Betts

Land-surface-BL-cloud coupling Alan 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 : 

Land-surface-BL-cloud coupling Alan K. Betts Atmospheric Research, Pittsford, VT akbetts@aol.com Co-investigators BERMS 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

Background references: 

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

Climate and weather forecast modelsHow well are physical processes represented?: 

Climate and weather forecast models How 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

Land-surface couplingModels differ widely [Koster et al., Science, 2004]: 

Land-surface coupling Models 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 [*]

Role of soil water, vegetation, LCL, BL and clouds in ‘climate’ over land: 

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

Daily mean fluxes give model ‘equilibrium climate’ state: 

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

SMI Rveg RH LCL LCC: 

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

What controls daily mean RH anyway?: 

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]

ERA40: soil moisture → LCL and EF: 

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

ERA40: Surface ‘control’: 

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

ERA-40 dynamic link (mid-level omega): 

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

Omega, P, E and TCWV: 

Omega, P, E and TCWV Linear relationship P with omega

Compare ERA-40 with 3 BERMS sites: 

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

Compare ERA-40 with BERMS: 

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

Large T, RH errors in 1996 - before BOREAS input: 

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

Global model improvements [ERA-40]: 

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

BERMS and ERA-40: T, RH : 

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

BERMS: Old Black Spruce: 

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

SW perspective: scale by SWmax: 

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

Fluxes scaled by SWmax: 

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

Seasonal Evaporative Fraction: 

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?

Cloud albedo and LW comparison: 

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

How do fluxes depend on cloud cover?: 

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

OA Summers 2001-2003 were drier than 1998-2000: 

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

PLCL → αcloud and LWnet: 

PLCL → αcloud and LWnet

Conclusions -1 : 

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

Conclusions - 2: 

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

Conclusions - 3: 

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

Comparison of T, Q, RH, albedos: 

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

Similar PLCL distributions: 

Similar PLCL distributions

Controls on LWnet: 

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

ERA-40 and BERMS average: 

ERA-40 and BERMS average ERA-40 has higher EF

EF to αcloud and LWnet: 

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

SW and LW feedback of EF: 

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

Cloud forcing; Cloud albedos: 

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)

SW and LW cloud forcing: 

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

Albedo, SW and LW coupling SW very tight: 

Albedo, SW and LW coupling SW very tight ALB:SRF = 1.45*ALB:TOA + 0.35*(ALB:TOA)2

Energy balance binned by PLCL: 

Energy balance binned by PLCL

Seasonal Cycle - 4: 

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

Diurnal Temp. range and soil water: 

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