Models Fail: Greenland and Iceland Land Surface Air Temperature Anomalies

I’m always amazed when global warming enthusiasts announce that surface temperatures in some part of the globe are warming faster than simulated by climate models. Do they realize they’re advertising that the models don’t work well for the area in question? And that their shouting it from the hilltops only helps to highlight the limited value of the models?

Greenland is a hotspot for climate change alarmism in more ways than one. A chunk of glacial ice sits atop Greenland, and as it melts, it contributes to the rise in sea levels. If surface temperatures in Greenland warm in the future, the warming rate will impact how quickly Greenland ice melts and its contribution to future sea levels. Greenland is also one of the locations around globe where land surface air temperatures in recent decades have been warming faster than simulated by models. See Figure 1, which is a model-data comparison of the surface air temperature anomalies of Greenland and its close neighbor Iceland. Somehow, that modeling failure turns into proclamations of doom, with the Chicken Littles of the anthropogenic global warming movement proclaiming we’re going to drown because rising sea levels.

Figure 1

Figure 1

A more detailed discussion of Figure 1: It compares the new and improved UK Met Office CRUTEM4 land surface air temperature anomalies for Greenland and Iceland (60N-85N, 75W-10W), for the period of January 1970 to February 2013, and the multi-model ensemble-member mean of the models stored in the CMIP5 archives, based on the scenario RCP6.0. As you’ll recall, the models in the CMIP5 archive are being used by the IPCC for its upcoming 5th Assessment Report. Based on the linear trends, since 1970, Greenland and Iceland surface air temperatures are warming at a rate that’s about 65% faster than predicted by the models. That’s not a very good showing for the models. And, for example, the disparity between the models and observations is even greater if we start the comparison in 1995, Figure 2. During the last 18 years, Greenland and Iceland land surface temperatures have been warming at a rate that’s more than 2.5 times faster than simulated by models. Obviously the modelers haven’t a clue about what causes land surface temperatures to warm there.

Figure 2

Figure 2

LOOKING AT THE RECENT WARMING PERIOD DOESN’T TELL THE WHOLE STORY

The data in Figure 1 covers a period of a little more than 40 years. Let’s look at a model-data comparison for the 40-year period before that, January 1930 to December 1969. Refer to Figure 3. During that multidecadal period, land surface air temperature anomalies in Greenland and Iceland actually cooled, and they cooled at a significant rate. On the other hand, the models show a miniscule long-term cooling from 1930 to 1969, but the trend is basically flat. The models fail again.

Figure 3

Figure 3

In our example in Figure 2, we looked at the trends from 1995 to present, so Figure 4 compares the models and data from January 1930 to December 1994. The data show cooling at a significant rate, about 0.25 deg C per decade, but now the models show warming.

Figure 4

Figure 4

TWO MORE REASONS FOR THIS EXERCISE

In addition to showing you how poorly the models simulate the land surface temperatures of Greenland and Iceland, another point I wanted to make was that you have to be wary of the start year of any study of Greenland surface temperatures. Figure 5 compares the models and data for Greenland and Iceland from 1930 to present. In it, the data and model output have been smoothed with 13-month running-average filters to minimize the monthly variations. Greenland and Iceland obviously cooled for much of the period since 1930. The break point between cooling and warming is probably debatable. But the most outstanding feature in the data is the extreme dip and rebound in the early 1980s. That dip appears about the time of the eruption of El Chichon in Mexico, and there’s another dip in 1991, which is when Mount Pinatubo erupted. Mount Pinatubo was a stronger eruption, so the 1982 dip appears unusual. Bottom line: keep in mind that any study of the recent warming of Greenland and Iceland surface temperatures will be greatly impacted by the start year.

Figure 5

Figure 5

The other point: Based on the linear trends (of the monthly data not the illustrated smoothed versions), land surface air temperature anomalies for Greenland and Iceland have not warmed since 1930. See Figure 6. Phrased another way, Greenland and Iceland surface temperatures have not warmed in 80+ years. But the models show they should have warmed about 1.3 deg C during that time. Granted, land surface temperatures now are warmer than then were in the 1930s and ’40s, but the models can’t simulate the cooling that took place from the 1930s to the latter part of the 20th Century, and they can’t be used to explain the recent warming.

Figure 6

Figure 6

Again, the models show no skill at being able to simulate surface temperatures. No skill at all.  Even for critical locations like Greenland.

STANDARD BLURB ABOUT THE USE OF THE MODEL MEAN

We’ve published numerous posts that include model-data comparisons. If history repeats itself, proponents of manmade global warming will complain in comments that I’ve only presented the model mean in the above graphs and not the full ensemble. In an effort to suppress their need to complain once again, I’ve borrowed parts of the discussion from the post Blog Memo to John Hockenberry Regarding PBS Report “Climate of Doubt”.

The model mean provides the best representation of the manmade greenhouse gas-driven scenario—not the individual model runs, which contain noise created by the models. For this, I’ll provide two references:

The first is a comment made by Gavin Schmidt (climatologist and climate modeler at the NASA Goddard Institute for Space Studies—GISS). He is one of the contributors to the website RealClimate. The following quotes are from the thread of the RealClimate post Decadal predictions. At comment 49, dated 30 Sep 2009 at 6:18 AM, a blogger posed this question:

If a single simulation is not a good predictor of reality how can the average of many simulations, each of which is a poor predictor of reality, be a better predictor, or indeed claim to have any residual of reality?

Gavin Schmidt replied with a general discussion of models:

Any single realisation can be thought of as being made up of two components – a forced signal and a random realisation of the internal variability (‘noise’). By definition the random component will uncorrelated across different realisations and when you average together many examples you get the forced component (i.e. the ensemble mean).

To paraphrase Gavin Schmidt, we’re not interested in the random component (noise) inherent in the individual simulations; we’re interested in the forced component, which represents the modeler’s best guess of the effects of manmade greenhouse gases on the variable being simulated.

The quote by Gavin Schmidt is supported by a similar statement from the National Center for Atmospheric Research (NCAR). I’ve quoted the following in numerous blog posts and in my recently published ebook. Sometime over the past few months, NCAR elected to remove that educational webpage from its website. Luckily the Wayback Machine has a copy. NCAR wrote on that FAQ webpage that had been part of an introductory discussion about climate models (my boldface):

Averaging over a multi-member ensemble of model climate runs gives a measure of the average model response to the forcings imposed on the model. Unless you are interested in a particular ensemble member where the initial conditions make a difference in your work, averaging of several ensemble members will give you best representation of a scenario.

In summary, we are definitely not interested in the models’ internally created noise, and we are not interested in the results of individual responses of ensemble members to initial conditions. So, in the graphs, we exclude the visual noise of the individual ensemble members and present only the model mean, because the model mean is the best representation of how the models are programmed and tuned to respond to manmade greenhouse gases.

CLOSING

Will the warming continue in Greenland and Iceland? If so, for how long? Or will the surface temperatures in Greenland and Iceland undergo another multidecadal period of cooling in the near future? The models show no skill at being able to simulate land surface air temperatures in Greenland and Iceland, so we can’t rely on them for predictions of the future.

We can add the surface temperatures of Greenland and Iceland to the growing list of climate model failures. The others included:

Scandinavian Land Surface Air Temperature Anomalies

Alaska Land Surface Air Temperatures

Daily Maximum and Minimum Temperatures and the Diurnal Temperature Range

Hemispheric Sea Ice Area

Global Precipitation

Satellite-Era Sea Surface Temperatures

Global Surface Temperatures (Land+Ocean) Since 1880

And we recently illustrated and discussed in the post Meehl et al (2013) Are Also Looking for Trenberth’s Missing Heat that the climate models used in that study show no evidence that they are capable of simulating how warm water is transported from the tropics to the mid-latitudes at the surface of the Pacific Ocean, so why should we believe they can simulate warm water being transported to depths below 700 meters without warming the waters above 700 meters?

I’ve got at least one more model-data post, and it’s about the land surface temperatures of another continent. The models almost double the rate of warming there.

Looks like I’ve got a lot of ammunition for my upcoming show and tell book. It presently has the working title Climate Models are Crap with the subtitle An Illustrated Overview of IPCC Climate Model Incompetence.

It’s unfortunate that the IPCC and the government funding agencies were only interested in studies about human-induced global warming. They created their consensus the old fashioned way: they paid for it. Now, the climate science community is still not able to differentiate between manmade warming and natural variability. They’re no closer to that goal than they were when they formed the IPCC. Decades of research efforts and their costs have been wasted by the single-sightedness of the IPCC and those doling out research funds.

They got what they paid for.

About Bob Tisdale

Research interest: the long-term aftereffects of El Niño and La Nina events on global sea surface temperature and ocean heat content. Author of the ebook Who Turned on the Heat? and regular contributor at WattsUpWithThat.
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13 Responses to Models Fail: Greenland and Iceland Land Surface Air Temperature Anomalies

  1. Frank says:

    Bob: One aspect of Figure 6 (and other parts of your presentation) can be misleading. When you show the multi-model mean and observed temperature on the same plot, one doesn’t expect the deviations (natural variability) from the multi-model mean to be as large as with the observed temperature. Much of the weather “noise” has been averaged out of multi-model mean, but noise hasn’t been averaged out of the observed temperatures (except via smoothing). For example, 2010 was an unusually warm year – deviating from the long term trend by at least 2 degC, but the largest deviations from the long term trend in the multi-model mean are about 0.5 degC. One needs a spaghetti graph of model runs to understand how noisy a typical model realization is.

  2. catweazle666 says:

    It’s amazing to think that – well into the second decade of the 21st century – there are “scientists” like Schmidt out there pretending that it is possible to meaningfully “model” a coupled forced non-linear complex dynamic system subject to an effectively infinite number of feedbacks – of which even of the ones we have identified, in many cases we don’t even know the sign, which may, as in the case of some clouds, change at regular intervals anyway. And then there’s a slight matter of extreme sensitivity to initial conditions, of course.

    Even more incredible is that they appear to be paid good money for it.

    You couldn’t make it up.

  3. Bob Tisdale says:

    Frank says: “One aspect of Figure 6 (and other parts of your presentation) can be misleading.”

    It’s not misleading because I’m not discussing the annual wiggles. I’m only discussing trends.

    Also, we’re not interested in the noise of the models because it is not weather related as the variations in the data are. Noise in the model outputs is simply noise created by the models.

  4. Bob, say: ”I’m always amazed when global warming enthusiasts announce that surface temperatures in some part of the globe are warming faster than simulated by climate models”

    One can cherry-pick places where is cooler than last year; but it doesn’t fit the theology. Always some places is warmer than last year; BUT also other places is cooler than previous year – and those things cancel each other. . Reason reporting temp for individual places is meaningless

  5. jim2 says:

    Does anyone know of a web site that has global average precipitation beyond the year 2000? If found a couple that go to 2000, but for some mysterious reason, there are none that I can find that extend past 2000.

  6. Bob Tisdale says:

    jim2: I generally use the CAMS-OPI precipitation data. It starts in 1979 and is a combination of rain gauge- and satellite-based data. It’s available through the KNMI Climate Explorer:
    http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

    I’ve used it in model-data precipitation comparisons:
    https://bobtisdale.wordpress.com/2012/12/27/model-data-precipitation-comparison-cmip5-ipcc-ar5-model-simulations-versus-satellite-era-observations/

    Regards

  7. Frank says:

    Bob: Thanks for the reply. When I look at Figure 6, for example, the first thing I see is the tremendous amount of variability in the observed temperatures (and the much smaller variability in the multi-model mean). The high variability in the observed temperature automatically means that the confidence interval for the observed trend should be large, making it difficult to determine “how inconsistent” the multi-model mean is from these observations, if indeed it is statistically inconsistent at all. Since you didn’t include a confidence interval for these trends, I’m reluctant to accept your conclusion that the models failed.

    Comparing observations and to the multi-model mean with proper statistics is tricky and I am often uncertain what to trust (despite following Lucia’s blog). If one considers only the mean and its standard error, many individual model runs are statistically inconsistent with the mean they produce. The standard deviation of the population (of all model runs), not the standard deviation of the mean (both calculated from all models runs) appears to be most appropriate for assessing whether observations are inconsistent with models. Unfortunately, when you focus on small areas of the globe (as in this post), the variability in observed temperature is much higher than it is for the whole globe or large chucks of it. Fortunately, you focus on the correctness of hindcasts over long enough periods of time to narrow the trend confidence interval, rather than forecasts covering the decade+ since a forecast was made (a favorite topic of Lucia).

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