"We find new methods informed by progressive learning to remove bias. "
If that were the case, you would apply increased weighting on weather stations that have been the most compliant (double readers, no change in location, no change due to rapid development in the area resulting in an urban heat island effect. But that is not what climate 'scientists' do. They given greater weight to the global readings despite that record not even being anywhere close to complete until the 1960's with the oceans completely uncovered until Argo was implemented. So, I call BS on that rationalization.
"100 years ago, they relied on mercury."
Again, you are correcting the temperature readings now from 100 years prior based on them using mercury? Really? I don't think you even understand how the temperature has been corrected. Nothing to do with mercury, has to do with applying 'smoothing' records where you discount actual readings. So, while the actual temperature records that are reported on your local news are the same as recorded at that point in time, you climate 'scientists' are saying - "no, it was not 106 degrees in Kansas on that day, it was actually 95 degrees. It is ridiculous.
"Now we have ensembles of sensors within a single unit, we quantify the variance, and find aggreement through methods like Kalman filters, quantile mapping, et al. Modern methods are quantitative and objective.
#15 | POSTED BY HORSTNGRABEN"
The fact is that NONE of that is required now as you can take satellite readings. Further, we have Argo and the weather balloon data to know the changes at the atmospheric levels. And those numbers simple do not support the underlying model of global warming.
Here is a graph showing the data manipulation plotted. realclimatescience.com
Now, before slaughtering the source, we are not even talking about their 'analysis' of the data - it is simply the plots of the raw data and the manipulated data.
Now mind you - I am not making any statement on the validity of Global Warming - I am just factually stating that the case for it using DATA ANALYSIS is sorely lacking and our models have been a disaster in forecast accuracy.