Guest post by Dr. Willie Soon, Dr. Ronan Connolly & Dr. Michael Connolly
Recently, a new paper which we co-authored with five other researchers was published in Earth-Science Reviews entitled, “Comparing the current and early 20th century warm periods in China”. The paper is paywalled, but the journal has kindly allowed free access to the article until 20th July 2018 at this link here. If you’re reading this post after that date, you can download a pre-print here:Soon et al, 2018 ESR – China SAT trends (PDF)
The Supplementary Information and data for the paper is available here (Excel file) :Soon et al, 2018 ESR – China SAT trends – SI
The paper is quite technical and focuses specifically on Chinese temperature trends. But, we think that it will still be of interest to many readers here, especially anybody who is interested in any of the following topics:
- Urbanization bias
- The homogenization of temperature data
- The “early 20th century warm period” found in many parts of the Northern Hemisphere, and
- Comparing temperature proxies to instrumental records
Background to the project
Most studies of regional temperature trends for China have found at least two warm periods for the last century – the current warm period, and an early warm period from the 1920s until the 1940s. As part of our 2015 study of Northern Hemisphere temperature trends (abstract here; preprint here), we did our own analysis of Chinese temperature trends. We found that – for rural China – the 1940s warm period was actually hotter than present.
This was quite surprising in terms of the current human-caused global warming explanation. In the 1940s, the atmospheric concentration of carbon dioxide (CO2) at 0.03% was still relatively close to the pre-industrial concentrations of 0.027-0.029%, while currently the concentration is about 0.04%. For that reason, the current Global Climate Models calculate that (a) there shouldn’t have been an early 20th century warm period, and (b) the current warm period should be the hottest on record. This can be seen from the plot below which shows the mean “hindcast” for China of all 41 of the “CMIP5” climate models. These are the models which were used for the most recent UN IPCC reports:
In our 2015 paper, we argued that the most likely reason for the poor performance of the climate models is that they are underestimating the role of natural climate change and overestimating the role of carbon dioxide. [See also Soon et al., 2011 (abstract here; pdf here)].
However, while we were finding a very warm 1940s period for China, other groups were finding that the recent warm period was the “hottest on record” by far. For instance, the figure below is adapted from Liu et al., 2017 (abstract, paywalled unfortunately) – a study entitled, “Unprecedented warming revealed from multi-proxy reconstruction of temperature in southern China for the past 160 years”:
Liu et al., 2017 did find a slight warm period in the 1940s, but they found that the recent warm period was much hotter.
When we combed through the literature on Chinese temperature trends, we found that there was actually a lot of debate over how the early-20th century and current warm periods compared to each other. Some studies found they were both quite similar, e.g., Ren et al., 2017 (abstract here; pdf here). But, others concluded that the earlier “warm period” was just a temporary localised blip, e.g., Li et al., 2017 (open access).
Why were different studies getting such different answers?
Along with two other colleagues (Prof. Hong Yan and Peter O’Neill), we decided to collaborate with three of the scientists who had reached the opposite conclusion to us (Prof. Jingyun Zheng, Prof. Quansheng Ge, Prof. Zhixin Hao) and investigate.
Prof. Zheng and Prof. Hao were both co-authors of the Liu et al., 2017 study mentioned above, i.e., the one claiming the current warm period was “unprecedented” in the last 160 years. And along with Prof. Ge, they have co-authored many papers which often reached very different conclusions from us, e.g., see Wang et al., 2018 (Open access). However, even though we each have different views on this subject, we all agreed that it is important to establish the reasons for the disagreements. Our new paper describes the results of this collaboration.
Summary of our key findings
1. Limitation of data for pre-1950s period
Probably, the biggest challenge for comparing the early 20th century and current warm periods in China is the shortage of long-term records. After the People’s Republic of China was founded in 1949, a nation-wide network of weather stations was installed across much of China, and so there is a relatively large amount of data for the post-1950s period. However, most of these stations were not available during the 1940s, i.e., at the time of the earlier warm period!
This can be seen from the figures below showing the number of stations in two of the most widely-used temperature datasets: the Climate Research Unit’s “CRUTEM” dataset and NOAA’s “GHCN” dataset.
There is a lot of ongoing work to try and track down and digitize more data for China in the pre-1950s period, e.g., the so-called “ACRE” project – see Williamson et al., 2017 (Open access). But, for now, the available data is quite limited.
Some of you may have noticed that there was a surprising sudden drop in station numbers after 1990 for version 3 of both datasets. This was particularly pronounced for the GHCN dataset, which had a relatively large number of stations for the 1961-1990 period. There are several legacy reasons for this odd fall off, e.g., when GHCN was initially compiled, several of their main data sources had only been updated to 1990. However, with version 4 of both datasets, this particular problem seems to have been mostly resolved.
2. The urbanization bias problem
A related problem is that most of those stations with data for the early-20th century are in urban areas. This makes sense in that it is harder to staff a weather station continuously for decades if it is located in an isolated spot far from any urban area. However, urban areas tend to be warmer than the surrounding countryside due to the “Urban Heat Island” (UHI) effect. Since China has experienced a dramatic increase in urbanization over the last few decades, many of these urban station records have experienced a strong “urban warming” due to the growth of the local UHI.
This urban warming is a real climatic effect, and it is a growing problem for the Chinese population, since most people in China now live in urban areas. However, urban areas account for less than 1% of the land area of China. So, if you want to study the actual climatic trends for China, you need to correct for this “urbanization bias” problem.
To study this problem, we ranked all 494 Chinese stations in the GHCN version 4 dataset based on how urbanized they are today. We then divided the stations into five subsets according to these rankings. Below are the results for the most urban and most rural subsets:
We found that the more urbanized the subset, the hotter the recent warm period seemed to be. This suggests that at least some of the recent “warming” is an artefact of urbanization bias. However, unfortunately, the more rural the subset, the less data there was for the early 20th century – leading to greater error bars. So, if we want to compare the two periods, we can’t just rely on the rural data.
Also, we found that most of the urban stations were in eastern China, while most of the rural stations were from western and central-China.
3. The homogenization debate
Urbanization bias is by no means the only non-climatic bias associated with the temperature records. There are many different ways in which changes to the thermometer station and its environment can artificially alter the values of the “measured temperatures” independently of the real climate. For example, station moves, changes in the time of observation, changes in the immediate surroundings of the thermometer, etc.
Several groups have come up with different statistically based “homogenization” algorithms to adjust the station records to attempt to correct for such “step change” non-climatic biases, such as the Menne & Williams, 2009 algorithm used by NOAA (Open access).
Meanwhile, studies such as Venema et al., 2012 (Open access) have tested these algorithms by artificially adding synthetic biases to unbiased data, and seeing how good they are at removing them. On these tests, the main algorithms seem to do quite well. This has led many people to assume that the “homogenized” temperature datasets are more reliable than the “raw” (unhomogenized) datasets.
As it happens, when the Chinese temperature datasets are homogenized, this tends to slightly reduce the warmth of the early 20th century period and increase the warmth of the current period. This has led some researchers to conclude that the 1940s warm period was at least partially an artefact of non-climatic biases, e.g., Li et al., 2017 (Open access).
However, in Sections 3.2.3-3.2.5 of the paper, we point out that it ain’t necessarily so. We show that the current homogenization algorithms have a theoretical flaw called the “urban blending problem”. Whenever there are a lot of urban stations, and the urban stations are used as neighbours for homogenizing rural stations, some of the urban heat island of the urban stations gets added into the “rural” station records. This means that after homogenization, all of the stations have some urban warming in their records – even if they had originally been rural!
We can see the results in the following plot which shows the effects of homogenization on a sample of 10 stations near Beijing. The plot is adapted from He and Jia, 2012 (Open access).
We can see that before homogenization, there was a very strong correlation between the 1978-2008 warming trend and the rate of urbanization. That is, most of the warming trend was a result of urbanization bias. After homogenization, the differences in trends between the stations are reduced, and all of the stations have similar rates of warming. But, while the most heavily urbanized station has been cooled, the rural stations have been warmed. Instead of the urbanization bias being removed by homogenization, it has been merely spread among all stations (rural and urban).
This urban blending effect seems to be the main reason why homogenization “cools” the 1940s warm period for China. So, before we can rely on the homogenized data for China, we have to avoid urban blending. However, because there are so few Chinese rural stations for the early warm period, nobody has yet managed to construct a homogenized Chinese dataset for the early 20th century which only uses rural stations.
4. Inconsistency of temperature proxies
One possible approach to overcoming the urbanization bias problem could be to use so-called “temperature proxies”. These are datasets constructed from temperature-influenced measurements such as tree rings and ice cores. Although these are usually only indirect measurements of temperature, if they are calibrated against local thermometer records, they can be used to estimate long-term temperature trends for the region. The records often cover hundreds (or even thousands) of years. They also tend to be located in isolated areas, such as in the mountains, and therefore are usually unaffected by urbanization.
We think that temperature proxies are a very powerful tool for the climate science community and several of our co-authors on this paper do a lot of work with temperature proxies. However, they also have their limitations and should be used cautiously. We have noticed that there has been a tendency for researchers to focus mostly on the bits where different proxy series agree with each other. But, in Section 3.5, we point out that we should be comparing and contrasting the proxy series, rather than just focusing on the points of agreement.
Below are four different proxies for the same region, i.e., northeast China (although Wiles et al., 2014 is technically for northeast Asia). All of them show warming and cooling periods, and it is possible to find points of agreement between any two series. However, when you try to directly compare any two specific periods, e.g., the 1920s-1940s and 1980s-present, the results depend on which series you pick!
For instance, Zhu et al., 2015 implies that both periods were similarly warm (and that there were also similar warm periods in the 19th century). However, Zhu et al., 2016 implies that the 1940s were one of the coldest decades since the 19th century!
Therefore, we believe more research is needed before temperature proxies can be used for directly comparing periods such as the early 20th century and recent warm periods.
Conclusions
So, what can we say? The main conclusions of our study are as follows:
- The 1940s warm period seems to have been a real phenomenon in China. However, the data is probably still too limited to establish exactly how warm it was compared to the current warm period.
- The current climate models are unable to reproduce this early-20th century warm period, and can only simulate the recent warm period. The models attribute the recent warming to greenhouse gas emissions, and calculate that greenhouse gas emissions should not have caused an early-20th century warm period. This suggests that the earlier warm period was probably due to natural climate change – and that the current models are underestimating natural climate change.
- But, given that the models can’t explain the earlier warm period, it is quite plausible that some (or even most) of the recent warm period could also be due to similar natural climate changes to the ones which caused the 1940s warm period. That would imply that the current models are also overestimating the role of greenhouse gases in the recent warm period.
Disclaimer:
Given the fact that conspiracy theories abound in climate science, we should stress that all of the research for this collaborative paper was carried out during our free time and at our own expense. None of us received any funding for this research. The motivation for all eight of us in writing this paper was to try to advance the science on this topic.
Ah the 1940’s blip resurfaces. Why do they insist in merging urban and rural records.
That’s the best way to get the results they are looking for.
Mosher will perform a drive-by and tell you it doesn’t matter.
The real problem is averaging. You can’t take data from one place, average it with a thousand other places, and expect a meaningful result. You just can’t. If you look at individual station records, some go up, some go down, some stay relatively static. Averaging them all together removes most of the meaningful data.
“We found that – for rural China – the 1940s warm period was actually hotter than present.”
Huge point. Same with the US, for the ’30s.
Or at least the actual NASA data from 1999 shows that the 1930s were hotter:
And here’s NASA’s Hansen before they changed the data in the 2000s:
“In the U.S. there has been little temperature change in the past 50 years, the time of rapidly increasing greenhouse gases — in fact, there was a slight cooling throughout much of the country.” -James Hansen, Chief Scientist, NASA, 1999
Look at all these spots that were hotter in the 1930s or ’40s, and considering the global data for ~ the ’30s is much spottier and less reliable than the US data, you could conclude that the US data is in fact a better representation of the globe than the sparse global data. The 1930s were likely hotter than today … globally!
Interesting point -As a comparison – If you look at annual pictures of most of the north american glaciers, there is significant melt in the 1930’s. Rarely are annual comparison photos shown, Almost always there is one photo of the early 1900’s with the next photo in the 1980’s and then early 2000’s to give the impression that most of the melt has been in the last few decades.
Most of the retreat of glacier Bay took place in the 19th century. that little fact quietly ignored,
Thanks for that Hansen 1999 chart, Eric. I think that is the temperature profile (the 1930’s/40’s are hotter than subsequent years) we have really been living under all this time. It shows up everywhere, all over the world, in both hemispheres.
The Hansen 1999 chart shows the year 1934 as being 0.5C hotter than 1998, which makes 1934 0.4C hotter than 2016, which means we have been in a temperature downtrend all this time and it continues to this day.
One of Michael Mann’s Climategate co-conspirators measured the 1934 temperature at (if memory serves) 0.46C hotter than 1998. This was in an email where they were discussing how to hide this 1930’s/40’s heat, otherwise there is no correlation between temperatures and CO2 levels, and this would blow up their CAGW hypothesis, so they decided the 1930’s/40’s heat had to disappear from the record.
They disappeared the heat from their Hockey Stick charts but they were not able to destroy the original data so we know exactly what they did
Their fraudulent actions have cost the human race enormous amounts of money and driven a good portion of the populace insane and paralyzed politicians with fear of carbon dioxide.
The Climategate participants cheated and lied. One of these days they need to be held to account for their actions.
A “pious fraud”: the ends justify the means. The “end” is saving the planet from what we believe might be catastrophic warming. Note the word “believe.”
I think that sums up what this has all been about.
No It was about saving their jobs. If we dont have to worry about the future climate why would we keep funding studies about it?
They should be charged for treason.
the sadest part of it all…..if they had left that alone….the models might stand a chance of being at least half right…and might even be able to show the ups and downs
…as it is…..it’s their models that have made total fools of them…and it’s their models that show they lied
The climistas insist that this 1940’s temperature blip documented in China is merely a regional phenomena so that they can ignore it.
Funny how this localized temperature blip keeps showing up in more and more temperature records from around the world.
That time period created what was known as the “Dust bowl.” As many as 8 years of drought and extremely hot. We have had nothing like that since.
Yes, the whole decade of the 1930’s and into the 1940’s was very hot. Hotter and more extreme than today by far.
“the most likely reason for the poor performance of the climate models is that they are”…..
Tuned to a fake temperature history…….that has the past adjusted down to show a faster rate of warming….that the models match exactly
No, now they are changing the data to match the models!
The real problem seems to be that there are not good enough records for a long enough time period to draw conclusions about possible causes. The tree ring proxies look as irregular as the disputes over Mann’s hockey stick would imply.
I find that most proxies are pretty useless. They’re either not representing temperature at all, or they have such poor temporal resolution as to make them useless for decadal comparisons.
I think Chinese papers will soon cleave to the official CAGW party line, just as much as they do in the West.
This extensive article describes the current situation in China and notes that skeptics have suddenly all but disappeared.
Official Chinese position seems to be that because they lack oil and gas they choose to see fossil fuels as evil, so long as it doesn’t stop them from using coal to produce power. They seek to sabotage the economies of the West while protecting their own.
I thought the Chinese were smarter than what they are showing. If they are going full green they will repeat the mistake of Germany and Denmark and all the other places that have seen more than a doubling of electricity prices. However the Chinese leadership clearly don’t believe in global warming because they are financing coal plants all over the world because they are the biggest producers of coal in the world.
False. Are they hypocrites? Absolutely. But they believe AGW is real.
https://reliefweb.int/report/china/chinas-oceanic-authority-calls-measures-rising-sea-levels
“…we decided to collaborate with three of the scientists who had reached the opposite conclusion to us…”
Vastly different approach than is taken by folks like Mann.
A different approach. Yes, one that approaches how “climate science” should be done.
One that seeks to find out what is happening rather than one that has decided what is happening and then seeks confirmation.
It’s a shame, actually shameful, that such a disclaimer needed to be made.
Who pays Mickey’s legal bills?
Charitable foundations?
Who funds them?
Why aren’t the answers to those questions presented as “accusations”?
In 1936 , in the small town that I live in there were ten days in a row with high temps above 110 deg F.
Eastern Ks .
No AC ….. and we have not come close to that since .
Like all climate data prior to systems that were specially designed to do climate research. The data is poor quality and not suitable for analysis. The instruments are probably uncalibrated. You can’t be sure the the reading was performed at the said time of day. The accuracy the instrument was read.
They can do all the homogenation they want but the error is too great
Silk out of a sow’s ear.
Gunga Din
In the case of climate research, I rather think its ear wax out a sows ear.
Sh*t out of a bulls ars* more like
And in the 1940s, lots of Chinese people had other things to worry about.
We had a friend in Hunan back then, maintaining an orphanage. Japanese bombed and strafed their city almost daily. Rough times. He made it through, then had a breakdown just after the Americans arrived. My dad sent him some US funds, and he wrote back that we’d made him a millionaire (in Chinese money).
Anyone with serious analytic comprehension of “homogenization” should realize that in a predominantly urban data base, such as GHCN, it will produce a global warming bias. Instead of relying blindly upon such ad hoc schemes, compilers of various indices should realize that the station records employed need to be independently vetted in order to exclude severely UHI-corrupted data. Sadly, the rigorous signal-analysis methods required for such vetting are terra incognita in “climate science,” leaving the resolvable distinction between UHI- and CO2-produced warming thoroughly and tendentiously muddled.
The UHI card is vastly overplayed by sceptics. What really matters in most cases is how UHI changes with time, not how big it is. Urban stations can be used to correct inhomogeneities in rural ones because over a few decades UHI is unlikely to change by much. UHI is likely to be dominated by nearby walls rather than by distant structures, so rural stations can have it as well.
I think you should visit surface station .com Rural station may not be the best example of a correct method to collect weather data.
OCW, absolutely correct for the one rural station I surveyed. The MMTS was right next to a wooden shed, definitely wasn’t 2m high, and was encroached upon by grass and weeds almost as high as the sensor.
They forgot the mandatory trash burner six feet from the sensor?
Sorry, you can’t “correct” one station with another. Each one is its own entity.
I live 13 straight-line miles from where I work. And at the same time of day, the temps can vary by as much as 20 degrees, sometimes more.
I don’t believe that they are correcting one station with another. The method that I have read about is to use the trends of surrounding stations to fill in for any missing data in another station.
The problem is that rural stations are more likely to have gaps in the record compared to urban station, this is what allows urban trends to influence rural trends.
MarkW, I fail to see a difference.
If the town is already large, then UHI at the center of it won’t change much in a few decades.
However, very few weather stations are located in the center of towns. Most are in rural areas near towns, and these areas can urbanize very fast.
I lived in Atlanta for 23 years starting in the mid-70’s. During that time the edge of the city expanded outwards between 20 and 30 miles. Those areas that went from rural to suburban to urban during that time would have seen a huge UHI increase.
It’s similar for small towns. Going from 15,000 to 20,000 in a few decades is trivial, and can also have a big impact on UHI.
The reverse is not true, if a town drops from 20,000 to 15,000, the buildings aren’t torn down and the roads aren’t torn up. Not in just a couple of decades.
It’s precisely the rate of change over many decades that makes UHI-afflicted stations totally unsuitable as a regional reference. The fact that rural stations don’t necessarily escape their own corruptive effects merely emphasizes the need for stringent vetting.
It also shows how futile the entire effort to “fix” the ground based sensor data is.
Almost all of the stations are contaminated. How they are contaminated and to what degree varies from site to site and is to a large degree undocumented.
The whole notion of “homogenisation” of urban temperature data is risible. Just looking at the graph comparing raw and ‘homogenised’ data, the slope of the raw data is 4 times that of the former (0.044 vs 0.011) , i.e. 75% at least of the trend is clearly deemed to be UHI induced.
75%? why not 90%, 100%, 110% 150%. Where does the ‘homogenisation’ factor come from?
Why even use such obviously and now demonstrably corrupted data? Its like a restaurant’s staff dumpster diving for ‘raw’ food then claiming to be worthy of a Michelin star or two.
What I’ve been told by climate “scientists” is that while they know the data is bad. It’s all they have.
And they also truely believe that using the right statistical methods they can silk purses out of sow’s ears. The fact that the methods they apply, force the data to show what they want to see is just proof to them that their methods are correct.
Thank you, authors, for pointing out that removing bias is not the same as changing the slope of a line.
No knowledge of this time period in China but of course the 1930’s drought and heat waves in North America are very well known and indisputable. Even by climate “scientists”. I have lived through a few less severe droughts and always found that they were accompanied by high temperatures. It sees obvious to me that the dry conditions created the high temperatures more so than the other way round. Rain breaking the drought or higher winter snowfall creating wetter conditions resulted in the end of excessive heat.
Nowhere in official Warmist climate science have I ever seen reference to changing humidity levels or their effect on temperatures. For anyone who understands the full effects of enthalpy on temperature, this lack of attention is a clear signal that the whole field is a farce of politics and eco-religion.
With the exception of Dr. Soon and his associates and a few others, I should add, who continue to do real science without regard for where it leads so long as it is directed toward the truth.
I should also point out that low winter temperatures also come about during high pressure/ low humidity conditions.
JH,
Rain cools.
It’s signature is all over the historic temperature record.
It can be ‘adjusted out’ mathematically.
Then, the rather mportant question arises for GCM modellers – for energy balance calculations, should one use raw temperatures, or those adjusted for rainfall, whose energetics should be elsewhere in the water equations? Geoff
An important point that is not being paid enough attention is that the early 20th C. warming and the late 20th C. warming appear to have been of different nature, and this is one of the causes why proxies often give different readings. The late warming appears to have increased preferentially minimum temperatures affecting more night and winter temperatures, while the early warming seems to have affected more daily and summer temperatures, indicating different contributions from different causes. As a result temperatures were more extreme and variable during the early warming, while the late warming has produced milder general temperatures. Precipitation patterns also appear to have been quite different between both periods. This all boils down that obtaining an average temperature from thermometer readings or proxies doesn’t give much information about the climate and its causes. But some people are just obsessed with numbers and you really can’t describe climate changes with a single number or graph. It is a lot more complex and variable than that.
An example of what I say. Mountaintop plants are responding differently to the late 20th C. warming versus the early 20th C. warming, showing their different nature:
Figure 1. Rate of change in species richness (mean, red line). The shaded grey area represents ± s.e.m.
From: Steinbauer, Manuel J., et al. “Accelerated increase in plant species richness on mountain summits is linked to warming.” Nature 556.7700 (2018): 231.
See more: http://www.co2science.org/articles/V21/jun/a7.php
Something even more fundamental is missing here: the assurance of total uniformity of measurement methods and locations. Much has changed in that regard throughout the 20th century, even with instrumented data, rendering uncertain whatever nuanced differences may be detected between warming episodes in century-long data series. With proxy data, insignificant coherence seems to be the rule.
Instead of this confusion, why not use satellite data for selected urban and rural areas with large greenfields and look at the real contribution of ecological change component of non-greenhouse effect [human impact effect]. This is the only way to say there is global warming or not associated with CO2.
Dr. S. Jeevananda Reddy
Dr.Reddy,
The method you suggest will not yield valid results, because there is no meaningfull gauge of pre-satellite vegetation.
A more meaningful method would collect and compare all previous and existing rural stations temps over a set period to establish a delta. Likewise for historical and existing urban stations.
Weighting between the two sets must be based on surface area, vis-a-vis station number. Thus, urban deltas are weighted in small urban land masses, and rural deltas are extrapolated over the bulk of land mass.
A first approach to look for suspicious UHI is to compare the troposphere temperatures ( here UAH) with the T2m.
The northern part of China matches very good ( mostly rual) but the southern part should have a big footprint of UHI. The figure was generated with the KNMI climate explorer.
Some of us have looked at Australian historic T data to conclude that –
1. Generally, the more remote weather stations have the worse quality data.
2. This hinders comparison of rural and urban data
3. Thus, factors affecting UHI are as yet poorly understood.
4. This is a severe impediment for attempts to homogenise.
5. Past homogenisation attempts failed to capture and quantify some variables.
6. One such variable is the cooling effect of water such as rain.
7. Another such variable is site moves found post-homogenisation.
8. Statistically, many stations show no warning trend in the past century when adjusted for 6 and 7.
8. The whole topic of historic temperatures is mired in suspected subjective bias towards warming, to support global warming conjecture.
Thank you, Dr Soon et al for this China work. It fits neatly with ours in prep. Geoff.
“Berkeley declares ‘climate emergency’ worse than World War II, demands ‘humane’ population control”
http://www.foxnews.com/politics/2018/06/13/berkeley-declares-climate-emergency-worse-than-world-war-ii-demands-humane-population-control.html
It has ALWAYS been about population control !!
From the nutcases and radical leftist loons of Berkeley. Michael Mann, a graduate from Berkeley himself, goes left like the rest of them. You see that in how they’re always hobnobbing with the most extreme leftist politicians. They don’t even try to hide it: climate “science” is politicized science which is leftist propaganda not science.
Dr Soon;
As always, it is good to hear from you.
You above article (co-auyhored with Ronan Connolly and William Connolly) reports the following.
“Liu et al., 2017 did find a slight warm period in the 1940s, but they found that the recent warm period was much hotter.
When we combed through the literature on Chinese temperature trends, we found that there was actually a lot of debate over how the early-20th century and current warm periods compared to each other. Some studies found they were both quite similar, e.g., Ren et al., 2017 (abstract here; pdf here). But, others concluded that the earlier “warm period” was just a temporary localised blip, e.g., Li et al., 2017 (open access).
Why were different studies getting such different answers?
Along with two other colleagues (Prof. Hong Yan and Peter O’Neill), we decided to collaborate with three of the scientists who had reached the opposite conclusion to us (Prof. Jingyun Zheng, Prof. Quansheng Ge, Prof. Zhixin Hao) and investigate.
Prof. Zheng and Prof. Hao were both co-authors of the Liu et al., 2017 study mentioned above, i.e., the one claiming the current warm period was “unprecedented” in the last 160 years. And along with Prof. Ge, they have co-authored many papers which often reached very different conclusions from us, e.g., see Wang et al., 2018 (Open access). However, even though we each have different views on this subject, we all agreed that it is important to establish the reasons for the disagreements. Our new paper describes the results of this collaboration.”
Such investigation of “disagreements” is how real science is done.
However, it is NOT how ‘climate science’ is conducted.
Therefore, your findings will be disregarded by all adherents to the politically-supported superstition that is warmunism (e.g. supporters of the UN FCCC and UN IPCC)..
Richard
“But, given that the models can’t explain the earlier warm period, it is quite plausible that some (or even most) of the recent warm period could also be due to similar natural climate changes to the ones which caused the 1940s warm period. That would imply that the current models are also overestimating the role of greenhouse gases in the recent warm period.”
Or… the models and Liu et al., 2017 are correct and and the earlier warm period wasn’t as warm as the current one because:
“the data is probably still too limited to establish exactly how warm it was compared to the current warm period”.
Nothing to see move along.
Whistling past the graveyard.
An impressive piece of detective work. I think I need to get the data and have a look at it myself.
It basically adds to our knowledge that the original USA land dataset shows the 1930’s to have been the hottest recent era. It also gets you wondering if that was the case globally. If it was, then it’s basically game over for the alarmists. (Although they will no doubt continue trying to convince the public otherwise)
A warm period is warmer than, not hotter than. The lack of technical precision among climate people is awful. However, this is a nice attempt at resolving a major issue and I would like to thank the authors for their diligence and efforts
Stephen Richards:
Please explain what you claim to be the difference between “warmer than” and “hotter than”.
Failure to define the terms they use is an “awful” practice adopted by people who try to denigrate an article by ‘nit-picking’ language used in it when they cannot fault what it says.
Another such ‘awful’ practice is ‘damning with faint praise’ such as providing thanks for “diligence and efforts”.
Richard
That is what homgenisation will do. It removes peaks by spreading them around. That is the point!
Warm period from about1920 to1950. I wonder if it was also a very dry period.
The claim that the present warm period is the warmest recorded is false and the temperature in the eighties and nineties incorporated into it this false warming is simply a fabrication. . It can be demonstrated that the global temperature for this 18-year period did nt rise and that this period should properly be regarded as a hiatus period. Its true temperature is shown in figure 15 of ny book “What Warming” and is steady for 18 years. The same can be observed in every monthly temperature chart issued by the UAH satellite service since 2008 that incorporates this period. This 18 year hiatus period takes a large chunk iut of the beginning of the current warm period and raises our suspicion that the rest of that temperature rise may also be fabricated. Especially since they ignore a cooling period from 2003 to 2012.