NASA's «GISS» temp uses land and ocean - based thermometers which measure «different parts of the system [UHI affected parking lots, asphalt heat sinks, AC exhaust air vents], different signal to noise ratio [we bias toward warm stations],
different structural uncertainty [we «homogenise» our data set to cool the past and warm the present to fit the global warming narrative].»
Not exact matches
There are basically two key points (explored in more depth here)-- comparisons should be «like with like», and
different sources of
uncertainty should be clear, whether
uncertainties are related to «weather» and / or
structural uncertainty in either the observations or the models.
We can derive the underlying trend related to external forcings from the GCMs — for each model, the underlying trend can be derived from the ensemble mean (averaging over the
different phases of ENSO in each simulation), and looking at the spread in the ensemble mean trend across models gives information about the
uncertainties in the model response (the «
structural»
uncertainty) and also about the forcing
uncertainty — since models will (in practice) have slightly
different realisations of the (uncertain) net forcing (principally related to aerosols).
Structural uncertainty is attenuated when convergent results are obtained from a variety of
different models using
different methods, and also when results rely more on direct observations (data) rather than on calculations.
This study addresses the challenge by undertaking a formal detection and attribution analysis of SCE changes based on several observational datasets with
different structural characteristics, in order to account for the substantial observational
uncertainty.