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Feedback from terrestrial productivity changes to the climate Model predictions of greenhouse gas emission at a regional scale Klaus Butterbach-Bahl Institute for Meteorology and Climate Research Atmospheric Environmental Research (IMK-IFU) Forschungszentrum Karlsruhe

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  • Feedback from terrestrial productivity changes to the climate

  • Model predictions of greenhouse gas emission at a regional scale

  • Klaus Butterbach-Bahl

  • Institute for Meteorology and Climate Research

  • Atmospheric Environmental Research (IMK-IFU)

  • Forschungszentrum Karlsruhe

  • Garmisch-Partenkirchen, Germany


Change climate

Nitrification

Soil C and N

turnover

Denitrification

Manag.

Soil microbial

community

Temp.

Physico-chemical environment

(radiat., soil T& - moist., compact., O2, C/N avail.)

Plant litter (QA/QN)

& Exudation

Biospere-Atmosphere Exchange of CH4 & N2O

Precip.

Plant species

composition

CO2

CH4-Prod.

Plant Physiology

(WUE, Photosynthesis)

N-Dep.

(O3, …)

CH4-Oxid.

Global Changes and soil N2O and CH4 exchange

Land use

Climate

change

Atmosph.

Compos.


+soil climate

microbial

activity

+soil

N-

cycling

-O2

avail.

+DOC

avail.

Increased N2O

Understanding the environmental feedbacks between C and N cycling

VOC

Increased SOC

NOx

CO2

90%

N2O

60-70%

Photosynthesis

CH4

Isoprenoid-

production

60-70%

Nitrification

Denitrification

CH4-Oxidation

Methanogenesis


The challenge: Regional fluxes climate

  • Bridgingthegap: processunderstanding ↔ ecosystem/ regional fluxes

  • Empirical/ statisticalapproachesforinventories

  • IPCC andemissionfactors

  • Robust (mostlikely), but canhardlybeusedfor

    • evaluatinglanduse/ landmanagementstrategies, or

    • assessingclimate-biospherefeedbacks

  • Processorientedmodeling

  • DayCent, DNDC, CERES, COUP, etc.

  • Complex processesdescriptions, highparameterdemand

  • still needfurtherdevelopmentand UC assessment, but

    • providerealisticsimulationofecosystemprocesses

    • allowtotesthypotheses,

    • canbeusedtoassess global changefeedbacks

4 | V. Name | Organisationseinheit | TT.MM.JJJJ



GIS coupling for GIS – the problem of data climate

GIS database for DNDC

Data source

Data content

format

ISRIC-

WISE

ESDB v1

Clay content

pH

bulk density

Polygon

10km x 10km Grid

Spatial

information

SOIL

Map of Topsoil

OrganicCarbon

SOC

1x1 km2

Grid

GIS

DATABASE

MM5

Temperature

Precipitation

PAR

Latitude

10x10 km2

Grid

CLIMATE

EMEP

N deposition

50x50 km2

Grid

DNDC

CAPRI

Crop type/area

1x1 km2 Grid

EUStat

Fertilizer input

Yield (kg/ha)

NUTS Regions

MANAGEMENT

Emission

Inventory

of European

agricultural soils

Sowing and

Harvest date

(2003)

LUCAS

Points


Global N climate2O budget


Model testing climate


Model testing climate


Model testing climate


Motivation climate

Strategy

Sites&Methods

Results

Australia

Global

Outlook

Model testing

Simulated N2O-emissions [g N ha-1]

Measured N2O-emissions [g N ha-1]


Global GIS climate


Global GIS climate


Global GIS climate


Global N climate2O inventory

Werner et al., 2007, JGR – Global Biogeochem. Cycl.


Interannual variability climate

Werner et al., 2007, JGR – Global Biogeochem. Cycl.


Comparison with earlier estimates climate

  • What is the area we are talking about?(all N2O emissions scaled to the area used in this study; orange: scaled / white: original)

  • Matson and Vitousek 1990: 1.8 Tg N yr-1 (2.4 Tg N yr-1)

  • Bouwman et al. 1995: 1.5 Tg N yr-1 (2.3 Tg N yr-1)

  • Potter 1998: 1.3 Tg N yr-1

  • Breuer et al. 2000: 2.6 Tg N yr-1 (3.55 Tg N yr-1)

  • Stehfest and Bouwman 2006: 1.5 Tg N yr-1 (1.17 Tg N yr-1)

  • This study: 1.3 Tg N yr-1 (± 0.3 SD)

Werner et al., 2007, JGR – Global Biogeochem. Cycl.


Predictions for Europe climate

GIS database for DNDC

Data source

Data content

format

ISRIC-

WISE

ESDB v1

Clay content

pH

bulk density

Polygon

10km x 10km Grid

Spatial

information

SOIL

Map of Topsoil

OrganicCarbon

SOC

1x1 km2

Grid

GIS

DATABASE

MM5

Temperature

Precipitation

PAR

Latitude

10x10 km2

Grid

CLIMATE

EMEP

N deposition

50x50 km2

Grid

DNDC

CAPRI

Crop type/area

1x1 km2 Grid

EUStat

Fertilizer input

Yield (kg/ha)

NUTS Regions

MANAGEMENT

Emission

Inventory

of European

agricultural soils

Sowing and

Harvest date

(2003)

LUCAS

Points



Identifying key regions climate

Crop yield - statistics

Simulated N2O

Simulated NO3 leaching

Crop yield - DAYCENT

Del Grosso et al., 2006, J. Env. Qual.


Inventorying soil N trace gas fluxes and identifying feedbacks

Simulated forest area of Europe: 1 410 477km2


Climate change feedbacks feedbacks

( 2031-2039) - (1991-2000) [A2 scenario]

ECHAM4  MCCM/MM5 regionalisation (60kmx60km)


Climate change feedbacks ( 2031-2039) - (1991-2000) feedbacks

Seasonal changes in soil moisture

Kesik et al., 2006, JGR - Biogeosciences

23 | V. Name | Organisationseinheit | TT.MM.JJJJ


Climate feedbacks on forest soil N feedbacks2O/NO emissions

NO

N2O

Temp.

Precip.

Kesik et al., 2006, JGR - Biogeosciences


Climate feedbacks on forest soil N feedbacks2O/NO emissions

Kesik et al., 2006, JGR - Biogeosciences

25 | V. Name | Organisationseinheit | TT.MM.JJJJ


Climate feedbacks on N feedbacks2O/N2 ratio. Is this realistic?

Kesik et al., 2006, JGR - Biogeosciences


+ feedbacks

+

+

+

?

+

+

?

+

+

?

?

?

?

?

C/N

ratio

+

Primary

production

+

+

?

+

Carbon Cycle

Land use change

Fossil fuel

burning

Industrial N2

fixation

Human

drivers

Atmospheric

CO2

Atmospheric

reactive N

Atmospheric

drivers

Climate warming

N2 fixation

Biogeochemical Cycles

Biologically

Available N

Denitrification

N-Cycle

Gruber & Galloway, Nature 2008


  • Summary feedbacks

  • Nr emission/ deposition processes are by far more complex as compared to e.g. CO2-exchange processes, due to

    • Complexity of involved processes

    • Complexity of feedbacks to environmental drivers

    • Nr cascading on landscape/regional and global scales

  • Understanding of microbial production and consumption processes under changing environmental conditions is still incomplete, and

    • Link between process understanding, field observations and model implementation cannot always be established

    • Closing the N cycling remains difficult due to uncertain N2 losses

  • Long-term measurements are needed [holistic approach]

  • Models need to address the regional scale and linking of biogeochemical models to hydrological models is needed


Atmosphere feedbacks

PLANT PROCESSES

  • Phenology

  • Ressource capture

  • Partitioning

  • Senescence

CO2, N2O,

NH3, NO

SOIL TRANSFERS

  • Heat (Fourier)

  • Water (Tipping bucket)

  • Nitrate (Convective)

mineral N

organic N

C-N BIOTRANSFORMATIONS

Mineralisation - Immobilisation

Nitrification - Denitrification

Soil

Groundwater

Process oriented modeling of ecosystem N- (C-) fluxes

Atmospheric N input

Human

management

Biomassremoval

Crop type/

fertilization

crop residues

absorption

Tillage/

drainage

drainage,

nitrate leaching


aerobic soil matrix feedbacks

anaerobic soil matrix

Conceptual model of an anaerobic balloon

CH4-consumption

CO2

CH4

DOC

CH4

CO2

CH4-production

denitrification

NO3-→ NO2-→ NO → N2O → N2

N2O

NO

NO3-

NO2-

NH4+

nitrification


Model evaluation: SOC dynamics feedbacks

Illinois, USA

Rothamsted, UK

wheat/fallow

Winter wheat

barley/potato/wheat/sugarbeet

Bad Lauchstädt, Germany

Waite, Australia

Li, Frolking, Butterbach-Bahl, Climatic Change, 2005


Model evaluation: N feedbacks2O fluxes

Li, Frolking, Butterbach-Bahl, Climatic Change, 2005


Model evaluation: NO fluxes [forests] feedbacks

Kesik et al., 2006, Biogeosciences


34 feedbacks | V. Name | Organisationseinheit | TT.MM.JJJJ


Eh changes driven in CH feedbacks4/N2O fluxes in rice paddies

Li et al., 2005,Glob.Biogeochem. Cycl.


Model evaluation: Soil water fluxes feedbacks

Altdorf

Flossenbürg


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