Preprints
https://doi.org/10.5194/gmd-2020-93
https://doi.org/10.5194/gmd-2020-93

Submitted as: model evaluation paper 28 Jul 2020

Submitted as: model evaluation paper | 28 Jul 2020

Review status: a revised version of this preprint is currently under review for the journal GMD.

Global evaluation of nutrient enabled version land surface model ORCHIDEE-CNP v1.2 (r5986)

Yan Sun1, Daniel S. Goll1,2, Jinfeng Chang3, Philippe Ciais1, Betrand Guenet1,4, Julian Helfenstein5, Yuanyuan Huang1,6, Ronny Lauerwald1,7, Fabienne Maignan1, Victoria Naipal1,8, Yilong Wang1,9, Hui Yang1, and Haicheng Zhang1,7 Yan Sun et al.
  • 1Laboratoire des Sciences du Climat et de 1’Environnement/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif sur Yvette, 91191, France
  • 2Department of Geography, University of Augsburg, Augsburg, Germany
  • 3Ecosystems Services and Management Program, International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria
  • 4Laboratoire de Géologie, UMR 8538, Ecole Normale Supérieure, PSL Research University, CNRS, Paris, France
  • 5Agroecology and Environment, Agroscope, Reckenholzstrasse 191, 8046 Zurich, Switzerland
  • 6CSIRO Oceans and Atmosphere, Aspendale 3195, Australia
  • 7Department Geoscience, Environment & Society, Universite libre de Bruxelles, 1050 Bruxelles, Belgium
  • 8Department of Geography, Ludwig-Maximilian University, Munich, Germany
  • 9Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

Abstract. The availability of phosphorus (P) and nitrogen (N) constrain the ability of ecosystems to use resources such as light, water and carbon. In turn, nutrients impact the distribution of productivity, ecosystem carbon turnovers and their net exchange of CO2 with the atmosphere in response to variation of environmental conditions both in space and in time. In this study, we evaluated the performance of the global version of the land surface model ORCHIDEE-CNP (v1.2) which explicitly simulates N and P biogeochemistry in terrestrial ecosystems coupled with carbon, water and energy transfers. We used data from remote-sensing, ground-based measurement networks and ecological databases. Components of the N and P cycle at different levels of aggregation (from local to global) are in good agreement with data-driven estimates. When integrated for the period 1850 to 2017 forced with variable climate, rising CO2 and land use change, we show that ORCHIDEE-CNP underestimates the land carbon sink in the North Hemisphere (NH) during the recent decades, despite an a priori realistic GPP response to rising CO2. This result suggests either that other processes than CO2 fertilization which are omitted in ORCHIDEE-CNP, such as changes in biomass turnover, are predominant drivers of the northern land sink, and/or that the model parameterizations produce too strict emerging nutrient limitations on biomass growth in northern areas. In line with the latter, we identified biases in the simulated large-scale patterns of leaf and soil stoichiometry and plant P use efficiency pointing towards a too severe P limitations towards the poles. Based on our analysis of ecosystem resource use efficiencies and nutrient cycling, we propose ways to address the model biases by giving priority to better representing processes of soil organic P mineralization and soil inorganic P transformation, followed by refining the biomass production efficiency under increasing atmospheric CO2, phenology dynamics and canopy light absorption.

Yan Sun et al.

 
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Yan Sun et al.

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Short summary
We evaluated the performance of nutrient enabled version land surface model ORCHIDEE-CNP v 1.2 against remote-sensing, ground-based measurement networks and ecological databases. We find that the simulated carbon, nitrogen and phosphorus fluxes among different spatial scales are generally in good agreement with data-driven estimates. However, the recent carbon sink in the Northern Hemisphere is substantially underestimated. Potential causes, as well as model development priorities are discussed.