Posted by Jeff Id on January 5, 2010
So I’ve learned a great deal playing around with the GHCN data, I think this is a reasonably significant post. Ya know, it’s hard to know anything until you try it yourself and I hope more of the readers here will. Again, there was a problem in my last CRU post, however, the more I look for it, the more avenues there are to explore. The issues have been corrected by avoiding the remaining possibilities in this post.
Of all the details worked out over the last two days, one is a decent gridded average of temperature data. Unfortunately for us skeptics it looks like Figure 1 which is pretty similar to the CRU plot.
Figure 1 – all data gridded
Yes, there is warming according to our temp stations, but I don’t think comrade Phil Climategate Jones would like this curve, because the warming in this curve happens entirely after 1975.
It’s nice to see a good quality CRU similar curve after the previous effort, but that’s how things happen when you do your work in public. The plot above uses all the data with each 5 digit temp station code averaged together individually, as my first post did. Anomaly is calculated over the entire series length.
The concern which was explored in some detail, regarded the hypothesis that the loss of stations in recent years created or biased the trend. It came about since so many stations are lost in recent years as Ken Fritsch pointed out in the recent CRU #3 thread.
Figure 2 – Stations per year GHCN per Ken Fritsch from KNMI database
I’ve run dozens of plots over the last several days, some of which contained an error in them created from data selection or a code problem in my previous post. Using the algorithm which averages together individual station ID numbers, I get very consistent CRUesque patterns. the warming is common to a variety of data sorting processes. This methods avoids the issues of data selection or code problems in the other methods and I’m confident in the accuracy of these results, but you should check them.
Several methods were employed to test the consistencey of result, including sorting for Rural and Urban, and sorting for several different time lengths of station data. All varieties so far produced very similar same results. There are, however, interesting revelations from examination of the slight differences.
Figure 3 – 100% Urban Data
Figure 3 is a plot is the urban data only. Of note is that the warming starts at 1978 with only slight warming beforehand and launches up about 1.2 C with no end in sight. Also, 1982 isn’t much reduced from around 1940 which is different from the global average in Figure 1. So the next thing I did was to plot the rural data.
Figure 4 – Rural station data.
That looks a great deal more like the satellite data. The temp rose and fell again prior to 1978 and rose again since 1978 is maybe 0.5C total. I tend to ignore data prior to 1900 due to the very small number of stations. I don’t think the drop in temps to 1900 levels in the early 70’s is the kind of curve that supports the high CO2 sensitivity claimed by climate science. Does anyone remember the snow storms of the early 70’s? Yeah, yeah just weather, I know.
One of the other avenues explored at great length , yet still isn’t finished, was how station starts and stops affect the trend in recent years. To explore that, one of the several methods I used was to sort data according to number of available data points. Below, I presented the gridded global average for all stations with at least 100 years (1200 points) of available data, since many of the stations in Figure 2 were started in 1950.
Figure 5 – Urban gridded data from stations with at least 1200 points
The urban data only in Figure 5 has an even steeper curve, you would expect this from longer series in this type of analyis. The temp rise since 1978 is about 1.2C. The rural 100 year curve is below.
Figure 6 Rural temperature stations with at least 1200 points
So the Rural stations show about 0.7C of warming since 1978. Visibly less warming than the urban stations by themselves. Also note the slight downtrend in recent years. Since the industrial revolution occurred a hundred years ago, it’s hard to imagine this curve is created by CO2. Still I’m not denying the heat capturing ability of CO2, just that the curves here don’t show a continuous warming but rather a short term recent spike.
So of course we should look at the difference between urban and rural stations.
Figure 7 – Difference between urban and rural data as identified by GHCN
Figure 8 – Difference between urban and rural data from GHCN stations with at least 100 yrs of data (1200 monthly points)
Look at that curve! Despite the crudeness of the categorization of thermometers, there is a clear warming bias for big city data. The curve in Figure 8 ends at 0.6C difference. What’s more, the trend between the two looks statistically significant. If Phil Climategate Jones and Michael Marx Mann can choose which data they want to show and hide the rest, I think it’s only fair to choose to look at trends only from Figure 8 since 1978 (even though it won’t make much difference). After all, one hundred percent of global warming has apparently occurred since that time. Let’s do a simple significance test.
Figure 9 – Statistical significance between rural and urban stations
Woah, it’s not even close, a trend of 0.12 and a no trend null hypothesis limit of +0.04. The difference between urban and rural warming is as great as the entire trend in UAH data over the same timeperiod.
Just how much trend do the ground stations show.
Figure 10 – All urban stations different Y scale from 11
Figure 11 – All rural stations different Y scale from 10
Even Figure 11 is still greater than UAH and RSS satellite data but it’s one heck of a lot less than the urban stations. Of course we would be remiss to not mention that WUWT has taught us what rural stations often look like.
What could go wrong with sophisticated technology like that?
The R code for this post is here.
The rural temps are also affected by the heat island influence. It is part of a degree, but it takes a flat line into an upward slope.
Jeff’s analysis is spot on – the conjunction of trend, adjustment and location requires more in depth investigation.
I’ve been looking at trends and adjustments in raw and adjusted data for over a week now. They are mapped here (by KevinS):
http://82.42.138.62/MapsNOAA.asp
http://82.42.138.62/MapsGISS.asp
The analysis is not quite ready to post yet, but it is clear there is a lot of warming in the raw data – even the rural data. It is far from uniform.
There is more information on the database that allowed production of these maps here: http://diggingintheclay.blogspot.com/2010/01/climate-database-development.html
Some people have reported difficulties with registration and apologies if that is still a problem. Fixed soon I hope.
I’m currently downloading the daily temp data from 1900-2009 of all 1300 Canadian stations from Envrionment Canada’s website. It’s about half done after 2 weeks. However, I have started to do some evaluations of this daily temp data, and have found something interesting. Indeed, for the one location, Belleville, Ont, the yearly average temperature has increased and followed the basic anomaly graphs we have seen, with an increase up until 1945, followed by a slight decrease until about 1980, followed by an increase until today.
But what does that actually mean? I have discovered that the over all temperature ranges for each year has been narrowing. That is, in the 1940s they had very hot summers, and lots of them (4.1% of the days above 30C), and very cold winters with more than twice the number of days below -20 than today. The number of hot days (over 30) dropped to just 1.7% between 1955 and 1983, then increased to 3.7% and remained there since 1984. The number of days below -20 has consistently dropped since the 1930’s to half.
Thus what we are seeing in the average temperatures is not a physical measurment of an increase in real temperatures, but a narrowing of the variation within the years over time.
We are seeing less cold and shorter winters, with virtually no change in the summer temperatures.
If this methodology can be applied to other areas of Canada, which I will do when I get the rest of the stations, and then applied to other global stations, and the same trend emerges, then AGW is dead in the water. The planet is not heating up at all. Just the variation between extreme temperatures is narrowing. Hardly scary.
I was checking out Charlotte, NC tabular data on the GISS website
Here’s the data
http://data.giss.nasa.gov/work/gistemp/STATIONS//tmp.425723140001.1.1/station.txt
Somehow they are missing July 2009 for Charlotte.
Well they gave a June-July-August temperature average of 25.7 for Charlotte.
That means they gave July a temperature of 26.5
I checked Charlotte out day by day and the temp there for July was 25.0
^^^ GISS somehow has the temperature there a full 1.5 degrees Celsius warmer than it really was for July.
What is really amazing is that the Charlotte data goes from 1881 to present day. Only 3 months are missing (May 1985), (Feb 2004), and (July 2009).
I checked July 2009 and GISS mad some “man-made” global warming to that month. Wonder what their great formula’s are doing to other sites where they “somehow” don’t have the data.
Excellent post Jeff. The basis of AGW is erroneous temperature analysis. The analysis is either mishandled, or intentionally biased. In either case, errors are made in assumptions/treatments of that data.
This post will be highlighted on RealClimate within the hour. The hockey stick returns to life. At least this time it is balanced, nuanced and believable. Great post. Thanks
Jeff, I very much like your blog and visit there often. As you note there seems to be great siting problems with now the majority of GHCN stations being really urban even though classified as rural. Then there are problems with the rural stations with the “new remote sensors” that can’t be located an adequate distance from the “base atation” due to cabling problems. It would be a real treat to read the reasoning for the closing of so many stations (I believe that most of these are still in operation). It would seem that most of the closures coinside with the supposed catastrophic rise in “global warming”.
Soon there will be a major change in our perspectives as it gets cooler and more and more people are drasticly effected by the “climate.”. With the shenagians that have beem noted by the :climate” experts it is no wonder that we know very little about our climate system even with Millions and millions being spent on computer power. When the controling authority over the research is more interested in political correctness and maintaining the cash flow than in finding the truth and knowlege our scientific system has a true problem. We were warned about this in the fare well address of Dwight Esienhouer. I believe he was well aware of what the future could be and rried to give us an insight.
Great Job, I just wish that a collection of data carried forwared from many of those “closed” stations could be collected and if they were mostly of what type and locations.
Thanks for your good work.
Bill Derryberry
(spell checker appears to not be working this morning my appologies.)
Question:How well is the Urban Heat Island Effect compensated for ?
I went to the GISS website and downloaded the temperature record for two locations I know a little about.
Here is the link
http://data.giss.nasa.gov/gistemp/station_data/
The two data sets I downloaded were
Hensley Field [ a Naval air station which started in 1923 and is substantially unchanged]
DFW airport was a cow pasture 1/2 way between Dallas and FW] prior to 1977 when a massive airport was built.
Since they are about 15 miles apart they should have the same temperature differences.
No raw data is available but the least “corrected” data had “USHCN corrections” applied.
DFW airport pattern makes a big “U” witgh the upturn starting in …..You guessed it 1977.
Who would think Jets and runways etc would have an effect on temperature ?
The Hensley fields pattern was an “L”. there was no uptick after 1977.
The “Fully Adjusted data for both don’t change the pattern at all.
Conclusion THE ADJUSTMENTS DON’T REMOVE THE PHONY WARMING FROM THE DFW RECORD..
Since this is the main station for the DFW area the whole area is mis-represented.
This is in the USA with the most accurate surface station records in the world ?
What must Asia be like ?
This is a non peer reviewed study although I have given enough data it can easily be replicated in 20 minutes.
I hate taking even the skeptics assertions at face value.
If you look into bias introduced by stations that have been dropped, you might consider looking into elevation. Since temperature in the atmosphere falls about 6.5 degC per kilometer, there is a significant opportunity for a warming bias to be introduced into the land record when higher elevation stations drop out. (All 20th century warming is roughly equivalent to a 100 m drop in each elevation.)
I don’t know if the algorithms used to extract “higher value” data from the raw temperature record includes elevation. If you have a grid cell with uneven terrain and one of a small number of stations dropped out, there should be a major discontinuity in the record. Step discontinuities in individual station records are often assumed to be due to relocation or equipment change and a given a step-function correction.
what about that wonderful graph from noaa that can be found here:
http://cdiac.ornl.gov/epubs/ndp/ushcn/ts.ushcn_anom25_diffs_urb-raw_pg.gif
Supposedly there is an explanation for all the adjustments but are even half of them capable of meeting common sense validation?
What are the fudge factors tossed into the mix for Jeff’s starting data?
The mid-1970s is when the Clean Air Acts started the progressive reduction in urban particulate pollution (smoke and haze). This produced the observed increasing minimum temperature trend due to increased early morning sunlight reducing minimum temperatures.
Which BTW accounts for well over 100% of the increase in surface temperature since that time. Something even Jones and Karl agree is significant.
http://www.ncdc.noaa.gov/oa/climate/mxmntr/mxmntr.html
Having the most powerful computer ever made means silch, really, crap data fed into crap mathematical models using whatever code will still produce crap.
Is there a definition ready at hand for RURAL and URBAN as far as classification of data points goes? Is the definition universally accepted and applied, or do different groups have different definitions?
Certainly, the notion of “rural” regarding US Census data is NOT always what you would expect.
Has anyone tried to classify the stations into an independent Rural-Urban grouping, on the basis of a GIS analysis of the surrounding land use or population density, rather than a zipcode or county-based classification. (I’m guessing that’s how it’s done now.)
Here’s Jeff Id’s rural temperature plot compared to the CRU data in red. I’ve offset the CRU data to match by eye, but the scales, and hence trends, are identical:
http://img99.imageshack.us/img99/8886/cruvsidrural.png
So the unlikely conclusion: Climate sceptic identifies a higher recent warming trend than CRU!
(Off topic) Not sure it this has been noted before, but I just read on the UK Met Office website the explanation by their chief scientist, Prof. Julia Slingo, of the proof that CO2 causes global warming.
The question put to this “scientist” is:
“How do you know CO2 is responsible for the change in climate and can you prove it? And how do we know that CO2 released is from human activities?”
Her response comes down to this:
“We are now rapidly approaching 390 parts per million which means it’s been a 40% increase. Most of that increase has happened in the last 50 years. And if we know that carbon dioxide is a greenhouse gas, it’s hard to believe that if you increase it by 40% you’re not going to do something to the temperature of the planet.”
So, it’s “hard to believe” that it won’t “do something” – there’s scientific rigor for you!
http://www.metoffice.gov.uk/corporate/pressoffice/in-depth/ask/julia-slingo.pdf
Try riding a motorbike in winter time.Even the warmth from a squirrel fart is perceptable when you are cold enough.
The Location of the above ‘Rural’ station so near to a badly insulated shiplap building with facing windows would certainly skew the figures.
Looks pretty similar to the PDO index with an added high level of TSI to give it a boost.
This is still a good post, to me it is the best real evidence for warming I’ve seen in years.
Of course the warming is only half of what has been claimed and the bias corrections are lacking so that the half a degree that remains is almost certainly a large overstatement but at least this work doesn’t have deliberate fudging of the data. If Philip B is right, as I suspect he is, there may indeed be no evidence for AGW or any warming in the CHCN data.
But, at least it’s possible that we may now learn the truth and it is possible there has been some warming. Prior to this I was starting to think it was all pure baloney.
Which data was used, raw or adjusted ? Because in a high percentage of station records I have checked in the GISS dataset there I have found a negative stair step adjustment with later years being adjusted DOWN significant amounts and gradually stepping up to no or little adjustments in the last decade/s.
I’m still not convinced the raw records, even with the UHI, show anything like the slope published by the warmers. Given that most adjustments I have seen are NOT UHI adjustments (which would grow over time, not shrink) I still think the data is being gamed.
Jeff is your analysis based on raw raw data or adjusted raw data? If it’s really raw raw data may I ask where you got it? I’ve been unable to find it. Even what they call raw is not really raw.
thanks
dave
So, we have diddled data in the code, we have Mann’s 0.5 degree y2k increase, we have the UHIE on both urban and ‘rural’ stations, we have the result of reducing particulates in the atmosphere in the 1970s.
If all of these effects are removed from the data, would would be the result? A hockey stick pointing -down-?
As a non-scienist but someone with a background in engineering I’ve picked up on what I see as two significant comments posted so far.
1) Since the mid 1970’s the winters were warmer but the summers were not hotter.
2) Polution in urban areas was reduced considerably since the mid 1970’s which would help explain much of the urba/rural warmth difference.
Wouldn’t these two facts pretty much account for the warming trend that most of us experienced as “weather” between 1975 and 1998?
Anthony,
I commented on Jeff’s site that I wondered what the relationship of the blade was to the mmts installation rate. Specifically the change in distance from the sensor to the building due to the cable run. Didn’t you have a graph of that data at one time?
REPLY: Not a graph, just an observation about the issue – A
Was the rural data here raw or adjusted? I’m asking because of FILNET. If FILNET was used to fill in missing rural station data, then the data is contaminated by the well known urban station problems.
Is the UAH the best data?
Jeff (07:28:04) :
Raw data was used.
Tom P (07:05:07) :
Identical? You sure?
Before I start, this post has nothing to do with trend but rather a problem in the data potentially created by local heating influence. This post calls into question the station data itself so claiming a trend of one magnitude or another is beside the point.
As you note in the rural plots, all of the warming happened in recent years. This would say CO2 had basically limited warming effect prior to 1978 – unless you use urban data of course. If you need me to explain the point further, it appears that urban bias is a real effect which has significance. It would be foolish to simply assume (eyes closed) that the same bias doesn’t exist in the rural stations as you can see from the last picture.
Scientifically we cannot ignore the reality of the problem, or the recent proof in climategate emails that those in power like Jones are happy to ignore it. In one email a scientist discussed removing points from the temperature curve in his presentations so people wouldn’t see the decline. What we need is good surfacestations.org style QC of the global station data, nothing more, and definitely nothing less.
Richard Wakefield (06:23:29) ….I have also been looking at Environment Canada’s data for the town of Smithers in central BC…..I have found that from 1943 to 2009 the summer and the winter have a different trends…a bit odd
For summer it is 0.013 deg-c/year
for winter it is 0.026 deg c/year
In other words summers have not really warmed up much but the winters have…???…the airport at Smithers is out of town (google earth)
Jeff ID: “I’m confident in the accuracy of these results, but you should check them.”
Now there is something you will never read in any Team literature.
Jeff,
I have trawled throught your site and sites such as climate audit and real climate etc and cannot find any explanation for the methods used in actually measuring the temperatures. Can anyone point me to where the specifications for the surface thermometers are kept for all this data? What I do not understand is how thermometers with a visible resolution of 2 deg F and and accuracy of +/- 4 deg F (at best!) can be used to provide monthly average data to 0.01 deg F? In my world, this is just not acceptable, and I have been measuring pressures and temperatures for over 25 years as part of my job. In my business, if a thermometer (talking about mercury or alcohol glass thermometers) has graduations of 2 deg C on the scale, the temperature is recorded to the nearest 1 deg C. If you average 1000 readings and get something like 12.77 Degrees, you have to round it up to 12C. Why are Climatologists carrying ‘false’ accurate temperatures to 2 decimal places?
Tom P (07:05:07) :
Hmmm. No. I think that you are missing the point.
Very interesting what you say about Clean Air Acts. Does anyone have dates for when these came into force around the world? The UK act was passed in 1956 and it marked the end of the London “pea-souper” smogs. Not 1970s as postulated above, at least in this industrialised country.
Also surely particulate emissions are growing in developing countries with all the industry and forest clearing that is going on now. Wouldn’t that cancel out the clean air acts in developed countries?
Great example of UHI here in Denver this AM
16 below zero at the airport (pretty much rural), 1 deg above at City Park
17 degress difference!
You can see this consistently in the hourly obs:
http://www.crh.noaa.gov/product.php?site=bou&product=rwr&issuedby=CO
Latest hourlys aas I post (9AM):
OZ035-036-038>040-071700-
…NORTHERN COLORADO FRONT RANGE…
CITY SKY/WX TMP DP RH WIND PRES REMARKS
DENVER INTL AP MOSUNNY -7 -11 83 SE7 30.32R WCI -21
DMNS CITY PARK N/A 10 6 82 CALM 30.33R
AURORA MOSUNNY 1 -8 66 W1 30.29R
FRONT RANGE AP SUNNY -6 -9 84 E6 30.32 WCI -18
CENTENNIAL MOSUNNY 2 -5 72 CALM 30.27R
BROOMFIELD MOSUNNY 1 -4 78 CALM 30.30R
LOVELAND MOSUNNY -8 -18 59 S5 30.37R WCI -19
$$
Still 17 degrees warmer in the city than the airport (-7 vs +10)
I see this all the time here. The odds UHI is undercompensated for is very very high.
“Veronica (08:29:31) :
[…]
Also surely particulate emissions are growing in developing countries with all the industry and forest clearing that is going on now. Wouldn’t that cancel out the clean air acts in developed countries?”
Not necessarily. A lot of that industrialization happens with very modern technology, for instance german or american companies building car production lines in say Mexico or India. China produces solar cells, they use the most modern equipment from germany (Roth+Rau machines for instance). Often developing countries can skip the bad solutions we had in the past.
This may be OT but I think it’s worth mentioning. It’s the irony that the first thing the (Green) Brits turn to when the weather gets a bit nippy is: fossil fuels.
This from the (Green) British Broadcasting Company.
“The mercury fell to -18C overnight in places and temperatures were typically between -8C and 0C at lunchtime.
The Arctic conditions are expected to continue for up to a week.
The National Grid has issued its second gas alert in three days, with demand expected to hit a new record of 454 million cubic metres on Thursday.”
As an oil industry geologist, and not a climate modeler, am I wrong in feeling a little smug?
May I also state that there’s no way I’m going to shave of what’s left of my hair and grow a goatee beard.
lws (06:46:41) :
That information on Dallas is a lovely find and makes a very good point.
There are three stations on the GISS site:
Dallas-Fort W; 32.9 N/97.0 W
Dallas/Faa Ap; 32.9 N/96.8 W
Dallas/Hensley Fld Nas; 32.7 N/97.0 W
All have the same WMO code 72259 and are classified as Urban (>4,000,000 popn). They are therefore adjusted which makes a substantial difference to the trends.
Your local knowledge suggests adjustment for Hensley field should not ‘require’ adjustment. That could make a big difference to local anomaly values. That is why this is so flawed. I am tempted to look into this station set further.
Richard Wakefield (06:23:29) : I’m currently downloading the daily temp data from 1900-2009 of all 1300 Canadian stations from Envrionment Canada’s website… I have found something interesting… We are seeing less cold and shorter winters, with virtually no change in the summer temperatures…
Ah, but there is more, Richard. Just, just, just have a look at this plot from Salehard, edge of Yamal, Russia. I think it is FordPrefect’s work but, arghh, I forget… What is stunning to me is that the seasonal difference shouts recent UHI from the rooftops. So I wonder if what you observe is undetected UHI.
It would sure be nice to have a surface temperature data roadmap that describes the process of taking actual thermometer data and ending up with global temperature graphs.
It would answer questions like:
1. How are daily averages for each station calculated?
2. What processes are applied to raw data to create the GHCN dataset?
3. What processes are applied to GHCN data to get CRU and GIStemp?
4. How is missing data handled in each dataset?
5. How are missing land and sea areas handled in the various datasets?
6. What raw information and process descriptions are missing that would be required to duplicate the work of GISS, CRU, etc.?
I envision an open-source “World Temperature Project” with some chapters missing, but at least identified as areas that need to be filled in. It would include the most recent versions of software, as available. Such a thing could also include information from surfacestations.org.
This would enable the web community to duplicate and verify the work that is being relied upon for so many policy decisions. And perhaps, using the power of the web, to create even better temperature datasets.
Richard Wakefield (06:23:29) :
Thus what we are seeing in the average temperatures is not a physical measurment of an increase in real temperatures, but a narrowing of the variation within the years over time.
Very nice analysis. As an outsider, I have trouble understanding the desire to come up with daily then monthly averages for each station, then to group stations geographically and average further to finally to come up with some average “global” temperature for a month. There’s way too much interesting data that’s tossed in that sort of strategy. Arithmetic averages between a daily high and a daily low miss so much variation when there are hourly readings available. Analyses such as yours provide ample justification for making full use of all the [raw!] data.
But then I’m a splitter, not a lumper.
Strange then that young Peters plot on Youtube of US rural sites show no warming?
Unadjusted raw data, is it now lost for ever?, did they hide it somewhere?, if so, could we try some enhanced interrogation techniques-no statistical of course- in order to make them confess where it is? ☺☺☺
ah, raw … I assumed as much … thanks for the answer though 🙂
Since almost all of met stations are on the Northern hemisphere, the result is skewed to warmer trend. Tropics, Antarctic and Southern globe have not definite trends as N extratropics. If every station occupies 10km2 on the whole surface, then plain average would do fine.
Until Arctic (where the CO2 rise in dry cold air should manifest mostly) shows 40ties as warm as present and falling again, there is no much sense in the whole CO2 warming theory.
http://climexp.knmi.nl/data/icrutem3_hadsst2_0-360E_70-90N_na.png
Thanks for the careful analysis of temperature data.
Here is a Christmas gift for all WUWT readers that I received from my good friend, Kirt Griffin of Climate Topics.
http://tinyurl.com/yfbgsjw
With kind regards,
Oliver K. Manuel
Nice effort but since we know the data has been grossly corrupted, there is really know what is going on – maybe we can get some reliability from the sat data.
When doing a station survey it is extremely important to note any light colored walls to the NORTH of the device.
Walls or landscape features can reflect and refract enough of the sun to increase temps by 15-20 degrees (or more) in an enclosed space (room). {I have used this ‘trick’ to make Senior housing warmer and more marketable}
Until someone tests the effects of this type of heating of a Stevenson screen – we need to remove these stations from the record.
Sorry but it should read:
Nice effort but since we know the data has been grossly corrupted, there is really “No Way To” know what is going on – maybe we can get some reliability from the sat data.
Jeff Id (08:14:45) :
“Identical? You sure?”
Yes, the same y-axis scale.
“This post calls into question the station data itself so claiming a trend of one magnitude or another is beside the point.”
Well, the fact that the recent rural trend from your analysis is greater than CRUs trend is hardly evidence of UHI bias in their plot – that’s a very relevant point to make.
“This would say CO2 had basically limited warming effect prior to 1978…”
Agreed in that no strong effect is discernible and consistent with the CRU data: their warming trend after 1978 is more than six times the slope of the trend before that year. The CO2 warming is understood to have been offset by cooling from particulate emissions during the previous decades.
“…it appears that urban bias is a real effect which has significance…”
Also agreed, and why UHI is compensated for by both CRU and GISS in deriving their gridded temperatures. This might explain why the CRU and GISS recent trends are below even your rural-station trend, which itself would not be immune to UHI effects. The UHI compensation is validated during the period of greatest warming through agreement with the satellite data.
“Scientifically we cannot ignore the reality of the problem…” and nor has it been by climate scientists in producing their results, as I think you might now be appreciating. I’d be interested to see any proof in the emails that UHI has been deliberately ignored – Jones has actually published papers on this subject!
But I appreciate your efforts in producing these plots. Those with contrarian beliefs about global warning, whether you with surface temperatures or Roy Spencer with satellite temperatures, are producing consistent results in agreement with others. This should give some pause to those who claim bias, or worse, in these temperature plots.
Jeff Id and Lucy Skywalker
Temperature information only means something if we are comparing like for like. In this respect we should always bear in mind that Thermometers are designed purely to measure the micro climate immediately around them.
I have been tracking a number of historic temperature records and there are two main reasons for recent temperature increases.
The first is UHI- cities have grown enormously since most weather stations were set up in open fields or parkland at the edge of what were then small towns. The thermometer is recording the change in its immediate microclimate as buildings grow around it.
The second factor is that many stations have physically moved. As an example I have been tracking the temp data for Bologna Italy which has been recording since 1814. In recent years it has moved some 20 miles and is now here.
http://server.gladstonefamily.net/site/LIPE
So the Bologna temperature record now resides at airport marconi –also known as borgo panigale. This is the Bologna temp record from 1814 to current. http://climatereason.com/LittleIceAgeThermometers/Europe.html
This study demonstrates the Bologna UHI effect is up to 6 degrees C.
http://www.springerlink.com/content/7lt5g728v38k0538/
The rural areas on the Adriatic side of the Appennine mountain chain have been cooling in recent decades so the recent uptick in Bologna is a combination of UHI/station change
Each record needs to be looked at individually otherwise we are in danger of comparing apples with oranges. There is simply no comparison between the temperature record of the site in parkland at Bologna University in 1814 and that now being taken at an airport 20 miles away in 2009.
No amount of ‘adjustment’ will disguise that we are talking about fundamentally different temperature records from very different locations which just happen to have the same name, but other than that bear no comparison to each other.
Tonyb
” Jimmy Haigh (08:42:12) :
This may be OT but I think it’s worth mentioning. It’s the irony that the first thing the (Green) Brits turn to when the weather gets a bit nippy is: fossil fuels. ”
There’s no wind blowing so we could have a 200 million wind turbines all producing nothing and solar’s a bit lacking in sun this time of year. Nuclear is not popular but could do the business. We’ve got lots of coal under the ground…not popular since the 80’s. Hobson’s choice methinks.
Jimmy Haigh (08:42:12) :
This may be OT but I think it’s worth mentioning. It’s the irony that the first thing the (Green) Brits turn to when the weather gets a bit nippy is: fossil fuels.
And 4×4’s, 4×4 drivers have been volunteering to undertake recovery duties of 2WD cars and deliveries for OAPs (old age pensioners)
How about a refund of the hugh road tax hit for them Gordon ?
Huge not Hugh, spelling is not my strong point!!!
Jeff ID:
Of course that is what the climate science says: According to the GCMs, prior to the mid-1970s, anthropogenic CO2 and sulfates more or balanced each other.
While I agree on the need to be cautious, urban islands are real sources of heat. Eventually, that isn’t a bias in temperature, but a source of actual heating of the local climate.
What we want is the mean value of the surface temperature field. If you could do it with an infinite budget (this is a gedanken experiment_ you’d do it e.g., with a resolution of 1-m.
It wouldn’t matter if a particular thermometer is sitting on an air conditioner or not. That’s the real surface temperature for that site. And the average over that 510 trillion or so thermometers (sure hope I did the math right) is the real mean temperature field.
No let’s do another thought experiment: ManBearPig launches an assault at all the power stations, and all of those urban heat islands shut down. Would there be a net difference in surface temperature in the next year? The short answer is “yes”, though for an average over the globe it’d be pretty small (most land surface is not urban, and 3/4 of the Earth is ocean, so…)
In practice, we want to do is replace that array of 510 trillion or so thermometers with a much more course grained array. Is this doable in principle?
Well the answer is yes, if all you care about is daily measurements, because of wind, which advects hot and warm turbulent cells over a given microphone. With a 3 m/s wind, you end up over a day with a column of air about 250-km long that you are averaging over.
Even if a site is in an urban location (say in a park so it’s well away from local heat sources), you should be able to recapture the average temperature over that hypothetical grid of 1-m spaced thermometers. But my point here is, what you don’t want is the urban site replaced by what it would have been the urban site was not there, you just want the “true” value for the urban site, and one biased by very localized heat sources,
This same argument applies mutatis mutandis to changes in environment from environmental effects due to land-usage changes, such as deforestation for agriculture or even (as we are seeing where I live) reforestration of old farms to provide for the timber market.
Phil Jones knew what he was talking about. If he had released the data, people would have found something wrong with it.
That should read “and one not biased by very localized heat sources,”
Atmospheric Nuclear testing was stopped in 1963.
“…Prof. Julia Slingo, of the proof that CO2 causes global warming. Her response comes down to this:
“We are now rapidly approaching 390 parts per million which means it’s been a 40% increase. Most of that increase has happened in the last 50 years. And if we know that carbon dioxide is a greenhouse gas, it’s hard to believe that if you increase it by 40% you’re not going to do something to the temperature of the planet.”
———-
Reply: The operative word for Julia is “if”, as in “if we know”. All that does is assume because there is no more spirited argument currently underway than HOW MUCH is CO2 a greenhouse gas? Does it trump water? Does the modeled forcing factor assigned to it make any sense?
So many questions and so few definitive answers, yet her final analysis is based on “belief” and “something”. That wouldn’t cut it where I work. I also “believe something” is going on, but to make a guess like Julia does wouldn’t be science and it certainly isn’t definitive enough to plunge the world into chaos because some non-scientific politicians and traders glom onto it and use it to their personal benefit.
The entire work that has been done by a couple of generations of climatologists since 1960 has apparently been “corrected” by a single blogger after two days of work. This is good news indeed! Let’s hope Jeff puts his remarkable talent to use in other fields as well – maybe we could have a cure for cancer by the end of this month and a working fusion reactor by the end of February. I would also recommend that all particle physicists send their data to Jeff without delay – who needs the LHC when there is not a single problem that cannot be solved by diligent bloggers?
I think this is land-only temperature and should not be compared to or mixed with land+ocean temperatures…?
“I tend to ignore data prior to 1900 due to the very small number of stations.”
For a Global Average, fine. But don’t ignore them altogether. They have quite a story to tell:
http://i49.tinypic.com/rc93fa.jpg
Juravj V:
Not if you average over 5°x5* grids before combining them to make the global mean.
The real problem you run into is if you have more stations in urban areas than in rural, if you just do an unweighted average over all stations, that 5°x5° grid point, you’ll end up with a net urban-heating bias in that 5°x5° grid.
OT
A Guardian newspaper exclusive:
http://www.guardian.co.uk/business/2010/jan/07/gas-rationing-national-grid-factories
Yes, most of the warming since 1988, which was the first year climate changed in most parts of Norway, has occured on the Nothern hemisphere. The data below show annual mean temperatures since the beginning of instrumental observation in capital of Norway, Oslo. I think everyone sees the steep and sudden warming trend. The data below is not homogenizied and are retrieved from various stations in Oslo area due to change in observation positions in the early years. But it should cover the trend pretty well.
Most of Norway, in particular not-costal areas, have already got their two degree warming forecasted for the globe by the IPCC. We live fine with that!
We now see a weak decline in temperatures for 2009 which gives a set back to 2001. Entering a new year we have so far the coldest weather since 1987; the last cold year below mean.
Talking about Arctic: Spitsbergen (Svalbard, governed by Norway), has experienced even warmer weather and a collapse due to lack of sea ice. Some believe this is caused by deviation in circulation pattern and change og wind direction.
I have know scientific background, but as lay man studied these things for 30 years or so. But I have to say that this religious thinking of CO2 as the devil must be wrong. I still though wonder why NAO + is stronger when it occurs. In ten years we will know?
Decade Mean
1820-29 5,5
1830-39 5,2
1840-49 5,1
1850-59 5,4
1860-69 5,1
1870-79 5,0
1880-89 5,4
1890-99 5,6
1900-09 5,5
1910-19 5,7
1920-29 5,8
1930-39 6,7
1940-49 5,7
1950-59 5,7
1960-69 5,3
1970-79 5,9
1980-89 5,7
1990-99 6,4
2000-09 7,1
There is a companion graph that is more instructive as it shows the corrections step-by-step.
I got hold of the “peer-reviewed” papers behind all those station adjustments on USCHN over the Christmas break and read them. You can find my thoughts on this at this link. However, let me give you a couple of highlights.
1. The time of observation bias provides a large correction, because the day to day temperature variation is quite large at many sites in the U.S. It is a stochastic correction because it depends on the temperature difference from a day behind to the current day. What the form of the correction over time suggests is that lots of COOP station observers were changing their observation schedule from midnight to late in the day from about 1900 to 1930 or so, and then switching to a morning observation time from 1930 to the present. There were some large changes apparently just after WWI and just before 1950. It looks like the correction is based solely on a study of 1958-1964 data, and I wonder if it is appropriate to all time.
2. The homogenization step simply compares lots of “nearby” stations with one another. It is meant mainly to look for step discontinuities, which means that slow drifts like UHI or degradation of station site will not be removed, but rather spread through stations uniformly most likely. Karl’s paper specifically suggests doing this process last, but you will notice that NCDC does this correction before removing UHI and that is out of order according to the boss. I think this presents a problem, but I can get no one else to render an opinion right now. Anthony, of course, has pointed out the smearing effect of this adjustment.
3. The correction for UHI effect is based on a study of paired rural-urban stations. I don’t know that the pairing was particularly effective because paired stations differ by plus or minus 9 degrees, and the correction is a regression based on this noisiness and looks almost insignificant, and is dominated by the largest stations (because the regression is weighted by population).
One interesting thing that strikes me as suspicious about the validity of these corrections is that the correction arrived at through homogenization is just about the mirror image of that for UHI. This does not seem likely to me, but rather indicates that doing corrections out of order has fouled them up. Moreover, the raw station differences in the UHI correction are not used, but rather these are weighted by size of climatic zone. So, the weighting factors are different for the different corrections, and I wonder what potential there is for strange interactions here..
The last peasouper in London occured during the winter of 1962-3. We remember it well. My mother had just returned to England after many years living in the Middle East. The week’s fog, where for several days you could not see the person who was walking alongside you in the street, drove her to go and stay with my sister in the West Country over Christmas. The snow was so deep in Somerset that she was housebound for nearly six weeks, but at least there was no fog there.
We did had thick fogs in at least some London suburbs in the 1970s but without the acrid yellow infusions which used to turn the fogs into peasoupers.
Can I repeat the question posed by lichanos. What is the protocol for defining stations? Has anyone ascertained how many “urban” stations were previously “rural”? It is all very well only selecting stations with 100-year records but do we know that each of these was always urban or have some of them been reclassified during the period? And, if so, when?
Interesting article, though I could do without the ad hom labeling.
Anthony;
Regarding the ‘truly rural’ and properly installed stations you have found through surfacestations.org, could you please supply the list of those stations to JeffId to run through the kind of processing he has done here?
I seem to recall having heard someone (GS?) on one of those ‘climategate debates’ making the (uncontested) claim that the bottomline result of the surfacestations effort was identical to the CRU result, and that the results had been ignored or buried. This last assertion really bothered me.
I realize it is a mostly-NA effort at this point, but the time seems ripe to offer JeffId a shot at plotting the net results of the good rural stations separately, even if they be a smallish group.
I will keep asking this question until someone can give me a reasonable and believable answer: If you have dozens of stations which show little or no warming and many which show cooling since 1895, how is it called global warming. In my simple mind, if some stations show cooling, then the globe is actually cooling and warming is only local. That is the physics of heat transfer as I understand it! For example, if one station in California shows warming (Petaluma) and another station nearby (Santa Rosa) shows cooling, what is local and what is globe? I used the public web interface for USHCN and looked at the Monthly Mean Temperature vs years, at the following link:
http://cdiac.ornl.gov/epubs/ndp/ushcn/ushcn_map_interface.html
Stephen
[REPLY – This much I can say: US (USHCN) stations, equally weighted, raw data, show 0.14C average warming per century. For adjusted temperatures (FILNET+SHAP+MMTS – outliers), it comes to 0.59C warming per century. Areas designated by NCDC as Urban (9% of USHCN stations) warm around 0.5C per century faster than non-urban. ~ Evan]
Carrick (09:45:02) : “…While I agree on the need to be cautious, urban islands are real sources of heat. Eventually, that isn’t a bias in temperature, but a source of actual heating of the local climate.”
Except that UHI-influenced temperature in essentially every case is used as the value for a much larger area than the UHI itself.
I’m a fan of sci-fi author Larry Niven. He co-wrote “Fallen Angels” which was published in 1991. Good story if you care to check it out.
He also wrote in another story that “heat is a by-product of civilisation”. I can’t find the source (I think it was a Ringworld story).
Is there not a grain of truth in that statement?
assuming a linear trend, the increases after 1978 can be modelled by:
temp(urban) = m(urban) * x = (m1(urban) + m2) * x (1)
temp(rural) = m(rural) * x = (m1(rural) + m2) * x (2)
where
x is he time after 1978
temp is the temperature increase after 1978
m1 is the slope due to UHI
m2 is the slope due to other influences such as ocean cycle, land use change GG
m is m1+m2
With an overall slope ration of
m(urban)/m(rural) = 2,
the equations (1,2) can be solved, if we make an assumption for the ratio
m1(urban)/m1(rural).
2 cases:
1. given there is no UHI in rural stations, i.e. m1(rural) = 0.
it follows then, that m1(urban) = m2. UHI and other factor contribute equally to the urban increase. the rural increase in only due to the other factors.
2. given rural station have half of urban UHI increase, i.e. m1(urban)/m1(rural)=2:
satisifying equations (1,2) then requires, that m2 = 0. that means all increase, both urban and rural was only due to UHI
Carrick (09:45:02) :
” Eventually, that isn’t a bias in temperature, but a source of actual heating of the local climate.”
I disagree a bit with this point and the context of it. While there are city sized chunks of ground air which show warming, the sections are very thin in relation to the lower atmosphere. Therefore, it represents a very very small fraction of the atmosphere. IMHO, trends in this data are therefore almost purely erroneous for determination of heat in the atmosphere and comparison to climate models.
Tom P (09:37:27) :
“Also agreed, and why UHI is compensated for by both CRU and GISS in deriving their gridded temperatures.”
You and I could sit in a room and disagree about the color of the walls. It would not be my fault either. You can’t be serious in claiming gridding data compensates for bias in warming? Somehow, I’m sure you are though.
Finally, you again have assigned contrarian belief to me I’m very tired of arguing this point. CO2 does capture heat better than some other gasses so we don’t disagree (I hope). I don’t believe warming is dangerous, nor do I believe it’s as large as these curves show. Urban warming effects also apply to many rural stations. I also don’t believe in the flatly ignorant solutions advocates are so fond of. So with that said, I just plot the data and let it take me where it will, unlike Michael Mann, and Phil Jones, I don’t have a choice in the matter.
Two points for Id/Skywalker…a third source of error would be from error associated with different types of instruments at the location. It would be hard to detect since it is mixed into the UHI effect.
The good news is that UHI effects for most locations now may have reached their max since all the various urbanization effects around the obervation sites are probably not going to get much worse. I have noticed that Phoenix and Tucson have not shown any change in temperature since the mid 90s…
@KevinKilty et al;
I haven’t noticed the topic of ‘Inversion Bubbles’ being discussed, although this may reflect my own myopia.
After spending collegiate years in the LA air basin, I was surprised to recognize the same ‘Smoggy Horizon’ back home in Oklahoma City,
, you know,
‘where the wind comes sweeping down the plain…’
My mom said; Well, it is because most cities are started near rivers, and they are thus built in valleys. When the warm wind is in the right direction, it can blow over the top of the valley and make a local inversion layer.
My realization of the day is; Within that layer, the UHI effect will tend to be trapped, just like the smog.
Hopefully this is valuable insight to more than just me.
TIA
ACakaTLakaRR
DirkH (08:38:40) :
“Veronica (08:29:31) :
[…]
Also surely particulate emissions are growing in developing countries with all the industry and forest clearing that is going on now. Wouldn’t that cancel out the clean air acts in developed countries?”
Not necessarily. A lot of that industrialization happens with very modern technology, for instance german or american companies building car production lines in say Mexico or India. China produces solar cells, they use the most modern equipment from germany (Roth+Rau machines for instance). Often developing countries can skip the bad solutions we had in the past.
Get a grip on reality. Been to china lately ??????????????? ( I have )
smoking chimneys almost wherever you look……..
Remember the Olympics, they had to shut down most industry over a large area prior so that the athletes could breath…..
Wee sheds at the side of the roads ( and beside the irrigation ditches ) in which there are smoldering rubbish fires going as a method of rubbish disposal……
We are exporting our pollution BIG time to china, as they apparently have no environmental controls.
Juraj V. (09:23:41) :
“Since almost all of met stations are on the Northern hemisphere, the result is skewed to warmer trend. Tropics, Antarctic and Southern globe have not definite trends as N extratropics. If every station occupies 10km2 on the whole surface, then plain average would do fine.
Until Arctic (where the CO2 rise in dry cold air should manifest mostly) shows 40ties as warm as present and falling again, there is no much sense in the whole CO2 warming theory.”
Reply: You’re correct that doing a simple average using such a small number of data points will not provide an accurate measure. If my maths are correct then based on 100km^2 grid this means 1,487,700 of the data points are extrapolated?
Land area of earth = 148,940,000km^2
Number of 10km X 10km gridded squares = 1,489,400
Current number of thermometer data points = 1,700
Extrapolated data points = 1,487,700
Even allowing for the fact that it is only the anomaly which is required, a crystal ball would probably give as good a result.
Then we have to think about how many data points are needed to cover the 361,132,000km² of water.
Sticking to the satellite data is a better bet, despite the issues regarding calibration, sensor degredation e.t.c.
“…Prof. Julia Slingo, of the proof that CO2 causes global warming”
Some further gems from the good professors article:
You mentioned models in your last answer, and people have asked whether we can really rely on models to tell us about the future of our climate?
“It’s a very good question, but of course we have to remember they are the only thing we have to tell us about the future. We are trying to look into the future to predict what’s going to happen based on the best science and our best understanding of how the climate system works. The only way to do that is through using these models.
I think what people find difficult to understand is what is this thing that we call a model? Well, it’s a huge computer code and it’s about solving the very fundamental equations of physics which describe the motion of the atmosphere, the motion of the oceans, how clouds form, how the land interacts with the sun’s rays, how it interacts with rainfall and so on and so on.
So what these models are is hundreds and thousands of lines of code which capture and represent our best understanding of how the climate system works. So they are not in a sense tuned to give the right answer, what they are representing is how weather, winds blow, rain forms and so forth, absolutely freely based on the fundamental laws of physics.”
So thats alright then! There is no “tuning” from Met Office Scientists and the models are completely impartial.
BTW Just seen the BBC evening news featuring a Profit Of Doom from the Met Office who again told us that despite the evidence of ones own eyes Global Warming is still a real phenomenon and in fact other parts of the world are experiencing unusually warm weather for the time of year.
So just when you thought it was safe to relax we are in fact still all doomed.
I new it, it’s a trick. The global warmist are taking all our money and building a big space ship to take them to a warmer universe……Cape and Trade is a space ship tax!!!!!
Wasn’t sure which thread to add this too, but it’s a pretty amazing video interview with John Hirst, head of the Met Office.
Jeff: Is this GHCN raw or GHCN adjusted data? It’s not indicated, so I am assuming adjusted.
USHCN raw data shows +0.14C per station (equal weighting) per century.
Adjusted data shows +0.59 per century.
Urban sites warm c. 0.5C faster than non-urban. 9% of USHCN sites are designated as urban (17% suburban, the rest, rural).
Jeff ID:
Now you’re breaching a whole new topic: Where the measurements need to be taken and which ones are meaningful: If you’re discussing tropospheric measurements, should they be taken near ground level, or above the atmospheric boundary layer?
But if you are discussing measurements in the boundary layer , obviously urban warming (and more generally local environmental changes associated with changes in surface covering) is an important regional effect.
There are multiple layers of errors here.
1) Climate data is known to be corrupt.
2) As noted by Stephen, if some are cooling and some are heating… we may have to look very closely as to why – data is insufficient.
3) Microclimates are everywhere and have been alluded to based upon altitude and wind, but there are many more factors to consider.
4) The CEC in Calif. identifies 16 zones but I have Identified at least that many SUB-zones within the “official climate zone”
5) Lets consider the strength of the data prior to making decisions or recommendations.
Steve
@Pete
Thanks for this. This is the first time I’ve ever seen anyone on the BBC actually question the assumed consensus on AGW. Normally it is presented as a foregone conclusion, for Instance Prof. Iain Stewarts “Climate Wars” series. Whoever had researched Andrew Neil’s questions had done a very good job – will probably be looking for alternative employment shortly! So now the Met are openly admitting that temperatures have not risen since 1998. But luckily their model had already predicted this would happen. It looks like history is being rewritten somewhere.
What next? The Met Office “discover” actually no link between 0.04 % CO2 in the atmosphere and atmospheric temperature ?
You can indeed do what you want with data.
To amuse myself with the snow outside, I decided to analyse CET winter and summer seasons since 1798.
I wanted to see how temperature changed through complete Hale Cycles.
Course, you can start with an odd cycle or not, and interestingly, the CET mean for a Hale cycle shows apparently different results depending on which you use – OE or EO.
If odd cycles start the 22-odd year winter seasons, you get: from 1843 – 1913 – stasis. Then: POW!! A 0.6C rise 1913 – 1933, before dropping back to stasis and rising steadily to 1998 but not coming close to the mildness of the early 20th century.
But if you do it the other way round, the result is a delight to warmers: from 1855 to 1986: STASIS. But 1986 to 2008 – close to 1 degree increase in winter temperature!!
Similar things in summer: POW in the early 20th century, with later 20th century less warm but warmer than 19th century; OR step change in 20th century with statis to 1986; then POW!!
To editor: please clip prior posting – accidently hit submit half way through……
Pete (12:04:46) :
“Wasn’t sure which thread to add this too, but it’s a pretty amazing video interview with John Hirst, head of the Met Office.”
Thanks for posting the Video, Pete, it was well worth watching.
John Hirst was so poor I was cringing for him most of the way through – what a complete and utter plonker. No wonder that the Met Office is a joke here in the UK and no-one I know believes anything they try to tell us.
For WUWT non-UK readers, this guy was taken apart in the video by Andrew Neil, a political presenter/consultant. Imagine what Lord Monckton would have done to the poor guy :-))
jorgekafkazar:
We’re to the question of quantifying how much that matters.
If you are averaging the temperature over a 250-km column of air being dragged across the microphone by advection, that tends to moderate the effect somewhat, especially if we are discussing temperature anomalies or trends.
Carrick “While I agree on the need to be cautious, urban islands are real sources of heat.”
Not necessarily. For example, if you have a temperature station in a middle of a field and then build a wall just to the north of the station, the average temps will all increase. There has been no net heating in that area though. If you place a temperature station in the shade just north of the wall it should register cooler temps. The average of the two stations should be the same as one station with no wall.
The more interesting issue is asphalt. I have measured temps from black roofs and white roofs. The white roofs have higher temps in the day and lower temps at night than the black roofs. It is my understanding that the higher temps we are seeing is not daytime highs rather it is nighttime lows being higher. Which is what I would expect asphalt to do.
The other issue is rural settings which typically have plants in the area. Areas with plants are cooler than areas without, because plants convert heat to chemical reactions and don’t reflect as much heat. Plants are heat sinks.
The UHI is the difference in temperature between a thermometer being surrounded by heat sinks and reflective surfaces. I have measured the differences within 60 feet and found 30+ degree temperature differences.
Re: Richard Wakefield’s analysis of historical Canadian temperatures.
You may be interested in this tidbit…About 6 years ago my son compared historical temperatures for 6 stations in Alberta (3 rural, 3 urban) for his Grade 6 science fair. I don’t recall all of the details, but his main summary was that over time the temperatures were getting “less cold” (ie, the summer maximums were staying about the same, but the winters were not as cold as they were in the past). Sorry I don’t have the details with me, but I thought you might appreciate this anecdotal confirmation of what you have been finding.
ps…his evaluators at the science fair did not appreciate his contention that “it isn’t getting warmer – it’s just getting less cold”!
Hamish
Thanks for the back up. I was in Cairo a while back and it stank and the air was yellow. In India the burning of dung fires for domestic cooking is causing particulate pollution – worst indoors of course – and aren’t the Chinese building a new coal fired power station every week now? I thought that I’d read somewhere that all these particulates in the air and settling out could affect the albedo of ice thousands of miles away…
I don’t think the Chinese are ahead of the west in the application of solar power. But I could be wrong!
TonyB (09:42:44) : Jeff Id and Lucy Skywalker
Temperature information only means something if we are comparing like for like…
Perhaps I’m being thick but I don’t understand what you are addressing in my comments… What I was pointing to was a graph of Salehard with Spring, Summer, Autumn and Winter records scaled to the mean of each. For a long time, patterns can be seen echoed across the seasons… then in the last few years, the WINTER record shoots up with a serious obvious anomaly that is not echoed in the other months. This is surely comparing apples with apples – it doesn’t matter here if the station is moved etc. What matters is that the winter record changes so drastically, out of step with the other seasons, as to show something is suspect, and make UHI a likely suspect. What this does not show is the UHI effect at all seasons. But it does show that the annual temperature is already suspect on account of the winter temperature.
Lucy Skywalker, thanks for that. It indeed follows a very similar line that my average temp does for Belleville. However, this appears to be “anomaly” (however that is definted) of the yearly average temperature trends.
What I did was plot out the extreme ends of each year. That is, the number of days in the year that were above 30C and the number of days below -20C. When you plot that along with the average temperature you get a very interesting result. That plot you see in the Russian location may show the same thing, but it needs to be evaluated on the daily, not monthly temps, so you can count the days at the extremes.
I’ve taken this further looking at the onset of spring and the onset of winter (winter’s duration). Getting interesting results from that, but not yet done.
The bottom line from what I see is in the 1930-1950’s there were far more hotter days and colder days that today. As seen in the first upswing of that Russian plot. Then, at the same period that the Russan data drops, Belleville shows the lowest hot days, and a somewhat lower number of cold days. Then from 1985 onwards, the number of hot days increased (but less than the first increase) but the number of cold days continued to drop. Hence the trend seems clear. A narrowing of the variation within the years since 1921 for this location.
Soon as I get this done, I’ll post it for all to have a look and verify. This MUST be verified.
GeneDoc (08:54:22) :
Yep, that is EXACTLY why I wanted to see what was going on in far more detail.
Besides, can someone explain to me what “anomaly” temperature actually means? I seem to see different definitions. How is it actually calculated?
Here is a challenge. See if any of you can answer this. Using daily temperature data of the max, min and mean, what calculation can be used to deturmine when winter ends and begins?
This is related to the article but first…
_________________________________________________
Has anyone else looked at this article from NOAA?
According to our current instruments, we know that, on average, Earth receives about 1,365 W/m2 in total solar irradiance. Piecing together clues from proxy datasets such as tree rings, scientists have evidence that this value has increased slightly over the last century. However, this increase accounts for less than 10 percent of the warming our world has experienced over the same period. Thus, the increase in total solar irradiance alone cannot account for all of the global warming observed since 1900.
http://www.climatewatch.noaa.gov/2009/articles/climate-change-incoming-sunlight
After Anthony’s blog on the new Climate.gov site I ran across that and was astounded that there was, in fact, an admitted up-trend in the output of the sun. Just out of curiosity, does anyone know why this graph ends in 2000?
From the NOAA graph, just eyeballing trough to trough, the delta from 1880 to 2000 looks like it’s about 1 W/m2 (close to 2/3 the IPCC forcing attributed to CO2 here: http://upload.wikimedia.org/wikipedia/commons/b/bb/Radiative-forcings.svg), and that the solar output increased and stabilized at the new higher level around 1950.
I’m having a little trouble reconciling a 1 W/m2 change from 1880 to 2000 indicated by NOAA, with the .15-ish value assigned to it by the IPCC (which baselines pre-Industrial as 1750) unless solar output was significantly higher in 1750 than it was in the 1880’s. Could anyone set me straight on this?
_________________________________________________
OK, now that I digressed for a sec: I’d be very curious about how significant the increase in solar output is when used against the rural or satellite records
I’d also be curious to see what T. J. Nelson and Ted Ladewski’s calculations (http://www.brneurosci.org/co2.html) on climate sensitivity would look like using the rural record instead of “0.57 degrees C (using the value cited by Al Gore and others)” between 1900 and 2000 – both with and without the solar output change factored in.
Jeff Id (10:48:54) :
“You and I could sit in a room and disagree about the color of the walls. It would not be my fault either. You can’t be serious in claiming gridding data compensates for bias in warming? Somehow, I’m sure you are though.”
I claim no such thing – you are picking an argument where there is none. Gridding as a procedure is used to produce a uniform dataset from nonuniformly distributed station data – it is not a procedure to compensate for UHI. I wrote “gridded temperatures” just because this is the dataset that CRU and GISS produce.
“I don’t … believe it’s as large as these curves show. Urban warming effects also apply to many rural stations.”
So how then do you explain the good agreement between the trends of ground and satellite data since 1979?
Easy. CO2 goes on summer vacation every year ….
Great post and great analysis. This is what I’ve been waiting to see. Cheers
Tom the difference between ground and sat is statistically significant. The ground data is biased high or the sat data low.
Lucy
I was AGREEING with your earlier comment abour undetected UHI whilst pointing out to Jeff that most data is highly suspect as we don’t know its provenance. Stations move. UHI affects the data. There are ‘known unknowns.’ It means we end up inadvertently comparing apples and oranges as the micro climate currently being measured is not the same as the one we started with.
Ideally-if not realistically-all data points need to be checked.
Tonyb
Some people have asked about how stations are categorized as rural or urban. I don’t know the answer to that but a better way is to grade the stations, rather than have a binary classification.
McKitrick and Michaels have a paper published – JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 112, D24S09, doi:10.1029/2007JD008465, 2007
– which compared temperature records to economic statistics for the area in question – as a proxy for urbanization. They found a strong correlation between temperature and urbanization.
“Local land surface modification and variations in data quality affect temperature trends in surface-measured data. Such effects are considered extraneous for the purpose of measuring climate change, and providers of climate data must develop adjustments to filter them out. If done correctly, temperature trends in climate data should be uncorrelated with socioeconomic variables that determine these extraneous factors.”
“We conclude that the data contamination likely leads to an overstatement of actual trends over land. Using the regression model to filter the extraneous, nonclimatic effects reduces the estimated 1980–2002 global average temperature trend over land by about half.”
To hoots of derision from the IPCC marketing department, once again demonstrating that “peer-reviewed literature” isn’t really the important issue.
Later Ren et al published a paper about China which demonstrated the UHI effect was about 38% of the stated Chinese warming.
Ren, G., Zhou, Y., Chu, Z., Zhou, J., Zhang, A., Guo, J. and Liu, X. 2008. Urbanization effects on observed surface air temperature trends in north China. Journal of Climate 21: 1333-1348.
So, quite amusing to find that Phil Jones has subsequently published a paper:
Jones, P. D., D. H. Lister, and Q. Li (2008), Urbanization effects in large-scale temperature records, with an emphasis on China, J. Geophys. Res., 113, D16122, doi:10.1029/2008JD009916
– which demonstrates large UHI effects, probably a necessity after his “seminal” 1990 paper with Wang was shown to be “flawed”. His 1990 paper had a large dependence on Chinese “data”, well fictitious data, well allegedly fictitious, but let’s not go there.
His early paper along with his control of the IPCC process on global temperature records has led to the UHI effect being effectively ignored.
So it will be fascinating to read the IPCC report in 2011.
By the way, the IPCC marketing department, realclimate.org, don’t seem to have done a post on the errors in Ren at al. (I couldn’t find one in a search). This leads me to conclude that they attacked the McKitrick paper because it was written by McKitrick – or, once Ren et al was in progress Phil Jones had told them he was soon to publish a recantation of his 1990 – 2007 viewpoint.
And a disclaimer that I have cherry picked 3 papers which show a large UHI effect. There are plenty of peer-reviewed papers which don’t.
But fascinating that Phil Jones is now in the “skeptics” camp! Bring on IPCC 2011!
Jeff Id (14:22:56) :
“Tom the difference between ground and sat is statistically significant. The ground data is biased high or the sat data low.”
There’s no statistical difference in the warming trends:
http://img85.imageshack.us/img85/2432/rssvscru.png
and hence no evidence for UHI bias.
Does anyone have any sources for ocean temp data? Rural and Urban trends are all very interesting but you’re only talking 30 percent of the planet.
[woodfortrees.org has HADsst2GL for download – RT mod]
There is some published literature on many of the points being discussed here.
For those interested some scientists to google are:
Eugenia Kalnay
De Laat & Maurellis
Happy Hunting
Of course, that is a useful observation, because there are obviously times when the urban stations appear colder than rural stations, and one way to explain this is through inversions in valleys where these cities sit. However, the UHI I think is also complicated by heated environments. A day or so ago I posted an observation that our campus, which is at the edge of the city, was about 4F warmer than my home which is about 4 miles east of the city. There was no “sky-view” effect (which many people insist is the bulk of UHI) because the sky was heavily overcast and snowing. I suspect the heated campus buildings and utility vaults explains this.
One question that I never see answered is –
‘is the concept of an average valid when applied to temperatures measured at different stations?’
Personally I believe the answer is no. What is the average temperature of Australia? The entire globe? To me it is about as valid as stating the average skin colour of humans. You are not in fact comparing the same data. You can only give an average of dependent values. The temperature for a station in the Antarctic is totally independent of the temperature of a station in the tropics. Even when you start talking about the average temperature of a single station over a period of a year you may not even be comparing the same thing.
Genghis:
And you will get a discontinuity in the temperature anomaly when the wall got put in place. If you have an algorithm for detecting these infrequent changes in local environment, the effect of the environmental change would subtract out to first order.
I’m not sure I understand your measurements here.
Are you measuring the surface temperature of the roof or the air above the roof? And if you did, did you have a radiation shield on your thermometer? Because surface radiation can interfere with your measurement in situations like this.
In the day time, the surface temperature of the black roof should be much warmer than the white surface. And if it is a black body radiator, its surface temperature should be cooler at night, not warmer.
I’m thinking it’s a bit more complex than this. The chlorophyl in the plants absorbs most of the infrared radiation from the Sun, so in the case of short grass, it will actually raise the temperature.
You do get cooler temperatures inside of a tall forest..but the explanation is more complex than just how much heat is being absorbed and reradiated by the surface of the plant.
Tom P:
I’ll note your “proof” contained no confidence intervals. That’s kind of sloppy on your part to make an assertion based on a graph with no uncertainty information.
In fact the trends are quite different. I get for the trends, 1.26°C/century UAH and 1.57°C/century HadCRU3v.
Subtracting the two two data sets (this operation removes a lot of common correlations in the separate data sets) and computing the trend + error, I get 0.31°±0.12°C/century (p < 0.01). Even inflating the uncertainties well above their admitted values (0.4° measurement uncertainties), I still get p < 0.1.
Anyway, even if there was no UHI, there's reason to expect satellite measurements (which don't include the boundary layer) to be different than measurements taken inside the boundary layer
I am no kind of expert but I think it is important to remember the point made by Jo Nova, http://joannenova.com.au/global-warming/, There is a lot of evidence about warming but very little evidence that mankind’s contributions are causing it. Again, not an expert, but I remember learning long ago that correlation is not causation.
Tom P (14:39:24) :
I learned something with this point. RSS is a very good trend match to CRU for the recent 30 years. Of course it still has the huge ugly step in the center of the timeseries which makes it generally inferior to UAH in my opinion. Christy demonstrated using sonde data that the step favors UAH, I independently came to the same conclusion using ground data. When the step is corrected, the curves match almost perfectly.
http://noconsensus.wordpress.com/2009/01/19/satellite-temp-homoginization-using-giss/
My point should have been that CRU has a statistically significant positive bias when compared to UAH satellite data. Hence, evidence for urban warming bias.
USA Today has a particularly un-academic (and very poorly argued) guest editorial today from Orrin H. Pilkey and Rob Young in North Carolina. Bottom line? We don’t need no stinkin’ measurements to know that it’s getting warm, and give us more money so we can convince the public better! Unbelievable gall.
http://blogs.usatoday.com/oped/2010/01/column-doubt-global-warming-the-planet-wont-tell-a-lie-.html
Maybe earlier than “now”; maybe around 2002, when temperatures began plateauing.
Broaching.
If it gets any colder they are going to have to site stations inside people’s homes to get the ‘right’ results!
TonyB (09:42:44) :
Jeff Id and Lucy Skywalker
Temperature information only means something if we are comparing like for like. In this respect we should always bear in mind that Thermometers are designed purely to measure the micro climate immediately around them.
And to inform the surrounding community of day to day temperature movements. A favorite ‘sport’ on our local ABC radio in the morning is to bait the duty forecaster about the previous days forecasting errors, chatting about max/min is obligatory (“wasn’t it warm last night”). I guess it makes sense that if you live in a city you are interested in the max/min’s of that city overnight. Your world is that city environment so the UHI is meaningful to you as an individual. I am saying this as there seems to be a disconnect between local air temperature that is measured at human height for local people and the ageing and extrapolation for climate purposes. One side says ‘so what’ about UHI , ‘this city is where I live and I want to know the temperature” and the other employs statistical gymnastics trying to remove UHI to create global stats. The global estimates here seem to be the looser.
Just a thought, if the aim is get a meaningful record of land insolation, would not the lowest maximum (not a max/min average) in a gridded area be the best estimate ?. A higher maximum may reflect local heating anomalies and the night minimum’s are just a re-radiation of the daily IR intake and as has been discussed,subject to local distortion. If the aim is record solar heating for climate purposes,the daily maximum only would be a better estimate that the current average.
Kent McQueen, wow, amazing. I think we may be on to something. Agazzis BC is showing the same trend. I’ll likely be hitting the same AB stations soon. Thanks.
Anthony, we need to pursue this much further than what I’m doing. Please contact me.
For those wanting to plot station data and compare GHCN and HadCRU stations as well as the HadCRU 5×5 grid, there is a new map-based graphing interface at http://www.appinsys.com/GlobalWarming/climate.aspx – click the Map Interface For Graphing button
GeneDoc:
Well a couple of points here. We know from other observations besides surface temperature measurements that it’s been warming for about 150 years. The climate models themselves suggest that until 1980 all of the warming was natural (anthropogenic CO2 was balanced by sulfate emissions). Again temperature measurements not needed there.
Finally, if we are going to take climate change seriously enough to discuss radically changing our economic system, yes more money to help monitor the system would be nice.
The Rural graph (Figure 6) reminds me of the 100+ year trend of the Barents Sea temperature, published last year as a peer-reviewed paper. But I am unable now to identify it. Someone please guide me to the paper?
Thank you Evan for your reply at Stephen (10:28:13) :
But I still have a bad feeling in my gut that there is something wrong with the concept of grids, or averaging or homogenizing, temp data to find a global temp base line. Is there a way to help me passed this block. To me it is like taking a plot of land and pile up dirt on one little spot. Yes we changed the average height of the plot of land, but except for the little plot, the rest of the land remains at the base line. To me, it doesn’t matter what the island effect is doing at its little pile, or even if we spread a little dirt on a few more little surrounding spots, we haven’t yet changed the base line, and as far as the base line is concerned, averaging etc. is meaningless. What I would like to see is all of the “good” rural stations, which show a zero to cooling trend, set to a grid to find out how big our holding and cooling baseline is; and compare that to “good” rural stations that show warming; and also compare those to “good” urban, etc. stations. Until we know what the baseline is doing, the rest is just local! In other words, if our plot of land is actually sinking, spreading the pile of dirt and more, may be a good thing, ha!
Stephen
Carrick:
Yes, there is warming. Yes, it’s unclear to what extent humans are responsible and it might be worth understanding whether and to what extent (I read you to be convinced by the models, I for one, am not, and there are many issues all over this site and others that provide ample latitude for doubt.
I read the USA Today piece to suggest that additional funding was needed to help convince the public that there’s a problem, not to actually study anything (after all, we know already!) There’s an odd claim that it’s not worth doing anything quantitative:
“Why is there so much confusion about whether the planet is warming? We believe a big part of the problem centers on the use of earth surface temperature data as a direct measure of warming. Where do you stick the thermometer? What matters most: Daytime highs? Nighttime lows? Summer or winter temperatures? Trying to determine whether the planet is warming in this fashion seems fraught with peril.
One doesn’t need to measure thousands of temperatures to find conclusive evidence that the planet is warming. The earth does the averaging for us. Many physical and biological characteristics show that the earth is warming and has been for decades.”
OK, so what’s that evidence?
“Glacier National Park in Montana is down to 26 named glaciers from 150 in 1850, and if this trend continues, the park is expected to be ice-free by 2020. Glaciers in the Himalayas are shrinking so rapidly that the summer flow of the major rivers (Indus, Ganges, Mekong, Yellow, Yangtze) they feed might eventually be affected.”
Hmm, glaciers have been retreating since the end of the Little Ice Age. Why wouldn’t the trend continue? Perhaps a rigorous, quantitative analysis would be informative? And they cite the now debunked alarm about the Himalayan glaciers.
“The rate of rise in sea level has sped up over the past century. A tide gauge on a pier in Duck, N.C., says the sea level is rising at 1.5 feet a century. The Arctic Ocean’s ice cover is shrinking and could disappear, endangering animals that depend on the ice for their survival.”
One tide gauge is evidence of global sea level rise of 0.5m/100 years! (off by an order of magnitude or two–and probably local subsidence). Arctic sea ice is back since 2007 and will likely be above average levels after this winter.
“One can argue for hours whether this year was warmer or colder than last. To us, it doesn’t matter. We should be reading the earth, not thermometers. The earth is clearly warming, and sea level is clearly rising.”
The science is settled. No use arguing. Don’t bother us with numbers. To me, this is “un-academic”. If you can’t quantify it, it’s not science. Rates matter. Whether it’s out of historical bounds matters.
“In order to convince a skeptical public that global change is real, scientists and funding agencies need to invest more in field measurements and monitoring rather than computer modeled predictions.”
I’m not sure how this is meant to be interpreted. My take was: More measurements are needed to convince the skeptical public, even though we don’t need any more convincing…but we’ll take your money. Maybe you see this differently, but I find it offensive.
“As for the skeptics, it’s time to get off the atmospheric temperature kick and read the earth. We also believe the science clearly indicates that humans are playing a new, and critical role in driving that warming. But, lack of clarity in the exact degree that humans are causing global warming should not be used as an excuse to ignore the monumental changes that rising sea level and changing climate will bring to the planet, and our society.”
Belief is not a concept that is helpful in science. Maybe it’s ok in climate science, but it’s a word that I forbid in my laboratory. And I encourage skepticism about data. This article takes the opposite tack: we know and we don’t appreciate anybody who’s skeptical. So what if the data are messy? We know the answer and don’t need to be bothered by problems with the data. But we’ll take more money because that will improve our proselytizing to convert the skeptics…Lack of clarity of the degree…again, don’t bother us with quantitative aspects. No one’s going to ignore sea level rises when they occur. I thought the argument was about whether there’s anything to be done about it and how soon it might occur. But of course that requires numbers…
In sum, I found this piece poorly argued, with blatantly false or misleading statements and insulting to the intelligence of the reader/taxpayer.
I think these results have not been interpreted correctly in this thread.
The rural data is land only ! Land only temperatures have warmed by a factor of 2 more than ocean temperatures since 1980.
As land covers 30% of the earth’s surface, land temperature increase should be about 1.5 times higher than land + ocean combined.
If instead this rural land only data matches well with HadCrut land + ocean data, there must be something wrong !
If we believe this rural data is correct, HadCrut land + ocean data should overstate warming by a factor of 1.5.
Actually, this matches well with what we expect from the comparison with UAH satelite data::
Here, HadCrut trend is approx. 20% above the UAH satellite data trend.
However, physics and climate models predict, that the troposphere should warm faster than the surface by a few ten percent.
In sum it can be concluded, that the HadCrut trend overstates warming by a factor of approx. 1.5.
This is still
Genedoc:
No, not really. I think they are pretty much broken actually.
To be clear, I do think that the physics of CO2 as a greenhouse gas is well settled (but that only gives about a 1.2°C/doubling of CO2), but I don’t think the rest of the physics is fully understood (water vapor feedback loop that supposedly enhances the CO2 feedback to as much as 6°C/doubling of CO2, effect of clouds etc). Nor do I think the models adequately capture the short-term fluctuations of climate (e.g. periods less than 10 years, a point agreed to by most modelers as far as I can tell), however, I have doubts it can capture all of the important long-term fluctuations either.
So in my opinion, the models get the physics of CO2 sensitivity wrong and completely misses most of the rest of regional scale climate fluctuations like the Arctic Oscillation that is wrecking havoc. On the up side, they do make produce some nice animations, great for PowerPoint slides to pitch for more funding. So there is that!
Carrick (16:26:41), Jeff Id (16:40:21) :
Both of your points rest on UAH and RSS have a different trend, 1.27 vs 1.53 C/century. There are three points here:
1. Is the difference in trends statistically significant? Any calculation which ignores serial correlation, such as Carrick’s, will overestimate the significance. You need to do the complete calculation based on an appropriate correlation model to show significance here – I very much doubt there is any based on a plot of the residuals, but I’m willing to be proved wrong.
2. Is the difference scientifically significant? This is a 20% difference in trends, not an important difference given the instrumental uncertainties. Here’s John Christ of UAH himself on the difference:
“When global trends are compared for 70S-85N (RSS domain) the difference [between UAH and RSS] is only 0.02 C/decade and is getting closer as the relative warm shift of RSS in 1992 is being mitigated by the relative cooler drift over the NOAA-15 period. So we are looking at relatively small difference issues in the larger context.”
3. Is the difference relevant to UHI? Obviously the difference in the trends is to do with the methodologies RSS and UAH and nothing to do with UHI. Hence attributing the satellite differences to UHI is incorrect.
Manfred (23:24:22) :
“If instead this rural land only data matches well with HadCrut land + ocean data, there must be something wrong !”
But it doesn’t match well. Here are the two plots, Jeff’s in black, CRU in red, offset to show the different recent trends:
http://img340.imageshack.us/img340/5856/cruvsruralid.png
In fact the difference in the slopes looks like it’s a little more than 1.5 – Jeff could give a more accurate figure for the period of 1978 to present. Hence, taking into account the ocean data as you quite rightly suggest, Jeff’s work is in good agreement with the CRU global average.
The thing that really jumps out at me when looking at any of these graphs, is just how small the scale is. We’re talking half degrees, which are dwarfed by daily, not to mention seasonal temperature ranges.
Urban Bias
I live in village in the UK that lies to the north of a railway line. The railway line is on a raised embankment for much of its path through the village and all the traffic passes through one short narrow tunnel under the line.
When I was cycling to work regularly through this tunnel, it was noticeable that if there was frost on the south side of the tunnel there was no frost on the north side, this before the sun had risen. So if a temperature sensor had been moved just 45 feet from one side of the tunnel to the other the record would have shown quite a different temperature profile.
Tom P (02:12:17) :
I checked it before I posted my last comment. UAH is statistically significantly different from CRU based on a Santer style lag 1 difference.
Tom, you keep saying this is in good agreement with the global average, this is not a gridded global dataset presented here. This is gridded LAND ONLY data which is not even averaged by individual hemisphere leaving the southern nations underrepresented.
The trend drops substantially when you do a north south average CRU style, but even that leaves the oceans unmeasured. It’s premature to look at trends in this data as global trends as the station weightings are not appropriate.
The purpose of this post was to demonstrate the urban warming bias, nothing more. I’m working toward a better global average but would like to improve the step detection algorithm and individual station ID number averaging before moving on.
Some Guy (02:33:56) :
I agree, none of these plots is terribly scary. Copenhagen was scary, cap and trade— scary. A little warming– kinda nice.
Jeff Id (05:53:26) :
“UAH is statistically significantly different from CRU based on a Santer style lag 1 difference.”
Wouldn’t the correlation be expected to reach far beyond just the next month of data – after all there is a seasonal signal in the series. If so, might restricting to lag 1 substantially overestimate the significance.
“… you keep saying this is in good agreement with the global average…”
Please see my comments to Manfred above – your rural data shows a larger warming trend than CRU global data.
“…none of these plots is terribly scary… little warming– kinda nice.”
Warming of about one degree is seen in the last thirty years. That’s much less than the total warming of 8 C from the last ice age 12,000 years ago. But there’s certainly a cause for concern if the warming trend continues at this rate – that wouldn’t be so nice.
As for crying over glacier melt, as long as the average sea level temp of the globe is 59 deg F, they will continue to melt, even without warming. The only way to get the glaciers back, is to reduce the average global temp to below freezing. And, you may get your wish… See:
http://www.c3headlines.com/2010/01/satellite-confirms-that-global-temps-continue-decline-trend-a-minus-151f-per-century-rate.html
And if your really scared about today’s melting, take another look at:
http://wattsupwiththat.com/2010/01/03/swiss-eth-glaciers-melted-in-the-1940s-faster-than-today/
Stephen
“”Carrick (00:56:00) :
Genedoc:
I read you to be convinced by the models…
No, not really. I think they are pretty much broken actually.
To be clear, I do think that the physics of CO2 as a greenhouse gas is well settled (but that only gives about a 1.2°C/doubling of CO2), but I don’t think the rest of the physics is fully understood (water vapor feedback loop that supposedly enhances the CO2 feedback to as much as 6°C/doubling of CO2, effect of clouds etc). Nor do I think the models adequately capture the short-term fluctuations of climate (e.g. periods less than 10 years, a point agreed to by most modelers as far as I can tell), however, I have doubts it can capture all of the important long-term fluctuations either.
So in my opinion, the models get the physics of CO2 sensitivity wrong and completely misses most of the rest of regional scale climate fluctuations like the Arctic Oscillation that is wrecking havoc. On the up side, they do make produce some nice animations, great for PowerPoint slides to pitch for more funding. So there is that!
“”
Your co2 sensitivity is still a bit high. However, absolute humidity variations with temperature really tend to quash the hypersensitivity of h2o vapor. Increasing temperature by 5 deg. C while assuming relative humidity stays constant (a common climatology assumption) results in less forcing due to h2o than is due to the co2 doubling. It would add about 1.3x more h2o which is a long way from an h2o doubling. The result is that the co2 and h2o could not cause even half the warming necessary to raise the T by 5 degrees. If we assumed the T increased by 2 deg. C, the increase in h2o vapor would be somewhat less than a 5 deg. C warming could cause. Again, though, the total warming caused by the co2 doubling and corresponding h2o vapor increase would amount to hardly more than the co2 warming by itself. The other factors which are ignored here are negative feedback and so will reduce the effect further. While the h2o attenuation effect is over twice that of co2, the limitation that absolute humidity with temperature places upon its growth gives one some serious limits as to how big an effect could be had.
Of course, the notion that a 1 deg C rise could generate a 5 deg C rise is simply a runaway condition that could never exist due to a total lack of stability. The absolute humidity limitation shows it cannot be the case.
This is based on the fundamental physics of radiative transfer in the atmosphere. It suggests that the complex models that do promote high sensitivity due to h2o feedback must be highly flawed and may not even based upon the actual physics.
Tom P (14:39:24) :
Regrettably, for your purposes, HADCRU figures must be considered toxic and self serving. There was no altruism at the University of East Anglia. Only the funding mattered, AFAIK.
OTOH, http://www.surfacestations.org/ has the chinks to show for it.
Moderator, please be advised, chinks are coins. Reference “Shakespeare in love”. I am suggesting that Surfacestations’ findings are more precious than gold.
“Shakespeare in love”
Apothecary. Tell me, in your own words.
Shakespeare. l-lt’s as if, my quill is broken. As if the organ of my imagination has dried up. As if the proud tower of my genius has collapsed.
A- lnteresting!
S- Nothing comes!
A-Most interesting.
S-lt’s like trying to pick a lock with a wet herring.
A-Tell me, are you lately humbled in the act of love ? How has it been ?
S- A goodly length in times past, but lately–
A- No, no! You have a wife, children ?
S-Aye. I was a lad of 18. Ann Hathaway was a woman, half as old again.
A- A woman of property ?
S- She had a cottage! One day she was three monthsgone with child, so–
A- And your relations ?
S- On my mother’s side, the Ardens.
A- No, your marriage bed?
S- Four years and a hundred miles away in Stratford. A cold bed, too, since the twins were born. Banishment was a blessing!
A- So, now you are free to love–
S- Yet cannot love, nor write it.
A-Here is a– a bangle… found in Psyche’s temple on Olympus. Cheap at fourpence. Write your name on a paper and feed it into the snake.
S- Will it restore my gift ?
A-The woman who wears the snake will dream of you, and your gift will return. Words will flow like a river. See you next week.
TomP:
I didn’t just ignore serial correlation. Had I done so, the result would have been significant at the 15 sigma level. When you subtract the two series before computing the trend in the residual, much of the serial correlation is removed, though I admit some still remains.
My best guess is p < 0.1, but I admit an improved statistical model is needed here, and it may increase the confidence intervals more.
This still is a substantially improved result over your blithe claim that “There’s no statistical difference in the warming trends.”
If you can reliably measure the difference between the two paradigms (which almost has to exist given they really aren’t measuring the same physical quantity using the exact same methodology), then of course it’s “scientifically interesting”, your cherry-picked quote of John Christy aside.
Or as is equally plausible, RSS is as flawed as people have been saying, and shouldn’t be agreeing so well with CRU. In other words the apparently strong agreement could be due to confirmation bias on the part of the RSS group.
If one focuses on the differences between UAH and CRU, which is a valid thing to do, one explanation for the differences has to do with an oversampling of urban versus rural sites in the surface temperature reconstruction using the surface temperature instrumentation.
So, no, you can’t say it’s “incorrect”, just that it is one of several explanations, and that includes errors in the UAH methodology.
As I’ve said above, the real problem here is one wouldn’t expect two methodologies to agree, even if both did everything “right”. There are differences to be expected between measurements in the boundary layers and measurements above it, for starts.
You seem to have some odd need to gloss over scientifically interesting questions, and have a great willingness to make statements of certainty where none exists. Why is that, do you suppose?
After 1975 or so, the coating on the temperature collection boxes in the USA was changed from whitewash (calcium carbonate) to latex paint. This has added a portion of degree of warming during the course of the day and warmth retention at night.
If the rural temps have increased by a fraction of a degree, the gradual implementation of this coating change could explain some of it.
Tom P,
The bias toward the northern hemisphere is the reason the trend is a bit higher in recent years. Don’t worry, when the whole globe is used together the truth of the matter is a little different. As I said above, you are premature in your judgment and reading too much into the trend. If you want, I’ve provided the code so you can do it yourself and see the answer before I write it up.
Carrick (09:42:49) :
“You seem to have some odd need to gloss over scientifically interesting questions…”
Not at all. The difference between UAH and RSS is worth looking at. But it’s curious that you discount the straightforward explanation of the difference given by John Christy and instead invoke confirmation bias by RSS without offering any evidence.
And why the bizarre accusation that I “cherry-picked” John Christy’s statement? Are there many more quotes where he offers alternative explanations?
Jeff Id (09:56:10):
“The bias toward the northern hemisphere is the reason the trend is a bit higher in recent year.”
OK, if we take nearly double the warming trend of your plot compared to HadCRUT to be “a bit”. But I look forward to your full writeup on this.
To Richard Wakefield
I am not at all a scientist and have measured nothing but I have noticed that over the past three years in Sydney (Australia) there has been an evening out of temperatures. The winters simply have not been cold for more than a few days.
I have not bought the children parkas for three years because it has been too warm for them in Sydney in winter.
Go back a five years and they had parkas. I hope someone with the ability to check this will do so. Is it simply my perception or is the Sydney winter disappearing? The last few summers have not been scorchers either.
I wish I had time to look at the data too. Here’s a thought. It seems that winters are warming since 1975 but summers are not, with the effect stronger in urban areas. This could be an urban heat island effect even in the rural stations— am I right in thinking that the UHI effect would warm winters more than summers, because air conditioning 20 degrees has less extreme effect than heating 50 degrees? (90 to 70 vs. 20 to 70).
Here are a couple more UHI implications to check for:
1. The warming should rise with the coldness of the winters at the station.
2. The warming should be greater at night than during the day — a bigger effect on the winter minimum than on the winter maximum daily temperature. (This is because more heat is needed at night, and this test holds holds time of year constant.)
Tom P:
The fact that their time series “happens” to line up so perfectly with CRU, when CRU doesn’t agree nearly as well with GISTemp (purportedly measuring the same quantity) is itself the evidence for confirmation bias. Of course the history of measurements of fundamental constants is replete with examples of confirmation bias, so it’s not like I’m suggesting the RSS guys are doing something people haven’t done 100 times before.
As I see it, CRU and GISTemp should agree, RSS and UAH should agree, but the two methods shouldn’t agree with each other, even if each measured what it purports to measure “perfectly”.
For what it’s worth, I actually like the job GISTemp does better than HadCRU, even though it shows more warming (how much warming is exhibited shouldn’t be a selection criterion, I like how GISTemp handles missing station data better, and the fact that their algorithms are online and fully replicable increases my confidence in what they are doing).
The fact GISTemp and UAH don’t disagree doesn’t disturb me particularly, with the one thing we should all agree on being that this disagreement doesn’t necessarily prove UHI contamination in the surface temperature record.
On the other hand, you no doubt selected CRU and RSS precisely because of the four temperature series, they were the only ones that agreed.
John Christy clearly understands that he’s measuring a different quantity than the surface temperature record. What he’s obviously saying is “given how they aren’t measuring the same quantities”, it’s interesting how close they are to each other, nonetheless.
Beyond that, bringing up a qualitative statement in an attempt to refute a statistical inference is just another version of appeal to authority on your part, which amounts to a form of logical fallacy on your part. (If the quantities vary by a statistically measurable amount, what Christy’s opinion of the degree of agreement is, is irrelevant.)
I simply MUST comment on that photo of the rural station.
I see urban heat island happening, even in this still photo. How?
Look at the flag, blowing left to right. Any heat from the building will be carried by the prevailing wind toward the met station.
Look at the time of year (the trees and field). At least at this time of year the heated building is affecting temp readings to some degree.
There are fully six windows exposed on that side of the building, allowing more heat to leach out of the building than a solid wall would. Not only that, but because the six are spread out changes in the wind direction will still likely allow the heat escaping one of the windows to blow directly past the met station.
Look at the closeness of the station to the building (the most obvious error in met station placement and probably the LOL as to why this image was chosen here). Because it is so close, there is less probability of the building’s escaping heat to disperse with the wind. In addition, it is so close as to be in the “draft” of the building, sheltered from the winds (by intent?) – but that puts it within the cocoon of the building’s warmth. Unless the wind comes from about a 350 heading around eastward to a 190 heading the station is sheltered by a building, not exposed. That is the exact opposite of what a rural station (actually ANY station) should be.
And finally, look at the height of the station. It is exactly the same height as the vertical center of the windows, meaning the escaping heat does not blow over or under it, but right TOWARD it.
If you built a FUNNEL from the building to the met station you could hardly direct the escaping building heat – and then cuddle it there – to the met station any better. Even if the wind shifts, it is most likely still going to be readings some of the escaping building heat.
ALL of this would be moot, if the flag was blowing the opposite direction.
Since prevailing winds do just that – prevail – the odds of this photo being taken while the winds came from some anomalous direction does exist, but is not altogether likely. In my own location, winds come from the west about 75-80% of the time. I actually saw a map of this about 7 years ago, and I still recall the general info on that map, but only generally. As I recall, winds from the NE/E/SE came less than 10% of the time, and from the N or S about 15% of the time. So, there if that one is similar to my area (the features of the photo show it could have been taken in my area as well as any other), the wind probably blows as shown about 75% of the time.
The photo screams urban heat island effect. Even as the fields tell us it is rural, not urban.
And one final, final point:
If that building is air conditioned in the summertime, the air conditioner is extracting heat from the inside and pumping it outside (that is what air conditioners do, after all). And no matter what side of the building the exhaust is on, the prevailing wind and the turbulence around the building will direct some of that toward that met station.
Air conditioners is one of my own pet suspects – specifically because of the rearranging of heat out of buildings and into the external atmosphere where temps are measured. I notice that the timing of the urban rise somewhat matches the beginning of the widespread use of air conditioners. Is it a coincidence? Possibly, but I hypothesize that there is a link. I understand that the increased curve (whatever that actually is – I don’t trust the HadCRU adjusted figures any more than anyone here) is higher for the topics – exactly where air conditioners make life bearable and make urbanization much more possible – this would tend to support such a speculation that in measuring the temps in places like Malaysia we are not measuring the overall heat level (which includes the temp indoors, too), but only the external heat level, which is biased because of the extra heat added from within buildings and then pumped out of doors.
TomP:
Nearly double?
HadCrut is 1.57C/century over that period, Jeff’s rural land stations is 1.90C/century.
That’s just about a 25% difference. But you’re comparing to the wrong data set:
HadCRUT3 is land+sea
As Manfred points out above, GHCN is land stations only.
crutem3vgl (just land stations) gives 2.11C/century.
GISS land temperature by comparison gives 1.83 C/century.
Carrick:
Your arguments become ever more contrived. Now you see the excellent agreement between data as being evidence of bias in RSS! And you characterise of my citing of Christy’s quotation as “bringing up a qualitative statement in an attempt to refute a statistical inference” when you admitted your “statistical inference” was a guess, and the “qualitative statement” put in numbers for differences between the trends!
But as to comparisons between data, I agree it makes sense to compare like with like. So we have from 1978 to present for just the land stations:
Jeff Id’s rural stations: 1.90 C/century
crutem3vgl: 2.11 C/century
GISS 1.83 C/century.
So the spread is just 15%, with Jeff’s value in between.
I would be very surprised if either the GISS or crutem trends were statistically different from Jeff’s value. Certainly there is no scientific significance in the differences. From all three datasets it looks like for the last thirty years we’ve had a land warming trend of 1.95±0.15 C/century.
If such warming were to continue it would certainly be a cause for concern.
There’s nothing contrived about the fact that satellite measurements don’t measure the same physical quantity as the surface measurements, and the default is they shouldn’t agree as well as they do (as I mentioned there are specific quantifiable differences that can and should be modeled).
It is “very surprising” that the agreement between RSS and HadCRUT are so tight compared to any other time measurements especially given what we know about things that are left out of HadCRUT, because of the way it grids temperature, it underestimates the SST trend for one thing.
When I see two quantities agree too well that I believe shouldn’t, of course my first reaction is “prove to me that you didn’t unintentionally distort your result to match.” I think taht should be your instinct too, and I’m sorry that it isn’t, because in my opinion it demonstrates your lack of objectivity.
Again.
…
You are glossing over the science in pursuit of your political agenda. Not interested.
There are corrections left out of CRUTemp (and Jeff ID’s analysis) that GISTemp has included, and one would expect CRUTemp and Jeff’s analysis to both run high, and they do. Looks to me like the trend analysis has enough fidelity to pick this out to me. I’d like to see them agree to at least 5% after everything is said and done and I believe that should be very doable.
Also, if you have the fidelity to measure, you have the fidelity to discover anomalies in the measurements. Whether those anomalies that arise are “scientifically interesting” is left up to observer. I’m involved in physical measurement, so I find it intrinsically interesting, I don’t know your background that well, so I don’t find it interesting that you don’t.
After all, yesterday’s systematic errors are the basis for today’s measurement methods of choice.
While I’m not saying I think GISTemp is the “end of the game” in surface measurements, I do think it is a pretty decent piece of work. Fully open software approach, full access to the data they use, and people are replicating it now right and left. Yes one can pick on it for how they categorize urban versus rural, for example, but that is where the surfacestations survey will help.
From my point of view, the satellite measurements match more closely the physical quantities simulated in the GCMs, so there is work to be done to reconcile the two approaches.
Of course, I agree.
But we also need to make sure the measured values are accurate. It’s going to be hard to reconcile climate models with measurement if for example you have a 25% error in your temperature time series.
Carrick (11:29:42) :
“I’d like to see them agree to at least 5% after everything is said and done and I believe that should be very doable.”
I’d be happy to see measurements of the same quantity agree to within their errors, rather than some arbitrary percentage. To wish for anyhting better is not really scientific.
“But we also need to make sure the measured values are accurate. It’s going to be hard to reconcile climate models with measurement if for example you have a 25% error in your temperature time series.”
Actually the spread in the projections is wider than the spread in the surface temperature measurements:
http://www.realclimate.org/index.php/archives/2009/12/updates-to-model-data-comparisons/
It’s not the accuracy in the land surface temperatures which is mainly holding back the modelling here, but rather better observations of temperatures and energy flow in the oceans and upper atmosphere.
TomP:
For somebody who isn’t a practitioner you sure have your share of advise for those of us who are! Seriously, to “agree” in science implies within the uncertainty of the measurement.
Nah. It’s the inability of the models to capture short term variability, and maybe long term too.