Interannual variability in terrestrial carbon exchange using an

ecosystem-fire model and inverse model results

Sergey Venevsky(1), Prabir K. Patra(2),

Shamil Maksyutov(3), and Gen Inoue(3)

(1) Obukhov Institute for Atmospheric Physics, Moscow 109017, Russia(2) Frontier Research Center for Global Change/JAMSTEC, Yokohama 2360001, Japan(3) National Institute for Environmental Studies, Tsukuba 305-8506, Japan

Outline of the presentation

1.Modelling of land-atmosphere carbon fluxes at global and regional scale – dynamic global vegetation model SEVER.

2.Description of human and lightning induced fires at global scale –SEVER-FIRE

3.Monthly-mean CO2 fluxes from 11 land regions using a time-dependent inverse (TDI) model.

4.Comparison of the regional net carbon exchange fluxes simulated by TDI and SEVER.

SEVER dynamic global vegetation model (Venevsky and Maksyutov, 2005)

Precip Temp, SWR

H2O

PrecipconvectCO2

Plant functional types distribution

NEE=Rh+C fire-NPP

C fire

Rh

NPP

Daily time step

0.5°x0.5°grid cell

VegetationcompositionFPC

Annual time step

Technical realisation of SEVER – modification of the LPJ DGVM to a daily time step

. State-of-the-art dynamic global vegetation model LPJ DGVM (Sitch, et.al., 2003, Thonicke et.al., 2001, Venevsky et.al., 2002) was taken as a basis for technical realisation of SEVER.

Advantages of the LPJ DGVM:•modular stucture with identified processes of vegetation dynamics and soil/biosphere biogeochemistry•successfuly reproduces vegetation composition and vegetation/soil carbon pools and fluxes at global scale •Code is avaiable on the Internet

Disadvantage:•Pseudodaily approach

Temp

day1

1 31

Tmonth

1

GPP

day1

gppmidmonth

311 16

DESIGN OF SEVER (Venevsky, Maksyutov, 2005):

Modification of LPJ modules from month/mid-month to a daily step

New radiation routine

New soil temperature routine

New fire model

SEVER/DGVM – Technical details

Temperature

Precipitation

Shortwave radiation

SOIL classification data: 9 classes at 0.5°x0.5° (Sitch et.al., 2003)

CLIMATE data:6-hr NCEP/NCAR reanalysis data for 1956-2002 at T62 resolution were interpolated to 0.5°x0.5° with correction to elevation.

a) No fire emissions considered: b) complete set

Minimum temperature

Maximum temperature

Precipitation

Shortwave radiation

Convective precipitation

Comparison of SEVER DGVM with LPJ DGVM (new version)

-10

-8

-6

-4

-2

0

2

4

1 41 81 121 161 201 241 281 321 361

EUROFLUX observed

SEVER calculated

NEE (g/m2)

day

Tharandt 2000

-8

-6

-4

-2

0

2

4

6

1 41 81 121 161 201 241 281 321 361

EUROFLUX observed

SEVER calculated

NEE(g(m2)

day

Bordeaux

0

0.2

0.4

0.6

0.8

1

gunn no

rso

rth

arba

yrsa

rrco

ll

SEVERcorrelation

LPJcorrelation

Correlations (r) between the observed and calculated monthly-mean NEE for all sites

SEVER: r2=0.75LPJ (new version) : r2=0.51

SEVER-FIRE global mechanistic fire model

1

365

Day

Fire moistureextinction level

lat lon

Fire weather danger

Flammabilitythreshold

Spread and termination

Lightningand humanignition

Carbon fire emission

Human induced ignitions: conceptual scheme

Timing

00.20.40.60.8

1

1 2 3 4 5 6 7 8 9 10 11 12

&

Wealthstatus * Population

density * Urban/rural

Timing

0

0.2

0.4

0.6

1 2 3 4 5 6 7 8 9 10 11 12

* Timing * accessibility

Lightning ignitions: conceptual scheme

--

+ +

Cumulonimbus

+

-

+

Elevation

MoistureDuff

MoistureDuff

LPC/LCC flashes

Smoldering probability

Stepwise validation of SEVER- FIRE model (1)

0

10

20

30

40

50

0 20 40 60

Number of annual CG flashes per km2 South America

OTD

SEVER-FIRE

02000400060008000

1000012000140001600018000

19

74

19

76

19

78

19

80

19

82

19

84

19

86

19

88

19

90

19

92

NationalfirestatisticsSEVER-FIRE

Number of human-induced fires. Spain

0100200300400500600700

1974

1977

1980

1983

1986

1989

1992

SEVER-FIRE

Nationalfirestatistics

Number of lightning fires. Spain

• Number of cloud-to-ground (CG) flashes is validated using data of the Optical Transient Detector for the continents (Christiensen et.al, 2002)

• Number of lightning/human fires is validated using data for Canada (Stocks, et.al., 2002) and Spain (Vasques, et.al.,2002)

Step validation of SEVER- FIRE model (2)

• Areas burnt for Canada (Stocks et.al., 2002), Spain (Vasques et.al., 2002), Africa (Barbosa et.al., 1998). Examples of complete step validation for North-West Alberta, Canada (Wielgolaski et.al., 2002), for lightning fires:

0

50000

100000

150000

200000

1989

1989

1989

1989

1989

1989

Albertaobserved

Albertacalculated

Total number of CG flashes. North-West Alberta

0

50

100

150

200

250

1961

1965

1969

1973

1977

1981

1985

1989

1993

Number offiresobserved

Numver offirescalculated

Number of lightning fires. North-West Alberta

0500

100015002000250030003500

19

62

19

66

19

70

19

74

19

78

19

82

19

86

19

90

19

94

Areaburntobserved(ha)Areaburntcalculated(ha)

Area of lighning fires (ha). North-West Alberta

Annual Flux (1997-2001) Flux Anomaly (1997-1998)

Lightning fires

Human induced fires

Comparison SEVER-FIRE vs CASA estimates, based on satelitte derived area burnt and CO

SEVER

CASA (van der Werf et.al., 2004)

Comparison of SEVER/DGVM fire flux with MODIS fire counts (seasonal cycle and spatial pattern)

gC/m2/mon

Simulated global CO2 fire emissionduring 1971-2002 (human and lightning cases)

1000

1200

1400

1600

1800

2000

2200

2400

2600

2800Human-casedignitions TgC/year

Lightning casedignitions TgC/year

Linear (Human-cased ignitionsTgC/year)

Linear (Lightningcased ignitionsTgC/year)

El Niño

Total averaged for 1971-2002 annual fire emissions 3581 TgC (3530 TgC for 1997-2001,Randerson, 2005)

Time-dependent Inverse Model (64 regions):Monthly fluxes (S) and associated covariance (CS) are calculated as:

)()( 01111

0 0GSDCGCGCGSS D

TSD

T

(1)

111 )(0

SDT

S CGCGC

(2)

G = Transport model operator, D = atmospheric CO2 data, CD = Data Covariance

Patra et al., GBC, October 2005

Rayner et al., 1999Gurney et al., 2004

Overview of TDI

Results –

Global and hemispherics

cale CO2 flux anomaly

Patra et al., GBC, October 2005Patra et al., GBC, July 2005

Anomaly= monthly fluxes – mean seasonal cycle

Climate control on regional CO2 flux anomaly(Patra et al., GBC, 16 July 2005)

Flux Anomaly Region/PC

Climate Oscillation Indices Meteorology

ENSO NAO Rain Temp.

Temp North America −0.41 (1) −0.47 (2) −0.32 (3) 0.33 (4)

Trop South America 0.48 (0) −0.20 (0) −0.47 (4) 0.57 (0)

Tropical Africa 0.71 (3) −0.17 (0) 0.42 (4) 0.36 (0)

Boreal Asia 0.21 (5) −0.36 (5) 0.17 (5) 0.23 (0)

Tropical Asia 0.61 (2) −0.18 (0) −0.63 (5) 0.41 (2)

Europe −0.18 (0) 0.45 (5) 0.18 (5) −0.47 (1)

   

   

   

Comparison of CO2 flux anomalies – TDI vs SEVER/DGVM

AB

Regional Flux Anomaly (1994-2004) : EuropeCiais et al., 2005 : 0.5 Pg-C for 2003

Processes associated with interannual CO2 flux variability– TDI vs SEVER/DGVM

AB

Seasonal Cycle and long-term means of CO2 fluxes – TDI vs SEVER/DGVM

Conclusions

1. Human induced fire, increased in last 20 yrs, carbon emission exceeds that from lightning, with a ratio 68/32, despite of small number of lightning fires (1/20 of human fire).

2. We simulated greater human induced fires during the 1997-98 El Niño event, and the emissions are highest in the tropics.

3. The ecosystem model simulated flux anomalies are fairly in phase and amplitude with those estimated using inverse modelling atmospheric CO2.

4. However, there still exists significant disagreements between the inversion and ecosystem flux amplitudes at the regional scale.