1. LANDFLUX workshop Toulouse May 2007 ECMWF Surface fluxes over land in ERA-40 Anton Beljaars (ECMWF) Land surface model in ERA-40 (TESSEL) Introduction to ERA-40 Land surface data assimilation in ERA-40 Evaluation of fluxes Alan Betts diagnostics Thanks to: Gianpaolo Balsamo, Alan Betts, Matthias Drusch, Pedro Viterbo, Ulf Andrae and many others

2. LANDFLUX workshop Toulouse May 2007 ECMWF Why is the land problem so difficult compared to the ocean problem? Fluxes can be written as: U 1,V 1,T 1,q 1 Lowest model level Surface 0, 0, T s, q s z1z1 OceanLand Transfer coefficients Known within 10%Very uncertain at the gridbox level due to effects of vegetation and heterogeneous terrain Surface temperature Well observed and rather persistent on synoptic time scale Highly variable, large diurnal cycle, but can be retrieved from satellite observations Wind Well observed (e.g. scatterometer data) Very uncertain at the gridbox level due to effects of heterogeneous terrain Boundary layer humidity Vertical integral is well observed by SSMI Observed by radio sondes and SYNOP stations Surface energy balance Not very useful (ocean residual is large) Very useful constraint (ground heat flux small on longer time scales)

3. LANDFLUX workshop Toulouse May 2007 ECMWF The land surface scheme (TESSEL) Root depth depends on vegetation type Canopy resistances depend on: radiation, air humidity, soil water (not ice) No root extraction or deep percolation in frozen soils Snow under high vegetation has low albedo Climatological land use data fields derived from 2’30” GLCC: Low vegetation cover High vegetation cover Low vegetation type High vegetation type One global soil type: loam High vegetation Low vegetation Interception reservoir Bare soil Exposed snow Snow under high vegetation Aerodynamic resistances depend on: roughness lengths and stability Lowest model level

4. LANDFLUX workshop Toulouse May 2007 ECMWF Snow model in TESSEL (2 tiles) Single layer snow pack with prognostic equations for (Douville et al. 1995): Snow mass (right hand side : snow fall, snow melt and snow evaporation) Snow temperature (right hand side: radiative heating, turbulent fluxes, basal heat flux) Snow density (right hand side: decrease to min 100 kg/m2 for fresh snow; relaxation to 300 kg/m2 in 3 days) Snow albedo (right hand side: reset to 0.85 for fresh snow, relaxation to 0.50 with a time scale of a month for cold snow and about 4 days for melting snow) Snow depth D from mass and density Snow cover increases linearly with snow mass (total cover at 15 kg/m2) Snow albedo is only used for “exposed snow” tile Tile with snow under high vegetation has albedo of 0.2 (Viterbo and Betts, 1999) Lowest model level 7 cm 21 cm 72 cm 189 cm Snow under vegetation Exposed snow

5. LANDFLUX workshop Toulouse May 2007 ECMWF The old model erroneously transform the available energy into evaporation. However, plants have limited transpiration in winter/spring, when the roots are frozen. The TESSEL model simulates this because the stress function relies on available water (excluding ice). Offline TESSEL evaluation with BOREAS data BOREAS evaporation: One-column integration Old TESSEL Jan 1994Jan 1996Jan 1995 Used 9 different datasets for offline testing: Cabauw FIFE BOREAS 1994-1996 NOPEX 1994-1996 Torne-Kalix (PILPS2E) …. Van den Hurk et al, 2000

6. LANDFLUX workshop Toulouse May 2007 ECMWF Deep drainage is the only mechanism for runoff in the old (ERA15) model (control). There is no mechanism for fast runoff and no peak associated to spring snowmelt. TESSEL (ERA40) restricts vertical water transfer in frozen soils. Fast runoff due to: (a) snowmelt over frozen soils, and (b) Soil water melt. BOREAS: runoff vs observations Betts et al, 2001. J. Geophys. Res., BOREAS special issue.

7. LANDFLUX workshop Toulouse May 2007 ECMWF BOREAS snow depth In the old (control) model, evaporation causes too early depletion of snow TESSEL (new) model limits snow evaporation, and depletion of snow (by melting) occurs later Van den Hurk et al, 2000. ECMWF Tech. Mem. 295, 42 pp.

8. LANDFLUX workshop Toulouse May 2007 ECMWF van den Hurk et al. (2000) Stand alone simulation with old land surface scheme (control) and new scheme (TESSEL or tile) using long time series from Cabauw (10-day averages) The two model versions are rather similar for Cabauw

9. LANDFLUX workshop Toulouse May 2007 ECMWF ERA-40 analysis system Global T159 (about 125 km resolution), 60 levels (top level at 10 Pa) 1957 - 2002 3DVAR with 6 hour cycling FGAT (first guess compared to observation at appropriate time; increment added at analysis time) Model first guess is converted into equivalent of observation using a forward model e.g. RT model for satellite radiances Atmospheric analysis variables: U,V,T,q,P s,O 3 Cloud prognostic variables (cover, condensate) are not analyzed and are copied from 1 st guess 0612180

10. LANDFLUX workshop Toulouse May 2007 ECMWF ERA-40 analysis system Model related parameters are computed during the first guess and during short range forecasts from 0 and 12 UTC e.g.: precipitation surface and top of the atmosphere fluxes cloud variables Model related parameters can have biases that are related to model deficiencies Post-processing of models fields every 3 hours for study of short time scale variability, e.g. diurnal cycles Comprehensive set of vertically integrated fields for budget studies, e.g. moisture covergence Some “physics” parameters (diffusion coefficients, mass fluxes, 3D precip) are archived for chemical transport modelling See: Uppala et al. 2005; QJRMS, 131, 2961-3012 and http://www.ecmwf.int/research/era

11. LANDFLUX workshop Toulouse May 2007 ECMWF Observations Conventional observations: Radio-sondes, pilot winds, profilers SYNOP’s SHIP’s Buoys Aircraft reports Satellite observations TOVS/ATOVS radiances Scatterometer winds SSMI 1DVAR retrievals of TCWV and winds Cloud track winds Density and quality of observations is (dependent on type) variable over the 40-year period

12. LANDFLUX workshop Toulouse May 2007 ECMWF Soil moisture analysis Soil moisture observations are not available on a global scale. Soil moisture analysis at ECMWF uses first guess errors (6 or 12 hour forecast compared to SYNOP observations) of temperature and humidity to correct soil moisture. Effectiveness of method depends on quality of model (particularly the land surface scheme). Three methods have been developed at ECMWF: –(i) Nudging using q observations only (Viterbo and Courtier 1995) –(ii) OI using q and T. ERA uses OI (Douville et al. 2000) –Kalman filter developed in ELDAS; still to be implemented operationally (Sueffert et al. 2004) Soil moisture reservoir Boundary layer reservoir in dry day-time conditions The use of boundary layer q to infer soil moisture assumes a perfect relation between E and. The method may be very good for E (what we want in NWP), but not necessarily good for.

13. LANDFLUX workshop Toulouse May 2007 ECMWF Time series of q at Cabauw (Netherlands) Data: Fred Bosveld, KNMI 2 m Above the surface 200 m Above the surface Data assimilation systems are very powerful in representing synoptic variability of the main atmospheric variables (U,V,T and q). This applies in particularly to T2 RH2 due to land surface data assimilation

14. LANDFLUX workshop Toulouse May 2007 ECMWF CRU/Hadley Centre http:///www.cru.uea.ac.uk/cru/info/warming Trends and interannual variability ERA-40 See: Simmons et al. 2004; JGR, 109, D24

15. LANDFLUX workshop Toulouse May 2007 ECMWF 2m temperature analysis increments in ERA-40 July 1986-1995 2m relative humidity analysis increments in ERA-40 July 1986-1995

16. LANDFLUX workshop Toulouse May 2007 ECMWF Soil moisture analysis increments in ERA-40; July 1986-1995

17. LANDFLUX workshop Toulouse May 2007 ECMWF Surface analysis increments in ERA-40 (1986-1995) Temperature (top 7-cm layer; K/6-hours) 2m Temperature (K/6-hours)

18. LANDFLUX workshop Toulouse May 2007 ECMWF Surface analysis increments in ERA-40 (1986-1995) Water (top 1m of soil; mm/6-hours ) Snow (mm of water equivalent/6-hours ) 2m Relative humidity (%/6-hours )

19. LANDFLUX workshop Toulouse May 2007 ECMWF July fluxes (positive=up): Open loop -OI LE H Drusch and Viterbo, 2007

20. LANDFLUX workshop Toulouse May 2007 ECMWF 1000hPa RMS-T errors: Open loop versus OI Europe (dot), N-Amer(solid), E-Asia (dash) Drusch and Viterbo, 2007

21. LANDFLUX workshop Toulouse May 2007 ECMWF Oklahoma meso-net (34N-36.8N/94.6W-99.9W); 72 stations with meteo and soil moisture. Drusch and Viterbo, 2007 Soil moisture (top 5 cm) Soil moisture (top 100 cm) Precipitation Downward solar radiation OI Open loop Observed

22. LANDFLUX workshop Toulouse May 2007 ECMWF Mackenzie basin averaged monthly evaporation (ERA40) Betts et al. 2003

23. LANDFLUX workshop Toulouse May 2007 ECMWF Data from the Boreal Ecosystem Research and Monitoring Sites (BERMS) Three different sites less than 100 km apart in Saskatchwan at the southern edge of the Canadian boreal forest (at about 54 o N/105 o W) : Old Aspen (deciduous, open canopoy, hazel understory, 1/3 of evaporation from understory) Old Black Spruce (boggy, moss understory) Old Jack Pine (sandy soil) Thanks to the Fluxnet-Canada Research Network ( A. Barr, T. A. Black, J. H. McCaughey)

24. LANDFLUX workshop Toulouse May 2007 ECMWF BERMS vs ERA-40 (Daily averages: Apr, May, Jun) ERA-40 follows observations with RMS error of about 2 K. 2m temperature Daily averages processed by Alan betts

25. LANDFLUX workshop Toulouse May 2007 ECMWF Rnet BERMS vs ERA-40 (Daily averages: Apr, May, Jun)

26. LANDFLUX workshop Toulouse May 2007 ECMWF Sensible heat flux (negative = up) BERMS vs ERA-40 (Daily averages: Apr, May, Jun)

27. LANDFLUX workshop Toulouse May 2007 ECMWF Latent heat flux (negative = up) BERMS vs ERA-40 (Daily averages: Apr, May, Jun)

28. LANDFLUX workshop Toulouse May 2007 ECMWF Ground heat flux BERMS vs ERA-40 (Daily averages: Apr, May, Jun)

29. LANDFLUX workshop Toulouse May 2007 ECMWF BERMS diurnal cycles (30-day averages) Rnet G SSHF SLHF

30. LANDFLUX workshop Toulouse May 2007 ECMWF Cabauw (The Netherlands, 52N, 5E) 7-day averages 1986-1997 Solar downward radiation Net surface radiation OBS ERA OBS ERA

31. LANDFLUX workshop Toulouse May 2007 ECMWF Cabauw (The Netherlands, 52N, 5E) 7-day averages 1986-1997 Sensible heat flux Latent heat flux OBS ERA OBS ERA

32. LANDFLUX workshop Toulouse May 2007 ECMWF Norunda (Sweden, 60N, 17E) 7-day averages 1996-1998 Solar downward radiation Net surface radiation OBS ERA

33. LANDFLUX workshop Toulouse May 2007 ECMWF Norunda (Sweden, 60N, 17E) 7-day averages 1986-1997 Sensible heat flux Latent heat flux OBS ERA

34. LANDFLUX workshop Toulouse May 2007 ECMWF Conclusions from ERA-40 NWP analysis is very efficient in reproducing synoptic variability Fluxes in ERA-40 are adjusted through soil moisture and temperature based on boundary layer budgets Assimilation assumes that no other model biases exist that affect boundary layer T and q. Data assimilation is very efficient in keeping 2m temperature and humidity errors under control Turbulent fluxes might be reasonable but are not bias free Soil moisture is not very good (implies that relation between soil moisture and EF is not realistic in TESSEL) Extensive verification is needed before conclusions can be drawn about quality

35. LANDFLUX workshop Toulouse May 2007 ECMWF Conclusions on re-analysis Possible strategy for land flux climatology could be: –Use re-analyses for base line data (no missing data; high time resolution) –Distinguish net radiation (Qn) and evaporative fraction (EF) –Correct Qn using top of the atmosphere radiation data –Document errors using as much verification material as possible, e.g. CEOP, FLUXNET, basin budgets, budgets based on precip minus moisture convergence –Correct EF based on independent observations –Many problems: (i) Different areas behave differently, (ii) high latitude processes are less documented, (iii) many data sparse areas, (iv) winter budgets are very subtle

36. LANDFLUX workshop Toulouse May 2007 ECMWF LANDFLUX AKB: 5/21/2007 - Solve for daily mean problem [Use model advective relations] - Separate R net and EF - Get SW dn from cloud: use α cloud concept - Get LW net from RH and cloud - Use a ‘Water Availability Variable’ to get EF(T, WAV) - Use diurnal temp. range to check LW net and daytime thermal budget - Use T skin as check - Check model EF to RH relationships etc

37. LANDFLUX workshop Toulouse May 2007 ECMWF ERA-40 Ohio-Tenn. river basin Cloud ‘albedo’: α cloud = 1- SW netSRF /SW netSRF (clear) SW netSRF = (1- α cloud )(1- α SRF ) SW dnSRF (clear)

38. LANDFLUX workshop Toulouse May 2007 ECMWF TOA and surface cloud albedos - tightly related α cloud = -SWCF SRF /SW netSRF (clear) α TOA = -SWCF TOA /SW dnTOA (clear)

39. LANDFLUX workshop Toulouse May 2007 ECMWF Surface cloud forcing has linear relation to α cloud - Clear-sky LW net depends on P LCL - Cloud forcing does not

40. LANDFLUX workshop Toulouse May 2007 ECMWF LW net on RH and α cloud Outgoing LW net falls as RH and cloud cover increase Higher RH means lower LCL & depth of ML LW coupling same for BERMS and ERA-40

41. LANDFLUX workshop Toulouse May 2007 ECMWF Net radiation variability depends mostly on α cloud R netSRF (clear) varies weakly CF SRF linear with α cloud

42. LANDFLUX workshop Toulouse May 2007 ECMWF EF depends on T and SMI-L1 -EF increases with SMI -Slope with T ≈ ‘equilibrium evaporation’

43. LANDFLUX workshop Toulouse May 2007 ECMWF Coupling of soil moisture, LCL and precipitation LCL descends with increasing SMI-L1 and precip. Highly coupled - precipitation increases SMI-L1 - wetter SMI increases evaporation from surface - falling precip. evaporates, lowering LCL

44. LANDFLUX workshop Toulouse May 2007 ECMWF Cloud-base and RH linked to EF scaled -LCL descends with increasing EF scaled and Precipitation -Coupled through soil-water

45. LANDFLUX workshop Toulouse May 2007 ECMWF Comments -Not all empirical relations may hold for real atmosphere -Observation based studies are needed -Extensive verification is needed