New code allows scientists to generate and analyze multiple models that vary in how processes are represented.
Researchers developed a new modeling software package that allows many alternative models to be posed, run, and analyzed as an ensemble, saving scientists time and providing a path to decrease uncertainty in modeling analyses.
There are many ways to represent real-world processes in computer models. But it is common that only a single representation is used in any given model, leading to results that are model specific. This new code now allows the modeling community to move away from the single-representation method to using many alternative models in a single study for a richer analysis that more broadly encompasses our current state of knowledge about ecosystem processes.
Alternative ways that real-world processes can be represented in computer models is a huge source of uncertainty in model output. Yet tools and modelling systems to examine these alternatives are not available. Researchers at Oak Ridge National Laboratory and a team of national and international collaborators have developed software that can combine alternative ways to represent many real-world processes into a complete set of all possible combinations of the alternatives. This will give a full range of possible model results and goes beyond the single-instance approach to running models. The software also includes novel tools for analysis of model sensitivity to alternative process models.
Contacts (BER PM)
Terrestrial Ecosystem Science
Earth and Environmental System Modeling
Anthony P. Walker
Oak Ridge National Laboratory
DOE Office of Science, Office of Biological and Environmental Research funded Oak Ridge National Laboratory Terrestrial Ecosystem Science SFA and Next Generation Ecosystem Experiments (NGEE) — Tropics.
Walker, A. P. et al. “The multi-assumption architecture and testbed (MAAT v1.0): R code for generating ensembles with dynamic model structure and analysis of epistemic uncertainty from multiple sources.” Geoscientific Model Development 11, 3159-3185 (2018). [DOI:10.5194/gmd-11-3159-2018]
SC-23.1 Climate and Environmental Sciences Division, BER
BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER
May 10, 2019
Quantifying Decision Uncertainty in Water Management via a Coupled Agent-Based Model
Considering risk perception can improve the representation of human decision-making processes in age [more...]
May 09, 2019
Projecting Global Urban Area Growth Through 2100 Based on Historical Time Series Data and Future Scenarios
Study provides country-specific urban area growth models and the first dataset on country-level urba [more...]
May 05, 2019
Calibrating Building Energy Demand Models to Refine Long-Term Energy Planning
A new, flexible calibration approach improved model accuracy in capturing year-to-year changes in bu [more...]
May 03, 2019
Calibration and Uncertainty Analysis of Demeter for Better Downscaling of Global Land Use and Land Cover Projections
Researchers improved the Demeter model’s performance by calibrating key parameters and establi [more...]
Apr 22, 2019
Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles
Representation of warm temperature events varies considerably among global climate models, which has [more...]
List all highlights (possible long download time)