Anthony Watts put his blog on hold for two days because he had to work on an urgent project.
Something’s happened. From now until Sunday July 29th, around Noon PST, WUWT will be suspending publishing. At that time, there will be a major announcement that I’m sure will attract a broad global interest due to its controversial and unprecedented nature.What has happened? Anthony Watts, President of IntelliWeather has co-written a manuscript and a press release! As Mr. Watts is a fan of review by bloggers, here is my first reaction after looking through the figures and the abstract.
Press release from WUWT:
The new improved assessment, for the years 1979 to 2008, yields a trend of +0.155C per decade from the high quality sites, a +0.248 C per decade trend for poorly sited locationsIn his press release, Anthony Watts does not explicitly state that these trends are for raw data. The manuscript does state this important "detail". The poorly sited locations are likely in cities where it is more difficult to find good locations. Thus what he found is that the Urban Heat Island (UHI) effect exists. I did not know that this was controversial.
[UPDATE: Maybe it does not have to be due to trends from the UHI. There are two other factors.
1) A fine alternative explanation would be that the location quality ratings were performed at the end and may thus be biased. Bad stations at the end of the period may have been better before and thus cooler. And Good stations at the end, may be been worse before and thus show a smaller trend in the raw data.
2) Rabett Run points out that the stronger time of observation bias for the rural stations is probably the main cause of the difference in the trends in the raw data. Even Steve McIntyre, co-author of the Watts et al. manuscript and blogger at Climate Audit, sees neglecting the TOB as a serious problem.]
Good news is that the study finds that after homogenization, the station quality is no longer a problem for the mean temperature. As you can see in Figure 17 of the paper below, after homogenization (adjustment) the trends for all stations show about the same trend, whether they are in urban, semi-urban or a rural environment.
Figure. Top left panel of Figure
And from the full Figure
Table. The mean temperature trends from Figure
|Class 1/2||Class 3/4/5||Class 3||Class 4||Class 5|
Also in Falls et al. (2011), which was co-authored by Anthony Watts, it was found that the homogenized mean temperatures had about the same trend for all quality classes (no significant differences were found). Also in Falls et al. this trend was about 0.3 °C per decade. Also in Falls et al. the tend in the raw data was 0.1 °C per decade smaller. Thus I cannot see this manuscript as unprecedented. Leroy (2012) will be happy that his new siting quality classification seems to work better as judged by the larger difference in the trends between the categories. That seems to be the main novelty. This result is worth a paper, I am not sure if it worth a press release.
In the press release it is also emphasised that the temperature trend after homogenization is stronger than in the raw data. Maybe Mr Watts thinks this is new, but, e.g., Menne et al. (2009) already stated that the introduction of automatic weather stations (the transition from Liquid in Glass thermometers to the maximum–minimum temperature system) caused a temperature decrease in the raw data of 0.3 to 0.4 °C. This temperature jump has to be and was removed by homogenization.
Had the new study found clear differences in the temperature trend in the homogenized data, the study would have been interesting for the general public. Because it is the homogenized data that used to compute large scale trends in the real climate. If the homogenized data would still be partially polluted by the urban heat island effect that would have been an error. The aim of homogenization is exactly to remove artificial changes from the raw data. It seems to do so successfully, now acknowledged by WUWT the second time.
If I were reviewer of this manuscript my main question would be to clarify the statement in the abstract that "[u]sing the new Leroy (2010) classification system on the older siting metadata used by Fall et al. (2011), Menne et al. (2010), and Muller et al. (2012), yields dramatically different results." If this relates to the climatologically important homogenized temperature trends, this statement does not seem to fit with the results. If this statement only relates to the raw data, this is an important disclaimer that should not be missing in an abstract.
StylePutting a manuscript on your homepage and inviting colleagues to review it is a good idea and can improve the manuscript. Sometimes press interest before publication is unavoidable. For example when CERN finds that the speed of light can be exceeded or finds a God particle. I am not sure whether the finding that the weather station classification scheme of Leroy (2010) is better than Leroy (1999) is of this category.
The press release does not explain why there was suddenly such a hurry, why WUWT had to be interrupted for two days and a press release had to be released on a Sunday, a day on which journalists will find it difficult to get a second opinion from a scientist. I hope my judgement of the manuscript is fair, I had only little time.
[Update: Maybe I was too fast to conclude that the paper shows that trends are due to the UHI. This is the part of the homogenization literature that I am not so familiar with yet; this literature is frighteningly huge. A fine alternative explanation would be that the location quality ratings were performed at the end and may thus be biased to stations where the location became worse.]
More posts on homogenisation
- Statistical homogenisation for dummies
- A primer on statistical homogenisation with many pictures.
- Homogenisation of monthly and annual data from surface stations
- A short description of the causes of inhomogeneities in climate data (non-climatic variability) and how to remove it using the relative homogenisation approach.
- New article: Benchmarking homogenisation algorithms for monthly data
- Raw climate records contain changes due to non-climatic factors, such as relocations of stations or changes in instrumentation. This post introduces an article that tested how well such non-climatic factors can be removed.
- A short introduction to the time of observation bias and its correction
- The time of observation bias is an important cause of inhomogeneities in temperature data.
- HUME: Homogenisation, Uncertainty Measures and Extreme weather
- Proposal for future research in homogenisation of climate network data.
Related external articles
- Initial thoughts on the Watts et al draft
- A very good summary of the main problems found in Watts et al. up to now.
- This Has Become Farce
- Appraisal of blog review (Quark Soup by David Appell).
- Bunny bait
- Rabett Run points out that the known stronger time of observation bias for the rural may be the main cause of the difference in the trends in the raw data.
- Two climate papers get hyped first, reviewed later. Isn’t that a bad idea?
- A well-written piece in the Washington Post, about the speed of light, Anthony Watts and Richard Muller.
- Watts et al: Clunk
- The sound of the drama queen's preprint hitting the Internet (Quark Soup by David Appell).
- Comments on the game changer new paper by Watts et al. 2012
- Roger Pielke Sr. rightly praises his friend Anthony Watts for his work on SurfaceStations.org.
ReferencesFall, S., Watts, A., Nielsen‐Gammon, J. Jones, E. Niyogi, D. Christy, J. and Pielke, R.A. Sr., 2011, Analysis of the impacts of station exposure on the U.S. Historical Climatology Network temperatures and temperature trends, Journal of Geophysical Research, 116, D14120, doi: 10.1029/2010JD015146, 2011.
Leroy, M., 1999: Classification d’un site. Note Technique no. 35. Direction des Systèmes d’Observation, Météo-France, 12 pp.
Leroy, M., 2010: Siting Classification for Surface Observing Stations on Land, Climate, and Upper-air Observations JMA/WMO Workshop on Quality Management in Surface, Tokyo, Japan 27-30 July 2010 http://www.jma.go.jp/jma/en/Activities/qmws_2010/CountryReport/CS202_Leroy.pdf
Menne, Matthew J., Claude N. Williams, Russell S. Vose, 2009: The U.S. Historical Climatology Network Monthly Temperature Data, Version 2. Bull. Amer. Meteor. Soc., 90, 993–1007. doi: 10.1175/2008BAMS2613.1.
Watts, Anthony, Evan Jones, Stephen McIntyre, John R. Christy, [plus additional co-authors that will be named at the time of submission to the journal]. An area and distance weighted analysis of the impacts of station 1 exposure on the U.S. Historical Climatology Network temperatures and 2 temperature trends. To be submitted, 2012.