<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<metadata xml:lang="en">
<Esri>
<CreaDate>20230518</CreaDate>
<CreaTime>13504500</CreaTime>
<ArcGISFormat>1.0</ArcGISFormat>
<SyncOnce>TRUE</SyncOnce>
</Esri>
<dataIdInfo>
<idCitation>
<resTitle>Temperature _Max_ – Change btw RCP 4_5 Mid and RCP 8_5 Mid_Century _degrees Fahrenheit_</resTitle>
</idCitation>
<idAbs/>
<idCredit/>
<idPurp>The difference in the annual average of maximum daily temperatures between the RCP4.5 and RCP8.5 climate scenarios at mid-century (2045-2054). The annual average for each time period is produced by taking the maximum temperature for each day (the daily high) and calculating an annual average, and then using this value to calculate an ensemble mean across the ten years included in a time period and the three model outputs. The annual average for the mid-century period under lower emissions (RCP4.5) is then subtracted from the mid-century high emissions projections (RCP8.5).
</idPurp>
<resConst>
<Consts>
<useLimit/>
</Consts>
</resConst>
</dataIdInfo>
<Binary>
<Thumbnail>
<Data EsriPropertyType="PictureX">/9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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==</Data>
</Thumbnail>
</Binary>
</metadata>
