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Land-surface-BL-cloud coupling
Alan K. BettsAtmospheric Research, Pittsford, VT
Co-investigatorsBERMS Data: Alan Barr, Andy Black, Harry McCaughey
ERA-40 data: Pedro Viterbo
Workshop on The Parameterization of the Atmospheric Boundary Layer
Lake Arrowhead, California, USA
14-16 June 2005
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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
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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
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Land-surface couplingModels differ widely [Koster et al., Science, 2004]
Precip SMI E 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
[*]
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Role of soil water, vegetation, LCL, BL and clouds in ‘climate’ over land
• SMI Rveg RH LCL LCC• Clouds SW albedo (cloud) at surface, TOA• LCL + clouds LWnet
• Clouds SWnet + LWnet= Rnet = E + H + G• Tight coupling of clouds means: - E ≈ constant
- H varies with LCL and cloud coverBut are models right?? [Betts and Viterbo, 2005] - DATA CAN TELL US
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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
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• RH gives LCL [largely independent of T]• Saturation pressure conserved in adiabatic motion• Think of RH linked to availability of water
SMI Rveg RH LCL LCC
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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]
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ERA40: soil moisture → LCL and EF
• River basin daily means• Binned by soil moisture and Rnet
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ERA40: Surface ‘control’
• Madeira river, SW Amazon
• Soil water LCL, LCC and LWnet
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ERA-40 dynamic link (mid-level omega)
• Ωmid → Cloud albedo, TCWV and Precipitation
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Omega, P, E and TCWV
• Linear relationship P with omega
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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
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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
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Large T, RH errors in 1996 - before BOREAS input
• -10K bias in winter
• NCEP/NCAR reanalysis saturates in spring
• Betts et al. JGR, 1998
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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
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BERMS and ERA-40: T, RH
• ERA-40 RH close to BERMS in summer
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BERMS: Old Black Spruce
• Cloud ‘albedo’: αcloud = 1- SWdown/SWmax
• Similar distribution to ERA-40
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SW perspective: scale by SWmax
surf, cloud give SWnet
Rnet = SWnet - LWnet
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Fluxes scaled by SWmax
• Old Aspen has sharper summer season
• ERA-40 accounts for freeze/thaw of soil
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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?
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Cloud albedo and LW comparison
• ERA-40 has low αcloud except summer
• ERA-40 has LWnet bias in winter?
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How do fluxes depend on cloud cover?
• Bin daily data by cloud • Quasi-linear variation• Evaporation varies less than other fluxes
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CO2 fluxes and clouds
• Flux progression from OJP,OBS to OA as expected
• Peak uptake at cloud = 0.35
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OA Summers 2001-2003 were drier than 1998-2000
• Radiative fluxes same, but evaporation higher with higher soil moisture
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PLCL → αcloud and LWnet
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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
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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, cloud
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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
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Comparison of T, Q, RH, albedos
• ERA-40 has small wet bias
• cloud is BL quantity: similar at 3 sites
• RH, PLCL also ‘BL’: influenced by local E
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Similar PLCL distributions
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Controls on LWnet
• Same for BERMS and ERA-40• Depends on PLCL [mean RH, & depth of ML]• Depends on cloud cover
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ERA-40 and BERMS average
• ERA-40 has higher EF
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EF to αcloud and LWnet
• Similar but EF for ERA-40 > OBS
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SW and LW feedback of EF
• Greater EF
• reduces outgoing LW
• increases surface cloud albedo
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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)
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SW and LW cloud forcing
• Tight relation of TOA TOA and ATM LWCF and SRF SWCF - linked
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Albedo, SW and LW coupling SW very tight
• ALB:SRF = 1.45*ALB:TOA + 0.35*(ALB:TOA)2
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Energy balance binned by PLCL
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Seasonal Cycle - 4
• Scaled SEB Convergence TCWV, cloud Rnet falls, E flat
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Diurnal Temp. range and soil water
• Similar behavior of DTR• Evaporation in ERA-40 is soil water dependent;
not in BERMS [moss, complex soils]