ILAMB 2.3.1

The International Land Model Benchmarking Package

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. Building upon past model evaluation studies, the goals of ILAMB are to:

• develop internationally accepted benchmarks for land model performance, promote the use of these benchmarks by the international community for model intercomparison,

• strengthen linkages between experimental, remote sensing, and climate modeling communities in the design of new model tests and new measurement programs, and

• support the design and development of a new, open source, benchmarking software system for use by the international community.

It is the last of these goals to which this repository is concerned. We have developed a python-based generic benchmarking system, initially focused on assessing land model performance.

Useful Information

• Documentation - installation and basic usage tutorials

• Sample Output

  • CLM - land comparison against 3 CLM versions and 2 forcings

  • CMIP5 - land comparison against a collection of CMIP5 models

  • IOMB - ocean comparison against a few ocean models

• Paper preprint which details the design and methodology employed in the ILAMB package

• If you find the package or the ouput helpful in your research or development efforts, we kindly ask you to cite the following reference (DOI:10.18139/ILAMB.v002.00/1251621).

ILAMB 2.3 Release

We are pleased to announce version 2.3 of the ILAMB python package. Among many bugfixes and improvements we highlight these major changes:

• You may observe a large shift in some score values. In this version we solidified our scoring methodology while writing a paper which necesitated reworking some of the scores. For details, see the linked paper.

• Made a memory optimization pass through the analysis routines. Peak memory usage and the time at peak was reduced improving performance.

• Restructured the symbolic manipulation of derived variables to greatly reduce the required memory.

• Moved from using cfunits to cf_units. Both are python wrappers around the UDUNITS library, but cfunits is stagnant and placed a lower limit to the version of the netCDF4 python wrappers we could use.

• The scoring of the interannual variability was missed in the port from version 1 to 2, we have added the metric.

• The terrestrial water storage anomaly GRACE metric was changed to compare mean anomaly values over large river basins. For details see the ILAMB paper.

Funding

This research was performed for the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation (RUBISCO) Scientific Focus Area, which is sponsored by the Regional and Global Climate Modeling (RGCM) Program in the Climate and Environmental Sciences Division (CESD) of the Biological and Environmental Research (BER) Program in the U.S. Department of Energy Office of Science.