Radical Rodent, since I was talking about the public, both uses of the word apply, but you're right, the scientists should be impartial, but aren't.

Martin, try this image of the 12 month running average: https://tamino.files.wordpress.com/2015/05/gissmovave.jpeg It doesn't look so much like a pause with the context of the latest data. Instead it just looks like a constant rise with 1998 as an outlier. I wasn't interested in climate back then, but I imagine it must have been quite a shock to those watching.

Tiny/Radical, 'interest' goes to the heart of what we were talking about. The people from whom you take your cues (mostly unknowingly, which is the beauty of it) definitely have an interest in preventing action on climate. Scientists on the other hand are mainly just interested in the subject - that is why they are scientists. If it wasn't climate it would be something else. The public are by and large uninterested or ignorant, but they are definitely not disinterested. They have a profound interest in the climate remaining favouarble but they also have a more pressing interest in day to day survival - it should surprise noone that the latter overrides the former.

Micky ,

I've spent 15 odd years having to not assume data is correct

the funny thing there is that the only people I've seen claiming that the temp data is correct are those "skeptics" who insist we should be using the raw data only. No adjustments, use the raw data, the raw data is the truth. A cursory look at a random selection of station data will tell anyone that the data is not all ok. So if we want to use it we need to deal with that in one way or another.

But say we chose to discard the indices altogether as beyond correction. What then? Are we driving blind? If we can't use indices constructed from thousands of thermometers around the world, then we must be even less sure about any proxy data that tells us about historical temperatures. The proxies are vague point estimates compared to the broad cover from the indices. So we know little about past temperatures and we don't know recent temperatures. But we have the physics of greehouse gasses and the knowledge that CO2 concentrations are rising steadily. That alone should make us alarmed and tells us we need to make some changes even before we start looking at other observable changes to the planet.

"Answer the question, please. A scientist should be able to give a better answer than hunter." EM

Or perhaps you could start your own discussion since the subject of this one is 'ARE we wasting time' not 'CAN we waste time'.

While most of us drift off topic when the relevant issues have been adequately discussed, you and Raff don't even start. He tossed off a lie about coming here in his spare time away from persuading the undecided about AGW. Funny how I don't believe he spends any time at all. What's your excuse? Why not wave your graph at the disinterested instead of us?

May 23, 2015 at 10:19 AM | Unregistered CommenterTinyCO2

It's quite flattering that we "3%" have so much influence and power, isn't it?

Raff "the only people I've seen claiming that the temp data is correct".

No you haven't. You've seen them pick holes in how the adjustments are made, especially when they're made decades after the event. Since GISS, HadCRUT4, RSS and UAH can differ on even the most recent measurements indicates the result is something of a guess. If it's a gues in 2015, it was a bloody dart board in 1850. Any interest in the RAW data is to demonstrate how much fiddling (necessary or not) has gone on. The temperature is climate science's most basic product. It's the thing that everything relies on, and in engineering terms it's crap. Phil Jones losing the original raw data was just another clue to how slap dash it all is. That doesn't mean the end product isn't vaguely right. However it's fed into highly complex software to predict the unpredictable. GIGO.

michael hart, it would be flattering if the other side weren't so... good at failure. And like all true losers, it always has to be someone else's fault. Cue the shadowy forces that are using us as their pawns.

Tiny, no you are wrong. There are people who object to adjustments per-se. Euan Mearns is an example - he even complained to Roy Spencer that the new UAH 6 adjusts the raw data. Go figure.

Martin, I think we can agree that we are unlikely to agree. The problem is that we don't understand climate in enough detail to know exactly what moves global temperature. But we know a lot, like the fact that volconoes tend to depress temps and that El Nino tends to raise temps. Take those influences out of the temperature record and there is no 'pause'. Tamino did a post on this some time back, well before this year's increase in temps. If you are interested I'll find it - but my guess is that you don't consider him a credible source.

Micky, the homogenization algorithms are indeed tested. Victor Venema, who is well known for work on homogenization, was involved in a study benchmarking homogenization algorithms - see http://variable-variability.blogspot.com/2012/01/new-article-benchmarking-homogenization.html

Removing alleged effects of volcanoes and El Nino to see what the trend would look like is a bit like -

lets assume the volcano effect is x, and El Nino effect is y, subtract them from the existing trend and what do we get?

Well it depends upon how big your x and y values are.

How big do want to make them?

The right amount to remove the pause..............

So many circles, so little knowledge.

Micky, you dont get to "[characterise] the measurement process, the variations in that process and the equipment used" for decades old data of unsure provenance. The paper is testing whether the algorithms work when applied to unknown simulated data. That is doubtless not as good as it could be if we set about instrumenting the earth now with new sensors. So how would you go about validating the homogenization methods?

All the main indices give very similar results, as do others such as those from Nick Stokes and even naive ones by skeptic Roger Andrews (see Euan Mearns blog). Everything shows that temps are rising. The real rate of change is bound to be somewhat different because there is patchy coverage, esp in the Arctic where temps are changing fastest. When combined with other evidence if change and with theory, we have what we need to act.

Not back, just passing through.

Micky H Corbett

Knowing each Trent engine intimately, with detailed testing of all parts and the complete engine, plus detailed telemetry in operation is a marvellous thing for an engineer. If the oil temperature in the cruise shows an upward temperature trend you can monitor until you are sure it is real, then take out the engine and check all the possible causes, finally repairing or replacing whatever was faulty.

I am not clear how you intend to apply this approach to climate change. Could you expand on how you would use an engineering approach to measuring and identifying the rate of warming and other climate change symptoms.

If current measurement technology is insufficient, what would you suggest replaces it? How much more investment and how many extra people would it need? How would you persuade the politicians to fund the extra effort.

How would you do test to identify and quantify the different effects such as albedo, solar insolatio, CO2, methane and Milankovich cycles?

I am not trying to be sarcastic, but I find it very difficult to envision how you can apply the testing, monitoring and diagnostic techniques you use on an engine to planet Earth.

The former is an object you designed, about which you can gather unlimited data and which is under your direct control. The latter is not designed, but the outcome of many processes.can only be monitored to a limited extent and is not sufficiently under your control to permit detailed testing or controlled trials.

Raff "Euan Mearns is an example"

Given you ability to misunderstand what people write, you'll forgive me for not taking your word for it.

EM, one of the things said about climate modelling is that they can’t hind cast because they don’t have the starting conditions. In other words, creating software that can mimic the ebb and flow of ice ages is impossible. Ditto the current interglacial, the last three warm periods or even the last 1000 years. In fact they don’t seem to model any further back than the 60s (find any model plot that shows further back). We did hear validation of Mann’s work at one point by a phrase about his hockey stick being broadly in agreement with models. Well we know how good the hockey stick is, don’t we.

Hang on, if we need highly detailed measurements and observations of ocean currents, and ice, and clouds, and the sun and all the other things that are being used to explain why no model predicted the pause then we’ve only been measuring any of those in detail since… well I’m not even sure we’re doing it all now. If just land temperatures, CO2 and aerosols aren’t enough to roughly model climate then we can’t model climate. Since we have to adjust raw temperature measurements we don’t even have highly accurate starting conditions going back to the 60s. If ‘near enough’ was good enough for broadly accurate modelling then they should be able to model from about 1850. But they can’t.

And don’t even start on ocean temperatures. When you look at a modern plot of ocean temperatures as sampled by ARGO, can you even begin to consider the old methods were useful? If the spare energy is going into the oceans, what have the oceans done in the past?

Think how badly the election voting models failed. They just didn’t have enough information of what influences the voters. They failed right up to the last minute. Think how wrong they would have been 1 year in advance, 5 years, 20 years, 60 years. Sure, they would have had a fifty, fifty chance of calling it for the Conservatives but could they have given any useful detail about percentages, the SNP, the Lib Dems or UKIP? You might dismiss this by claiming that people are random, but are they much more random than the climate?

Micky H Corbett

Your argument is known as the "impossible standards" straw man.

It simplifies down to a claim that because you cannot measure climate parameters to the standards used in precision engineering, you cannot usefully measure them at all.

Unfortunately policy has to be made on the hoof by those who turn up (ie.the politicians and Civil Servants) using the data available.

In practice the temperature data is probably as good as you are likely to get.

Consider accuracy.

Any competent engineer can design an automatic weather station capable of measuring temperature to 0.1C twice a day. This gives a sample of 730 measurements per year per station. The data for global temperature comes from 1250 off these high quality stations, giving a sample size n=266450 for the annual global means.

The accuracy of the mean increases in proportion to √n. Thus if n=1 you can give the measurement to the nearest 0.1C. If n=100 you can give a mean accurate to 0.01C. If n>266450 you can meaningfully write a global annual average temperature accurate to 0.002C.

Consider uncertainty.

Despite Martin A's sky dragon slayer approach to statistics the uncertainty in the climate data can be calculated reliably.

The data follow a normal distribution. If you plot a frequency distribution 95% of the individual measurements are less than two standard deviations from the mean.
An alternative way of say it; You can be 95% confident that the temperature of the system you are measuring is within 2SD of the mean of your sample.

Since all the groups calculating global temperatures use the same raw data it is not surprising that they quote similar uncertainties, on the close order of +/- 0.1C.

Consider variation and the number of stations.

Variation in the temperature data comes from four sources. One is the 0.1C uncertainty in individual measurements., Another is the day-to-day variation at each station due to weather. Thirdly there is the seasonal variation at each station. Finally there is the variation between the climate at different stations.

Taking measurements throughout the year minimises the effect of the first three. To determine how many stations you need to compensate for the fourth plot a graph; standard deviation on the Y axis and sample size, the number of stations, on the X axis.

At small sample sizes the SD is large because random sampling variations play a large part. As sample size increases the SD decreases. Above a critical sample size the graph flattens as the SD of the actual temperatures becomes dominant. This is the optimum number of stations. Any larger number gives little improvement

For analysis of global temperature that optimum is the 1250 stations they use.

In summary.

The climate monitoring system has been designed using what look to me like engineering principles to give the best output that physical and matematical limitations allow. This is the maximum quality of input likely to be available to the policy makers.

Refusing to make policy because you cannot have an impossible level of information is irrational.

Radical Rodent

Try here.

Read the section headed Means. If you want more follow the link to standard error.

Micky H Corbett

If I arrive at your garage complaining that my car is detonating and pinking, with the temperature gauge reading well above normal you, as a mechanic, would diagnose that it was overheating.

II arrive at Slartibartfast's with a planet showing retreating glaciers, shrinking land ice sheets on Greenland and Antarctica, rising sea levels, increasing ocean heat content, climate zones and biomes moving to higher latitudes and higher altitudes, rising tropopause, cooling stratosphere and rising surface temperatures.He would also diagnose overheating.

I don't believe in climate change. I look at the variety of changes in the Earth system, all consistent with increasing energy input, and regard it as probable. Since these changes have consequences for our civilisation, policies to respond are necessary.

What I do not do is take expertise developed in the controlled environment of engineering and try to apply it to an Earth system in which such specialised knowledge is not applicable. It is an error I see from people who are professionals and engineers, when operating outside their own area of expertise.

Micky H Corbett

Given your question I would also have chosen D.

"The reason why a mechanic would think your car is overheating is that it is a well characterised process and had been observed many times."

This is where your professional experience is leading you astray. You work in a field where the physics is long established and you can reliably apply the equations. Your machinery then operates accordingly. Theory, practice and measurement agree to the limits of your measurement technology. Concepts such as standard deviation and probability are no longer necessary. You also have 80 years of prior jet engineering experience available.

The problem comes when you move into climate science.

Thanks to the USAF the physics of the CO2 greenhouse effect is well understood, even by your standards.

The problem is understanding how that interacts with a planet much more complex and less predictable than a Trent. Under those conditions your information can never be complete, only sufficient, and statistics is a necessary part of your analysis.

There is also no past experience to guide us, nobody having tried to double the CO2 concentration before.