A canopy conductance and photosynthesis model for use in a GCM land surface scheme

Cox, Peter M.; Huntingford, Chris; Harding, R. J.

doi:10.1016/s0022-1694(98)00203-0

Cited by 376 publications

(365 citation statements)

References 14 publications

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“…The BB stomatal model was introduced into land surface models in the mid-1990s (Bonan, 1995;Sellers et al, 1996b;Cox et al, 1998) and is now commonly used to simulate stomatal conductance. To more accurately account for A as c s approaches zero, Leuning (1990) modified the BB equation by replacing c s with c s -G, where G is the CO 2 compensation point for photosynthesis (including respiration in the light).…”

Section: Stomata In Global Models: Empirical and Optimization Approacmentioning

confidence: 99%

Stomatal Function across Temporal and Spatial Scales: Deep-Time Trends, Land-Atmosphere Coupling and Global Models

et al. 2017

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Section: Stomata In Global Models: Empirical and Optimization Approacmentioning

confidence: 99%

Stomatal Function across Temporal and Spatial Scales: Deep-Time Trends, Land-Atmosphere Coupling and Global Models

et al. 2017

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“…On the demand side, 21 of the 23 model variants simulated canopy stomatal conductance (g s ), using one of four principal schemes to solve for g s , E, and leaf-level photosynthesis (if simulated) given ambient incoming radiation, air temperature, and humidity: Jarvis-type (Jarvis, 1976) (four model variants), Leuning-type (Leuning et al, 1995) (four model variants), Ball-Woodrow-Berry-Collatz (Ball et al, 1987;Collatz et al, 1991) (11 model variants), or a constant ratio of internal to external leaf CO 2 concentration (2 model variants). Balsamo et al (2009), Best et al (2011, Clapp and Hornberger (1978), Clark et al (2011), Cox et al (1998), de Rosnay and Polcher (1998, Ducoudre et al (1993), Foley et al (1996), Gerten et al (2004), Haxeltine and Prentice (1996), Jacobs (1994), Krinner et al (2005), Medvigy et al (2009), Monteith (1995, Niu et al (2011), Oleson et al (2010, Running and Coughlan (1988), Schaefer et al (2008), Sellers et al (1996), Van den Hurk et al (2000), Verseghy (1991) and Zhan et al (2003).…”

Section: Ecosystem Model Overview and Selectionmentioning

confidence: 99%

Mechanisms of water supply and vegetation demand govern the seasonality and magnitude of evapotranspiration in Amazonia and Cerrado

Christoffersen

Restrepo‐Coupe

Arain

et al. 2014

Agricultural and Forest Meteorology

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“…For each PFT, the C fluxes are calculated using a coupled photosynthesisstomatal conductance model on each model timestep (typically 30 to 60 min; Cox et al, 1998). These fluxes are then time-averaged (usually every 10 days) before being passed to TRIFFID (Top-down Representation of Interactive Foliage and Flora Including Dynamics), JULES' dynamic global vegetation model, which updates the vegetation distribution, based on the net C available to it and competition with other vegetation types, and soil C in each model grid box on a longer timestep (usually every 10 days; Cox, 2001).…”

Section: Model Descriptionmentioning

confidence: 99%

Global evaluation of gross primary productivity in the JULES land surface model v3.4.1

et al. 2017

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Abstract. This study evaluates the ability of the JULES land surface model (LSM) to simulate gross primary productivity (GPP) on regional and global scales for [2001][2002][2003][2004][2005][2006][2007][2008][2009][2010]. Model simulations, performed at various spatial resolutions and driven with a variety of meteorological datasets (WFDEI-GPCC, WFDEI-CRU and PRINCETON), were compared to the MODIS GPP product, spatially gridded estimates of upscaled GPP from the FLUXNET network (FLUXNET-MTE) and the CARDAMOM terrestrial carbon cycle analysis. Firstly, when JULES was driven with the WFDEI-GPCC dataset (at 0.5 • × 0.5 • spatial resolution), the annual average global GPP simulated by JULES for 2001-2010 was higher than the observation-based estimates (MODIS and FLUXNET-MTE), by 25 and 8 %, respectively, and CAR-DAMOM estimates by 23 %. JULES was able to simulate the standard deviation of monthly GPP fluxes compared to CARDAMOM and the observation-based estimates on global scales. Secondly, GPP simulated by JULES for various biomes (forests, grasslands and shrubs) on global and regional scales were compared. Differences among JULES, MODIS, FLUXNET-MTE and CARDAMOM on global scales were due to differences in simulated GPP in the tropics. Thirdly, it was shown that spatial resolution (0.5 • × 0.5 • , 1 • × 1 • and 2 • × 2 • ) had little impact on simulated GPP on these large scales, with global GPP ranging from 140 to 142 PgC year −1 . Finally, the sensitivity of JULES to meteorological driving data, a major source of model uncertainty, was examined. Estimates of annual average global GPP were higher when JULES was driven with the PRINCETON meteorological dataset than when driven with the WFDEI-GPCC dataset by 3 PgC year −1 . On regional scales, differences between the two were observed, with the WFDEI-GPCC-driven model simulations estimating higher GPP in the tropics (5 • N-5 • S) and the PRINCETON-driven model simulations estimating higher GPP in the extratropics (30-60 • N).

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A canopy conductance and photosynthesis model for use in a GCM land surface scheme

Cited by 376 publications

References 14 publications

Stomatal Function across Temporal and Spatial Scales: Deep-Time Trends, Land-Atmosphere Coupling and Global Models

Stomatal Function across Temporal and Spatial Scales: Deep-Time Trends, Land-Atmosphere Coupling and Global Models

Mechanisms of water supply and vegetation demand govern the seasonality and magnitude of evapotranspiration in Amazonia and Cerrado

Global evaluation of gross primary productivity in the JULES land surface model v3.4.1

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