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Land-Surface-BL-Cloud Coupling: Understanding Physical Processes and Improving Forecast Accuracy

This study examines the coupling between land surface, boundary layer, and clouds to understand the physical processes and improve forecast accuracy. Data from flux towers and reanalysis models are used to assess the representation of key climate processes. The study focuses on the role of soil moisture, vegetation, relative humidity, and cloud cover in the land surface-climate relationship.

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Land-Surface-BL-Cloud Coupling: Understanding Physical Processes and Improving Forecast Accuracy

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  1. Land-surface-BL-cloud couplingAlan K. BettsAtmospheric Research, Pittsford, VTakbetts@aol.comCo-investigatorsBERMS Data: Alan Barr, Andy Black, Harry McCaugheyERA-40 data: Pedro ViterboWorkshop on The Parameterization of the Atmospheric Boundary Layer Lake Arrowhead, California, USA 14-16 June 2005

  2. Background references • Betts, A. K., 2004: Understanding Hydrometeorology using global models. Bull. Amer. Meteorol. Soc., 85, 1673-1688. • Betts, A. K and P. Viterbo, 2005: Land-surface, boundary layer and cloud-field coupling over the Amazon in ERA-40. J. Geophys. Res., in press • Betts, A. K., R. Desjardins and D. Worth, 2004: Impact of agriculture, forest and cloud feedback on the surface energy balance in BOREAS. Agric. Forest Meteorol., in press • Preprints: ftp://members.aol.com/akbetts

  3. Climate and weather forecast modelsHow well are physical processes represented? • Accuracy of analysis: fit of model to data [analysis increments] • Accuracy of forecast : growth of RMS errors from observed evolution • Accuracy of model ‘climate’ : where it drifts to [model systematic biases] • FLUXNET data can assess biases and poor representation of physical processes and their coupling

  4. Land-surface couplingModels differ widely [Koster et al., Science, 2004] Precip SMI lE clouds Precip vegetation vegetation BL param dynamics soils RH microphysics runoff Cu param LW,SW radiation Rnet , H SMI : soil moisture index [0<SMI<1 as PWP<SM<FC] αcloud: ‘cloud albedo’ viewed from surface [*]

  5. Role of soil water, vegetation, LCL, BL and clouds in ‘climate’ over land • SMI Rveg RH LCL LCC • Clouds SW albedo (acloud) at surface, TOA • LCL + clouds LWnet • Clouds SWnet + LWnet= Rnet = lE + H + G • Tight coupling of clouds means: - lE ≈ constant - H varies with LCL and cloud cover But are models right?? [Betts and Viterbo, 2005] - DATA CAN TELL US

  6. Daily mean fluxes give model ‘equilibrium climate’ state • Map model climate state and links between processes using daily means • Think of seasonal cycle as transition between daily mean states + synoptic noise

  7. SMI RvegRH LCL LCC • RH gives LCL [largely independent of T] • Saturation pressure conserved in adiabatic motion • Think of RH linked to availability of water

  8. What controls daily mean RH anyway? • RH is balance of subsidence velocity and surface conductance • Subsidence is radiatively driven [40 hPa/day] + dynamical ‘noise’ • Surface conductance Gs = GaGveg /(Ga+Gveg) [30 hPa/day for Ga =10-2; Gveg= 5.10-3 m/s]

  9. ERA40: soil moisture → LCL and EF • River basin daily means • Binned by soil moisture and Rnet

  10. ERA40: Surface ‘control’ • Madeira river, SW Amazon • Soil water LCL, LCC and LWnet

  11. ERA-40 dynamic link (mid-level omega) • Ωmid → Cloud albedo, TCWV and Precipitation

  12. Omega, P, E and TCWV • Linear relationship P with omega

  13. Compare ERA-40 with 3 BERMS sites Focus: • Coupling of clouds to surface fluxes • Define a ‘cloud albedo’ that reduces the shortwave (SW) flux reaching surface - Basic ‘climate parameter’, coupled to surface evaporation [locally/distant] - More variable than surface albedo

  14. Compare ERA-40 with BERMS • ECMWF reanalysis • ERA-40 hourly time-series from single grid-box • BERMS 30-min time-series from Old Aspen (OA)Old Black Spruce (OBS)Old Jack Pine (OJP) • Daily Average

  15. Large T, RH errors in 1996- before BOREAS input • -10K bias in winter • NCEP/NCAR reanalysis saturates in spring • Betts et al. JGR, 1998

  16. Global model improvements [ERA-40] • ERA-40 land-surface model developed from BOREAS • Reanalysis T bias of now small in all seasons • BERMS inter-site variability of daily mean T is small

  17. BERMS and ERA-40: T, RH • ERA-40 RH close to BERMS in summer

  18. BERMS: Old Black Spruce • Cloud ‘albedo’: αcloud = 1- SWdown/SWmax • Similar distribution to ERA-40

  19. SW perspective: scale by SWmax • - asurf, acloud give SWnet - Rnet = SWnet - LWnet

  20. Fluxes scaled by SWmax • Old Aspen has sharper summer season • ERA-40 accounts for freeze/thaw of soil

  21. Seasonal Evaporative Fraction • Data as expected OA>OBS>OJP • ERA-40 too high in spring and fall • Lacks seasonal cycle • ERA a little high in summer?

  22. Cloud albedo and LW comparison • ERA-40 has low αcloud except summer • ERA-40 has LWnet bias in winter?

  23. How do fluxes depend on cloud cover? • Bin daily data by acloud • Quasi-linear variation • Evaporation varies less than other fluxes

  24. OA Summers 2001-2003 were drier than 1998-2000 • Radiative fluxes same, but evaporation higher with higher soil moisture

  25. PLCL→ αcloud and LWnet

  26. Conclusions -1 • Flux tower data have played a key role in improving representation of physical processes in forecast models • Forecast accuracy has improved • Mean biases have been greatly reduced • Errors are still visible with careful analysis, so more improvements possible

  27. Conclusions - 2 • Now looking for accuracy in key climate processes: will impact seasonal forecasts • Are observables coupled correctly in a model? • Key non-local observables: • BL quantities: RH, LCL • Clouds: reduce SW reaching surface, acloud

  28. Conclusions - 3 • Cloud albedo is as important as surface albedo [with higher variability] • Surface fluxes : stratify by αcloud • Clouds, BL and surface are a coupled system: stratify by PLCL • Models can help us understand the coupling of physical processes

  29. Comparison of T, Q, RH, albedos • ERA-40 has small wet bias • acloud is BL quantity: similar at 3 sites • RH, PLCL also ‘BL’: influenced by local lE

  30. Similar PLCL distributions

  31. Controls on LWnet • Same for BERMS and ERA-40 • Depends on PLCL [mean RH, & depth of ML] • Depends on cloud cover

  32. ERA-40 and BERMS average • ERA-40 has higher EF

  33. EF to αcloud and LWnet • Similar but EF for ERA-40 > OBS

  34. SW and LW feedback of EF • Greater EF • reduces outgoing LW • increases surface cloud albedo

  35. Cloud forcing; Cloud albedos • SWCF:TOA = SW:TOA - SW:TOA(clear) • LWCF:TOA = LW:TOA - LW:TOA(clear) • SWCF:SRF = SW:SRF - SW:SRF(clear) • LWCF:SRF = LW:SRF - LW:SRF(clear) Atmosphere cloud radiative forcing are the differences • SWCF:ATM = SWCF:TOA - SW:SRF • LWCF:ATM = LWCF:TOA - LW:SRF Define TOA and SRF cloud albedos ALB:TOA = 1 - SW:TOA/SW:TOA(clear) cloud=ALB:SRF = 1 - SW:SRF/SW:SRF(clear)

  36. SW and LW cloud forcing • Tight relation of TOA TOA and ATM LWCF and SRF SWCF - linked

  37. Albedo, SW and LW couplingSW very tight • ALB:SRF = 1.45*ALB:TOA + 0.35*(ALB:TOA)2

  38. Energy balance binned by PLCL

  39. Seasonal Cycle - 4 • Scaled SEB Convergence TCWV, cloud Rnet falls, E flat

  40. Diurnal Temp. range and soil water • Similar behavior of DTR • Evaporation in ERA-40 is soil water dependent; not in BERMS [moss, complex soils]

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