Modelling Land Surface in a climate model E. Kowalczyk CSIRO Marine and Atmospheric Research Cape...
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Transcript of Modelling Land Surface in a climate model E. Kowalczyk CSIRO Marine and Atmospheric Research Cape...
Modelling Land Surface in a climate model
E. Kowalczyk
CSIRO Marine and Atmospheric Research
Cape Grim Tumbarumba
Role of the land surface schemes (LSS) in climate models Surface energy and water balance in climate model Representation of vegetation processes in CABLE
Description of land hydrology and soil temperature
- soil moisture & temperature
- snow accumulation, melting and properties
Examples of use of CABLE coupled to a climate model
Outline
Fast biophysical processes
Canopy conductancephotosynthesis, leaf respiration
Carbon transfer,Soil temp. & moistureavailibity
Slow biogeographicalprocesses
Vegetation dynamics & disturbance
Land-use and land-cover change
Vegetation change
Autotrophic andHeterotrophic
respiration
Allocation
Intermediate timescalebiogeochemical processes
Phenology
Turnover
Nutrient cycle
Solution of SEB;canopy and ground
temperatures and fluxes
Soil heat and moisture
Surface water balance
Update LAI,Photosyn-thesis capacity
Physical-chemical forcingT,u,Pr,q, Rs,Rl,CO2
Radiationwater, heat, & CO2 fluxes
days years
Biogeo-chemicalforcing
Time scale of biosphere-atmosphere interactions
Atmosphere
minutes
S net + L net – G = H + λE
Tf
Tg
LSS calculates exchanges of moisture, energy,momentum and trace gassesat the land-atmosphere interface.
Land surface important characteristics forcalculation of SEB: albedo, leaf area index, canopy height, surface moisture.
Key task is to calculateSurface Energy Balance:
Role of the Land Surface Scheme (LSS) in GCM
H
λEL
S
Changes of land featuresorography, vegetation,
albedo, etc
Runoff
PrecipEvap
Transpiration
Atmosphere-LandCoupling
iver
Infiltration
Drainage
Precip.Evap.
Surface Water Balance in Climate Model
Prec – Evap – Runoff = ΔSnow + ΔSoilMoist
Land surface important characteristics: soil hydraulic properties & depth vegetation properties; rooting depth leaf area index, max carboxylation rate
Interface to GCM or offline
Canopy radiation;sunlit & shaded visible &near infra-red,albedo stomata transp.
& photosynthesis
Carbon fluxes;GPP,NPP, NEP
SEB & fluxes;for soil-vegetationsystem:Ef , Hf , Eg , Hg;
evapotranspiration
soil moisture snow
carbon pools; allocation & flow
The general structure of CABLE
soil temp. soil respiration
Email: [email protected] the CABLE secured website with your supplied password at https://teams.csiro.au/sites/cable/default.aspx
Kowalczyk et al., CMAR Research Paper 013, 2006. http://www.cmar.csiro.au/e-print/open/kowalczykea_2006a.pdf
The main features of CABLE • a coupled model of stomatal conductance, photosynthesis and the partitioning of
absorbed net radiation into latent and sensible heat fluxes
• the model differentiates between sunlit and shaded leaves i.e. two-big-leaf sub-models for calculation of photosynthesis, conductance and leaf temperature
• the radiation submodel calculates the absorption of beam and diffuse radiation in visible and near infrared wavebands, and thermal radiation
• the vegetation is placed above the ground allowing for full aerodynamic and radiative interaction between vegetation and the ground
• the plant turbulence model by Raupach et al. (1997)
• a multilayer soil model is used; Richards equations are solved for soil moisture and heat conduction equation for soil temperature
• the snow model computes temperature, density and thickness of three snowpack layers.
• biogeochemical model CASA CNP for carbon, nitrogen and phosphorus
including symbiotic nitrogen fixation ( Wang, Houlton and Field,2007).
Role of the land surface schemes (LSS) in climate models Surface energy and water balance in climate model Representation of vegetation processes in CABLE
Description of land hydrology and soil temperature
- soil moisture & temperature
- snow accumulation, melting and properties
Examples of use of CABLE coupled to a climate model
Outline
Representation of vegetation processes in CABLE
Canopy representation
CABLE
Coupled model of stomatal conductance and photosynthesis
The two-leaf model ( sunlit & shaded ) of Wang & Leuning [1998] is used to calculate 6 variables:
• Tf - leaf temperature• Ds - vapour pressure deficit• Cs - CO2 concentration at the leaf surface• Ci - intercellular CO2 concentration of the leaf• Gs - stomatal conducatnce• An - net photosynthesis
The set of six equations is used to solve simultaneously for photosynthesis, transpiration, leaf temperature and sensible heat fluxes for a each leaf
Vegetation parameters required for CABLE
Geographically explicit data
LAI – leaf area index
fractional cover C3/C4 - fraction the model calculates: z0 – roughness length
α – canopy albedo
VEGETATION TYPE 1 broad-leaf evergreeen trees 2 broad-leaf deciduous trees 3 broad-leaf and needle-leaf trees 4 needle-leaf evergreen trees 5 needle-leaf deciduous trees 6 broad-leaf trees with ground cover
/short-vegetation/C4 grass (savanna) 7 perennial grasslands 8 broad-leaf shrubs with grassland 9 broad-leaf shrubs with bare soil10 tundra11 bare soil and desert12 agricultural/c3 grassland13 ice
A grouping of species that show close similarities in their response to environmental control have common properties such as: - vegetation height - root distribution - max carboxylation rate - leaf dimension and angle, sheltering factor, - leaf interception capacity
Role of the land surface schemes (LSS) in climate models Surface energy and water balance in climate model Representation of vegetation processes in CABLE
Description of land hydrology and soil temperature
- soil moisture & temperature
- snow accumulation, melting and properties
Examples of use of CABLE coupled to a climate model
Outline
Multilayer soil model
Z1=0.02m
ZN
ZN-1
Z1
Z2Z3
ZN-1
ZN
ground heatsensible
Net Solar + Net Long wave
evap
Thickness of soil layers (m) 0.022 0.058 0.154 0.409 1.085 2.872
Soil moisture model
ZN
Z1
Z2Z3
ZN-1
drainage
Surface runoff calculated as saturation excess ( + effects of topography if coupled to a climate model)
precipitation + snow melt
Drainage calculated as excess of soil field capacity or gravitational drainage
plant ET
surf runoff
soil evap
Soil moisture is calculated from the solution of Richard’s equation. The assumed form of relationship between the hydraulic conductivity, matric potential and the soil moisture is that of Clapp and Hornberger (1978).
SaturationSaturation: water fills in all available pore space
Soil Moisture: some terms and concepts
Available WaterAvailable Water: amount of water in the soil between the field capacity and the permanent wilting percentage
Field CapacityField Capacity: water that remains in soil beyond the effects of gravity.
Permanent WiltingPermanent Wilting: amount of water after the permanent wilting point is reached
Soil moisture Soil moisture : quantity of water in soil, θ = Vwater / Vsoil Є ( 0 , 0.5 )
Soil parameters required for CABLESoil types:
Coarse sand/Loamy sand
Medium clay loam/silty clay loam/silt loam
Fine clay
Coarse-medium sandy loam/loam
Coarse-fine sandy clay
Medium-fine silty clay
Coarse-medium-fine sandy clay loam
Organic peat
Permanent ice
Soil Properties: - water balance: saturation wilting point field capacity hydraulic cond. at saturation matric potential at saturation
- heat storage: albedo, specific heat, thermal conductivity density
- soil depth
Post, W., and L. Zobler, 2000Global Soil Types
Variation of hydraulic conductivity with water potential
K
wet dry
The soil parameters used in the CSIRO climate models.
Soil types:• (1) Coarse sand/Loamy sand (5) Coarse-fine sandy clay• (2) Medium clay loam/silty clay loam/silt loam (6) Medium-fine silty clay• (3) Fine clay (7) Coarse-medium-fine sandy clay loam• (4) Coarse-medium sandy loam/loam (8) Organic peat (9) Permanent ice
SOIL Type 1 Type 2 Type 3 Type 4 Type 5 Type 6 Type 7 Type 8 • density 1600 1600 1600 1600 1600 1600 1600 1300 soil density kg/m3• sfc 0.143 0.301 0.367 0.218 0.31 0.37 0.255 0.45 field capacity (m3/m3)• swilt 0.072 0.216 0.286 0.135 0.219 0.283 0.175 0.395 wilting point (m3/m3)• ssat 0.398 0.479 0.482 0.443 0.426 0.482 0.420 0.451 saturation (m3/m3)• hyds*10-6 166.0 4.0 1.0 21.0 2.0 1.0 6.0 800.0 hydraulic cond. at saturation (m/s)• sucs -0.106 -0.591 -0.405 -0.348 -0.153 -0.49 -0.299 -0.356 matric potential at saturation • bch 4.2 7.1 11.4 5.15 10.4 10.4 7.12 5.83 b parameter in Clapp-Hornberger relations• clay 0.09 0.30 0.67 0.20 0.42 0.48 0.27 0.17 fraction of clay• sand 0.83 0.37 0.16 0.60 0.52 0.27 0.58 0.13 fraction of sand• silt 0.08 0.33 0.17 0.20 0.06 0.25 0.15 0.70 fraction of silt• css 850 850 850 850 850 850 850 1920 soil specific heat (kJ/kg/K)• dry soil thermal conductivity is calculated as: sand*0.3 + clay*0.25 + silt*0.265 [W/m/K]
• Thickness of soil layers (m) 0.022 0.058 0.154 0.409 1.085 2.872
Role of the land surface schemes (LSS) in climate models Surface energy and water balance in climate model Representation of vegetation processes in CABLE
Description of land hydrology and soil temperature
- soil moisture & temperature
- snow accumulation, melting and properties
Examples of use of CABLE coupled to a climate model
Outline
Snow modelling
Modelling of snow evolution
Snow - properties
- high albedo
- good thermal insulator
- density increases with time
-Snow accumulation-Snow albedo-Snow metamorphism and thermal properties-Snow cover interaction with vegetation-Snow melting
Modelling of snow evolution
Snow - properties
- high albedo
- good thermal insulator
- density increases with time
Snow state variables:- temperature- density- age- mass
Snow diagnostic variables:- snow albedo- depth- effective conductivity
Snow-Free Spatially Complete Product
January 2002, 0.86µm
Overlaying the Snow Albedo Statistics onto the Snow-Free Spatially Complete Albedo
Using NISE Snow Extent and Type to Overlay the Snow Albedo Statistics
Crystal Schaaf, Boston University)
www.nasa.gov/.../content/95040main_snowcover.jpg
The Moderate Resolution Imaging Spectroradiometer (MODIS), flying
aboard NASA’s Terra and Aqua satellites, measures snow cover over the entire globe every day,
cloud cover permitting.
The image shows snow cover
(white pixels) across North America from February 2-9, 2002.
SNOWMIP I Col de Porte
CSIRO observations
Role of the land surface schemes (LSS) in climate models Surface energy and water balance in climate model Representation of vegetation processes in CABLE
Description of land hydrology and soil temperature
- soil moisture & temperature
- snow accumulation, melting and properties
Examples of use of CABLE coupled to a climate model
for C4MIP phase one study.
Outline
Fast biophysical processes
Canopy conductancephotosynthesis, leaf respiration
Carbon transfer,Soil temp. & moistureavailibity
Slow biogeographicalprocesses
Vegetation dynamics & disturbance
Land-use and land-cover change
Vegetation change
Autotrophic andHeterotrophic
respiration
Allocation
Intermediate timescalebiogeochemical processes
Phenology
Turnover
Nutrient cycle
Solution of SEB;canopy and ground
temperatures and fluxes
Soil heat and moisture
Surface water balance
Update LAI,Photosyn-thesis capacity
Physical-chemical forcingT,u,Pr,q, Rs,Rl,CO2
Radiationwater, heat, & CO2 fluxes
days years
Biogeo-chemicalforcing
Time scale of biosphere-atmosphere interactions
Atmosphere
minutes
Negative feedback Neutral Positive feedback
Major regulatory mechanisms that lead to either positive or negative feedbacksof C cycle to climate warming
Photosynthesis Respiration
Nutrient availability Decomposition
Length ofgrowing seasons Drought
Warming- or nutrientprone species
Stress-tolerantspecies
diminishing
acclimation acclimation
Luo Annu. Rev. Ecol. Evol. 2007
Increased evapotranspiration
CSIRO Carbon-climate simulation • C4MIP phase I simulation:
– Coupled CABLE (CSIRO Atmosphere Biosphere Land Exchange LSS) with CCAM (Cubic Conformal Atmospheric Model).
– Used prescribed SST, carbon fluxes from ocean, fossil fuel and land use change from 1900 to 2000.
– Two atmospheric CO2 concentrations used: 1) prescribed historical CO2 globally uniform, 2) a result of atmospheric transport of all carbon fluxes including biospheric fluxes.
– Two simulations:
RUN1: biosphere sees prescribed historical CO2 from 1900 to 2000 RUN2: biosphere sees prescribed historical CO2 from 1900 to 1970,
then CO2 is kept constant at 1970 level from 1971 to 2000.
Law, Kowalczyk & Wang, Tellus, 58B, 427-437, 2006.
C-CAM
CABLE
photosynthesis
Fossil Fuel CO2
emissionsfluxes
Ste
m
RootsSoil Carbon
CO2
release
CO2 uptake
atmospheric transport
CO2
Carbon cycle in C-CAM coupled carbon-climate model
CABLE interface to C-CAM
Land-use and land-cover change
fluxes
Ocean Carbonfluxes
heterotrophic respiration
Phenology
hydrology
Conformal-cubic C48 grid used for C4MIP simulations
Resolution is about 220 km
Model forcing and modelled climate
Sea Surface Temperature:
HadISST1.1 dataset, 1x1o, monthly
CO2 concentration
Law Dome (pre 1958), then South Pole and Mauna Loa, smoothed
1900 2000
1900 2000
Land air temperature
1900 2000
Carbon fluxes through 20th century
GPP – photosynthesis increases as atmospheric CO2 increases
NPP (photosynthesis minus plant respiration) and soil respiration increase with increasing CO2
NEE (net exchange with atmosphere) starts ~neutral (tuned) and becomes sink
1900 2000
Map of output locations
Red: atmospheric sampling sites, blue: flux tower sites
Atmospheric data ‘see’ CO2 sources/sinks from a larger region than flux towers
Mauna Loa
Barrow
Ulaan Uul
Cape Rama
South Pole
WLEF
Tapajos
Seasonal cycle: amplitude and phase
Model Observations Peak to peak
amplitude – too low in northern mid-latitudes
Month of minimum, out by 4-5 months in southern hemisphere
Data: GLOBALVIEW-CO2 (2003)
Seasonal cycle: NH sites
Barrow Ulaan Uul
Mauna Loa Cape Rama
Blue: obs
Green: CABLE
Red: CASA
Data: GLOBALVIEW-CO2 (2003)
Seasonal cycle: southern hemisphere
South Pole
Blue: obs, green: model, red: CASA
Contribution of source from each semi-hemisphere
Data: GLOBALVIEW-CO2 (2003)
CO2 at Mauna Loa
1960 2000
CO2 concentration (ppm)
Annual growth of CO2 (ppm/yr)
Model: red, Observed: blue
Data: Keeling et al (2005)
CO2 Growth Rate Components at South Pole Station (ppm/yr)
Fossil fuel
Total
Land use
Biosphere
Ocean
Future plans
- Model simulated GPP,NPP,RP, Rs increased steadily over 20th century with NEP changing from being slightly positive (source) to being slightly negative (sink)
- Tropical rainforest and savanna were main contributors to global NEP variability
- CO2 fertilization effect was strongest for tropical forest, savanna and C3 grass/agriculture
- Simulated seasonal CO2 cycles were mostly good for Northern hemisphere stations and poor for Southern hemisphere
Conclusions
- implement new biogeochemical model- improve vegetation phenology - participate in the 2nd C4MIP experiment
Potentially important feedbacks in coupled climate-carbon cycle system.
Albedo (α)
Increase in α
Absorbed Sw decrease
Rn decrease
H & EL
Cloudiness & Precip. decrease
Increase in α
Reduction in Soil moisture
Sw increase
Rn increase
(+) (-)Response of the terrestrial biosphere to:
• increasing CO2 • climate change
climate variability
Example of a simple albedo feedbacks