TY - JOUR
T1 - Model parameterization to represent processes at unresolved scales and changing properties of evolving systems
AU - Luo, Yiqi
AU - Schuur, Edward A.G.
N1 - Funding Information:
Financial support for this work was partially provided by U.S. DOE DE‐SC0006982, 4000161830, U.S. National Science Foundation (NSF) grant DEB 1655499, and subcontract 4000158404 from Oak Ridge National Laboratory (ORNL) to Northern Arizona University.
Funding Information:
Yiqi Luo yiqi.luo@nau.edu Edward A. G. Schuur Department of Biological Sciences Center for Ecosystem Sciences and Society Northern Arizona University Flagstaff AZ USA carbon cycle data assimilation ecological forecasting ecosystem modeling parameterization uncertainty U.S. Department of Energy 4000161830 DE‐SC0006982 U.S. National Science Foundation DEB 1655499
Funding Information:
Financial support for this work was partially provided by U.S. DOE DE-SC0006982, 4000161830, U.S. National Science Foundation (NSF) grant DEB 1655499, and subcontract 4000158404 from Oak Ridge National Laboratory (ORNL) to Northern Arizona University.
Publisher Copyright:
© 2019 John Wiley & Sons Ltd
PY - 2020/3/1
Y1 - 2020/3/1
N2 - 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.
AB - 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.
KW - carbon cycle
KW - data assimilation
KW - ecological forecasting
KW - ecosystem modeling
KW - parameterization
KW - uncertainty
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U2 - 10.1111/gcb.14939
DO - 10.1111/gcb.14939
M3 - Review article
C2 - 31782216
AN - SCOPUS:85078585469
SN - 1354-1013
VL - 26
SP - 1109
EP - 1117
JO - Global Change Biology
JF - Global Change Biology
IS - 3
ER -