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Progress in understanding land-surface-atmosphere
coupling over the Amazon
Alan Keith Betts, Atmospheric Research, [email protected]
Maria Assuncao Silva Dias, CPTEC, [email protected]
Amazon in Perspective: Integrated Science for a Sustainable Future
Manaus, November 18, 2008
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Ten year perspective from LBA• Many coupled processes
• Shortwave and longwave fluxes: clouds
- Coupling of LWnet to diurnal temperature range
• Aerosols
- role of fires in vertical transport
- cloud microphysics & precipitation
• Partition of moisture convergence into column water vapor, cloud & precipitation
• Surface-cloud-boundary layer coupling
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Solar Input
Surface Radiation
Budget
Vegetation
Soil Temperature Soil Moisture
Runoff
SHLH
BL θ, q
Large Scale Dynamics
Precipitating clouds
BL - Clouds
aerosol
Stomatal closure
all clouds
Betts et al 1996 “The land surface atmosphere interaction: A review based on observational and global modeling perspectives” JGR, 101, D3, 7209-7225
DiurnalSeasonalCentury
TIME SCALES
(snow)
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Top of the Atmosphere Radiation Budget
Surface Radiation Budget
Land Use & Land Cover
Soil Temperature Soil Moisture
Runoff
Aerosol, clouds,greenhouse gases
albedo
SW
LW
Aerosol, clouds
SH,LHfCO2
Emissions
BL θ, q, LCL
[CO2],[XY]I aerosol
Large Scale and Mesoscale Dynamics
Precipitating clouds
aerosol,fire
BL - Clouds
Stomatal closure
Betts et al revisited
DiurnalSeasonalDecadal
TIME SCALES
Atmospheric profiles of
θ, q, θE [CO2],[[XY]i aerosol
[XY]I concentration of several chemical species
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Surface Energy Balance
Rnet = SWnet + LWnet = H + λE + G
• the split between surface processes and atmospheric processes
• the split between SW and LW processes• the partition between clear-sky, aerosol and cloud
processes in the atmosphere• the partition of the surface Rnet into H and λE
- controlled by the availability of water for evaporation and by vegetation
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Surface SWnet
SWnet = SWdown- SWup =(1- αsurf)(1- αcloud) SWdown(clear)
• surface albedo αsurf = SWup /SWdown
• effective cloud albedo- a scaled surface short-wave cloud forcing, SWCF
αcloud = - SWCF/SWdown(clear)where
SWCF = SWdown - SWdown(clear) [Betts and Viterbo, 2005; Betts, 2007]
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Amazon sub-basins
• 41: Tapajos, Xingu..
• 42: Madeira
• 43: Amazonas
• 44: Negro
• 45: Purus, Jurua…
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Cloud albedo: ERA-40 data
• Transformation of SWCF to αcloud
• Seasonal cycle OK: small daily variability: biased???
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Cloud albedo: ISCCP data
• Different clear-sky flux: Aerosol differences• ERA-40 systematic high bias in αcloud ≈ +7% • ISCCP has more daily variability
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Rondonia forest & pasture : SWCF [data: von Randow et al 2004]
• More dry season cloud over pasture
• Aerosol ‘gap’ in September burning season
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Energy balance: forest and pasture
• In July, pasture has 8% higher surface albedo and 7% more cloud
• Pasture LWnet is greater (surface warmer, BL drier)
• Pasture Rnet ≈ 15% less than forest
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LWnet - RH - cloud coupling
• Point comparison: ERA40 and Jaru tower
• Humidity and cloud greenhouse effects
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LWnet and diurnal T range (DTR)
• Rainy seasonDTR and LWnet
small
• Dry seasonDTR and LWnet
large
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LWnet and DTR – monthly mean data
• Mean LWnet and DTR correlated [Betts: JGR, 2006]
Slope ≈ 1
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Aerosol-biomass burning issues
- role of fires in vertical transport
- cloud microphysics & precipitation
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ECMWF experimental monthly aerosol
analysis using MODIS 550nm data
• Sept. 2003
• Sept. 2004
Morcrette, 2008, personal communication
& Benedetti et al., ECMWF Newsletter #116
Sea salt Desert dust Black carbonOrganic matterSulphates
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Biomass Burning Longo et al. ACPD, 2007;CATT-BRAMS
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Plume Rise of Vegetation Fires
• Heat flux: Forest: 30-80kWm-2; Savannah: 5-25 kWm-2
• Fire size distribution broad: 13 (±15)ha- fire plume reaches 4-8km
SMOCC ‘Dry’: 20 Sept. 2002 ‘Wet’: 27 Sept. 2002
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1-D plume model, driven by Fire Emission model & coupled to 3-D CATT-BRAMS gives realistic dispersion
• Mid-tropospheric CO matches AIRS much better with sub-grid 1-D plume rise model [Freitas et al. 2006]
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Cloud Microphysics
PRECIPITATION
CCN Activation
Cloud/Aerosol RadiativeTransfer
AEROSOLS
IN Activation
Aerosol Wet Removal
Aerosol-cloud-precipitation feedbacks
Cloud Dynamics
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TRMMPrecipitation
Radar vs
MODIS AOT
Lin et al2006
liquid +ice
CWF ~ CAPE
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Series of papers on Aerosol-Precipitation coupling
• Martins, Silva Dias, Gonçalves (2008):Modeling the impacts of biomass burning aerosols on precipitation in the Amazonian region using BRAMS: a case study for 23 September 2002 – JGR (accepted)
• Martins et al 2008 : Cloud condensation nuclei from biomass burning during the Amazonian dry-to-wet transition season – Meteo. Atmos . Phys. (accepted)
• Gonçalves, Martins, Silva Dias, 2007 – Shape parameter analysis using cloud spectra and gamma functions in numerical modelling during LBA in Amazon – Atmos Research. (in press)
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nnn
t
D
D
DD
DNDn exp
1
)()(
1
0
200
400
600
800
2 7 12 17 22
DIÂMETRO (m)
dN/d
D (1
,5
m.c
m3 )-1
1 2 3 4
5 6 7 8
9 10 11 12
13 14 15 16
17 18 19 20
m
Shape parameter in cloud droplet diameter
distribution
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R = 0.74
2.00
3.00
4.00
5.00
6.00
7.00
300 600 900 1200 1500
CONCENTRAÇÃO DE GOTÍCULAS (cm-3)
PA
RÂ
ME
TR
O D
E F
OR
MA
Sha
pe p
aram
eter
Cloud droplet concentration (cm-3)
Observed shape parameter vs CCN concentrationNumerical experiments : BRAMS
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Microphysics parameters used in numerical experiments
CCN300 CCN450 CCN600 CCN900 CCN900R
CCN concentration 300 450 600 900 900
Shape parameter –
cloud droplet and
pristine ice
2 3 4 5 5
Shape parameter –
all remaining
water categories
1 1 1 1 1
Radiative effect None None None None Absorbing
and
reflecting
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Vertical structure of cloud and ice water mixing rate observed at the time of
maximum liquid water path
CCN300
CCN600
CCN450
CCN900
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Vertical transport processes
• Local fire-driven transports and convection
• Larger-scale forcing, cloud & precipitation
• Cloud-BL coupling
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Precipitation and cloud coupling to vertical motion in ERA-40 reanalysis
• Partition of moisture convergence into TCWV, αcloud, and precipitation
• Note high bias of αcloud from ISCCP; but precip. generally low
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Land-surface-BL Coupling
• SMI-L1 = (SM- 0.171)/(0.323-0.171) (soil moisture index)• PLCL stratified by Precip. & SMI-L1 or EF • Highly coupled system: only PLCL observable: Mixed layer depth
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Cloud - BL coupling Jaru forest – Noon ±2h
• Cloud amount coupled to cloud-base & RH
• Temperature decreases as cloud increases
• θE is flat: regulated by cloud transports
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Conclusions
• Our understanding of physical processes has broadened extensively
- Coupling of clouds to surface, BL and LWprocesses
- Aerosol interactions: radiative & microphysical
- Our ability to assess physical biases in models
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Top of the Atmosphere Radiation Budget
Surface Radiation Budget
Land Use & Land Cover
Soil Temperature Soil Moisture
Runoff
Aerosol, clouds,greenhouse gases
albedo
SW
LW
Aerosol, clouds
SH,LHfCO2
Emissions
BL θ, q, LCL
[CO2],[XY]I aerosol
Large Scale and Mesoscale Dynamics
Precipitating clouds
aerosol,fire
BL - Clouds
Stomatal closure
Betts et al revisited
DiurnalSeasonalDecadal
TIME SCALES
Atmospheric profiles of
θ, q, θE [CO2],[[XY]i aerosol
[XY]I concentration of several chemical species
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Vertical motion and structure
• Ascent and dry in February [wet season]• Descent and dry in August [dry season]
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Stability differences
• Unstable for deep convection in Feb
• Stable for deep convection in Aug