I am currently comparing contrasting and comparing various IAM models. In terms of Mimi - I have been reviewing the input SSP data for GIVE, FAIRv2 and FAIRv1.6.2.
Given that historical information should all be consistent - i.e. it should not matter what the SSP scenario is, the historical emissions data should all be the same. However, it appears that the SSP585 data is not consistent, for what Leach et al call Montreal gases and Aerosols. For example the ch2cl2 montreal gas for the 585 scenario is different to the 119, 245 and 370 scenarios. (Note: this all seems to stem from taking the averages from the Leach et al paper (although confusingly the Python replication data (called as such in the Mimiv2 files (GitHub - FrankErrickson/MimiFAIRv2.jl))) appear to be consistent.
Am I missing something here?
Hi @rmjohnst thank you for the question! I’m going to move the question over the MimiFAIRv2 repository, since this is a model-specific rather than Mimi-specific question, and we can carry on the conversation there.
Thank you for taking the trouble to look into this.
Yes you are right - the averages I was talking about (for the montreal gases and aerosols) are those that appeared in the python_replication_data and that were, in turn, derived from the annual means from the file C:\Users\rjohn.julia\packages\MimiFAIRv2\7ivpN\data\python_replication_data\Original Leach et al (rcmip-emissions-annual-means-v5-1-0.csv).
And I will surely be asking more questions re: any inconsistencies I discover in the input data for the various models
P.S. The Mimi Framework is a very impressive piece of work!! - I am slowly becoming accustomed to the api etc. - but even now am finding it very logical and efficient to work with.
@rmjohnst thank you, I am in touch with my colleagues and we’ll sort out the inputs here. The models on the Mimi platform are open-source and developed by a host of users, many still in progress development especially if they have not been used in a publication, so these things can happen.
For MimiFAIRv2 I also aim to add a Mimi API Monte Carlo at some point soon, since it is important to run that model in Monte Carlo mode for proper results.
The Mimi teams hosts the platform, adds technical support and feature improvements, etc. but are not in charge of every model so to get accurate and timely responses I would suggest generally posting a model-specific question to the Github repository of the given model as an Issue and tagging the developers. In this case of course several of the MimiFAIRv2 developers happen to be Mimi developers, so this worked as well! Just explaining why you may get pointed to the repository if you post package-specific questions here
I’m glad to hear that the Framework is working well for you!
Will post issues with particular models to appropriate Github repository in future.
Look forward to the Mimi Monte Carlo API - that would be a good addition.
Great! Feel free to cross post here if the developers overlap, but we can keep the conversations on model specific repos when appropriate just to keep information available and allow collaboration.
Yes the Monte Carlo API is built, documented, and implemented for several models, but it looks as if MimIFAIRv2 does not implement it yet. It has a Monte Carlo function but it isn’t built with the Mimi API so I’ll do that at some point when it becomes useful for folks.