Version 8 (modified by jchang, 18 months ago) (diff)

Update model description for ORCHIDEE-GMv3.2 used for publication


This version of ORCHIDEE has been used in a publication under review. << Reference to be added as soon as the manuscript is open for review >>.


ORCHIDEE is a process-based ecosystem model developed for simulating carbon, water and energy fluxes in ecosystems, from site level to global scale (Ciais et al., 2005; Krinner et al., 2005; Piao et al., 2007). ORCHIDEE-GM (Chang et al., 2013) is a version specifically developed to integrate the management of grassland (Chang et al., 2013). The equations describing management in ORCHIDEE are derived from PaSim? (Riedo et al., 1998; Vuichard et al., 2007a; Vuichard et al., 2007b; Graux et al., 2011). Accounting for management practices, such as mowing, livestock grazing and fertilizer application, on a daily basis, ORCHIDEE-GM is capable of simulating the dynamics of leaf area index, biomass, and carbon fluxes of managed grasslands. ORCHIDEE-GM v1 was evaluated, and some of its parameters calibrated, using eddy-covariance net ecosystem exchange and biomass measurements from 11 European grassland sites representative of a range of management practices. The model successfully simulates the net carbon budget of these managed grasslands (Chang et al., 2013). Ref(Chang et al., 2015c) added a parameterization of adaptive management through which farmers react to a climate-driven change to previous-year productivity. Though a full nitrogen cycle is not included in ORCHIDEE-GM, the positive effect of nitrogen fertilizers on grass photosynthesis rates, and thus on subsequent ecosystem productivity and carbon storage, is parameterized with an empirical function calibrated from literature estimates (v2.1 (Chang et al., 2015c)). ORCHIDEE-GM v2.1 was applied over Europe to calculate the spatial pattern, inter-annual variability, and the trends of potential productivity, i.e. the productivity that maximizes simulated livestock densities assuming an optimal management system in each grid cell (Chang et al., 2015c). This version was further used to simulate the net carbon budget, budget trends, and the GHG balance of European grasslands during the last five decades at a spatial resolution of 25 km (Chang et al., 2015a). Ref (Chang et al., 2017) recently updated the model to version 2.2 with the general parameterizations from ORCHIDEE Trunk.rev3623 (, and a new parameterization limiting grazing practices under specific conditions such as frost, snow cover and wet soil. ORCHIDEE-GM v2.2 was driven by projected future climate change to provide a European‐wide assessment of the future changes in productivity and phenology of grassland, and their consequences for management intensity and the carbon balance.

At the global scale, ORCHIDEE-GM v3.1 is a development of v2.1, and includes a parameter adjustment for the C4 grassland biome and implements a specific strategy for wild herbivores (Chang et al., 2016a). Combining livestock production information, ORCHIDEE-GM v3.1 was applied to reconstruct a series of global gridded maps containing a time-dependent history of grassland management intensity. These maps are model-dependent, and provide a unique opportunity for models with explicit representation of grassland management to make a more accurate estimate of global carbon and GHG budgets of grassland (Chang et al., 2016a) – as we have here. In this study, ORCHIDEE-GM v3.1 has been updated with i) the parameterizations of v2.2 and ii) the general parameterizations from ORCHIDEE-MICT (Guimberteau et al., 2018) rev5308 ( to a new version named ORCHIDEE-GM v3.2. ORCHIDEE-MICT (Guimberteau et al., 2018) is a version of ORCHIDEE with improved interactions between soil carbon, soil temperature and hydrology, and a fire module.

Code access


DOI 10.14768/20190319001.1
Creator Jinfeng Chang
Affiliation LSCE, CNRS
Title TBD
Publisher Institut Pierre Simon Laplace (IPSL)
PublicationYear 2020
ResourceType Software
Rights This software is distributed under the CeCILL license
Subject Land surface model, pixel-to-point comparison, biomass
DataManager Karim Ramage (IPSL)
DataCurator Josefine Ghattas (IPSL)
ContactPerson Philippe Ciais (LSCE/CNRS)
FundingReference The European Research Council Synergy grant ERC-2013-SyG-610028 IMBALANCE-P