doi:10.3808/jei.200500046
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Further Exploration of Terrestrial Vegetation Response(s) to ENSO Variability Using a Mechanistic Vegetation Simulation Model

M. Manobavan1* and N. S. Lucas2

  1. Centre for Earth and Environmental Science Research, Kingston University, Penrhyn Road, Kingston upon Thames, Surrey, KT1 2EE, UK
  2. University of Wales, Swansea, Singleton Park, Swansea, Wales, SA3 3DQ, UK

*Corresponding author. Email: b4v4n@yahoo.co.uk

Abstract


The need for analysis of terrestrial vegetation interactions and interannual climate variability such as the ENSO is paramount as terrestrial vegetation is an important boundary condition for the climate system, and is the primary interactive surface for the atmosphere. Contemporary investigations have been focusing on non-synergistic investigations to approach this within the Earth System bar a few. A complete understanding of these interconnective processes within the Earth System is impossible without the synergistic approach involving the incorporation of remotely sensed data. This paper follows on the research of Manobavan et al. (2003), who using a synergistic approach showed/hypothesised that terrestrial South American vegetation may have become resilient to interannual climatic perturbations by the El Ni駉 Southern Oscillation (ENSO). This paper further explores this possibility via simulations performed using the Hybrid Version 4.1, which was conditioned to incorporate ENSO related climatic variability. The results of this simulation modelling exercise support this hypothesis and show that the interannual perturbations such as the ENSO enhance the homeostatic property of the system rather than hinder it within its geophysiological limits. Moreover, this paper combines modelling and geophysiology thus attempting to provide a 'holistic' mechanistic explanation to the speculated behaviour of the terrestrial vegetation under perturbation by the ENSO.

Keywords: Geophysiological limits, interannual climatic variability, synergistic approach, vegetation simulation modelling


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