1. Slide 1 PA Surface III of IV - training course 2006 Slide 1 Introduction General remarks Model development and validation The surface energy budget Soil heat transfer Soil water transfer Surface water fluxes Initial conditions Snow Conclusions and a look ahead Layout

2. Slide 2 PA Surface III of IV - training course 2006 Slide 2 Evaporation: Idealized surfaces Lake Snow Wet vegetation Dry vegetation Bare ground

3. Slide 3 PA Surface III of IV - training course 2006 Slide 3 Potential evaporation, bucket model Potential evaporation -The evaporation of a large uniform surface, sufficiently moist or wet (the air in contact to it is fully saturated) Evaporation efficiency -Ratio of evaporation to potential evaporation Bucket modelBudyko 1963, 1974 Manabe 1969 1 0

4. Slide 4 PA Surface III of IV - training course 2006 Slide 4 A general, algebraic formulation Two limit behaviours -Bare soil: Evaporation dependent on soil water (and trapped water vapour) in a top shallow layer of soil (~ 20 mm). -Vegetated surfaces: Evaporation controlled by a canopy resistance, dependent on shortwave radiation, water on the root zone (~ 1-5 m deep) and other physical/physiological effects.

5. Slide 5 PA Surface III of IV - training course 2006 Slide 5 Transpiration: The big leaf approximation Sensible heat (H), the resistance formulation Evaporation (E), the resistance formulation (the big leaf approximation, Deardorff 1978, Monteith 1965)

6. Slide 6 PA Surface III of IV - training course 2006 Slide 6 Some plant science r s, the stomatal resistance of a single leaf. Physiological control of water loss by the vegetation. Stomata (valve-like openings) regulate the outflow of water vapour (assumed to be saturated in the stomata cells) and the intake of CO 2 from photosynthesis. The energy required for the opening is provided by radiation (Photosynthetically Active Radiation, PAR). In many environments the system appears to be operate in such a way to maximize the CO 2 intake for a minimum water vapour loss. When soil moisture is scarce the stomatal apertures close to prevent wilt and dessication of the plant.

7. Slide 7 PA Surface III of IV - training course 2006 Slide 7 Jarvis approach(1) Dickinson et al 1991

8. Slide 8 PA Surface III of IV - training course 2006 Slide 8 Jarvis approach(2) Shuttleworth 1993

9. Slide 9 PA Surface III of IV - training course 2006 Slide 9 Bare ground evaporation Soil (bare ground) evaporation is due to: -Molecular diffusion from the water in the pores of the soil matrix up to the interface soil atmosphere (z 0q ) -Laminar and turbulent diffusion in the air between z 0q and screen level height All methods are sensitive to the water in the first few cm of the soil (where the water vapour gradient is large). In very dry conditions, water vapour inside the soil becomes dominant

10. Slide 10 PA Surface III of IV - training course 2006 Slide 10 TESSEL transpiration

11. Slide 11 PA Surface III of IV - training course 2006 Slide 11 TESSEL root profile

12. Slide 12 PA Surface III of IV - training course 2006 Slide 12 TESSEL evaporation: shaded snow Evaporation is the sum of the contribution of the snow underneath (with an additional resistance, r a,s,to simulate the lower within canopy wind speed) and the exposed canopy. The former is dominant in early spring (frozen soils) and the latter is dominant in late spring.

13. Slide 13 PA Surface III of IV - training course 2006 Slide 13 TESSEL bare ground evaporation

14. Slide 14 PA Surface III of IV - training course 2006 Slide 14 Tiles Interception layer Bare ground Snow on low vegetation High vegetation with snow beneath Sea ice / frozen lakesLow vegetation Open sea / unfrozen lakes High vegetation Sea and iceLand

15. Slide 15 PA Surface III of IV - training course 2006 Slide 15 TESSEL geographic characteristics Dependent on vegetation type 1 m Global constant Root depth Root profile Dependent on vegetation type Global constants LAI r smin MonthlyAnnualAlbedo Dominant low type Dominant high type Global constant (grass) Vegetation type Fraction of low Fraction of high FractionVegetation TESSELERA15Fields

16. Slide 16 PA Surface III of IV - training course 2006 Slide 16 Interception (1) Interception layer represents the water collected by interception of precipitation and dew deposition on the canopy leaves (and stems) Interception (I) is the amount of precipitation (P) collected by the interception layer and available for “direct” (potential) evaporation. I/P ranges over 0.15-0.30 in the tropics and 0.25-0.50 in mid-latitudes. Leaf Area Index (LAI) is (projected area of leaf surface)/(surface area) 0.1 < LAI < 6 Two issues -Size of the reservoir -C l, fraction of a gridbox covered by the interception layer T=P-I; Throughfall (T) is precipitation minus interception

17. Slide 17 PA Surface III of IV - training course 2006 Slide 17 Interception: Canopy water budget

18. Slide 18 PA Surface III of IV - training course 2006 Slide 18 TESSEL: interception Interception layer for rainfall and dew deposition

19. Slide 19 PA Surface III of IV - training course 2006 Slide 19 Example: Deep tropics interception Viterbo and Beljaars 1995 Interception Evaporation Interception ARME, 1983-1985, Amazon forest Accumulated water fluxes

20. Slide 20 PA Surface III of IV - training course 2006 Slide 20 Case study: Aerodynamic resistance (1) Cabauw (Netherlands), is a grass covered area, where multi- year detailed boundary layer measurements have been taken Observations were used to force a stand-alone version of the surface model for 1987 The first model configuration tried had z 0h =z 0m

21. Slide 21 PA Surface III of IV - training course 2006 Slide 21 Case study: Aerodynamic resistance (2) Beljaars and Viterbo 1994

22. Slide 22 PA Surface III of IV - training course 2006 Slide 22 Case study: Aerodynamic resistance (3)

23. Slide 23 PA Surface III of IV - training course 2006 Slide 23 Case study: Aerodynamic resistance (4)

24. Slide 24 PA Surface III of IV - training course 2006 Slide 24 Case study: Aerodynamic resistance (5)

25. Slide 25 PA Surface III of IV - training course 2006 Slide 25 Runoff and infiltration Infiltration is that part of the precipitation flux that contributes to wet the soil Runoff occurs in -Parts of the watershed where hydraulic conductivities are lowest (Horton mechanism, in upslope areas) -Parts of the watershed where the water table is shallowest (Dunne mechanism, in near channel wetlands) Runoff depends on orography, nature and moisture state of the soil, precipitation intensity, and sub-grid scale effects Runoff in NWP/climate models should be called runoff generation (upon application of a routing algorithm becomes “runoff”)

26. Slide 26 PA Surface III of IV - training course 2006 Slide 26 TESSEL: runoff generation Surface runoff is based on a maximum infiltration limit concept, but with no sub-grid scale variability of either the precipitation flux or the top soil water content. Physically, it is the Hortonian concept, but applied at the wrong spatial scales (the model grid-box) Deep runoff is free drainage at the bottom All computations are performed with soil liquid water only. The frozen fraction of the surface is impervious to vertical water fluxes

27. Slide 27 PA Surface III of IV - training course 2006 Slide 27 Introduction General remarks Model development and validation The surface energy budget Soil heat transfer Soil water transfer Surface water fluxes Initial conditions Snow Conclusions and a look ahead Layout

28. Slide 28 PA Surface III of IV - training course 2006 Slide 28 Initial conditions: general remarks There are no routine direct observations of soil temperature and water at a global scale Long time scales in deeper soil temperature and water: Forecast drifts are possible. A way of controlling model drift (initialisation of soil variables) is needed State variables (soil temperature and water) are non- linearly related (via the equations of the land-surface scheme) to observations (near-surface atmospheric variables)

29. Slide 29 PA Surface III of IV - training course 2006 Slide 29 Soil water initial conditions Simple method 1: (Initial values)=(short term forecast values) -Some form of relaxation to climatology in the land surface scheme (ECMWF prior to 1993) or outside (NCEP global model, NCEP ETA model prior to 1998) -Problem: Model results difficult to validate Simple method 2: (Initial values)=(short term forecast values) -No relaxation to climatology -Problems: It can lead to drift due to: (a) Errors in the atmospheric forcing; (b) Deficiencies in the land surface scheme. Used at ECMWF August 1993-December 1994 Use of proxy information -Screen level RH (ECMWF, 1994, NUDGING) -Screen level T and RH [(ECMWF, 1999, Optimal interpolation (OI)] -Rainfall rates -Radiometric screen temperature (infrared, microwave)

30. Slide 30 PA Surface III of IV - training course 2006 Slide 30 Soil moisture increments The nudging coefficient, D, is such that a specific humidity analysis increment of 1.5 g/kg fills 150 mm of water in the soil in 9 days (24 days in ERA15) Exception conditions: (a) Snow; (b) ; (c) Operational at ECMWF during 1994-1999. Used for ERA15 ECMWF 1994-1999: Nudging

31. Slide 31 PA Surface III of IV - training course 2006 Slide 31 Optimal Interpolation vs nudging (1) Use is made of temperature (T a ) and relative humidity (RH a ) information (instead of specific humidity only) Increments of soil moisture linked in an optimal way to T a and RH a increments, with coefficients depending on location (instead of an empirical global coefficient used in nudging) Increments decrease in no information situations: low zenith angle, high wind speed, precipitation, cloudiness (instead of the constant value used in nudging)

32. Slide 32 PA Surface III of IV - training course 2006 Slide 32 Optimal Interpolation vs nudging (2) OINudging

33. Slide 33 PA Surface III of IV - training course 2006 Slide 33 Soil moisture increments Douville et al 2000 Nudging OI

34. Slide 34 PA Surface III of IV - training course 2006 Slide 34 Temperature/humidity errors Douville et al 2000 T RH

35. Slide 35 PA Surface III of IV - training course 2006 Slide 35 LE and soil moisture Douville et al 2000 LE Soil water, root zone Soil water, layer 4

36. Slide 36 PA Surface III of IV - training course 2006 Slide 36 July 1988 increments: NOTILES, Nudging

37. Slide 37 PA Surface III of IV - training course 2006 Slide 37 July 1988 mean increments: NOTILES, OI

38. Slide 38 PA Surface III of IV - training course 2006 Slide 38 July 1988 mean increments: TESSEL, OI

39. Slide 39 PA Surface III of IV - training course 2006 Slide 39 Soil water increments: USA June

40. Slide 40 PA Surface III of IV - training course 2006 Slide 40 Case study: Europe, May-June1994 (1) ECMWF German (DWD) Day 2 forecasts

41. Slide 41 PA Surface III of IV - training course 2006 Slide 41 Case study: Europe, May-June1994 (2)

42. Slide 42 PA Surface III of IV - training course 2006 Slide 42 Case study: Europe, May-June1994 (3)

43. Slide 43 PA Surface III of IV - training course 2006 Slide 43 Case study: Europe, May-June1994 (4) Observations suggest the model has not enough low clouds, and a warm and dry bias at the surface. In data assimilation the observations only control the state of the atmosphere. Initial conditions for the surface state variables (e.g., soil moisture) were taken from short-term forecasts. They reflect the errors in the surface forcing (precipitation, radiation and clouds) in those forecasts. A “free-running” soil moisture will have a inaccurate time evolution, even with a perfect surface model. Cycling the short-term forecasts for soil moisture is tantamount to an extended model integration. x t x b x a Analysis trajectory Background trajectory H (x) y time Atmospheric data assimilation

44. Slide 44 PA Surface III of IV - training course 2006 Slide 44 Case study: Europe, May-June1994 (5) Control + 2 (one month) data assimilation experiments -FREWATControlNo initialisation of soil water -WATSimple initialisation of soil water -WATCLDWAT + prognostic cloud scheme (Tiedtke, 1993) WAT uses information from screen-level humidity and temperature observations. If the short-term forecast parameters are warm and dry, the system produces a soil water increment (ie, adds soil water in the root layer); the converse also happens (Viterbo, 1996).

45. Slide 45 PA Surface III of IV - training course 2006 Slide 45 Case study: Europe, May-June1994 (6) 21 Jun1 Jun 2q Bias over Europe 1 Jun21 Jun 2T Bias over Europe FREWAT WAT WATCLD

46. Slide 46 PA Surface III of IV - training course 2006 Slide 46 Case study: Europe, May-June1994 (7) Errors in the surface radiative forcing can affect the soil water in the root zone, which in turn affect the partitioning of surface energy into sensible and latent heat. This has a large impact on the quality of summer forecasts. The impact exists not only on near-surface parameters but throughout the atmosphere. It is essential to include in data assimilation a procedure to initialise soil moisture based on indirect usage of observations. 500 hPa Bias Europe 500 hPa RMS Europe FREWAT WAT WATCLD