Note: clearly satellites can see urban heat, as demonstrated by this recent paper unveiled at the 2010 AGU meeting by NASA. See: Satellites Image the Urban Heat Islands in the Northeast. It can also be demonstrated that the UHI biases the thermometers upwards. As cities grow, so does the increased bias. In that paper NASA says:
The compact city of Providence, R.I., for example, has surface temperatures that are about 12.2 °C (21.9 °F) warmer than the surrounding countryside…
So when you see images like this one above, where the satellites can clearly see the UHI, wouldn’t it make sense to then just look at the biggest low pass filter heat sink on the planet, the oceans, to see what the difference might be? After all, we don’t have urban heat islands in the oceans. Frank Lansner thinks it is worth exploring in this guest post. – Anthony
UAH reveals Urban Heat
Guest post by Frank Lansner
How UAH (University of Alabama, Huntsville) satellite temperature data supports Urban Heat (UHI) as a real and significant factor when estimating global temperatures.
Northern Hemisphere temperatures in recent years:
Fig1. UAH global temperatures trend equals global sea surface temperatures: The black temperature graph – average RSS+UAH satellite NH (Land + Sea) – has a smaller warming trend than the other (brown) land data series – but in fact resembles the cooler Sea Surface Temperature trend. (The blue graph “CSST” is an average of the rather similar SST´s: MOHSST6, HADSST1, HASSST2, ERSST.v3b, HADISST1and Kaplan SST 98.)
The satellite data represents both land and ocean temperatures – and yet they resemble only the SST´s. Why ?
Satellite temperatures and SST do have one thing in common: They are for sure without the UHI warming error from the cities and airports – they are excluding UHI:
Fig2. Now we split the UAH data up in a land fraction and an ocean fraction. Both still seems to yield considerably lower temperature trends than the land data (brown) measured from mostly cities and airports on the ground.
So UAH land temperatures have colder temperature trend than the ground based land temperatures. Are the land-data deviations due to general issues with the satellite data then? Perhaps the satellite data happens to show colder trends for some “known” reasons etc?
Not likely: There is a good resemblance between the UAH ocean temperature trends and then the directly measured ocean data, SST (“CSST”). This shows that satellite data (and thus also satellite land data) are indeed useful and likely to be correct.
So, unless the satellites always starts to fail just when flying over land, the deviation between land data measured on the ground (mostly from cities and airports) vs. satellite land data is likely to originate mostly from the ground based land measurements. This “extra heat trend” seen in the ground based land temperature data may be explained by UHI + possibly faulty adjustments of data and siting problems.
– One more result might also support the correctness of UAH data:
Systems will always seek equilibrium.
On fig 2 we see a pattern of gabs between the UAH land and ocean data. However, after the gabs the UAH land and ocean data these data unite again and thus despite the temporary deviations, they still seem to produce a common trend.
Is it surprising that the temperatures over land and sea will seek equilibrium? Or would it rather be surprising if they did not? What force should maintain a still bigger difference in temperatures between land and see trends?
Fig 3. Lets focus on the temporary gabs between satellite land and ocean temperatures. The green curve represents a de-trended version, just the difference between the land and ocean temperature data from satellite. From fig 3 it appears to some degree that land and sea temperatures align or reaches equilibrium mostly when temperature do not change fast.
Lets take a look at the same phenomenon in the decades just before the satellite age – I use original temperature data published en around 1974-84 for this:
Fig 4. On this illustration we have confirmed, that the land-AIR temperatures are fastest to reach a temperature change “100%”, then the Marine-AIR temperatures comes soon after “80%” and finally the sea water surface temperatures reaches the new temperature level. Again it seems, that after a given time ocean temeperatures and land temperatures tends to find equilibrium. The bac-to-equilibrium-between-land-and-sea-surface-temperatures seems to happen whithin few years, escpecially if general warming/cooling pauses or reverses.
With a reasonable argumentation that also the Land fraction of satellite data is a good indicator of land temperatures, lets look at the “extra heat” seen in the ground based land temperature measurements (mostly from cities and airports). How much “extra heat” do the ground based land data contain?
Fig5. The extra heat in CRUTEM3 land data compared to UAH on NH is 0,103 K per decade.
Fig6. On global scale, the extra heat in CRUTEM3 land data compared to UAH on NH is 0,088 K per decade. (0,23K over 26 years from 1981 to 2007).
If the extra heat in data measured on land is applied to a period 1900-2010 – just to get a rough idea of the possible impact – using 35-40% land area as hadcrut does – we get global extra heat of +0,34 to +0,39 K added to the overall warming of the Earth related to the extra heat occurring when measuring from cities, Airports etc.
0,34-0,39 K is roughly half the supposed global warming 1900 – 2010 , but in this context we cannot claim to have quantitative precision, obviously. But the rough estimate of 0,34-0,39 K suggests that the impact of “extra heat” that cannot be detected by satellites plays an important role when trying to estimate global temperature trends.
The problem of “extra heat” in land temperatures (likely to be UHI and more) is escalated by GISS because they extrapolate the ground based land temperature measurements over the oceans in stead of using real ocean data:
Fig7. In the case of Hadcrut temperature series they use around 35-40% land data when calculating global data, but GISS have a temperature product using roughly twice this fraction for land area as fig 7 shows.
Fig 8 until around 2008 this illustration of land vs ocean temperatures was online at the NASA/GISS website. As we have seen, satellite data indicates that land temperatures from ground has trend around twice the trend of land data from satellite data – and as almost twice the warming trend of SST, ocean data. This tendency is confirmed on fig 8. From 1880 to 2007 we have an ocean warming trend around 0,6K and for land its around 1,2 K – twice.
Again, we saw from 30 years of satellite temperatures that global satellite data matches ocean temperatures rather closely. If valid, then the fig 8 indicates a 0,6 K faulty extra heat, UHI etc from 1880 to 2007.
****
Article from which most graphics where taken:
Review and feedback of the above article by E.M.Smith, Musings from the Chiefio:
“The rewritten past”: http://chiefio.wordpress.com/2010/12/13/the-rewritten-past
Lucy Skywalker says: “Seriously. This looks like being potentially a damn good and important paper.”
Lucy, the post is so error filled it leaks like a collender. I’m considering writing a rebuttal post based on the maps I presented in this comment:
http://wattsupwiththat.com/2010/12/16/uah-and-uhi/#comment-552206
John Day says:
December 17, 2010 at 4:42 am
What are you on about John?
John Day said:
You mean that cold water, being slightly denser, must always sink to the bottom?
Totally correct, old chap. This also explains why CO2, being 50% more dense than O and N, always sinks to the lowest levels of the atmosphere and is never found more than a few hundred meters above the ground. Also explains why we are all dead because the CO2 concentration near the ground is almost 100%. Stout fellow, Humbug!
/sarc off
————————————————————————–
No John, Baa (and I) mean that warm seawater does not sink below colder seawater – other things being equal – just as warm CO2 does not sink below cold CO2.
Try first year high school physics sometime, John. I found it pretty interesting at the time.
Excellent forensic analysis, uncovering the massive error in the GISS data that basically eliminates the claimed global warming.
But I am always struck at the minuscule scale of this feared warming. When I hear alarmist claim nature will collapse if we see a 1° C change over a decade it makes this biology major cringe at the ignorance on display. My local ‘nature’ outside Washington DC experiences annual temperature swings from -9° t0 +39° C, and sometimes even larger swings. Year to year seasonal averages swing by a few degrees all the time. Heck, over the course of a day we see changes of many degrees.
So when I see fractional changes in annual temperature estimates I have to laugh. Few living organisms could even detect such small changes, let alone be concerned with them. If we can handle daily and annual variances an order of magnitude greater than this noise I am confident the end of the world is not upon us.
Maybe instead of a rebuttal we could have some plain science instead.
Just a thought. 🙂
currently at SEATAC 42 F
currently outside the big city 30 F
Looks like UHI is 12 F higher (at least in that area)
So, while UHI data will very depending on local climate being wet, dry, tropical, desert, etc. So why spend the effort to massage and modify UHI to fit some global climate agenda? $$$$$$$$$$$
By basic GHG theory (ignoring feedbacks), the mid-tropospheric satellite trends should be about 1.2 times the surface trend (both positive & negative trends). So, if UAH trends are 1C/century, then surface trends would be .83C/century.
If there are overall negative feedbacks (which empirical data in fact suggests), the surface trends will be less than .83C.
As a starting point, I think sat-temps are high enough in the atmosphere to eliminate or minimalize UHI “contamination”, but IMO it’s not impossible that UHI could produce plumes of localized warm areas above/downwind of large developed regions at sat-temp elevations, but I don’t know for sure. Convective activity over large UHI areas could quickly transport heated UHI air to the 15,000 ft sat-temp altitude (and above). But “developed” areas are actually only a small percentage of total area.
jimmi says:
December 17, 2010 at 2:25 am (Edit)
Er, it really would be a good idea to look at the southern hemisphere data as well, as Steven Mosher suggests, before you all jump to too many conclusions..
#######
here is a hint. Anytime you see anybody ( micheal mann or Frank Lansner) do a chart where they show the NH, and do not discuss the SH, your warning lights should go on. if only to ask a question
Frank Lansner says:
December 16, 2010 at 11:51 pm (Edit)
Dr. Phil says:
December 16, 2010 at 8:18 pm
“Great article, but really I don’t understand the basic premise behind correcting for UHI affects. Aren’t the urban temperature measurements real measurements? Urban areas are warmer than non-urban, so what?”
Hi Dr. Phil!
basically all cities in the world have grown massively from their 1900 size to their 2010 size. Therefore, all temperature stations in or around these urban areas does not just have a warm urban temperature, no theyve gotten a lot warmer during 1900-2010.
##################
Actually not.
Yes Virginia there is an explanation that hot ocean water rises and it’s not a ‘travesty.’ Really. Actually, Unbelievable.
We now have two converging explanations that may better help us understand natural phenomena comprising global warming. AND COOLING. The key to this understanding are the concepts of a `torque’ and the of natural power of `swirling vortices’ as these phenomena that relate to the role of the atmosphere, the oceans, the Earth’s `molten outer core,’ and formation of Earth’s magnetic field on climate change.
Adriano Mazzarella (2008) for one criticized the GCM modelers reductionist approach. He realized that the reductionist approach fails to account for many of the factors comprise a more robust holistic approach to global warming AND COOLING.
One of these factors is itself just a part but an important part of a larger process that might be described as a single unit comprised of the `Earth’s rotation/sea temperature.’ Holistically, however, we see that included in this single unit are changes in `atmospheric circulation which, like a torque,’ that themselves can, for example, can cause `the Earth’s rotation to decelerate which, in turn, causes a decrease in sea temperature.’
Similarly, UCSB researchers (results to be published in the journal Physical Review Letters) `filled the laboratory cylinders with water, and heated the water from below and cooled it from above,’ to better understand the dynamics of atmospheric circulation and `swirling natural phenomena’ observed in nature.
As applied to Earth science, it won’t be long before it can be conclusively shown that Trenberth is never going to find the global warming that he is looking for in the deep recesses of the ocean. The reason is simple: it’s not there. No matter how much AGW True Believers may wish otherwise, global cooling is not proof of global warming.
Soon, the mathematics of the UCSB researchers will help reveal that given differences in ocean temperature, for example, in an especially a real world example of the Earth rotating on its axis with warm water at the bottom of an ocean of colder water on the top, that the cold water will sink. The difference in the temperature from top to bottom is itself a `causal factor’ that drives the flow downward.
I think we all knew this already as a simple process of convection. But, let’s hope that a sensible mathematical representation will make the process more accessible and hopefully will also make the government science authoritarians stop acting like persecutors of Galileo.
Frank Lasner: You are not showing the UAH/RSS data correctly the way it should be shown: with full resolution and color overlay. Your averaging destroys data so that you don’t understand what is going on. To fully understand what I am saying look at Figure 7 in my book. The super El Nino of 1998 in particular is very important but you can’t even see it in your display. It divides the temperature regime of the last thirty years into two segments which must not be fitted to a single temperature curve. The left half in the eighties and nineties shows ENSO oscillations which in your display simply get lost. The mean temperature of these oscillations is a horizontal, straight line. Which means no warming for the twenty year period involved. In case you have forgotten this is Hansen’s warming about which he said in 1988 that “.. we can ascribe with a high degree of confidence a cause and effect relationship between the greenhouse effect and the observed warming.” The UAH/RSS temperature curve proves it is total bullshit but you simply don’t know how to look at satellite data or how to interpret it. Read my book before you dream up any more dead ends.
I always get a bit wary when I see phrases like
* “they resemble only the SST´s.”
* “seem to yield considerably lower temperature trends …”
* “There is a good resemblance … ”
* “From fig 3 it appears to some degree …”
Could you quantify these relationships? Maybe calculate a correlation coefficient? Maybe do a t-test? People are good at seeing trends — whether or not they truly exist in the data! I for one would like to see a more detailed statistical analysis before accepting the conclusions.
Rob Vermeulen says:
December 17, 2010 at 2:20 am
“The trajectories diverge only because you somehow decided arbitrarily that the temperature sets coincided in 1981.”
No, trends for CRUTEM (land) NH is + 0.108 K/decade compared to UAH land NH.
Globally (some still haven noticed? see fig 6) trend for CRUTEM land is + 0,088 K/decade warmer than UAH land.
Trends are not depening on start year.
K.R. Frank
PS: I can see there is a lot of comments that needs a comment 🙂 I will catch up later in the Danish night. Everythings going Christmas around here 🙂
Bob Tisdale says:
December 17, 2010 at 5:48 am
Lucy Skywalker says: “Seriously. This looks like being potentially a damn good and important paper.”
Lucy, the post is so error filled it leaks like a collender. I’m considering writing a rebuttal post based on the maps I presented in this comment:
http://wattsupwiththat.com/2010/12/16/uah-and-uhi/#comment-552206
Then post your damn rebuttal and save your snark for the Warmist blogs.
And then we drift off into more models, statistical analysis, data handling and before you know it we are talking about obtuse and obscure mathematics not science. But hey, I guess it keeps some folk happy. I have a nasty suspicion it’s a load of BS, but that’s only based on twenty years as an Instrumentation and Control Engineer, before I became an academic. Complex data analysis and the interpretation of instrumentation data are hardly new to me (or the use of it to baffle mere mortals). The high acronym count is always a clue.
The basic limitations of our measurement systems and the lack of long term data that would cover, what would appear to be, long term cycles, really calls into question the ability of anyone, no matter how smart, to determine such small temperature changes over the last thirty years let alone hundreds years.
But then I guess we’ve got to keep those grants coming in and those egos massaged on all sides of the climate debate!
Hi Bob!
As I wrote earlier, i will make some global updates between Christmas and New year.
From your graphs and others i believe global view yields a different result.
K.R. Frank
Bob Tisdale wrote :
“So let me use another method to show that most of the differences between TLT anomalies and Surface Temperature anomalies are not a result of UHI effect. The following gif animations include two maps. Each represents the temperature anomalies in 2009 compared to a base period of 1979-1980. In effect, they are showing the change from the 1979-1980 average temperature to the 2009 temperature. The first compares UAH TLT anomalies and GISTEMP LOTI. As you can see, there are significant differences between the two datasets in Africa, South America, and in parts of Southern Asia that should not have significant UHI components:”
http://i51.tinypic.com/15gcne9.jpg
I found this picture instructive.
First it confirms Frank’s observation, of elevated warming “measured” with GISS over land. Elevated in 2 ways, against warming over sea surface and against UAH satellite data.
Then there is correlation with urbanization, though there are above mentioned areas, where Bob would not expect UHI.
However, South America may be also explained with massive land use changes turning forests into farming areas.
Same for Africa plus African data has long been put into question with the very few stations.
South Asia correlates quite well with UHI and land use change in my view.
I would like to see above graphics extended over a longer time than just 1 year, because we have learned this year (with El Nino) that there are significant multi month lags between ground and satellite data sets.
Colin from Mission B.C. says:
December 17, 2010 at 9:32 am (Edit)
Bob Tisdale says:
December 17, 2010 at 5:48 am
Lucy Skywalker says: “Seriously. This looks like being potentially a damn good and important paper.”
Lucy, the post is so error filled it leaks like a collender. I’m considering writing a rebuttal post based on the maps I presented in this comment:
http://wattsupwiththat.com/2010/12/16/uah-and-uhi/#comment-552206
Then post your damn rebuttal and save your snark for the Warmist blogs.
###############
Colin, I think it’s fair to say that Bob Tisdale and I are on opposite sides of the AGW debate. That said, Bob’s work is always complete and detailed and open. I cannot say the same for Frank’s work. Bob raises a host of valid points which frank will have to address. There are other points as well that need to be adressed ( mistaking ideas about the land records and how to compare them with UAH/RSS data, failure to look at both hemispheres, lack of statistical tests etc )
Still, since I suggested on the other thread that somebody should take a look at UAH and RSS land versus GISS/CRU land, I’ll suggest that Frank start over and keep it simple.
Start with RSS land and CRU Land. The whole globe, and then do three latitude bands. describe the data and the methods completely.
Steven and Bob, at least so far I have not found errors in NH numbers, but the global numbers from my part needs a lot more work, no doubt!!!
When i come foreward with work (now 20 times on watts?) it IS the point that you guys can take a look at it, and im very greatful for this, so thanks to you both.
The “errors” Bob mentioned about the CSST being source of my findings: No, i cant see any difference when changing with the single SST´s. BUT as I said, globally there appears to be a somewhat more complicated picture. If there is a significant difference between SH and NH this needs to be analysed before any conclusions, this is true, Steven and Bob.
BUT 🙂
SH has a LOT LESS LAND with big urbanization! So that the global impact of the “UHI” problems do appear less than NH, and perhaps… this just confirms my findings for NH?
Ok, time will tell 🙂
K.R. Frank
Manfred says:
December 17, 2010 at 10:45 am
Bob Tisdale wrote :
http://i51.tinypic.com/15gcne9.jpg
I would like to see above graphics extended over a longer time than just 1 year, because we have learned this year (with El Nino) that there are significant multi month lags between ground and satellite data sets.
,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
and really useful would be a picture with the difference of above pictures, as this is what we are talking about.
Frank, the problem is that folks cant check your work because you need to provide
links to the data and a clear description of methods.
That is why I suggest you start with ONE land surface record and ONE sat record.
When you do that, for example, I could code it up in R for people so they can check for themselves.
kramer says:
December 16, 2010 at 1:09 pm
Anthony, why can’t somebody just calculate the earth’s temperature using data from stations unaffected by UHI? I’d be more trusting of what this data says even if it showed an increase (and I suspect it would).
REPLY: Easier said than done- the problem is that only a handful have been identified in the USA that are free from UHI and siting biases, and we haven’t even begun to look at the entire world. – Anthony
It doesn`t matter, use the best stations you have then compare them with the same number of stations at cities and airports, you are only measuring the microclimate around the stations anyway. This should indicate the temperature trend of those rural micro climates against the urban trend of the micro climates around the cities and airports where the majority of the stations are situated. Steve Mac did this using Petersons data, it showed a 0.7 degree difference and he did not use pristine rural sites.
Steve Mac,
Last week, Peterson sent me a list of the 289 sites used in this study, together with the classification into urban and rural. As I noted previously, there are many puzzles in the allocation of sites to urban and rural with many “urban” sites seemingly being at best very small towns and, in some cases, rural themselves. So, in that sense, it would seem unsurprising if Peterson didn’t observe any difference between the two networks.
Assuming nothing, I downloaded raw daily data for 282 out of 289 sites. (The other 7 sites either had id number discrepancies or were not online at GHCND.) From this, I calculated average monthly TMAX and TMIN temperatures for all the sites and then calculated 1961-1990 anomalies. I then calculated simple averages of the “raw” anomalies for the two networks BEFORE any jiggery-pokery. Even if all the subsequent adjustments are terrific, from a statistical point of view, it’s always a good idea to see what your data looks like at the start. Here is a plot (with a 24 month smooth.)
As you see in the bottom panel, there is an observable trend in the difference between Peterson-urban and Peterson-rural sites. The delta over 100 years is just under 0.7 deg C.
http://i446.photobucket.com/albums/qq187/bobclive/peters26.gif
Comparison of Peterson Sites with Major League Sports Franchises to Rural Network
Same thing,
http://i446.photobucket.com/albums/qq187/bobclive/peters27.gif
It does not matter where in the word this is undertaken the results will be the same.
You might need complex maths to get the exact temperature but you don`t them to get the trend.
How many rural sites give a higher trend from 1900 to 1990-5 than urban, 1% 5%.
IN the 1990s, literally hundreds of Australian stations were deleted from the GISS network. Nearly all of these are still operational, including about 44 rural or semi-rural sites that now form part of the Australian Bureau of Meteorology’s “High Quality” network.
You don`t data after 1995.
Steven Mosher says:
December 17, 2010 at 8:30 am
Frank Lansner says:
December 16, 2010 at 11:51 pm (Edit)
Dr. Phil says:
December 16, 2010 at 8:18 pm
“Great article, but really I don’t understand the basic premise behind correcting for UHI affects. Aren’t the urban temperature measurements real measurements? Urban areas are warmer than non-urban, so what?”
Hi Dr. Phil!
basically all cities in the world have grown massively from their 1900 size to their 2010 size. Therefore, all temperature stations in or around these urban areas does not just have a warm urban temperature, no theyve gotten a lot warmer during 1900-2010.
##################
Actually not.
++++++++++++++++++++
Actually YES.
Hong Kong’s Urban Heat Island
Fri, 2010-02-05 09:00 — Mr Tall
The theme of my first Hong Kong climate change article was simple: average yearly temperatures here have been going up more less steadily since the Hong Kong Observatory (HKO) starting keeping track of them, but they have skyrocketed in recent decades. Yet over the past 60 years, essentially all of this warming has occurred at night, i.e. the average nighttime lows are much higher than those in the past, while daytime highs are just the same.
This pattern is in fact the signature effect of a phenomenon that has been termed the ‘urban heat island’, or UHI.
http://www.batgung.com/climate-change-urban-heat-island-hong-kong
That the urban area has been warming up much more rapidly than the “countryside” is thus evident’.
Bob Tisdale wrote :
“So let me use another method to show that most of the differences between TLT anomalies and Surface Temperature anomalies are not a result of UHI effect. The following gif animations include two maps. Each represents the temperature anomalies in 2009 compared to a base period of 1979-1980. In effect, they are showing the change from the 1979-1980 average temperature to the 2009 temperature. The first compares UAH TLT anomalies and GISTEMP LOTI. As you can see, there are significant differences between the two datasets in Africa, South America, and in parts of Southern Asia that should not have significant UHI components:”
http://i51.tinypic.com/15gcne9.jpg
ooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo
Actually this issue has already been solved with sound mathematics by McKitrick and supportive of Frank’s results. McKitrick correlated land temperature trends with economic development / land use change etc. (over longer time scales than 1 year). the result was that at least 50% of warming was due to non GHG causes.
It is also interesting to note that this blog discussion lead into this direction.
The other innovative perspective of the same question was Frank’s postulation of long term equilibrium between sea surface and land based anomalies, not visible in ground based measurements.
Only if we assume a quicker response of land temperatures AND a trend increase over time, a steeper trend on land may be sustained long term.
However, in periods of declining temperatures (or even not increasing trends), this difference should collapse. The steeper land temperature increase would then only be a short term property but not long term.
Manfred says: “I would like to see above graphics extended over a longer time than just 1 year, because we have learned this year (with El Nino) that there are significant multi month lags between ground and satellite data sets.”
The available time periods from the KNMI Climate Explorer for that type of map are one to twelve months. The TLT data of course could be lagged a few months, but it doesn’t make any “heat islands” show up over land. Here it iis with a 3-month lag:
http://i54.tinypic.com/11790e0.jpg
You continued, “and really useful would be a picture with the difference of above pictures, as this is what we are talking about.”
There is an option to create maps of the differences between two datasets using the KNMI Climate Explorer, but, first, there isn’t an option for selecting the base years as I had for these maps, and, second, I haven’t been able to make the option work comparing surface temp (GISTEMP or NCDC) and UAH TLT. The “blink comparator” method will have to suffice for now.