Introduction to the Community Land Model · DEMETER, BIOME2/3, LPJ Coupling...
Transcript of Introduction to the Community Land Model · DEMETER, BIOME2/3, LPJ Coupling...
Introduction to the Community Land Model
Edouard Davin ([email protected])
Community Land Model tutorial
Goals
Which processes are represented in the Community Land Model (CLM)?
What makes CLM different from TERRA_ML?
How is CLM coupled to COSMO?
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Content
Introduction Historical perspective on land surface modelling TERRA_ML vs CLM Ingredients of CLM
Energy balance and radiative fluxes Turbulent fluxes and stomatal conductance Hydrology Subgrid-scale heterogeneity Transient land use Biogeochemical processes Vegetation dynamics
Offline validation of CLM Coupling strategy
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Community Earth System Model (CESM)
A climate model freely available to the scientific community Info and download: http://www.cesm.ucar.edu/models/cesm1.2/ CLM is the land component of CESM
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Figure: P. Lawrence
Community Earth System Model (CESM)
A climate model freely available to the scientific community Info and download: http://www.cesm.ucar.edu/models/cesm1.2/ CLM is the land component of CESM
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• CESM has ~1.5M lines of code
• CLM has ~0.2M lines of code
Figure: P. Lawrence
Community Land Model (CLM)
More info and documentation: http://www.cesm.ucar.edu/models/clm/
CLM versions: CLM3.5 coupled to COSMO as subroutine CLM4.0 coupled to COSMO using OASIS; basis for this presentation and
tutorial CLM4.5 coupled to COSMO using OASIS
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Where to find detailed information
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http://www.cesm.ucar.edu/models/cesm1.1/clm/CLM4_Tech_Note.pdf
What is a Land Surface Model?
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What is a Land Surface Model?
…something that solves the surface energy, water (and carbon) balances
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Rn=λE+SH+GdSdt
=P−E−Rs−Rg
What is a Land Surface Model?
…something that solves the surface energy, water (and carbon) balances
Based on first principles: conservation of energy and mass!
Common to all LSMs; degree of complexity depends on the approach used to compute these fluxes
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Rn=λE+SH+GdSdt
=P−E−Rs−Rg
HISTORICAL PERSPECTIVE ON LAND SURFACE MODELLING
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Evolution of climate models
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IPCC, 2014
Evolution of LSMs
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1970 1980 1990
Bucket model: Bucket model: Manabe, 1969
Sellers et al. (1997) classification
1st generation LSM:•“bucket” model•No explicit treatment of vegetation
Evolution of LSMs
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1970 1980 1990
Bucket model: Bucket model: Manabe, 1969
Sellers et al. (1997) classification
BATS:BATS:Dickinson, 1984SiB:SiB:Sellers et al., 1986
1st generation LSM:•“bucket” model•No explicit treatment of vegetation
2nd generation LSM:•“Big-leaf” approach•Stomatal conductance
Evolution of LSMs
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1970 1980 1990
Bucket model: Bucket model: Manabe, 1969
SiB2:SiB2:Sellers et al., 1992LSM:LSM:Bonan, 1995
BATS:BATS:Dickinson, 1984SiB:SiB:Sellers et al., 1986
Sellers et al. (1997) classification
1st generation LSM:•“bucket” model•No explicit treatment of vegetation
2nd generation LSM:•“Big-leaf” approach•Stomatal conductance
3rd generation LSM:•Photosynthesis•Carbon cycle
Evolution of LSMs
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Vegetation dynamics(biogeography)
1970 1980 1990
Bucket model: Bucket model: Manabe, 1969
BATS:BATS:Dickinson, 1984SiB:SiB:Sellers et al., 1986
SiB2:SiB2:Sellers et al., 1992LSM:LSM:Bonan, 1995
IBIS:IBIS:Foley et al., 1996
Sellers et al. (1997) classification
1st generation LSM:•“bucket” model•No explicit treatment of vegetation
2nd generation LSM:•“Big-leaf” approach•Stomatal conductance
3rd generation LSM:•Photosynthesis•Carbon cycle
Evolution of LSMs
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1st generation LSM:•“bucket” model•No explicit treatment of vegetation
2nd generation LSM:•“Big-leaf” approach•Stomatal conductance
3rd generation LSM:•Photosynthesis•Carbon cycle
Vegetation dynamics(biogeography)
1970 1980 1990
Bucket model: Bucket model: Manabe, 1969
BATS:BATS:Dickinson, 1984SiB:SiB:Sellers et al., 1986
SiB2:SiB2:Sellers et al., 1992LSM:LSM:Bonan, 1995
IBIS:IBIS:Foley et al., 1996
TERRA_ML CLM
Sellers et al. (1997) classification
Evolution of LSMs
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no canopy layer “Big-leaf” approach
1970 1980 1990
Bucket model: Bucket model: Manabe, 1969
BATS:BATS:Dickinson, 1984SiB:SiB:Sellers et al., 1986
“2-leaf” approach Multi-layer canopy
Multi-layer hydrology“bucket” hydrology
2000 2010
CLM3:CLM3:Oleson et al., 2004
CLM4.5:CLM4.5:Oleson et al., 2013ORCHIDEE:ORCHIDEE:Ryder et al., 2013
sunlit
shaded
2-layer hydrology
Vertical discretisation
1st generation LSMBucket modelBucket model
Empirical modelsNPP = f(T , P)
MIAMIMIAMI
Empirical modelsBiome= f(T,P)
KKööppen, Holdridgeppen, Holdridge
Biogeophysical models Biogeochemical models Biogeographical models
2nd generation LSMBATS, SiB, ISBA,BATS, SiB, ISBA,
SECHIBASECHIBA
TERRA_MLTERRA_MLMechanistic models
TEM, BIOME-BGC, CARAIB, TEM, BIOME-BGC, CARAIB, SILVAN, CENTURY, FBMSILVAN, CENTURY, FBM
Concept of PFT, competition
BIOMEBIOME
Diagnostic models (satellite)CASA,TURCCASA,TURC
3rd generation LSM(coupling stomatal conductance-NPP)
SiB2, LSM, MOSESSiB2, LSM, MOSES
PFT=f(NPP)DEMETER, BIOME2/3, LPJDEMETER, BIOME2/3, LPJ
Coupling physics-biogeochemistry-biogeographyCLMCLM, ORCHIDEE, JULES, IBIS, ORCHIDEE, JULES, IBIS Adapted from N. Viovy
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70s
80s
90s
Today
TERRA_ML VS CLM
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Surface temperature and energy balance
Radiation fluxes
TERRA_ML CLM4.0
• Single interface with one temperature (t_g) and bulk fluxes
• Distinguishes temperature and energy fluxes for canopy (tv) and ground (tg)
• Fluxes based on grid scale albedo and temperature.
• Technically in src_radiation
• Canopy radiative scheme• 2-stream approximation of the
radiative transfer equations• Explicit treatment of diffuse and
direct light
Stomatal conductance
• BATS-based• Empirical Jarvis-type approach
• Ball-Berry approach• Coupling with photosynthesis• “2-leaf” canopy with diffuse/direct
light
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Soil hydrology
Runoff
Snow processes
TERRA_ML CLM4.0
• Richards’ equation solved for multi-layer soil column
• Richards’ equation solved for multi-layer soil column
• Groundwater model• Water table depth determined
• Surface runoff: Hillel, 1980• Subsurface runoff when layer is
at field capacity
• TOPMODEL-based approach• Surface runoff: saturation and
infiltration excess• Subsurface runoff function of
water table depth
• Single mass balance equation• New option for multi-layer
scheme?
• Multi-layer scheme• Solid and liquid content• Melt-freeze cycles• Accumulation and compaction
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Surface parameters
TERRA_ML CLM4.0
• ?
• MODIS-based (LAI, PFT distribution)
• IGBP Global Soil Data Task 2000 for soil texture and soil organic matter with vertical profile
Subgrid-scale heterogeneity
• Only accounts for partial coverage of snow.
• Tile approach in ICON-TERRA
• Tile approach• Vegetated (17 PFTs),• Crop, Urban, lake, glacier
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Biogeochemistry
TERRA_ML CLM4.0
• Carbon-nitrogen module• Prognostic phenology• BVOCs
Ecosystem dynamics
• LPJ-based approach
Land use change
• Change in crop and pasture over time
• Biogeophysical and biogeochemical effects
Multi-layer soil structure
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Davin et al., Clim. Dyn., 2011
CLM4.0
Hyd
rolo
gy
Temp
erature
ENERGY BALANCE AND RADIATIVE FLUXES
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How CLM balances energy at the surface
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Canopy energy balance
Ground energy balance
Overall energy balance
Tg
Tv
Ground/canopy radiative fluxes
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Diffuse solar radiation Longwave radiationDirect solar radiation
Direct ground albedo (vis/nir)
Diffuse ground albedo (vis/nir)Oleson et al., Tech. Note, 2010
TURBULENT FLUXES AND STOMATAL CONDUCTANCE
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Formulation of turbulent fluxes
The flux is driven by the gradient of the quantity considered The role of turbulence is represented through a bulk aerodynamic
resistance (inverse of bulk aerodynamic coefficient)
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E=1ra
(qs−qa)
SH=1ra
(Ts−Ta)
ra
Ta, qa
Ts, qs
Aerodynamic resistance
Atmospheric forcing
Temperature or humidity gradient
How to deal with qs ?
E=1ra
(qs−qa)
0≤β≤1
E=β1ra
(qsat (Ts)−qa )=βEpot /¿
¿
qs not easy to estimate, thus we introduce qsat(Ts) instead
Surface humidity at saturation
“Beta-factor” to represent limited availability of moisture
Epot
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How to deal with qs ?
E=1ra
(qs−qa)
E=β1ra
(qsat (Ts)−qa )=βEpot /¿
¿
qs not easy to estimate, thus we introduce qsat(Ts) instead
E=1
ra+rs(qsat (Ts )−qa )
Or using the electrical network analogy:
Surface resistance (to be discussed later)
ra
qa
qs
qsat
rs
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Separation of evaporation and transpiration
Eveg=1
ra+rc(qsat (Tveg )−qa)
soil resistance
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Figure: Sellers et al., 1997
Esoil=1
ra+rsoil( qsat (Tsoil)−qa )
canopy resistance: represents the vegetation control on transpiration
Partitioning of evapotranspiration
E = soil evaporation + interception + transpiration
Half of the water flux to the atmosphere is conveyed by plants !
Biological processes play a major role in controlling evapotranspiration.
Dirmeyer et al., 2006
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TERRA_ML: 2nd generation; Biophysical models
Stomatal behaviour represented based on empirical relations (Jarvis et al., 1976)
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Figure: Bonan, 2002
gstom = f (PAR, W, T, δe)
stomatal conductance
Light limitation
Water stress
temperature
Air humidity
Limitation of 2nd generation LSMs
Vegetation explicitly represented in 2nd generation LSMs but...
Stomatal conductance is calculated empirically without considering the actual process controlling stomatal functioning
Maximisation of water use efficiency (photosynthesis/water)
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CLM: 3rd generation; Photosynthesis model
Stomatal conductance explicitly related to photosynthetic assimilation using Ball-Berry conductance model (Collatz et al. 1991):
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gstom = mAcs
hs p + b
m empirical coefficient derived from
observationsA photosynthetic assimilationcs CO2 concentration at the leaf surface
hs relative humidity at the leaf surface
p atmospheric pressureb minimum value of gstom
Figure: Sellers et al., 1997
Photosynthetic assimilation
AC: Efficiency of the photosynthetic enzyme system (Rubisco limitation)
AL: Amount of light captured by the leaf chlorophyll (Light limitation)
AS: Capacity of the leaf to utilize or export the products of photosynthesis (Capacity utilization limitation)
AC and As mainly depend on Vcmax (maximum rate of carboxylation) which includes the effect of water stress and nitrogen limitation
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A = min ( Ac,Aj ,Ae ) (Farquhar et al. , 1980)
Scaling from leaf to canopy
Photosynthesis and stomatal conductance calculated separately for sunlit and shaded leaves: “2-leaf model”
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Bonan et al., JGR, 2011
Account for vertical gradient of nitrogen in the canopy decline in foliage nitrogen (per unit area) with depth in canopy yields decline in photosynthetic capacity
HYDROLOGY
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Oleson et al., 2010
TERRA_ML CLM4.0
COSMO model doc
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Oleson et al., 2010
TERRA_ML CLM4.0
COSMO model doc
River routing
Water table depth-dependent subsurface runoff
TOPMODEL-based
Soil water dynamics: Richards’ equation
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Soil moisture change over time
Hydraulic conductivity dependent on water content and soil type
Vertical water flux (Darcy’s law)
Sink term (evapotranspiration, runoff)
• Mineral and organic properties• Vertical profile
TOPMODEL-based runoff
Figure: D. Lawrence
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Surface runoff
TOPMODEL-based runoff
water table depth depends on aquifer water storage
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Subsurface runoffSurface runoff
Figure: D. Lawrence
Subgrid-scale topography
fsat depends on soil moisture state (water table depth) and fmax
fmax integrates the effect of subgrid-scale topography (input dataset)
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Figure: P. Lawrence
SUBGRID-SCALE HETEROGENEITY
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How to deal with subgrid-scale heterogeneity?
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Figure: P. Houser
Climate model grid
Subgrid land cover
Problem with averaging surface parameters
Relations between surface parameters and fluxes are non-linear
Averaging surface parameters over heterogeneous terrain will yield wrong fluxes:
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F x F x( ) ( )
Figure: F. Ament
Subgrid hierarchy in CLM4.5
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Oleson et al., 2013
Spatially-varying input parameters
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Oleson et al., 2013
Will be found in surfdata_xxx.nc
Global datasets aggregated to model grid
PFT-specific input parameters
Optical properties Leaf angle Leaf/stem reflectance Leaf/stem transmittance
Morphological properties Leaf dimension Root distribution
Photosynthetic parameters Specific leaf area Leaf carbon-to-nitrogen ratio m (slope of conductance-photosynthesis relationship)
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Will be found in pft_physiology.xxx.nc
TRANSIENT LAND USE
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PFT mapping
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Lawrence and Chase, JGR, 2007
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Lawrence et al., JAMES, 2011
Biogeophysical effect
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Lawrence et al., J. Clim., 2012
Biogeochemical effect
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Lawrence et al., J. Clim., 2012
BIOGEOCHEMICAL PROCESSES
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The carbon cycle
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IPCC, 2007
Anthropogenic emissions
In black: preindustrial state of reservoirs and fluxes
In red: anthropogenic perturbation
Importance of terrestrial ecosystems
Land use change: ~1/3 of cumulative anthropogenic CO2 emissions since preindustrial
~25% of cumulative anthropogenic CO2 emissions since preindustrial were absorbed by the terrestrial biosphere (land sink)
How will the terrestrial carbon sink be affected by climate change?
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IPCC, 2014
Terrestrial carbon cycle models
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Figure adapted from Krinner et al., 2005
CO2 CO2
Terrestrial carbon cycle models
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CO2 CO2
Allocation
Function of temperature, moisture availability,
nutrient and light limitation
Example: if the plant lacks water or nitrogen it
tends to grow roots
Figure: N. Viovy
waterlight
Ro
ots
Figure adapted from Krinner et al., 2005
Terrestrial carbon cycle models
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CO2 CO2
Phenology: Senescence
Triggered by reduction in temperature,
daylength or moisture
Temperate and boreal deciduous trees shed
their leaves when days become shorter and
temperature falls below a critical threshold.
Tropical raingreen trees shed their leaves
when moisture availability becomes critical
Evergreen trees have a continuous leaf
turnover all over the year
Figure adapted from Krinner et al., 2005
Terrestrial carbon cycle models
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CO2 CO2
Heterotrophic respiration
Organic matter is decomposed by microbes
and other microorganisms
Microbial activity, and thus decomposition,
increases with warmer temperatures
The rate of decomposition is also a function of
soil moisture: Figure: B
onan, 2002
Figure adapted from Krinner et al., 2005
Nutrient limitation
Plant growth is limited by nutrient availability
Nitrogen is usually the most important nutrient at mid and high latitudes (present in chlorophyll)
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Coupled Carbon-Nitrogen dynamics
2 competing processes in a changing climate:
CO2 fertilization effect limited by N availability: negative impact on NPP
More N mineralization in warmer soil: positive impact on NPP
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Thornton et al., 2009
VEGETATION DYNAMICS
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Classical biogeography
Vegetation can be mapped as a function of climate
Consider only climax vegetation (in equilibrium with climate) Only one-way interaction
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Holdridge, 1967
Vegetation dynamics (CLM-CNDV)
Use Plant Functional Type (PFT) instead of biomes Competition for light, water and nutrients Successional dynamics
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Bonan, 2008Sitch et al., 2003
Broadleaf evergreenBroadleaf raingreengrass
OFFLINE VALIDATION OF CLM
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Evaluation against FLUXNET sites
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Source: D. Lawrence
Global partitioning of evapotranspiration
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Source: D. Lawrence
Annual mean albedo
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Lawrence et al., JAMES, 2011
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Lawrence et al., JAMES, 2011
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Bonan et al., JGR, 2011
• Overestimation of GPP in the tropics
• Alleviated in CLM4.5
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Bonan et al., JGR, 2011
• Same behaviour for ET
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Castillo et al., J. Clim., 2012
CLM4-CNDV MODIS-derived
COUPLING STRATEGY
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Variables exchanged
Atmosphere Land: atmospheric temperature, U and V wind components, specific water vapour content, height of first atmospheric level, surface pressure, direct shortwave downward radiation, diffuse shortwave downward radiation, longwave downward radiation, precipitation
Land Atmosphere: surface albedo, outgoing longwave radiation, latent and sensible heat fluxes, momentum fluxes
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Coupling for turbulent fluxes
COSMO does not use directly surface fluxes in its turbulent scheme but uses instead surface states (e.g. surface temperature) and surface transfer coefficients (e.g. TCH)
Therefore, the surface fluxes from CLM passed to COSMO have to be inverted to recalculate “effective” transfer coefficients (and effective qv_s) that can by used by the turbulence scheme
An option for surface flux boundary conditions will be implemented in the next revision of the turbulence scheme (M. Raschendorfer)?
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COSMO-CLM2
“Subroutine coupling”: COSMO-CLM (various versions) coupled to CLM3.5 Evaluation: Davin et al., Clim. Dyn. [2011]; Davin and Seneviratne, Biogeosciences [2012];
Lorenz et al., [2012]
Currently in use within various projects, but no plans to implement it in official COSMO code
OASIS coupling: Uses OASIS3-MCT as external coupler Upgrade to CLM4.0/CLM4.5 Upgrade to COSMO5.0 Code will be distributed via the redmine server
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Code structure
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COSMO
cesm/CLM4.0
Oasis
interface
Oasis
interface
OASIS3-MCTlibraries
Executable 1
Executable 2
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Performance on a Cray XE6 (CSCS)
Time-to-solution increases by 30% (0.44 res) to 100% (0.11 res) in the “subroutine coupling” approach compared to COSMO-TERRA
Almost no increase in time-to-solution with OASIS3-MCT
Uncoupled Coupled
0.44 deg. resolutionCOSMO: 8x16 procs
CLM4: 32
5:00 ±2min 5:14 ±4min
0.11 deg. resolutionCOSMO: 32x24 procs
CLM4: 256
20:48 ±40min 21:26 ±30min
Wall time on rosa (Cray XE6; CSCS) for 1 year of simulation
Redmine server for code management
http://code.hzg.de/ Register online to get an
account Project “CCLM-CLM” Version used for training
course will be uploaded on the server
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