The Blackboard

Is it stated for each model that aerosols were included?

John–
The modeling groups are permitted a range of choices within the constraint of the SRES. So, some models include aerosols. But the parameterization they chose is up to the professional judgment of the modelers.

I think some of the spread in model projections is due to each group making slightly different choises.

There is a table in the AR4 detailing some choices for each model.

Lucia-
I noticed when I was importing the data for sresa1b a while back that they don’t all have a common time interval. Some start in 1850,1860,2000,2001 or 2004 like GISS ER. When calculating a trendline, I think it might be a good idea to calculate it over an interval where the data represents the same number of models throughout. I suspect that calculating over the whole interval of time with a different number of models on different intervals might skew the results in some way. Just a thought.

Speaking of GISS ER, did you notice that strange dip around 2100?

Yes! I noticed that dip, and didn’t even blog about the weirdness. It’s weird– isn’t it?

I’ve got a note in to Greet Van “xxx” about the some of the data sets.

I’m not sure what you mean about “the same number of models throughout”. In this post, I didn’t compute any trend lines. The red curve is just the average of over the 38 runs. There are 38 runs all the way from 1980-2030 (and even well before.) I added the dashed 2C/century line so people could see the average is increasing at 2C/century– as the IPCC said it does for these cases.

For now, when doing analyses, I will be using these 38 runs for precisely the reason you mention. For some analyses, the it’s convenient to have matching numbers. But, as it happens, I’m never looking at anything prior to 1900, so the different start dates for the different runs won’t affect anything I’m currently looking at.

Why different number of runs for individual models?

Wouldn’t the s.d. of your model average GMST change if you changed the number of runs assigned to specific models? If so, doesn’t this prove by simple logic that the s.d of the model mean does not in any way represent weather noise?

Clark-

The modeling community gets together in various forums and comes up with SRES etc. The propose a variety of challenges. Then individual groups decide which SRES to run, how many of each case to run etc.

The statistical tests will be based on what you are describing as simple logic. However, the tests are made systematic so we can quantify and estimate the probability that one would get a particular result given some paricular hypothesis.

So, I always try to explain the hypothesis, explain why that particular hypothesis is relevant as a matter of simple logic and then apply the test.

One test is going to relate to the residuals to ordinary least squares fits, one will relate to the variability of 8 year trends… and so on.

“Having set everything to common 1980-1999 baselines, the temperature anomalies for data and observations are forced to agree, on average, during that period.” I’ll put in the token skeptic comment regarding using GISTemp, Hadcrut and NOAA: with all the historical and current adjustments to these datasets, one could state rather that the “observed” temperatures are forced to agree with the models…

Fred–
If I were to show the full hindcast, we’d see that the data don’t fully agree with the models. Certaintly, they don’t agree with individual model runs.

The figure 2 is interesting .
So all the complex models come with is T = at + b or in other words dT/dt = a ?
Why bother with LES when the truth is the simplest form and a 0D model – a straight line ?

There is one striking exception : years 92 – 93 .
In general the red doesn’t even get the right sense of variation of the blue , let alone the right value .
But in 92-93 it gets both remarkably right .

As this is filtered , the interpretations are tricky .
Is there something special in those early 90ies years that would explain that remarkable behaviour ?

TomVonk-

Yes, Mt. Pinatubo. Which happened before AR4. You do the math.

Lucia,

Are my eyes playing tricks on me, or is the spread between the one-SD curves widening over time? If so, why would that be?

So if we do an annual correlation between Merge L & O , and Models, and find that 92-93 is the period that correlates best, but that it was the period with the most un Model like conditions, and that these conditions were reported before the Models were designed, what should we conclude?

TomVonk,

It’s not correct to directly compare the variation in the red and blue curves. The red curve is the average of many different model runs, and as such will generally be less variable than the observations, which are just a single realization of the weather. Averaging together a bunch of different “model weather” realizations tends to hide the variability present in the models because high temperature fluctuations in some runs cancel with low temperature fluctuations in others.

The red line matches the blue better at Mt. Pinatubo because all the models have Pinatubo in them, so when you average them together you still get a net cooling. By contrast they don’t all get the big 1998 peak from El Nino, because while they do have internal ENSO variability, it’s stochastic and the El Ninos they generate happen at random times with varying strengths. So the El Ninos and La Ninas mostly cancel each other out in the average. (Sometimes people compare the models to an “ENSO corrected” set of observations to compensate.)

Francois–

The spread does increase over time. This is a symptom of the fact that the spread is not “weather noise!”

The spread is the spread in model projections of GMST at any month. Since the individual models project slightly different things on average, the spread is due to “model weather noise” (that is, the variability for an individual model) plus the spread between the average that we would get if we ran any particular model one bajillion times.

Since we basedlined everything to 1980-1999, the spread is minimized in that region. Basically, we’ve forced the difference in model averages to zero during the baseline period, and in that period, the spread is dominated by “model weather noise”.

Pi–
Actually, some models don’t have Mt Pinatubo in them. I’d need to look up the proportion. But, some models do have Mt. Pinatubo in them.

You’ll notice the model spread is a little larger near the eruption. The fact that some models include the eruption and others don’t likely contributes to the spread.

lucia,

Have you made the 38 temperature time series available for download anywhere?

I made a comment about El Chichon in 1982, but I got the years mixed up. We do see El Chichon in the observations; I’m not sure why we don’t see it much in the models, unlike Pinatubo.

Pi.
No. They are available from the climate explorer. I’m not going to take the responsibility of maintaining an archive which would require me to institute some form of quality control. In anycase, I’m not sure it would be permitted. It’s not “my” data and copyright might be an issue.

Not sure whether this has any bearing on what you’re doing right now, but I wanted to pass it along.

http://people.iarc.uaf.edu/~sa....._LIA_R.pdf

[...] We could calculate the central tendency from the models ourselves! Should you feel the need to see the average computed over the model runs that have been made available at the climate explorer, I’ve plotted them here [...]

[...] It’s September 29, and I’m finally reporting the test of the hypothesis that the global means surface temperatures are rising at a rate of 2C/century, ( which is ‘purt dang close to the trend represented by the average of models in the IPCC AR4: That is to say, it represents a central tendency for the predictions of IPCC models used in the AR4. For graph of average of model runs compared to 2C/century, read 1.) [...]

[...] As usual, I’ll use the data I downloaded from The Climate Explorer, discussed earlier here. [...]

[...] Since this is a blog, documentation will be less involved than one might expect of a journal article. I will take the liberty of assuming you all have access to Santer17 which itself describes the caveats to the test. To minimize typing of equations, I will refer to various hypotheses and equations numbers using the values in that paper. Also, I will only compare models to one data set for GMST. It is the one I call “Merge 3″ and represents the average of NOAA/NCDC, GISS Land/Ocean and HadCrut. Also, I have not obtained all runs included in the IPCC AR4 and extending to the current period. Instead, I have used the runs discussed in an earlier post. [...]

[...] For this test, I use the 38 runs downloaded from The Climate Explorer are discussed here. [...]