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
Aug 24, 2019
New Approach for Studying How Microbes Influence Their Environment
A diverse group of scientists suggests a common framework and targeting of known microbial processes [more...]
Aug 08, 2019
Nutrient-Hungry Peatland Microbes Reduce Carbon Loss Under Warmer Conditions
Enzyme production in peatlands reduces carbon lost to respiration under future high temperatures. [more...]
Aug 05, 2019
Amazon Forest Response to CO2 Fertilization Dependent on Plant Phosphorus Acquisition
AmazonFACE Model Intercomparison. The Science Plant growth is dependent on the availabi [more...]
Jul 29, 2019
A Slippery Slope: Soil Carbon Destabilization
Carbon gain or loss depends on the balance between competing biological, chemical, and physical reac [more...]
Jul 15, 2019
Field Evaluation of Gas Analyzers for Measuring Ecosystem Fluxes
How gas analyzer type and correction method impact measured fluxes. The Science A side- [more...]
List all highlights (possible long download time)