On the relative role of fire and rainfall in determining vegetation patterns

1. On the relative role of fire and rainfall in determining vegetation patterns in tropical savannas: a simulation study Allan Spessa[1]*, Rosie Fisher[2,3] ** [1] National Centre for Atmospheric Science (NCAS-Climate), Dept Meteorology, Reading University, UK, [2] Terrestrial Science Section, National Center for Atmospheric Research (NCAR), USA, [3] formerly Dept Animal and Plant Sciences, Sheffield University, UK// * a.spessa@reading.ac.uk ** rfisher@ucar.edu • Although the tropical savannas study show seasonal rainfall patterns, the amount of rainfall and the length of the dry season varies markedly along transects and between continents. As per expectation, JULES-ED-SPITFIRE simulates fire activity during the dry season in all cases, and this fire activity tends to be greatest in sites with an immediate amount of rainfall, that is, where fuel is neither limiting (driest sites) nor too moist (wettest sites) (Figures available upon request). • Three PFTs are simulated for the tropical savannas (Fig. 3): tropical broad-leaved evergreen trees ‘rainforest’ trees (which are moderately deep-rooted, shade tolerant and are vulnerable to drought stress and fire); tropical broad-leaved raingreen (‘savanna’) trees (which are deep-rooted, shade intolerant, exhibit a drought deciduous phenology, and are generally resilient to fire and low soil moisture); and C4 grasses (which are fast growing, have shallow roots, are shade intolerant, and are moderately fire resilient). • Without fire, trees generally increase in biomass as rainfall increases (Fig. 3). TrBlEg trees dominate in high MAP sites, TrBlRg trees at mid-range MAP sites, and C4 grasses at low MAP sites. Ecotone ‘zones’ are evident. Exceptions at some sites due to soil moisture and rainfall not being well-correlated. Without fire, trees, especially TrBlEg trees, favoured more than grasses as rainfall increases. Probably due to differential effects of resource competition for light and water availability. • Fire sharply reduces rainforest tree biomass and results in increase in savanna trees, particularly in mid-range MAP sites (Fig. 3). Increased grass productivity at these sites. • Probable mechanisms: after fire introduced, grass biomass increases wrt rainfall because there is reduced canopy cover (since fire selects TrBlRg over TrBlEg trees) and thus reduced competition for soil moisture and light. The increased growth opportunity for TrBlRg trees and grasses promotes even more fire (fine dry leaf litter from grasses and savanna trees). • With-fire simulations produce more reasonable biomass estimates than without-fire simulations; compared with published field studies (Brazil: Satchi et al. 2007 GCB; northern Australia: Beringer et al. 2007 GCB; Africa: Higgins et al. 2009 Ecology). But this is difficult to assess at a GCM resolution. Need more ‘point-based’ simulations in relation to long term ecological experiments that control fire treatments (unfortunately few LTEs exist). • Fire, rainfall and their interaction determine vegetation patterns in the tropical savannas. JULES-ED-SPITFIRE results indicate complex interactions among fire-induced mortality, and resource competition for light and soil moisture. • These processes are known drivers of observed vegetation patterns in the tropical savannas (Sankaran et al. 2005 Nature), highlighting the need to capture both demographic and eco-physiological processes in ecosystem models, with similar implications for Earth System Models simulating vegetation-climate feedbacks. • Tropical savannas cover 18% of the world’s land surface, comprise 15% of the total terrestrial carbon stock, with an estimated mean net primary productivity (NPP) of 7.2 tC ha-1 yr-1 (ca. two-thirds of tropical forest NPP), are the most frequently burnt biome (fire return intervals = 1-2 years in highly productive areas), and are a major source of emissions (38 % total annual CO2 from biomass burning, 30% CO, 19 % CH4 and 59 % NOx). Fires shape community structure and function and nutrient redistribution, as well as the biosphere-atmosphere exchange of trace gases, momentum and radiative energy. • Several climate modelling studies indicate rainfall will change significantly in many fire-affected forest biomes in future, including the tropical savannas of Africa, South America and Australia (2007 IPCC 4th Assessement Report). How this will affect the future carbon cycle, and thus, the capacity of forests to continue moderating rising CO2 impacts via carbon sequestration, depends on several important factors- not least of which, is our ability to simulate present and future vegetation and fire dynamics. • The QUEST Earth System model (QESM) is a pioneering effort to model a comprehensive range of earth system processes, motivated by the fact that feedbacks such those between climate and vegetation are critically important (http://www.quest-esm.ac.uk/). The Joint UK Land Environment Simulator (JULES) is the land surface component of the QESM, and within JULES are embedded a new vegetation dynamics model (ED) (Fig. 1) which is coupled to a new fire dynamics and emissions model (SPITFIRE) (Fig. 2). • The present study is a snapshot of on-going work to investigate and evaluate the JULES-ED-SPITFIRE model offline across a range of different biomes in the tropical, temperate and boreal zones; as a precursor to full model coupling in the QESM. In this study, we assess the relative importance of fire versus rainfall in determining simulated vegetation patterns in tropical savannas using JULES-ED-SPITFIRE. Results Introduction Conclusions ROI Figure 2 Systems diagram of SPITFIRE, showing how it interacts vegetation and climate. Note how SPITFIRE accounts for the three fundamental requisites for fire to occur: i) a sufficient amount of fuel, ii) sufficiently dry enough fuel; and iii) an ignition source (red dashed circles). Figure 1 The site/patch/cohort hierarchy in ED, and its use of linked lists and dynamic memory allocation to flexibly book keep simple to complex ecosystems without having to predefine arrays. MAP (mm) Simulation Experiment JULES-ED-SPITFIRE used to simulate fire, vegetation and their interaction at 62 GCM-resolution sites located along large-scale rainfall gradients in the tropical savannas of the Brazilian Cerrado, west Africa, and northern Australia. Observed climate fields (CRU TS2.1 1901-2002) used to drive the model, with a spinup based on a repeating a decade-long climatology from 1750 to 1901. Global observed [CO2] fields used. At each site, all possible combinations of two fire treatments and three rainfall treatments were examined.Fire: i) fire set at a low fixed ignition rate (starting with zero ignitions per patch in 1750, linearly increasing to one ignition per patch in 2002), and no fire. Rainfall: i) -20% of daily rainfall, ii) no change to daily rainfall, and iii) +20% of daily rainfall.No influence of humans/land use or lightning in these experiments. Natural vegetation only ie. no agricultural land. ED (Ecosystem Demography) Model The original ED was developed and applied to an Amazonian forest by Moorecroft et al. (2001 Ecological Monographs).Global version produced by Rosie Fisher (Los Alamos National Energy Laboratory) (Fisher et al 2010 New Phytol) and Allan Spessa.ED simulates seven Plant Functional Types (PFTs): C3 grass, C4 grass, Boreal Needleaved Sumergreen (larch), Temperate Broadleaved Summergreen (oaks, birch etc), Tropical Broadleaved Evergreen (rainforest), Tropical Broadleaved Deciduous (savanna trees), Temperate Needleleaved Evergreen (pine). This is not hard-wired. ED can flexibly incorporate more PFTs.ED is based on ‘gap’ model principles and the concepts of patches and cohorts (Fig. 1). The patch structure in ED is defined by time since disturbance by tree mortality or fire. Newly disturbed land is created every year, and patches represent stages of re-growth. Patches with sufficiently similar characteristics are merged. Within each ED patch, plants of a given PFT with similar height and succesional stage are grouped into cohorts. Cohorts compete for resources (e.g. light). The profile of light through the canopy is used by the JULES photosynthesis calculations. SPITFIRE (Spread & Intensity of Fires and Emissions) ModelCurrent versions of SPITFIRE*: i) LPJ-DGVM-SPITFIRE (Global: Thonicke, Spessa, Prentice, et al. 2010 Biogeosciences); ii) LPJ-DGVM-SPYTHFIRE ^ (FireMAFS project, where parameter sensitivity in SPITFIRE is explored across different biomes: Gomez-Dans, Spessa, Wooster, Lewis In review Ecol. Model.); iii) LPJ-GUESS-SPITFIRE (CarboAfrica: Lehsten, Thonicke, Spessa et al 2008 Biogeosciences); and iv) ED-SPITFIRE (Tropical savannas: Spessa and Fisher 2010 EGU abstracts.) (Fig. 2). * All models driven by observed climate data. ^ LPJ vegetation model bypassed by EO-based data (biomass, PFT distributions). Figure 3 Simulated above-ground biomass of tropical broad-leaved evergreen ‘rainforest’ trees, tropical broad-leaved raingreen (drought deciduous) ‘savanna’ trees and tropical C4 grasses across the rainfall gradient (Brazil, West Afica and Nth Australia sites combined) in the presence versus the absence of fire. Methods