From the “we told you so” department comes this paper out of China that quantifies many of the very problems with the US and global surface temperature record we have been discussing for years: the adjustments add more warming than the global warming signal itself
A paper just published in Theoretical and Applied Climatology finds that the data homogenization techniques commonly used to adjust temperature records for moving stations and the urban heat island effect [UHI] can result in a “significant” exaggeration of warming trends in the homogenized record.
The effect of homogenization is clear and quite pronounced. What they found in China is based on how NOAA treats homogenization of the surface temperature record.
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.”
Basically what they are saying here is that the heat sink effect of all the concrete and asphalt surrounding the station swamps the diurnal variation of the station, and when it is moved away, the true diurnal variation returns, and then the homogenization methodology falsely adjusts the signal in a way that increases the trend.
You can see the heat sink swamping of the diurnal signal in the worst stations, Class 5, nearest urban centers in the graphs below. Compare urban, semi-urban, and rural for Class 5 stations, the effect of the larger UHI heat sink on the Tmax and Tmin is evident.
In Zhang et al, they study what happens when a station is moved from an urban to rural environment. An analogy in the USA would be what happened to the signal of those rooftop stations in the center of the city, such as in Columbia, SC when the station was moved to a a more rural setting.
The Zhang et al paper studies a move of Huairou station in Beijing from 1960 to 2008, and the resultant increases in trend that result from the adjustments from homgenization being applied, resulting in a greater trend. They find:
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.
The figures 4 and 5 from the paper are telling for the effect on trend:
Now here is the really interesting part, they propose a mechanism for the increase in trend, via the adjustments, and illustrate it.
The larger effects of relocations, homogenization, and urbanization on Tmin data series than on Tmax data series in a larger extent explain the “asymmetry” in daytime and nighttime SAT trends at Huairou station, and the urban effect is also a major contributor to the DTR decline as implied in the “asymmetry” changes of the annual mean Tmin and Tmax for the homogeneityadjusted data at the station.
In my draft paper of 2012 (now nearing completion with all of the feedback/criticisms we received dealt with, thank you. It is a complete rework. ), we pointed out how much adjustments, including homogenization, added to the trend of the USCHN network in the USA. This map from the draft paper pretty much says it all: the adjusted data trend is about twice as warm as the trend of stations (compliant thermometers) that have had the least impact of siting, UHI, and moves:
The Zhang et al paper is open access, an well worth reading. Let’s hope Petersen, Karl, and Menne at NCDC (whose papers are cited as references in this new paper) read it, for they are quite stubborn in insisting that their methodology solves all the ills of the dodgy surface temperature record, when it fact it creates more unrecognized problems in addition to the ones it solves.
Effect of data homogenization on estimate of temperature trend: a case of Huairou station in Beijing Municipality Theoretical and Applied Climatology February 2014, Volume 115, Issue 3-4, pp 365-373,
Lei Zhang, Guo-Yu Ren, Yu-Yu Ren, Ai-Ying Zhang, Zi-Ying Chu, Ya-Qing Zhou
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
Download the PDF (531 KB) Open Access
h/t to The Hockey Schtick
UPDATE 1/30/14: Credit where it is due, Steve McIntyre found and graphed the physical response to station moves three years ago with this comment at Climate Audit.
Here’s another way to think about the effect.
Let’s suppose that you have a station originally in a smallish city which increases in population and that the station moves in two discrete steps to the suburbs. Let’s suppose that there is a real urbanization effect and that the “natural” landscape is uniform. When the station moves to a more remote suburb, there will be a downward step change. E.g. the following:
The Menne algorithm removes the downward steps, but, in terms of estimating “natural” temperature, the unsliced series would be a better index than concatenating the sliced segments.