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Model parameterization to represent processes at unresolved scales and changing properties of evolving systems
1Department of Biological Sciences, Center for Ecosystem Sciences and Society, Northern Arizona University, Flagstaff, AZ, USA.
Global Change Biology
|November 30, 2019
Summary
Ecosystem models face uncertainty due to parameterization. This study shows that varying parameter values, estimated using data assimilation, better accounts for unresolved processes and changing ecosystem properties, improving model predictions.
Area of Science:
- Ecological modeling
- Environmental science
- Computational science
Background:
- Modeling is crucial for scientific research, especially for predicting ecosystem responses to global change.
- Model uncertainty often stems from parameterization, the process of assigning values to model parameters.
- Traditional parameterization assumes constant values, but models calibrated at one site often fail at another without re-tuning.
Purpose of the Study:
- To illustrate that varying parameter values are necessary for accurate ecosystem modeling.
- To explain that parameter variation accounts for processes at unresolved scales and evolving system properties.
- To highlight the role of data assimilation in rigorously estimating parameter values.
Main Methods:
- Utilizing data assimilation for statistically rigorous estimation of parameter values.
- Analyzing how parameter values vary with time, space, and experimental treatments.
- Demonstrating the impact of parameter variation on model predictions.
Main Results:
- Parameter values in ecosystem models are not constant and vary across sites, time, and conditions.
- Varying parameters allows models to incorporate interactions from unresolved scales and changing system properties.
- Data assimilation provides a rigorous approach to estimate these varying parameters.
Conclusions:
- Parameter variation is essential for improving the accuracy and reliability of ecosystem models.
- Data assimilation offers a powerful tool to understand the drivers and extent of parameter variation.
- Further research is needed to fully comprehend the implications of parameter variation for global change modeling.
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