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

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. “

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.).

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, andSoil Biogeochemistry (‘ECOSSE’)

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

• 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

GrassPFT 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. PFT2. Height3. 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 andfire-induced tree

mortalitySource: Veiko Lehsten

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 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.

• 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?

Why are Tropical Savannas Important?

JULES-ED-SPITFIRE 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)

More JULES-ED-SPITFIRE runs… This time ignitions vary spatially based on information from GFEDv3 (van der Werf et al 2010)

TrBlEg TrBlRg

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

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.

ED-SPITFIRE Summary 21. 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).

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.).

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

uncalibrated

calibratedMODISsatellite

White = 0% disparity

Light pink ~ 1% disparity

Dark red ~ 20% disparity

Dark blue > 40% disparity.

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.).

Sarawak & Sabah

Coastal

Central-South Kalimantan

Montane

West Kalimantan

EastKalimantan

El Niño years: 1997, 1998, 2002, 2004, 2006

Cross-spectral time series analysis of the number of weeks peak fire lags

minimum rainfall @ 1 deg. resolution and 52 week (1 year) frequency.

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

1997 to 2002.

0

10000

20000

30000

40000

50000

60000

70000

1997 1998 1999 2000 2001 2002

An

nu

al A

rea

Bu

rnt

(sq

km

s)

year

Observed Base run

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

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

0

20000

40000

60000

80000

100000

120000

140000

1997 1998 1999 2000 2001 2002

An

nu

al

Are

a B

urn

t (s

q k

ms

)

year

Observed

20% decrease in live grass moisture & dead fine fuel moisture

20% decrease in above parameters in DEFORESTED grid cells ONLY. Deforested grid cell: > 5% tree loss as indicated by EO data.

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.

George Pankiewicz © Crown copyright Met Office

Projected increase in fire risk due to climate change

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

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.

• 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 20th 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

Conceptual diagram of observations available for testing carbon-climate models

Randerson et al 2009 GCB

Parameter Use Available globally?

Temporal availability

Examples Reliability Issues

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

Yes Start 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

Yes As 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.

Hotspots Ignition patterns Yes 1990s onwards ATSR fire atlas Depends on product Detection limitation due to cloud, sun glint, fire size & power....

Burned Area 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

No 2004 onwards MSG SEVIRI, GOES

Theoretically, v good Small fires, saturation, cloud cover

Parameter Use Available globally?

Temporal availability

Examples Reliability Issues

Biomass dynamic vegetation model

Yes/No 2006+ 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)

Yes 1980s+ SSM/I, AMSR, SMOS, SAR

Sensor dependent

Only top most layer, vegetation cover dependent.

Vegetation Moisture

Fire models (fire risk)

No 2000+ MODIS, MERIS Untested No official products released, experimental.

Albedo dynamic vegetation models, fire models

Yes 2000+ MODIS MCD43, GlobALBEDO

OK

Model-EO Data Comparison Issues1. 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…

MODEL vs EO data Burnt Area

EO data = GBS-Global Burnt Series product, (AVHRR GAC, JRC-Ispra).

MODEL = LPJ-SPITFIREThonicke, 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.

• 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, +0.15oC).

• “ 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

• 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.