1. MODIS NDVI Nov. 2007 (NASA) Allan Spessa Modelling Interactions and Feedbacks among Climate, Vegetation, and Biogeochemical Cycles: What’s been done (including my modest contributions) and Where to next? National Centre for Atmospheric Science (NCAS-Climate), Dept Meteorology, Reading University

2. 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: 10.1111/j.1365-2486.2008.01626.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. “

3. 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. 2010 Biogeosciences). 2.LPJ-DGVM-SPYTHFIRE (Fire Modelling and Forecasting project- Model Evaluation/ Data assimilation; Gomez-Dans, Spessa, Wooster, Lewis Ecological Modelling In review. Seasonal fire risk forecasting: Spessa et al in progress). 3.LPJ-GUESS-SPITFIRE (CarboAfrica project: Lehsten et al. 2008 Biogeosciences, 2009 Biogeosciences; Africa-revisited and Northern Australia: Spessa et al. in progress). 4.JULES-ED-SPITFIRE (QUEST ESM and JULES projects: Spessa, Fisher, Clark, Harris in progress). 5.CLM-ED-SPITFIRE (NCAR: Fisher et al in progress.). 6.JSBACH-SPITFIRE (MPI-Met: Kloster et al in progress.).

4. 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.) http://www.quest-esm.ac.uk/ 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’)

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

6. 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

7. 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.

8. 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.

9. 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!

10. ED-SPITFIRE and ecological succession ED-SPITFIRE and fire-induced tree mortality Source: Veiko Lehsten

11. 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

12. 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 TS2.1 1901-2002) to drive the model, with a spinup based on a repeating a decade-long climatology from 1750 to 1901. 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.

13. 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 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, 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 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?

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

15. 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)

19. More JULES-ED-SPITFIRE runs… This time ignitions vary spatially based on information from GFEDv3 (van der Werf et al 2010) TrBlEgTrBlRg C4 grass Note: nice climate-determined gradients for biomass. Nutrient grad in Amazonia missing. TROBIT project? Note: nice result for cohort distributions reflecting fire disturbance

20. ED-SPITFIRE 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.

21. ED-SPITFIRE 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. 2007 GCB; northern Australia: Beringer et al. 2007 GCB; Africa: Higgins et al. 2009 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 available).

22. 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. 2010 Biogeosciences). 2.LPJ-DGVM-SPYTHFIRE (Fire Modelling and Forecasting project- Model Evaluation/ Data assimilation; Gomez-Dans, Spessa, Wooster, Lewis Ecological Modelling In review. Seasonal fire risk forecasting: Spessa et al in progress). 3.LPJ-GUESS-SPITFIRE (CarboAfrica project: Lehsten et al. 2008 Biogeosciences, 2009 Biogeosciences; Africa-revisited and Northern Australia: Spessa et al. in progress). 4.JULES-ED-SPITFIRE (QUEST ESM and JULES projects: Spessa, Fisher, Clark, Harris in progress). 5.CLM-ED-SPITFIRE (NCAR: Fisher et al in progress.). 6.JSBACH-SPITFIRE (MPI-Met: Kloster et al in progress.).

23. 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

26. uncalibrated calibratedMODIS satellite

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

28. 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. 2010 Biogeosciences). 2.LPJ-DGVM-SPYTHFIRE (Fire Modelling and Forecasting project- Model Evaluation/ Data assimilation; Gomez-Dans, Spessa, Wooster, Lewis Ecological Modelling In review. Seasonal fire risk forecasting: Spessa et al in progress). 3.LPJ-GUESS-SPITFIRE (CarboAfrica project: Lehsten et al. 2008 Biogeosciences, 2009 Biogeosciences; Africa-revisited and Northern Australia: Spessa et al. in progress). 4.JULES-ED-SPITFIRE (QUEST ESM and JULES projects: Spessa, Fisher, Clark, Harris in progress). 5.CLM-ED-SPITFIRE (NCAR: Fisher et al in progress.). 6.JSBACH-SPITFIRE (MPI-Met: Kloster et al in progress.).

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

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

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

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

34. Observed versus ED-SPITFIRE simulated area burnt (changed parameter values) across Borneo, 1997 to 2002.

35. Scholze et al (2006) PNAS Changes to fire frequency under climate change? Wildfire frequency (red, increase; green, decrease). Burnt area is function of soil moisture, and simple fuel threshold. Ignitions are assumed to be ever present.

36. George Pankiewicz © Crown copyright Met Office Projected increase in fire risk due to climate change 2020s2080s 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

37. 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.

38. Collaborate with CEH, Wallingford obtaining latest version of JULES, and implementing ECOSSE and FUN into existing JULES-ED-SPITFIRE framework. Latter is due to be completed by March 2011 (Doug Clark QESM). Improved photosynthesis at cohort level (Phi Harris TROBIT). Ensure latest version of JULES is version containing organic soil hydrology (Eleanor Blyth). Model Evaluation. Use observed data to test, improve and constrain JULES (incl. ED-ECOSSE-FUN- SPITFIRE). FLUXNET data, riverflow data, MODIS data on vegetation cover, NPP, LAI, burnt area etc Driving data: J. Sheffield data, Princeton. 3hrly with all fields for JULES runs. Using these data for ED- SPITFIRE paper. Also refer to Blyth et al (2010) GMD, Blyth et al (2010) J Hydrometeorology. C-LAMP system metrics (Randerson et al 2009 GCB). Implement improved JULES into HadGEM3. HadGEM3 is at UM version 7.4-7.5. Isolate the main effects of climate variability, fire, land use change, nitrogen, CO2 physiological effect on carbon fluxes through 20 th century. Switch processes on/off. Interactions? e.g. land use/deforestation and fire? Loads of EO-based studies, but very little modelling to date. Fire and Nitrogen ? Loads of experimental studies, but very little modelling to date. Examine land-atmosphere feedbacks. Does HadGEM3-JULES capture ENSO, summer droughts etc? What is the impact of improved biogeochemical process description on future delivery of ecosystem services? Future Plans wrt HadGEM3-JULES

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

40. ParameterUse Available globally? Temporal availability ExamplesReliabilityIssues Landcover Constrain PFTs/vegetation types Yes Depending on product, but often yearly, or snapshot GLC2000, MODIS Fair for broad landscape classes Mapping landcover to PFT is not obvious fAPAR Constraining dynamic vegetation model’s C assimilation through photosynthesis YesStart at 1980s AVHRR, MODIS, MERIS Quite accurate Assimilation into a dynamic vegetation model not trivial Efficiency models (e.g. Monteith) Constraining dynamic vegetation model calculation of GPP YesAs fAPAR MODIS PSN product Issues with capturing plant stress correctly, not globally validated. Try to bypass ample parts or dynamic vegetation model, or assimilation not trivial. HotspotsIgnition patternsYes1990s onwardsATSR fire atlasDepends on product Detection limitation due to cloud, sun glint, fire size & power.... Burned Area Yes 2000 onwards, 1990s? MODIS BA product (MCD45A1) Good for savannas, other biomes need further validation Cloud cover, partial burning, overstory... Combusted biomass Emissions, general fire dynamics No2004 onwardsMSG SEVIRI, GOES Theoretically, v goodSmall fires, saturation, cloud cover

41. ParameterUse Available globally? Temporal availability ExamplesReliabilityIssues Biomass dynamic vegetation model Yes/No2006+ ALOS PALSAR, ESA BIOMASS* Reasonable Only really tested on tropical forests, poor results for savannas, poor understanding of the signal, suboptimal sensors Soil Moisture dynamic vegetation model, fire models (through fire risk) Yes1980s+ SSM/I, AMSR, SMOS, SAR Sensor dependent Only top most layer, vegetation cover dependent. Vegetation Moisture Fire models (fire risk) No2000+MODIS, MERISUntested No official products released, experimental. Albedodynamic vegetation models, fire models Yes2000+MODIS MCD43, GlobALBEDO OK

42. Model-EO Data Comparison Issues 1. 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. 2.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. 3.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 JULES, soil moisture calculated at each of 4 layers down to 200cm. 4. EO data is NOT truth. User beware. 5. Closer dialogue between EO experts and modellers needed Precedence? C-LAMP and ESA ‘Essential Climate Variables’ projects. JULES community & NCEO…

43. MODEL vs EO data Burnt Area EO data = GBS-Global Burnt Series product, (AVHRR GAC, JRC-Ispra). MODEL = LPJ-SPITFIRE Thonicke, Spessa, Prentice et al (2010) Biogeosciences Variable = Incidence of burning 1981-2002 GBS fails to detect fires in boreal regions. LPJ-SPITFIRE is natural veg only. No deforestation fires.

44. As atmospheric CO2 concentrations increase, amount of CO2 plants take up should rise, but N constrains amount of CO2 plants can use. Rising temperatures increase organic matter decomposition, making more N available for increased plant growth, which results in increased C storage. ORCHIDEE-CN model. Between 1860 and 2100, accounting for N dynamics substantially decreases terrestrial C storage (up to 50% mainly in mid-latitudes), and thus increase atmospheric CO2 concentrations (+48ppm) potentially accelerating climate change (+29 W/m, +0.15 o C). “ Predictions of future climate change need to account for the potential impacts of nitrogen dynamics on the global carbon cycle.“  Interaction between fire frequency and N availability. Increase fire frequency  decrease soil Nitrogen (volitisation and consumption of litter), though post-fire flushes of inorganic, or plant-available, nitrogen can be expected. Some PFT winners, some losers  consequence for vegetation patterns and carbon? Zaehle S et al. (2010) Terrestrial nitrogen feedbacks may accelerate future climate change Geophys. Res. Letters, 37, L01401: doi:10.1029/2009GL041345. Climate-carbon feedbacks, and nitrogen

46. Originally introduced to NOz by cattle industry in 1940s. Proved to be not so palatable for cows, plus is highly invasive. Inhibits the process of nitrification in the soil (like it does in Africa). Gamba can increase its own competitive superiority over native Oz grasses. High productivity in low-nitrogen ecosystems. Ammonium is its preferred nitrogen source. Prevents nitrification and accumulating ammonium. Due to high productivity, Gamba grass fires are 8-10 x more intense. Kills trees, unlike native grass fires. Over 12 years, 50% reduction in tree canopy cover in Darwin rural areas. Impacts on Carbon? Ecosystem services? Nitrogen-fire interactions…. Possible future application of new version of JULES-ED-ECOSSE-FUN-SPITFIRE? African gamba grass (Andropogon gayanus) fire in northern Australia tropical savannas.