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

Walker, Anthony P.; Ye, Ming; Lu, Dan; De Kauwe, Martin G.; Gu, Lianhong; Medlyn, Belinda E.; Rogers, Alistair; Serbin, Shawn P.

doi:10.5194/gmd-11-3159-2018

Title: 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

Journal Article · Fri Aug 10 00:00:00 EDT 2018 · Geoscientific Model Development (Online)

DOI:https://doi.org/10.5194/gmd-11-3159-2018· OSTI ID:1465033

^[1]; Ye, Ming ^[2];

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^[1]; Medlyn, Belinda E. ^[4];

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Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Florida State Univ., Tallahassee, FL (United States)
Univ. of New South Wales, Sydney, NSW (Australia)
Western Sydney Univ., Penrith, NSW (Australia)
Brookhaven National Lab. (BNL), Upton, NY (United States)

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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC05-00OR22725; SC0012704

OSTI ID:: 1465033

Alternate ID(s):: OSTI ID: 1466608

Report Number(s):: BNL-207962-2018-JAAM

Journal Information:: Geoscientific Model Development (Online), Vol. 11, Issue 8; ISSN 1991-9603

Publisher:: European Geosciences UnionCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 11 works

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