*If you would use this data, let me know what variables you are interested in*
Experiment title 
: ACCESS-ESM1-5 PI initialised ensembles
Summary :
Initialise 10 ensemble members, from 10 distinct initial conditions, and run these for 10 years (1000 years total). All run under pre-industrial forcing.
Scientific motivation:
To explore the predictability of internal variability in the ACCESS-ESM1-5 model.
I often use data from the Decadal Climate Prediction Project (DCPP, part of CMIP). These are hindcast ensembles initialised yearly from observations, and run with historical forcing. It’s nearly impossible to separate predictability from initial conditions vs that from climate change, so the global warming trend provides apparent ENSO forecast skill at 10 years lead time, which I suspect is unphysical. Model drift, due to the initialisation from observations, is also a confounding factor.
Therefore, I want to run a set of initialised perfect-model forecasts (i.e. model forecasts of the model itself) of a pre-industrial control run, to understand the predictability of ENSO in ACCESS-ESM1.5, when ignoring the effects of climate change and model drift.
I’d hope this experiment could also be utilised to understand the predictability of other aspects of the earth system (e.g. other climate modes, sea ice extent, drought, etc)
Experiment Name : init_pi
People : Jemma Jeffree
Configuration: ACCESS-ESM1-5 PI control:
Initial conditions: 10 distinct initial conditions, 5+ years apart, taken from a pre-industrial control simulation. Open to requests for particular initialisation years.
Run plan: 10 ensemble members, using micro-scale perturbations, for 10 years each
Simulation details:
Total KSUs required : 1,000
Total storage required : Depends on the variables saved. Personally, I only want monthly SST, 20 degree isotherm, surface winds (~10GB total), but it’d be great to keep other variables that the community is interested in. I’d be keen to keep only monthly variables, given this is an experiment for interannual predictability, and ideally these would nearly all be 2d.
Storage lifetime :
Long term data plan :
Outputs:
Restarts:
Related articles:
Analysis:
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