Title: Ecosystem biogeochemistry model parameterization: Do more flux data result in a better model in predicting carbon flux?

Abstract

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 indicatemore » 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.« less

Authors:

Zhu, Qing ^[1]; Zhuang, Qianlai ^[1]

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+ Show Author Affiliations

1. Purdue Univ., West Lafayette, IN (United States)

Publication Date:

Mon Dec 21 00:00:00 EST 2015

Research Org.:

Purdue Univ., West Lafayette, IN (United States)

Sponsoring Org.:

Office of Science (SC), Biological and Environmental Research (BER). Earth and Environmental Systems Science Division

OSTI Identifier:

1435594

Grant/Contract Number:  

FG02-08ER64599

Resource Type:

Accepted Manuscript

Journal Name:

Ecosphere

Additional Journal Information:

Journal Volume: 6; Journal Issue: 12; Journal ID: ISSN 2150-8925

Publisher:

Ecological Society of America

Country of Publication:

United States

Language:

English

Subject:

59 BASIC BIOLOGICAL SCIENCES; Adjoint Method; AmeriFlux GPP and NEP; l data assimilation; data coverage; data length; model parameterization; terrestrial ecosystem modeling