From the “fighting denial with denial” department comes this desperate ploy and press release written to snare headlines with gullible media. Meanwhile, just a couple of days ago the UK Met office said the global warming pause may continue.
Global warming ‘hiatus’ never happened, Stanford scientists say
A new study reveals that the evidence for a recent pause in the rate of global warming lacks a sound statistical basis. The finding highlights the importance of using appropriate statistical techniques and should improve confidence in climate model projections.
An apparent lull in the recent rate of global warming that has been widely accepted as fact is actually an artifact arising from faulty statistical methods, Stanford scientists say.
The study, titled “Debunking the climate hiatus” and published online this week in the journal Climatic Change, is a comprehensive assessment of the purported slowdown, or hiatus, of global warming. “We translated the various scientific claims and assertions that have been made about the hiatus and tested to see whether they stand up to rigorous statistical scrutiny,” said study lead author Bala Rajaratnam, an assistant professor of statistics and of Earth system science.
The finding calls into question the idea that global warming “stalled” or “paused” during the period between 1998 and 2013. Reconciling the hiatus was a major focus of the 2013 climate change assessment by the Intergovernmental Panel on Climate Change (IPCC).
Using a novel statistical framework that was developed specifically for studying geophysical processes such as global temperature fluctuations, Rajaratnam and his team of Stanford collaborators have shown that the hiatus never happened.
“Our results clearly show that, in terms of the statistics of the long-term global temperature data, there never was a hiatus, a pause or a slowdown in global warming,” said Noah Diffenbaugh, a climate scientist in the School of Earth, Energy & Environmental Sciences, and a co-author of the study.
Faulty ocean buoys
The Stanford group’s findings are the latest in a growing series of papers to cast doubt on the existence of a hiatus. Another study, led by Thomas Karl, the director of the National Centers for Environmental Information of the National Oceanic and Atmospheric Administration (NOAA) and published recently in the journal Science, found that many of the ocean buoys used to measure sea surface temperatures during the past couple of decades gave cooler readings than measurements gathered from ships. The NOAA group suggested that by correcting the buoy measurements, the hiatus signal disappears.
While the Stanford group also concluded that there has not been a hiatus, one important distinction of their work is that they did so using both the older, uncorrected temperature measurements as well as the newer, corrected measurements from the NOAA group.
“By using both datasets, nobody can claim that we made up a new statistical technique in order to get a certain result,” said Rajaratnam, who is also a fellow at the Stanford Woods Institute for the Environment. “We saw that there was a debate in the scientific community about the global warming hiatus, and we realized that the assumptions of the classical statistical tools being used were not appropriate and thus could not give reliable answers.”
More importantly, the Stanford group’s technique does not rely on strong assumptions to work. “If one makes strong assumptions and they are not correct, the validity of the conclusion is called into question,” Rajaratnam said.
A different approach
Rajaratnam worked with Stanford statistician Joseph Romano and Earth system science graduate student Michael Tsiang to take a fresh look at the hiatus claims. The team methodically examined not only the temperature data but also the statistical tools scientists were using to analyze the data. A look at the latter revealed that many of the statistical techniques climate scientists were employing were ones developed for other fields such as biology or medicine, and not ideal for studying geophysical processes. “The underlying assumptions of these analyses often weren’t justified,” Rajaratnam said.
For example, many of the classical statistical tools often assume a random distribution of data points, also known as a normal or Gaussian distribution. They also ignore spatial and temporal dependencies that are important when studying temperature, rainfall and other geophysical phenomena that can change daily or monthly, and which often depend on previous measurements. For example, if it is hot today, there’s a higher chance that it will be hot tomorrow because a heat wave is already in place.
Global surface temperatures are similarly linked, and one of the clearest examples of this can be found in the oceans. “The ocean is very deep and can retain heat for a long time,” said Diffenbaugh, who is also a senior fellow at the Woods Institute. “The temperature that we measure on the surface of the ocean is a reflection not just of what’s happening on the surface at that moment, but also the amount of trapped heat beneath the surface, which has been accumulating for years.”
While designing a framework that would take temporal dependencies into account, the Stanford scientists quickly ran into a problem. Those who argue for a hiatus claim that during the 15-year period between 1998 and 2013, global surface temperatures either did not increase at all, or they rose at a much slower rate than in the years before 1998. Statistically, however, this is a hard claim to test because the number of data points for the purported hiatus period is relatively small, and most classical statistical tools require large numbers of data points.
There is a workaround, however. A technique that Romano invented in 1992, called “subsampling,” is useful for discerning whether a variable – be it surface temperature or stock prices – has changed in the short term based on limited amount of data. “In order to study the hiatus, we took the basic idea of subsampling and then adapted it to cope with the small sample size of the alleged hiatus period,” Romano said. “When we compared the results from our technique with those calculated using classical methods, we found that the statistical confidence obtained using our framework is 100 times stronger than what was reported by the NOAA group.”
The Stanford group’s technique also handled temporal dependency in a more sophisticated way than in past studies. For example, the NOAA study accounted for temporal dependency when calculating sea surface temperature changes, but it did so in a relatively simple way, with one temperature point being affected only by the temperature point directly prior to it. “In reality, however, the temperature could be influenced by not just the previous data points, but six or 10 points before,” Rajaratnam said.
Pulling marbles out of a jar
To understand how the Stanford group’s subsampling technique differs from the classical techniques that had been used before, imagine placing 50 colored marbles, each one representing a particular year, into a jar. The marbles range from blue to red, signifying different average global surface temperatures.
“If you wanted to determine the likelihood of getting 15 marbles of a certain color pattern, you could repeatedly pull out 15 marbles at a time, plot their average color on a graph, and see where your original marble arrangement falls in that distribution,” Tsiang said. “This approach is analogous to how many climate scientists had previously approached the hiatus problem.”
In contrast, the new strategy that Rajaratnam, Romano and Tsiang invented is akin to stringing the marbles together before placing them into the jar. “Stringing the marbles together preserves their relationships to one another, and that’s what our subsampling technique does,” Tsiang said. “If you ignore these dependencies, you can alter the strength of your conclusions or even arrive at the opposite conclusion.”
When the team applied their subsampling technique to the temperature data, they found that the rate of increase of global surface temperature did not stall or slow down from 1998 to 2013 in a statistically significant manner. In fact, the rate of change in global surface temperature was not statistically distinguishable between the recent period and other periods earlier in the historical data.
The Stanford scientists say their findings should go a long way toward restoring confidence in the basic science and climate computer models that form the foundation for climate change predictions.
“Global warming is like other noisy systems that fluctuate wildly but still follow a trend,” Diffenbaugh said. “Think of the U.S. stock market: There have been bull markets and bear markets, but overall it has grown a lot over the past century. What is clear from analyzing the long-term data in a rigorous statistical framework is that, even though climate varies from year-to-year and decade-to-decade, global temperature has increased in the long term, and the recent period does not stand out as being abnormal.”
###
Debunking the climate hiatus
Bala Rajaratnam, Joseph Romano, Michael Tsiang, Noah S. Diffenbaugh
Abstract
The reported “hiatus” in the warming of the global climate system during this century has been the subject of intense scientific and public debate, with implications ranging from scientific understanding of the global climate sensitivity to the rate in which greenhouse gas emissions would need to be curbed in order to meet the United Nations global warming target. A number of scientific hypotheses have been put forward to explain the hiatus, including both physical climate processes and data artifacts. However, despite the intense focus on the hiatus in both the scientific and public arenas, rigorous statistical assessment of the uniqueness of the recent temperature time-series within the context of the long-term record has been limited. We apply a rigorous, comprehensive statistical analysis of global temperature data that goes beyond simple linear models to account for temporal dependence and selection effects. We use this framework to test whether the recent period has demonstrated i) a hiatus in the trend in global temperatures, ii) a temperature trend that is statistically distinct from trends prior to the hiatus period, iii) a “stalling” of the global mean temperature, and iv) a change in the distribution of the year-to-year temperature increases. We find compelling evidence that recent claims of a “hiatus” in global warming lack sound scientific basis. Our analysis reveals that there is no hiatus in the increase in the global mean temperature, no statistically significant difference in trends, no stalling of the global mean temperature, and no change in year-to-year temperature increases.
The paper is open access, read it here
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“The finding highlights the importance of using appropriate statistical techniques and should improve confidence in climate model projections.”
What qualifies these authors, over all other climate scientists, to be the ones to define which statistical techniques are most appropriate for dealing with temperature data? I suspect that the statistical technique that most improved confidence in climate model projections was the one they determined to be the most ‘appropriate’, and it just happened to be a technique invented by one of the authors.
They are the ones that most closely match the models. That makes them the experts.
Give me a million bucks and I will prove to you that pigs can fly.
maltesertoo,
“Give me a million bucks and I will prove to you that pigs can fly.”
To Paris in December?
michael (-:
maltesertoo:
Give me a million bucks and I will prove to you that pigs can fly.
We’re in a free market. I’ll prove it for half a mil!
My model can prove anything…
For some reason, this reminded me of the show “Pumpkin chunkin”.
“While the Stanford group also concluded that there has not been a hiatus, one important distinction of their work is that they did so using both the older, uncorrected temperature measurements as well as the newer, corrected measurements from the NOAA group.”
When they say “older, uncorrected temperature measurements,” are they referring to the ‘raw’ data? Did they use any charts in their paper? I’m surprised none were posted in this article. It would be nice to see a chart comparing the raw data with the corrected data, as well as one comparing the old statistical technique to the new.
It would also be instructive to see the evidence for their claim that “the statistical confidence obtained using our framework is 100 times stronger than what was reported by the NOAA group.”
‘100 times stronger’ refers to their new tooth paste that is better than anyone else’s tooth paste so hurry and buy your tube before the mobs buy it all out! 🙂
I tried using a “novel statistical framework” to show my bank that I didn’t have an overdraft and that, in fact, they owed me money. Have a guess how impressed they were and what the outcome was.
The boys are nothing if not predictable: if you don;t like the data, go back and change it.
This won’t fly. The satellite data are the gold standard.
Okay, they are clearly using the NASA GISS Global Surface Air Temperature Anomaly as measured by meteorological stations.
http://data.giss.nasa.gov/gistemp/graphs_v3/Fig.A.gif
Let’s not delve into the details of this temperature reconstruction for now.
Their main point is, trend of 1997-2013 is not statistically different from trend of 1950-1997. Let’s accept that as well.
Now, what’s the rate of warming according to GISTEMP during the 65 years from 1950 to 2014? It is 175 mK/decade. I wonder if applying the same method it can be shown to be statistically different from a zero trend or not…
But that question is not asked in the paper.
Dumb question – were all these meteorology stations around in 1880? Is it just the mean value? Wouldn’t the number of earlier stations represent a less dense sample?
DWR54:
I write to congratulate you. In the 5 hours this thread has existed you have already contributed to it with 16 posts and every one of your posts is silly.
That is a remarkable amount and rate of disruptive behaviour from an individual (assuming you are one person). Well done!
Richard
I was just thinking the same thing myself
May they live in interesting times.
Maybe, just maybe, cooling will be about to start.
By 2030, it will be very interesting:
“It’s not cooling, it’s inverse warming.”
“Global warming has not stopped, it’s just a downward
adjustment and temperatures are actually trending up.”
” Negative trend? There’s no negative trend: it’s a statistical
artifact of faster warming.”
“The thermometer is upside down.”
“The Earth is going into a new ice age.”
“Build more windflails.”
“It’s not a minus sign, it’s a dash.”
“That’s not ice in the Arctic, it’s surfactant froth.”
“Polar bears? Aren’t they extinct from global warming?”
“Temperature’s are not falling. You’re using an incorrect
statistical technique.”
“Be quiet and pay your carbon tax. Be thankful you’ve
got one. What would the weather be like without them?”
“Cooling is what warming does.”
…
Yeah.
Right.
We’re just warming in a different direction.
Rubbish!
Cherry pick, convoluted nonsense using custom tailored statistic runs.
1a) The analysis is not based on temperatures, only on anomalies, without error bars.
1b) HadCRUT4 does not use infilling so is not considered valid…? Only GISTemp is thoroughly adjusted enough. Again, no error bar ranges for ‘adjusted temperatures’ before anomalies.
1c) The chosen base period is 1950-1980. Odd that 1950 through 1975 is another ‘hiatus’ period in temperatures.
2a) Strawman! Insisting that current hiatus statements are based upon a claim that Earth’s current hiatus trend is lower than 1950-1997’s trend is classic straw man smoke and mirrors.
2b) Comparing trends from land series that undergo massive adjustments allegedly correcting temperature station changes, alterations and moves is a false approach without full defined error bars for all adjustments and changes. Without a clear defined and universally accepted rationale for adjustment, only the original temperature should be used.
3a) As the period 1950-1975 is a hiatus period, so too is 1880-1916 a relative hiatus.
And it goes on.
4a) Assume false premise
4b) Assign assumptions
4c) Accept and utilize extremely malleable and historically modified data.
4d) Mandate that infilling data is a requirement
4e) Skip utilizing actual temperature slope changes over time, instead use a very vague concept of Global Temperature anomaly. Devise multi-step parameters.
4f) Calculate Ordinary least-squares (OLS) regressions.
4g) Further calculate OLS autocorrelation function (ACF) and partial autocorrelation function (PACF) in assigning a ‘Temporal dependence’ factors and correcting for them. An odd approach includes the rejection of the NULL hypothesis (simple dependence).
…
All of this for simply calculating and comparing the slope for given sections of the temperature record? Something that any qualified meteorologist or student of weather can easily do?
The more convoluted the posturing, the louder and more insistent are wild claims, the more likely a snake oil salesman is defrauding the people.
“…Datasets used in analysis
The NASA GISTEMP dataset uses the 1951-1980 average as the baseline period and estimates anomalies up to 1200 km from the nearest measurement station, allowing for broad spatial coverage. The NOAA data reconstructs land data for unobserved regions using a method called “empirical orthogonal teleconnections.”
The HadCRUT4 data does not use any spatial infilling and thus has gaps in grid squares with very sparse (or no) data. The HadCRUT4 data therefore does not account for warming in the Arctic and Antarctic regions,
leading to documented coverage bias (Cowtan and Way, 2014).”
1) HADRCUT4 may not account for some warming or cooling in the Arctic 80N+, but the area is so tiny relative to the size of the planet it hardly makes any difference.
2) If you are so concerned with this, why not use DMI covering 80N+ that uses real observations (balloon, buoys, ship & plane readings etc) that organisations use for helping generate correct weather forecasts.
3) Infilling data is making up nonsense because there is no way you can know what may be happening there. The weather on it’s own can easily distinguish between huge temperature changes. Infilling from land to ocean surface is the worst technical science rubbish that can be ever done. Temperatures on the water surface change significantly slower to those on land. Might as well stick a tail on a donkey with numbers on it and use that.
4) Satellite data covers far more of Antarctica than GISS ever will and it shows no warming.
Therefore the claim that Antarctica is warming when it is not with real observations, shows that infilling the grids with no observation has cause this result and difference. GISS shows far more warming than any other global data set because of infilling over Antarctica and Arctic coverage. This is despite satellites especially UAH having far more coverage of these regions than GISS does. The infilling also seems to be deliberately extreme on just odd occasions to get those record temperature peaks that none of the others do.
HACRUT4 has it’s faults and is doing some of the GISS tricks, but it is far better observation data set than a made up one from GISS.
Same old nonsensical argument. 15 years of rising temperatures is a catastrophic trend that requires immediate, drastic action from mankind. 15 years of without rising is statistically insignificant.
The problem is not enough time has elapsed since the 1998 to 2013 period has passed, they only incrementally adjust the temperatures upward about once a year. In another 10 years, with 10 more years of adjusting upwards, the 1998 to 2013 period will show a significant rise. Remember in 2000 when James Hansen summarized the 20th century temperature trend as having no significant trend up or down? 10 years later, slower than the speed of grass growing but growing none the less, James Hansen’s temperature trends for the 80s and 90s grew into enough of an upward trend to make himself and many colleagues rich from telling us about a dangerous temperature trend that emerged from the data years and years after the measurements were taken.
So, I’m still trying to find the proof that the warming since 188x is statistically significant, given the presence of AMO, PDO, and autocorrelation. Where is this proof? I can’t find it despite endless Google searches.
?dl=0
?dl=0
When I run my own Monte Carlo analysis of trends on autocorrelated noise that has the same spectrum as GISS or Hadcrut4, I get a very tiny amount of warming above the 95% confidence interval only for the most adjusted temperature set – GISS, and lower than 95% confidence for Hacrut4.
By the standards I”m using, the hiatus is also statistically insignificant. So I agree with the authors of the paper cited in this article. But so is the entire AGW idea…or at least the coefficient of C02 log2 function is ridiculously small….
Peter
Method: Generate noise of equivalent RMS and spectrum of length 8x that of the record in question. Run Monte Carlo simulation to find band of 95% of trends of the length of the record in question.
Source: I also note this is rough draft work: https://www.dropbox.com/sh/6eweroc97i0dlk9/AAAhPRyxAb2XtJQp2MOpwqEEa?dl=0
I looked at the GISS dat using the n equivalent method Willis discused a while back for time series data. I had to adjust the equations for slope error a bit to get it to match typical errors generated with random time series data. So I am not sure the numbers are correct, but for the whole GISS record the slope was not significantly different from zero at alpha = 0.05. But for the first and second half of the data I did get slightly significant trends different from zero.
If you’re going to try to say someone else is using “faulty statistics” you should be using “tried and true” statistics not “…a novel statistical framework…”
I always find a statement that the confidence is 100 times more as a good reflection on how we can rely on such information. Why not state that they are 99 times more confident or 101times . It’s all so meaningless and the fact that any one could take such fiction seriously is a bit disturbing.
Or 97 times better, because 97 is the magic number.
Don’t dictators always get 97% of the vote. Or is it 99%?
Gloria Swansong September 17, 2015 at 1:49 pm
Don’t dictators always get 97% of the vote. Or is it 99%?
—————————
97% of the dictators get 99% of the vote with a confidence level of 95%.
Gloria Swansong: “Don’t dictators always get 97% of the vote. Or is it 99%?”
Some dictators have managed to get >100%.
A bit like the climate “scientists” who reckon CO2 is responsible for >100% of the increase in the Earth’s temperature…
sophocles,
You have the right screen name. You wrote:
By 2030, it will be very interesting:
“It’s not cooling, it’s inverse warming.”
“Global warming has not stopped, it’s just a downward
adjustment and temperatures are actually trending up.”
” Negative trend? There’s no negative trend: it’s a statistical
artifact of faster warming.”
“The thermometer is upside down.”
“The Earth is going into a new ice age.”
“Build more windflails.”
“It’s not a minus sign, it’s a dash.”
“That’s not ice in the Arctic, it’s surfactant froth.”
“Polar bears? Aren’t they extinct from global warming?”
“Temperature’s are not falling. You’re using an incorrect
statistical technique.”
“Be quiet and pay your carbon tax. Be thankful you’ve
got one. What would the weather be like without them?”
“Cooling is what warming does.”
I’ll add another ‘reason’: “Cooling is just a warming reciprocal”: one over Warming.
And my fav: “Climate change is happening!“
‘Climate change is real and it’s happening now’ is the warmist shibboleth.
The statistical method appears to assume that data points that are associated in time will be about the same.
By saying the pause is short term and a data point in the context of climate change, it assumes that the data in the pause is associated with the warming that occurred before.
So the pause can be seen as part of the hypothesised picture that the pause is an artifact that when looked at the broader context is part of warming.
So the pause can be ignored as this statistical method shows.
If my understanding is correct, this is a circular argument, as the conclusion is driven by the assumption that data points will be the same.
It is used to disprove the Null Hypothesis, where it was postulated that where there is no difference between the global atmospheric surface temperature over 18 years then additional atmospheric CO2 was not a significant driver of temperature.
The CO2 hypothesis was used as a call to stop CO2 production as temperature would rise dangerously.
Some thoughts.
If the hypothesis that temperature does not vary much because data pints are linked then there is no danger of a rapid rise in temperature, because there has not been any.
So this paper, if believed, predicts slow temperature rise.
Its weakness is that it uses, as pointed out above,small data sets to illustrate large data sets which are available.
A statistical comment would be good.
RagDuke comes to mind.
I read the article by Rajaratnam et al. in “Climatic Research” and wrote a comment but discovered that they do not want comments. I am including the comment I wrote here. The Article is called
“Debunking the climate hiatus” comment follows.
From the abstract we read:
“….We apply a rigorous, comprehensive statistical analysis of global temperature data that goes beyond simple linear models to account for temporal dependence and selection effects.
…Our analysis reveals that there is no hiatus in the increase in the global mean temperature,
…We find compelling evidence that recent claims of a “hiatus” in global warming lack sound scientific basis.”
On the contrary, compelling evidence is that this paper lacks sound scientific basis. To start with, lets take the first sentence of the abstract above. Ernest Rutherford, of whom you undoubtedly learned in school, assesses it this way: “If your experiment needs statistics you should have done a better experiment.”
Next, when you say “Our analysis reveals…” you are not correctly analyzing your observables. The section of global temperature included as part of the hiatus usually begins with the 1997 part of the super El Nino of 1998. It got started there when the observers were interested in including the maximum number of years into the observed hiatus and the Super El Nino fortuitously added to this. But this is incorrect because the super El Nino has a completely different origin from the rest of the data surrounding it.The correct start for counting the years of the present hiatus should be the year 2002, the ending date of the step warming of 1999. That step warming followed closely on the heels of the super El Nino of 1998 and is the only real warming since 1979. In three years it raised global mean temperature by a third of a degree Celsius. For comparison, the entire temperature rise for the twentieth century was only 0.8 degrees. This quick temperature rise should have attracted attention and it did attract Hansen’s attention. He noted that all of the first decade of the twenty-first century was warmer than the twentieth century was, except for one (1998), and declared this wonderful gift to be greenhouse warming. Unfortunately he had greenhouse warming on his brain and did not know how wrong he was. You can’t start greenhouse warming without injecting carbon dioxide into the atmosphere and we know this did not happen in 1999. And you can’t stop it without plucking all the carbon dioxide molecules out of the air which obviously did not happen in 2002. The correct hiatus temperature graph using NOAA’s ERSST v.4, startimg in 2002, does not have an upward slope and is a horizontal straight line. But it there are still no regular ENSO oscillation there. For the first seven years of the century there was no ENSO but in 2008 suddenly a La Nina cooling appeared. Probably the one Trenberth was cursing in Climategate emails. It is followed gy an El Nino warm peak in 2010 which gave us hope that ENSO had returned. I speak of its return because the super El Nino, not part of ENSO, had interrupted an ENSO wave train that was active in the eighties and nineties. Such super warm peaks are rare and usually happen on centennial time scales. It looks now like the temperature fluctuations following the 2010 El Nino have returned to the same doldrums we had in the first decade of this century. This does not promise much warming ahead. The prognosticators are putting their hopes on the return of another super El Nino but there is no chance of that and a regular El Nino is the best they can get, followed of course by a La Nina to balance it, . And now lets look at some technical data. The so-called “pause-buster” temperature data-set from NOAA is called ERSSTv.4. If you plot a linear curve with it starting in 1997 as most hiatus curves have done you do get slightly more warming than its version 3 showed. If you start in 2002 you get no warming as I mentioned. The reason for choosing 2002 is because that is the year when the construction of the hiatus platform by the step warming starting in 1999 was complete. If I understand correctly, the entire purpose of this paper is to prove the non-existence pf this hiatus. How would it strike you guys if I told you that this is not the first but the second hiatus you people have attacked? I bet you did not know that the first of these hiatuses existed in the the eighties and nineties. You are ignorant of it because it was successfully covered up by a phony warming called “late twentieth century warming.” I discovered it accidentally in satellite records while doing research for my book “What Warming?” in 2008. It turned out that there was no warming at all from 1979 to 1997. Comparing this to ground-based temperature curves I found that this temperature section was covered up by a phony warming. I also discovered that this phony warming had originated with HadCRUT3 and put a warning about it into the preface of the book when it came out. Nothing happened. I next discovered that GISS and NCDC had collaborated with HadCRUT in this conspiracy because of identical computer footprints in their data. These are high spikes near the ends of years. Two of them sit right on top of the super El Nino of 1998, easily recognizable by comparison with satellite curves. Luckily they still don’t control the satellites or we would never have known about any of this. If you seriously want to prove that there is no hiatus you have to make your proof include both hiatuses, nit just one. As to calibrating these hiatus data, luckily ENSO was active during the eighties and nineties and produced a wave train of five El Ninos with La Nina valleys in between in the middle of the hiatus. In a situation like this the global mean temperature is at the center point of a line connecting an El Nino peak with its neighboring La Nina valley. I put dots at these points wherever possible in this wave train and discovered that the dots formed a horizontal straight line 18 years long. Eighteen years of no warming, just like today. You will find its graph as figure 15 in my book. This calibrates the hiatus that is now covered up by fake warming. You can do it yourself by downloading the data from satellites. If you showed the true temperature instead of the fake one in official temperature curves you would have an eighteen year horizontal step in that smoothly rising temperature curve they foist upon us now. As to your last claim that ” ..We find compelling evidence that recent claims of a “hiatus” in global warming lack sound scientific basis.” — it is nothing more than just another pseudo-scientific boast intended to falsify the temperature record.
http://rhymeafterrhyme.net/no-warming-pause-now-theres-a-surprise/
No great assumptions they say? It seems to me they’re making the assumption that local effects extrapolate to global effects and that’s a fundamental and huge assumption.
It somehow pictures the state of cimate science. Even with the homogenized temperature records, they have to use extremely advanced statistical methods to find what they want.
If it was so visible, that they claim it is, why do you need all that statistics to see it.
Without climate science, nobody would have noticed the globe getting warmer.
“Using a novel statistical framework…”
Oh dear…
I am curious how the cult of CAGW will respond to CGC.
The corollary of the observational fact (and roughly a couple of dozen other observations and analysis results) that there has been no warming for 18 years, is the majority of the warming in the last 150 years was due to solar cycle changes, rather than the increase in atmospheric CO2. If that is true global warming is reversible.
The solar observations (shrinking sunspot size, shorter sunspot lifetime, decreasing solar 2.8 Mhz flux, sudden drop in sunspot number) are changing quarter by quarter, continuing to support the assertion the sun cycle has been interrupted rather than is just slowing down.
What has held back the cooling are solar wind bursts from persistent coronal holes and a complex mechanism that is related to how solar cycle changes cause large cyclic geomagnetic field changes.
The solar wind bursts create a charge differential which in turn causes there to a movement of electrical charge from high latitude regions of the planet and the equator which cause warming.
The coronal holes are now starting to move to high latitude regions of the sun where they no longer affect the earth and/or are starting to dissipate.
If I understand the mechanisms, when the coronal holes are no longer producing solar wind bursts, there will be significant cooling, say 0.5C over a few years. We are going to experience the cooling phase of a Dansgaard-Oeschger cycle.
.
http://sait.oat.ts.astro.it/MmSAI/76/PDF/969.pdf
“we found that the statistical confidence obtained using our framework is 100 times stronger than what was reported by the NOAA group”
Statistical confidence? Do you really need statistics to plot measured data over a timeline?
Since the “subsamples” are not measured data, how can they claim these subsamples reflect actual temperatures?
—
“In reality, however, the temperature could be influenced by not just the previous data points, but six or 10 points before,” Which on is it, six or ten? How would they know how they were “influenced”, given the subsamples are imaginary data?
Statistics are generally used in climate science for hiding inconveniences and this topic is a good example, plus many others and SM/Willis has made a good job of exposing their worth in the past. Smoothing of data can be useful, but it should never be instead of the raw data as it can hide secrets.
The reason for the pause and why in the near future the pause may be claimed to have finished.
http://i772.photobucket.com/albums/yy8/SciMattG/RSS%20Global_v1997-01removal_zpszk83g0xi.png
When we get a strong El Nino to show it’s effects in global temperatures in about 6 months time. Whether it will do a step up like it did previously is unknown, but maybe the signs are not this time with a lack of response so far with the satellites?
http://www.woodfortrees.org/plot/rss-land/from:2015/plot/uah-land/from:2015
The continents didn’t start warming up from El Nino 1997/98 until around January 1998, so there is plenty of time yet.
http://www.woodfortrees.org/plot/rss-land/from:1997.5/to:1998.5/plot/uah-land/from:1997.5/to:1998.5
This year the El Nino started about 2 months earlier, so maybe looking for a sign in November?
What I love about roof-top solar is the 20-year payback. You have 20 year life expectancy solar panels sitting on a 15-year shingled roof that has to be replaced every 12-15 years. What could go wrong? And the installation of solar panels happens 5 years after the shingles are laid.
I suspect that if you used 1000 year periods instead of 15 years and used the Greenland ice core data you could easily find that the planet is still cooling.
In fact, I would also think that using 10 year periods might give a different answer as well. So, we can see right away they are cherry picking to get the answer they want. Not very scientific.
I also suspect one could apply this technique to a sine wave and claim the line is still going up even after it has started to go down.