Cowtan_global_land_digitized_decadal.zip

   size       date   time    filename                                    description
--------+------------------+-------------------------------------------+------------------------------------------
 287803   02/02/2015 02:24   Cowtan_global_land_unadjusted.png           graph (screenshot)
 412690   02/06/2015 06:34   Cowtan_global_land_digitized_decadal.png    graph with digitized points marked
   5575   02/06/2015 05:09   Cowtan_global_land_digitized_decadal.json   WebPlotDigitizer saved calibration & data
   1310   02/06/2015 04:35   Cowtan_global_land_digitized_decadal.csv    data points: 24 unadjusted, then 24 adjusted
   1192   02/06/2015 05:21   Cowtan_global_land_digitized_decadal2.csv   data points: year, unadj_temp, adj_temp
  17920   02/06/2015 07:23   Cowtan_global_land_digitized_decadal1.xls   spreadsheet
  22258   02/06/2015 06:05   Cowtan_global_land_digitized_decadal1.htm   spreadsheet exported to web page
  22235   02/06/2015 06:22   Cowtan_global_land_digitized_decadal1.html  prettified spreadsheet exported to web page

Cowtan_global_land_unadjusted.png is a screenshot captured from Dr. Kevin
Cowtan's video entitled, "NOAA Paraguay data"
https://www.youtube.com/watch?v=qRFz8merXEA

The screenshot depicts a graph with two plots of global land surface
temperature data: unadjusted data in red, and NOAA's adjusted data in pink,
from 1900 to 2014.

Using WebPlotDigitizer, I digitized the temperatures for each plot at ten
year intervals, from each end.  That is, I digitized the unadjusted and
adjusted temperatures for each year ending in "0" or "4":
1900, 1904, 1910, 1914, 1920, 1924, 1930, 1934, 1940, 1944, 1950, 1954,
1960, 1964, 1970, 1974, 1980, 1984, 1990, 1994, 2000, 2004, 2010, 2014.

I included years ending in "0" so that decadal trends could be calculated
starting with year 1900, which is the earliest year in Dr. Cowtan's graph.
I included years ending in "4" so that decadal trends could be calculated
ending with year 2014, which is the latest year for which data exists.
(I didn't include the other years because digitizing all that data is very
tedious!)

I loaded all the data into a spreadsheet in Excel, with these column headers:
year   unadj_temp   adj_temp

I then added ten calculated columns:

adj-unadj = the difference between adjusted and unadjusted temperature

50yr_unadj_diff = the difference in unadjusted temperatures between the
designated year and fifty years earlier. If positive, it indicates warming
compared to fifty years earlier; if negative, it indicates cooling.

50yr_adj_diff = the difference in adjusted temperatures between the
designated year and fifty years earlier.

50yr_%warm_from_adj = the percentage of the warming which is due to the
adjustments (shown as "100%" if the unadjusted data indicated cooling).

70yr_unadj_diff = the difference in unadjusted temperatures between the
designated year and seventy years earlier.

70yr_adj_diff = the difference in adjusted temperatures between the
designated year and seventy years earlier.

70yr_%war_from_adj = the percentage of the warming which is due to the
adjustments.

114yr_unadj_diff = the difference in unadjusted temperatures between 2014
and 1900.

114yr_adj_diff = the difference in adjusted temperatures between 2014
and 1900.

114yr_%war_from_adj = the percentage of the warming which is due to the
adjustments.

114yr_increase_by_adj = the percentage by which the adjustments increased
the reported warming.