TERRATERRA

Soil Vegetation Atmosphere Transfer across

Models and Scales

• Main features of the TERRA ICON version

• TILE approach,

• Multi-layer snow model

• External parameters for ICON • Offline land simulations - structure

OutlineOutline

H

LE RT

RS

G

Physical processesPhysical processes

Physical processesPhysical processes

Model RMSE: Model RMSE: ICONICON vs. GME vs. GMEfor Europe, June 2012for Europe, June 2012

PSPS DDDD FFFF

T2MT2M TD2MTD2M

Components

Modeling component Current status

Surface energy balance • Surface temperature is area weighted average of temperature of snow covered and snow free surface fraction

• TILE-Approach for land points using 23 land use classes + snow

Soil transfers 7-layer soil model + 1 climate layerLayer-depth between 1 cm and 14.58 mSolution of the heat conduction equation Bugfix

Frozen soils Temperature and soil type dependent computation of fractional freezing/meltingof total soil water content in 6 active soil layers

Vegetation One-layer – Evapotranspiration after Dickinson (1984) – interception reservoir

ComponentsComponents

Modeling component Current status

Snow • One layer – prognostic variables : snow temperature, snow water equivalent, snow density, snow albedo• Multi-layer snow model

Freshwater Lakes FLake

Sea-ice Sea-ice model

Ocean Prescribed surface temperature (analysis)Charnock formulation for roughness length

Urban areas • Modified surface roughness, leaf area index, plant coverage • Detailed consideration possible

Surface boundary layer Application of the turbulence scheme at the lower model boundary and iterative interpolation – Consideration of TILES

ComponentsComponents

• Based on TERRA from the COSMO model

• Main developments for ICON:

• Treatment of subgrid heterogeneities using a TILE approach,

• Improved multi-layer snow model • ICON interface structure developments to enable offline land

simulations

• Implementation and validation, intercomparison studies with ECMWF HTESSEL

Features of ICON-TERRAFeatures of ICON-TERRA

TERRA structure TERRA structure

0.00-0.01

0.01-0.03

0.03-0.09

0.09-0.27

0.27-0.81

0.81-2.43

2.43-7.29

7.29-21.87

FLake

H1 LvE1 H2 LvE2 H3 LvE3 H7LvE7H4 LvE4 H5 LvE5 H6 LvE6

T IE/MOSAICAccount for non-linear effects of sub-grid inhomegeneities at surface on the exchange of energy and moisture between atmosphere and surface (cf. Ament&Simmer, 2006)

mosaic approach

surface divided in N subgrid cells

tile approach

N dominant classes

(e.g. water, snow, grass)

(Figure taken from

Ament&Simmer, 2006)

if

if1

1

i

N

i

f

Nfi

1

Sub-grid surface schemes Sub-grid surface schemes

Example Lindenberg areaExample Lindenberg area

(Figure taken from

Ament, 2006)

Model RMSE: Impact from TILESModel RMSE: Impact from TILESfor Europe, June 2012for Europe, June 2012

1 TILE1 TILE

3 TILES3 TILES

T2MT2M TD2MTD2M

PSPSDDDD FFFF

PSPSDDDD FFFF

T2MT2M TD2MTD2M

Treatment of SnowTreatment of Snow

Snow Snow

Insulation effect: Decoupling of soil from atmosphere (30%-90% of the snow mantle is air)

Albedo Effect: Higher albedo than any other natural surface (0.4-0.85 for bare ground/low vegetation, 0.2-0.33 for snow in forests)

Snow melting prevents rise of surface temperature above 0°C for a long period in spring – impact on hydrological cycle and energy budget at surface

Snow Model

One layer – prognostic variables : snow temperature, snow water equivalent, snow density, snow albedo

Multi-layer – Vertical profiles in snow pack; considers equations for the snow albedo, snow temperature, density, total water content and content of liquid water. Therefore phase transitions in the snow pack are included.

G. Balsamo, 2007

Main effects

High AlbedoLow Density

Low AlbedoHigh Density

Snow aging processesSnow aging processesAlbedo and densityAlbedo and density

Processes in deep snow packProcesses in deep snow pack

Treatment of the diurnal cycle for T2M in deep snow pack: Limit for thickness of L1-L2:•1st layer: 25 cm, one-layer scheme : 1.5 m for heat transfer•2nd layer: 2 m•3rd layer: unlimited

Processes in deep snow packProcesses in deep snow pack

Model Bias

No-Tilesnlev_snow=3

One-layersnow scheme

Multi-layersnow scheme

T2MT2M TD2MTD2M

PSPSDDDD FFFF

T2MT2M TD2MTD2M

PSPSDDDD FFFF

Processes in deep snow packProcesses in deep snow pack

Model RMSE

No-Tilesnlev_snow=3

One-layersnow scheme

Multi-layersnow scheme

T2MT2M TD2MTD2M

PSPSDDDD FFFF

T2MT2M TD2MTD2M

PSPSDDDD FFFF

Multi-layer snow scheme performs as well as single layer scheme for deep snow pack

Confronting the model Confronting the model with reality with reality

––External parametersExternal parameters

HLE

H LE

Impact of external parametersImpact of external parameters

Numerical Weather Prediction and Climate Application

externalparametersontarget grid

orographyGLOBEASTER

soil dataDSMWHWSD

land use(GLC2000,GLCC,GlobCover)

Process ChainProcess Chain

Sochi

Uncertainties: Land-Sea MaskUncertainties: Land-Sea Mask

GLC2000 land use classes(currently used to derive land-sea mask)

Globcover 2009

GLCC USGS land use / land cover system

Uncertainties: Land-Sea MaskUncertainties: Land-Sea Mask

GLOBE Orography: HSURFGLOBE Orography: HSURF

Orography & Land use: Z0Orography & Land use: Z0

Land use: LAI_MAXLand use: LAI_MAX

Land use: Evergreen ForestLand use: Evergreen Forest

Land use: Surface EmissivityLand use: Surface Emissivity

Albedo-MODIS: ALB_DIFF CLIMAlbedo-MODIS: ALB_DIFF CLIM

Soil-DMSW: Soil TypeSoil-DMSW: Soil Type

Soil-CRU: T_CLSoil-CRU: T_CL

Lakes: Lake depthLakes: Lake depth

LL a a ICOICONN d d

Offline land-surface simulationin the ICON framework

J. Helmert, M. Köhler, D. Reinert

• Existing land-surface reanalysis: ERA-Interim/Land, MERRA-Land

• State-of-the-art land-surface datasets covering the most recent decades for consistent land initial condition to NWP and climate

• Idea: Analysis-driven land-surface simulations for SVAT model development

• Benefit: Easy to test changes in land processes, which need long spinup times (snow, soil temperature/water/ice, vegetation)

MotivationMotivation

What do we need?What do we need?

Forcing !Forcing !

0.00-0.010.01-0.030.03-0.09

0.09-0.27

0.27-0.81

0.81-2.43

2.43-7.29

7.29-21.87

H1 LvE1 H2 LvE2 H3 LvE3 H4 LvE4 H5 LvE5 H6 LvE6

What do we need?What do we need?

Reanalysis 3h-intervalReanalysis 3h-intervalSW, LW, p, T, rh, wind, RR

ECMWF example: FluxesECMWF example: FluxesBalsamo et al. (2012): ERA Report Series No. 13Balsamo et al. (2012): ERA Report Series No. 13

ECMWF example: Soil moistureECMWF example: Soil moistureBalsamo et al. (2012): ERA Report Series No. 13Balsamo et al. (2012): ERA Report Series No. 13

TESSELERA-Interim/LandERA-Interim

ECMWF example: SnowECMWF example: SnowBalsamo et al. (2012): ERA Report Series No. 13Balsamo et al. (2012): ERA Report Series No. 13

Link between atmosphere and soil by exchange of fluxes of heat, moisture, and momentum – New: with TILE approach

Demand for realistic surface and soil characteristics – external

parameters

Flexible ICON interface structure offers several coupling options:

TERRA-ICON into COSMO, Offline SVAT-Mode, 3rd party SVAT

Soil-vegetation atmosphere transfer modeling in ICON

SummarySummary

Benefit: SVAT model + external parameters add complex surface characteristics into numerical weather prediction

Improves prediction of key weather parameters near the land surface