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?

2Community Land Model tutorial

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


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


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


• 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


http://www.cesm.ucar.edu/models/clm/

Where to find detailed information


http://www.cesm.ucar.edu/models/cesm1.1/clm/CLM4_Tech_Note.pdf

What is a Land Surface Model?



…something that solves the surface energy, water (and carbon) balances


Rn=λE+SH+GdSdt

=P−E−Rs−Rg


…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


Rn=λE+SH+GdSdt

=P−E−Rs−Rg

HISTORICAL PERSPECTIVE ON LAND SURFACE MODELLING


Evolution of climate models


IPCC, 2014

Evolution of LSMs


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


1970 1980 1990



BATS:BATS:Dickinson, 1984SiB:SiB:Sellers et al., 1986


2nd generation LSM:•“Big-leaf” approach•Stomatal conductance

Evolution of LSMs


1970 1980 1990


SiB2:SiB2:Sellers et al., 1992LSM:LSM:Bonan, 1995





3rd generation LSM:•Photosynthesis•Carbon cycle

Evolution of LSMs


Vegetation dynamics(biogeography)

1970 1980 1990




IBIS:IBIS:Foley et al., 1996





Evolution of LSMs





Vegetation dynamics(biogeography)

1970 1980 1990




IBIS:IBIS:Foley et al., 1996

TERRA_ML CLM


Evolution of LSMs


no canopy layer “Big-leaf” approach

1970 1980 1990



“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


70s

80s

90s

Today

TERRA_ML VS CLM



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


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


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


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


15

42

4215

Davin et al., Clim. Dyn., 2011

CLM4.0

Hyd

rolo

gy

Temp

erature

ENERGY BALANCE AND RADIATIVE FLUXES


How CLM balances energy at the surface


Canopy energy balance

Ground energy balance

Overall energy balance

Tg

Tv

Ground/canopy radiative fluxes


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


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)


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


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


Separation of evaporation and transpiration

Eveg=1

ra+rc(qsat (Tveg )−qa)

soil resistance


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


TERRA_ML: 2nd generation; Biophysical models

Stomatal behaviour represented based on empirical relations (Jarvis et al., 1976)


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)


CLM: 3rd generation; Photosynthesis model

Stomatal conductance explicitly related to photosynthetic assimilation using Ball-Berry conductance model (Collatz et al. 1991):


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


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”


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



Oleson et al., 2010

TERRA_ML CLM4.0

COSMO model doc


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


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


Surface runoff

TOPMODEL-based runoff

water table depth depends on aquifer water storage


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)


Figure: P. Lawrence

SUBGRID-SCALE HETEROGENEITY


How to deal with subgrid-scale heterogeneity?


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:


F x F x( ) ( )

Figure: F. Ament

Subgrid hierarchy in CLM4.5


Oleson et al., 2013

Spatially-varying input parameters


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)


Will be found in pft_physiology.xxx.nc

TRANSIENT LAND USE


PFT mapping


Lawrence and Chase, JGR, 2007


Lawrence et al., JAMES, 2011

Biogeophysical effect


Lawrence et al., J. Clim., 2012

Biogeochemical effect


Lawrence et al., J. Clim., 2012

BIOGEOCHEMICAL PROCESSES


The carbon cycle


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?


IPCC, 2014

Terrestrial carbon cycle models


Figure adapted from Krinner et al., 2005

CO2 CO2



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




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




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


Nutrient limitation

Plant growth is limited by nutrient availability

Nitrogen is usually the most important nutrient at mid and high latitudes (present in chlorophyll)


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


Thornton et al., 2009

VEGETATION DYNAMICS


Classical biogeography

Vegetation can be mapped as a function of climate

Consider only climax vegetation (in equilibrium with climate) Only one-way interaction


Holdridge, 1967

Vegetation dynamics (CLM-CNDV)

Use Plant Functional Type (PFT) instead of biomes Competition for light, water and nutrients Successional dynamics


Bonan, 2008Sitch et al., 2003

Broadleaf evergreenBroadleaf raingreengrass

OFFLINE VALIDATION OF CLM


Evaluation against FLUXNET sites


Source: D. Lawrence

Global partitioning of evapotranspiration


Source: D. Lawrence

Annual mean albedo





• Overestimation of GPP in the tropics

• Alleviated in CLM4.5



• Same behaviour for ET


Castillo et al., J. Clim., 2012

CLM4-CNDV MODIS-derived

COUPLING STRATEGY


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


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)?


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

Community Land Model tutorial 82

Code structure


COSMO

cesm/CLM4.0

Oasis

interface

Oasis

interface

OASIS3-MCTlibraries

Executable 1

Executable 2


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

Community Land Model tutorial 85

http://code.hzg.de/

introduction to the community land model · demeter, biome2/3, lpj coupling...

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