Model predictions of greenhouse gas emission at a regional scale

Präsentation zum Thema: "Model predictions of greenhouse gas emission at a regional scale"—  Präsentation transkript:

1 Model predictions of greenhouse gas emission at a regional scale
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 Physico-chemical environment
Global Changes and soil N2O and CH4 exchange Change Manag. Nitrification Denitrification CH4-Prod. CH4-Oxid. Biospere-Atmosphere Exchange of CH4 & N2O Land use (radiat., soil T& - moist., compact., O2, C/N avail.) Physico-chemical environment Soil C and N turnover Soil microbial community Precip. Temp. Climate change Plant litter (QA/QN) & Exudation Plant Physiology (WUE, Photosynthesis) Plant species composition CO2 N-Dep. (O3, …) Atmosph. Compos.

3 Understanding the environmental feedbacks between C and N cycling
VOC Increased SOC NOx CO2 90% N2O +soil microbial activity N- cycling 60-70% Photosynthesis -O2 avail. +DOC CH4 Isoprenoid- production 60-70% Increased N2O Nitrification Denitrification CH4-Oxidation Methanogenesis

4 The challenge: Regional fluxes
Bridging the gap: process understanding ↔ ecosystem/ regional fluxes Empirical/ statistical approaches for inventories IPCC and emission factors Robust (most likely), but can hardly be used for evaluating land use/ land management strategies, or assessing climate-biosphere feedbacks Process oriented modeling DayCent, DNDC, CERES, COUP, etc. Complex  processes descriptions, high parameter demand still need further development and UC assessment, but provide realistic simulation of ecosystem processes allow to test hypotheses, can be used to assess global change feedbacks 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 LUCAS Sowing and Harvest date (2003) Points

7 Global N2O budget

8 Model testing

9 Model testing

10 Model testing

11 Model testing Simulated N2O-emissions [g N ha-1]
Motivation Strategy Sites&Methods Results Australia Global Outlook 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: Tg N yr-1 Breuer et al. 2000: 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: 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 LUCAS Sowing and Harvest date (2003) 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
NO Emissions Minimum Scenario kt N a-1 Average Scenario Maximum Scenario 1990 1995 2000 45 38 98 85 99 248 220 254 Simulated forest area of Europe: km2

22 Climate change feedbacks
( ) - ( ) [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
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 + − + + + + ? + − ? + + ? − + + − + ? ? ? N-Cycle Carbon Cycle
Land use change Fossil fuel burning Industrial N2 fixation Human drivers + − + + + Atmospheric CO2 Atmospheric reactive N Atmospheric drivers Climate warming + ? + − ? ? + + ? Primary production Carbon Cycle N2 fixation − ? C/N ratio + + 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 C-N BIOTRANSFORMATIONS
Process oriented modeling of ecosystem N- (C-) fluxes Atmospheric N input Human management Atmosphere PLANT PROCESSES Phenology Ressource capture Partitioning Senescence Biomass removal CO2, N2O, NH3, NO Crop type/ fertilization crop residues absorption SOIL TRANSFERS Heat (Fourier) Water (Tipping bucket) Nitrate (Convective) mineral N organic N Tillage/ drainage C-N BIOTRANSFORMATIONS Mineralisation - Immobilisation Nitrification - Denitrification Soil drainage, nitrate leaching Groundwater

30 Conceptual model of an anaerobic balloon
aerobic soil matrix CH4-consumption CO2 CH4 anaerobic soil matrix 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 barley/potato/wheat/sugarbeet Winter wheat 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