Slide 1 PA Surface III of IV - training course 2013 Slide 1 Introduction General remarks Model...

PA Surface III of IV - training course 2013 Slide 1

Introduction

General remarks

Model development and validation

The surface energy budget

The surface water budget

The surface CO2 budget

Soil heat transfer

Soil water transfer

Snow

Initial conditions

Conclusions and a look ahead

Layout


Soil science miscellany (1)

The soil is a 3-phase system, consisting of

- minerals and organic matter soil matrix

- water condensate (liquid/solid) phase

- moist air trapped gaseous phase

Texture - the size distribution of soil particles

Hillel 1982, 1998


Soil science miscellany (2)

Structure - The spatial organization of the soil particles

Porosity - (volume of maximum air trapped)/(total volume)

Composition

Water content

Hillel 1982,1998

Reference:Hillel 1998 Environmental Soil Physics, Academic Press Ed.


Soil properties

Rosenberg et al 1983

Arya 1988


Skin layer at the interface between soil (snow) and atmosphere; no thermal inertia, instantaneous energy balance

At the interface soil/atmosphere, each grid-box is divided into fractions (tiles), each fraction with a different functional behaviour. The different tiles see the same atmospheric column above and the same soil column below.

If there are N tiles, there will be N fluxes, N skin temperatures per grid-box

There are currently up to 6 tiles over land (N=6)

TESSEL

N,...,1i

i

TTG i,sksi,ski

for tileindex


TESSEL skin temperature equation

Grid-box quantities

4,

,

,

,

, , ,

, ,

(1 )

( )

( , )

( ) 0

i S g T g

h i L

sk i

skp L p

h i L L i L s i sat s

sk i s

i

sk i

sk i

R R

C u C T gz C

C

T

T

u a q a q pT

TT

fraction Tile

,

i

iiskisk

iii

iii

C

TCT

ECE

HCH


Ground heat flux

2

2s

g

T

T

g

Ts

g

z

Tk

t

T

k

Cz

T

zz

G

t

TC

soil, shomogeneou anFor

ydiffusivit Thermal C

tyconductivi Thermal

capacityheat volumetric Soil

In the absence of phase changes, heat conduction in the soil obeys a Fourier law

Boundary conditions:• Top Net surface heat flux• Bottom No heat flux OR prescribed climate


Diurnal cycle of soil temperature

summer

winter

bare sod

Rosenberg et al 1983

50 cm depth

surface

summer


TESSEL

Solution of heat transfer equation with the soil discretized in 4 layers, depths 7, 21, 72, and 189 cm.

No-flux bottom boundary condition

Heat conductivity dependent on soil water

Thermal effects of soil water phase change

↓ 10.6 ~ 55.8 d

↓0.1~0.6 d

↓ 1.1~5.8 d

Time-scale for downward heat transfers in wet/dry soil


TESSEL soil energy equations

0G

TTG

DD5.0

TTG

D

GGTT

t

C

2/41

i1i,ski,sk2/1

1jj

1nj

1n1j

2/1j,T1n2/1j

j

1n2/1j

1n2/1jn

j1n

jj

conditions Boundary

1,...,4j

DjTj

j-1

j+1

Gj+1/2

Gj-1/2


Case study: winter (1)

Model vs observations, Cabauw, The Netherlands, 2nd half of November 1994

Soil

140 m

2 m


Case study: winter (2)Soil Temperature, North Germany, Feb 1996: Model (28-100 cm) vs OBS 50 cm

Observations: Numbers

Model: Contour


Model bias:- Net radiation (Rnet) too large

- Sensible heat (H) too small

But (Tair-Tsk) too large (too large diurnal cycle)

Therefore f(Ri) problem

- Soil does not freeze (soil temperature drops too quickly seasonally)


T air

T skin

Rnet H LE

G

))(( skairHnairp TTRifCUCH

Stability functions

Soil water freezing

Viterbo, Beljaars, Mahfouf, and Teixeira, 1999: Q.J. Roy. Met. Soc., 125,2401-2426.


Winter: Soil water freezing

water frozen Soil

T

I

Iwfs t

Lz

T

zt

T)C(

Soil heat transfer equation in soil freezing condition

)T(f)T(II

z

T

zt

T

T

fL)C( Twfs

Apparent heat capacity



1 Oct 1 Jan1 Dec

1 Nov 1 Feb

Germany soil temperature: Observations vs Long model relaxation integrations

Observations

Control

Stability

Stab+Freezing


Northern Hemisphere


Soil water freezing acts as a thermal regulator in winter, creating a large thermal inertia around 0 C.

Simulations with soil water freezing have a near-surface air temperature 5 to 8 K larger than control.

In winter, stable, situations the atmosphere is decoupled from the surface: large variations in surface temperature affect only the lowest hundred metres and do NOT have a significant impact on the atmosphere.

Europe

850 hPa T RMS forecast errors

Stab+freezing

Control


Introduction

General remarks





Soil heat transfer

Soil water transfer

Snow

Initial conditions


Layout


More soil science miscellany

3 numbers defining soil water properties

- Saturation (soil porosity) Maximum amount of water that the soil can hold when all pores are filled 0.472 m3m-3

- Field capacity “Maximum amount of water an entire column of soil can hold against gravity” 0.323 m3m-3

- Permanent wilting point Limiting value below which the plant system cannot extract any water 0.171 m3m-3

Hillel 1982Jacquemin and Noilhan 1990


Schematics

extractionroot ie k,source/sin water Soil

flux water Soil

water soil 12

33

S

skgmF

mm

Sz

F

t ww

Hillel 1982

Root extraction The amount of water transportedfrom the root system up to the stomata(due to the difference in the osmoticpressure) and then available fortranspiration

Boundary conditions:Top See laterBottom Free drainage or bed rock


Soil water flux

1

12

ty conductivi hydraulic

y diffusivit hydraulic

)(

ms

sm

zF w

Darcy’s law

> 3 orders of

magnitude> 6 orders of

magnitude

Mahrt and Pan 1984


TESSEL: soil water budget

Solution of Richards equation on the same grid as for energy

Clapp and Hornberger (1978) diffusivity and conductivity dependent on soil liquid water

Free drainage bottom boundary condition

Surface runoff, but no subgrid-scale variability; It is based on infiltration limit at the top

One soil type for the whole globe: “loam”


A new hydrology scheme

A spatially variable hydrology scheme is being tested following Van den Hurk and Viterbo 2003

Use of a the Digital Soil Map of World (DSMW) 2003

Infiltration based on Van Genuchten 1980 and Surface runoff generation based on Dümenil and Todini 1992

Van den Hurk and Viterbo 2003


A new hydrology scheme(2)

Dominant soil type from FAO2003 (at native resolution of ~ 10 km)

█coarse █medium █med-fine █fine █very-fine █organic Soil Diffusivity

Soil Conductivity

control

control


TESSEL soil water equations (1)

2/412/412/12/1

2/11

111

2/11

2/1

,

12/1

12/1

1

conditionsBoundary

5.0

ws

jjj

nj

nj

jwnj

jwj

nj

nj

nj

nj

w

FEYTF

DDF

SD

FF

t

Dj

j-1

j+1

Fj+1/2

Fj-1/2

j ↓ 11.7 ~ >> d

↓0.1~19.7 d

↓ 1.2~195.9 d

Time-scale for downward water transfers in wet/dry soil


TESSEL soil water equations (2)

2

32

,/,

,/,//,

tscoefficien Hydraulic

1,...,4j

n vegetatio(H/L) high/lowfor separate j,layer at extractionRoot

b

satsat

satsat

b

satsat

jjliqjLHj

jliqjLHjLHLHjw

b

DR

DRCS


Introduction

General remarks





Soil heat transfer

Soil water transfer

Snow

Initial conditions


Layout


Snow

Snow insulates the ground (30% to 90% of the snow mantle is air)

A snow covered surface has a higher albedo than any other natural surface (0.2-0.3 in the presence of forests, 0.5-0.8 for bare ground/low vegetation)

Snow melting keeps the surface temperature at 0 C for a long period in spring


Snow energy budget

waterliquidoffractionTf

capacitywaterliquidSnowS

G

Q

L

QGHELRt

T

T

TfSLDC

sn

Cl

m

s

msnsn

sn

snClfsn

fluxheat Basal

melting ofHeat

nsublimatio ofheat Latent

fraction Snow

Meltwater

fusion ofheat Latent

sn

f

msn

wffm

C

M

L

t

S

CLMLQ

Apparent heat capacity


Snow mass budget

)(

mass) (snow equivalent water Snow

Snowfall

Snowfall

snowpackthelevingwaterliquidRunoffR

S

F

F

RFcEcFt

S

sn

l

snlsnsn

Snow mass (S) and snow depth (D)

mD

C

SD

sn

snsn

w

area, covered-snow for thedepth Snow

density Snow


Metamorphism, density, albedo, Density

- Weighted average between current density and the density of fresh snow, in case of snowfall

- Overburden, thermal metamorphism (new formulation)

Albedo

- Exponential relaxation with different time scales for melting and non-melting snow

- Surface albedo for high vegetation regions with snow underneath from MODIS

Snow cover fraction

- Function of snow depth (10 cm deep – fully cover)

1.0,1min sn

sn

S

c


Case study: Boreal forest albedo (1)


0

-1

Forecast day0 10

Northern Hemisphere

FAL

CON


Forecast day0 10

-1

0

-2

-3

Eastern Asia

CON

FAL

850 hPa temperature bias20 forecasts every 3 days, March-April 1996

No data assimilation

Considering a lower value of snow Forest ALbedo was beneficial.



The surface albedo is the direct regulator of the energy available to the surface. The albedo of natural surfaces has a limited range (0.1-0.3), but in non-forested snow covered areas can reach values up to 0.8.

Snow covered boreal forests have a much lower albedo than grassland to their south and tundra to their north; the presence of boreal forests has a direct control on the climate of high-latitudes.

1997Operational

Bias(FAL)

1996Operational

Bias(CON)

PA Surface III of IV - training

course 2013

30R1

32R1

Dutra et al. 2008

Slide 34

Snow sublimation, role of roughness


Snow insulation, role of snow density (1)New snow formulation improves

snow mass and snow density.

Lower snow densities -> increased snow thermal insulation

Dutra et al. 2010


Snow insulation, role of snow density (2)

Increased snow thermal insulation

Stronger decoupling of land –atmosphere

Warm T2M bias are reduced (soil was warming the atmosphere)

Clear impact on long climate runs

Small impact on short range forecasts

Hazeleger et al 2010

Liquid Water in the snow-pack

A diagnostic formulation can take into account liquid water in the snow pack without new prognostic variable (similar to soil ice)

Slide 37PA Surface III of IV - training course 2013


course 2013

Liquid Water in the snow-pack (II)

The liquid reservoir is function of temperature, snow mass and snow density

Slide 38


course 2013

Snow density: I. New snow

Slide 39


course 2013

Snow density: II. Snow on the ground

In the old scheme rainfall infiltrates directly into the soil even in presence of snow on the ground. In the new snow scheme it is intercepted by the snow-pack

Slide 40


course 2013

Forest-Snow Albedo

Albedo of Forest areas with snow underneath was fixed to 0.15 (quite dark). A MODIS-derived vegetation dependent albedo is used.

Slide 41


course 2013

Open-Area Snow Albedo

Open area albedo is aging from 0.85 in case of fresh snow to 0.5 for old snow, however in case of new snowfall the albedo was istantaneously re-whitened to 0.85, regardless of the snowfall amount.

Slide 42


course 2013

Snow fractionSnow fraction is made dependent of snow

depth rather than snow mass.

A 10cm snow depth is required for full coverage.

Slide 43


course 2013

Verification

Slide 44


course 2013

SNOW-MIP2

Slide 45


course 2013

GSWP-2 and Runoff verification

Slide 46

MODIS albedo as snow verification product

Slide 47PA Surface III of IV - training course 2013


course 2013

Summary and conclusions for snow processes impactThe new snow scheme first validated in offline simulations (SNOW-MIP2

and GSWP2) and showed improvements on the water cycle and on the land albedo

Further tests in forecasts mode both at ECMWF and in a climate model have demonstrated that snow can improve near-surface temperatures in cold climate.

The introduction of multi-layer snow scheme is recognized as high priority in the next years to improve the representation of the diurnal cycle and introduce the right level of decoupling between snow and atmosphere.

Perspectives for snow modelling

Slide 48

Slide 1 PA Surface III of IV - training course 2013 Slide 1 Introduction General remarks Model...

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