Evaluation of the ORCHIDEE ecosystem model over Africa against 25 years of satellite‐based water and carbon measurements. Issue 8 (14th August 2014)

• Record Type: Journal Article
• Authors: Traore, Abdoul Khadre; Ciais, Philippe; Vuichard, Nicolas; Poulter, Benjamin; Viovy, Nicolas; Guimberteau, Matthieu; Jung, Martin; Myneni, Ranga; Fisher, Joshua B.
• Is Part Of: Journal of geophysical research. Volume 119:Issue 8(2014)
• Page Start: 1554
• Page End: 1575
• Publication Date: 2014-08-14
• Subjects: Geobiology -- Periodicals; Biogeochemistry -- Periodicals; Biotic communities -- Periodicals; Geophysics -- Periodicals; 577.14
• DOI: 10.1002/2014JG002638
• Languages: English
• ISSNs: 2169-8953

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>Few studies have evaluated land surface models for African ecosystems. Here we evaluate the Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) process‐based model for the interannual variability (IAV) of the fraction of absorbed active radiation, the gross primary productivity (GPP), soil moisture, and evapotranspiration (ET). Two ORCHIDEE versions are tested, which differ by their soil hydrology parameterization, one with a two‐layer simple bucket and the other a more complex 11‐layer soil‐water diffusion. In addition, we evaluate the sensitivity of climate forcing data, atmospheric CO<sub>2</sub>, and soil depth. Beside a very generic vegetation parameterization, ORCHIDEE simulates rather well the IAV of GPP and ET (0.5 < <italic>r</italic> < 0.9 interannual correlation) over Africa except in forestlands. The ORCHIDEE 11‐layer version outperforms the two‐layer version for simulating IAV of soil moisture, whereas both versions have similar performance of GPP and ET. Effects of CO<sub>2</sub> trends, and of variable soil depth on the IAV of GPP, ET, and soil moisture are small, although these drivers influence the trends of these variables. The meteorological forcing data appear to be quite important for faithfully reproducing the IAV of simulated variables, suggesting that in regions with sparse weather station data, the model uncertainty is strongly related to uncertain meteorological forcing. Simulated variables are positively and strongly correlated with precipitation but negatively and weakly correlated with temperature and solar radiation. Model‐derived and observation‐based sensitivities are in agreement for the driving role of precipitation. However, the modeled GPP is too sensitive to precipitation, suggesting that processes such as increased water use efficiency during drought need to be incorporated in ORCHIDEE.</p> </abstract>