ohw21-proj-cmip-ard

An OceanHackWeek21 project to learn & document examples of CMIP6 workflows, turning big climate projection data into useful inputs for modelling and analysis.

Goals of this project include building clean, well-commented Jupyter notebooks that document CMIP6 workflows for generating analysis ready datasets (ARD) for specific examples. A stretch-goal for this project is to complete and publish examples, possibly in a Pangeo Gallery?

This effort will leverage all the great work across the Pangeo & other open-source communities and may include:

• cmip6_preprocessing
  
• xgcm
  
• regionmask
  
• intake-esm
  

Use case #1: SST & surface nitrate projections for Arabian Sea ecological modelling

A hypothetical ecological model of the Arabian Sea includes basin averaged SST and surface nitrate as inputs. Researchers would like to explore ecosystem responses to future climate change projections under both SSP1-2.6 and SSP5-8.5 scenarios. A monthly timeseries through 2100, averaged over the Arabian Sea, is required from a range of CMIP6 models to start the effort, and ouput in a useful format for import into R is required. Another useful final product would be regridding all models onto a common grid for further comparison.

Tasks:
0. What CMIP6 models should we include in our ensemble and why? How do they perform for our areas of interest and with what known biases? (Out of scope - especially for this hypothetical example - but a literature review might be the first step here)

1. Loading SST and surface nitrate for your chosen CMIP6 multi-model ensemble (in this case arbitrarily choose a few models only, keeping in mind hub memory limits)
2. Visualise the spread of values across the multi-model ensembles for each scenario.
3. Do we need to de-drift each CMIP model and how?
4. Build an appropriate mask for computing the Arabian Sea spatially-averaged timeseries.
5. Generate the region-average timeseries considering appropriate weighting for each model's grid. Format the timeseries data in a useful, human readable way.

Stretch task:

6. Regrid the multi-model ensemble onto a single, common grid for further comparison.

Other use cases?