For one example, note https://data.giss.nasa.gov/gistemp/graphs_v4/ https://data.giss.nasa.gov/gistemp/graphs_v4/ which leads to http://www.columbia.edu/~mhs119

For one example, note https://data.giss.nasa.gov/gistemp/graphs_v4/ https://data.giss.nasa.gov/gistemp/graphs_v4/ which leads to http://www.columbia.edu/~mhs119/Temperature/ http://www.columbia.edu/~mhs119/Temperature/ which leads to https://data.giss.nasa.gov/gistemp/ https://data.giss.nasa.gov/gistemp/ which leads to https://data.giss.nasa.gov/gistemp/sources_v4/ https://data.giss.nasa.gov/gistemp/sources_v4/ which should give you some non-source controlled Python code translated from Fortran code which tried to use a weighted average from stations in something like a 1250 mile radius based on the unproved assertion that deviations from a 30-year mean were correlated well even though levels were not. Representative comment: In Step 5: 8000 subboxes are combined into 80 boxes, and ocean data is combined with land data; boxes are combined into latitudinal zones (including hemispheric and global zones); annual and seasonal anomalies are computed from monthly anomalies. I can't believe I missed this: https://data.giss.nasa.gov/gistemp/sources_v4/gistemp.html https://data.giss.nasa.gov/gistemp/sources_v4/gistemp.html: > In 2007, David Jones' team, Climate Code Foundation, reprogrammed the whole procedure in python, as a product of the Clear Climate Code project and the Climate Code Foundation, http://clearclimatecode.org/ http://clearclimatecode.org/ That's all the source control you'll ever get. Don't bother clicking on that link.