What was done
As early as the 1960s, automated weather systems began to replace human observers as collectors of meteorological data such as temperature and precipitation.  This transition, though helpful in many ways, presents a significant challenge to the climate community because of its potential to introduce systematic biases and inhomogeneities into long-term climate records.  In the present study, the authors examine the effect of automation on the continuity of temperature and precipitation data at five stations located in various climatic zones across Canada.

What was learned
The authors begin with an informative review of known biases that have resulted from the conversion from human to automated weather observation, including those due to (1) different instrument sensitivities, degrees of precision and time-responses when recording environmental variables, (2) data processing algorithms, (3) timing of observations, and (4) siting biases, all of which factors, the authors show, are influenced to different degrees, depending on seasonal and daily meteorological conditions.  Then, over the one-year period of overlap the authors investigated, they report typical temperature biases "on the order of a few tenths of a degree Celsius - very comparable indeed to the global warming estimated at barely 0.5°C per 100 years".  In addition, they report that automated measurement techniques underestimate precipitation by 13% in the moderate-to-heavy precipitation category (greater than 5 mm per day).

What it means
According to the authors, their study emphasizes that "the availability of at least one or two years of concurrent conventional and automated observations is crucial to the development of adjustment factors" that can be used to remove biases introduced into temperature and precipitation data sets by the change from human to automated data collection.  If left in the time series, they say, such biases could "lead to false conclusions or at least seriously impair results in studies of climate trend and variability, detection of climate change, analysis of extreme events, calculation of certain climatological indicies and normals, etc."