Allan Spessa Exploring the Effects of Fire and Rainfall Variability on Vegetation and Carbon in Tropical Savannas using the Coupled Land-Vegetation-Fire Model, JULES-ED-SPITFIRE University Photo: www4.ncsu.edu

The problem- and the opportunity! Sitch et al (2008) Evaluation of the terrestrial carbon cycle, future plant geography and climate-carbon cycle feedbacks using five Dynamic Global Vegetation Models (DGVMs) Global Change Biology. 14, 2015–2039, doi: /j x The DGVMs examined showed more divergence in their response to regional changes in climate than to increases in atmospheric CO2 content. All DGVMs simulated cumulative net land carbon uptake over the 21st century for four SRES emission scenarios. For most extreme emissions scenario, 3/5 DGVMs simulated an annual net source of CO2 from the land to the atmosphere at end of the 21st century. Under this scenario, cumulative land uptake differed by 494 PgC among DGVMs. This range ca. 50 years of anthropogenic emissions at current levels. “ A greater process-based understanding of large-scale plant drought responses and interaction with wild- fire and land-use, is needed, and this should filter into the next generation of DGVMs. “

Cover 18% of the world’s land surface. Comprise 15% of total terrestrial carbon stock, estimated mean net NPP of 7.2 tC ha -1 yr -1 (ca. two-thirds of tropical forest NPP). Most frequently burnt biome (fire return intervals = 1-2 years in highly productive areas). Major source of emissions (38 % total annual CO 2 from biomass burning, 30% CO, 19 % CH 4 and 59 % NOx). Fires  community structure and function and nutrient redistribution, and biosphere- atmosphere exchange of trace gases, momentum and radiative energy. GCM studies  future rainfall patterns changes in many fire-affected forest biomes, including tropical savannas of Africa, South America and Australia (2007 IPCC 4 th Assessment Report). More extreme climate patterns (e.g. droughts) predicted. How this will affect the future carbon cycle? What is the capacity of forests to continue moderating rising [CO 2 ] via carbon sequestration? How well can we simulate contemporary vegetation dynamics, fire dynamics, and fire- vegetation interactions? Why are Tropical Savannas Important?

Building Tools to Examine Fire-Vegetation Interactions: Coupling Dynamic Vegetation Models to SPITFIRE 1.LPJ-DGVM-SPITFIRE (Global vegetation distributions, fire regimes and emissions from wildfires: Thonicke, Spessa, Prentice, et al Biogeosciences; Regional applications: Spessa, Thonicke et al in progress). 2.LPJ-DGVM-SPYTHFIRE (Fire Modelling and Forecasting project- Data assimilation; seasonal fire risk forecasting: Gomez-Dans, Spessa, Wooster, Lewis in progress). 3.LPJ-GUESS-SPITFIRE (CarboAfrica project: Lehsten, Spessa, Thonicke et al Biogeosciences, 2009 Biogeosciences). 4.JULES-ED-SPITFIRE (QUEST ESM and JULES projects: Spessa &Fisher in progress). 5.CLM-ED-SPITFIRE (NCAR: Fisher et al in progress). 6.JSBACH-SPITFIRE (MPI-Met: Kloster et al in progress).

QUEST-UK EARTH SYSTEM MODEL (Reading univ, Met Office, CEH, Bristol univ, Oxford univ, Cambridge univ, UEA, Sheffield univ, Leeds univ, Lancaster univ, et al.) JULES= Joint UK Land Environment Simulator. Community Model. INCLUDES NEW MODULES FOR: Vegetation Dynamics (‘ED’), Fire Disturbance & Emissions from biomass burning (‘SPITFIRE’), Diffuse Radiation & Photosynthesis, Nitrogen (‘FUN’), Soil Physics; Hydrology, and Soil Biogeochemistry (‘ECOSSE’)

Original ED developed and applied to an Amazonian forest by Moorecroft et al. (2001) Ecological Monographs. Seven (7) PFT version embedded within IMOGEN-MOSES2.2 (JULES) produced by Rosie Fisher (formerly Sheffield univ., now NCAR) (Fisher et al (2010) New Phytologist ). Litter dynamics, fire dynamics, fire-induced plant mortality, and emissions added subsequently. Plant Functional Types: 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). Not hard-wired  ED can flexibly incorporate more PFTs. ED is based on ‘gap’ model principles and the concepts of patches and cohorts. Quite different from traditional DGVMs (eg LPJ, TRIFFID etc). The Ecosystem Demography ‘ED’ model

Introducing the Patches Concept in ED Bare Ground Grass PFT 1 Tree PFT 1 Tree PFT 2 1 y.o.5 y.o. 15 y.o. 30 y.o.60 y.o. 90 y.o. (eg. TRIFFID in JULES) Age-based patch structure. (ED) PFT-based tile structure. 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 composition characteristics are merged.

Introducing Cohorts in ED ‘Cohorts’ of vegetation, merged according to: 1.PFT 2.Height 3.Successional stage 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, soil moisture). The profile of light through the canopy is used by the JULES photosynthesis calculations  GPP.

The site/patch/cohort hierarchy in ED Number of patches and cohorts changes every year/month/day respectively, and is much larger for complex forest ecosystems than for simple (eg tundra) ecosystems. ED uses linked lists and dynamic memory allocation, available in FORTRAN 90, to permit flexible bookkeeping of simple to complex ecosystems without having to predefine arrays. The alternative approach to this problem would be to define very large arrays for all the variables, which would then mostly be empty. Inefficient!

ED-SPITFIRE and ecological succession ED-SPITFIRE and fire-induced tree mortality

Ignitions Fuel Consumed Area Burned Fuel Moisture & Fire Danger Index Rate of Spread & Fire Duration Fire Intensity Emissions (trace greenhouse gases + aerosols) Human- caused Lightning- caused Plant Mortality Fuel Load & Fuel Structure Wind speed Temperature Relative Humidity Rainfall Vegetation Dynamics Model Population Density & land-use ‘Offline’ SPITFIRE Systems Diagram

Testing and tuning global ED-SPITFIRE New version of the coupled fire-vegetation model only recently completed. First steps… examining first order patterns in fire seasonality, burnt area, PFT distribution and plant productivity by running JULES-ED-SPITFIRE ‘offline’along large-scale simulation transects through different biomes (tropical savannas, Russian boreal and western USA temperate) ‘Offline’ in this case means: use observed climate fields (CRU TS ) to drive the model, with a spinup based on a repeating a decade-long climatology from 1750 to Also, global observed [CO2] fields. In this study, model 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. At each site, all possible combination of two fire treatments and three rainfall treatments were examined. o 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. o 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.

Simulation Transects Brazil- Cerrado West Africa- Sahel Northern Australia- AWDT

Simulated average burnt area is highest where neither fuel load nor fuel moisture are limiting (matches observed system behaviour, refer e.g. Spessa et al (2005) GEB)

Simulated PFTs ED simulates three PFTs for the tropical savannas, defined as: 1. Tropical broad-leaved evergreen trees (TrBlEg) ‘rainforest’ trees deep-rooted, shade tolerant, vulnerable to drought stress and fire. 2. Tropical broad-leaved raingreen (TrBlRg) (‘savanna’) trees deep-rooted, shade intolerant, drought deciduous phenology, moderately resilient to drought stress and fire. 3. C4 grasses shallow roots, shade intolerant, vulnerable to fire and drought but fast growing.

Summary 1 1. Without fire, trees generally increase in biomass as rainfall increases. 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. 2.Exceptions at some sites due to soil moisture and rainfall not being well- correlated. 3. 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.

Summary 2 1.Fire sharply reduces rainforest tree biomass and results in increase in savanna trees, particularly in mid-range MAP sites. Increased grass productivity at these sites. 2.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). 3.With-fire simulations produce more reasonable biomass estimates than without-fire simulations; compared with published field studies (Brazil: Satchi et al GCB; northern Australia: Beringer et al GCB; Africa: Higgins et al Ecology). 4.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).

Conclusions 1.Fire, rainfall and their interaction determine vegetation patterns in the tropical savannas. ED-SPITFIRE results indicate complex interactions among fire-induced mortality, and resource competition for light and soil moisture. 2.These processes are known drivers of observed vegetation patterns in the tropical savannas (Sankaran et al Nature), highlighting the need to capture both demographic and eco- physiological processes in ecosystem models. 3.Some questions relevant to the issue of global vs regional scales…. a)How best to use EO data, often the product of a global algorithm/calibration, to test, contrain and improve fire and vegetation models? b)Should we have regional-specific PFTs if our ultimate goal is global scale? c)Can we simplify the task by using ‘representative’ transects like this study? d)Human-caused fire ignition rates are neither constant, linear nor simple functions of pop density (LPJ-SPITFIRE paper). Fire ignitions vary according to land use/ socio- economic factors that change within/between regions. Can we account for this?

Thank you for your attention!