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Slide 1 PA Surface III of IV - training course 2013 Slide 1 Introduction General remarks Model development and validation The surface energy budget The.

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Presentation on theme: "Slide 1 PA Surface III of IV - training course 2013 Slide 1 Introduction General remarks Model development and validation The surface energy budget The."— Presentation transcript:

1 Slide 1 PA Surface III of IV - training course 2013 Slide 1 Introduction General remarks Model development and validation The surface energy budget The surface water budget The surface CO2 budget Soil heat transfer Soil water transfer Snow Initial conditions Conclusions and a look ahead Layout

2 Slide 2 PA Surface III of IV - training course 2013 Slide 2 Soil science miscellany (1) The soil is a 3-phase system, consisting of -minerals and organic mattersoil matrix -watercondensate (liquid/solid) phase -moist air trappedgaseous phase Texture - the size distribution of soil particles Hillel 1982, 1998

3 Slide 3 PA Surface III of IV - training course 2013 Slide 3 Soil science miscellany (2) Structure - The spatial organization of the soil particles Porosity - (volume of maximum air trapped)/(total volume) Composition Water content Hillel 1982,1998 Reference: Hillel 1998 Environmental Soil Physics, Academic Press Ed.

4 Slide 4 PA Surface III of IV - training course 2013 Slide 4 Soil properties Rosenberg et al 1983 Arya 1988

5 Slide 5 PA Surface III of IV - training course 2013 Slide 5 Skin layer at the interface between soil (snow) and atmosphere; no thermal inertia, instantaneous energy balance At the interface soil/atmosphere, each grid-box is divided into fractions (tiles), each fraction with a different functional behaviour. The different tiles see the same atmospheric column above and the same soil column below. If there are N tiles, there will be N fluxes, N skin temperatures per grid-box There are currently up to 6 tiles over land (N=6) TESSEL

6 Slide 6 PA Surface III of IV - training course 2013 Slide 6 TESSEL skin temperature equation Grid-box quantities

7 Slide 7 PA Surface III of IV - training course 2013 Slide 7 Ground heat flux In the absence of phase changes, heat conduction in the soil obeys a Fourier law Boundary conditions: TopNet surface heat flux BottomNo heat flux OR prescribed climate

8 Slide 8 PA Surface III of IV - training course 2013 Slide 8 Diurnal cycle of soil temperature summer winter bare sod Rosenberg et al cm depth surface summer

9 Slide 9 PA Surface III of IV - training course 2013 Slide 9 TESSEL Solution of heat transfer equation with the soil discretized in 4 layers, depths 7, 21, 72, and 189 cm. No-flux bottom boundary condition Heat conductivity dependent on soil water Thermal effects of soil water phase change 10.6 ~ 55.8 d 0.1~0.6 d 1.1~5.8 d Time-scale for downward heat transfers in wet/dry soil

10 Slide 10 PA Surface III of IV - training course 2013 Slide 10 TESSEL soil energy equations DjDj TjTj j-1 j+1 G j+1/2 G j-1/2

11 Slide 11 PA Surface III of IV - training course 2013 Slide 11 Case study: winter (1) Model vs observations, Cabauw, The Netherlands, 2 nd half of November 1994 Soil 140 m 2 m

12 Slide 12 PA Surface III of IV - training course 2013 Slide 12 Case study: winter (2) Soil Temperature, North Germany, Feb 1996: Model ( cm) vs OBS 50 cm Observations: Numbers Model: Contour

13 Slide 13 PA Surface III of IV - training course 2013 Slide 13 Model bias: -Net radiation (Rnet) too large -Sensible heat (H) too small But (Tair-Tsk) too large (too large diurnal cycle) Therefore f(Ri) problem -Soil does not freeze (soil temperature drops too quickly seasonally) Case study: winter (3) T air T skin Rnet H LE G Stability functions Soil water freezing Viterbo, Beljaars, Mahfouf, and Teixeira, 1999: Q.J. Roy. Met. Soc., 125,

14 Slide 14 PA Surface III of IV - training course 2013 Slide 14 Winter: Soil water freezing Soil heat transfer equation in soil freezing condition Apparent heat capacity

15 Slide 15 PA Surface III of IV - training course 2013 Slide 15 Case study: winter (4) 1 Oct 1 Jan 1 Dec 1 Nov 1 Feb Germany soil temperature: Observations vs Long model relaxation integrations Observations Control Stability Stab+Freezing

16 Slide 16 PA Surface III of IV - training course 2013 Slide 16 Northern Hemisphere Case study: winter (5) Soil water freezing acts as a thermal regulator in winter, creating a large thermal inertia around 0 C. Simulations with soil water freezing have a near-surface air temperature 5 to 8 K larger than control. In winter, stable, situations the atmosphere is decoupled from the surface: large variations in surface temperature affect only the lowest hundred metres and do NOT have a significant impact on the atmosphere. Europe 850 hPa T RMS forecast errors Stab+freezing Control

17 Slide 17 PA Surface III of IV - training course 2013 Slide 17 Introduction General remarks Model development and validation The surface energy budget The surface water budget The surface CO2 budget Soil heat transfer Soil water transfer Snow Initial conditions Conclusions and a look ahead Layout

18 Slide 18 PA Surface III of IV - training course 2013 Slide 18 More soil science miscellany 3 numbers defining soil water properties -Saturation (soil porosity)Maximum amount of water that the soil can hold when all pores are filled0.472 m 3 m -3 -Field capacityMaximum amount of water an entire column of soil can hold against gravity0.323 m 3 m -3 -Permanent wilting pointLimiting value below which the plant system cannot extract any water0.171 m 3 m -3 Hillel 1982 Jacquemin and Noilhan 1990

19 Slide 19 PA Surface III of IV - training course 2013 Slide 19 Schematics Hillel 1982 Root extraction The amount of water transported from the root system up to the stomata (due to the difference in the osmotic pressure) and then available for transpiration Boundary conditions: TopSee later BottomFree drainage or bed rock

20 Slide 20 PA Surface III of IV - training course 2013 Slide 20 Soil water flux Darcys law > 3 orders of magnitude > 6 orders of magnitude Mahrt and Pan 1984

21 Slide 21 PA Surface III of IV - training course 2013 Slide 21 TESSEL: soil water budget Solution of Richards equation on the same grid as for energy Clapp and Hornberger (1978) diffusivity and conductivity dependent on soil liquid water Free drainage bottom boundary condition Surface runoff, but no subgrid-scale variability; It is based on infiltration limit at the top One soil type for the whole globe: loam

22 Slide 22 PA Surface III of IV - training course 2013 Slide 22 A new hydrology scheme A spatially variable hydrology scheme is being tested following Van den Hurk and Viterbo 2003 Use of a the Digital Soil Map of World (DSMW) 2003 Infiltration based on Van Genuchten 1980 and Surface runoff generation based on Dümenil and Todini 1992 Van den Hurk and Viterbo 2003

23 Slide 23 PA Surface III of IV - training course 2013 Slide 23 A new hydrology scheme(2) Dominant soil type from FAO2003 (at native resolution of ~ 10 km) coarse medium med-fine fine very-fine organic Soil Diffusivity Soil Conductivity control

24 Slide 24 PA Surface III of IV - training course 2013 Slide 24 TESSEL soil water equations (1) DjDj j-1 j+1 F j+1/2 F j-1/ ~ >> d 0.1~19.7 d 1.2~195.9 d Time-scale for downward water transfers in wet/dry soil

25 Slide 25 PA Surface III of IV - training course 2013 Slide 25 TESSEL soil water equations (2)

26 Slide 26 PA Surface III of IV - training course 2013 Slide 26 Introduction General remarks Model development and validation The surface energy budget The surface water budget The surface CO2 budget Soil heat transfer Soil water transfer Snow Initial conditions Conclusions and a look ahead Layout

27 Slide 27 PA Surface III of IV - training course 2013 Slide 27 Snow Snow insulates the ground (30% to 90% of the snow mantle is air) A snow covered surface has a higher albedo than any other natural surface ( in the presence of forests, for bare ground/low vegetation) Snow melting keeps the surface temperature at 0 C for a long period in spring

28 Slide 28 PA Surface III of IV - training course 2013 Slide 28 Snow energy budget Apparent heat capacity

29 Slide 29 PA Surface III of IV - training course 2013 Slide 29 Snow mass budget Snow mass (S) and snow depth (D)

30 Slide 30 PA Surface III of IV - training course 2013 Slide 30 Metamorphism, density, albedo, Density -Weighted average between current density and the density of fresh snow, in case of snowfall -Overburden, thermal metamorphism (new formulation) Albedo -Exponential relaxation with different time scales for melting and non-melting snow -Surface albedo for high vegetation regions with snow underneath from MODIS Snow cover fraction -Function of snow depth (10 cm deep – fully cover)

31 Slide 31 PA Surface III of IV - training course 2013 Slide 31 Case study: Boreal forest albedo (1)

32 Slide 32 PA Surface III of IV - training course 2013 Slide 32 0 Forecast day 010 Northern Hemisphere FAL CON Case study: Boreal forest albedo (2) Forecast day Eastern Asia CON FAL 850 hPa temperature bias 20 forecasts every 3 days, March-April 1996 No data assimilation Considering a lower value of snow Forest ALbedo was beneficial.

33 Slide 33 PA Surface III of IV - training course 2013 Slide 33 Case study: Boreal forest albedo (3) The surface albedo is the direct regulator of the energy available to the surface. The albedo of natural surfaces has a limited range ( ), but in non- forested snow covered areas can reach values up to 0.8. Snow covered boreal forests have a much lower albedo than grassland to their south and tundra to their north; the presence of boreal forests has a direct control on the climate of high-latitudes Operational Bias (FAL) 1996 Operational Bias (CON)

34 Slide 34 PA Surface III of IV - training course R1 32R1 Dutra et al Slide 34 Snow sublimation, role of roughness

35 Slide 35 PA Surface III of IV - training course 2013 Slide 35 Snow insulation, role of snow density (1) New snow formulation improves snow mass and snow density. Lower snow densities -> increased snow thermal insulation Dutra et al. 2010

36 Slide 36 PA Surface III of IV - training course 2013 Slide 36 Snow insulation, role of snow density (2) Increased snow thermal insulation Stronger decoupling of land –atmosphere Warm T2M bias are reduced (soil was warming the atmosphere) Clear impact on long climate runs Small impact on short range forecasts Hazeleger et al 2010

37 Slide 37 Liquid Water in the snow-pack A diagnostic formulation can take into account liquid water in the snow pack without new prognostic variable (similar to soil ice) Slide 37 PA Surface III of IV - training course 2013

38 Slide 38 PA Surface III of IV - training course 2013 Liquid Water in the snow-pack (II) The liquid reservoir is function of temperature, snow mass and snow density Slide 38

39 Slide 39 PA Surface III of IV - training course 2013 Snow density: I. New snow Slide 39

40 Slide 40 PA Surface III of IV - training course 2013 Snow density: II. Snow on the ground In the old scheme rainfall infiltrates directly into the soil even in presence of snow on the ground. In the new snow scheme it is intercepted by the snow-pack Slide 40

41 Slide 41 PA Surface III of IV - training course 2013 Forest-Snow Albedo Albedo of Forest areas with snow underneath was fixed to 0.15 (quite dark). A MODIS-derived vegetation dependent albedo is used. Slide 41

42 Slide 42 PA Surface III of IV - training course 2013 Open-Area Snow Albedo Open area albedo is aging from 0.85 in case of fresh snow to 0.5 for old snow, however in case of new snowfall the albedo was istantaneously re-whitened to 0.85, regardless of the snowfall amount. Slide 42

43 Slide 43 PA Surface III of IV - training course 2013 Snow fraction Snow fraction is made dependent of snow depth rather than snow mass. A 10cm snow depth is required for full coverage. Slide 43

44 Slide 44 PA Surface III of IV - training course 2013 Verification Slide 44

45 Slide 45 PA Surface III of IV - training course 2013 SNOW-MIP2 Slide 45

46 Slide 46 PA Surface III of IV - training course 2013 GSWP-2 and Runoff verification Slide 46

47 Slide 47 MODIS albedo as snow verification product Slide 47 PA Surface III of IV - training course 2013

48 Slide 48 PA Surface III of IV - training course 2013 Summary and conclusions for snow processes impact The new snow scheme first validated in offline simulations (SNOW-MIP2 and GSWP2) and showed improvements on the water cycle and on the land albedo Further tests in forecasts mode both at ECMWF and in a climate model have demonstrated that snow can improve near-surface temperatures in cold climate. The introduction of multi-layer snow scheme is recognized as high priority in the next years to improve the representation of the diurnal cycle and introduce the right level of decoupling between snow and atmosphere. Perspectives for snow modelling Slide 48


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