Guest essay by M.S.Hodgart (Visiting Reader Surrey Space Centre University of Surrey)
A feature of the politicised debate – if such it may be called – over AGW (anthropogenic global warming) and so-called ‘climate change’- is the tendency on both sides to cite only the evidence supporting their views and to ignore what does not. Scientists of course are supposed to be above this sort of thing and to take into account all relevant evidence.
One finds a lot of partiality when it comes to interpretation of the trend in climate data – particularly the available time series of average temperature measurements on the surface of this planet. Is it going up or down or has it paused? What is happening?
Sceptical commentators were the first to draw attention to a recent pause or hiatus in global temperatures and are naturally tempted to see this as being persistent for as long as possible. The ‘warmist’ climate scientists – those that compiled the IPCC reports including those who work for or presumably get their research funding from the UK Meteorological Office have tended the other way. For a long time they were in a state of denial of any pause – not even conceding any reduction in warming rate – presumably because anything that detracted from the sacred dogma that an uncontested increase in atmospheric CO2 must entail a rise in temperature was very unwelcome.
But where both sides of the debate are often referring to the same data one must ask why it is not possible to come to a more objective conclusion.
I focus first on the time series of remote sensed TLT satellite measurements released by Remote Sensing Systems . I also look again at the HadCRUT4 data which were the object of my analysis in the WUWT of September 2013. It should be emphasised that the physical accuracy of any of these data is not under review here and is a separate issue.
Plotted either as monthly or annual updates the time series of globally averaged temperature measurements shows a substantial random-looking scatter from one month to the next (or year to the next). This scatter and a general lack of knowledge as to what exactly drives the temperatures makes it difficult to determine the trend. Yet so many people debate, write and comment as if the trend in these data were entirely obvious. They think they know – ignoring the fact that the scatter in the data makes for a significant problem, not least in establishing what a trend means. The distinguished econometrician Phillips has memorably written (see his introduction)
No one understands trends. Everyone sees them in data.
also (and not altogether ironically)
A statistician is a fellow that draws a line through a set of points based on unwarranted assumptions with a foregone conclusion.
In other words be careful if you run a linear regression on data like these. In the spirit of impartiality and with all respect for his warning I try here to draw reliable conclusions about the trend from these particular cited data. I must however put on record that like our ‘climate lord’ Matt Ridley I am a ‘luke-warmist’. My sympathies are with the ‘sceptics’ because there seems to have arisen an officially-sponsored global warming industry and a general scare-mongering by and of the scientifically ignorant. It has for example become a political ‘fact’ – contrary to all biology and chemistry – that CO2 in the atmosphere at present or worst-case future concentrations is or will be a pollutant i.e. a poison. It is not; its presence is essential to plant growth and therefore our survival. The material bulk of all trees and crops derives and is converted from CO2 in the air. Trees and crops grow out of the air not the ground! See the brilliant “Fun to imagine” TV series by Feynman. It is difficult to take seriously an unremitting propaganda that is prepared to distort the science as badly as this.
Lord Monckton and the RSS data
Viscount Monckton of Brenchley is a prominent climate sceptic. In a recent release to WUWT he emphasises what seems to him an obvious fact that global surface temperatures have paused for almost two decades. He is not alone in this view but let us see how he comes to this conclusion. He appeals first to the TLT satellite measurements released by Remote Sensing Systems (RSS). By the simple procedure of linear regression on their monthly data he finds for effectively a zero slope (his last cited month was September 2015) going back to February 1997. I replicate his result in my fig 1 (the red line). In consequence it seems obvious to him – and to so many others – that indeed global warming has stopped for all this time. But has it?
His problem
The problem is that he has chosen to disregard all the prior months of available measurements going back to January 1977. A linear regression over all these months yields a line (brown) with a slope of 0.12 deg C/decade. Although he acknowledges this effect he does not seem to realise that this longer regression makes his conclusion untenable, whatever assumptions are made as to what the linear regression achieves.
He probably assumes that the slope resulting from linear regression determines the trend in global temperature. In other words “whatever I choose to calculate and the way I do it defines the observed effect”. If he does then he runs into a flat contradiction. The red line gives him his “Pause” (he uses a capital letter); but the brown line says that over the same time interval temperatures continued to rise. So which ? The trend can’t be doing both. The RSS web-site plots only the longer span regression. For them there is no pause.
If however he were to make the more orthodox assumption that linear regression estimates a linear trend there are still difficulties. It could be that the data back to 1997 conforms to a classical signal + noise model with a straight line of some slope and offset (the signal) which one cannot see because of an obscuring random variation (the noise). The standard model is
1
(i) (ii)
where z[k] is the time series, the variable k is a count in months or years (it is easiest to start at zero), and the signal= trend in (i) is defined by the offset a and rate b. The noise terms v[k] in (ii) are introduced in order to give an account of that random-looking fluctuation we can see in the time series. Ideally they answer to a description of ‘white’ noise but the terms here exhibit some limited correlation – approximating what electrical engineers call ‘low pass noise’. Linear regression estimates an offset 
and slope 
which are in error from the true a and b because of that scatter. There are then two problems – the minor one being that his zero slope is at best a likely estimate – it is not definite.
More importantly it is confusing to decide over just what span of years this model (1) could be valid. We could postulate that model (1) applies over a limited span. But it is asking a lot of Nature to oblige Monckton with even an approximation to a linear model as which just happens to start in Feb 1997. If it applies over all the years then the two regressions are estimating the same trend and the flat red regression is a ‘freak’ due to a chance combination of noise terms. Again one would conclude that only the longer regression had any validity.
Fig 1 RSS monthly data and linear regressions. Red line from Feb 1979 to September 2015 (Monckton’s regression). Blue line: regression from mid 1973. Brown line: regression through all data.
But there is hope for Lord Monckton still. It can be shown that the assumption that a linear trend runs over the whole is unlikely to be true. The difference in slope between the two regressions of 0.12 deg C/decade is too large to be attributable to ‘chance’ – as one can readily determine. The two regressions and also a third regression (blue line) calculated from mid-1993 with an intermediate slope strongly suggests that beneath the noise the trend is not following a straight line.
All three lines can be reconciled if we allow that there is a non-linear trend – as indeed the IPCC scientists readily concede in ‘Box 2.2’ of their latest report AR5. There has to be something more complicated than a straight line beneath the noise. A generalisation of (1) is the classic
2
where z[k] are again the data points, and the signal = trend s[k] follows an assumed but unknown curve. The v[k] are again noise terms. The curve hidden in the data can be assumed to cover the whole span of years. Model (1) is at best an approximation over a limited span.
A linear regression is not invalidated by this model but the computed slope has to be interpreted differently. It will have to be seen as an average of a trend with some actual variation within the span of years.
Accordingly the overall regression (brown line) computes an average trend of something which is non-linear between the years 1979 and 2015. But Monckton’s regression in principle is also no more than an average trend. So yes: there is a ‘Pause’ but its strict interpretation is that “an estimate of the average trend from Feb1997 to Sept 2015 happens to have a zero slope”. But no: he has not demonstrated what is the most likely actual trend over this time.
As I show below it is much more likely that temperatures were still rising past 1997 and that Monckton only gets his Pause from a later date. As many others have pointed out it is easy to get fooled in statistical analysis by an apparent pattern suggested by what turns out to be the influence of a random component in the data.
Monckton’s construction does have one useful consequence: he has shown that none of these linear regressions (including his own) is likely to be estimating a straight line.
Alternative stochastic model?
In this deterministic trend model (2) there is assumed to be some unknown but well-defined curve or line concealed by low-pass noise i.e. strictly a weak sense stationary stochastic process. We need to be aware of a substantial literature which views the entire time series as a generalised non-stationary stochastic process. It is ‘all noise’. This approach is the preferred choice of econometricians who have taken a look at climate data. In his extensive publications Professor Terence Mills has looked at both approaches but favours the all-stochastic. If identification of ARIMA processes is your meat then there is plenty to work on. I wish you luck! In my opinion the stochastic approach leads to paradox and a terminological confusion. The data series has to be regarded as the output of a feed-forward and feed-back machine whose input is a white noise. If this were true then every possible time series is ‘random’. So where is your anthropogenic global warming ? I will follow the climate scientists and stay with deterministic trend estimation in general and (2) in particular.
Estimating a non-linear trend
If we have to fall back on the generalisation which is (2) then we shall have to estimate s[k] while only having access to the data z[k]. This is an exercise in curve fitting– for which there are a plethora of methods.
The difficulty with all methods of curve fitting is that there are essentially two kinds of error to contend with: the random error or variance due to the omni-present noise v[k] ; and a systematic error or bias due to the poor fit of a proposed fitting function to the unknown hidden signal s[k]. Whatever method is adopted the unavoidable problem is to decide if the computed curve is over-fitting (too much random error) or is under-fitting (too much bias error). There is a model selection problem.
In my earlier release to WUWT back in 2013 analysis of the HadCRUT4 data I proposed using a cubic loess – which Mills shows is superior to quadratic or linear loess – and also a polynomial regression In the case of loess the problem is to decide on the effective window width and with a polynomial to decide on the degree.
For loess if the window width is too narrow random error dominates over the systematic and if too wide vice versa. For a polynomial regression if the degree is too high random error dominates over the systematic and if too low vice versa. There are many model identification methods designed to guide a choice – starting perhaps with Akaike Information Criteria, modifications such as that by Hurvich and Tsai and many more. There are also various forms of cross validation technique. But they seem to me (having tried some of them) to be uncertain and unreliable. Statistical experts may disagree.
Corroborating curve fitting
Whatever the procedure the would-be statistician is left with a degree of freedom in allocating a crucial parameter. Some years ago however I stumbled on the fact that a combination of cubic polynomial loess and a standard polynomial regressions offer a unique choice of window width for the former and degree for the latter which gives the least disparity between the two generated curves. The one selects the other. The combination is self-selective. This idea seemed to work well on the HadCRUT4 data. This serendipitous result is now found to apply to the RSS data. In fig 2 a (half) window width of 168 months for a cubic polynomial loess and a polynomials degree of 5 give the closest agreement to each other (shown in blue dashed lines with no attempt to distinguish between them).
These very similar curves are perhaps the most likely deterministic estimates of the trend but they cannot be the exact truth. The uncertainty is again due to the noise present in the data. Assuming however that they are ‘close enough’ what they have in common, if we disregard the discernable oscillation, is a depiction of a rising trend followed by a pause effectively starting around 2003 – and not 1997.
Alternative segmented linear regression
The shape of these curves provides also a motivation for a different idea: to apply a split or segmented regression. The idea is to run two regressions over all the data years but with a break point which offers the least discontinuity between the two segments.
The break point is found after a trial and error search to be September 2003. Monckton still gets his pause but it is now reduced to the last 12 years. The first segment of the proposed regression in fig 2 from 1997 to 2003. finds for a computable rate of 0.16 deg C/decade. There is a pause after that over which the trend is indeed flat. The trend does not literally switch in slope on the month of September 2003. The purpose is to provide a meaningful computable rate.
Fig 2 RSS monthly data Jan 1979 to September 2015. Dashed blue curves: cubic polynomial loess with 168 month half window width; polynomial regression with degree 5. Continuous red lines: segmented linear regression with break point September 2003.
However each regression is seen by comparison with the loess and polynomial curves to be an acceptable approximation. The two segments are plausible averages over respectively separate ranges of data. The apparently contradictory or competitive regressions in fig 1 are now explained by more than just positing average slopes of a non-linear trend. Some information has been gleaned as to what that trend consists.
Application to HadCRUT4 data
The RSS data tell us nothing about global trends before 1979 and one has to turn to the publicly available land and sea-based surface measurements. The UK compilation HadCRUT4 goes back to 1850 but the two US series go back only to 1880. It is not my intention to try and assess the accuracy and reliability of any of these compilations. It is clearly a difficult exercise relying on measurements which were never intended for a systematic global experiment. Particular difficulties must be associated with sea temperature measurements which historically were very crude indeed. The series is of course under continual review from both its compilers and from sceptical critics – which can only be a good thing. Avoiding the very important issue of measurement error what can be inferred about the trend in global temperature if we should decide to trust HadCRUT4? To repeat: in my previous submission to WUWT in September 2013 I used this self-checking combination of a high degree polynomial fit and a cubic loess. But now let try something simpler – a succession of split linear regressions. We will need more than one break year. The same criterion will be adopted: that there needs to be the least discontinuity between successive regressions. All the break years meeting this requirement have to be searched and discovered by trial and error.
The result of this exercise is shown in fig.3 on the annually updated time series.
Fig 3 HadCRUT 4.4 annual boxed connected points to 2014 . Discrete heavy spots are Met Office approved discrete decadal averages. Brown lines are sequential regression segments. Arbitrary start from 1870; break point years 1910, 1942, 1975, 2005. Estimated r.m.s noise 
= 0.098 deg C. Red lines estimate average trend; discovered break point year 1941; post-war average trend 0.087± 0.012 (2 s.d) deg C /decade from 1941 to 2014.
I start on the same year 1870 as in my previous report to WUWT. We need four break years – splitting the trend estimate into five segments (see brown lines). It should be noted that these break years are discovered – not arbitrary choices. The heavy points also depicted are discrete decadal averages of temperature located in the middle of each decade – a simple statistic which the UK Met Office has long favoured and was adopted for the first time by the IPCC in their AR5 report (see part 2.4.3 AR5 )
As can be seen the proposed line regressions are in excellent agreement with these averages. This agreement surely promotes confidence in both procedures. Comparison with my earlier presentation also shows a good agreement with optimally chosen cubic loess and polynomial regression. One can see a broad similarity with the RSS time series from the 80s onwards. The temperatures started rising from 1975 and no pause is found until a break year of 2005 (two years later than for the RSS data). With this latest version of HadCRUT4 (now issue 4.4) we now get a low warming rate (of about 0.01 deg C/decade) from 2005 (compare flat response with the RSS data). I have not included the year 2015 which was not completed when running all these calculations.
One should emphasise that (i) these computed lines are probabilities not certainties; (ii) they are not meant to be taken literally but to be seen as approximants to some postulated smooth curve which is hidden from view and for which the loess and polynomial regressions may be better estimates.
The split regression segments graphically convey the impression that there were two long periods when temperatures were actually falling. Temperatures fell from at least 1870 to 1910, but rose from 1910 to 1942. They then were falling again from 1942 to 1975. From 1975 to 2005 warming resumed with a probable rate of 0.20 deg C /decade. But the warming did not persist at this rate. It seems to me to be probable that a third half- period has begun in which if there is now a pause (but with revised HadCRUT4.4 it is now a very slow warming).
This recent pause looks to be a continuation of an oscillation of global temperatures with a period of slightly more than 60 years going right back through the record imposed on a generally rising mean trend. I am not of course the first ‘sceptic’ to point this out.
I come to much the same conclusion as in my 2013 report. It seems that the much simpler sequential regressions are as convincing a way of specifying the trend in the data as my previous effort using polynomial regression and cubic loess.
What is the matter with the UK Met Office and the IPCC scientists?
In the summer of 2013 the UK Met Office, and the academics which they support, called a press conference in London to concede (reluctantly) a pause or a ‘hiatus’ in global temperatures and also confess they hadn’t clue as to why it was happening. The rather critical BBC journalist David Shukman who was present noted that
….the scientists say .. pauses in warming were always to be expected. This is new – at least to me…I asked why this had not come up in earlier presentations. No one really had an answer, except to say that this “message” about pauses had not been communicated widely..
Indeed! The press conference coincided with a reports by the Met Office (report 1, report 2, report 3) on the same theme. What the Met Office scientists did not discuss or even concede in that 3-part report is the presence of substantial oscillation over the historical record. This oscillation surely cannot be attributed to increasing concentration of atmospheric CO2 and it accounts for half the faster rate of warming in the 80s and 90s.
I find it troubling that presumably intelligent scientists (and they have competent statisticians also) cannot bring themselves to acknowledge – let alone explain or even properly discuss – the statistical fact that two extended cooling periods have featured in the past while CO2 levels were presumably always rising .
The reader will find the same statistical obfuscation in the two most recent reports (AR4 and AR5) released by the IPCC. A pause (or hiatus or standstill) is most unwelcome. Yet there is surely something to explain here for those who believe in the dominant anthropogenic effect on global warming. Since at least 1958 with the Keeling measurements (Mauna Loa etc) – and no doubt long before that – atmospheric CO2 levels have been rising monotonically (after seasonal averaging). It is hard to avoid the impression that there has been political pressure not to acknowledge the obvious: that an ever-rising concentration of atmospheric CO2 cannot be the only effect determining global surface temperature.
Trend v. average trend
In principle an oscillation does not have a trend. There is a need therefore to identify a mean trend which discounts that obvious oscillation. As suggested before one can differentiate
trend in the data = mean trend in the data + quasi-periodic oscillation
How then to estimate this mean trend? My previous effort was perhaps too elaborate. The following may be more convincing. One can construct a split regression with just two segments (the two red lines in fig. 3). To my mind the lines are steering a convincing middle course between the oscillating trend conveyed by the multiple split regressions. They may be about right. The break year of 1941 is again not an arbitrary choice: it has to be searched in order to ensure the least discontinuity between the two regressions with this construction. This notional mean trend is being estimated by two average trends computed by linear regression between favourable years. The post war average trend is found to be 0.087 ± 0.011 ( 2 s.d) deg C /decade i.e. less than 0.1 deg/decade which is half the rate of the actual trend which peaked (temporarily) in the 80s and 90s. The error limits are computed after first estimating the standard deviation of the noise of 
0.098 deg C.
It is extraordinary that in their various releases neither the UK Met Office nor the IPCC seem to want to confront these statistical facts in their own data. It is of course unwise to make a projection into the future but if we trust neither the elaborate computer climate models favoured by the Met Office nor the projection of Mills- type all-stochastic models this is all we have got. One can only note that in the 85 years from now to 2100 the projected increase could be around 0.0087 ´ 85 = 0.74 degrees. Could this be realistic and if so is that a cause for alarm? I only ask.
Lies damn lies and statistics. Think the clue is complex coupled non linear chaotic system which to me means no trend unless you need to manufacture one after the fact. Up and down like a fiddlers elbow statisticians need to earn a living like alarmists need to author alarmism. Alarmists need to define a trend sceptics have no need to create a trend where no trend exists just question the authenticity of alarmism which is exactly what Christopher Monckton does very well. The planet has warmed, fact but only a little and most likely will continue to warm a little until it cools a little or a lot. Does this essay add to the debate or argument think not, does it expose any flaws that might question the authenticity of the proposition that Co2 if it does cause warming then its not a lot an assertion that the pause illustrates quite well.
Christopher Monckton did not say the science is settle that was the UN, they need certainty those who question alarmism define uncertainty. Weather unpredictable climate chaotic trends pure supposition I certainly wouldn’t put money on it. Trying to define a trend is speculating again that at some point in time we might be able to influence weather or climate which beggars belief, as if.
M. S. Hodgart: I fear you have come late to the party and you overestimate the reasonableness of the warming proponents. They don’t just disagree – they will go to any lengths, including bending and twisting data to hang on to the notion that CO2 alone is the explanation of the coming runaway warming disaster they divined about 30 years ago. You also grossly underestimate and underappreciate the position and awareness of skeptics. Some have pointed out a few things on this above for your edification. Your analysis is known to the world because of skeptics pointing out there are other things than CO2.
You seem to be unaware exactly why the ‘pause’ is such a battlefield. Briefly, the longer this period of statistically no global warming lasts, the smaller the effect of CO2 in the earths temperatures. Climate sensitivity, previously thought to be (from the ‘theory’) 3-5 degrees per doubling of CO2 with feedbacks, has been shrinking to the ~1 to 1.5 level, a point at which CO2 has only a modest effect on temperature.
Indeed, skeptics are not saying the globe didn’t warm or even saying (Monckton states this in every one of his analyses, too) that we won’t have more global warming, nor that it couldn’t be harmful. Thinking skeptics have not simply been sniping contrarians. We have been holding feet to the fire of a enfranchised group that has been guilty of monstrous excesses in their zeal to prosecute their views, which would lead to enormous economic burdens on humankind and restrictions on freedoms. We point out that they entirely exclude the possibility of benefits of warming and CO2 (already greening the planet). You seem unaware of Climategate, or you believe that what was in it was just boys being boys or some such rationalization. You seem unaware of the egregious whitewashes of the behaviour of climate scientists in a half dozen so-called investigations. Indeed, I think you need to find the time to review all the issues before you come here and give us a gentle lecture on our schoolyard behavior.
That’s one for the gold file..awesome..
Hodgart appears to be one of those warming proponents you speak of. First he sets up a strawman he names “Lord Monckton,” and then he proceeds to bash said strawman, displaying utter incomprehension of what the real Lord Monckton had written. Time and time again, Lord Monckton has said his starting point is the latest available temperature data and he calculates the farthest month in the past where the trend is zero; in other words, he’s trying to find whether there is a Pause, based on the latest readings, and if there is, how long has there been a Pause.
Since Hodgart didn’t understand something that simple, I guess being a visiting reader doesn’t guarantee reading comprehension. Otherwise, he deliberately misrepresented the writing of the real Lord Monckton just to earn warmist points.
Katherine,
I concur, I’ve seen Mr. Monckton explain the matter several times, and this author seems to have bent over backwards trying t accuse him of somethi9ng . .
I studied under Dr Hodgart and he is an exceptional engineer. He knows maths. What he is saying here is not incorrect mathematically, but it does misrepresent the scientific position because Monkton only needs to prove that the exponential increase in CO2 has not resulted in any kind of reliable trend in increased temperatures. He doesn’t need to prove a pause – only that the trend over the last 17 years has not fitted the expectation based on AGW theory – the bar is thus much lower than as Dr Hodgart presents it.
As you very correctly say in your introduction, trends are a meaningless statistical artifice. If you use a trend to identify some causal mechanism then you can say that the trend has been useful (in a Kuhnian manner), but you have still not said that the trend actually “means” anything. In my opinion, however, using the trend to derive the mechanism is very much the wrong way round as it pre-supposes that the trend itself has meaning and what you are doing is subject to a great deal of confirmatory bias
Trend analysis, on the other hand, is a way to test your theories of underlying mechanisms. When postulating an atmospheric CO2 driven increase in global temperature, a trend analysis is a method to refute this (something our very own Willis does pretty regularly in addressing the various cycle-theories). One thing which comes out of the above very clearly – to me – is that a trend analysis of global temperatures has pretty well refuted the atmospheric CO2-driven temperature increase theory, at least as concerns CO2 (and other “greenhouse gases”) playing the dominant role in temperature change. The trends seen here simply do match the known concentration changes of these gases in the atmosphere, either on the very simple (two-segment) model, or the more accurate multi-segment model.
If you need statistical techniques to prove a trend or deny a trend and both could be argued to be correct, then it seems to that no real trend exists.
Well, then I guess the “precautionary principle” mandates we blow our brains out to save the planet, just in case, ya know? ; )
There is no reason to believe that global temperature as a function of time is a straight line function or even piecewise linear for that matter. The climate change we have been experiencing is caused by the sun and the oceans and that cause is not a linear nor piecewise linear function. Despite all the claims, there is no real evidence that CO2 has any effect on climate. There is no such evidence in the paleoclimate record. There is evidence that warmer temperatures cause more CO2 to enter the atmosphere but there is no evidence that this additional CO2 causes any more warming. If additional greenhouse gases caused additional warming then the primary culprit would have to be H2O which depends upon the warming of just the surfaces of bodies of water and not their volume but such is not part of the AGW conjecture. In other words CO2 increases in the atmosphere as huge volumes of water increase in temperature but more H2O enters the atmosphere as just the surface of bodies of water warm. We live in a water world where the majority of the Earth’s surface is some form of water.
The AGW theory is that adding CO2 to the atmosphere causes an increase in its radiant thermal insulation properties causing restrictions in heat flow which in turn cause warming at the Earth’s surface and the lower atmosphere. In itself the effect is small because we are talking about small changes in the CO2 content of the atmosphere and CO2 comprises only about .04% of dry atmosphere if it were only dry but that is not the case. Actually H2O, which averages around 2%, is the primary greenhouse gas. The AGW conjecture is that the warming causes more H2O to enter the atmosphere which further increases the radiant thermal insulation properties of the atmosphere and by so doing so amplifies the effect of CO2 on climate. At first this sounds very plausible. This is where the AGW conjecture ends but that is not all what must happen if CO2 actually causes any warming at all.
Besides being a greenhouse gas, H2O is also a primary coolant in the Earth’s atmosphere transferring heat energy from the Earth;s surface to where clouds form via the heat of vaporization. More heat energy is moved by H2O via phase change then by both convection and LWIR absorption band radiation combined. More H2O means that more heat energy gets moved which provides a negative feedback to any CO2 based warming that might occur. Then there is the issue of clouds. More H2O means more clouds. Clouds not only reflect incoming solar radiation but they radiate to space much more efficiently then the clear atmosphere they replace. Clouds provide another negative feedback. Then there is the issue of the upper atmosphere which cools rather than warms. The cooling reduces the amount of H2O up there which decreases any greenhouse gas effects that CO2 might have up there. In total, H2O provides negative feedback’s which must be the case because negative feedback systems are inherently stable as has been the Earth’s climate for at least the past 500 million years, enough for life to evolve. We are here. The wet lapse rate being smaller then the dry lapse rate is further evidence of H2O’s cooling effects.
The entire so called, “greenhouse” effect that the AGW conjecture is based upon is at best very questionable. A real greenhouse does not stay warm because of the heat trapping effects of greenhouse gases. A real greenhouse stays warm because the glass reduces cooling by convection. This is a convective greenhouse effect. So too on Earth..The surface of the Earth is 33 degrees C warmer than it would be without an atmosphere because gravity limits cooling by convection. This convective greenhouse effect is observed on all planets in the solar system with thick atmospheres and it has nothing to do with the LWIR absorption properties of greenhouse gases. the convective greenhouse effect is calculated from first principals and it accounts for all 33 degrees C. There is no room for an additional radiant greenhouse effect. Our sister planet Venus with an atmosphere that is more than 90 times more massive then Earth’s and which is more than 96% CO2 shows no evidence of an additional radiant greenhouse effect. The high temperatures on the surface of Venus can all be explained by the planet’s proximity to the sun and its very dense atmosphere. The radiant greenhouse effect of the AGW conjecture has never been observed. If CO2 did affect climate then one would expect that the increase in CO2 over the past 30 years would have caused an increase in the natural lapse rate in the troposphere but that has not happened. Considering how the natural lapse rate has changed as a function of an increase in CO2, the climate sensitivity of CO2 must equal 0.0.
The AGW conjecture talks about CO2 absorbing IR photons and then re radiating them out in all directions. According to this, then CO2 does not retain any of the IR heat energy it absorbs so it cannot be heat trapping. What the AGW conjecture fails to mention is that typically between the time of absorption and radiation that the same CO2 molecule, in the lower troposphere, undergoes roughly a billion physical interactions with other molecules, sharing heat related energy with each interaction. Heat transfer by conduction and convection dominates over heat transfer by LWIR absorption band radiation in the troposphere which further renders CO2’s radiant greenhouse effect as a piece of fiction. Above the troposphere more CO2 enhances the efficiency of LWIR absorption band radiation to space so more CO2 must have a cooling effect.
This is all a matter of science
WillHaas! ANOTHER FINE COMMENT (other one I recently saw was on the “Gosh, a New Model…” thread, here: http://wattsupwiththat.com/2016/01/20/gosh-a-new-model-based-study-puts-temperature-increases-caused-by-co2-emissions-on-the-map/#comment-2124875 )
@ur momisugly Other non-technical major readers like I:
Read the above Haas comment for a fine elaboration on this:
Thank you, willhaas! An excellent summary.
“The post war average trend is found to be 0.087 ± 0.011 ( 2 s.d) deg C /decade i.e. less than 0.1 deg/decade which is half the rate of the actual trend which peaked (temporarily) in the 80s and 90s.”
And you have used this trend over about 70 years to suggest small change till 2100. Yet in discussing LOESS you say
“For loess if the window width is too narrow random error dominates over the systematic and if too wide vice versa.”
That is generally true, and for LOESS you seem to get to a “balance” scale of a decade or two, not 70 years. If we are looking for the CO2 effect on a trend, then the amount of CO2, and its rate of increase, changed hugely over those 70 years. As you say, regression is a means of estimating a trend. It is very unlikely that the estimated ternd in 2015 is correctly determined by postwar average. And even less likely that this trend will continue with ever increasing emission.
M S Hodgart, This is a really welcome contribution to the “hiatus debate”. This may be because it is exactly what I have been doing with climate time series for many years! I have always called it segmented regression.
The fundamental theoretical problem is identifying the segments, or in other words the positions of step changes, and it may well be impossible to do so with statistical “certainty”. As you will know assorted techniques have been proposed for identifying step changes in time series, which often rely on non-linear and iterative methods, which are (for me at least) troublesome if not impossible to compute. I rely on an old and tested method used in SQC for identifying abrupt changes in output parameters from a production line, with the intention of intervening as swiftly as possible to avoid unacceptable product quality degradation.
The method I use is to form the cumulative sum of the series relative to a suitable base value. With historical data, which is what climate observations necessarily are, this base value is usually (and for good reasons) the mean of the observations over the time period of interest. When applied to climate parameters of various types these cusum profiles are often very striking. What is seemingly a jumbled mass of data points often transforms into a clear pattern that is characterised by approximately linear segments (with scatter) interrupted by sharp changes of slope. There are often also segments that seem to exhibit gradual curvature, also with scatter, and others where the cusum plot is clearly rather chaotic. Interpretation of these patterns is simple, though subjective. I’ll not elaborate on the properties of cusum patterns or curves – it would take more time than I have at the moment, except to say the a roughly linear cusum indicates a stable sequence of original values.
Applying this method to temperature series such as the CET data immediately reveals stable periods of various durations, from a few years to well over 100 years, as well as excursions from a linear pattern that can readily be associated with external forcings, particularly volcanoes. One very interesting outcome from this sort of analysis is in its application to single site data from northern Europe and Russia. Virtually every site has the same cusum pattern, whose most striking feature is a sharp angle change (thus a step change) that occurs mainly in late 1987. Subsequent to that date the temperatures are stable right up to the present. It looks as if “The Hiatus” began in late 1987 for this part of the world, with a gradually later onset as one goes eastwards towards Vladivostok, where the change began about a year later.
Anyone can verify this assertion regarding the hiatus by downloading temperature series from many sources, for example KNMI and the Met Office, and fitting a linear model to data from 1987 to 2015. The slopes that yu will find are seldom statistically significant. Regressions starting some years before 1987 will show significant slopes. Fitting a linear model to data that are fundamentally not linear is ubiquitous in the global warming industry, resulting the confusion that we are very aware of.
Sorry I can’t post diagrams – some help needed I suppose! I could provide endless examples using email.
Great comment for a layperson like me, Mr. Edwards! Thank you. As has been said many times on WUWT, only a master of a given scientific subject can write with the clarity it takes to explain it to a non-technical major.
Lots of good stuff, like this:
Re; your diagrams and examples
A suggestion (and a hope!): Write an article (doesn’t need to be long, you know) for WUWT, including your graphics, etc… in a Word doc — attach that to an e mail (or, just write it in the e mail body directly, if the attachment thing won’t work for you) and send it to Anthony. You are such a FINE, long-term, WUWT “colleague” of Anthony’s that I have no doubt that he would not mind you using his “fire hose” In Box. Might need to send it more than once, though, for he may overlook it. OR ask (use the word “moderat0r” spelled out with an “o” instead of a zero) a moderat0r for help in how to submit your article. When he or she sees that it is YOU who is the author, they will GLADLY help you, I think!
And if monitoring and replying to those who comment on your article in the thread below it is NOT appealing to you, just ignore it. Many authors do, never responding at all. Your choice.
With admiration,
Janice
Student in the back of the WUWT classroom (who sometimes runs up front and writes stuff on the board, bwah, ha, ha, ha, haaaaaaaa!)
#(:))
M.S.Hodgart, in his balanced article, referred to “ … the tendency on both sides to cite only the evidence supporting their views and to ignore what does not. Scientists of course are supposed to be above this sort of thing and to take into account all relevant evidence.“. There’s one problem with this view : When a theory has been put forward, it matters not how much evidence there is supporting it – a single fact can disprove it. The burden is greater on proponents of a theory than it is on opponents (but that still doesn’t entitle anyone to ignore anything relevant).
MSH also says “The problem is that [Viscount Monckton of Brenchley ] has chosen to disregard all the prior months of available measurements going back to January 1977. “. That is incorrect, VMofB took ALL data into account, as has been explained thoroughly by other commenters.
The rest of MSH’s article, re segmented linear trends, oscillations, etc, makes sense.
robinedwards36 – I calculate segmented linear temperature trends using a very simple and relatively objective technique: I simply optimise using both date and temperature as variables. ie, I allow the intermediate segment ends to move horizontally as well as vertically. Here’s one I did a while ago:
http://members.iinet.net.au/~jonas1@westnet.com.au/Hadcrut4MultiPhaseTrend20140508.JPG
I say ” relatively objective” because one still needs to decide how many segments.
And while we are also taking about oscillations:
http://members.iinet.net.au/~jonas1@westnet.com.au/hadleycurvefit20111114.jpg
Indeed, the CUSUM technique. I love it. Used it. Most appropriate for these “anomalies”. Do you know who developed it and for whom it was developed? An interesting story. Taken up by engineers in the USA during WWII but I am not aware that it was published, in England, until the mid 1950s. The original CUSUM technique was given to the USA department of Defense by a very famous mathematician, John ….. fill in the blanks. He decided not to publish it because it was so “simple” to detect a change in a trend!
CO2 has gone up, temperature has not followed.
And it definitely hasn’t followed the Hockey Stick. As such the theory CO2 leads to warming is wrong!
It really is that simple.
Why? Why, for example, is it not possible that natural factors have offset the CO2 warming over the past decade or so?
1. According to AGWers, CO2 drives climate. If natural drivers, such as the oceans, can negate CO2, CO2 is not the controlling driver. If those negating forces have overwhelmed the increase in atmospheric CO2 (given it is, indeed, driving anything in climate), then, CO2 (and it may all be net natural, too, you know) is not likely to lead to warming. That is, the null hypothesis, that natural (non-CO2) drivers control the climate of the earth, stands. The burden of proof still lies at the feet of those who assert CO2 can do ANY-thing to change the climate of the earth. Not proven. Moreover, given: CO2 UP. WARMING STOPPED. — the evidence is running against AGW. The IPCC models assume CO2 as a controlling climate driver and have been proven unfit for purpose.
2. In the past, CO2 levels have been signicantly lower, yet the temperature on earth was high enough that you can find palm tree fossils just south of the Canadian border in the U.S…. and tree stumps where glaciers now reign… and Oetzi climbed an Alp in what became Italy around 3,000 B.C. died, then was buried under meters of snow and ice… .
3. Ice core proxies reveal that CO2 levels lag temperature increases by a quarter cycle.
John Finn:
You ask
It is wrong because it is observed to be wrong: i.e. CO2 rose but temperature did not.
CO2 may sometimes lead to warming but that is not demonstrated.
CO2 does not always lead to warming is demonstrated.
Why CO2 does not always lead to warming is another matter. Perhaps temperature would have risen recently were it not for being offset by “natural factors”. If so, then that would mean “natural factors” have effects of as great a magnitude as the CO2 warming. And, why would one assume “natural factors” did not provide all of the warming before the ‘Pause’ when “natural factors” certainly did contribute nearly 100% of the warming from the LIA prior to the industrial revolution?
Richard
JohnFinn – if you ask “ is it not possible that natural factors have offset the CO2 warming over the past decade or so? then you must also ask “ is it not possible that natural factors caused the warming over the previous decades?“.
richardscourtney – apologies, I hadn’t read your comment when I wrote mine.
I once opined: “If natural variation is occasionally in charge (at random), it is always in charge.”
Finn
why weren’t natural factors the cause for the warming in the first place, every time you post you invalidate yourself.
It is indeed that simple. But this site simply refuses having this graph available here.
So why don’t we have a graph of CO2 increased since the measurements started in 1958 vs temperature on this site? Forget the CO2 of a million years ago always quoted here.
Such a graph exists at climate4you.com. Why not here?
If it is that simple, have this graph here, shows the CO2 increased from 1958 to about 1978 while temperature was about the same or even decreased slightly, then temperature increased until about 2000 (with obviously the El Ninio of 1998) and then no more increase until just now.
Indeed it is that simple. So simple that when Judith Curry and others are asked to testify they NEVER show this graph, EVER. You are correct, it is that simple. This site should show the graph, it is available.
But NO, this site will not show it!
Mr. Hodgart, it’s always possible to get a better fit to a data set using polynomials, but that’s hardly the point of a statistical approach to analyzing the data. Absent some sort of theory, curve/line fitting is a pretty useless (and largely automated) activity. Why chose 1, 2, 3, or 4 factors if you have no idea what those factors might be and no way to measure them?
The purpose of Monkton’s analysis is to point out that during he past 19 odd years CO3 has increased dramatically and AGT has not. That’s the entire point, there is no other. We have one factor, atmospheric CO2 fraction, with one hypothetical effect, increased AGT. Data collected by RSS clearly falsifies that hypothesis. It’s demonstrated that prior to 1997, both CO2 and AGT were rising together. After 1997 that relationship stopped.
There’s nothing more to be said for it. Clearly there is some factor that was driving changes in AGT, but the data in hand demonstrate it isn’t CO2.
If I were to throw a bone to the loons who selectively ignore historic decreases in AGT that occurred during times of increasing CO2, I might suggest that the “Pause” could actually be a stunted “decline” if it weren’t for accumulated CO2, but that would require them to have some clue as to what the factors driving temperature actually are, and they’ve never bothered to even suggest one other than CO2, even after consuming billions in research funding clearly squandered on a useless fantasy.
The biggest problem is the belief that any “trend” to be found looking backwards has some meaning in the future.
I am again reminded that my old statistics professor said “Statistics is an attempt to find meaning when there is none!” Still true today. What has happened in the past, linear of not, does NOT predict the future. No amount of statistical gobbledygook will ever change that.
I appreciate the points Mr. Hodgart made about the fallacies of using linear regression statistical methods when the break points cannot be known (or assumed). He shed a lot of light on this issue. Thank you!
Perhaps part of the problem is pedantry in the application of ‘pause’ and ‘hiatus’ in the debate.
In Fig 3 (HadCRUT 4.4) above, putting aside the controversy over its recent “SST corrections etcetera” versus HadCRUT 3, one could just as validly say that there are ‘plateaus’ centred around 1945 and developing around 1910 by a simple process known as “eyeball”. (A plateau means a relative flatness compared with the typically steeper sides as seen on some mountains). The earliest study that I know of to imply this is by two Russians, Lyubushin & Klyastorin (2003):
http://www.biokurs.de/treibhaus/180CO2/Fuel_Consumption_and_Global_dT-1.pdf
See their figure 5. (using their 2003 temperature data)
Both plateaus are preceded by somewhat similar warming periods via the aforementioned eyeball method (whereas if CO2 was a major driver, the more recent warming should be steeper).
As an analogy to ‘linear trend pause’, the average height of a mountain plateau can be determined or alternatively it might be approximated to the apex of a sine wave….. take your pick or expert statistical opinion.
Sorry, should be ‘developing around 2010’
….I find it troubling that presumably intelligent scientists (and they have competent statisticians also) cannot bring themselves to acknowledge – let alone explain or even properly discuss – the statistical fact that two extended cooling periods have featured in the past while CO2 levels were presumably always rising …
Their jobs are on the line. What would you do?
Be honorable..get a new job !!
You go, Marcus! Amen.
Hi Janice !! No more CC ?? LOL
Ugh. Don’t even mention that sickening thing’s name… likely to summon it from the underworld… .
And, Hi!
“If you want to keep a secret, you must also hide it from yourself.”
― George Orwell, 1984
All this sound and fury over temperatures is beside the point. Arguing alleged effects without even proving the cause.
According to IPCC AR5 the atmospheric CO2 concentration increased by 40%, from 278 ppm around 1750 to 390.5 ppm in 2011, a difference of about 240 GtC, aka the hockey stick/blade. How they know this is based on WAGs, SWAGs, assumptions, and “expert” opinions. The foregone assumption is that this increase cannot possibly be caused by natural variations therefore it must be due to mankind, i.e anthropogenic sources.
In the same time frame IPCC estimates/WAGs/SWAGs/assumes/opines that anthropogenic sources added about 555 +/- 85 GtC (+/- 15%!!). That’s twice the increase and a problem IPCC et al have been trying kick under the rug.
IPCC AR5 Table 6.1 partitions this 555 GtC anthropogenic sources (375 +/- 30 FF & Cement, 180 +/- 80 land use) among the various allegedly invariable natural sinks (rugs) and sources.
IPCC AR5 Table 6.1………GtC……..+/- GtC……..+/- %
Anthro Generation…………555………….85…..….15.3%
FF & Cement……………….375………….30…..……8.0%……..67.6%
Net land use………………..180…………80……….44.4%……..32.4%
Anthro Retained………..….240…………10…………4.2%………43.2%
Anthro Sequestered………-315………………………..…………-56.8%
Ocean to atmos…………..-155……..…..30…..…..-19.4%
Residual land sink……….-160……..……90…..…..-56.3%
So the CO2 increase between 1750 & 2011 that cannot possibly be ‘splained by natural processes (Considering the huge uncertainties how would they even know?), but natural processes can easily ‘splain the sinking and sweeping precisely 56.8% of the anthro contribution under the rug.
@Nicholas Schroeder
AFAIK according to Henry’s Law there ought to be around fifty times more CO2 absorbed in the oceans than there is in the atmosphere.
Now given your quotation:
“According to IPCC AR5 the atmospheric CO2 concentration increased by 40%, from 278 ppm around 1750 to 390.5 ppm in 2011, a difference of about 240 GtC,”
Applying the Henry’s Law factor of 50, this must require a net source of CO2 at around 50 times 240 GtC … or 12,000Gtc.
As the IPCC claim that anthropogenic CO2 emissions were 555 GtC over the same period, this only accounts for ~ 5% of the reported increase …. leaving the other 95% to come from natural sources.
IF Henry’s Law works as stated, then only a fraction of the claimed increase in global temperature can be Anthropogenic!
Bullseye. Causality is the real issue. CO2 is still going up, temperatures, not so much. The lines are diverging. Causality has never been proven and will not as there are too many exogenous variables and the actual mechanism is still unknown
Could be summed up as there is a pause in the data which doesn’t necessarily mean a pause in AGW (the mean of the last 10 years in the RSS (-2015.92) is only 0.03°C more than the 10 years before). It just highlights the hubris about the science being robust and settled, and alarmists down playing uncertainty until its need to debunk something like the pause.
As a interesting exercise, I have looked at the trend from 1979 to each year from 2000 onwards and the trends become progressively smaller (with some minor variation) from about 2006 onwards. I have also looked at the successive 30 year trends from 1979-2009 through 1984-2014 and those 30 years trends also become successively lower.
I have not continued past that since WoodForTrees does not seem to have data past mid 2014. This means that the data I have, particularly for the sequential 30 year trends is inadequately small.
Of note though, is whilst the temperature trend has been mostly lowering (around the 0.015 range) and certainly fluctuating, the Mauna Loa CO2 trend (around 1.7) has been steadily increasing, and the Anthropogenic CO2 output has been exponentially increasing. I find it hard to see any correlation between these at all.
But just looking at the raw temp data, it seems obvious to me that it is not linear, whereas the raw CO2 data seems very linear (with an overlaid seasonal sinusoid).
These data can be easily accessed at http://woodfortrees.org/ for anyone who want to play themselves.
“to cite only the evidence supporting their views”
I’m not a scientist and barely remember it and math from 45 years ago, but I ask the question
What evidence? Warmists don’t believe in their data without “homogenization”, deniers question that.
Even now scientists are starting to question satelite data, governments spent hundreds of millions if not billions on climate satelites that scientists said are a good idea, and will produce meaningful data.
Obama said “trust the science”, what he really meant was “trust the scientist that he trusts”.
The upshot is we can’t trust our politicians, why should we trust the scientists, let alone the science?
You can do all the charts and graphs you like, but if no one trusts the original data anymore, what’s the point? So is AGW real? I don’t think anyone really knows, the only reality is some are making a lot of money and obtaining a great deal of prestige from it. I certainly don’t believe it, unless someone can actually prove it.
The problem is …Obama and his liberal socialist circle…Just look at what the Democratic nominee’s are for president..(1) a socialist that can’t add two plus two ..and (2) A socialite non Madonna that believes if you erase the words ” classified ” from a document , it is no longer ” classified ” !!
statistical nerd fight … using mostly made up data … seems like a waste of time …
Yup. If, as RD50 asserts, it really is that simple, then the discussion is what is chaotic, perhaps not the CO2 story.
I’m trying to think of an analogy to the way much Climate Science modeling is practiced, and this is the best I can come up with:
Imagine you are flying a plane from New York to Los Angeles with a computer tracking all the minute course adjustments that you make to keep the plane aloft due to turbulence.
Over Kansas City, you program the plane to use the New York to Kansas City
“trend” to finish the remaining Kansas City to Los Angeles leg of the flight. You leave the cockpit and go flirt with a flight attendant.
Anyone care to bet if you’ll make it to your destination?
Lol. I like it!
I think… your final words will be: Hey! Where did all that water come from?
… sorta like how water (the only proven-effective greenhouse gas) and the oceans overwhelm AGW fantasy science in which it sank, about 10 years ago (and really, never got off the ground, never made a prima facie case shifting the burden of proof to its detractors, the science realists; the null hypothesis that nature drives climate stands).
“jmarshs says: January 21, 2016 at 6:32
Anyone care to bet if you’ll make it to your destination?”
I would say yes you would. Planes can pretty much take off and land themselves these days. An automatic landing system was implemented at London Heathrow in 1965 I think.
When I lived in New Zealand (NZ) I knew someone who worked for the NZ Govn’t negotiating air routes to various countries. He used to joke that a new security device was being introduced in the cockpit, a pitbull dog. It was to keep the pilots away from the controls.
You completely missed the point. The “autopilot” does not exist in my analogy.
The issue has to do with finding so called “trends” in chaotic, non-linear systems.
The belief of many in the Church of Climatology is that they can — from first principles tuned to (faulty) historical temperature data — determine future “trends” in the climate system.
The piloting of an airplane requires instantaneous feedback and continuous corrections.
If we believe that we can control the temperature of the Earth, then we need two things: 1) enough power to affect the system and 2) timely feedbacks to make corrections.
Neither of which are available to us.
And besides, what is the optimal temperature of the Earth anyway?
Oh. (shrug) I guess I did, too. I thought you had to be on autopilot to leave the cockpit. I figured that you had programmed the AP via Kansas City and that Patrick (being from Australia) was just unfamiliar with U.S. geography, so the vector diff wasn’t registering with him. Well, jmarshs. Learn something new every day… .
@Janice
Maxwell’s Demon drove a steak through the heart of Laplace’s Demon.
lol,
Stake
People often make a fundamental mistake in thinking about data. I see this article making it. It is a statistician’s professional job to avoid making this mistake. Stephen Jay Gould understood the issue very well and explained it pretty well.
Let me start with something else first, and come back to the big one. Fitting a straight line to this kind of data can be useful as a summary, but as a rule it’s worse than useless outside the range of the observed data. For example, suppose we discover that there is a trend of one degree per century. Since the Earth’s temperature is roughly 300 absolute, that means that in thirty thousand years the temperature will be less than absolute zero. Which is physically impossible. You can avoid that absurdity by fitting a straight line to the logarithms of the absolute temperatures, but you still get the absurdity at the other end that the Earth will eventually outblaze the Sun, all due to CO2 no doubt. PROJECTING A TREND FROM A PHYSICALLY MEANINGLESS FORMULA INTO THE FUTURE IS ALWAYS FOOLISH.
But that’s not the big issue. The flaw in people’s thinking is that there is the signal, some real, true, underlying, Platonic, *smoothly varying* global temperature, and that this is hidden by meaningless pesky noise that just gets in the way, and if we want to understand what’s really going on we have to get rid of the noise to see the big picture.
But statisticians (and evolutionary biologists, who would have nothing to study without it) know that VARIATION IS JUST AS REAL as anything else in the data. And in the temperature data, the year-to-year data *is* the big picture. Figure 1 in this article shows a trend of about 0.1 degree/decade, 0.01 degree per year. That’s the “signal” that the article is looking at in various ways. But if you compute the absolute difference of the temperatures between successive years, you find the average is close to 0.1. To me, THAT is the real signal. Year to year, the variation is huge compared with the trend.
There is a lot to learn from this, but this comment is already too long. But one thing is clear: the air and sea are affected by many things operating on many time scales so this is a very complex system, and we should be trying to understand what can jerk the planetary temperature around 0.1 degree from year to year.
The point about variation being real and being interesting applies to most situations.
Trend or no trend – and where does it (or doesn’t it) start. The whole debate is really futile, I think. We should simply stop debating it, because we simply cannot know with any scientific certainty. It all therefore becomes a matter of opinion – to which each of us is entitled to have his/her own. Let us accept the agreement reached by the International Meteorological Organisation’s conference in Warsaw in 1935 : “Climate” was to be represented at a particular site (and we may choose the whole globe, I suppose) by an averaged 30-years span of meteorological data, called “climate normal”. The first period was nominated to be between 1901 – 1930; then on to 1931 – 1960, 1961 – 1990 and (our present period) 1991 – 2020. Let the warmists be right : the global “climate normals” do show a temperature rise since 1901. But so what?? Let us look at it with detached interest and address the question again in 2020. And, most importantly, refuse to be alarmed – as it certainly doesn’t warrant alarm (yet!).
The issue is not do we understand the climate. Engineers constantly struggle (and succeed) to control systems that they don’t understand.
The issue is: Do we possess the power to affect changes, and are timely feedback mechanisms in place to allow for corrections? And do we know what an optimal global temperature is?
M.S.Hodgart,
“His problem (Mr. Monckton’s)
The problem is that he has chosen to disregard all the prior months of available measurements going back to January 1977.”
Let me explain something, kid. When we are talking about yesterday, we grown-ups don’t include things that happened last week. See how that helps to sort of keep us from just rambling all over with no constraints on our considerations? . . Never mind, it’s hard to explain . .
JohnKnight: Fig 2 should help you over your difficulty
Pfft
According to Greenland and other Ice Core data our Holocene Interglacial is in long-term decline.
When considering the scale of temperature changes that alarmists anticipate because of Man-made Global Warming and their view of the disastrous effects of additional Man-made Carbon Dioxide emissions, it is useful to look at climate change not from the point of view of annual or decadal changes but from a longer term, centennial or millennial perspective.
The current, warm Holocene interglacial has been the enabler of mankind’s civilisation for the last 10,000+ years. It’s congenial climate spans from mankind’s earliest farming to the scientific and technological advances of the last 100 years.
But:
• the last millennium 1000AD – 2000AD encompassing the Medieval warm Period has been the coldest millennium of the current Holocene interglacial.
• each of the notable high points in the Holocene temperature record, (the early Holocene Climate Optimum – Minoan – Roman – Medieval – Modern), have been progressively colder than the previous high point.
• for its first 7-8000 years the early Holocene, including its high point “Climate Optimum”, had virtually flat temperatures, an average drop of only ~0.007 °C per millennium.
• but the more recent Holocene, since a “tipping point” at ~1000BC, has seen a temperature diminution at more than 20 times that earlier rate at about 0.14 °C per millennium.
• the Holocene interglacial is already 10,000 – 11,000 years old and judging from the length of previous interglacials the Holocene epoch should be drawing to its close: in this century, the next century or this millennium.
• the beneficial warming at the end of the 20th century to the Modern high point has been falsely transmuted into being “the Great Man-made Global Warming Scare”.
• eventually this late 20th century modern temperature blip will come to be seen as just noise in the system in the longer term progress of comparatively rapid cooling over the last 3000+ years.
The much vaunted and much feared “fatal” tipping point of +2°C would only bring Global temperatures close to the level of the very congenial climate of “the Roman warm period”.
Were possible to reach the “horrendous” level of +4°C postulated by Warmists, that extreme level of warming would still only bring temperatures to about the level of the previous Eemian maximum, a warm and abundant epoch, when hippopotami thrived in the Rhine delta.
Global warming protagonists should accept that our interglacial has been in long-term decline for the last 3000 years or so and that any action taken by man-kind will make no difference whatsoever. And it’s implausible that any action by Man-kind could reverse the inexorable in the short period of the coming century.
Were the actions by Man-kind able to avert warming they would eventually reinforce the catastrophic cooling that is bound to return relatively soon.
see
https://edmhdotme.wordpress.com/2015/06/01/the-holocene-context-for-anthropogenic-global-warming-2/
I have always had a problem with doing a linear curve fitting exercise, when the temperature record is far from linear. If you look at the record, you can see many discontinuous places where a linear curve will fit. Oddly enough, I have never seen anybody trying to fit anything but a linear curve. If you were desperately trying to prove disaster from exponential temperature you would be trying to fit an exponential growth, aka Mann’s famous hockey stick.
But the discontinuities are the interesting parts. It would be naive in the extreme to assume that a linear curve on such a noisy plot can actually predict anything. In fact, no sooner does a trend line appear, than it it disappears. In basic statistics, we learned how rapidly the error bars diverged even with good data. The error bars on the temperature record makes the predictive value of a straight line unusable within a couple of years.
The only worthwhile model I have ever seen took the recovery from the last glacial period, added the AMO/PDO and got surprisingly good results.
Any data set coming out of HadCUT should be thrown away. They are cheats that were caught red-handed.