Data Methods: Extreme Heat Season

Using Cal-Adapt's open source Python library climakitae, input data was retrieved. Climate model simulations were retrieved for historical and future global warming levels (GWL): 0.8, 1.5, 2.0, 2.5, and 3.0 °C. This retrieval process pulls the 30 year time window around the year (+/- 15 years) in which the global warming level is reached. A 30 year window is used to look at typical conditions over a long period centered on the global warming level.

Inland water was masked from the data to discard artificially high values over water that are not representative of the variable. Leap days were removed so that every year has 365 days.

For percentile-based thresholds, a per-gridcell historical temperature threshold was calculated using the 0.8 GWL for each simulation. This value represents an extreme heat threshold unique to the historical conditions at each location.

For each fixed maximum temperature threshold, or historically calculated percentile threshold, the number of years in which each calendar day exceeds the threshold was counted across the 30-year window for each GWL, for each day of the year from 1 (January 1) to 365 (December 31). This count is expressed as a percent of the 30 years. Absolute thresholds range from 65 to 135 °F and percentile-based thresholds from the 90th to the 99th percentile.

Next, we aggregated the gridded data by spatial unit (e.g. county, city, etc) by taking a median of all the grid cells within the boundary. A median is used to capture the typical frequency of extreme heat days within the area of interest without introducing bias from any anomalous grid cells.

Finally, the data was aggregated across simulations using a mean. The final data product is a table for each county containing the following variable:

• Percent of years in which the day is an extreme heat day (for each GWL, threshold, and day of year)