Abstract
Computer models are ubiquitous tools used to represent systems across many scientific and engineering domains. For any given system, many computer models exist, each built on different assumptions and demonstrating variability in the ways in which these systems can be represented. This variability is known as epistemic uncertainty, i.e. uncertainty in our knowledge of how these systems operate. Two primary sources of epistemic uncertainty are (1) uncertain parameter values and (2) uncertain mathematical representations of the processes that comprise the system. Many formal methods exist to analyse parameter-based epistemic uncertainty, while process-representation-based epistemic uncertainty is often analysed post hoc, incompletely, informally, or is ignored. In this model description paper we present the multi-assumption architecture and testbed (MAAT v1.0) designed to formally and completely analyse process-representation-based epistemic uncertainty. MAAT is a modular modelling code that can simply and efficiently vary model structure (process representation), allowing for the generation and running of large model ensembles that vary in process representation, parameters, parameter values, and environmental conditions during a single execution of the code. MAAT v1.0 approaches epistemic uncertainty through sensitivity analysis, assigning variability in model output to processes (process representation and parameters) or to individual parameters. In this model description paper we describe MAAT and, by using a simple groundwater model example, verify that the sensitivity analysis algorithms have been correctly implemented. The main system model currently coded in MAAT is a unified, leaf-scale enzyme kinetic model of C3 photosynthesis. In the Appendix we describe the photosynthesis model and the unification of multiple representations of photosynthetic processes. The numerical solution to leaf-scale photosynthesis is verified and examples of process variability in temperature response functions are provided. For rapid application to new systems, the MAAT algorithms for efficient variation of model structure and sensitivity analysis are agnostic of the specific system model employed. Therefore MAAT provides a tool for the development of novel or "toy" models in many domains, i.e. not only photosynthesis, facilitating rapid informal and formal comparison of alternative modelling approaches.
Original language | English |
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Pages (from-to) | 3159-3185 |
Number of pages | 27 |
Journal | Geoscientific Model Development |
Volume | 11 |
Issue number | 8 |
DOIs | |
State | Published - Aug 10 2018 |
Funding
Acknowledgements. The MAAT modelling framework and sensitivity analysis component of this research was supported as part of the ORNL Terrestrial Ecosystem Science, Science Focus Area, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research. The multi-assumption leaf-scale photosynthesis model component of this research was supported as part of the Next Generation Ecosystem Experiments-Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research. Oak Ridge National Laboratory is operated by UT-Battelle, LLC, under contract DE-AC05-00OR22725 to the United States Department of Energy. Brookhaven National Laboratory is managed under contract no. DE-SC0012704 to the United States Department of Energy. We thank Lisa Jansson (BNL) for assistance with graphic design.
Funders | Funder number |
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Office of Biological and Environmental Research | |
U.S. Department of Energy | |
Office of Science | |
Oak Ridge National Laboratory |