Here’s a test for the CMIP5 models, assuming that there are 1×1 monthly gridded datasets available for ocean temperature.
1. For each gridbox in the ocean between 0-2000M, detrend the temperature timeseries and measure the variance.
2. Calculate the zonal mean of the variances (i.e. average out the longitudinal dimension).
3. For each depth level, normalize the data by standard deviations.
4. Produce an image plot of the variances by latitude and depth.
5. Do the same using interpolated Argo data and compare the plots.
Why do I think this could be important? Because one can use the variance of the timeseries to calculate the average power of the seasonal signal. Perhaps this can be used to assess heat uptake? I did this last year against the CMIP3 models. In my opinion, none of the models adequately matched the Argo pattern. See for yourself:
https://sites.google.com/site/climateadj/ocean_variance
Then again, maybe I messed up or it doesn’t matter. All the usual caveats apply.