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But are the climate scientists always correcting obvious errors in the data and reducing measurement error when they "clean" the data? In many--probably most--cases, they are, but the mismanagement of some of the data and the politicking in the climate research community does raise doubts--in my mind, at least. When you combine opacity of methods and lack of reproducibility in climate data management with sensitivity of climate models to inputs--to say nothing of the incentives resulting from politicization--you're leaning pretty hard on the integrity and the infallibility of the climate scientists.


Don Wheeler has made similar points many times in his books and articles. His advice, like yours above, is to first check the data for homogeneity (using process behavior charts). Coupled with this is his admonition that all outliers are evidence...though the evidence may be of problems in data collection rather than the process being studied.

I think that this first step in data analysis is under-appreciated, even among the scientific community. Perhaps the techniques for checking data are not taught in a rigorous way, and as a result, everyone cleans their data out of necessity, but no one is comfortable defending the process.

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