Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2018-71
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Model description paper
19 Apr 2018
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).
The Multi-Assumption Architecture and Testbed (MAAT v1.0): Code for ensembles with dynamic model structure including a unified model of leaf-scale C3 photosynthesis
Anthony P. Walker1, Ming Ye2, Dan Lu1, Martin G. De Kauwe3, Lianhong Gu1, Belinda E. Medlyn4, Alistair Rogers5, and Shawn P. Serbin5 1Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
2Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, Florida, USA
3ARC Centre of Excellence for Climate Extremes, Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia
4Hawkesbury Institute for the Environment, Western Sydney University. Locked Bag 1797 Penrith, New South Wales, Australia
5Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, New York, USA
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 informally and incompletely, or is ignored. In this model description paper we present the Multi-Assumption Architecture and Testbed (MAAT v1.0) designed to formally and more completely analyse process-representation based epistemic uncertainty. MAAT is a modular modelling code that can simply and efficiently vary model structure (process representation) during runtime, allowing the generation of large ensembles that vary in process representation, as well as in parameters, parameter values, and environmental conditions. MAAT v1.0 approaches epistemic uncertainty through sensitivity anlaysis, 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. 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 specifc system model employed. Therefore MAAT provides a tool for development of novel or "toy" models in many domains, i.e. not only photosynthesis, facilitating rapid informal and formal comparison of alternative modelling approaches.
Citation: Walker, A. P., Ye, M., Lu, D., De Kauwe, M. G., Gu, L., Medlyn, B. E., Rogers, A., and Serbin, S. P.: The Multi-Assumption Architecture and Testbed (MAAT v1.0): Code for ensembles with dynamic model structure including a unified model of leaf-scale C3 photosynthesis, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-71, in review, 2018.
Anthony P. Walker et al.
Anthony P. Walker et al.
Anthony P. Walker et al.

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Short summary
Large uncertainty is inherent in model predictions due to imperfect knowledge of how to describe the processes that a model is intended to represent. Yet methods to quantify and evaluate this model hypothesis uncertainty are limited. To address this, the multi-assumption architecture and testbed (MAAT) automates the generation of all possible models by combining multiple representations of multiple processes. MAAT provides formal framework for quantification of model hypothesis uncertainty.
Large uncertainty is inherent in model predictions due to imperfect knowledge of how to describe...
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