The researchers found that when compared to observations, almost every CMIP5 model fails, no matter whether the multidecadal variability is assumed to be forced or internal.

The basic questions for climate models is whether they realistically simulate observations, and to what extent can future climate change be predicted? It’s an important concept as political and environmental action is predicated upon it.

A new paper by Timothy DelSole of George Mason University and Michael Tippett of Columbia University looks into this by attempting to quantify the consistency between climate models and observations using a novel statistical approach. It involves using a multivariate statistical framework whose usefulness has been demonstrated in other fields such as economics and statistics. Technically, they are asking if two time series such as observations and climate model output come from the same statistical source.

To do this they looked at the surface temperature of the North Atlantic which is variable over decadal timescales. The reason for this variability is disputed, it could be related to human-induced climate change or natural variability. If it is internal variability but falsely accredited to human influences then it could lead over estimates of climate sensitivity. There is also the view that the variability is due to anthropogenic aerosols with internal variability playing a weak role but it has been found that models that use external forcing produce inconsistencies in such things as the pattern of temperature and ocean salinity. These things considered it’s important to investigate if climate models are doing well in accounting for variability in the region as the North Atlantic is often used as a test of a climate model’s capability.

The researchers found that when compared to observations, almost every CMIP5 model fails, no matter whether the multidecadal variability is assumed to be forced or internal. They also found institutional bias in that output from the same model, or from models from the same institution, tended to be clustered together, and in many cases differ significantly from other clusters produced by other institutions. Overall only a few climate models out of three dozen considered were found to be consistent with the observations.

Recently Michael Mann, in particular, has said there is no such thing as internal climate variability, maintaining that oscillations seen in proxies of pre-industrial temperature can be explained as an artifact of volcanic activity. The researchers find no evidence for this in the North Atlantic data.

The researchers have a book being published by the Cambridge University Press later this year called “Statistical Methods for Climate Scientists.”

This article appeared on the Global Warming Policy Forum website at https://www.thegwpf.com/climate-models-fail-in-key-test-region/