Climate is changing; climate has always changed. It is a chaotic system where local microclimate effects can be strong. How can any global average be sure to represent adequately the local nuances and variations (anomalies)? In the existing input data, the spatial and temporal coverage is highly variable. What spatial resolution is needed to capture local responses to changes in long-term weather patterns? How can one station, say in the lee of mountains and in an arid rain shadow, area adequately represent an entire region, when another 1-200km away with completely different altitude, cloud cover, rain/humidity and temperature responds differently but is only present in the record for a fraction of the time of the arid one?
Using anomalies is necessary yes, but how can you be sure the response of these two stations will track each other over long periods? The program requires an overlap of only 20 years minimum when they both report. What GIStemp does is assume that one station will always be a good proxy for others in the area.
If climatic systems are cyclical (whether regular or not), modelling in engineering systems aspires to at least 3 full cycles to show stability of the relationship between two datasets. We just don’t have that quantity of data.
Other examples: the Arctic; China; Turkey. You might say these are cherrypicks, but ask yourself this – if a cycle shows up in one station but an adjacent one has continuous warming, which one is right? Is this not a case of the differences I suggest above? In this case should we not be concerned if one or other of them drops out of the data record used by GIStemp?
This was a response to a blog comment (thread here) about GIStemp and the calculation of global average temperatures, er, anomalies. Stopping by at E.M.Smith’s blog this evening I found he had just posted a link and discussion of agricultural degree days which kind of supported the point I was making. By overlaying climate stations on a degree day map, we can look at the spatial resolution of data reporting with respect to temperature variations within the landscape. Does this look like adequate coverage?
In the USA I would expect coverage to be adequate (although I will probably follow up and look at temporal coverage also – the ‘Station Drop-out’ issue). For other parts of the world not so.
As anna v commented yesterday evening on WUWT:
“… a 1C anomaly in a region that has an average temperature of 273K has a completely different physical manifestation than a 1C anomaly where the average temperature is 288K.”
So the question is – are all anomalies created equal?