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No, there doesn't need to be a justification or a "theory borne out by observation" for this particular method any more than we need a "theory borne out by obse

No, there doesn't need to be a justification or a "theory borne out by observation" for this particular method any more than we need a "theory borne out by observation" for the distributive method in algebra. There's no gentle way to say this, but: you're trying to argue in a field in which you clearly have even less understanding than I do. The "adjustments" made to the graph do not make it any less accurate for what it is representing; it is not just fiddling around with numbers or making up data. It's a very simple, and very well-understood, way to represent data. > These effects can cause an increase in measured temperatures over time even if overall the local climate saw no increase in temperature. How does that even make sense? If the human activities at the airport location are causing an increase in temperatures at the airport, then that by definition is an increase in the temperature of the local climate. If the temperature is simply slightly higher than surrounding areas, but the overall trend of the temperature is not, then it can still be adjusted into a graph with temperature data from surrounding areas using the exact same methods that were used in the linked example.

InclinedPlane 17y ago
These adjustments are more than just putting the distributive method into practice. If you have 2 separate time series which do not overlap and you are attempting to put forward a theory about the long-term trend of the underlying data, then you very much do need to provide a justification for any adjustment you make to each time series. How does that even make sense? If the human activities at the airport location are causing an increase in temperatures at the airport, then that by definition is an increase in the temperature of the local climate. Climate and local weather are not the same. If I build a parking lot the local temperature may increase by several degrees C on average, yet this represents an inconsequential change to the local climate within a radius of, say, 100km. More so, it represents an inconsequential change to the climate of the entire Earth. The significance of the urban heat island effect with relation to temperature records is that the proportion of weather stations which are affected by an increased urban heat island effect (due to increased urbanization) exceeds the percentage of the Earth affected by many orders of magnitude. Yes, urban heat island effects can be accounted for, but I think the criticism is that for the most part they have not been.

InclinedPlane 17y ago

These adjustments are more than just putting the distributive method into practice. If you have 2 separate time series which do not overlap and you are attempting to put forward a theory about the long-term trend of the underlying data, then you very much do need to provide a justification for any adjustment you make to each time series. How does that even make sense? If the human activities at the airport location are causing an increase in temperatures at the airport, then that by definition is an increase in the temperature of the local climate. Climate and local weather are not the same. If I build a parking lot the local temperature may increase by several degrees C on average, yet this represents an inconsequential change to the local climate within a radius of, say, 100km. More so, it represents an inconsequential change to the climate of the entire Earth. The significance of the urban heat island effect with relation to temperature records is that the proportion of weather stations which are affected by an increased urban heat island effect (due to increased urbanization) exceeds the percentage of the Earth affected by many orders of magnitude. Yes, urban heat island effects can be accounted for, but I think the criticism is that for the most part they have not been.