Sample Models like Observations

Sample Models Like Observations

Scientific Motivation

Model geophysical fields are not always directly comparable to observations. Observations have limited spatial sampling (e.g., we do not have surface temperature observations over much of the ocean) and may have detection limits (e.g., spaceborne lidars cannot see through thick clouds).

Despite known differences between model fields and observed products, they are frequently compared. Such observational limitations can be important when drawing conclusions about model performance. For example, Cowtan et al (2015) found that by accounting for differences in HadCRUT4 and “climate model temperature fields accounts for 38% of the discrepancy in trend between models and observations over the period 1975–2014.” This finding helped to reconcile model-observational differences in the inferred transient climate response (Richardson et al., 2016).

In order to compare models and observations, post-processing is required to transform model fields into synthetic observed fields.

Proposed Hacking

During the CMIP6 Hackathon we will:

  • produce a publicly available code base that can be used to transform model fields into synthetic observed fields
  • make model-based synthetic observations publicly available for CMIP6 models
  • an obvious synthetic observation to include is surface air temperature, but the code could produce other synthetic observations as well (e.g., MSU temperature fields)

Anticipated Data Needs

Historical and future scenario surface fields including tas, tos, ts, sftof/sftlf, and sic.

Anticipated Software Tools

CDAT would work (NetCDF IO, regridding, masking), but other tools (e.g., xarray, ESMF) could also work. The goal would be to produce a library (e.g., in GitHub) that would be easy to use (e.g., with a conda environment) along with CMIP6 synthetic observation datasets.

Desired Collaborators

I proposed this idea because it seems like this is a project that would be generally useful to the climate community. This project could benefit from scientists who have experience in software development (to write a solid, reliable library) or scientists who see value in creating software to create their own synthetic observational dataset (from model fields).

4 Likes

Hi,

I developed some code to project model results onto observation space. It uses ESMF grid to locstream interpolation. There’s definitely room for improvement but it might be an example to build from?

best,
raf

1 Like

Sound like there is some good synergy between this project and the CMIP6 Ocean Atlas project.

1 Like

I just learned about another project in this space:

pyinterp was created in response to the interpolation problems we have experienced during our studies. For example, interpolate variables from a 2D field, a time series, or a 3D field. These fields often have land/sea masks and these problems must be managed. In addition, we sometimes interpolate models defined on irregular grids. For example, interpolate the MIT/GCM fields.
The libraries that we found were not sufficient mainly in terms of computation time. That’s why I created this project.
The current version is a first draft, but it is already being used in operational chains. I think it would be useful for the community to have such a working tool. But to make progress, I really need feedback.

1 Like

I have initialized some examples to use with binder. This gives an idea of what can be done with the library.

This proposal is very interesting and could have very beneficial outcomes well beyong CMIP6 community. Please keep us informed of its outcome !

@fbriol, it’s still on my todo list to provide feedback to you on pyinterp

Hi @fbriol / @raphaeldussin - are you going to work on this project? I am the lead, but I think I’d like to work on the zonal SST gradient project. Would either of you like to take over this project?

Hello,

The library we use allows us to do colocation. If you explain to me with a concrete example your need, I can see what can be done with pyinterp and xarray.

@pochedley I’m gonna focus on the Water masses of the future project that I proposed, but I can help whoever wants to use easy_coloc