Collaborative Project: The problem of bias in defining uncertainty in computationally enabled strategies for data-driven climate model development. Final Technical Report.
- Univ. of New Mexico, Albuquerque, NM (United States)
The objective of the project is to develop strategies for better representing scientific sensibilities within statistical measures of model skill that then can be used within a Bayesian statistical framework for data-driven climate model development and improved measures of model scientific uncertainty. One of the thorny issues in model evaluation is quantifying the effect of biases on climate projections. While any bias is not desirable, only those biases that affect feedbacks affect scatter in climate projections. The effort at the University of Texas is to analyze previously calculated ensembles of CAM3.1 with perturbed parameters to discover how biases affect projections of global warming. The hypothesis is that compensating errors in the control model can be identified by their effect on a combination of processes and that developing metrics that are sensitive to dependencies among state variables would provide a way to select version of climate models that may reduce scatter in climate projections. Gabriel Huerta at the University of New Mexico is responsible for developing statistical methods for evaluating these field dependencies. The UT effort will incorporate these developments into MECS, which is a set of python scripts being developed at the University of Texas for managing the workflow associated with data-driven climate model development over HPC resources. This report reflects the main activities at the University of New Mexico where the PI (Huerta) and the Postdocs (Nosedal, Hattab and Karki) worked on the project.
- Research Organization:
- Univ. of New Mexico, Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- DOE Contract Number:
- SC0010843
- OSTI ID:
- 1252216
- Report Number(s):
- DOE-UNM-GH-05102016
- Country of Publication:
- United States
- Language:
- English
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