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Feedback from terrestrial productivity changes to the climate

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

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  1. 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

  2. Change 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.

  3. +soil 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

  4. The challenge: Regional fluxes • 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

  5. Process oriented modeling of biosphere-atmosphere exchange

  6. GIS coupling for GIS – the problem of data 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

  7. Global N2O budget

  8. Model testing

  9. Model testing

  10. Model testing

  11. Motivation Strategy Sites&Methods Results Australia Global Outlook Model testing Simulated N2O-emissions [g N ha-1] Measured N2O-emissions [g N ha-1]

  12. Global GIS

  13. Global GIS

  14. Global GIS

  15. Global N2O inventory Werner et al., 2007, JGR – Global Biogeochem. Cycl.

  16. Interannual variability Werner et al., 2007, JGR – Global Biogeochem. Cycl.

  17. Comparison with earlier estimates • 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.

  18. Predictions for Europe 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

  19. Identifying key regions and interannual variabilities

  20. Identifying key regions Crop yield - statistics Simulated N2O Simulated NO3 leaching Crop yield - DAYCENT Del Grosso et al., 2006, J. Env. Qual.

  21. Inventorying soil N trace gas fluxes and identifying feedbacks Simulated forest area of Europe: 1 410 477km2

  22. Climate change feedbacks ( 2031-2039) - (1991-2000) [A2 scenario] ECHAM4  MCCM/MM5 regionalisation (60kmx60km)

  23. Climate change feedbacks ( 2031-2039) - (1991-2000) Seasonal changes in soil moisture Kesik et al., 2006, JGR - Biogeosciences 23 | V. Name | Organisationseinheit | TT.MM.JJJJ

  24. Climate feedbacks on forest soil N2O/NO emissions NO N2O Temp. Precip. Kesik et al., 2006, JGR - Biogeosciences

  25. Climate feedbacks on forest soil N2O/NO emissions Kesik et al., 2006, JGR - Biogeosciences 25 | V. Name | Organisationseinheit | TT.MM.JJJJ

  26. Climate feedbacks on N2O/N2 ratio. Is this realistic? Kesik et al., 2006, JGR - Biogeosciences

  27. + + + + ? − + + ? + − + ? ? ? ? ? − 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

  28. Summary • 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

  29. Atmosphere 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

  30. aerobic soil matrix 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

  31. Model evaluation: SOC dynamics Illinois, USA Rothamsted, UK wheat/fallow Winter wheat barley/potato/wheat/sugarbeet Bad Lauchstädt, Germany Waite, Australia Li, Frolking, Butterbach-Bahl, Climatic Change, 2005

  32. Model evaluation: N2O fluxes Li, Frolking, Butterbach-Bahl, Climatic Change, 2005

  33. Model evaluation: NO fluxes [forests] Kesik et al., 2006, Biogeosciences

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

  35. Eh changes driven in CH4/N2O fluxes in rice paddies Li et al., 2005,Glob.Biogeochem. Cycl.

  36. Model evaluation: Soil water fluxes Altdorf Flossenbürg

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