By Andy May
Nicola Scafetta has just published a new paper in Climate Dynamics examining evidence of the urban heat island (UHI) effect (Scafetta, 2021). The paper is not paywalled and can be downloaded here. In summary, Scafetta shows that part of the recent warming shown in the HadCRUT 4 global temperature record may be due to the UHI effect. He uses an analysis of diurnal maximum (Tmax) and minimum (Tmin) temperatures, climate model output, and a comparison of sea surface temperatures (SST) to land temperatures to estimate the possible influence on the HadCRUT 4 record.
The various land temperature records are not specifically corrected for the UHI effect, instead NOAA and the Hadley Climatic Research Centre rely on homogeneity algorithms to smooth through anomalies. NOAA calls their homogenization process “PHA” and the Hadley Centre’s algorithm is similar, see this post or Menne and Williams’ 2009 article (Menne & Williams, 2009a) for a discussion of temperature homogenization. While these algorithms do lower the temperature in the cities, they also raise temperatures in the rural areas around the cities. To make matters worse, the past 70 years have been a period of rapid population growth and increasing urbanization. The world population has grown from 2.5 billion people in 1950 to 7.5 billion in 2020. The UHI in London has been estimated to be as much as 2.8°C in the summers between 1990 and 2006.
UHI causes Tmin to rise more than Tmax and it causes the diurnal temperature range (DTR) to decrease with time. Scafetta uses the Hadley Climatic Research Unit temperature records (HadCRUT) and the Coupled Model Intercomparison Project 5 (CMIP5) data to examine this problem. He compares an ensemble mean of the CMIP5 Tmax and Tmin data to the HadCRUT data and examines the differences between them.
The CMIP5 models have been tuned to the HadCRUT global and regional anomalies, so local anomalies, like UHI may be visible in maps of the difference between the two datasets. The CMIP5 models do not parameterize cities, so differences over cities, and surrounding areas, might indicate UHI influence remaining in the HadCRUT record.
Figure 1. These are the global Tmax (red) and Tmin (blue) anomalies from HadCRUT (A) and CMIP5 (B). The differences are shown in C and D. Source: (Scafetta, 2021).
Figure 1 compares the global Tmin (blue) and Tmax (red) anomaly records from HadCRUT4 to the CMIP5 ensemble mean values that Scafetta used in his study. The records are anomalies from 1945-1954. Comparing the decades 1945-1954 and 2005-2014, the Tmin-Tmax (DTR) differences are different. The HadCRUT4 DTR is 0.25 and the CMIP5 DTR is 0.1. In both cases Tmin warmed more than Tmax.
Figure 2 shows how the Tmin-Tmax anomaly differences are distributed in the HadCRUT4 dataset.
Figure 2. Global distribution of HadCRUT Tmin-Tmax anomalies (DTR). Orange, purple and reds mean that Tmin is warming faster than Tmax. White areas, including the oceans have no Tmin and Tmax data. (Scafetta, 2021).
As you can see in Figure 2, most of the land areas show a positive value, meaning Tmin is rising faster than Tmax. The HadCRUT data shown in Figure 2 show that large areas in North America and Asia have Tmin increasing much faster than Tmax. This is most apparent in the rapidly urbanizing China and in the growth areas of the United States and Canada.
In Figure 3 we see how the CMIP5 ensemble Tmin -Tmax (DTR) anomalies are distributed. The modeled Tmin-Tmax anomalies are much more subdued and closer to zero than the measured and homogenized values.
Figure 3. Global distribution of CMIP5 Tmin-Tmax anomalies (DTR). At the poles, the far north, and parts of Asia and central Africa show Tmin is warming slightly faster than Tmax. Most of the rest of the world is near zero, including the oceans. (Scafetta, 2021).
Only near the poles do the models show much of an increase in Tmin-Tmax, along with scattered areas in Asia and Africa. Greenland is a large island with a very small population, ~56,000 people, and it shows little difference between the modeled values and the measured values. The actual values vary from -0.2 to 0.2 and the modeled values are between 0 and 0.2.
Scafetta shows with numerous examples “that the land climatic record is affected by significant non-climatic biases.” Tmin and Tmax do not exist in the SST (sea surface temperature) records, but we can compare the SST records to the HadCRUT land records via the CMIP5 model ensemble. When this was done, Scafetta found that, after accounting for the thermodynamic differences between land and ocean, the CMIP5 simulations match the warmer land records, but significantly overestimate the SST. A land simulation of the temperature difference between the 1940 to 1960 average and the 2000-2020 average showed a model to HadCRUT difference of only 0.06°C. Comparing CMIP5 (+0.69°C) to HadSST (+0.41°C) over the oceans, showed a warming of 0.28°C, which is five times higher.
The land temperature warming according to HadCRUT is about one degree from the 1940-1960 period to the 2000-2020 period. If the CMIP5 models and HadSST records are accurate, then the land records have a bias of +0.36°C. This is almost a 60% error. We have discussed the large impact of corrections on the temperature record before, see this post for more on the topic.
Discussion
Scafetta’s study shows a potential systemic bias in the land HadCRUT records. Most of the bias, as shown in Figure 2, is in areas with rapid urban development over the study period of 1940 to 2020. There are other anomalies, the notable one in Bolivia may be due to rapid deforestation in that area. The anomalies in arid parts of North Africa may be due to a reverse urban effect, since in these area’s urbanization may create a cooler area, relative to the surrounding rural areas.
All the data used in the study has error. Definitive conclusions cannot be drawn. But it does seem that the land portion of the HadCRUT 4 record is warmer than it should be, relative to SST. It is also likely that this warm bias has leaked into the CMIP5 models. Recent DTR values (Tmax-Tmin) have decreased more than the CMIP5 models have predicted. This could be a problem with the models in urban areas, or it could be due to the homogenization algorithms used by the Hadley Climatic Research Centre smearing urban heat island warming over large areas. Either way, Scafetta has shown that these datasets are not consistent and, one or more of them, may contain significant systemic bias.
One final point. When the data is corrected for the apparent bias described above, and compared to the independent lower troposphere UAH global mean temperature (Spencer, et al., 2017), we see that the corrected HadCRUT record is closer to it than the original, which is in black, in Figure 4B. This comparison shows that the apparent bias detected by Scafetta’s study has some empirical support.
Figure 4A compares the original HadCRUT 4.6 record, in black, to Scafetta’s corrected one in red. The CMIP5 model ensemble mean, in yellow, is shown along with 106 independent model runs in green. Figures 14A and 14B use the same colors. Figure 14B adds the UAH global lower troposphere average temperature in blue. All curves are anomalies to the 1940-1960 period.
Relative to 1940 to 1960, the original HadCRUT curve shows 0.59°C of warming and 0.48°C using Scafetta’s corrections. The UAH record shows 0.44°C. The CMIP5 climate models show 0.78°C of warming.
It is possible, according to Scafetta’s correction, that non-climatic biases may have contributed a fifth of the reported HadCRUT global warming since 1940-1960. It is also possible that the CMIP5 climate models may overestimate warming by a third. These are significant problems.
Works Cited
Menne, M., & Williams, C. (2009a). Homogenization of Temperature Series via Pairwise Comparisons. Journal of Climate, 22(7), 1700-1717. Retrieved from https://journals.ametsoc.org/jcli/article/22/7/1700/32422
Scafetta, N. (2021, January 17). Climate Dynamics. Retrieved from https://doi.org/10.1007/s00382-021-05626-x
Spencer, R., Christy, J., Braswell, W. (2017), UAH Version 6 global Satellite Temperature Products: Methodology and Results, Asia-Pac J Atmos Sci 53:121-130.
I am trying but I just can’t see UHI having an effect in Central or Northern Australia.
lee,
these weather stations are all situated at airports or settlements – the only places to have paving, new buildings and aircraft/equipment.
There is nothing of signicance in either Central or Northern Australia.
There are 21 sites in the whole of Northern Territory and a perhaps maybe six more depending how you want to define Central Australia all with fractured record histories. The area you are talking about will be 2-3 million km² the whole area really needs to be ignored.
Yep. Nothing of significance there at all, for such a large UHI effect.
This come about because the temperature record from a poorly-sited and erratically-staffed weather station at some lonely little fly-blown settlement is weighted to represent the temperature for a vast area of uninhabited land.
Well let’s look at the weather stations in the NT and particularly around central Australia and Alice Springs-
NT Weather Observations Map (bom.gov.au)
and now for those start dates-
Alice Springs 1940
Yuendemu 1952
Curtin Springs 1953
Jervois 1966
Kulgera 1968
Yulara 1983
Territory Grape 1987
Arltunga 1988
Watarrka 1990
Rabbit Flat 1996
Now Ernabella Mission had records from 1935 to 1983 and started up again as Pukatja in 1997
So there’d be plenty of room for homogenizing and pasteurizing that lot with automatic weather stations kicking in when you’ve largely got Alice Springs Airport and Ernabella stretching back.
I’d like to know where the airport one is situated because I can recall a single round hangar at ‘A Town Called Alice’ (see the film with Peter Finch) as a boy bouncing up and down from Darwin to Adelaide on the mail run in propellor engined DC4s and the airport has changed a bit. Anyhow you wouldn’t want to base too much climate conniptions and dooming on that very sparse and piddling record.
Alice Springs temperatures have been HEAVILY ADJUSTED by the climate scammers and now bear no resemblance to temperature that were actually measured.
The metadata for Alice Springs does not give the date of automation but most were converted in the 1990s and that coincides with a step up in the record.
The post office goes back to 1873. The trend there is indicative of the dry period that resulted in the federation drought. The place was measured as warmer in the 1890s than now despite the impact of jet streams and automation of the airport weather station.
Scafetta’s map shows the variation for only ten years up to 2014. Few of those places would have changed much in that period, neither would the whole of Siberia. AGW is predicted to to raise mins faster – that is what is observed. Next.
Ok two periods separated, but Australia is upside down, all the anomaly in the north none in the south. Nothing in Europe, India big UHI footprint in Siberia and Columbia – the map is showing artifacts. But I guess Andy wants to show us anything that might appear to show its anything but CO2.
Well as YOU have PROVEN many many times
It is NOT CO2 causing the slight but beneficial warming.
You know there is absolutely ZERO evidence that it does.
The map is at the very least showing failures in the homogenization process.
Columbia University has a big UHI footprint. Maybe you meant Colombia, which has gone to over 51 million people from 16 million in 1960. Over 77% of the population lives in urban areas.
India? Europe? Australia? The Himalayas? etc. Without an analysis of the correllation between Scafetta’s map and ecomonomic development and with the expected global bias towards warming at night removed, I remain skeptical this is showing anything at all.
Loydo,
Since when is warming at night a BIAS? It’s not a BIAS so why would you want to remove it?
And you think India, Europe, and Australia haven’t seen urban growth? Jeessh! Talk about cultural bias!
WRONG AGAIN loy-dumbest.
UHI causes mins to rise faster.. Smearing of urban data over large ares and then homogenising the crap out of everything does the rest.
Next…
How about some empirical evidence of warming by atmospheric CO2 ??
We are all still waiting and laughing.
Which Figure, bozo.. be specific
Title figure is Figure 2, which shows a MASSIVE increase in urban warming in Siberia from the 1950s to 2010 (using approx period centers for brevity)
This is totally in line with the MASSIVE urban expansion over that period.
The Australian change looks like it more to do with data adjustment and homogenisation than anything real.
“Scafetta’s map shows the variation for only ten years up to 2014”
.
FFS…. the title says 2005-2014 MINUS 1945-1954
It use two 10-year periods 60 years apart
Learn to read, idiot !!
As poly’s comment shows, you don’t need anything of significance to start picking up UHI.
If the infrastructure hasn’t changed for the period UHI can’t explain the change in anomaly
All it takes is for the infrastructure to be repainted to have an impact on the temperature different from the past. Or that dark, heat absorbent asphalt be used to pave over light, reflective concrete. Or that old buildings are not scrubbed and dirt and grime buildup change the heat being absorbed by the building over time.
Do you *ever* stop and try to think things through before hitting the “post comment” button?
Of course he stops to think before posting.
How could he be so consistently wrong if it wasn’t intentional?
Maybe he’s just lucky! 🙂
Main graph is urban heat changes from around 1950 – around 2010.
You really think there has been no infrastructure changes !!
You are yet again showing yourself to be nothing but a complete moron.!
And your evidence that the infrastructure hasn’t changed is????
Of course you have no evidence, just a strong belief that the models can’t be wrong.
Only a complete and absolute moron thinks that infrastructure hasn’t changed in 60 years
Has Loy really been locked in his padded cell that long ?????
poly
You write:
” these weather stations are all situated at airports… ”
Yeah. Why don’t you show any REAL example?
I can do the work for you, by showing a graph in which the allegedly UHI-burdened station at Anchorage International Airport (Alaska) is compared with the pristine CRN station Kenai, located in the middle of nowhere, about 50 km away.
A) Google Maps
https://www.google.de/maps/place/60%C2%B043'25.0%22N+150%C2%B026'53.9%22W/@60.7236,-151.5689109,233585m/data=!3m1!1e3!4m5!3m4!1s0x0:0x0!8m2!3d60.7236!4d-150.4483?hl=en
B) The chart based on the raw, unadjusted GHCN daily data (I perform NO Homogenization of any kind):
https://drive.google.com/file/d/1OhCuDiAFUT80Ws4S8XopciaWQTp4rorn/view
The absolute temperatures measured by the two stations differ by 2 °C.
It should be evident that if UHI was such a problem, the Anchorage station would present much higher anomalies, shown by a trend differing from that for the KENAI station. This is not the case.
*
Unfortunately, unlike that of GHCN V3, the GHCN daily station list (over 100,000 stations, of which 40,000 for temperature) does not contain context specific information, like rural vs. suburban vs. urban etc.
That makes a chart like that out of V3’s unadjusted variant, impossible for GHCN daily:
https://drive.google.com/file/d/10ztwxF-P9LJeF0iPY8ZSReQfyQN8KkmB/view
What a pity!
*
But nonetheless, all stations located at airports are named such that their location is visible (“… airport”, “… AP” and the like).
It took me a lot of time, but I managed to collect in 2019 840 of them for CONUS in the GHCN daily data set, and could compare them with 71 USHCN stations selected by ‘surfacestations.org’ as ‘well-sited’:
https://drive.google.com/file/d/1tbreucKhA5wCgFtPcgKIWGuCiwHe4zCC/view
If you don’t believe in the correctness of my data processing, then… feel free to do the job yourself!
J.-P. D.
Bindidon,
I looked at the cooling degree-day data for both locations, Anchorage Intl and Kenai, over the past 258 months. Kenai shows a zero trend in cooling degree-days while Anchorage Intl shows a definite upward trend with a massive peak in 2019.
There is *definitely* a difference in measurements at these two stations. UHI is an obvious suspect for Anchorage Intl.
Here’s the graph for Anchorage Intl. I thought you could post two graphs but apparently not.
Tim Gorman
What exactly are your sources, and what is your ‘cooling degree-day’ data?
Nothing describes what you mean with your strange graphs; my graphs explain exactly what we all need to understand them.
Moreover, I hope you managed to keep in mind how important it is not to consider absolute values, but departures from a mean instead. Discussions about absolute TMIN or TMAX is irrelevant when comparing stations.
*
The graph I posted, comparing Anchorage Intl AP and Kenai 29 ENE, is based on GHCN daily:
ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/
In the station list you see
USW00026451 61.1689 -150.0278 36.6 AK ANCHORAGE INTL AP 70273
USW00026563 60.7236 -150.4483 86.0 AK KENAI 29 ENE CRN 70342
and the data you find in the ‘all’ directory in the files named
USW00026451.dly
USW00026563.dly
Caution: don’t click on the ‘all’ link: that would result in the display of over 100,000 directory entries on your monitor.
J.-P. D.
go to: degreedays.net
My graphs aren’t strange. They are monthly cooling degree-days graphed for the past 258 months, just like I said.. Climate *is* the entire temperature profile at a location, both daily and seasonal. Integrating that temperature profile tells you the climate at a location, anomalies do not.
Is it the concept of degree-days that you don’t understand? Cooling degree-days are calculated by integrating the temperature profile above a certain set point. They are exactly the same as growing degree-days and heating degree-days.
Anomalies do nothing but hide what the climate is doing. Degree-days were developed to overcome that. If I tell you that the anomaly for yesterday in Kansas City, Kansas was 2degC exactly what does that tell you about the climate in Kansas City? Degree-days are being used by all kinds of engineers to size HVAC units for all kinds of buildings. That process depends on knowing *exactly* what the climate is. Anomalies simply won’t tell you that!
Cooling degree-days and heating degree-days tell you a lot about different locations, far more than anomalies can tell you. There is absolutely no reason why degree-days can’t be compared between stations. In fact, I have been an advocate for the climate scientists to abandon anomalies and move to using degree-days. There is no reason why degree-days can’t be calculated from automated stations going back to 1980 in some cases. Add them all up from around the globe and track what is happening with the total each month.
What I have found by looking at the cooling degree-day data from around the globe in places like India, Brazil, Siberia, the US, Japan, and China is that the number of cooling degree-days are stagnant or trending down over the past ten years instead of trending up. When you find one that is trending up, like the Anchorage Intl station, it is suspicious.
GHCN gives you Tmax and Tmin. So-called climate scientists use these to calculate a mid-range temperature. They call it an “average” but it isn’t an average. It is a mid-range value. If you look at daily temperature profiles they approach a sine wave, sometimes distorted (sometimes distorted a lot by weather fronts) but still approaching a sine wave. The average of a sine wave is about .67Vpeak, it is not a mid-range value. So what the so-called climate scientists start off with is already meaningless. What does a mid-range value tell you? Ans: nothing.
Since the GHCN data values don’t include an uncertainty value for each station, the so-called climate scientists just ignore uncertainty in all their calculation. If a station has a +/- 0.5C uncertainty and you calculate (Tmax-Tmin)/2 then the uncertainty in the calculated value becomes +/- 0.7C. When you add 30 monthly mid-range values together to get a monthly mid-range value your uncertainty becomes sqrt(30) x 0.7 = +/- 3.8C. All of a sudden your uncertainty overwhelms any “anomaly* you might be trying to identify.
I’ve been through all this a dozen times with different people. If I had my druthers and I were in charge of the world I would tell the climate scientist to start using enthalpy in their calculations. Enthalpy takes into consideration temperature, humidity, and pressure (i.e. altitude). By just using temperature alone you don’t really calculate heat content, you don’t actually calculate anything meaningful. Have you ever wondered why a temp in D*ath Valley feels cooler than the same temp in Miami?
Tim Gorman
Your graphs look more and more strange to me.
Simply because the difference between the latest peaks in Anchorage Intl AP and Kenai Mun AP does not match the data in the stations
USW00026451 61.1689 -150.0278 36.6 AK ANCHORAGE INTL AP 70273
USW00026523 60.5797 -151.2392 27.7 AK KENAI MUNI AP 70259
Here are two graphs covering 1954-2020, one for the differences between the TMAX and TMIN absolute data
https://drive.google.com/file/d/1v-nF3WyBsGFK9dDIe1mcM6YNSKxC4s00/view
and one for the anomalies wrt 1981-2010
https://drive.google.com/file/d/1j1IKUIjSagArXbG95q1ZIa7tSHuB0sbw/view
Trends for 1954-2020, in °C / decade
Tmin absol
Kenai Mun AP: 0.48 +- 0.17
Ancho Intl AP: 0.48 +- 0.16
Tmax absol
Kenai Mun AP: 0.34 +- 0.16
Ancho Intl AP: 0.31 +- 0.17
Tavg anomalies
Kenai Mun AP: 0.40 +- 0.05
Ancho Intl AP: 0.39 +- 0.04
Where, do you think, is UHI visible for Anchorage vs. Kenai?
J.-P. D.
Go to: degreedays.net and study up on what a degree-day is. It is *not* temperature. It is an integral of the temperature profile above or below a specified set point.
If Tmax goes up then, of course, the integral of the temperature profile will go up as well. If Tmax goes down then the integral of the temperature profile will go down. The integral is basically the area under the temperature profile curve above/below a set point.
If Tmin goes up then (for heating degree-days) then there is less area under the temperature profile and the heating degree-day value goes down. If Tmin goes down then the integral will go up since you need more heat.
It is truly a simple concept. And it gives *much* more information about the climate than a mid-point. An annual cooling degree-day value of 1000 vs 2000 tells you that the climate at the 2000 cooling degree-day location will require much more air conditioning capacity than the 1000 cooling degree-day location.
All you have to do is add up all the heating degree-day values around the globe and all the cooling degree-day values around the globe and you should be able to tell directly if the globe is warming or cooling and when it is doing it. No averaging, no homogenizing data, no adjusting data, no fiddling data. It will very much simplify modeling requirements and will also make projections for the future much simpler. The projections would be easy to validate against current measurements.
I truly believe that the reason this will never happen is that it will also prevent the alarmists from scaring people that the earth is going to turn into a cinder. There are lots of professionals out there that understand what degree-day values are and how they can be used. It would be pretty hard to baffle them with BS about degree-day values. The very same degree-day process can be used to track growing degree-days which lots of farmers and agricultural experts track in doing things like projecting crop growth, date of maturity, etc. Again, it would be pretty hard to baffle these people with BS over growing degree-day values and trends.
As for the GHCN data, I simply don’t believe it tells you anything. If Tmax were trending up then the cooling degree-day data would show it since the size of the integral would also being going up. And, as I pointed out, Tavg is *NOT* an average, it is a mid-point value. It simply doesn’t provide anything meaningful at all. Tmax and Tmin values of 60/30 will give the same mid-point value as 70/20 yet the climate associated with each pair is obviously different.
If you think the people at degreedays.net are somehow not capable of accurately calculating the integral of the temperature profile at these stations then you need to provide a reason for your belief. Too many professionals use this data to size HVAC capacity requirements. If the data was wrong it would soon be noticed – but I have never heard of that happening.
Tim Gorman
Here are, for 1954-2020, the graphs for TMIN
https://drive.google.com/file/d/1MTnuH6E2EJUllHjKe9iUPQTq5f9iNPbR/view
and TMAX
https://drive.google.com/file/d/110SwkYg3p9N2-I3Lwo-CkittHnTFl31O/view
out of GHCN daily’s absolute data.
My impression, Sir, is that your are victim of a statistical artifact.
J.-P. D.
There are no statistical artifacts in degree-day calculations. There are not any statistics involved! Do you know what an integral is? Why do you think there are statistics involved in calculating an integral?
I see that as a mystery also. The northern Russia (on the Kara Sea) anomaly makes perfect sense. Oil was discovered there after the war and there are large communities of workers building lots of infrastructure all over the place. But, I’m not sure what is going on in Australia. The Alaska anomaly is also easy to explain.
Andy, IMO one has to take two effects into account: With ongoing forcing the Tmax will increase more on drylands than in wetlands due to the limited evaporation over dry areas. This influences the DTR ( the difference between Tmax and Tmin). On the other hand UHI should increase the Tmin more, also the GHG effect will do so in reducing the OLR during night times. IMO the DTR is influenced by many factors, the simple conclusion: Tmin is warming faster than Tmax due to UIH is not sufficient.
Unless it’s all smeared out, sorry, “homogenised” from a single weather station in cairns.
It’s been years since I saw it, yet there was a study that showed even small towns can easily have UHI, although it may well be a question of site setting, which is often just as distorting.
From personnal experience I’d agree. I used to live in rural central France. I did a lot of leisure cycling, and still do. The area consisted of small villages and hamlets usually about 7kms apart with precious little between as most farms were set back from the road. During low wind summer days there was a notcable increase in temperature in the “Built up” areas. Not so detectable for 6 months of the year.
This is not noticable in the UK where I cycle.
The population of the French Department was 371,102 mostly concentrated in one town population 136,221 in an area of 2,130 sq mi. In the UK county population 802,700, largest town population 248,752 in an area of 983 sq mi.
So I’d say that in a lot of parts of the UK the UHI covers a large area with quite a gradual increase from lowest temperature to city centre, whereas in a lot of France with low populations and population density the UHI effect is very noticable.
You don’t need a large population to have a measurable UHI.
Absolutely correct! I live about 20 km from a small city of about 12,000 people. The countryside is mostly forested while the city is, like almost all cities, brick and mortar buildings packed together with a maze of asphalt roads and many hillsides with sparse veg, exposing soil and bedrock. Its almost always a few degrees C warmer in the city, a gradient very visible on my auto thermometer. And this true for all seasons of the year. So IMO, the UHI effect is likely a planet altering phenom, given the extent of cities everywhere. Flying into Toronto, at over 500 km/h, one flies over dense metropolis for at least 15 or 20 minutes, not inconsequential.
Thanks, Andy, interesting presentation of data utilizing the Scafetta report as a start point. I note that the upward trend in global temperatures start at around 1980, directly following the alarm in the 1970’s about a new glacial cycle starting. The whole mess of CAGW looks to be a normal climate cycle with both UHI added and some misconduct to spice it up.
Well said.
“The whole mess of CAGW looks to be a normal climate cycle with both UHI added and some misconduct to spice it up.”
I think that sums it up nicely.
How come I see data agreement until about ’60, then things suddenly become ,er…competitive? Did the holy profits of doom of the seventies start manipulating the data in the ’60s already, or is this like cancer; “we find it more because we test more” while inventing ever more complex reasons and ever more profitable non-solutions?
Your mistake is in considering the Hockey Stick chart as representing reality.
The real temperature profile of the Earth has the 1930’s as being as hot or hotter than today, and then the temperatures declined from the 1930’s to the late 1970’s, when climate scientists were starting to claim the Earth might be entering a new ice age, and then the temperatures started warming and warmed up to the present day, and we are now currently cooler than it was in the 1930’s.
The Hockey Stick shows just the opposite. It shows a steady increase in temperatures from the 1940’s and practically erases the cold of the 1970’s and shows current temperatures to be much higher than in the Early Twentieth Century.
The Hockey Stick chart was created by Alarmists in order to promote the Human-caused Climate Change scam. They had to artificially eliminate the warmth of the Early Twentieth Century because if it was just as warm then as it is now, then that means CO2 has had little effect on the Earth’s temperatures since there is much more CO2 in the Earth’s atmosphere now than there was in the 1930’s, yet it is no warmer now than then. So the alarmists had to erase this obvious obstacle to their Human-caused Climate Change narrative.
Here’s the real surface temperature profile of the Earth (below), represented by the regional US surface temperature chart which shows the warmth of the Early Twentieth Century and the cold of the late 1970’s. These inflection points have been bastardized in the Hockey Stick chart to make it appear that the Earth’s temperatures are at the warmest point in human history, but it’s all a Big Lie, supported only by a computer-generated Hockey Stick chart.
And here’s a comparison of the US regional chart with a bogus, bastardized Hockey Stick chart:
http://www.giss.nasa.gov/research/briefs/hansen_07/
The Hockey Stick chart is the ONLY “evidence” the alarmists have to promote their scheme and it’s all made up out of thin air in their computers, and does not resemble any written temperature record of the past.
I’m told that even if you feed baseball scores into Mann’s “model” it will contrive a Hockey Stick.
Or random numbers from the phone book.
I noticed on the graphs from NASA with the U.S. vs Globe that the scales are different. What propaganda! Makes the Globe graph look absolutely terrifying.
Also My estimates from the graph show basically 0.0 deg to 0.0 deg between 1960 and 2000 on the U.S. graph. Meanwhile the Globe graph shows 0.6+ for the same time frame. That means an area the size of the U.S. would need to have increased by 1.2+ deg in order to get a 0.6+ deg increase. I can’t imagine that kind of an increase over 40 years wouldn’t have been trumpeted all over the media!
I suppose someone could say the remaining land area of the globe would only have to increase to 0.7 or 0.8 to raise the entire average. But, you increasingly need to then explain why the U.S. is entirely different from the remainder of the world. The sum of the parts have to add to the whole. That in a nutshell is the problem I have with GAT, no subparts are available that can be checked against the whole!
“But, you increasingly need to then explain why the U.S. is entirely different from the remainder of the world.”
Actually, the world is not different from the U.S.
The U.S. temperature chart is a regional surface temperature chart. If you look at other regional temperature charts from around the world, their temperature profiles resemble the U.S. temperature profile, where the Early Twentieth Century shows to be just as warm then as it is now.
The only chart that differs from the regional surface temperature charts is the bastardized instrument-record, Hockey Stick chart which was generated by a computer and a lot of dishonest imagination in an effort to promote the Human-caused Climate Change scam.
Re Fig. 4: this says the black line represents the “HadCRUT4 global temperature” record. It clearly shows 2020 as the warmest full year in that record. Yet according to the HadCRUT4 source data that isn’t right; 2016 was the warmest year, 2020 second: https://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/time_series/HadCRUT.4.6.0.0.annual_ns_avg.txt
Most odd.
I don’t know for sure, but Nicola submitted the paper for peer review in September and it was published in January, so he couldn’t have used a full year of data for 2020. It wasn’t available.
In late 2020, my observation is that it started to get cold where I am in Florida. I did some checking of reported “average” temperatures for my location vs. forecast temperatures (which turned out to be reasonably accurate) and consistently came out with temperatures below average by a typical 3 degrees F.
Okay, but something the author should have clarified or that the reviewers should have picked up on.
From the article: “If the CMIP5 models and HadSST records are accurate,”
They are not accurate. They are at complete odds to the written regional surface temperature charts which all show it was just as warm in the Early Twentieth Century as it is today.
If you look at a temperature chart and it does not show the Early Twentieth Century to be as warm as today, then you are looking at a bogus, bastardized, modern-era Hockey Stick chart which is a false represenation of the Earth’s temperatures.
That’s not to say it is not useful to poke holes in the Hockey Stick chart as it exists. But still, we have to keep in mind that the Hockey Stick chart does not represent reality. It’s a figment of Alarmist imagination.
Inconsistencies are important for sure. One of Nicola’s talents is showing them clearly.
I agree. Anything that refers to and lends legitimacy to the HatcruD charts is flawed in my book.
Yes.
Compared with the same time period……
My brain is foggy this morning but, for whatever reason, this article seems relevant. It points out that the warming in the United States has predominantly been in the night.
Yep, night time temperatures are rising faster over most of the world. Winter temperatures are also rising faster than summer temperatures. Tropical temperatures are barely rising. Hmmm, is this anything to worry about?
Exactly. “Global Milding” is no catastrophe. Nor is loss of ice mass. Growing ice and chilling climate is to be feared, not a warming climate.
Temperatures derived from ice core records from Greenland and Antarctica from a lecture I have on the Milankovitch Cycles pretty clearly indicate that it was warmer a few thousand years ago, and we are heading for the next big ice age.
Reference lecture eleven from “The Physics of History” by professor David J. Helfand, Ph.D., “The Great Courses” from “The Teaching Company.”
Yes, it is a worry for all temperate zone fruit production that has a minimum winter chill requirement for fruit setting.
Give a location you think global warming might threated fruit production. Chilling typically happens in a range between 35degF and 50degF. Where do you see fruit production today with low enough temperatures to “chill” that are threatened by warmer nights?
Andy, as you know Frank Lansner has done studies on weather stations showing differences depending on exposure to ocean breezes or not. His recent publication Temperature trends with reduced impact of ocean air temperature Lansner and Pederson (2018)
“Temperature data 1900–2010 from meteorological stations across the world have been analyzed and it has been found that all land areas generally have two different valid temperature trends. Coastal stations and hill stations facing ocean winds are normally more warm-trended than the valley stations that are sheltered from dominant oceans winds.
Thus, we found that in any area with variation in the topography, we can divide the stations into the more warm trended ocean air-affected stations, and the more cold-trended ocean air-sheltered stations. We find that the distinction between ocean air-affected and ocean air-sheltered stations can be used to identify the influence of the oceans on land surface. We can then use this knowledge as a tool to better study climate variability on the land surface without the moderating effects of the ocean.
We find a lack of warming in the ocean air sheltered temperature data – with less impact of ocean temperature trends – after 1950. The lack of warming in the ocean air sheltered temperature trends after 1950 should be considered when evaluating the climatic effects of changes in the Earth’s atmospheric trace amounts of greenhouse gasses as well as variations in solar conditions.
My synopsis:
https://rclutz.wordpress.com/2018/04/30/pushing-for-climate-diversity/
Interesting Ron, thanks
Yet again, Andy May plays an absolute blinder with this article. My respect for the man rises ever further. In my opinion, the key take-away point that he makes is that the climatariat DOES NOT remove heat from the UHI’d temperature records that they use for modelling – they simply spread excess heat from known UHI’d sites around other nearby weather station records. This really is a show-stopper for me and completely invalidates so much modelled garbage (at least in terms of historic data presentation) produced to say that our world is warming due to CO2. If the effect was trivial (say, 0.001C), you could cry “irrelevant”. May, however, implies that the error creates a significant fraction of the overall supposed global warming. It is thus far too important just to homogenise away.
Here is the conundrum. UHI is a real temperature increase. Yet, what causes it? Not CO2! Uh oh, all the crony capitalists and politicians have bet on the wrong horse. Windmills and solar panels are the wrong solution. However, momentum will be conserved and we will continue down the wrong path for quite some time. Basically, until friction removes momentum no actual solution will arrive, that is, the citizens get tired of being poor.
Where is spatial error accounted for?
This again? Berkley Earth conclusively proved that UHI wasn’t skewing climate data
Berkley Earth – Mandy Rice-Davies applies (MRDA).
Being a UK resident and citizen her name may be known to you.
Berkley Earth has been conclusively refuted.
Got a reference for that please?
Berkeley Earth’s methodology was absolute NONSENSE.
Even a dumb-ass like you must realise that !!
It really takes GROSS INCOMPETENCE (or deliberate malfeasance) to say they can find no UHI effect on thermometers in urban areas…
And that is what Berkeley Earth deliver in spades.
fred250
“Berkeley Earth’s methodology was absolute NONSENSE.”
________________
Yet it came so highly recommended…
https://wattsupwiththat.com/2011/03/06/briggs-on-berkeleys-best-plus-my-thoughts-from-my-visit-there/
They got CONNED.
Berkeley were rabid warmistas right from the start.
Muller’s daughter was head of the pack, and Muller himself had made several anti-CO2 comments before they even formed.
Sorry to have to wake you up to reality, yet again, rusty !
Muller was NEVER a “skeptic”..
Always a believer…. just a con-artist.
http://www.populartechnology.net/2012/06/truth-about-richard-muller.html
Here’s Muller from 2003.
I’m sorry, you lost me at “Berkeley”.
From http://berkeleyearth.org/about/
OUR MISSION AND PURPOSEGlobal warming is the defining environmental challenge of our time. The need for quality, unbiased scientific information about global warming could not be more urgent.
They claim they want “unbiased scientific information”, yet they have already come to a conclusion as to the portent of the information they do not have, as certainly the data must indicate that “GW is the defining environmental challenge of our time.”
Usually, griff, science involves not coming to conclusions before the information is available. And, usually, griff, scientists at least attempt to show that they only want the truth, even if it is counter to their hypothesis.
They are being disingenuous when they say that they want “unbiased” scientific information. It is what leftists are, disingenuous, they can’t help themselves.
BTW: Did you notice that there is not one person of color on the the Berkeley Earth board of directors, or even, “on the team”? Yet another example of Leftist disingenuousness.
griff
You have once again demonstrated that you appear to be incapable of critical thinking. Yes, Mosher addressed the issue, and seems to believe it himself. However, it was a flawed analysis. Instead of comparing urban-core temps with the surrounding rural areas, he should have looked at the rural temperatures downwind, and compared them with those upwind from the urban areas. It has been documented in other studies that large cities not only increase the temperatures downwind, but also even impact rain. All too often people find what they start out looking for, even when it has no more validity than a cloud looking like some familiar object. And then gullible people like you say, “See, the science supports what I believe.”
Mosher is not a scientist
He is a hired mouthpiece to try to sell the Berkeley con-job.
A failed literature wannabe.
He addressed nothing….. he is not capable of it.
Short Memories?
HadCRUT4 data? Hadley Climate Research Unit Temperature? @East Anglia University?
The liars who were caught in the email disclosures fiddling with the data because temperatures were not rising fast enough. That HadCRUT?
https://wattsupwiththat.com/2018/10/29/how-bad-is-hadcrut4-data/
Interesting to see London mentioned as I live outside the city in the countryside and make journeys in and out of the suburbs and on numerous occasions I have seen nearly double the claimed 2.8C on my car thermometer in a period of about 45 minutes. And car does not show tenths of a degree so it could be double. I have also seen a change of 3C going from edge of Gatwick Airport to the south of London 8 miles to my house. I think the UHI effect is much larger than they either claim or make allowance for.
Could be.
I live near the center of a town in central Florida. My eldest son recently moved about a ten minute drive away and more rural. We have compared temperatures, and while they are essentially the same during the day, at nighttime his have been significantly lower.
So the poorly-collected temperature data is manipulated incorrectly and fed into unverified models that are wrong by design = this is supposedly what predicts doom? And people think I’m crazy for saying that any real changes in climate are >95% natural. Wake me when the climate “science” crowd gets around to doing real science.
I have occassionally driven Interstate 10 late a night in the summer months through Phoenix.
The UHI effect is easily +15ºF on the night time lows going from wide open desert (80ºF) to going through downtown Phoenix passing SkyHarbor airport (~95ºF) at 1 AM.
Just going into town (nearest is pop ~10,000 about 10 miles away) I sometimes see as much as a 5 degree(F) temperature difference.
The only reason somebody would take an “average” is to introduce weightings which allow them to put their thumb on the scale. Anybody experienced with science would understand that any unknown systematic error in weighting, such as UHI, could lead to grossly misleading results.
A graph of the median yearly daily high temperatures of a significant sample, say n>100, of well-understood and vetted weather stations across a region would inform far far more.
I instruct statistics, but I have only the vaguest understanding of these “averages” (HatCrud etc.). Given this, I fail to see how they may be responsibly presented to the public because, in no way, could the public be expected to understand what they do or do not represent.
If you want to understand “climate” then you must look at the entire temperature profile. Mid-range (Tmax-Tmin divided by two is *not* an average, it is a mid-range) temperatures tell you absolutely nothing about the temperature profile anywhere. If the climate scientists were really interested in “climate” they would use the integral of the temperature profile as a much better metric for measuring climate. This info is available at sites like degreedays.net. If they *really* wanted to be proper they would calculate enthalpy at each of the stations and integrate that to get the actual average heat content at a location.
The “average” databases are almost worthless because no one uses uncertainty for the measurements and propagates this through their calculations of a “global average” temperature. If this were to be done the uncertainty interval of their final calculation would be wider than any differential temperature values they are trying to identify! Because these are independent measurements of different things it is simply not possible to assume that the uncertainties will somehow cancel out if enough individual, independent measurements of different things are combined.
They are also trending non-stationary time series by averaging them. The means, variances, and distributions of all the station data populations are all different. They don’t even realize that doing this increases the variance to where you end up with higher variances than the actual trend mean value. That is 0.5 deg +/- 5 deg. You can see what this does for the possible range of what the actual GAT could be.
To exclude the effect of deforestation on Tmax and Tmin from the hypothesis creates great doubt in the veracity of the conclusion. Seriously, Australia has removed 40% of forests across the continent in 200 years, that’s millions of square kilometres. After years of reading the debates and scientific studies about climate, my personal conclusion, broadscale deforestation is a primary cause to variability in Tmax and Tmin.
If there is a temperature trend over a region for the last 60 years look for the measurement errors.
In Australia, the UHI is only a small portion of the measurement errors. The two most significant factors are relocation of rural manual recordings from the Post Office to nearby rural airports, then the introduction of automatic weather stations with fast response electronic transducers. Added to by the gradual increase in the size of aircraft and frequency of service to many rural airports.
Much of Australia’s Tmax can be traced to jet streams – literally. What comes out of the exhaust of jet engines as planes ready for take off.
Tmins have been missed because the calibration of the gauges considered that -8C was an impossibility in Australia so that was the bottom of the range for calibration. It was not until a fellow observed that his local official gauge was stuck at -8C when he was recording -12C nearby.
Show me a temperature trend other than zero and close inspection will reveal the measurement error.
The UAH lower troposphere temperature is a meaningless number with regard to surface temperature. The actual numbers are around freezing – who knows where that is in the atmosphere other than it being near cloud base and what relationship it has with the surface. It should be constantly recalibrated against the moored buoys to avoid its obvious bias.
Andy,
You remarked, “The anomalies in arid parts of North Africa may be due to a reverse urban effect, since in these area’s urbanization may create a cooler area, relative to the surrounding rural areas.”
There is an alternative hypothesis. These areas are very dry, thus water vapor is contributing little, if anything, to nighttime temperature increases. The “well-mixed” CO2 should be affecting the globe uniformly, if it indeed even has a significant effect. If there is an as-yet unidentified source of warming that has been responsible for warming these last 12,000 years, then it is going to be seen primarily in the Tmax and Tmax will therefore increase faster than Tmin in these dry areas.
Conversely, your Fig. 2 makes the case that Tmin is increasing more rapidly in heavily forested areas where high rainfall and transpiration increase the absolute humidity. Water vapor seems to be more responsible for increases in Tmin than CO2 is.
Correct, that was what I was thinking, I just didn’t explain it as well as you did.