Improved representation of plant functional types and physiology in the
Joint UK Land Environment Simulator (JULES v4.2) using plant trait
information
Anna B. Harper1,Peter M. Cox1,Pierre Friedlingstein1,Andy J. Wiltshire2,Chris D. Jones2,Stephen Sitch3,Lina M. Mercado3,4,Margriet Groenendijk3,Eddy Robertson2,Jens Kattge5,Gerhard Bönisch5,Owen K. Atkin6,Michael Bahn7,Johannes Cornelissen8,Ülo Niinemets9,10,Vladimir Onipchenko11,Josep Peñuelas12,13,Lourens Poorter14,Peter B. Reich15,16,Nadjeda A. Soudzilovskaia17,and Peter van Bodegom17Anna B. Harper et al. Anna B. Harper1,Peter M. Cox1,Pierre Friedlingstein1,Andy J. Wiltshire2,Chris D. Jones2,Stephen Sitch3,Lina M. Mercado3,4,Margriet Groenendijk3,Eddy Robertson2,Jens Kattge5,Gerhard Bönisch5,Owen K. Atkin6,Michael Bahn7,Johannes Cornelissen8,Ülo Niinemets9,10,Vladimir Onipchenko11,Josep Peñuelas12,13,Lourens Poorter14,Peter B. Reich15,16,Nadjeda A. Soudzilovskaia17,and Peter van Bodegom17
1College of Engineering, Mathematics, and Physical Sciences, University
of Exeter, Exeter, UK
2Met Office Hadley Centre, Exeter, UK
3College of Life and Environmental Sciences, University of Exeter,
Exeter, UK
4Centre for Ecology and Hydrology, Wallingford, UK
5Max Planck Institute for Biogeochemistry, Jena, Germany
6ARC Centre of Excellence in Plant Energy Biology, Research School of
Biology, Australian National University, Canberra, Australia
7Institute of Ecology, University of Innsbruck, Austria
8Systems Ecology, Department of Ecological Science, Vrije Universiteit,
Amsterdam, the Netherlands
9Institute of Agricultural and Environmental Sciences, Estonian
University of Life Sciences, Tartu, Estonia
10Estonian Academy of Sciences, Tallinn, Estonia
11Department of Geobotany, Moscow State University, Moscow 119234,
Russia
12CSIC, Global Ecology Unit CREAF-CSIC-UAB, Cerdanyola del Vallès,
08193 Barcelona, Catalonia, Spain
13CREAF, Cerdanyola del Vallès, 08193 Barcelona, Catalonia, Spain
14Forest Ecology and Forest Management Group, Wageningen University,
P.O. Box 6700 AA, Wageningen, the Netherlands
15Department of Forest Resources, University of Minnesota, Saint Paul, Minnesota, USA
16Hawkesbury Institute for the Environment, University of Western
Sydney, Penrith, New South Wales, Australia
17Institute of Environmental Sciences, Leiden University, Leiden, the
Netherlands
1College of Engineering, Mathematics, and Physical Sciences, University
of Exeter, Exeter, UK
2Met Office Hadley Centre, Exeter, UK
3College of Life and Environmental Sciences, University of Exeter,
Exeter, UK
4Centre for Ecology and Hydrology, Wallingford, UK
5Max Planck Institute for Biogeochemistry, Jena, Germany
6ARC Centre of Excellence in Plant Energy Biology, Research School of
Biology, Australian National University, Canberra, Australia
7Institute of Ecology, University of Innsbruck, Austria
8Systems Ecology, Department of Ecological Science, Vrije Universiteit,
Amsterdam, the Netherlands
9Institute of Agricultural and Environmental Sciences, Estonian
University of Life Sciences, Tartu, Estonia
10Estonian Academy of Sciences, Tallinn, Estonia
11Department of Geobotany, Moscow State University, Moscow 119234,
Russia
12CSIC, Global Ecology Unit CREAF-CSIC-UAB, Cerdanyola del Vallès,
08193 Barcelona, Catalonia, Spain
13CREAF, Cerdanyola del Vallès, 08193 Barcelona, Catalonia, Spain
14Forest Ecology and Forest Management Group, Wageningen University,
P.O. Box 6700 AA, Wageningen, the Netherlands
15Department of Forest Resources, University of Minnesota, Saint Paul, Minnesota, USA
16Hawkesbury Institute for the Environment, University of Western
Sydney, Penrith, New South Wales, Australia
17Institute of Environmental Sciences, Leiden University, Leiden, the
Netherlands
Correspondence: Anna B. Harper (a.harper@exeter.ac.uk)
Received: 27 Jan 2016 – Discussion started: 01 Feb 2016 – Revised: 13 May 2016 – Accepted: 20 May 2016 – Published: 22 Jul 2016
Abstract. Dynamic global vegetation models are used to predict the response of vegetation to climate change. They are essential for planning ecosystem management, understanding carbon cycle–climate feedbacks, and evaluating the potential impacts of climate change on global ecosystems. JULES (the Joint UK Land Environment Simulator) represents terrestrial processes in the UK Hadley Centre family of models and in the first generation UK Earth System Model. Previously, JULES represented five plant functional types (PFTs): broadleaf trees, needle-leaf trees, C3 and C4 grasses, and shrubs. This study addresses three developments in JULES. First, trees and shrubs were split into deciduous and evergreen PFTs to better represent the range of leaf life spans and metabolic capacities that exists in nature. Second, we distinguished between temperate and tropical broadleaf evergreen trees. These first two changes result in a new set of nine PFTs: tropical and temperate broadleaf evergreen trees, broadleaf deciduous trees, needle-leaf evergreen and deciduous trees, C3 and C4 grasses, and evergreen and deciduous shrubs. Third, using data from the TRY database, we updated the relationship between leaf nitrogen and the maximum rate of carboxylation of Rubisco (Vcmax), and updated the leaf turnover and growth rates to include a trade-off between leaf life span and leaf mass per unit area.
Overall, the simulation of gross and net primary productivity (GPP and NPP, respectively) is improved with the nine PFTs when compared to FLUXNET sites, a global GPP data set based on FLUXNET, and MODIS NPP. Compared to the standard five PFTs, the new nine PFTs simulate a higher GPP and NPP, with the exception of C3 grasses in cold environments and C4 grasses that were previously over-productive. On a biome scale, GPP is improved for all eight biomes evaluated and NPP is improved for most biomes – the exceptions being the tropical forests, savannahs, and extratropical mixed forests where simulated NPP is too high. With the new PFTs, the global present-day GPP and NPP are 128 and 62 Pg C year−1, respectively. We conclude that the inclusion of trait-based data and the evergreen/deciduous distinction has substantially improved productivity fluxes in JULES, in particular the representation of GPP. These developments increase the realism of JULES, enabling higher confidence in simulations of vegetation dynamics and carbon storage.
Dynamic global vegetation models (DGVMs) are used to predict the response of vegetation to climate change. We improved the representation of carbon uptake by ecosystems in a DGVM by including a wider range of trade-offs between nutrient allocation to photosynthetic capacity and leaf structure, based on observed plant traits from a worldwide data base. The improved model has higher rates of photosynthesis and net C uptake by plants, and more closely matches observations at site and global scales.
Dynamic global vegetation models (DGVMs) are used to predict the response of vegetation to...
