1. Biodiversity and Climate Research Centre, Frankfurt (BiK-F)
Global Fire Ecology and Emissions from Biomass Burning: Patterns, Processes and Simulation Modelling
Allan Spessa
Biodiversity and Climate Research Centre, Frankfurt (BiK-F) 1

2. Talk Overview Why is fire important in the earth system?
An overview of comtemporary fire patterns (ca. last 100 years).
Why simulate fire and emissions from biomass burning?
Modelling fire-vegetation interactions within the earth system. Introducing the dynamic fire model SPITFIRE (Spread and Intensity of Fires and Emissions), and the dynamic vegetation models ED (Ecosystem Demography) and LPJ (Lund Potsdam Jena).
Model validation and data assimilation. Introduction to EO data.
Tropical peat fires. Deforestation-fire-climate interactions.
Simulating future fire- priorities and challenges?

3. Why is Fire Important in the Earth System I ?
1. Atmosphere forcing, atmospheric chemistry, and land-atmosphere feedbacks
Global warming: Fire  greenhouse gases CO2, CO, CH4 etc  absorb thermal infrared radiation. On average, about 3 Pg of carbon into the atmosphere per annum, and 0.6 PgC from peat and deforestation fires. Much higher during drought events e.g. El Niño.
Global cooling: Fires  aerosols  scattering and absorption of incoming solar radiation.
Clouds: Smoke and haze can reduce rain droplet formation.
Burnt areas are darker (lower albedo)  increase in radiation absorbed  increase convective activity.
Black carbon from boreal forest fires falling on snow/ice, thereby reducing its reflective capacity.

4. Why is Fire Important in the Earth System II & III?
2. Plant reproduction & survival
Hot fires kill grasses and trees.
However, many plant species need intense fires to initiate germination and reproduce. Consequences for ecological succession, land cover and carbon.
3. Carbon fluxes and biogeochemistry
Increase fire frequency  more grass and fewer trees i.e. less carbon; & vice-versa.
Increase fire frequency  decrease soil Nitrogen (volitisation and consumption of litter).
Peat is normally a below-ground carbon sink. Vulnerable to droughts & fires  potentially very large source of trace gases and aerosols.

5. Eucalyptus regnans (Mountain Ash)1 2 3

6. Fire functioning and feedbacks in the earth system, illustrating the three fundamental requisites for fire to occur: i) a sufficient amount of fuel, ii) sufficiently dry enough fuel; and iii) an ignition source.

7. Mouillot & Field (2005) Fire history and the global carbon budget
Mouillot & Field (2005) Fire history and the global carbon budget. Global Change Biology. 11:

8. Mouillot & Field (2005) Fire history and the global carbon budget
Mouillot & Field (2005) Fire history and the global carbon budget. Global Change Biology. 11:

10. Why make Simulation Models to Study Fire?
It is simple to make a fire- one just needs 3 things:
sufficient plant litter (fuel) + dry conditions + ignition source
However, scientific picture of fire is complicated because:
climate and soils → grass & tree survival → how much fuel available for burning → fire frequency and intensity?
fires → grass & tree survival → how much fuel available for burning?
weather → how dry the fuel is?
weather → how many fires are lightning-caused?
human behaviour, land use → how many fires are lit by people?
Models can be used to capture complex processes and interactions, and make them more tractable for analysis.
Models are a formal hypothesis of our system understanding.
Models are needed for prediction e.g. How will future climate change affect fire activity and emissions from fire? How will carbon uptake by the terrestrial biosphere change in future due to fire?
But model development and validation must precede interesting applications… 

11. Climate-carbon feedbacks, and fires
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. “

12. Building Tools to Examine Fire-Vegetation Interactions:
Coupling Dynamic Vegetation Models to SPITFIRE
LPJ-DGVM-SPITFIRE (Global vegetation distributions, fire regimes and emissions from wildfires: Thonicke, Spessa, Prentice, et al Biogeosciences).
LPJ-DGVM-SPITFIRE (Fire Modelling and Forecasting project- Model Evaluation/ Data assimilation; Gomez-Dans, Spessa, Wooster, Lewis Ecological Modelling In review. Cross-spectral time-series analysis of fire weather versus fire activity and emissions: Spessa et al in progress).
LPJ-GUESS-SPITFIRE (CarboAfrica project: Lehsten et al Biogeosciences, Biogeosciences; Africa-revisited and Northern Australia: Spessa et al. in progress).
JULES-ED-SPITFIRE (QUEST ESM and JULES projects: Spessa, Fisher, Clark, Harris in progress).
CLM-ED-SPITFIRE (NCAR: Fisher et al in progress.).
JSBACH-SPITFIRE (MPI-Met: Kloster et al in progress.). 12

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

14. Surface rate of fire spread (ROS), and thus area burnt, simulated by SPITFIRE is based on equations developed by Dick Rothermel & co-workers (USDA) in the laboratory and field during and 1970s for forest fire management (Rothermel 1972, Wilson 1982…).
ROS is directly proportional to energy produced by ignited fuel (fuel load, wind speed, surface area to volume).
ROS is inversely proportional to the amount of energy required to ignite fuels (fuel moisture & fuel bulk density).

15. 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’)

16. Schematic of the main connections between components of JULES-QESM
Dynamic vegetation model (ED) and crop model
Fire model (SPITFIRE)
Amount and types of fuel
disturbance
vegetation amounts and
properties (e.g. height, LAI)
soil and fuel moisture
vegetation debris
JULES
Surface energy balance, soil T and moisture, photosynthesis
NPP available for growth
canopy radiation and T
BVOC model (MEGAN)
organic content
NPP/ N demand
soil T and moisture
soil T and moisture
Soil C and N model (ECOSSE)
Plant N uptake model (FUN)
N availability
N extraction

17. The Ecosystem Demography ‘ED’ model
Original ED developed and applied to an Amazonian forest by Moorecroft et al 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 ).
I subsequently added litter dynamics, fire dynamics, fire-induced plant mortality, and emissions; and produced global simulations- which are currently being checked against EO data.
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, BETHy etc).

18. Introducing the Patches Concept in ED
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.
Age-based patch structure. (ED)
PFT-based tile structure.
(eg. TRIFFID in JULES)
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.

19. Introducing Cohorts in ED
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.
‘Cohorts’ of vegetation,
merged according to:
PFT
Height
Successional stage

20. 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! 20

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

22. 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.
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.

23. Why are Tropical Savannas Important?
Cover 18% of the world’s land surface.
Comprise 15% of total terrestrial carbon stock, estimated mean net NPP of 7 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 CO2 from biomass burning, 30% CO, 19 % CH4 and 59 % NOx).
Fires  community structure and function and nutrient redistribution, and biosphere- atmosphere exchange of trace gases, water, 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 4th 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 [CO2] via carbon sequestration?
How well can we simulate contemporary vegetation dynamics, fire dynamics, and fire- vegetation interactions?

24. JULES-ED-SPITFIRE Simulation Transects
Brazil- Cerrado
West Africa- Sahel
Northern Australia-
AWDT

25. 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)

29. More JULES-ED-SPITFIRE runs… This time ignitions vary spatially based on information from GFEDv3 (van der Werf et al 2010)
Note: Reasonable climate-determined gradients for biomass. Nutrient-determined gradient in Amazonia missing.
TrBlEg
TrBlRg
Note: Result for cohort distributions reflects fire disturbance, as expected.
C4 grass

30. ED-SPITFIRE Summary 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.
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. 30

31. ED-SPITFIRE Summary 2
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.
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 GCB; northern Australia: Beringer et al GCB; Africa: Higgins et al 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 available). 31

33. Building Tools to Examine Fire-Vegetation Interactions:
Coupling Dynamic Vegetation Models to SPITFIRE
LPJ-DGVM-SPITFIRE (Global vegetation distributions, fire regimes and emissions from wildfires: Thonicke, Spessa, Prentice, et al Biogeosciences).
LPJ-DGVM-SPITFIRE (Fire Modelling and Forecasting project- Model Evaluation/ Data assimilation; Gomez-Dans, Spessa, Wooster, Lewis Ecological Modelling In review. Cross-spectral time-series analysis of fire weather versus fire activity and emissions: Spessa et al in progress).
LPJ-GUESS-SPITFIRE (CarboAfrica project: Lehsten et al Biogeosciences, Biogeosciences; Africa-revisited and Northern Australia: Spessa et al. in progress).
JULES-ED-SPITFIRE (QUEST ESM and JULES projects: Spessa, Fisher, Clark, Harris in progress).
CLM-ED-SPITFIRE (NCAR: Fisher et al in progress.).
JSBACH-SPITFIRE (MPI-Met: Kloster et al in progress.). 33

34. Conceptual diagram of observations available for testing carbon-climate models
Randerson et al 2009 GCB

35. Key Earth Observation Data Sources
Burned Areas
Aiming to exploit existing (and in many cases validated) ‘level 2’ products, rather than re-process ‘level 1’ raw’ observations.
‘Burned Area’ Products
(based on detection of spectral reflectance changes)
Active Fires
Active Fire Detections
(based on detection of thermal anomalies)
Use these level 2 products to derive key ‘level 3’ information for model evaluation and optimisation – e.g. ‘fire rate of spread’.
From D.Roy (South Dakota State Uni.)

36. Key Datasets: Radiative Power  Fuel consumption
Sahelian Zone Fires, 1-14 February 2004 30 20 10
Fuel Combustion Rate (tonnes/sec)

37. Potential to Discriminate Surface & Crown Fire Activity
N. America fire ‘intensity’ mean ~ 70 MW/fire pixel; increasing in
proportion to % tree cover: conclusion  dominated by crown fires
Russian fire ‘intensity’ mean ~ 42 MW/fire pixel; no relationship with %
tree cover: conclusion  dominated by surface fires

38. Simulated vs Observed Fire Activity: How well are we doing?
MODIS Rapid Fire Detections (NASA)
LPJ-SPITFIRE simulated area burnt ( average) 38

39. MODEL vs EO data Burnt Area
Variable = Incidence of burning
MODEL = LPJ-SPITFIRE
Thonicke, Spessa, Prentice et al (2010) Biogeosciences
GBS fails to detect fires in boreal regions.
EO data = GBS-Global Burnt Series product, (AVHRR GAC, JRC-Ispra).
LPJ-SPITFIRE is natural veg only. No deforestation fires.

40. Average carbon losses from above and below ground wildfires, 1997-2008 (tonnes km-1 year-1).
Global Fire and Emissions Database V2 (Guido van der Werf )

41. LPJ-SPITFIRE simulated carbon emissions from fire, 1996 to 2002 (tonnes km-1 year-1)x x x
GLOBAL ANNUAL AVERAGE
~ 2.3 Pg

43. FireMAFS project: Gomez-Dans, Spessa, Wooster, Lewis
Assessment and optimisation of SPITFIRE using EO data, and Bayesian probability and Markov Chain Monte Carlo (MCMC) techniques
FireMAFS project: Gomez-Dans, Spessa, Wooster, Lewis

46. uncalibrated
MODIS
satellite
calibrated

47. White = 0% disparity
Light pink ~ 1% disparity
Dark red ~ 20% disparity
Dark blue > 40% disparity.

48. Model-EO Data Comparison Issues
Which EO product to use? Confusing for the non-expert. Many EO products available. Older products offer longer time series but are less accurate than modern sensors. Algorithms and instruments are ever-changing.
Modelled variables often not directly measured by satellites. Models distil processes/ synthesise suites of variables. e.g. Mapping remotely sensed landcover to Plant Functional Types or Crop Functional Types is not obvious.
Mismatch between resolution of model output and available EO data (time and space). Makes model validation difficult e.g. fire radiative power data is very coarse scale but has very high temporal resolution (opposite to fire model); albedo products (was 16 day, now 8 day running average) but simulated plant and fire dynamics are daily; soil moisture radar measures upper soil moisture (~ 20cm) but in most land surface models, soil moisture calculated at each of several layers down to 200cm.
EO data is NOT truth. User beware.
Closer dialogue between EO experts and modellers needed Precedence? Examples: C- LAMP, ESA ‘Essential Climate Variables’ projects…

50. Building Tools to Examine Fire-Vegetation Interactions:
Coupling Dynamic Vegetation Models to SPITFIRE
LPJ-DGVM-SPITFIRE (Global vegetation distributions, fire regimes and emissions from wildfires: Thonicke, Spessa, Prentice, et al Biogeosciences).
LPJ-DGVM-SPITFIRE (Fire Modelling and Forecasting project- Model Evaluation/ Data assimilation; Gomez-Dans, Spessa, Wooster, Lewis Ecological Modelling In review. Cross-spectral time-series analysis of fire weather versus fire activity and emissions: Spessa et al in progress).
LPJ-GUESS-SPITFIRE (CarboAfrica project: Lehsten et al Biogeosciences, Biogeosciences; Africa-revisited and Northern Australia: Spessa et al. in progress).
JULES-ED-SPITFIRE (QUEST ESM and JULES projects: Spessa, Fisher, Clark, Harris in progress).
CLM-ED-SPITFIRE (NCAR: Fisher et al in progress.).
JSBACH-SPITFIRE (MPI-Met: Kloster et al in progress.). 50

51. ~ 25% of emissions occur at the deforestation frontier
Average carbon losses from above and below ground wildfires, (gC m-1 year-1).
~ 25% of emissions occur at the deforestation frontier
Global Fire and Emissions Database V2 (Guido van der Werf )

52. Study Regionalisation
Island of Borneo
Global Land Cover 2000
Study Regionalisation 1 ID
Region name
Notes 1
Sarawak, Sabah (Malaysia)
Lowland areas 2
East Kalimantan (Indonesia) 3
Central South Kalimantan (Indonesia) 4
West Kalimantan (Indonesia) 5
Montane (Malaysia and Indonesia)
Mountain area > 500 m 6
Coastal (Malaysia and Indonesia)
< 30 percent landmass 5 2 4 3
6: coastal

53. Total devastation after the forest fires of 1997-98 in East Kalimantan
Total devastation after the forest fires of in East Kalimantan. Around 5.2 million hectares were burnt.
Burnt peat swamp forest (left) and unburnt forest (right).
Post-fire regeneration of tropical trees is very slow and patchy due to seed loss, grass invasion, increased fire susceptibility and thin bark of trees.

54.
wwf.org.uk/Orangutan
UNEP-WCMC (2007)

55. Central-South Kalimantan
El Niño years:
1997, 1998, 2002, 2004, 2006
Sarawak & Sabah
East
Kalimantan
Central-South Kalimantan
West Kalimantan
Montane
Coastal

57. Cross-spectral time series analysis of the number of weeks peak fire lags minimum 1 deg. resolution and 52 week (1 year) frequency.

58. Observed vs LPJ-SPITFIRE simulated area burnt (base run) across massively fire affected and deforested island of Borneo, 1997 to 2002.

59. Cochrane (2003) Fire science for rainforests. Nature 421: 913-919

60. Observed versus LPJ-SPITFIRE simulated area burnt (with changed parameter values) across Borneo, 1997 to 2002.

62. Changes to fire frequency under climate change
Wildfire frequency (red, increase; green, decrease).
Burnt area is function of soil moisture, and a fuel threshold (> 300 gC per sq m? if yes, then it burns).
Ignitions are assumed to be ever present.
Are these realistic assumptions?
Scholze et al (2006) PNAS

63. Projected increase in fire risk due to climate change in the Amazon: what does this mean for burnt area and emissions?
2020s
2080s
Proportion of climate model simulations projecting “high” fire risk (McArthur fire danger index)
Ensemble of simulations with HadCM3 climate model
Golding and Betts (2008) Glob. Biogeochem. Cycles
George Pankiewicz © Crown copyright Met Office

64. Fire functioning and feedbacks in the earth system, illustrating the three fundamental requisites for fire to occur: i) a sufficient amount of fuel, ii) sufficiently dry enough fuel; and iii) an ignition source.

65. Thank you for your attention!