Effect of plant dynamic processes on African vegetation responses to climate change: Analysis using the spatially explicit individual‐based dynamic global vegetation model (SEIB‐DGVM)

Abstract

[1] We applied a dynamic global vegetation model (DGVM) to the African continent. After calibration, the model reproduced geographical distributions of the continent's biomes, annual gross primary productivity (GPP), and biomass under current climatic conditions. The model is driven by the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES) A1B scenario of rising CO 2 , and by climate changes during the twenty-first century resulting from the change in CO 2 concentrations, simulated by a coupled Model for Interdisciplinary Research on Climate (MIROC) ocean atmosphere model. Simulations under this condition revealed time lags between environmental change and biome change, with the extent of these lags depending largely on the type of biome change. A switch in forest type was accompanied by the longest delay in biome change among all changes classified, indicating that resident trees largely prevent the establishment of nonresident tree types adapted to the new environment, and that tree growth requires additional years after successful establishment. In addition, assumptions for tree dispersal, which determine whether nonresident tree types can be established, modified the patterns of biome change under the twenty-first-century environment: under the assumption that nonresident tree types cannot be established even if environmental conditions change, the extent of the forest type switch and the development of forest and savanna were suppressed, while forest dieback was enhanced. These changes accompanied a slowing of the increasing trend in net primary productivity (NPP), biomass, and soil carbon during the twenty-first century and in subsequent years. These results quantitatively demonstrate that both patch dynamics and invasive tree recruitment significantly modify the transient change in vegetation distribution and function under a changing environment on the African continent.Citation: Sato, H., and T. Ise (2012), Effect of plant dynamic processes on African vegetation responses to climate change: Analysis using the spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM),

Potential Vegetation and Carbon Redistribution in Northern North America from Climate Change

“…Large potential changes in response to climate change has lead scientists to examine the transient response [14,16,24,51,55,56]. The transient response of vegetation to climate change may introduce a time-lag to equilibrium as species have withdrawal-invasion interactions dependent on the climate change rate that can influence terrestrial carbon stocks.…”

“…TRIFFID is a process-based model that uses a top-down approach ideal for large domain simulations, and can simulate land-surface interactions when coupled with JULES [21,22]. SEIB-DGVM is a spatially explicit forest model that scales up to a larger domain to research the transient response [23,24]. Despite this progress, additional work is needed to examine the transient response mechanistically over large domains.…”

Simulating long‐distance seed dispersal in a dynamic vegetation model

“…DGVMs are often coupled with global climate models (GCMs) to simulate the bi-directional feedback between biosphere and atmosphere; climate-induced vegetation shifts can affect CO 2 and water exchange between land and air, which influence climate (Quillet et al, 2010). However, DGVMs seldom impose any limitations on migration (but see Sato & Ise, 2012). This assumption of full migration could have significant impacts on predictions of future climate change, and needs to be addressed within a DGVM framework.…”

Non-Gaussian data assimilation of satellite-based leaf area index observations with an individual-based dynamic global vegetation model

“…We used version 2.71 (Sato and Ise, 2012) but with minimal modifications for DA. The model simulates daily states, but the original model outputs were only once per year.…”

“…Here, the 10-year forcing data are repeated for the 107-year simulation, and the last 7 years from years 101 to 107 use the actual climate forcing of 2001 to 2007; thus, we refer to years 101 to 107 as 2001 to 2007. The daily climate data were generated by the procedure of Sato and Ise (2012) with updated information available at the SEIB-DGVM web page, based on the monthly Climate Research Unit observation-based data (CRU-TS3.22 0.5 • monthly climate time series) (Harris et al, 2014) and the daily data from the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis (Kalnay et al, 1996). We chose the study area at one of the AsiaFlux sites, the Siberia Yakutsk larch forest site at Spasskaya Pad, the middle basin of the Lena River (62 • 15 18 N, 129 • 14 29 E).…”