International Land Model Benchmarking

As earth system models (ESMs) become increasingly complex, there is a growing need for comprehensive and multi-faceted evaluation of model projections. The International Land Model Benchmarking (ILAMB) project is a model-data intercomparison and integration project designed to improve the performance of land models and, in parallel, improve the design of new measurement campaigns to reduce uncertainties associated with key land surface processes.

1 December 2022 ILAMB intake Catalog

We are pleased to announce that the reference datasets that we have reprocessed and can be mass downloaded via ilamb-fetch are now also available as an intake catalog. Intake is a lightweight set of python tools for loading and sharing data in data science projects. It allows you to write python code referencing the ILAMB datasets by name, and then intake manages the download, using cached versions if available on your system.

26 May 2022 New Datasets Added to ILAMB

We have added 5 new datasets to the ILAMB collection. Please run ilamb-fetch to update your local collection and check ilamb.cfg for details on how to include them in your local runs. Alternatively you may browse some results against a subset of CMIP6 models. The new additions include:

2 July 2021 Help Us Make ILAMB Better

Help us learn what scientists think about model performance by evaluating pairs of model biases on this feedback form. Simply click on which bias plot you consider to be ‘better’ from the 20 randomly given pairs. While our intention is for you to select the model with the lower error relative to observations, please use whatever definition of ‘better’ makes sense to you as you examine the differences between the plots. We will use your collective responses to evaluate how well our methodolgy captures community opinion. For context, each plot represents either the Gross Primary Productivity (gpp), Sensible Heat (hfss), or Surface Air Temperature (tas) bias of a model in the CMIP5 or CMIP6 era, relative to a reference data product. When you are finished, click the ‘complete’ button to see how well your choices align with our current methodologies.

Check out our news archive for older posts.