Ecosystem biogeochemistry model parameterization: Do more flux data result in a better model in predicting carbon flux?
- Purdue Univ., West Lafayette, IN (United States)
Reliability of terrestrial ecosystem models highly depends on the quantity and quality of thedata that have been used to calibrate the models. Nowadays, in situ observations of carbon fluxes areabundant. However, the knowledge of how much data (data length) and which subset of the time seriesdata (data period) should be used to effectively calibrate the model is still lacking. This study uses theAmeriFlux carbon flux data to parameterize the Terrestrial Ecosystem Model (TEM) with an adjoint-baseddata assimilation technique for various ecosystem types. Parameterization experiments are thus conductedto explore the impact of both data length and data period on the uncertainty reduction of the posteriormodel parameters and the quantification of site and regional carbon dynamics. We find that: the modelis better constrained when it uses two-year data comparing to using one-year data. Further, two-year datais sufficient in calibrating TEM’s carbon dynamics, since using three-year data could only marginallyimprove the model performance at our study sites; the model is better constrained with the data thathave a higher‘‘climate variability’’than that having a lower one. The climate variability is used to measurethe overall possibility of the ecosystem to experience all climatic conditions including drought and extremeair temperatures and radiation; the U.S. regional simulations indicate that the effect of calibration datalength on carbon dynamics is amplified at regional and temporal scales, leading to large discrepanciesamong different parameterization experiments, especially in July and August. Our findings areconditioned on the specific model we used and the calibration sites we selected. The optimal calibrationdata length may not be suitable for other models. However, this study demonstrates that there may exist athreshold for calibration data length and simply using more data would not guarantee a better modelparameterization and prediction. More importantly, climate variability might be an effective indicator ofinformation within the data, which could help data selection for model parameterization. As a result, we believe ourfindings will benefit the ecosystem modeling community in using multiple-year data to improve modelpredictability.
- Research Organization:
- Purdue Univ., West Lafayette, IN (United States)
- Sponsoring Organization:
- Office of Science (SC), Biological and Environmental Research (BER). Earth and Environmental Systems Science Division
- Grant/Contract Number:
- FG02-08ER64599
- OSTI ID:
- 1435594
- Journal Information:
- Ecosphere, Vol. 6, Issue 12; ISSN 2150-8925
- Publisher:
- Ecological Society of AmericaCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
A new theory of plant-microbe nutrient competition resolves inconsistencies between observations and model predictions
|
journal | March 2017 |
Coupling the Canadian Terrestrial Ecosystem Model (CTEM v. 2.0) to Environment and Climate Change Canada's greenhouse gas forecast model (v.107-glb)
|
journal | January 2018 |
Similar Records
Quantifying Dissolved Organic Carbon Dynamics Using a Three‐Dimensional Terrestrial Ecosystem Model at High Spatial‐Temporal Resolutions
Quantifying global N2O emissions from natural ecosystem soils using trait-based biogeochemistry models