http://reallyrealclimate.blogs.....t-for.html

We are not talking about predictive models here, we are talking about a historic event for which we should have all of the data. The assumption behind the models is that we know all of the physics necessary to make projections for the future. But if we do not know enough physics, and if we do not know enough about natural variation, to explain what natural variability caused an event that has already occured, the current flat trend, then how is this possible?

I was looking at the wiggles in the graph above and it appears that the runs which show a flat trend for the current decade are only able to stay within the range of the other models because they have some suspiciously unphysical jumps. For example, the orange wiggle jumps 0.5 degs in 2-3 years in the 2050’s and again in the 2070’s. Such a jump has no precident in the temperature record that I can find.

I think that re-enforces you point about how outlier model runs are not an appropriate way to estimate the uncertainity in the IPCC projections.

I have a login to another place–but I didn’t find time series. Maybe I didn’t look hard enough though! So, I’ll go back and have another look.

Lucia

here is one place to start. there is another location, but I cant find it.

“The problem you have is that there is one realisation of the real world and from any short period you cannot constrain the statistics of intrinsic variability that is longer than that period.”

This reminds me of a paper by Hansen, Schmidt and others whose abstract goes like this:

“Our climate model, driven mainly by increasing human-made greenhouse gases and aerosols, among other forcings, calculates that Earth is now absorbing 0.85 +/- 0.15 watts per square meter more energy from the Sun than it is emitting to space. This imbalance is confirmed by precise measurements of increasing ocean heat content over the past 10 years.”

http://pubs.giss.nasa.gov/docs.....etal_1.pdf

So my question would be, how can the imbalance be confirmed by “precise measurements” over a period that Gavin claims is shorter than the intrinsic variability of the climate system?

The next question that I have has do with the idea that 10 years does not constrain the intrinsic variability, but that somehow 30 years does. How in the world do you arrive at the conclusion that 30 years covers all of the intrinsic variability. Certainly you cannot do this by looking at any multi century temperature record, where 30 year trends vary as readily as 10 year trends.

Beyond that, I have a problem with dismissing 10 year trends for another reason. I do agree that there are strong elements of variability that effect 10 year trends. But the warming community seems to imply that they understand what these elements are. So if you have a flat 10 year temperature trend and you can show that such a trend is caused by ENSO or PDO or Volcanoes, or solar, etc., then you might be able to say that the natural elements of variability have overriden the CO2 forcing trend that would otherwise be seen. But the current flat temperature trend (which is actually 11 years long) has no explanations in natural variability. Gavin’s own ENSO data clearly shows that the flat trend was not caused by ENSO. Of the .2C per decade temp rise that one would expect from CO2 forcing, only 0.01625 C can be attributed to ENSO. So where is the missing 0.183 C. It wasn’t hidden by volcanoes. The claim is that solar variability is far too weak to override CO2 forcing. So if the current flat trend is due to natural variability, what are the elements of natural variability that are giving it to us? And if we don’t know enough about natural variability to answer that question, then how can we extract the climate sensitivity signal from a natural variability that we do not completely understand.

By the way, I looked at Gavin’s ENSO corrected data from 1998 to the present and compared it to the uncorrected data for HadCrut3v here:

http://reallyrealclimate.blogs.....t-for.html

Notice that the difference is hardly impressive. I think this soundly puts to rest the warmer claim that the data for the last decade has been cherry picked for it’s ENSO effect, and that minus the ENSO effect we would see the CO2 forcing signal clearly. In fact the ten year flat trend has nothing to do with ENSO.

I hacked together a method for determining the ENSO effect back in May, here:

http://reallyrealclimate.blogs.....fects.html

And it is worth noting that the Thompson method does not disagree with the conclusion that I reached at that point, which was that the current flat trend is not ENSO related.

In order for all of us to have a fruitful discussion of this, a few of us (not just gavin) need access to the time series for those trends. I can get access to gridded data, but post processing all that to obtain the time series in the IPCC documents is a bit much to expect of members of the public trying to make up their minds.

But, you must also remember: I base my hypothesis test in the central tendency predicted by the IPCC on the features of the real earth data. Obviously, if I believe we need to test the estimate of the mean tendency, I’m not going to blindly accept the idea that the model runs must provide correct estimates of the higher order moments (i.e. standard deviations) or other features of the time series (spectral features, autocorrelation etc.)

I might be willing to buy the idea that squinting at the spaghetti graphs is useful if the standard errors for the 8 year trends that gavin estimated based on that output appeared remotely realistic. It doesn’t.

His standard errors are larger than one would expect based on the full thermometer record, including periods with no volcanic eruptions, no measurement error etc. This suggests that there is something wrong with either a) the magnitude of the interannual variation or b) features like the autocorrelation or spectra of the GMST.

Of course, there may be nothing wrong, but in that case, the apparent mis-match must be explained. Until it is, we really aren’t going to agree on anything by squinting at those graphs because we disagree about the ultimate meaning of those graphs vis-a-vis falsifying the prediction for the central tendency against measured data.

So, with luck, it may turn out there is someway for people like me to access the time series. I have the time and patience to plot them, and I even take requests! So, if they are available, I’ll show them and we can discuss them.

Your second answer to em was more evasive than the first. Oh well.

What are you talking about? In comment 3968, I provided a lengthy qualitative discussion of the meaning of the underlying climate trend. I avoid mathematical terms like “compact support” and “manifold”, and extensive dissertations on chaos and the various disagreements on whether or not the system is ergodic.

If the way to get them is to write a proposal to the IPCC repository, I’d be happy to do that.

On the cookie bets: I might propose a cookie bet with gavin, but we’d need to negotiate a trivial climate oriented bet, as those are the best ones one which to bet a dozen home baked cookies. It may, of course, turn out that gavin doesn’t like cookies. Worse, like my sister, he could be gluten intolerant, and we’d have to switch to something like meringues (which don’t ship well.) Also, I already have the bet on GISS temp with Tilo, and I can’t risk having to cook a billion cookies at Christmas!

One is on the trend. Two are just below the 3C central trend, but they’re not far off. And they’re all in the shaded area of the other AR5 plot.

(Incidentally, I can see these particular lines individually in the AR5 pdf because my graphics card is slow enough that the lines are not all rendered at the same time. Double-clicking on the image before it has fully rendered helps slow the rendering).

Gavin might send you to the IPCC repository to get access to individual runs. I hope he doesnt.
To get access you have to write a short proposal and then they get back to you.

It would nice if GISS made the individual runs for ModelE available. Gavin is one of the best in making stuff available. So, hope springs eternal.

Anyway, I have an avalanche hitting me now otherwise I would comment more.

Gavin,

Thanks for showing up and commenting. Leave a haiku if you get a chance. or make a climate cookie bet.
ask lucia she’ll explain

I posted A2 because it has the fewest strings of spaghetti. For the distinct ones, at the edge, you can see that those that are low stay low. Those that are high stay high.

But even at that, I’m not entirely sure that criss-crossing is entirely weather noise. We know that criss-crossing occur if some models have different over-all time constants for responding to the climate. This coupled with rebaselining will result in variations.

Gavin:
If we are going to discuss the statistics of Model E (which I would like to do) is it possible for me to get access of time series of individual runs? Steve Moscher told us where to get runs, but they are all averages over 5 realizations. If I had the individual trajectories, I could also show readers graphs so they would know what we are dicussing too.

On this: “so you need to use models to do the attribution.” Sure. I’ve never stated models are useless. I’m just saying the central tendency of the current prediction looks like it has over-predicted the trend and that is based on statistical measures. I know the world’s weather is only one realization, but it is the only realization that we can be sure is governed by the real earth’s physics.

PS. do not confuse the distribution of a random variable (the trend) with the uncertainty in defining the trend in a single realisation. They are not the same. Try some monte carlo experiments with synthetic AR(1)+trend time series to see.

I have ran a bunch of monte carlo with white noise+ trend and also AR(1) + trend series around June 14. I compared the distribution of the randome variable (the trend) to the uncertainty in defining the trend from a single realization.

For white noise, I saw what I thought I’d see based on what my Sophomore year math book says about estimating the distribution of the trend based on the uncertainty in a realization and what the econometrics texts say for red noise.

What are you suggesting I would see?

Figure 10.5 in Chapter 10 of AR4 WG1 report page 763 shows a spaghetti graph of the 21 IPCC scenario runs.

When I look at the A1B plot closely, 4 or perhaps 5 of the 21 projections show cooling or very little warming by 2008. It’s not obvious because not all the lines start from 0 in 2000. For some of the lines it’s hard to tell where they start.

In my view there is a yellow line, 2 green lines and a dark blue line at the bottom when you look at about 2008. The possible 5th is another dark blue one, but you can’t see its starting point.

By the end of the century, the yellow line shows 2C of warming and the others are between 2C and 3C warming.

How does that square with your statistic? It seems to me that there is more like a 20-25% chance that the observed temperatures match the scenario range.

if short term trends have little or no meaning, what was Hansen testifying about in 1988 after 10 years of warming??

We are currently at the end of ten years of neutral. Should he be testifying that there is a disaster of neutral weather ahead we have to do something about??

Basically all the hoopla is over a 20 year period. How many times have we been told there needs to be at least 30 years??

You don’t have a 30 year warming period.

You seem to be arguing that the details of what made up the IPCC projection and what their uncertainty is completely irrelevant to your falsification attempt. That seems like an odd stance to me.

You seem to think the characterstics of the actual weather shouldn’t be used to estimate uncertainties. This seems an odd stance to me.

Actually, if I were asking the questions: “Would I consider the earth’s single realization an outliers compared to the batch of models runs?” I would look at this your way. I think that’s an important question when developing models– since you do want to know that at a minimum, your full spread did capture the earth’s trajectory.

However, that’s not the question I’m asking. It’s also not the one most people who don’t spend their lives developing climate models ask. I am asking:

Is the central tendency communicated by the IPCC consistent with the earth’s data?

To answer the second question, we must look to the empirical data and find the range of trends consistent with the empirical data. And the answer to this question is “No”.

I think if you compare our questions, you will likely see why I don’t give your uncertainty ranges precedence over those of the data. I also think you will understand why your model uncertainty ranges are larger than mine. In fact, your uncertainties are required to be larger than the ones I use.

If models were perfect, and people’s ability to develop SRES were perfect, our uncertainties would be identical The models weather trajectories would simply provide the uncertainty bounds associated with all weather in what we call the population of all weather we are trying to predict.

However, since the models are imperfect, and the SRES have variations, your uncertainties become larger.

On some other issues: I view the graphs at 800% in illustrator. I’ll make a few graphs to show people tomorrow to show a few other issues associated iwth comparing data to that graph. But, now I need to watch the Colbert report!