wave activity.
The question we propose to explore here is to what extent snowpack melt is a gradual process due to seasonal
warming and to what extent it comes in spurts driven by springtime and early summer heat waves. Are there
predictable pre-conditions that favor smooth versus episodic snowmelt? These questions have bearing on water
resources and their management in that gradual snowpack melt is amenable to efficient capture and storage in
engineered reservoirs, while strong episodic melting can be more challenging to manage and store and can lead to
We propose to first explore the heat-melt dynamics via multidisciplinary statistical analysis techniques applied to
historical observations of daily temperature (Tmax and Tmin), snow water equivalent (SWE), and fluctuations in
reservoir storage. We then propose to identify the modes of climate variability as they are manifested in Pacific sea
surface temperatures (SST) that impact heat wave activity over the mountainous West, and California's Sierra Nevada
in particular. We will then quantify seasonal predictability of heat wave activity in association with these modes and
quantify extended-range sub-seasonal predictability o
Need and Benefit
With climate change and S2S forecasting becoming more prevalent in the discussion of water planning and
management, our research strategy seeks to understand and exploit predictability of heat waves during the spring
snowmelt season to help Reclamation water managers make enhanced reservoir and river system operational
decisions. During the past nine years, every state in the Western United States has experienced drought that have
affected the economy both locally and throughout the United States through agricultural production, water supply, and
energy. This underscores the importance of effective and efficient water management. By virtue of the snowmelt
dominated nature of many basins with Reclamation projects, information pertaining to snow extent and melt
characteristics is of similar broad importance and value. Improved information on snowmelt timing, variability, and
magnitude will aid water managers in meeting the many competing demands on their system (e.g. water temperature,
water deliveries, hydropower, recreation, and environmental flows) with variable and scarce supplies.
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