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Title: Model parameterization to represent processes at unresolved scales and changing properties of evolving systems

Abstract

Abstract Modeling has become an indispensable tool for scientific research. However, models generate great uncertainty when they are used to predict or forecast ecosystem responses to global change. This uncertainty is partly due to parameterization, which is an essential procedure for model specification via defining parameter values for a model. The classic doctrine of parameterization is that a parameter is constant. However, it is commonly known from modeling practice that a model that is well calibrated for its parameters at one site may not simulate well at another site unless its parameters are tuned again. This common practice implies that parameter values have to vary with sites. Indeed, parameter values that are estimated using a statistically rigorous approach, that is, data assimilation, vary with time, space, and treatments in global change experiments. This paper illustrates that varying parameters is to account for both processes at unresolved scales and changing properties of evolving systems. A model, no matter how complex it is, could not represent all the processes of one system at resolved scales. Interactions of processes at unresolved scales with those at resolved scales should be reflected in model parameters. Meanwhile, it is pervasively observed that properties of ecosystems changemore » over time, space, and environmental conditions. Parameters, which represent properties of a system under study, should change as well. Tuning has been practiced for many decades to change parameter values. Yet this activity, unfortunately, did not contribute to our knowledge on model parameterization at all. Data assimilation makes it possible to rigorously estimate parameter values and, consequently, offers an approach to understand which, how, how much, and why parameters vary. To fully understand those issues, extensive research is required. Nonetheless, it is clear that changes in parameter values lead to different model predictions even if the model structure is the same.« less

Authors:
ORCiD logo [1];  [1]
  1. Department of Biological Sciences Center for Ecosystem Sciences and Society Northern Arizona University Flagstaff AZ USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1582470
Grant/Contract Number:  
4000161830; DE‐SC0006982
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Global Change Biology
Additional Journal Information:
Journal Name: Global Change Biology Journal Volume: 26 Journal Issue: 3; Journal ID: ISSN 1354-1013
Publisher:
Wiley-Blackwell
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Luo, Yiqi, and Schuur, Edward A. G. Model parameterization to represent processes at unresolved scales and changing properties of evolving systems. United Kingdom: N. p., 2020. Web. doi:10.1111/gcb.14939.
Luo, Yiqi, & Schuur, Edward A. G. Model parameterization to represent processes at unresolved scales and changing properties of evolving systems. United Kingdom. https://doi.org/10.1111/gcb.14939
Luo, Yiqi, and Schuur, Edward A. G. Sun . "Model parameterization to represent processes at unresolved scales and changing properties of evolving systems". United Kingdom. https://doi.org/10.1111/gcb.14939.
@article{osti_1582470,
title = {Model parameterization to represent processes at unresolved scales and changing properties of evolving systems},
author = {Luo, Yiqi and Schuur, Edward A. G.},
abstractNote = {Abstract Modeling has become an indispensable tool for scientific research. However, models generate great uncertainty when they are used to predict or forecast ecosystem responses to global change. This uncertainty is partly due to parameterization, which is an essential procedure for model specification via defining parameter values for a model. The classic doctrine of parameterization is that a parameter is constant. However, it is commonly known from modeling practice that a model that is well calibrated for its parameters at one site may not simulate well at another site unless its parameters are tuned again. This common practice implies that parameter values have to vary with sites. Indeed, parameter values that are estimated using a statistically rigorous approach, that is, data assimilation, vary with time, space, and treatments in global change experiments. This paper illustrates that varying parameters is to account for both processes at unresolved scales and changing properties of evolving systems. A model, no matter how complex it is, could not represent all the processes of one system at resolved scales. Interactions of processes at unresolved scales with those at resolved scales should be reflected in model parameters. Meanwhile, it is pervasively observed that properties of ecosystems change over time, space, and environmental conditions. Parameters, which represent properties of a system under study, should change as well. Tuning has been practiced for many decades to change parameter values. Yet this activity, unfortunately, did not contribute to our knowledge on model parameterization at all. Data assimilation makes it possible to rigorously estimate parameter values and, consequently, offers an approach to understand which, how, how much, and why parameters vary. To fully understand those issues, extensive research is required. Nonetheless, it is clear that changes in parameter values lead to different model predictions even if the model structure is the same.},
doi = {10.1111/gcb.14939},
journal = {Global Change Biology},
number = 3,
volume = 26,
place = {United Kingdom},
year = {Sun Jan 12 00:00:00 EST 2020},
month = {Sun Jan 12 00:00:00 EST 2020}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1111/gcb.14939

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