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TERRA Soil Vegetation Atmosphere Transfer across Models and Scales

Explore the TERRA-ICON model featuring the TILE approach, snow models, and external parameters. Understand the physical processes, subgrid surface schemes, and snow effects on energy exchange. Discover the advancements in offline land simulations and intercomparison studies. Uncover the impact of subgrid heterogeneities and snow processes on the hydrological cycle. Validate the model through RMSE comparisons and confront it with reality using external parameters for realistic simulations. Benefit from coupling options and complex surface characterizations for numerical weather prediction and climate applications.

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TERRA Soil Vegetation Atmosphere Transfer across Models and Scales

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  1. TERRASoil Vegetation Atmosphere Transfer across Models and Scales

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

  3. Physicalprocesses RS RT LE H G

  4. Physicalprocesses

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

  6. Components Components

  7. Components

  8. Features of ICON-TERRA • 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

  9. TERRA structure H2 LvE2 H3 LvE3 H1 LvE1 H4 LvE4 H5 LvE5 H6 LvE6 H7 LvE7 0.00-0.01 0.01-0.03 FLake 0.03-0.09 0.09-0.27 0.27-0.81 0.81-2.43 2.43-7.29 7.29-21.87

  10. Sub-gridsurfaceschemes 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) tile approach N dominant classes (e.g. water, snow, grass) mosaic approach surface divided in N subgrid cells (Figure taken from Ament&Simmer, 2006)

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

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

  13. Treatment of Snow

  14. Snow Main effects • 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

  15. Snow agingprocesses Albedo anddensity High AlbedoLow Density Low AlbedoHigh Density

  16. Processes in deepsnow pack • Treatment ofthediurnalcyclefor T2M in deepsnow pack: Limit forthicknessof L1-L2: • 1st layer: 25 cm, one-layerscheme : 1.5 m forheattransfer • 2nd layer: 2 m • 3rd layer: unlimited

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

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

  19. Confrontingthemodelwithreality – Externalparameters

  20. Impact ofexternalparameters LE H H LE

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

  22. Uncertainties: Land-SeaMask Sochi

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

  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 Offline land-surface simulationin the ICON framework J. Helmert, M. Köhler, D. Reinert

  34. Motivation • 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)

  35. What do weneed? Forcing !

  36. What do weneed? Reanalysis 3h-interval H2 LvE2 H3 LvE3 H1 LvE1 H4 LvE4 H5 LvE5 H6 LvE6 SW, LW, p, T, rh, wind, RR 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

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

  38. ECMWF example: Soilmoisture 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. Summary Soil-vegetation atmosphere transfer modeling in ICON • 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 Benefit: SVAT model + external parameters add complex surface characteristics into numerical weather prediction Improves prediction of key weather parameters near the land surface

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