terra terra soil vegetation atmosphere transfer across models and scales
TRANSCRIPT
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