terrestrial ecv assessment [wp3.4] aim -to use cci fire and soil moisture observations to derive...

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Terrestrial ECV assessment [WP3.4] Aim - to use CCI fire and soil moisture observations to derive functional relationships to optimize fire model parameters constrained by land cover type - resulting fire CO2 emissions will be translated into atmospheric CO2 concentrations and compared to CCI GHG ated assessment of CCI terrestrial ECVs impact in the MPI-ESM

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Page 1: Terrestrial ECV assessment [WP3.4] Aim -to use CCI fire and soil moisture observations to derive functional relationships to optimize fire model parameters

Terrestrial ECV assessment [WP3.4]

Aim

- to use CCI fire and soil moisture observations to derive functional relationships to optimize fire model parameters constrained by land cover type

- resulting fire CO2 emissions will be translated into atmospheric CO2 concentrations and compared to CCI GHG

• Integrated assessment of CCI terrestrial ECVs impact in the MPI-ESM

Page 2: Terrestrial ECV assessment [WP3.4] Aim -to use CCI fire and soil moisture observations to derive functional relationships to optimize fire model parameters

Fuel moisture

Fuel availability Ignition Source

JSBACH - SPITFIRE (Lasslop et al., 2014)

• Fire Model in MPI ESM - JSBACH-SPITFIRE

• State of the art process based fire model

• fully integrated in the MPI-ESM• reflects the “fire triangle”: a fire needs

an ignition source and fuel and the fuel has to be dry enough

Terrestrial ECV assessment [WP3.4]

Page 3: Terrestrial ECV assessment [WP3.4] Aim -to use CCI fire and soil moisture observations to derive functional relationships to optimize fire model parameters

burned area [% of grid box] (2006-2008)

CCI-MERIS / CCI Merged

GFEDv3 / GFEDv4

JSBACH SPITFIRE v1/v2

Terrestrial ECV assessment [WP3.4]

Page 4: Terrestrial ECV assessment [WP3.4] Aim -to use CCI fire and soil moisture observations to derive functional relationships to optimize fire model parameters

burned area [% of grid box]

CCI-MERIS / CCI Merged

GFEDv3 / GFEDv4

JSBACH SPITFIRE v1/v2

&

CCI SM

Terrestrial ECV assessment [WP3.4]

Page 5: Terrestrial ECV assessment [WP3.4] Aim -to use CCI fire and soil moisture observations to derive functional relationships to optimize fire model parameters

burned area [% of grid box]CCI FIRE

CCI-MERIS / CCI Merged

GFEDv3 / GFEDv4

JSBACH SPITFIRE v1/v2

&

CCI SM

Soil Moisture [%]

Nor

mal

ized

frac

tion

of b

urne

d ar

ea [%

]

Terrestrial ECV assessment [WP3.4]

Page 6: Terrestrial ECV assessment [WP3.4] Aim -to use CCI fire and soil moisture observations to derive functional relationships to optimize fire model parameters

burned area [% of grid box]CCI FIRE

CCI-MERIS / CCI Merged

GFEDv3 / GFEDv4

JSBACH SPITFIRE v1/v2

&

CCI SM

Soil Moisture [%]

Nor

mal

ized

frac

tion

of b

urne

d ar

ea [%

]

• CCI –MERIS burned area peaks at a higher soil moisture compared to GFED products and the distribution is wider

• CCI – MERGED burned area are higher for high soil moisture (>25%) compared to ESA – MERIS or GFED

• JSBACH-SPITFIRE peaks at a too high soil moisture and the distribution is too wide

Terrestrial ECV assessment [WP3.4]

Page 7: Terrestrial ECV assessment [WP3.4] Aim -to use CCI fire and soil moisture observations to derive functional relationships to optimize fire model parameters

• we varied two parameters in JSBACH – SPITFIRE to optimize width and peak position of the soil moisture / burned area relationship in ~70 experiments running from 1850 to 2006• (a) conversion soil moisture to fuel moisture• (b) ignition rate

a lower fuel moisture improves the peak position, while lower ignition rates improve the width of the distribution improvements are however small, i.e. default values perform reasonable well further not well constrained parameters in the fire model are currently tested

Δ peak position

Δ w

idth

of t

he d

istrib

ution

Terrestrial ECV assessment [WP3.4]