Carbon LAnd Model intercomparison Project (C-LAMP)

The purpose of this model-measurement intercomparison is to allow the international scientific community to evaluate the performance of biogeochemical models normally coupled to general circulation models (GCMs). Through a rigorous set of metrics, terrestrial models are scored based on their performance as compared to best-available site, field, and satellite observations. To this end, we encourage you to provide feedback on the experimental protocol, the metrics used to evaluate model performance, and the observational datasets available for use in the intercomparison.

Large-Scale Biosphere-Atmosphere (LBA) Model Intercomparison Project (LBA-MIP)

The objective of the LBA-Model Intercomparison Project (LBA-MIP) is to bring together international biosphere-atmosphere modeling groups to understand how different models simulate the ecosystems and biogeophysical processes in the Amazon of South America. Forcing and validation data were provided by the Large-Scale Biosphere-Atmosphere (LBA) Experiment in Amazonia. The LBA-MIP is the result of discussions held during the Carbon Synthesis Workshop and the 10th LBA-ECO Meeting held in Brasilia in early October 2006. This initiative is led by Luis Gustavo de Goncalves, Inez Fung, Humberto da Rocha and Scott Saleska. For more information, please e-mail the LBA-MIP Organizers.

Climate Model Experiments

Included here are diagnostics and analyses of various climate model experiments. The model results include spin-up, control, and transient runs from different configurations of the NCAR Community Climate System Model (CCSM).

Parallel Climate Model (PCM) Cluster Analysis

Multivariate Spatio-Temporal Clustering (MSTC) was applied to the monthly time series output from a fully coupled general circulation model (GCM) called the Parallel Climate Model (PCM). Results from an ensemble of five 99-yr Business-As-Usual (BAU) transient simulations from 2000 to 2098 were analyzed. MSTC is a powerful tool for model developers and environmental decision makers who wish to understand long, complex time series predictions of models.