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Enhancing Land-Surface Cloud Coupling: Modeling Insights and Evaluation

Explore the role of soil moisture, vegetation, clouds in climate dynamics over land, critic modeling accuracy, and compare ERA-40 and field data to enhance cloud-surface coupling understanding.

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Enhancing Land-Surface Cloud Coupling: Modeling Insights and Evaluation

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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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