Abstract
Abstract. The Land surface Processes and eXchanges (LPX) model is a fire-enabled dynamic global vegetation model that performs well globally but has problems representing fire regimes and vegetative mix in savannas. Here we focus on improving the fire module. To improve the representation of ignitions, we introduced a reatment of lightning that allows the fraction of ground strikes to vary spatially and seasonally, realistically partitions strike distribution between wet and dry days, and varies the number of dry days with strikes. Fuel availability and moisture content were improved by implementing decomposition rates specific to individual plant functional types and litter classes, and litter drying rates driven by atmospheric water content. To improve water extraction by grasses, we use realistic plant-specific treatments of deep roots. To improve fire responses, we introduced adaptive bark thickness and post-fire resprouting for tropical and temperate broadleaf trees. All improvements are based on extensive analyses of relevant observational data sets. We test model performance for Australia, first evaluating parameterisations separately and then measuring overall behaviour against standard benchmarks. Changes to the lightning parameterisation produce a more realistic simulation of fires in southeastern and central Australia. Implementation of PFT-specific decomposition rates enhances performance in central Australia. Changes in fuel drying improve fire in northern Australia, while changes in rooting depth produce a more realistic simulation of fuel availability and structure in central and northern Australia. The introduction of adaptive bark thickness and resprouting produces more realistic fire regimes in Australian savannas. We also show that the model simulates biomass recovery rates consistent with observations from several different regions of the world characterised by resprouting vegetation. The new model (LPX-Mv1) produces an improved simulation of observed vegetation composition and mean annual burnt area, by 33 and 18% respectively compared to LPX.
Highlights
We examined the relationship between CG strikes and the daily distribution of precipitation using the Climate Prediction Center (CPC) US Unified Precipitation data (Higgins and Centre, 2000; Higgins et al, 1996) provided by the NOAA/OAR/ESRL PSD (Physical Sciences Division), Boulder, Colorado, USA
We re-gridded the data for the period to 0.5◦ resolution to serve as a benchmark for the model simulations, we continue to use GFED3 for comparison with results from Kelley et al (2013).We use a burnt area product for southeastern Australia based on ground observations of the extent of individual fires during the fire year (July–June) for the period from July 1970 to June 2009 on a 0.001◦ grid (Bradstock et al, 2014)
As the normalised mean error metric (NME) and Manhattan metric (MM) metrics are the sum of the absolute spatial variation between the model and observations, the comparison of scores obtained by two different models shows the relative magnitude of their biases with respect to the observations, and the improvement can be expressed in percentage terms
Summary
The Land surface Processes and eXchanges (LPX) dynamic global vegetation model (DGVM) incorporates fire through a coupled fire module (Prentice et al, 2011) as fire is a major agent in vegetation disturbance regimes (Bond and Van Wilgen, 1996) and contributes to changes in interannual atmospheric carbon fluxes (van der Werf et al, 2008; Prentice et al, 2011). Arora and Boer, 2005; Kloster et al, 2010; Thonicke et al, 2010; Li et al, 2012; Prentice et al, 2011; Pfeiffer et al, 2013), LPX explicitly simulates lightning ignitions, fuel load, susceptibility to burning, fire spread and fire-induced mortality. – In arid areas, where fire is limited by fuel availability, LPX simulates too much net primary production (NPP) resulting in unrealistically high fuel loads and generating more fire than observed To address these shortcomings in the version of LPX running at Macquarie University (here termed LPX-M), we re-parameterised lightning ignitions, fuel moisture, fuel decomposition, plant adaptations to arid conditions via rooting depth, and woody plant resistance to fire through bark thickness. We begin by describing the basic fire parameterisations in LPX (Sect. 2) and go on to explain how these parameterisations were changed in LPX-Mv1 (Sect. 3) before evaluating whether these new data-derived parameterisations improve the simulation of vegetation patterns and fire regimes (Sect. 4)
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