Recurrent patterns behind extreme heatwaves in a warming climate

<p>Data generated and used in the &#39;Recurrent patterns behind extreme heatwaves in a warming climate&#39; by Prashant Neel.&nbsp;</p> <p>Spreadsheets are labelled by region (PNW or Siberia) and Scenario SSP1-2.6 or SSP2-4.5 from the EC-Earth model output. Heatwave days for this study are&nbsp; 28^th June 2021 in PNW, 20^th June 2020 in Siberia and 25th July 1988 in Siberia.</p> <p>1. MaxTemp_ spreadsheets show daily maximum temperature calculated by averaging the maximum 2m surface temperatures over a region from 50&deg;N to 90&deg;N and 60&deg;E to 180&deg;E in Siberia, the area containing the town of Verkhoyansk and bordered by high relief, and from 65&deg;N to 70&deg;N and 130&deg;W to 137&deg;W in the Pacific Northwest (PNW).&nbsp;</p> <p>2. _corr_ spreadsheets show the generated pattern correlation&nbsp;for daily mean 500 hPa geopotential height (z500) data for June and July days against the z500 data for the two heatwaves. For Siberia, this was performed over the region of 50&deg;N to 90&deg;N and 60&deg;E to 180&deg;E and for the PNW this was 30&deg;N to 70&deg;N and 70&deg;W to 145&deg;W. The latitude-corrected Pearson correlation coefficient was found for each day compared to the heatwave day (28^th June 2021 in PNW and 20^th June 2020 in Siberia). Those labelled 1988 were for the Siberian heatwave on 25th July 1988.</p> <p>3. The &#39;wavenumber_reanalysis&#39; spreadsheet shows the predominant Rossby wavenumber for the reanalysis data for the week preceding each day. Daily mean June and July ERA5 reanalysis data was averaged over 35&deg;N-70&deg;N to create a single value per longitude. A rolling 7-day mean was then be found, truncating the first 6 days of June each year. Then, the fast fourier transform (FFT) was performed to generate amplitudes of each wavenumber for a given day, based on the average v250 of the week prior. The amplitudes were converted to standard deviations from the mean.&nbsp;</p> <p>&nbsp;</p> <p>4. Spreadsheets ending in &#39;_all&#39; summarise all this data for each region and scenario.</p> <p>&nbsp;</p> <p>&nbsp;</p>