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03/06/2015 Modelling of regional CO2 balance Tiina Markkanen with Tuula Aalto, Tea Thum, Jouni Susiluoto and Niina Puttonen.

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Presentation on theme: "03/06/2015 Modelling of regional CO2 balance Tiina Markkanen with Tuula Aalto, Tea Thum, Jouni Susiluoto and Niina Puttonen."— Presentation transcript:

1 03/06/2015 Modelling of regional CO2 balance Tiina Markkanen with Tuula Aalto, Tea Thum, Jouni Susiluoto and Niina Puttonen

2 Contents Modeling framework in Snowcarbo Models REMO – regional climate model JSBACH – land surface scheme Demonstration of model performance Regional Local Conclusions and future perspectives 03/06/2015Finnish Meteorological Institute2

3 Modelling framework in Snowcarbo Models applied: REgional climate MOdel of MPI -M, Hamburg REMO Produce regional climatic forcing Land surface scheme (LSS) of GCM ECHAM JSBACH Produce regional CO2 balance consisting of assimilation and emissions in ecosystems 03/06/2015Finnish Meteorological Institute3

4 Modelling framework in Snowcarbo Relatively high spatial resolution ~18km High time resolution 1 hour Stays close to actual weather of target years 2001-2011 Climate initialised once per day REMO and JSBACH are offline coupled REMO does not get feedback from JSBACH but interacts with its own surface scheme 03/06/2015Finnish Meteorological Institute4

5 03/06/2015Finnish Meteorological Institute5 Validation against various data REMO2008 JSBACH Land cover classification from remote sensing Detailed meteorology Observed climate (ECWMF) CO 2 concentration field 3D REMO2008 tracer Anthropogenic and ocean CO 2 sources, fires Indicators of regional CO 2 balance Vegetation type classification from remote sensing CO 2 flux (NEE) field 2D CO2 flux Flux and concentration data Snow cover and phenology related variables Snow data Snow data, Phenology, etc.

6 03/06/2015Finnish Meteorological Institute6 Validation against various data REMO2008 JSBACH Land cover classification from remote sensing Detailed meteorology Observed climate (ECWMF) CO 2 concentration field 3D REMO2008 tracer Anthropogenic and ocean CO 2 sources, fires Indicators of regional CO 2 balance Vegetation type classification from remote sensing CO 2 flux (NEE) field 2D CO2 flux Flux and concentration data Snow cover and phenology related variables Snow data Snow data, Phenology, etc.

7 REMO: forcing and initialisation Regional climate model requires as boundary data Atmospheric conditions Wind speeds, Temperature, Humidity Sea surface temperature, ice cover Surface parameter fields As initial data in addition to those above Soil temperature and moisture Sources of initial and boundary meteorological data General circulation models Re-analysis data products, here ERA-Interim, ECMWF 03/06/2015Finnish Meteorological Institute7

8 REMO: basic characteristics Dynamic core of DWD operational model Physics and surface model from ECHAM Surface parameter maps for Surface background albedo, roughness length, vegetation ratio, leaf area index, forest fraction, soil field capacity Rotated spherical grid Close to rectangular  Resolution applied in Snowcarbo 0.1667° 03/06/2015Finnish Meteorological Institute8

9 REMO: surface parameter maps Standard land cover of USGS classified according to Olson cover types (n=100) Parameter values allocated to each Olson type Parameters aggregated from 1km USGS map to maps of resolution of the model In Snowcarbo the USGS map is replaced by National Corine Land Cover (CLC), European CLC and Globcover datasets  Allocations from new land cover classes to Olson ones needed 03/06/2015Finnish Meteorological Institute9

10 REMO: influence of land cover – forest fraction 03/06/2015Finnish Meteorological Institute10 Standard USGS land coverNational Corine land cover

11 REMO: In this project runs in forecast mode Model initialised daily at 6pm Spun-up until midnight in order to let the flowfield to develop into a reasonable state Run for 24 consequent hours with hourly output  Weather stays close to observed  Sensitive to meteorological boundary  Not very sensitive to surface parameterisation 03/06/2015Finnish Meteorological Institute11

12 03/06/2015Finnish Meteorological Institute12 Validation against various data REMO2008 JSBACH Land cover classification from remote sensing Detailed meteorology Observed climate (ECWMF) CO 2 concentration field 3D REMO2008 tracer Anthropogenic and ocean CO 2 sources, fires Indicators of regional CO 2 balance Vegetation type classification from remote sensing CO 2 flux (NEE) field 2D CO2 flux Flux and concentration data Snow cover and phenology related variables Snow data Snow data, Phenology, etc.

13 Modelling framework in Snowcarbo: JSBACH LSS of ECHAM to account for Surface energy partitioning – e.g. water balance Carbon cycle In offline coupled mode JSBACH is used to account for ecosystem carbon balance - CO2 exchange Process model Processes described down to as small scale as possible Limited by computational resources 03/06/2015Finnish Meteorological Institute13

14 JSBACH: characteristics 4 tiles, i.e. 4 PFTs (plant functional type) for each grid cell Photosynthesis of C3 and C4 plants Radiation in canopy Carbon storages in soil and vegetation Q10 approach for soil decomposition LAI (leaf area index) dynamics described with four phenology models 03/06/2015Finnish Meteorological Institute14

15 JSBACH: parameterisations for PFTs Tropical broadleaf evergreen trees Tropical broadleaf deciduous trees Temperate broadleaf evergreen trees Temperate broadleaf deciduous trees Coniferous evergreen trees Coniferous deciduous trees Raingreen shrubs Deciduous shrubs C3 grass C4 grass Tundra Swamp (not used) Crops Glacier 03/06/2015Finnish Meteorological Institute15 Phenology Photosynthesis Carbon storage sizes Decomposition rates of carbon storages Albedo for NIR and VIS Roughness length, LAImax, etc. Dynamic vegetation Nitrogen cycle PFT distribution is revised with more detailed land cover products

16 03/06/201516 ECHAM5 bottom layer JSBACH radiation Thermal and hydrological conditions CO 2 concentration H & LE CO 2 flux Albedo & roughness length Light absorbtion in canopy Photosynthesis (unlimited water) ECHAM soil model Photosynthesis (water limited) Carbon pool model + land use change phenology Land boundary properties LAI fAPAR NPP Gross assimilation, R d g c stressed g c unstressed

17 03/06/201517 REMO JSBACH radiation Thermal and hydrological conditions CO 2 concentration H & LE CO 2 flux Albedo & roughness length Light absorbtion in canopy Photosynthesis (unlimited water) ECHAM soil model Photosynthesis (water limited) Carbon pool model + land use change phenology Land boundary properties LAI fAPAR NPP Gross assimilation, R d g c stressed g c unstressed Offline coupling in Snowcarbo

18 03/06/2015Finnish Meteorological Institute18 Validation against various data REMO2008 JSBACH Land cover classification from remote sensing Detailed meteorology Observed climate (ECWMF) CO 2 concentration field 3D REMO2008 tracer Anthropogenic and ocean CO 2 sources, fires Indicators of regional CO 2 balance Vegetation type classification from remote sensing CO 2 flux (NEE) field 2D CO2 flux Flux and concentration data Snow cover and phenology related variables Snow data Snow data, Phenology, etc.

19 03/06/201519 JSBACH Light absorbtion in canopy Photosynthesis (unlimited water) ECHAM soil model Photosynthesis (water limited) Carbon pool model + land use change phenology Land boundary properties LAI fAPAR NPP Gross assimilation, R d g c stressed g c unstressed REMO in tracer mode radiation Thermal and hydrological conditions CO 2 concentration H & LE CO 2 flux Albedo & roughness length

20 03/06/201520 JSBACH Light absorbtion in canopy Photosynthesis (unlimited water) ECHAM soil model Photosynthesis (water limited) Carbon pool model + land use change phenology Land boundary properties LAI fAPAR NPP Gross assimilation, R d g c stressed g c unstressed REMO in tracer mode radiation Thermal and hydrological conditions CO 2 concentration H & LE CO 2 flux Albedo & roughness length Observed climate (ECWMF) Anthropogenic and ocean CO 2 sources, fires

21 REMO tracer run Uses the same meteorological initial and boundary data as the first REMO run Additionally Gets CO2 flux estimates of vegetation from JSBACH Utilizes prescribed anthropogenic and land fire emissions from a database Ocean sources from a database Requires background CO2 concentrations Produces 3D CO2 concentration fields 03/06/2015Finnish Meteorological Institute21

22 Demonstration of model performance JSBACH was forced regionally with REMO derived climatic forcing Forecast mode for climate model Standard land cover in both models Default carbon storages in JSBACH for flux measurement sites Sodankylä and Hyytiälä About a decade of measurements as climatic forcing 1000 years spin up for soil carbon storages with present climate 03/06/2015Finnish Meteorological Institute22

23 Demonstration of model performance Regional daily average NEE 03/06/2015Finnish Meteorological Institute23

24 Demonstration of model performance Daily NEE at Sodankylä Scots pine site (gm -2 s -1 ) 03/06/2015Finnish Meteorological Institute24

25 Demonstration of model performance Daily NEE at Hyytiälä Scots pine site (gm -2 s -1 ) 03/06/2015Finnish Meteorological Institute25

26 Conclusions and future perspectives  Assimilation and emissions of CO2 in ecosystems explicitly modeled  Offline coupled REMO – JSBACH framework produces CO2 balance in high temporal and in relatively high regional resolution  National level estimates of CO2 balance can be extracted from the regional maps To be done  Evaluation of the results against CO2 flux and concentration data  Adjustment of the relevant parameters in order to produce better regional estimates 03/06/2015Finnish Meteorological Institute26

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