According to the authors, "Our analysis shows that "data homogenization for [temperature] stations moved from downtowns to suburbs can lead to a significant overestimate of rising trends of surface air temperature."
The paper corroborates the prior work of Anthony Watts, Joseph D'Aleo, et al, finding leading meteorological institutions in the USA and around the world have so systematically tampered with instrumental temperature data that it cannot be safely said that there has been any significant net “global warming” in the 20th century.
Theoretical and Applied Climatology February 2014, Volume 115, Issue 3-4, pp 365-373,
Open Access
Effect of data homogenization on estimate of temperature trend: a case of Huairou station in Beijing Municipality
Lei Zhang,
Guo-Yu Ren,
Yu-Yu Ren,
Ai-Ying Zhang,
Zi-Ying Chu,
Ya-Qing Zhou
Download PDF (531 KB)
Abstract
Daily minimum temperature (Tmin) and maximum temperature (Tmax) data of Huairou station in Beijing from 1960 to 2008 are examined and adjusted for inhomogeneities by applying the data of two nearby reference stations. Urban effects on the linear trends of the original and adjusted temperature series are estimated and compared. Results show that relocations of station cause obvious discontinuities in the data series, and one of the discontinuities for Tmin are highly significant when the station was moved from downtown to suburb in 1996. The daily Tmin and Tmax data are adjusted for the inhomogeneities. The mean annual Tmin and Tmax at Huairou station drop by 1.377°C and 0.271°C respectively after homogenization. The adjustments for Tmin are larger than those for Tmax, especially in winter, and the seasonal differences of the adjustments are generally more obvious for Tmin than for Tmax. Urban effects on annual mean Tmin and Tmax trends are −0.004°C/10 year and −0.035°C/10 year respectively for the original data, but they increase to 0.388°C/10 year and 0.096°C/10 year respectively for the adjusted data. The increase is more significant for the annual mean Tmin series. Urban contributions to the overall trends of annual mean Tmin and Tmax reach 100% and 28.8% respectively for the adjusted data. Our analysis shows that data homogenization for the stations moved from downtowns to suburbs can lead to a significant overestimate of rising trends of surface air temperature, and this necessitates a careful evaluation and adjustment for urban biases before the data are applied in analyses of local and regional climate change
Related:
New paper asks: 'Would the 'real' temperature dataset please stand up?'; finds 'We have no ability to know' the true temperature data
Theoretical and Applied Climatology February 2014, Volume 115, Issue 3-4, pp 365-373,
Open Access
Effect of data homogenization on estimate of temperature trend: a case of Huairou station in Beijing Municipality
Lei Zhang,
Guo-Yu Ren,
Yu-Yu Ren,
Ai-Ying Zhang,
Zi-Ying Chu,
Ya-Qing Zhou
Download PDF (531 KB)
Abstract
Daily minimum temperature (Tmin) and maximum temperature (Tmax) data of Huairou station in Beijing from 1960 to 2008 are examined and adjusted for inhomogeneities by applying the data of two nearby reference stations. Urban effects on the linear trends of the original and adjusted temperature series are estimated and compared. Results show that relocations of station cause obvious discontinuities in the data series, and one of the discontinuities for Tmin are highly significant when the station was moved from downtown to suburb in 1996. The daily Tmin and Tmax data are adjusted for the inhomogeneities. The mean annual Tmin and Tmax at Huairou station drop by 1.377°C and 0.271°C respectively after homogenization. The adjustments for Tmin are larger than those for Tmax, especially in winter, and the seasonal differences of the adjustments are generally more obvious for Tmin than for Tmax. Urban effects on annual mean Tmin and Tmax trends are −0.004°C/10 year and −0.035°C/10 year respectively for the original data, but they increase to 0.388°C/10 year and 0.096°C/10 year respectively for the adjusted data. The increase is more significant for the annual mean Tmin series. Urban contributions to the overall trends of annual mean Tmin and Tmax reach 100% and 28.8% respectively for the adjusted data. Our analysis shows that data homogenization for the stations moved from downtowns to suburbs can lead to a significant overestimate of rising trends of surface air temperature, and this necessitates a careful evaluation and adjustment for urban biases before the data are applied in analyses of local and regional climate change
New paper asks: 'Would the 'real' temperature dataset please stand up?'; finds 'We have no ability to know' the true temperature data
It's Official: 2013 was the 4th7th10th107th... warmest year since the Little Ice Age
Would You Like Your Temperature Data Homogenized, or Pasteurized?
[Illustrations, footnotes and references available in PDF version]
Updated August 27, 2010 This paper is, as intended, a work in progress as a compilation of what’s current and important relative to the data sets used for formulating and implementing unprecedented policy decisions seeking a radical transformation of our society and institutions.
Authors veteran meteorologists Joe D’Aleo and Anthony Watts analyzed temperature records from all around the world for a major SPPI paper, Surface Temperature Records – Policy-driven Deception? The startling conclusion that we cannot tell whether there was any significant “global warming” at all in the 20th century is based on numerous astonishing examples of manipulation and exaggeration of the true level and rate of “global warming”.
That is to say, leading meteorological institutions in the USA and around the world have so systematically tampered with instrumental temperature data that it cannot be safely said that there has been any significant net “global warming” in the 20th century.
"it cannot be safely said that there has been any significant net “global warming” in the 20th century."
ReplyDeleteseriously? denying the world has warmed?
http://wattsupwiththat.com/2014/01/29/important-study-on-temperature-adjustments-homogenization-can-lead-to-a-significant-overestimate-of-rising-trends-of-surface-air-temperature/
ReplyDeleteThe authors wanted to accurately study the influence of the urban heat island effect and thus removed the cooling effect of two relocations using a special homogenization method. Without this method the importance of urbanization would have been underestimated.
ReplyDeleteThis special homogenization method is not used for global climate data, where the aim is to remove the effect of urbanization as well. Thus this post is unfortunately inaccurate. For details, please have a look at my blog.