The paper appears to affirm a number of criticisms of skeptics that station losses, fabricated/infilled data, and positively-biased 'adjustments' to temperature data have created a positive skew to the data and overestimation of warming during the 20th and 21st centuries.
Graphs from the paper below show that use of both valid and 'non-valid' station data results in a mean annual Northern Hemisphere temperature over 1C warmer at the end of the record in 2013 as compared to use of 'valid' weather station data exclusively.
In addition, the paper shows that use of the sharply decreasing number of stations with valid data produces a huge spike in Northern Hemisphere temperatures around ~2004, which is in sharp contrast to much more comprehensive satellite data showing a 'pause' or even cooling over the same period, further calling into question the quality of even the 'valid' land-based stations (urban heat island effects perhaps?).
Using temperature data from "valid" stations only, and a base period of 1961-1990, the warmest temperatures were in the first half of the 20th century. |
Highlights
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- Introduce the concept of a valid station and use for computations.
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- Define indices for data quality and seasonal bias and use for data evaluation.
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- Compute averages for mean and five point summary plus standard deviations.
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- Indicate a monotonically decreasing data quality after the year 1969.
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- Observe an overestimation of temperature after including non-valid stations.
Abstract
Starting from a set of 6190 meteorological stations we are choosing 6130 of them and only for Northern Hemisphere we are computing average values for absolute annual Mean, Minimum, Q1, Median, Q3,Maximum temperature plus their standard deviations for years 1800–2013, while we use 4887 stations and 389 467 rows of complete yearly data. The data quality and the seasonal bias indices are defined and used in order to evaluate our dataset. After the year 1969 the data quality is monotonically decreasing while the seasonal bias is positive in most of the cases. An Extreme Value Distribution estimation is performed for minimum and maximum values, giving some upper bounds for both of them and indicating a big magnitude for temperature changes. Finally suggestions for improving the quality of meteorological data are presented.
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