Hey,

I'm hoping that someone can help me figure out whats going wrong here. I'm trying produce a multi-model mean of a 2D (x-z dimensional) field. It's a fairly complex preprocessor, several of the stages can be quite slow, and I'll need to run it over lots (dozens?) of model datasets. With that in mind, I'm trying to keep it lightweight:

  prep_transect: # For extracting a transect
    custom_order: true
    time_average:
    regrid:
      target_grid: 1x1
      scheme: linear
    zonal_means:
      coordinate: longitude
      mean_type: mean
    extract_levels:
      levels: [0.1, 0.5, 1, 10, 20, 40, 80, 120, 160, 200, 240, 280, 320, 360, 400, 440, 480, 520, 560, 600, 640, 680, 720, 760, 800, 840, 880, 920, 960, 1000, 1200, 1400, 1600, 1800, 2000, 2200, 2400, 2600, 2800, 3000, 3200, 3400, 3600, 3800, 4000, 4200, 4400, 4600, 4800, 5000, 5200, 5400, 5600, 5800]
      scheme: linear
    multi_model_statistics:
      span: full
      statistics: [mean, ]

(The extract_levels field is a bit silly, please don't worry about it too much.)

The problem that I'm seeing now is that the multi_model_statistics part doesn't produce any results. I think that this is because it can't find a time overlap between the files:

2019-03-12 15:56:35,921 UTC [29013] DEBUG   esmvaltool.preprocessor._multimodel:304 Multimodel statistics: computing: ['mean']
2019-03-12 15:56:35,923 UTC [29013] INFO    esmvaltool.preprocessor._multimodel:313 Time overlap between cubes is none or a single point.check datasets: will not compute statistics.

The first step of the preprocessor is to take a time average, as this reduces the workload of the function by an order of magnitude or more. However, I suspect that this is the reason why it can't find any overlap in the time range between the models.

Perhaps people can suggest a better way to do this - or perhaps a way to get the multi-model mean function to ignore the time overlap?

Cheers!