Spin up with a Machine Learning approach

What is it about?

Aim: develop a spinup acceleration procedure which is model version independent. The idea is to develop a python tool set which can applied to the ORCHIDEE family of models.

How can I contribute to this effort?

Please contact the D.Goll if you want to join. Some example we would benefit from are:

• data from conventional spinup simulations
• expertise how to link it to other tools, like libIGCM, ORCHIDAS etc.
• expertise how to host/distribute/maintain the software
• machine learning, python

Task force members

Daniel Goll, Yan Sun, Jinfeng Chang, Yilong Wang, Yuanyuan Huang, Vladislav Bastrikov, Nicolas Viovy Matt McGrath?

Status reports

26/01/2021

• DONE: Proof of concept for ORCHIDEE-CNP v1.2
• ONGOING: Finding a common setup for pixel selection applicable to all ORCHIDEE versions
• ONGOING: Collecting data from other ORCHIDEE versions for testing
• ONGOING: Translating matlab into python code
• ONGOING: Cleaning the code
• ONGOING: Recruiting task force members

16/02/2021

Yan gave a presentation on progress with python coding, results on CNP and trunk, and timeline for next 2 months.

• Input files: restart + climate forcing (not hist file as might ORCHIDEE might introduce noise)
• K-means clustering: add plot which shows the total distance vs k to monitor if the chosen number of cluster paranmeter is well chosen (part of the monitoring info for user)
• Add checks and quality statistics to monitor if each steps performs well & stop the procedure is results fail minimum quality criteria (e.g. stop if machine learning fails to predict training pixels)
• Externalize all parameters of the routines in one file.

Work distribution:

• Matt: Provide trunk v4.0 data (EQ files, + results from 200yr after scratch w/o anal spinup)
• Yilong refines & extend coding of tool 1&2
• Run tests with the refined tools for other forcings (everyone)
• Yan will focus next month on PhD defens (20.March)

03/03/2021

• First version of python tools are available for testing
• Yilong gave an overview

Next steps:

• put code and documentation on github (Daniel, Vlad, Yilong)
• add documentation on how to run the tools; adapt them to other models (Yan,Yilong)

• all attempt to run the tools with their model data (keep a log on github about what model data used)

information/suggestions on run the tools:

• user specification files: need more information, e.g. what file name corresponds to Equilibirum information what to info from transient run (Yan)
• things to improve: figure labelling, user spec file (simplify)
• try to use qsub to avoid blocking nodes on obelix

16/03/2021

• github has been setup and some initial test and exchanges were done
• next: everyone try and test the tool on the two available datasets (CNP, trunk); report bugs, improvmenets, etc on github
• ongoing: acquire data from other model (versions): CABLE, ORCHIDEE-MICT, ORCHIDEE-<any>

• next meeting will be scheduled after discussion with Yan after her defence

01/04/2021

• github code status: YY could run the code, DG did some test modifying some inputs, all detected (minor) problems are listed in issues in github
• TODO1 (yan): provide information in README how to insert data from other simulation; separate the user specification files into experiment specific (e.g. path to model output, forcing period (for tool 3), etc) and model version specific (e.g. CNP, MICT, Trunk, CABLE, etc).
• TODO2 (yan): provide a tool 2 output which condense the information from now multiple files into a single file.
• TOdO3 (yan): work on the manuscript (incl. results from test with other model versions (if feasible from TODO4) and CABLE)
• TODO4 (YY, DG, all): test the tools 1 and 2 when TODO1 and TODO2 are ready.
• TODO5 (DG): discuss with project team about the running scripts.
• TODO6( Yan) : code a evaluation tool (tool 3); check criterias are (1) high priority (total land C stock), (2) medium priority (land C stock on pixel), (3) others / drift over forcing period (i.e. climate loop).