2015
DOI: 10.1002/2014jd021963
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Investigation of North American vegetation variability under recent climate: A study using the SSiB4/TRIFFID biophysical/dynamic vegetation model

Abstract: Recent studies have shown that current dynamic vegetation models have serious weaknesses in reproducing the observed vegetation dynamics and contribute to bias in climate simulations. This study intends to identify the major factors that underlie the connections between vegetation dynamics and climate variability and investigates vegetation spatial distribution and temporal variability at seasonal to decadal scales over North America (NA) to assess a 2-D biophysical model/dynamic vegetation model's (Simplified… Show more

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Cited by 28 publications
(49 citation statements)
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“…Although we have no observed largescale vegetation condition data before 1980, a decreasing LAI trend from the 1950s to the 1980s has been confirmed and identified by a product based on a biophysical/ dynamic vegetation model (Fig. 11d, Cox et al 2001;Song 2013;Zhang et al 2015). Consistent with the LAI changes, another dynamic vegetation model produces interdecadal variability in surface albedo in the Sahel (Kucharski et al 2013).…”
Section: Discussionsupporting
confidence: 64%
“…Although we have no observed largescale vegetation condition data before 1980, a decreasing LAI trend from the 1950s to the 1980s has been confirmed and identified by a product based on a biophysical/ dynamic vegetation model (Fig. 11d, Cox et al 2001;Song 2013;Zhang et al 2015). Consistent with the LAI changes, another dynamic vegetation model produces interdecadal variability in surface albedo in the Sahel (Kucharski et al 2013).…”
Section: Discussionsupporting
confidence: 64%
“…Boone et al: The interactions between ISBA-MEB in SURFEXv8 past 2 decades, LSMs have evolved considerably to include more biogeochemical and biogeophysical processes in order to meet the growing demands of both the research and the user communities (Pitman, 2003;van den Hurk et al, 2011). A growing number of state-of-the-art LSMs, which are used in coupled atmospheric models for operational numerical weather prediction (Ek et al, 2003;Boussetta et al, 2013), climate modeling (Oleson et al, 2010;Zhang et al, 2015), or both Masson et al, 2013), represent most or all of the following processes: photosynthesis and the associated carbon fluxes, multi-layer soil water and heat transfer, vegetation phenology and dynamics (biomass evolution, net primary production), sub-grid lateral water transfer, river routing, atmosphere-lake exchanges, snowpack dynamics, and near-surface urban meteorology. Some LSMs also include processes describing the nitrogen cycle (Castillo et al, 2012), groundwater exchanges (Vergnes et al, 2014), aerosol surface emissions (Cakmur et al, 2004), isotopes (Braud et al, 2005), and the representation of human impacts on the hydrological cycle in terms of irrigation (de Rosnay et al, 2003) and ground water extraction (Pokhrel et al, 2015), to name a few.…”
mentioning
confidence: 99%
“…While originally developed for predator-prey competition, the LV equations have been used in a number of DGVMs (Arora and Boer, 2006;Brentnall et al, 2005;Cox, 2001;Zhang et al, 2015). The use of the classical form of the LV equations for modelling competition between PFTs, however, leads to an amplified expression of dominance in that the dominant PFT ends up occupying a disproportionately large fraction of a grid cell, leading to little coexistence between PFTs.…”
Section: R K Shrestha Et Al: An Assessment Of Geographical Distribmentioning
confidence: 99%