Ross McKitrick writes:
I give a demonstration of why the Parker and BEST analyses don’t disprove the evidence of contamination of temperature data, and outline what it would likely take to settle the issue properly.
Cheers, Ross
ENCOMPASSING TESTS OF SOCIOECONOMIC SIGNALS IN SURFACE CLIMATE DATA: I have a new paper out in Climatic Change on the question of whether surface climate data are biased by non-climatic factors relating to socioeconomic development:
Rather than try to settle the debate once and for all, I focus on why the various attempts to show the data are not contaminated do not disprove the results showing that they are. The problem has been that authors use non-overlapping data sets and different methods, and end up talking past each other. The way to settle matters, I argue, is to adopt an encompassing framework in which both types of results can be demonstrated on the same data set, where one arises in a restricted subset of another model, and the restrictions can formally be tested.
I give two examples, one replicating a Parker-style equivalence between nighttime minimum trends in calm and windy conditions, then showing that this persists in a temperature data set that can be shown to be correlated with population growth. I also replicate the BEST-type results that rural trends are slightly greater than those of urban areas, and show that this result appears in a restricted subset of a larger model in which socioeconomic growth is significantly correlated with temperature trends. In both cases the restrictions necessary to yield the model that supposedly shows no data contamination are rejected. Data/code archive here.
Posted at http://www.rossmckitrick.com/.
McKitrick, Ross R. (2013) Encompassing Tests of Socioeconomic Signals in Surface Climate Data
Climatic Change doi 10.1007/s10584-013-0793-5
http://link.springer.com/article/10.1007%2Fs10584-013-0793-5
Abstract
The debate over whether urbanization and related socioeconomic developments affect large-scale surface climate trends is stalemated with incommensurable arguments. Each side can appeal to supporting evidence based on statistical models that do not overlap, yielding inferences that merely conflict but do not refute one another. I argue that such debates are only be resolved in an encompassing framework, in which both types of results can be demonstrated as restricted forms of the same statistical model, and the restrictions can be tested. The issues under debate make such data sets challenging to construct, but I give two illustrative examples. First, insignificant differences in warming trends in urban temperature data during windy and calm conditions are shown in a restricted model whose general form shows temperature data to be strongly affected by local population growth. Second, an apparent equivalence between trends in a data set stratified by a static measure of urbanization is shown to be a restricted finding in a model whose general form indicates significant influence of local socioeconomic development on temperatures.
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I have just read an interesting book that bears on this subject. I read a book “With Brass and Gas” by Munson Baldwin. This is a history of ballooning in the 19th century. In this book toward the end he relates from period articles some of the things that make a balloon go up and down. Over water the balloons lose altitude (These were all hydrogen balloons) due to the cooler air over water. The balloons gain altitude over towns, fields, and even individual farms due to the warmer air over these locations.
This phenomenon has never properly been documented temperature wise much less integrated into regional or general circulation models…
Ross – I think it would have been worthwhile pointing out that Watts 2012 did demonstrate that surface climate data are biased by non-climatic factors relating to station siting, and that factors relating to socioeconomic development are only another possible source of bias from non-climatic factors, not the only one.
Now don’t you suppose the Chinese knew what was going on in their cities?
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No one disputes urban areas are warmer, but the AGWers argue that there is no evidence for an increasing trend in urban temperatures. This is partly true.
They miss that the urban influence in the warming in the global surface data is mainly due to increasing urbanization, both horizontally and vertically (taller buildings). Population changes are a good proxy for urbanization changes.
Is part of the point here that on windy days, the UHI effect will be dispersed over a wide area and therefore the upward impact on temperature will be severely diluted within the heat source urban area? I guess that makes sense.
The point then is you need to use a dataset on non-windy days or nights, which immediately opens you up to charges of cherry picking. Worse still, it would probably give the alarmist data keepers the opportunity to somehow manipulate the past into being cooler yet again.
Data-diddling leaves fingerprints? Who knew?
The other issue is that raise urban temperatures are then given a equal weighting with rural temps that would generally cover a much larger area per station.
Urban areas occupy but a small percentage of the land surface and should ONLY be applied to the area they represent. There is a large urban bias in the global surface temperature calculation because of this.
Slightly OT But Nuticelli is still ascribing the Moscow heatwave in 2010 to AGW.
http://discussion.guardian.co.uk/comment-permalink/24402058
The idea that it is measurement errors, UHI or natural variability is verboten.
I tried to post a link to NOAA pointing out that it was probably natural variability but am currently banned.
http://www.noaanews.noaa.gov/stories2011/20110309_russianheatwave.html
However, another post has been deleted as well. They weren’t on ‘pre–moderation’ so they can be seen to be deleted.
It might be funny if a whole string of deleted comments turned up. It would be obvious that he was on weak ground… he he he.
Here’s an example of urban bias… a simple little calc that even AGW catastrophists may be able to manage.
Suppose in an area of 1000km^2 there are 3 temp stations.
Station A shows an increase of 1 deg C in 10 years
Station B shows an increase of 1.2 deg C in 10 years
Station C shows a decrease of 0.2 C in 10 years
What is the average temperature change for the region in those 10 years?
Now suppose I tell you that A is in an urban area of 50 km^2
and B is in another urban area of 100km^2
C is a rural temp unaffected by UHI rises.
What is the area weighted average temperature rise ?
An of course, under GISS or similar, they would probably “homogenise” the rural reading up to something like 0.8C increase in 10 year (totally changing the real rural temperature trend) to sort of match the 2 urban temps, then calculate on unweighted areas.. which gives a change of .. ?
Then they would apply an “adjustment” that lowers 10 year old temps by 0.3 degrees
And suddenly …. !!
Assuming that satellite data are insensitive to urban heat island effects. I wonder if it can be used to settle the whole thing. Maybe it’s possible to select a grid where the strongest urbanisation has taken place after 1979, the start of the satellite data, and compare the local temp trend with the satellite trends.
Look at slide 52 here:
https://docs.google.com/viewer?url=http://wattsupwiththat.files.wordpress.com/2012/07/watts-et-al-station-siting-7-29-12.ppt
That’s an order of magnitude difference from the official. 10 (ten) times difference.
The very definition of urban vs. rural is silly, that is, e.g. all settlements with less than ten thousand inhabitants are classified “rural”. At the same time it was amply demonstrated that even pretty small settlements may have considerable UHI, roughly proportional to the logarithm of local population density (~0.25°C/doubling). This relation only breaks down at a very low threshold, happily exceeded by most rural sites.
Therefore what counts is not immediate population density, but its trend over the last century. As global population has doubled almost twice during that timespan and the entire world has got urbanized, temporal UHI must be present almost everywhere around people.
It is a well known fact global population distribution is fractal-like and measurement points (weather stations) are not randomly distributed over land surface relative to this fractal. This is so, because it is much cheaper to operate / maintain stations close to spots of habitation and/or economic activity (like airports) than in the middle of nowhere.
Ross, there’s a small typo here.
‘I argue that such debates are only be resolved in an encompassing framework’
Land surface warming is highly variable given the chaotic weather patterns that overlay it and the onfluence of UHI effects amonst many other things.
But there are no socio-economic factors in ocean heat content, it represents at least 90% of the energy accumulated by the effect of rising CO2 and shows no sign of urban effects of any oth anthropogenic influence except the warming from the rising anthropogenic CO2.
http://onlinelibrary.wiley.com/doi/10.1002/grl.50382/abstract
Volcanic eruptions and El Niño events are identified as sharp cooling events punctuating a long-term ocean warming trend, while heating continues during the recent upper-ocean-warming hiatus, but the heat is absorbed in the deeper ocean. In the last decade, about 30% of the warming has occurred below 700 m, contributing significantly to an acceleration of the warming trend. The warming below 700 m remains even when the Argo observing system is withdrawn although the trends are reduced.
I am monitoring temperatures in my garden, one sensor in a shady corner the other next to the house. They are within 50ft of each other. temperature differences vary with conditions and the maximum difference between the max. temperatures is 5C. This is far more than I expected but an indication that UHI is alive and well and that all temperature data must be accurately taken and recorded on temperature sensors that conform to basic positional standards to try to remove UHI errors.
@ur momisugly johnmarshall
Trouble is, how do you accurately identify all UHI factors and remove them from the temperature station. They may be very close to the device, (eg evap units), there may be a UHI effect over a larger area. Could also be prevailing wind issues at a particular time of day in certain weather patterns.. You just don’t know !! UHI exists, but is obviously VERY badly accounted for.
This is what makes the land surface readings so meaningless, especially once you let GISS or HadCrud at them !!
On but sort of off-topic to this: Skeptics have been talking about UHI for years… and yet, True Believers are now shrieking that we deny the effect of UHI? I’m surprised there has been nothing on that farce.
@- AndyG55
“This is what makes the land surface readings so meaningless, especially once you let GISS or HadCrud at them !!”
Until recently land surface weather stations were intended to measure the macroscopic changes in weather from day to day and season to season. They were certainly NOt designed to detect small trends over decades and its a minor miricale that ANY significant data can be derived from them.
Deriving the global warming signal from ocean temperatures, moving plant type regions and the land ice mass balance is probably a lot more reliable than the extensive processing required to find the warming trend in data from surface temperature measurements.
However, even given the inadequacy of the surface temperature record in recording the observed warming, it is still possible to see that it is confirmed by data sources that are not contaminated by any UHI or siting issues. The surface trend without a UHI component is confirmed by measurement by satellite of global troposphere trends and sea surface trends.
Neither of which have had any significant urban build-up over the period in question.
“not contaminated by any UHI or siting issues. ”
BS !!
izen says:
June 18, 2013 at 2:55 am
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The satellite data, as you suggest, does not have the same contamination problems. Apart from the step change in and around the 1998 super El Nino it is all but flat both before and after that event. There is no first order correlation seen in that record between rising CO2 levels and temperatures (at least not unless the Super El Nono of 1998 was caused by CO2, which I do not understand any one suggests). The satellit data therefore suggests that the warming seen in the land based thermometer record between say 1979 and about 1996/7 may well be the result of contamination of that record by UHI, siting issues, station drop outs and questionable adjustments/homogenisation.
Ocean temperature is the only important data since this deals with energy and because of the heat capacity of the oceans compared to that of the atmosphere. However, pre ARGO the data is riddled with uncertainties, inconsistencies and potential errors bars are so high that it is not fit for purpose (ie., it cannot shed light on whether the oceans at any layer have warmed to any significant extent). ARGO may be able to provide this info, but the duration of the data set is too short, there is still insufficient coverage given the vastness of the oceans, issues abound with its calibrationand splicing onto earlier ocean temp data sets, and of course, ARGO buoys drift such that they never make like for like measurements and the drift may in itself lead to a bias (the drifting may be influenced by currents which themselves have a distinct temperature profile different to the ocean at large).
HadCrud and Giss have clearly climbed more than RSS and UHA atmospheric temperatures.
Now since HadCrud and GISS have not been subject to any adjustments, this is an obvious sign of urban heat effects.
http://www.woodfortrees.org/plot/hadcrut4gl/from:1979/plot/rss/from:1979/plot/uah/from:1979/plot/gistemp/from:1979
QED.
John Silver says: @ur momisugly June 18, 2013 at 1:03 am
Look at slide 52 here:
https://docs.google.com/viewer?url=http://wattsupwiththat.files.wordpress.com/2012/07/watts-et-al-station-siting-7-29-12.ppt….
>>>>>>>>>>>>>>>>>>>>>>>>>>
John, I get
and then a request to LOG in
Whilst discussing UHI, Tokyoboy recently posted a comment on another thread to the effect that last century Tokyo warmed by 3degC whilst an adjacent Japanese Island just 180km away showed no warming.
I suspect that Tokyo is one of the weather sites used in the world series, and the weather station on the adjacent nearby Japanese Island is not used.
izen says:
June 18, 2013 at 1:58 am
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This response seems to be citing the Trenbreth, et al. abstract. Isn’t this the missing heat paper that tries to reconcile a calculated energy balance discrepancy by putting it into deep ocean? I’m not sure I quite understand how you can warm the deep ocean without warming the surface. Nor do I understand how this supposed effect negates any discussion on the effects of population density on measured temperature changes.
Every day on BBC weather forecast the presenter says ” tonight it will be X degrees C in the countryside with the the towns and cities 2 or 3 degrees warmer….”.
Quelle surprise!