03/06/2015 modelling of regional co2 balance tiina markkanen with tuula aalto, tea thum, jouni...
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18/04/23
Modelling of regional CO2 balance
Tiina Markkanen
with Tuula Aalto, Tea Thum, Jouni Susiluoto and Niina Puttonen
Contents• Modeling framework in Snowcarbo
• Models
• REMO – regional climate model
• JSBACH – land surface scheme
• Demonstration of model performance
• Regional
• Local
• Conclusions and future perspectives
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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
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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
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Validation against various data
REMO2008 JSBACH
Land cover classification from
remote sensing
Detailed meteorology
Observed climate(ECWMF) CO2 concentration
field 3D
REMO2008 tracer
Anthropogenic and ocean CO2 sources,
fires
Indicators of regional CO2 balance
Vegetation type classification from
remote sensing
CO2 flux (NEE) field 2D
CO2 flux
Flux and concentration data
Snow cover and phenology related
variables
Snow data
Snow data,Phenology, etc.
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Validation against various data
REMO2008 JSBACH
Land cover classification from
remote sensing
Detailed meteorology
Observed climate(ECWMF) CO2 concentration
field 3D
REMO2008 tracer
Anthropogenic and ocean CO2 sources,
fires
Indicators of regional CO2 balance
Vegetation type classification from
remote sensing
CO2 flux (NEE) field 2D
CO2 flux
Flux and concentration data
Snow cover and phenology related
variables
Snow data
Snow data,Phenology, etc.
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 18/04/23Finnish Meteorological Institute 7
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°
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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
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REMO: influence of land cover – forest fraction
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Standard USGS land cover National Corine land cover
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
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Validation against various data
REMO2008 JSBACH
Land cover classification from
remote sensing
Detailed meteorology
Observed climate(ECWMF) CO2 concentration
field 3D
REMO2008 tracer
Anthropogenic and ocean CO2 sources,
fires
Indicators of regional CO2 balance
Vegetation type classification from
remote sensing
CO2 flux (NEE) field 2D
CO2 flux
Flux and concentration data
Snow cover and phenology related
variables
Snow data
Snow data,Phenology, etc.
Modelling framework in Snowcarbo: JSBACHLSS 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
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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
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JSBACH: parameterisations for PFTsTropical broadleaf evergreen treesTropical broadleaf deciduous trees
Temperate broadleaf evergreen treesTemperate broadleaf deciduous trees
Coniferous evergreen treesConiferous deciduous trees
Raingreen shrubsDeciduous shrubs
C3 grassC4 grassTundra
Swamp (not used)Crops
Glacier
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• 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
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ECHAM5 bottom layer
JSBACH
radiation
Thermal and hydrological conditions
CO2 concentration
H & LE
CO2 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 propertiesLAI
fAPAR
NPP
Gross assimilation, Rd
gc stressed
gc unstressed
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REMO JSBACH
radiation
Thermal and hydrological conditions
CO2 concentration
H & LE
CO2 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 propertiesLAI
fAPAR
NPP
Gross assimilation, Rd
gc stressed
gc unstressed
Offline coupling in Snowcarbo
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Validation against various data
REMO2008 JSBACH
Land cover classification from
remote sensing
Detailed meteorology
Observed climate(ECWMF) CO2 concentration
field 3D
REMO2008 tracer
Anthropogenic and ocean CO2 sources,
fires
Indicators of regional CO2 balance
Vegetation type classification from
remote sensing
CO2 flux (NEE) field 2D
CO2 flux
Flux and concentration data
Snow cover and phenology related
variables
Snow data
Snow data,Phenology, etc.
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JSBACH
Light absorbtion in canopy
Photosynthesis (unlimited water)
ECHAM soil model
Photosynthesis (water limited)
Carbon pool model + land use change
phenology
Land boundary propertiesLAI
fAPAR
NPP
Gross assimilation, Rd
gc stressed
gc unstressed
REMO in tracer mode
radiation
Thermal and hydrological conditions
CO2 concentration
H & LE
CO2 flux
Albedo & roughness length
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JSBACH
Light absorbtion in canopy
Photosynthesis (unlimited water)
ECHAM soil model
Photosynthesis (water limited)
Carbon pool model + land use change
phenology
Land boundary propertiesLAI
fAPAR
NPP
Gross assimilation, Rd
gc stressed
gc unstressed
REMO in tracer mode
radiation
Thermal and hydrological conditions
CO2 concentration
H & LE
CO2 flux
Albedo & roughness length
Observed climate(ECWMF)
Anthropogenic and ocean CO2 sources,
fires
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
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Demonstration of model performanceJSBACH 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
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Demonstration of model performance
Regional daily average NEE <start animation>
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Demonstration of model performance
Daily NEE at Sodankylä Scots pine site (gm-2s-1)
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Demonstration of model performance
Daily NEE at Hyytiälä Scots pine site (gm-2s-1)
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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
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