Session 5: Model development options

CMIP7 CMIP7 Workshop
,
21

Shayn McGregor: Tropical variability modelling:

WhySpatialCCmatters
WhySpatialCCmatters1355×1029 123 KB

ProjChangeVariability
ProjChangeVariability1317×1030 154 KB

CausesModelDiscrepency
CausesModelDiscrepency1221×919 134 KB

22

Thanks Ben, totally agree. At least in the land surface context, empirical / machine learning approaches have been super useful for defining how well a model is utilising the information it’s been given in its parameters and inputs… and I think that’s a really defensible basis on which to define whether a model is doing well or not. It’s obviously only one way that one might decide on performance expectations a priori, but I really like it because it’s not an application-specific definition of what makes a model ‘good enough’.

1 Like
23

The question of coupler vs soft coupling via ins/outs/restarts has been briefly discussed. In principal the coupler approach would be the way to go. However this is unlikely to be the method chosen initially because

  • we’d have two sets of technical developments ongoing at the same time
  • coupling between the land (CABLE/JULES) and other components of ACCESS is currently either within the same executable (UM including UKCA) or via the ‘coupling’ section of the UM and the UM’s state vector array (in the case of communication with MOM) implying we’d have to manipulate those parts of the system of model systems as well.

The ambition (at the moment) is to use the soft coupling approach initially, clearly identify what information needs to be passed between components and get something working, and then transition to a coupler methodology. But that is on a longer-than-CMIP7 timeline being realistic.

1 Like