Institute for Atmospheric and Climate Science ETH Zürich

Sonia Seneviratne, ETH Zürich

Sonia I. Seneviratne and Christoph Schär Land-Atmosphere-Climate Interactions Winter term 2006/07

Modeling of the coupled land-atmosphere system. Part (b)

Sonia I. Seneviratne

Institute for Atmospheric and Climate Science ETH Zürich

sonia.seneviratne@env.ethz.ch

Sonia Seneviratne, ETH Zürich

3

Plan

24.10.2006 SIS 1. Introduction

31.10.2006 CS 2. Land surface processes in the global energy and water cycles (a) 07.11.2006 SIS 2. Land surface processes in the global energy and water cycles (b) 14.11.2006 CS 2. Land surface processes in the global energy and water cycles (c) 21.11.2006 Reserve date (Hydrologie-Seminar, ENSEMBLES)

28.11.2006 EJ Discussion of Exercises (1)

05.12.2006 SIS 3. Feedback processes, threshold effects (a)

12.12.2006 Reserve date (AGU)

19.12.2006 SIS 3. Feedback processes, threshold effects (b) 09.01.2007 EJ Discussion of Exercises (2)

16.01.2007 CS 4. Modeling of the coupled land-atmosphere system (a) 23.01.2007 SIS 4. Modeling of the coupled land-atmosphere system (b) 30.01.2007 SIS 5. Outlook, open issues

SIS = Sonia I. Seneviratne, CS = Christoph Schär, EJ = Eric Jäger

Sonia Seneviratne, ETH Zürich

Outline

• Land surface models as part of global climate models

• Levels of complexity

• Plot-scale processes

• “Bucket” model

• Biophysical models (BATS, ...)

• Physiological models (SIB2, ...)

• Subgrid-scale heterogeneity

• Land dynamics

• Assessments and data products

Sonia Seneviratne, ETH Zürich

The Coupled 5

Climate System

Ocean

Feb 9, 2000

Sea Ice

Atmosphere

Land Surfaces

May 1989

Sonia Seneviratne, ETH Zürich

Climate model: Components

Atmosphere: dynamics + physics

Cryosphere

(ice, snow) Oceans

Land (vegetation, soil processes)

Processes computed by Land Surface Model (LSM) other names: Land Surface Scheme (LSS), Soil-

Vegetation Transfer Scheme (SVAT), Dynamic Global Vegetation Model (DGVM)

Sonia Seneviratne, ETH Zürich

7

Land surface models

• calculate land surface processes:

»fluxes (radiation, energy, water), runoff

»changes in storage (soil moisture, snow, soil heat content/temperature profile, ...)

• driver data (offline/online):

»precipitation, incoming shortwave radiation, incoming longwave radiation, wind, atmospheric humidity, air temperature, ....

• solving the radiation, energy and water balances

Energy

Water

Sonia Seneviratne, ETH Zürich

Land surface models: Levels of complexity

Plot-scale processes

“bucket” model biophysical models

physiological models

(photosynthesis, biochemistry)

Land dynamics specified or a fixed function of e.g. T

phenology (observed/

modeled)

dynamic vegetation not accounted

for (“big leaf approach”)

“mosaic” approach

(horizontal heterogeneity of land cover) TOPMODEL, catchment land surface model, ...

(impact of topography/catchments’

definition on runoff generation) Subgrid-scale

heterogeneity

Sonia Seneviratne, ETH Zürich

9

Levels of complexity: Plot-scale processes

“Bucket” model

(Manabe 1969, Mon. Weather Rev.)

Biophysical models

(e.g. “Biosphere-Atmosphere Transfer Scheme/BATS”, Dickinson 1984, Geogr. Monogr. AGU)  

Physiological models

(e.g. “Simple Biosphere model 2/

SiB2”, Sellers et al. 1996, J. Climate)  

(Dickinson 1984; Sellers et al. 1997, Science)

Sonia Seneviratne, ETH Zürich

Plot-scale processes: “Bucket” model

LH = L ! C _W u _a ( q _s " q _a )

Boundary layer theory (see Lecture 3, p. 23)

u

horizontal wind at height z

(e.g. z

=10m)

q

, T

specific humidity and temperature at the surface

q

, T

specific humidity and temperatur at height z

C

, C

aerodynamic transfer coefficients for humidity and heat

c

, L specific heat of air, latent heat of vaporization

" air density

SH = c _p ! C _H u _a ( T _s " T _a )

! use “resistance” to represent limitation of evaporation

• aerodynamic resistance (turbulent-diffusion term impeding evaporation)

• limitation due to soil moisture deficit (“ " -factor”; 0 ! " ^{! 1)}

!

E = LH / L = " # ^q

( ) ^T

^{$ q}

r

%

&

' (

) * = # E

!

SH = " c

T

# T

r

$

% & ' ( )

T

temperature at reference height

q

saturation water vapour specific humidity

q

specific humidity at reference height

r

aerodynamic resistance

! limiting factor based on soil moisture content

E

potential evaporation function of wind speed,

roughness length

Sonia Seneviratne, ETH Zürich

12

Plot-scale processes: “Bucket” model

! use “resistance” to represent limitation of evaporation

• aerodynamic resistance (turbulent-diffusion term impeding evaporation)

• limitation due to soil moisture deficit (“ " -factor”; 0 ! " ^{! 1)}

!

E = LH / L = " # ^q

( ) ^T

^{$ q}

r

%

&

' (

) * = # E

!

SH = " c

T

# T

r

$

% &

' ( )

T

temperature at reference height

q

saturation water vapour specific humidity

q

specific humidity at reference height

r

aerodynamic resistance

! limiting factor based on soil moisture content

E

potential evaporation

Analogy: Ohm’s law

!

I = V r

I: current

V:

potential difference

resistance

g:

conductance (1/r)

resistances in series resistances in parallel

!

r = r

+ r

r₁ r₂

r r₁ r₂ r

!

1 r = 1

r

+ 1 r

!

g = g

+ g

Sonia Seneviratne, ETH Zürich

Plot-scale processes: “Bucket” model

! use “resistance” to represent limitation of evaporation

• aerodynamic resistance (turbulent-diffusion term impeding evaporation)

• limitation due to soil moisture deficit (“"-factor”; 0 ! " ^{! 1)}

(Sellers et al. 1997, Science)

!

" = W

0.75Wmax

Dependence of

"

!

E = LH / L = " # ^q

( ) ^T

^{$ q}

r

%

&

' (

) * = # E

!

SH = " c

T

# T

r

$

% & ' ( )

T

temperature at reference height

q

saturation water vapour specific humidity

q

specific humidity at reference height

r

aerodynamic resistance

! limiting factor based on soil moisture content

E

potential evaporation

Sonia Seneviratne, ETH Zürich

14

Plot-scale processes: “Bucket” model

Bucket hydrology

(Sellers et al. 1997, Science)

!

"= W

0.75W_max

Dependence of

"

!

Runoff: - if W

! W

, R

= 0

- if W

> W

, Ri = W

-W

, W

=W

Sonia Seneviratne, ETH Zürich

Plot-scale processes: “Bucket” model

Vegetation types

!

W

uniform for all the globe

In original formulation (Manabe 1969), albedo and roughness length also uniform fields Later on: albedo and roughness length specified based on vegetation distribution (desert, forest) in snow-free regions

Very simplified parameterization; known issues:

• bare soil evaporation is overestimated in all regimes

• no representation of vegetation control (stomatal resistance)

• no representation of other limiting factors (linked to stomatal resistance): radiation, temperature, vapor pressure deficit, vegetation state

• in stress-free conditions: will still overestimate evapotranspiration (potential evaporation instead of potential evapotranspiration [non-zero value of stomatal resistance])

! In general: overestimation of evapotranspiration in almost all regimes

Sonia Seneviratne, ETH Zürich

16

Plot-scale processes: “Bucket” model

Sonia Seneviratne, ETH Zürich

Levels of complexity: Plot-scale processes

“Bucket” model

(Manabe 1969, Mon. Weather Rev.)

Biophysical models

(e.g. “Biosphere-Atmosphere Transfer Scheme/BATS”, Dickinson 1984, Geogr. Monogr. AGU)  

Physiological models

(e.g. “Simple Biosphere model 2/

SiB2”, Sellers et al. 1996, J. Climate)  

(Dickinson 1984; Sellers et al. 1997, Science)

Sonia Seneviratne, ETH Zürich

18

Plot-scale processes: Biophysical models

(Dickinson 1984)

• Explicit canopy

• Accounting of main physical processes occurring within the vegetation stand and in the soil

• Evaporation from 3 distinct sources (interception layer, bare soil

evaporation, plant transpiration)

• Leaf and snow drip

• Infiltration

• Percolation

Sonia Seneviratne, ETH Zürich

Total Evapotranspiration ET:

With:

E

: Bare soil evaporation

E

: Evaporation from interception storage (on soil and vegetation surface) E

: Snow sublimation

TR: Plant transpiration ET = E _b + E _i + E _s + TR

Plot-scale processes: Biophysical models

(Dickinson 1984)

Sonia Seneviratne, ETH Zürich

20

Plot-scale processes: Biophysical models

(Dickinson 1984)

• Explicit canopy

• Accounting of main physical processes occurring within the vegetation stand and in the soil

• Evaporation from 3 distinct sources (interception layer, bare soil

evaporation, plant transpiration)

• Leaf and snow drip

• Infiltration

• Percolation

Root zone Surface layer

allows long-term climate memory

(1 m, i.e. ca 500mm water storage

vs. 150mmn for bucket model)

Sonia Seneviratne, ETH Zürich

Plot-scale processes: Biophysical models

(Dickinson 1984)

• Explicit canopy

• Accounting of main physical processes occurring within the vegetation stand and in the soil

• Evaporation from 3 distinct sources (interception layer, bare soil

evaporation, plant transpiration)

• Leaf and snow drip

• Infiltration

• Percolation

• Biophysical control of

evapotranspiration through stomatal resistance r

stomate

Stomate density:

10‘000 - 100‘000 / cm

Sonia Seneviratne, ETH Zürich

22

Plot-scale processes: Biophysical models

!

E = " # ^q

( ) ^T

^{$ q}

r

%

&

' (

) * = # E

Bucket model

Biophysical models (transpiration at leaf level)

!

E

= " q

( ) T

^{# q}

r

+ r

$

%

&

&

' ( ) )

Biophysical models (transpiration for whole canopy)

!

E

= " q

( ) T

^{# q}

r

+ r

$

%

&

&

' ( ) )

T

temperature of the foliage

q

water-vapour specific humidity of the air within the canopy r

aerodynamic resistance to moisture and heat transfer through

the boundary layer at the leaf surface r

stomatal resistance

r

canopy resistance (integral mean of the resistance of the individual leaves assumed to act in parallel)

g_s stomatal conductance (1/r_s) g_c canopy conductance (1/r_c) LAI leaf area index

Sonia Seneviratne, ETH Zürich

Plot-scale processes: Biophysical models

(Sellers 1997)

!

r _s = r _{s min} f (PAR, T, W , " e) ^r

minimum stomatal resistance PAR photosynthetically active radiation

T temperature

W soil moisture

# e vapour pressure deficit

g_s stomatal conductance (1/r_s) g_c canopy conductance (1/r_c) LAI leaf area index

#l leaf water potential

Sonia Seneviratne, ETH Zürich

24

Plot-scale processes: Biophysical models

Biophysical models: overview

• much more detailed representation of land surface processes than for the bucket model

• main shortcomings of bucket model are addressed

• explicit representation of vegetation control (stomatal resistance)

• geographical variation of relevant parameters (rooting depth, albedo, minimum

stomatal resistance, soil hydrological parameters, ...) depending on vegetation and soil types (look-up tables)

• however:

• no explicit representation of carbon assimilation/photosynthesis, i.e. no impact of CO

concentration on evapotranspiration (may be relevant for climate change)

• high dependence of evapotranspiration on r

and calibrated curves representing dependence of r

on PAR, T, W, $e (based on few observations)

! State-of-the-art representation of land surface processes in current climate

models

Sonia Seneviratne, ETH Zürich

Levels of complexity: Plot-scale processes

“Bucket” model

(Manabe 1969, Mon. Weather Rev.)

Biophysical models

(e.g. “Biosphere-Atmosphere Transfer Scheme/BATS”, Dickinson 1984, Geogr. Monogr. AGU)  

Physiological models

(e.g. “Simple Biosphere model 2/

SiB2”, Sellers et al. 1996, J. Climate)  

(Dickinson 1984; Sellers et al. 1997, Science)

Sonia Seneviratne, ETH Zürich

26

Plot-scale processes: Physiological models

• (e.g. SiB2, Sellers et al. 1996)

• Explicit simulation of plant photosynthesis (including nutrients uptake, enzyme kinetics, electron transport, and light interception by chloroplasts in plant leaves)

• Leaf conductance g

(1/r

) is computed as a function of net CO

assimilation (Ball 1988)

• Canopy carbon and water fluxes are computed simultaneously and consistently

(Sellers 1997)

Sonia Seneviratne, ETH Zürich

Plot-scale processes: Physiological models

• Leaf conductance g

(1/r

) is computed as a function of net CO

assimilation (Ball 1988)

(Sellers 1997)

!

g

= m A

c

h

p + b

m empirical coefficient derived from observations

A

net CO

assimilation

c

CO

concentration at the leaf surface h

relative humidity at the leaf surface p atmospheric pressure

b minimum value of g

A assimilation

R_d leaf respiration rate A - R_d net flux of CO₂

Sonia Seneviratne, ETH Zürich

28

Physiological models: overview

• allow the explicit representation of carbon assimilation/photosynthesis

• important in the context of climate change (CO

-water relationships, carbon cycle, ...)

• however:

• do also depend on calibrated relationships and few observations

• some uncertainty remains: i.e. CO

fertilization, enhanced water-use efficiency,...

(several on-going field experiments)

• (SiB2) phenology/vegetation activity specified from remote sensing measurements (i.e. no data for time periods without measurements)

• still “big-leaf” approach: no accounting of subgrid-scale heterogeneity

Plot-scale processes: Physiological models

Sonia Seneviratne, ETH Zürich

Land surface models: Levels of complexity

Plot-scale processes

“bucket” model biophysical models

physiological models

(photosynthesis, biochemistry)

Land dynamics specified or a fixed function of e.g. T

dynamic vegetation not accounted

for (“big leaf approach”)

“mosaic” approach

(horizontal heterogeneity of land cover) TOPMODEL, catchment land surface model, ...

(impact of topography/catchments’

definition on runoff generation) Subgrid-scale

heterogeneity

phenology (observed/

modeled)

Sonia Seneviratne, ETH Zürich

30

Subgrid-scale heterogeneity: Mosaic approach

“Mosaic” approach (Koster and Suarez 1992)

(P. Houser 2006)

Sonia Seneviratne, ETH Zürich

Subgrid-scale heterogeneity: Mosaic approach

ECMWF land surface scheme “TESSEL” (“tiles”)

Sonia Seneviratne, ETH Zürich

32

Subgrid-scale heterogeneity: Mosaic approach

Issues: How about forcing (precipitation, radiation) downscaling? How about feedbacks?

“extended mosaic” approach (Molod et

al. JHM 2003) % extension of standard

mosaic approach within planetary

boundary layer

Lateral soil water flux due to topography Beven and Kirkby 1979; Walko et al. 2000

Subgrid-scale heterogeneity: TOPMODEL approach

(R. Stöckli)

(P. Houser 2006)

Partitioning between

evapotranspiration and runoff &

depth of water table dependent on topography within grid cell/computational unit

Sonia Seneviratne, ETH Zürich

34

Subgrid-scale heterogeneity: TOPMODEL approach

CLSM: Catchment-based land surface model (Koster et al. 2000):

• uses the hydrological catchment as computational unit

• avg. catchment size: 50km

• range: 10-150km

• TOPMODEL approach for runoff, MOSAIC approach for evapotranspiration

(S. Mahanama; R.D. Koster)

Sonia Seneviratne, ETH Zürich

Land surface models: Levels of complexity

Diurnal processes “bucket” model biophysical models

physiological models

(photosynthesis, biochemistry)

Land dynamics specified or a fixed function of e.g. T

dynamic vegetation not accounted

for (“big leaf approach”)

“mosaic” approach

(horizontal heterogeneity of land cover) TOPMODEL, catchment land surface model, ...

(impact of topography/catchments’

definition on runoff generation) Subgrid-scale

heterogeneity

phenology (observed/

modeled)

Sonia Seneviratne, ETH Zürich

36

Land dynamics: Phenology

(Reto Stöckli, NASA / ETH Zürich)

Sonia Seneviratne, ETH Zürich

Land dynamics: Phenology

Biophysical models: Simple representation of phenology effects with

dependence on temperature (e.g. BATS, Dickinson 1984; Dickinson et al. 1993)

ok as first approximation (both temperature and soil moisture effects are

included to some extent)

Sonia Seneviratne, ETH Zürich

38

Land dynamics: Phenology

Other approaches:

• Specify phenology from observations (e.g. NDVI): captures interannual variations, however no mechanistic representation

• For e.g. climate-change scenarios mechanistic model is preferable (e.g. growing

degree days, ...)

Sonia Seneviratne, ETH Zürich

Dynamic Global Vegetation Models (DGVMs)

Recent development: Dynamic Global Vegetation Models

• include full vegetation dynamics (phenology, mortality, fire disturbance, competition, vegetation shifts, ...)

• may be particularly relevant within climate change (climate shifts as a consequence of global warming)

[" t raditional LSMs: land cover maps, no disturbances]

Sonia Seneviratne, ETH Zürich

40

Dynamic Global Vegetation Models (DGVMs)

(http://www.pik-potsdam.de/lpj/;

Sitch et al. 2003)

Sonia Seneviratne, ETH Zürich

(Sitch et al. 2003)

Dynamic Global Vegetation Models (DGVMs)

LPJ

Sonia Seneviratne, ETH Zürich

42

The Development of Climate Models

(IPCC TAR)

Sonia Seneviratne, ETH Zürich

Land Surface Models: Intercomparison, Data products

Project for the Intercomparison of Land-surface Parameterization Schemes (PILPS) http://pilps.mq.edu.au/

Isotopes in Project for Intercomparison of Land-surface Parameterization (iPILPS) http://ipilps.ansto.gov.au/

Global Soil Wetness Project (GSWP) http://www.iges.org/gswp/

Land Data Assimilation Systems (LDAS) http://ldas.gsfc.nasa.gov/

Land Information System (LIS)

http://lis.gsfc.nasa.gov/