1. TERRA TERRA Soil Vegetation Atmosphere Transfer across Models and Scales

2. Main features of the TERRA ICON version TILE approach, Multi-layer snow model External parameters for ICON Offline land simulations - structure Outline

3. H LE RTRT RSRS G Physical processes

5. Model RMSE: ICON vs. GME for Europe, June 2012 PSDD FF T2M TD2M

6. Components Modeling componentCurrent status Surface energy balanceSurface 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 transfers7-layer soil model + 1 climate layer Layer-depth between 1 cm and 14.58 m Solution of the heat conduction equation Bugfix Frozen soilsTemperature and soil type dependent computation of fractional freezing/melting of total soil water content in 6 active soil layers VegetationOne-layer – Evapotranspiration after Dickinson (1984) – interception reservoir Components

7. Modeling componentCurrent status Snow One layer – prognostic variables : snow temperature, snow water equivalent, snow density, snow albedo Multi-layer snow model Freshwater LakesFLake Sea-iceSea-ice model OceanPrescribed surface temperature (analysis) Charnock formulation for roughness length Urban areas Modified surface roughness, leaf area index, plant coverage Detailed consideration possible Surface boundary layerApplication of the turbulence scheme at the lower model boundary and iterative interpolation – Consideration of TILES Components

8. 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-TERRA

9. 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 H1H1 LvE1LvE1 H2H2 LvE2LvE2 H3H3 LvE3LvE3 H7H7 LvE7LvE7 H4H4 LvE4LvE4 H5H5 LvE5LvE5 H6H6 LvE6LvE6

10. T IE/MOSAIC Account 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) Sub-grid surface schemes

11. Example Lindenberg area (Figure taken from Ament, 2006)

12. Model RMSE: Impact from TILES for Europe, June 2012 1 TILE 3 TILES T2M TD2M PS DD FF PS DD FF T2M TD2M

13. Treatment of Snow

14. 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

15. High Albedo Low Density Low Albedo High Density Snow aging processes Albedo and density

16. Processes 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

17. Processes in deep snow pack Model Bias No-Tiles nlev_snow=3 One-layer snow scheme Multi-layer snow scheme T2M TD2M PS DD FF T2M TD2M PS DD FF

18. Processes in deep snow pack Model RMSE No-Tiles nlev_snow=3 One-layer snow scheme Multi-layer snow scheme T2M TD2M PS DD FF T2M TD2M PS DD FF Multi-layer snow scheme performs as well as single layer scheme for deep snow pack

19. Confronting the model with reality – External parameters

20. H LE H Impact of external parameters

21. Numerical Weather Prediction and Climate Application external parameters on target grid orography GLOBE ASTER soil data DSMW HWSD land use (GLC2000, GLCC, GlobCover) Process Chain

22. Sochi Uncertainties: Land-Sea Mask

23. GLC2000 land use classes (currently used to derive land-sea mask) Globcover 2009 GLCC USGS land use / land cover system Uncertainties: Land-Sea Mask

24. GLOBE Orography: HSURF

25. Orography & Land use: Z0

26. Land use: LAI_MAX

27. Land use: Evergreen Forest

28. Land use: Surface Emissivity

29. Albedo-MODIS: ALB_DIFF CLIM

30. Soil-DMSW: Soil Type

31. Soil-CRU: T_CL

32. Lakes: Lake depth

33. L a ICON d L a ICON d Offline land-surface simulation in the ICON framework J. Helmert, M. Köhler, D. Reinert

34. 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) Motivation

35. What do we need? Forcing !

36. 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 H1H1 LvE1LvE1 H2H2 LvE2LvE2 H3H3 LvE3LvE3 H4H4 LvE4LvE4 H5H5 LvE5LvE5 H6H6 LvE6LvE6 What do we need? Reanalysis 3h-interval SW, LW, p, T, rh, wind, RR

37. ECMWF example: Fluxes Balsamo et al. (2012): ERA Report Series No. 13

38. ECMWF example: Soil moisture Balsamo et al. (2012): ERA Report Series No. 13 TESSEL ERA-Interim/Land ERA-Interim

39. ECMWF example: Snow Balsamo et al. (2012): ERA Report Series No. 13

40. 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 Summary Benefit: SVAT model + external parameters add complex surface characteristics into numerical weather prediction Improves prediction of key weather parameters near the land surface