A dynamical systems analysis of the data assimilation linked ecosystem carbon (DALEC) models

Area of Science:

• Earth system science
• Ecology
• Climate science

Background:

• Accurate modeling of the carbon cycle is crucial for understanding climate change.
• Existing carbon cycle models, despite complexity, have significant uncertainties.
• The Data Assimilation Linked Ecosystem Carbon (DALEC) model offers a simplified approach to forest carbon dynamics.

Purpose of the Study:

• To analyze the dynamical structure of the DALEC model.
• To identify key parameters influencing forest carbon cycle dynamics within DALEC.
• To investigate the implications of model parameterization for predicting carbon stock trends.

Main Methods:

• Qualitative analysis of the DALEC model's dynamical structure.
• Examination of parameter dependencies for evergreen and deciduous forest simulations.
• Identification of critical parameter thresholds indicating stable versus unsustainable conditions.

Main Results:

• DALEC model dynamics for both forest types are driven by a few critical parameters.
• A limit point exists where model behavior shifts from stable to unsustainable.
• Typical parameter values for forests are found near this critical limit point.

Conclusions:

• The sensitivity of DALEC to key parameters near a critical threshold complicates trend prediction.
• Insufficient data and parameterization challenges impact the reliability of carbon cycle models.
• These findings have significant implications for the application of data assimilation techniques in Earth system modeling.