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> 1960s seemed to be unusually cool Note that the set of thermometers and temperatures that make it into the GHCN comprise repeated convenience samples. The te

by nanis 6y ago

> 1960s seemed to be unusually cool Note that the set of thermometers and temperatures that make it into the GHCN comprise repeated convenience samples. The techniques used to smear individual observations over vast swathes of territory tend to leave much to be desired. Here's an animation[1] I made way back when I thought maybe thinking scientifically about how to evaluate the existing climate record for the recent past was acceptable. I have not touched the GHCN recently nor have I updated the analyses, but it did not take much time to produce the videos many years ago. Anyone curious should be able to repeat the same thing of plotting coordinates of temperature stations with fresh data with no trouble. So, for the original question, the answer is we do not know. However, data are subject to selection bias and that affects what kind of inferences you can make. But do compare the distribution of observation sources in the 60s to periods before and after. [1]: https://www.youtube.com/watch?v=h95uvT67bNg https://www.youtube.com/watch?v=h95uvT67bNg GHCNv3 (20141118 qca) Temperature Station Locations 1702-2014

blakebreeder 6y ago
>The techniques used to smear individual observations over vast swathes of territory tend to leave much to be desired. I am by no means an expert, but after farting around with climate data, even today I'm curious how they smear data across comparatively smaller swathes of territory. I've noticed, in my area (south west), that many of the stations are located in seemingly unique microclimates--like the tops of mountains, on the shores of rivers, etc. It roadblocked my layman research because distance to a given station was not very indicative of how that area's climate is.
- nanis 6y ago
  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.

by nanis 6y ago

blakebreeder 6y ago

>The techniques used to smear individual observations over vast swathes of territory tend to leave much to be desired. I am by no means an expert, but after farting around with climate data, even today I'm curious how they smear data across comparatively smaller swathes of territory. I've noticed, in my area (south west), that many of the stations are located in seemingly unique microclimates--like the tops of mountains, on the shores of rivers, etc. It roadblocked my layman research because distance to a given station was not very indicative of how that area's climate is.

nanis 6y ago
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.