Decoding the precision of historical temperature observations
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CitationRhines, Andrew, Martin P. Tingley, Karen A. McKinnon, and Peter Huybers. 2015. Decoding the Precision of Historical Temperature Observations. Q.J.R. Meteorol. Soc. 141, no. 693: 2923–2933. Portico. doi:10.1002/qj.2612. http://dx.doi.org/10.1002/qj.2612.
AbstractHistorical observations of temperature underpin our ability to monitor Earth’s climate. We identify a pervasive issue in archived observations from surface stations, wherein the use of varying conventions for units and precision has led to distorted distributions of the data. Apart from the original precision being generally unknown, the majority of archived temperature data are found to be misaligned with the original measurements because of rounding on a Fahrenheit scale, conversion to Celsius, and re-rounding. Furthermore, we show that commonly used statistical methods including quantile regression are sensitive to the finite precision and to double-rounding of the data after unit conversion. To remedy these issues, we present a Hidden Markov Model that uses the differing frequencies of specific recorded values to recover the most likely original precision and units associated with each observation. This precision-decoding algorithm is used to infer the precision of the 644 million daily surface temperature observations in the Global Historical Climate Network database, providing more accurate values for the 63% of samples found to have been biased by double-rounding. The average absolute bias correction across the dataset is 0.018 ◦C, and the average inferred precision is 0.41 ◦C, even though data are archived at 0.1 ◦C precision. These results permit better inference of when record temperatures occurred, correction of rounding effects, and identification of inhomogeneities in surface temperature time series, amongst other applications. The precision-decoding algorithm is generally applicable to rounded observations–including surface pressure, humidity, precipitation, and other temperature data–thereby offering the potential to improve quality-control procedures for many datasets.
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