There will be a meeting report, which is in preparation, and outcomes will be assimilated into an update of the SST CCI user requirements document.
An interesting outcome was the appetite among some users for ensembles of sea surface temperature (SST) datasets. This might seem surprising, since satellite-derived datasets are already large, and the prospect of a meaningful ensemble (100 to 1000 realisations was mentioned) would seem to be an obstacle. However, a number of counter-balancing points were raised:
- creating an ensemble can be the only practical way to represent some correlated error structures (particularly errors that evolve on the long term through a multi-decadal dataset, such as those associated with imperfect harmonisation across series of sensors)
- other forms of more detailed uncertainty information, including error covariance matrices, can also represent and large additional data volume
- once a user has an analysis or model run set up for a particular experiment using an SST dataset, it can be much easier for them to explore uncertainty by re-running the same analysis for variants of the SST dataset, than to analyse the dataset uncertainty information and assess its impact (which involves more thinking and work!
- the ensemble generation can involve runs that explore structural uncertainty (effects of data processing choices where there is some element of judgement about parameters, etc) -- and there is no other way to obtain the resulting effects (these choices/parameters generally can't be analytically propagated, and sensitivity to them has to be found by tweaking the choices and re-running)
We can't feasibly produce a full ensemble version of our climate data records within SST CCI phase 2, but we will stay alert to opportunities to progress towards that capability as we are doing the project.
