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

mailto:[email protected]

https://teams.csiro.au/sites/cable/default.aspx

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).






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






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

http://4e.plantphys.net/image.php?id=43

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






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






for C4MIP phase one study.

Outline

Fast biophysical processes

Canopy conductancephotosynthesis, leaf respiration

Carbon transfer,Soil temp. & moistureavailibity

Slow biogeographicalprocesses

Vegetation dynamics & disturbance


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


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


Seasonal cycle: southern hemisphere

South Pole

Blue: obs, green: model, red: CASA

Contribution of source from each semi-hemisphere


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

Thank you [email protected]

http://upload.wikimedia.org/wikipedia/commons/f/f4/Leaf_1_web.jpg

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

Modelling Land Surface in a climate model E. Kowalczyk CSIRO Marine and Atmospheric Research Cape...

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