Guest Post by Willis Eschenbach

[UPDATE]: I have added a discussion of the size of the model error at the end of this post.

Over at Judith Curry’s climate blog, the NASA climate scientist Dr. Andrew Lacis has been providing some comments. He was asked:

Please provide 5- 10 recent ‘proof points’ which you would draw to our attention as demonstrations that your sophisticated climate models are actually modelling the Earth’s climate accurately.

To this he replied (emphasis mine),

Of note is the paper by Hansen, J., A. Lacis, R. Ruedy, and Mki. Sato, 1992: Potential climate impact of Mount Pinatubo eruption. Geophys. Res. Lett., 19, 215-218, which is downloadable from the GISS webpage.

It contains their model’s prediction of the response to Pinatubo’s eruption, a prediction done only a few months after the eruption occurred in June of 1991:

Figure 1. Predictions by NASA GISS scientists of the effect of Mt. Pinatubo on global temperatures. Scenario “B” was Hansen’s “business as usual” scenario. “El” is the estimated effect of a volcano the size of El Chichón. “2*El” is a volcano twice the size of Chichón. The modelers assumed the volcano would be 1.7 times the size of El Chichón. Photo is of Pinatubo before the eruption.

Excellent, sez’ I, we have an actual testable prediction from the GISS model. And it should be a good one if the model is good, because they weren’t just guessing about inputs. They were using early estimates of aerosol depth that were based on post-eruption observations. But with GISS, you never know …

Here’s Lacis again talking about how the real-world outcome validated the model results. (Does anyone else find this an odd first choice when asked for evidence that climate models work? It is a 20-year-old study by Lacis. Is this his best evidence he has?) But I digress … Lacis says further about the matter:

There we make an actual global climate prediction (global cooling by about 0.5 C 12-18 months following the June 1991 Pinatubo volcanic eruption, followed by a return to the normal rate of global warming after about three years), based on climate model calculations using preliminary estimates of the volcanic aerosol optical depth. These predictions were all confirmed by subsequent measurements of global temperature changes, including the warming of the stratosphere by a couple of degrees due to the volcanic aerosol.

As always, the first step in this procedure is to digitize their data. I use a commercial digitizing software called “GraphClick” on my Mac, there are equivalent programs for the PC, it’s boring tedious hand work. I have made the digitized data available here as an Excel worksheet.

Being the untrusting fellow that I am, I graphed up the actual temperatures for that time from the GISS website. Figure 2 shows that result, along with the annual averages of their Pinatubo prediction (shown in detail below in Figure 3), at the same scale that they used.

Figure 2. Comparison of annual predictions with annual observations. Upper panel is Figure 2(b) from the GISS prediction paper, lower is my emulation from digitized data. Note that prior to 1977 the modern version of the GISS temperature data diverges from the 1992 version of the temperature data. I have used an anomaly of 1990 = 0.35 for the modern GISS data in order to agree with the old GISS version at the start of the prediction period. All other data is as in the original GISS prediction. Pinatubo prediction (blue line) is an annual average of their Figure 3 monthly results.

Again from their paper:

Figure 2 shows the effect of E1 and 2*El aerosol son simulated global mean temperature. Aerosol cooling is too small to prevent 1991 from being one of the warmest years this century, because of the small initial forcing and the thermal inertia of the climate system. However, dramatic cooling occurs by 1992, about 0.5°C in the 2*El case. The latter cooling is about 3 σ [sigma], where σ is the interannual standard deviation of observed global annual-mean temperature.This contrasts with the 1-1/2 σ coolings computed for the Agung (1963)and El Chichon (1982) volcanos

So their model predicted a large event, a “three-sigma” cooling from Pinatubo.

But despite their prediction, it didn’t turn out like that at all. Look at the red line above showing the actual temperature change. If you didn’t know there was a volcano in 1991, that part of the temperature record wouldn’t even catch your eye. Pinatubo did not cause anywhere near the maximum temperature swing predicted by the GISS model. It was not a three-sigma event, just another day in the planetary life.

The paper also gave the monthly predicted reaction to the eruption. Figure 3 shows detailed results, month by month, for their estimate and the observations.

Figure 3. GISS observational temperature dataset, along with model predictions both with and without Pinatubo eruptions. Upper panel is from GISS model paper, lower is my emulation. Scenario B does not contain Pinatubo. Scenario P1 started a bit earlier than P2, to see if the random fluctuations of the model affected the result (it didn’t). Averages are 17-month Gaussian averages. Observational (GISS) temperatures are adjusted so that the 1990 temperature average is equal to the 1990 Scenario B average (pre-eruption conditions). Photo Source

One possibility for the model prediction being so far off would be if Pinatubo didn’t turn out to be as strong as the modelers expected. Their paper was based on very early information, three months after the event, viz:

The P experiments have the same time dependence of global optical depth as the E1 and 2*El experiments, but with r 1.7 times larger than in E1 and the aerosol geographical distribution modified as described below. These changes crudely account for information on Pinatubo provided at an interagency meeting in Washington D.C. on September 11 organized by Lou Walter and Miriam Baltuck of NASA, including aerosol optical depths estimated by Larry Stowe from satellite imagery.

However, their estimates seem to have been quite accurate. The aerosols continued unabated at high levels for months. Optical depth increased by a factor of 1.7 for the first ten months after the eruption. I find this (paywall)

Dutton, E. G., and J. R. Christy, Solar radiative forcing at selected locations and evidence for global lower tropospheric cooling following the eruptions of El Chichon and Pinatubo, Geophys. Res. Lett., 19, 2313-1216, 1992. As a result of the eruption of Mt. Pinatubo (June 1991), direct solar radiation was observed to decrease by as much as 25-30% at four remote locations widely distributed in latitude. The average total aerosol optical depth for the first 10 months after the Pinatubo eruption at those sites is 1.7 times greater than that observed following the 1982 eruption of El Chichon

and from a 1995 US Geological Service study:

The Atmospheric Impact of the 1991 Mount Pinatubo Eruption ABSTRACT The 1991 eruption of Pinatubo produced about 5 cubic kilometers of dacitic magma and may be the second largest volcanic eruption of the century. Eruption columns reached 40 kilometers in altitude and emplaced a giant umbrella cloud in the middle to lower stratosphere that injected about 17 megatons of SO2, slightly more than twice the amount yielded by the 1982 eruption of El Chichón, Mexico. The SO2 formed sulfate aerosols that produced the largest perturbation to the stratospheric aerosol layer since the eruption of Krakatau in 1883. … The large aerosol cloud caused dramatic decreases in the amount of net radiation reaching the Earth’s surface, producing a climate forcing that was two times stronger than the aerosols of El Chichón.

So the modelers were working off of accurate information when they made their predictions. Pinatubo was just as strong as they expected, perhaps stronger.

Finally, after all of that, we come to the bottom line, the real question. What was the difference in the total effect of the volcano, both in observations and in reality? What overall difference did it make to the temperature?

Looking at Fig. 3 we can see that there is a difference in more than just maximum temperature drop between model results and data. In the model results, the temperature dropped earlier than was observed. It also dropped faster than actually occurred. Finally, the temperature stayed below normal for longer in the model than in reality.

To measure the combined effect of these differences, we use the sum of the temperature variations, from before the eruption until the temperature returned to pre-eruption levels. It gives us the total effect of the eruption, in “degree-months”. One degree-month is the result of changing the global temperature one degree for one month. It is the same as lowering the temperature half a degree for two months, and so on.

It is a measure of how much the volcano changed the temperature. It is shown in Fig. 3 as the area enclosed by the horizontal colored lines and their respective average temperature data (heavier same color lines). These lines mark the departure from and return to pre-eruption conditions. The area enclosed by each of them is measured in “degree – months” (degrees vertically times months horizontally).

The observations showed that Pinatubo caused a total decrease in the global average temperature of eight degree-months. This occurred over a period of 46 months, until temperatures returned to pre-eruption levels.

The model, however, predicted twice that, sixteen degree-months of cooling. And in the model, temperatures did not return to pre-eruption conditions for 63 months. So that’s the bottom line at the end of the story — the model predicted twice the actual total cooling, and predicted it would take fifty percent longer to recovery than actually happened … bad model, no cookies.

Now, there may be an explanation for that poor performance that I’m not seeing. If so, I invite Dr. Lacis or anyone else to point it out to me. Absent any explanation to the contrary, I would say that if this is his evidence for the accuracy of the models, it is an absolute … that it is a perfect … well, upon further reflection let me just say that I think the study and prediction is absolutely perfect evidence regarding the accuracy of the models, and I thank Dr. Lacis for bringing it to my attention.

[UPDATE] A number of the commenters have said that the Pinatubo prediction wasn’t all that wrong and that the model didn’t miss the mark by all that much. Here’s why that is not correct.

Hansen predicted what is called a “three sigma” event. He got about a two sigma event (2.07 sigma). “Sigma” is a measure of how common it is for something to occur. However, it is far from linear.

A two sigma event is pretty common. It occurs about one time in twenty. So in a dataset the size of GISSTEMP (130 years) we would expect to find somewhere around 130/20 = six or seven two sigma interannual temperature changes. These are the biggest of the inter-annual temperature swings. And in fact, there are eight two-sigma temperature swings in the GISSTEMP data.

A three sigma event, on the other hand, is much, much rarer. It is a one in a thousand event. The biggest inter-annual change in the record is 2.7 sigma. There’s not a single three sigma year in the entire dataset. Nor would we expect one in a 130 year record.

So Hansen was not just making a prediction of something usual. He was making a prediction that we would see a temperature drop never before seen, a once in a thousand year drop.

Why is this important? Remember that Lacis is advancing this result as a reason to believe in climate models.

Now, suppose someone went around saying his climate model was predicting a “thousand-year flood”, the huge kind of millennial flood never before seen in people’s lifetimes. Suppose further that people believed him, and spent lots of money building huge levees to protect their homes and cities and jacking up their houses above predicted flood levels.

And finally, suppose the flood turned out to be the usual kind, the floods that we get every 20 years or so.

After that, do you think the flood guy should go around citing that prediction as evidence that his model can be trusted?

But heck, this is climate science …

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