Evaluating modifications of the soil module terra
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Evaluating modifications of the soil module TERRA. Felix Ament, MeteoSwiss. COSMO General Meeting, September 2007. Dry soil moisture bias. OPRerational COSMO, two-layer version. Testsuite, multi-layer version. Soil moisture. T2m. Strong dry out bias!. Negative effect on T2m forecast.

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Evaluating modifications of the soil module TERRA

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Evaluating modifications of the soil module terra

Evaluating modifications of the soil module TERRA

Felix Ament,

MeteoSwiss

COSMO General Meeting, September 2007


Dry soil moisture bias

Dry soil moisture bias

OPRerational COSMO, two-layer version

Testsuite,multi-layer version

Soil moisture

T2m

  • Strong dry out bias!

  • Negative effect on T2m forecast.


Handling the dry out problem

Handling the dry out problem

ECMWF.


Design of terra standalone experiments

Design of TERRA standalone experiments

  • Atmospheric Forcing: COSMO analysis data

  • Domain: see left; 64x61 gridpoints at 7km resolution

  • Period: year 2006 plus December 2005 for spin up

  • Initialization: Operational COSMO analysis

Meteorological Forcing: T, p, u, q, Qdown

COSMO analysis

Precipitation RR

SVAT „TERRA“

  • Simulation of

  • Energy balance

  • Soil processes

  • Annual cycle of vegetation

Working in the dark – nearly no or insufficient observations!

time


Nudged mulitlayer versus two layer analysis of the water budget

Rain

Evaporation

Surface Runfoff

Snow

Intermediate Runfoff

SM

Ground Runfoff

Nudged mulitlayer versus two layerAnalysis of the water budget

Features of “Nudged Multilayer”:

  • Despite Nudging, LE is reduced in July/August and Tmax is higher.

  • Most of the nudged water (=residuum) is converted into runoff.

  • Remarkable: Less precipitation.

Nudged multilayerOperational 2-layer


Ctl standalone versus opr 2 layer

CTL standalone versus OPR 2-layer

Features of “CTL standalone”:

  • Again, reduced LE in July / August (no response in T_2m due to external forcing)

  • Dry out in summer, but recovers until the end of the year.

  • Higher runoff.

  • Do we really have a dry-out problem?

  • Probably, the T_2m diagnosis is misleading?

Doubts


Sensitivity experiments

Sensitivity experiments

Lower boundary

Drainage &diffusion

Vegetation

Exchange


Lower boundary condition i concepts

Lower Boundary Condition I- concepts

RIGIDGWATER

dry

wet

medium

rigid lid

Free drainage

ground water


Lower boundary condition ii ground water condition

Lower Boundary Condition IIGround water condition

GWATER

Problem: Definition of soil moisture gradient at top of water

Solution: Solve Darcy equation with these simplifications:

  • F is constant below centre of lowest layer

  • D is constant there, too

  • K varies only linearly with Q :


Drainage and capillary rise i

Drainage and capillary rise I

BROOKS1BROOKS2

  • CTL: Rijtema (1969), e.g. for drainage K:

  • Brooks and Corey (1964) – much more popular

  • However, Brooks and Corey formulation requires three parameters to derive drainage and capillary rise (depending on soil moisture) – they are not well defined.

    • BROOKS1: 6 type DWD soil classification; lookup table adopted from R. Grasselt (UBonn)

    • BROOKS2: 6 type DWD soil classification; lookup table from J. Helmert (DWD) adopted from Shao and Irannejad (1999)


Drainage and capillary rise ii

Drainage and capillary rise II

Ecoclimap

  • PEDO

  • fields of soil pro-perties (e.g. pore volume)

Rawls and Brakensiek, 1989

DWD classification

USDA classification

  • BROOKS3

  • 11 classes

  • Lookup by Shao

  • not fully done!

  • ECOSOIL

  • 6 classes

  • Lookup table by DWD


Drainage and capillary rise iii

Runoff_g

Drainage and capillary rise III

MACROPOR

Marcopores

  • help to infiltrate water rapidly during rainfall

  • might avoid runoff generation of saturated top layer

Parameterization (adopted from VEG3d, e.g. Braun 2002)

mit Fmax=10 und Qmin=0.5.


Vegetation i

Vegetation I

VEGPARA

  • Minimal / maximal stomatal resistance as well as plant albedo have constant value in TERRA CTL

  • VEGPARA uses spatially varying values depending on land-use

CTL

CTL


Vegetation ii

Vegetation II

ECOVEG

External vegetation parameters prescribed by ECOCLIMAP dataset (Mason et al., 2002):

  • Exhibits more variabilty

  • Systematic higher root depth

  • More detailed seasonal cycle (not shown)

(all maps are valid for July)


Evaluating modifications of the soil module terra

Vegetation III

ROOTDIST

ROOTDIST

  • Linear root depth distribution

CTL

  • Uniform root depth

Recipe

  • Diagnose soil moisture stress function fSM,loc for each layer separately

  • Determine mean SM stress by average weighted by layer thickness Dz and root density rroot

  • Extract transpired water proportional to fSM,loc Dz rroot


Atmospheric exchange i

Atmospheric exchange I

ZOLOC

Local roughness length z0,local

  • CTL roughness depends not only on local conditions, but also on variance of orography to account for gravity wave drag.

     Very high roughness length over mountainous areas.


Atmospheric exchange ii

Atmospheric exchange II

NP89

Top Layer SM at Lindenberg

Dickinson, 1984: BATS scheme

Designed for a two layer soil module!

Noilhan and Platon, 1989 (NP89): ISBA scheme, Meso-NH


Result i bare soil evaporation np89

Rain

Evaporation

Surface Runfoff

Snow

Intermediate Runfoff

SM

Ground Runfoff

Result I - bare soil evaporation NP89

  • Significant reduction of Evaporation during spring and fall, …

  • … but no effect during summer!


Result ii budget summary

Rain

Evaporation

Surface Runfoff

Snow

Intermediate Runfoff

SM

Ground Runfoff

Result II – Budget Summary

Deviations in mm


Conclusions

Conclusions

  • COSMO TERRA-ML is very robust; modifications have in general surprisingly small impact

  • TERRA-ML standalone has proven to be useful tool to asses the midterm effect of model modification.

  • However, objective decisions about implementation of modification is difficult, due to lack of observational data.

  • Scientifically the following modification can reasonably be recommended:

    • NP89 (removes high evaporation in spring & fall)

    • VEGPARA (better representation of forest)

    • (GWATER (counteracting dry-out))

    • (BROOKSX (being state-of-the-art))

  • Outlook:

    • Cross studies (e.g. BROOKS and GWATER)

    • Long term integration to reach model balance.

    • Combination with improved T_2m diagnosis.


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