The Integrated Carbon Observation System

Transcription

1 The Integrated Carbon Observation System, Director General of ICOS RI, Helsinki, Europe Helsinki, 26. February 2015

2 The vision and the scientific mission of ICOS fundamental understanding of carbon cycle, greenhouse gas budgets and pertubations and underlying processes, ability to predict future changes, verify the effectiveness of policies aiming to reduce greenhouse gas emissions, technical and scientific innovation, education and capacity building. Seite 2

3 The vision of ICOS All important parameters State of the art techniques Representative networks Optimum data streams Relevant data products A community to produce higher level output Long-term financial security of all partners Seite 3

4 The ICOS station network today Atmosphere Ecosystems Oceans Seite 4

5 The ICOS station network today.and anticipated ICOS station network 2020 Atmosphere Ecosystems Oceans Seite 5

6 Our main data product: maps provide information on space on risks on safe passages on alternatives Distribution of CO 2 fluxes in Europe at mid day in July 2009 in the Werner model L. Kutsch world [curtesy: Martin Heimann]

7 Verification of policies to reduce GHG emissions Seite 7 Sources: IPCC; Peters et al. 2012a; Le Quéré et al. 2012; CDIAC Data; Global Carbon Project 2012

8 Verification of policies to reduce GHG emissions Post-Kyoto agreement (2020) Develop observing techniques Establish baselines Critical Verification Period Robust verification, fine grid Initial capability, ERIC (2015) Fully operational (2020) Future enhancements (2030 ) Seite 8

9 Development of ICOS R&D, design studies CarboEurope-IP, CarboOcean-IP Other research projects National networks Preparatory phase (EC-FP7) ICOS ERIC 2015 European Research Infrastructure Consortium EU funding Transition phase ESFRI Roadmap 2006 ICOS listed [courtesy J.D. Paris] Seite 9 Construction phase National contributions Operational phase National contributions + EU funding

10 Some remarks about data archiving Your data have to be traceable! Raw data Raw data QC Half hourly flux data mv, 20 Hz Metadata Conversion mv -> physical unit Flux calculation µmol m -2 s -1 Metadata Gap-filling Flux partitioning Uncertainty calculation of GF Metadata Sensor, calibration Flux data QC Process annotation Process annotation Page 10

11 Quality Control and gap-filling for Eddy-Covariance Data Stationariätstest Integral turbulence characteristic test U * - Grenzwert Model Microclimate Data Rebmann et al. 2004, Foken et al. 2004, Goeckede et al. 2004

12 Quality Control and gap-filling for Eddy-Covariance Data CO 2 -Flüsse (µmol m -2 s -1 ) Turbulent Flux Storage Term Fc Friction velocity CO 2 -Konzentration (ppm) : m 18:00 00:00 06:00 12: m m m m m :00 18:00 00:00 06:00 12:00

13 Quality Control and gap-filling for Eddy-Covariance Data Fluxes (µmol m -2 s -1 ) Turbulent Flux Storage Term Fc Friction velocity 12:00 18:00 00:00 06:00 12: Friction velocity (m s -1 ) Value Stat. Test :00 18:00 00:00 06:00 12:00 Value ITC Test

14 Quality Control and gap-filling for Eddy-Covariance Data Fluxes (µmol m -2 s -1 ) Turbulent Flux Storage Term Fc Friction velocity Friction velocity (m s -1 ) : :00 00:00 06:00 12:002 Value Stat. Test Value ITC Test :00 18:00 00:00 06:00 12:00

15 Some remarks about data archiving Raw data Raw data QC Half hourly flux data mv, 20 Hz Metadata Conversion mv -> physical unit Flux calculation µmol m -2 s -1 Metadata Gap-filling Flux partitioning Uncertainty calculation of GF Metadata Sensor, calibration Flux data QC Process annotation Process annotation Which test? Which threshold? Software? Archive Archive Archive Page 15

16 What is an archive? Floppy disks on your attic. <Define more and better archives here> Page 16

17 Example for archiving (and publishing) services Page 17

18 Scetch of the possible ICOS ecosystem data structure Instrument Computer or other data storage and processing unit Data post-processing and QC Data archive Feedback between PI and ETC Data processing, usage for production of L3 products Product, e.g. scientific publication or report Seite 18 Joint ETC/MSA meeting

19 External e-infrastructures Cloud L0 Archive PI Arch doi L0 L0, ol1 L0, ol1, pil1, pil2 L0 L1, L2 doi L0 doi TC Arch L1, L2 doi L1, L2 doi doi system CP Arch PI ETC Carbon Portal L1, L2 doi L3 doi il3doi doi Web interface doi Special external users e.g. fluxnet doi External L3 producers doi doi

20 Cloud L0 Archive PI Arch doi L0 L0, ol1 L0, ol1, pil1, pil2 L0 L1, L2 doi L0 doi TC Arch L1, L2 doi L1, L2 doi doi system CP Arch PI ETC Carbon Portal L1, L2 doi L3 doi il3doi doi Web interface doi Special external users e.g. fluxnet doi External L3 producers doi doi

21 Workflow and reponsibilities of handling ICOS Data (ENVRI Reference Model, 21 Science Werner viewpoint) L. Kutsch 20/03/2015

22 ENVRI Reference Model Information Viewpoint: ICOS Data Lifecycle /03/2015

23 European CO 2 fluxes Distribution of CO 2 fluxes in Europe at mid day in July 2009 in the Werner model L. Kutsch world [curtesy: Martin Heimann]

24 SOCAT global data integration and Ocean CO 2 fluxes A mapping method Gas transfer parameterisation, wind speed A SOCAT data product (synthesis or gridded) Surface water fco 2 (here ) Air-sea CO 2 flux (here ) Page 24 [curtesy: Dorothee Bakker]

25 Data quality Part 2: The (biological)challenge of data quality Seite 25

26 Background: a study on forest carbon use efficiency Page 26 Nutrient availability as the key regulator of global forest carbon balance M. Fernández-Martínez et al. 2014

27 Background: a study on forest carbon use efficiency 0% The disturbance zone 50% The zone of miraculous fertility Page 27 Fernández-Martínez et al., Nature Climate Change 2014

28 Integrated Carbon Balance Hainich NEE GPP TER SR HET Litter CWDR WM WOOD GWI D WOOD WR LR SR AUT SOM CWD D CWD CR GCRI D SOM D CR NPP FLUX DOC Matter flux to soil: NPP from inventory: Litterfall Fine root production Gross Wood Increment Gross Coarse Root increment Ground vegetation aboveg. Ground vegetation belowg. NEP:

29 Integrated Carbon Balance Hainich Eddy-covariance, leaf gas exchange measurements GPP GPP NEE TER SR HET Litter CWDR WM WOOD GWI D WOOD WR LR SR AUT SOM CWD D CWD CR GCRI D SOM D CR NPP FLUX DOC <<5 Matter flux to soil: NPP from inventory: Litterfall Fine root production Gross Wood Increment Gross Coarse Root increment Ground vegetation aboveg. Ground vegetation belowg. NEP:

30 Integrated Carbon Balance Hainich Chamber derived model GPP (g C m -2 ) Level 4 Average GPP Hainich Level 4: 1504 g C m -2 y -1 Chamber:1651 g C m -2 y Page 30

31 Integrated Carbon Balance Hainich Soil carbon stocks, stock changes, soil respiration, Soil balances from fluxes GPP NEE TER SR HET Litter CWDR WM WOOD GWI D WOOD WR LR SR AUT SOM CWD CR GCRI DOC <10 D SOM Matter flux to soil: D CWD D CR NPP from inventory: Litterfall Fine root production Gross Wood Increment Gross Coarse Root increment Ground vegetation aboveg. Ground vegetation belowg. NPP FLUX NEP:

32 Soil carbon balance Kutsch et al. 2010, Biogeochemistry Page

33 Soil carbon balance Kutsch et al. 2010, Biogeochemistry Page 33

34 Kutsch et al. 2010, Biogeochemistry Page 34

35 Respiration from chamber measurements and standardized eddy covariance L4 products Kutsch et al. 2010, Biogeochemistry Page 35

36 Respiration from chamber measurements and eddy covariance (g C m -2 y -1 ) Foliage Stems Soil aut Soil het TER EC Page 36

37 Integrated Carbon Balance Hainich NEE Wood increment, tree ring analyses, litterfall., fine root turnover GPP TER SR HET Litter CWDR WM WOOD GWI D WOOD WR LR SR AUT SOM CWD CR GCRI DOC <<5 D SOM Matter flux to soil: D CWD D CR NPP from inventory: Litterfall Fine root production Gross Wood Increment Gross Coarse Root increment Ground vegetation aboveg. Ground vegetation belowg. NPP FLUX NEP:

38 Wood and Total NPP Total NPP Modeled NPP (gc m -2 year -1 ) Inventory + Exp.+litter fall Wood NPP +/- 95% intervall repeated inventory Dendrometers + Exp. + litter fall Wood NPP dendrometers Mund et al Year Page 38

39 Integrated carbon balance Hainich Mean annual fluxes (g C m -2 y -1 ) SR HET 472 ± 150 SOM DSOM 20 (1 35) DOC <<10 Litterfall 233 ± 50 Woody Litterfall 45 Carbon transfer to soil: 493 ± % CWD D CWD 0 Trunks, branches, twigs Coarse roots GPP 1635 ±130 (1504) Fine root turnover 148 ± 50 Groundvegetation (oberird.) 37 ± 20 Groundvegetation (unterird.) 30 ± 20 NEE 274 ± 300 (479) TER 1361 ± 300 (1024) LR 307 ± 100 Tree mortality CWDR 20 WR 129 ± Coarse root turnover DWood 256 ( ) CUE = 16 % 53 % SR AUT 434 ± 150 NPP from Fluxes: 764 ± 150 NPP from Inventory: 719 ( ) NEP: 211 ( )

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41 Topography of Hainich 480 Altitude (m above sea level) South Distance from tower (m) North Page 41

42 Direct Measurements of horizontal advection 10 Horizontal advection (µmol CO 2 m -2 s -1 ) m : : : :00 Date in : : :00 Kutsch et al. 2008, Ecol.Appl. Page 42

43 Towers in Complex Terrain Lavarone, Italy Aberfeldy, UK Kutsch and Kolari, Nature Climate Change, subm. Page 43

44 Towers in Complex Terrain Collelongo, Italy Fukushida, Japan Kutsch and Kolari, Nature Climate Change, subm. Page 44

45 Slope matters Carbon user efficiency a R² = Fukushida, Japan Carbon user efficiency b R² = Total slope in fluxtower area (m) Total slope in fluxtower area (m) Kutsch and Kolari, Nature Climate Change, subm. Page 45

46 Terrain can be more complex than a simple slope Laoshan, China Kutsch and Kolari, Nature Climate Change, subm. Page 46

47 Terrain can be more complex than a simple slope Wetzstein Spruce Forest site N W Located on a plateau of a hill Page 47

48 The situation at Wetzstein: Page 48

49 Slope matters 0.6 Carbon Use efficiency Slope Total slope > 300 m Total slope < 300 m Non-slope Kutsch and Kolari, Nature Climate Change, subm. Page 49

50 History matters Carbon use efficiency R² = R² = Age (years) Carbon Use efficiency p<0.01 a b Afforestation Disturbance Kutsch and Kolari, Nature Climate Change, subm. Page 50

51 The remaining data set Carbon Use efficiency p=0.42 High nutrients b Low nutrients a Page 51 Kutsch and Kolari, Nature Climate Change, subm.

52 Some questions to discuss Is every statistical correlation a scientific story? How do we treat *magic numbers*? Do we have to select our sites more thoroughly? o Can we develop strategies to reject (or at least flag) data from sites that are prone by terrain problems? o Which meta-data on site properties have to be provided with the data? o Are there enough ideal sites? Can we support EC measurements by bottom-up integration of independent measurements? Page 52

53 Thank you Thank you! Seite 53

54 Thank you Thanks to the many people who make ICOS possible. Seite