How accurately do climate models measure trends in global temperature?

Image By NOAA’s (National Oceanic and Atmospheric Association’s) Geophysical Fluid Dynamics Laboratory [Public domain], dated March 4, 2014, via Wikimedia Commons

It’s fairly certain that global warming is occurring. Just take a look at the Arctic Ice video on page 1 of this article. The video sequence is made up of actual images from 1997 through 2014. It is not speculation.

But then see the image of the 2 globes above. This image, created by NOAA in 2014, is a projection of ice thickness in the 2050s (projected at 54% of 1955, or 270 cm thickness in 2050), based on what ice thickness was in the 1950s (in 1955 to be exact, 500 cm thickness), factoring in rates of disappearance in 10-year segments.

Have scientists found the correct model for assessing and predicting the speed of global warming?

Two scientists set out to discover why measurements and predictions of climate were consistently inaccurate, and whether a trend of accuracy or inaccuracy of prediction could be found.

One factor confusing the issue of whether the earth is in a warming trend has been the 15-year cooling trend, which commenced at the turn of the century, due to reduced sunspot activity (the Maunder Minimum). This period is referred to in the article below as a “hiatus” in global warming, and is discussed on the next page of this article series.

The article below summarizes how scientists validated their predictive models by comparing predictive climate models to facts in 15-year periods.

Step One: Compare predictive accuracy of simulated versus observed temperature trends, using 114 fifteen-year periods (1900-2012).

To explain the puzzling discrepancy between model simulations and observations, Jochem Marotzke and Piers M. Forster proceeded in two steps. First, they compared simulated and observed temperature trends over all 15-year periods since the start of the 20th century. For each year between 1900 and 2012 they considered the temperature trend that each of the 114 available models predicted for the subsequent 15 years.

They then compared the results with measurements of how the temperature actually rose or fell. By simulating the average global temperature and other climatic variables of the past and comparing the results with observations, climatologists are able to check the reliability of their models. If the simulations prove more or less accurate in this respect, they can also provide useful predictions for the future.

The 114 model calculations withstood the comparison. Particularly as an ensemble, they reflect reality quite well: “On the whole, the simulated trends agree with the observations,” says Jochem Marotzke. The most pessimistic and most optimistic predictions of warming in the 15 subsequent years for each given year usually differed by around 0.3 degrees Celsius.

Step Two: Compare simulation sensitivity and assumptions to random climate fluctuations, as influencers of climate study outcome.

In a second step, the two scientists are now analysing why the simulations arrived at disparate results [from the factual observations]. This analysis can also explain why the various predictions for the past 15 years deviate from the actual observed trend.

Random fluctuations and three physical reasons come into question to explain this: The model calculations are based on different amounts of radiant energy from the sun that impinge on the Earth’s surface and are stored as a result of the greenhouse effect, e.g. due to atmospheric carbon dioxide. However, their predictions also respond with different degrees of sensitivity to changes in this radiant energy, for example if the carbon dioxide content of the atmosphere doubles.

In other words, the models assume different proportions of energy that warm the Earth’s surface and the proportion that is sooner or later radiated back into space. Finally, all the climate models assume different amounts of energy stored on the Earth that is transferred to the ocean depths, which act as an enormous heat sink.

The scientists felt random variation explained discrepancies in climate patterns.

Using a statistical method, Marotzke and Forster analysed the contributions of the individual factors and found that none of the physical reasons explains the distribution of predictions and the deviation from the measurements. However, random variation did explain these discrepancies very well.

In particular, the authors’ analysis refutes the claim that the models react too sensitively to increases in atmospheric carbon dioxide: “If excessive sensitivity of the models caused the models to calculate too great a temperature trend over the past 15 years, the models that assume a high sensitivity would calculate a greater temperature trend than the others,” Piers Forster explains. But that is not the case, despite the fact that some models are based on a degree of sensitivity three times greater than others.

To read the full article, which frames the need for this analysis within the debate over whether global warming is fact or fiction, see the original article, here.

Click next page to read about the Maunder Minimum which is thought to be responsible for the “hiatus” in global warming over the first 15 years of the 21st Century.