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)

Next meetings: 2. March 10h00 (Paris time)