TY - JOUR

T1 - ORCHIDEE-SOM

T2 - Modeling soil organic carbon (SOC) and dissolved organic carbon (DOC) dynamics along vertical soil profiles in Europe

AU - Camino-Serrano, Marta

AU - Guenet, Bertrand

AU - Luyssaert, Sebastiaan

AU - Ciais, Philippe

AU - Bastrikov, Vladislav

AU - De Vos, Bruno

AU - Gielen, Bert

AU - Gleixner, Gerd

AU - Jornet-Puig, Albert

AU - Kaiser, Klaus

AU - Kothawala, Dolly

AU - Lauerwald, Ronny

AU - Peñuelas, Josep

AU - Schrumpf, Marion

AU - Vicca, Sara

AU - Vuichard, Nicolas

AU - Walmsley, David

AU - Janssens, Ivan A.

N1 - This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, and USCCC. The ERA-Interim reanalysis data are provided by ECMWF and processed by LSCE. The FLUXNET eddy covariance data processing and harmonization was carried out by the European Fluxes Database Cluster, AmeriFlux Management Project, and Fluxdata project of FLUXNET, with the support of CDIAC, ICOS Ecosystem Thematic Center, and the OzFlux, ChinaFlux, and AsiaFlux offices. Marta Camino-Serrano, Philippe Ciais, Jopes Peñuelas, and Ivan A. Janssens acknowledge funding from the European Research Council Synergy grant ERC-2013-SyG-610028 IMBALANCE-P. Ronny Lauerwald acknowledges funding from the European Union Horizon 2020 research and innovation program under grant agreement no. 703813 for the Marie Sklodowska-Curie European Individual Fellowship “C-Leak”. Bertrand Guenet acknowledges funding from the ANR-14-CE01-0004 DeDyCAS project. Sara Vicca is a postdoctoral research associate of the Fund for Scientific Research, Flanders.

PY - 2018/3/15

Y1 - 2018/3/15

N2 - Current land surface models (LSMs) typically represent soils in a very simplistic way, assuming soil organic carbon (SOC) as a bulk, and thus impeding a correct representation of deep soil carbon dynamics. Moreover, LSMs generally neglect the production and export of dissolved organic carbon (DOC) from soils to rivers, leading to overestimations of the potential carbon sequestration on land. This common oversimplified processing of SOC in LSMs is partly responsible for the large uncertainty in the predictions of the soil carbon response to climate change. In this study, we present a new soil carbon module called ORCHIDEE-SOM, embedded within the land surface model ORCHIDEE, which is able to reproduce the DOC and SOC dynamics in a vertically discretized soil to 2gm. The model includes processes of biological production and consumption of SOC and DOC, DOC adsorption on and desorption from soil minerals, diffusion of SOC and DOC, and DOC transport with water through and out of the soils to rivers. We evaluated ORCHIDEE-SOM against observations of DOC concentrations and SOC stocks from four European sites with different vegetation covers: A coniferous forest, a deciduous forest, a grassland, and a cropland. The model was able to reproduce the SOC stocks along their vertical profiles at the four sites and the DOC concentrations within the range of measurements, with the exception of the DOC concentrations in the upper soil horizon at the coniferous forest. However, the model was not able to fully capture the temporal dynamics of DOC concentrations. Further model improvements should focus on a plant- A nd depth-dependent parameterization of the new input model parameters, such as the turnover times of DOC and the microbial carbon use efficiency. We suggest that this new soil module, when parameterized for global simulations, will improve the representation of the global carbon cycle in LSMs, thus helping to constrain the predictions of the future SOC response to global warming.

AB - Current land surface models (LSMs) typically represent soils in a very simplistic way, assuming soil organic carbon (SOC) as a bulk, and thus impeding a correct representation of deep soil carbon dynamics. Moreover, LSMs generally neglect the production and export of dissolved organic carbon (DOC) from soils to rivers, leading to overestimations of the potential carbon sequestration on land. This common oversimplified processing of SOC in LSMs is partly responsible for the large uncertainty in the predictions of the soil carbon response to climate change. In this study, we present a new soil carbon module called ORCHIDEE-SOM, embedded within the land surface model ORCHIDEE, which is able to reproduce the DOC and SOC dynamics in a vertically discretized soil to 2gm. The model includes processes of biological production and consumption of SOC and DOC, DOC adsorption on and desorption from soil minerals, diffusion of SOC and DOC, and DOC transport with water through and out of the soils to rivers. We evaluated ORCHIDEE-SOM against observations of DOC concentrations and SOC stocks from four European sites with different vegetation covers: A coniferous forest, a deciduous forest, a grassland, and a cropland. The model was able to reproduce the SOC stocks along their vertical profiles at the four sites and the DOC concentrations within the range of measurements, with the exception of the DOC concentrations in the upper soil horizon at the coniferous forest. However, the model was not able to fully capture the temporal dynamics of DOC concentrations. Further model improvements should focus on a plant- A nd depth-dependent parameterization of the new input model parameters, such as the turnover times of DOC and the microbial carbon use efficiency. We suggest that this new soil module, when parameterized for global simulations, will improve the representation of the global carbon cycle in LSMs, thus helping to constrain the predictions of the future SOC response to global warming.

KW - Sustainability Science

UR - http://www.scopus.com/inward/record.url?scp=85044169140&partnerID=8YFLogxK

U2 - 10.5194/gmd-11-937-2018

DO - 10.5194/gmd-11-937-2018

M3 - Journal articles

AN - SCOPUS:85044169140

VL - 11

SP - 937

EP - 957

JO - Geoscientific Model Development

JF - Geoscientific Model Development

SN - 1991-959X

IS - 3

ER -