Climate models fail in key test region
Date: 07/06/21
Dr David Whitehouse, GWPF Science Editor
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.
https://www.thegwpf.com/climate-models-fail-in-key-test-region/
