Where the data is more ambiguous is with the long - term multidecadal trends, where such data is easily contaminated by artifacts due to
changes in instrumentation over time, switches from one satellite to another, etc..
Lots of factors make measuring global temperature a difficult task, such as sparse data in remote places, random measurement errors and
changes in instrumentation over time.
The answer to this lies in how the different datasets deal with having little or no data in remote parts of the world, measurement errors,
changes in instrumentation over time and other factors that make capturing global temperature a less - than - straightforward task.
Not exact matches
Secondly, I expect that the
changes in instrumentation, inaccuracy of a single Secchi - depth - to - chlorophyll conversion, and variation
in sampling effort prevent identification of trends
over the last century.
«The identified biases include station moves,
changes in instrumentation, localized
changes in instrumentation location,
changes in observation practices, and evolution of the local and microsite station environment
over time.»
Meteorological data collection is
changing with the time
over land and
in / on oceans with network and
instrumentation changes.