M - Inpe

Interação entre
Aerossóis e Chuva
Maria Assunção Faus da Silva Dias
Departamento de Ciências Atmosféricas – IAG/USP
Curso sobre Sensoriamento Remoto e
Modelagem dos Processos de Formação da Precipitação
PROJETO CHUVA
UFRJ, 27 de outubro de 2011
Residence time
(or lifetime) ! of
a trace
constituent
in the
atmosphere
τ=
𝑀
𝐹
M is the amount
of the
constituent in
the
atmosphere (in
kg) and
F is the rate of
its removal
(in kg s1) from
the atmosphere.
CO2
Tamanhos
Fração
Tamanho
PM10
D ≤ 10 µm
PM2.5 (fração respirável)
D ≤ 2,5 µm
PM1
D ≤ 1 µm
Ultrafinos
D ≤ 0,1 µm
PM10-PM2,5 (fração grossa)
2,5 µm ≤ D≤ 10 µm
Primários – diretamente emitidos para o ar
Secundários – formados no ar por reações químicas
Direct Condensation Nuclei
Counter with Automatic
Photographic Recording, and
General Problems of
“Absolute” Counters
R. Jaenicke, H. J. Kanter
Journal of Applied
Meteorology
Volume 15, Issue 6 (June
1976) pp. 620-632
Pressão de
vapor
T
Supersaturação
Pressao de
vapor de
saturação
Teoria do crescimento de gotas
em nuvens quentes
Wallace Hobbs cap 6
Fletcher
Deforestation rates in Amazonia
Total land use change area: 14% of the 5.5 million Km2
Amazonia deforestation in km² per year 1977-2000
29.059
30000
25000
21,130
20000
15000
19,832
18,161
17,860
13,810
13,786
17,383 17,259
14,896
13,227
11,130
10000
5000
0
77
/8
8*
88
/8
9
89
/9
0
90
/9
1
91
/9
2
92
/9
4
94
/9
5
95
/9
6
96
/9
7
97
/9
8
98
/9
9
99
/0
0*
*
Deforested area in Km² per year
35000
INPE data
23
-A
08 ug
-S -9
25 ep- 2
-S 92
31 ep-O 92
12 c t-J 92
20 an-A 93
13 pr-9
31 -Jul 3
-A -9
08 ug 3
-S -9
26 ep 3
-N -93
23 ov -M 93
21 ar- 9
19 Jun 4
-A -94
15 ug9
07 Oc t 4
-F -94
25 eb-M 9
05 ay 5
-A -9
24 ug 5
-A -9
05 ug 5
-N -9
12 ov - 5
-M 9
24 ar- 5
-A 96
01 ug
-S -9
09 ep 6
-S -96
04 ep-O 96
30 c t-M 96
28 ar-9
16 -Jul 7
-A -9
29 ug 7
-S -9
07 ep- 7
-N 97
03 ov - 97
19 Jan
-A -98
20 pr-9
24 -Jul 8
-A -9
03 ug 8
-S -98
09 ep- 9
12 Oc t 8
-N -98
28 ov -J 98
29 an-M 99
07 ar-J 99
u
14 n-9
19 Jul 9
-A -9
16 ug 9
-S -99
25 ep- 99
02 Oc t
-F -9
24 eb- 9
-A 00
11 pr-0
0
21 Jul-O 00
22 c t-A 00
pr
-0
1
Mass concentration (μg/m³)
Aerosol Concentrations in Amazonia
Aerosol Concentrations in Amazonia changes from very low
values of 5-12 μg/m³ to very high 500 μg/m³
in areas affected by biomass burning
Alta Floresta Aerosol Mass Concentration 1992-2001
600
500
Coarse Mode
Fine Mode
400
300
200
100
0
Biomass burning plume covers millions of km2
Biomass burning trajectories
Altitude (m)
Freitas Longo and Silva Dias, 1996
23
-A
u
11 g-S 92
e
11 p-9
-O 2
19 ct-D 92
e
25 c-9
-M 2
ar
13 -93
-J
01 ul-S 93
e
16 p-9
-S 3
e
04 p-9
-F 3
e
06 b-9
-J 4
u
19 n-A 94
u
01 g-N 94
o
18 v-9
-A 4
pr
24 -95
-J
21 ul-A 95
u
05 g-N 95
o
02 v-9
-A 5
p
27 r-9
-A 6
u
06 g-S 96
e
29 p-S 96
e
30 p-9
-M 6
a
02 r-9
-A 7
u
22 g-A 97
u
25 g-9
-O 7
23 ct-D 97
e
19 c-9
-A 7
p
01 r-9
-A 8
ug
27 -A 98
u
01 g-9
-O 8
03 ct-N 98
o
28 v-9
-J 8
a
16 n-9
-A 9
p
24 r-9
-J 9
u
11 n-A 99
u
11 g-S 99
ep
25 -9
-O 9
c
18 t-9
-F 9
e
23 b-M 00
a
23 y-S 00
e
08 p-0
-A 0
pr
-0
1
Ratio EBC/FPM (%)
23
-A
08 ug
-S -9
2
25 ep
-S -92
e
31 p-O 92
12 ct-J 92
20 an
-A -93
p
13 r-9
- 3
31 Ju
-A l-9
3
08 ug
-S -9
3
26 ep
-N -93
23 ov-M 93
21 ar-J 94
19 un
-A -9
u 4
15 g-O 94
07 ct
-F -94
25 eb
-M -95
05 ay
-A -9
5
24 ug
-A -9
5
05 ug
-N -9
5
12 ov-M 95
24 ar
-A -96
01 ug
-S -9
6
09 ep
-S -96
04 ep-O 96
30 ct
-M -96
a
28 r-9
- 7
16 Ju
-A l-9
7
29 ug
-S -9
7
e
07 p-N 97
03 ov-J 97
19 an
-A -98
p
20 r-9
- 8
24 Ju
-A l-9
8
03 ug
-S -9
8
09 ep-O 98
12 ct
-N -9
8
28 ov-J 98
29 an
-M -99
07 ar-J 99
u
14 n-9
- 9
19 Ju
-A l-9
9
16 ug
-S -9
9
25 ep-O 99
02 ct-F 99
24 eb
-A -00
p
11 r-0
-J 0
21 ul-O 00
22 ct
-A -00
pr
-0
1
Black carbon concentration (ng/m³)
Equivalent black carbon concentration and ratio to FPM
50000
Alta Floresta black carbon fine mode aerosol 1992-2001
45000
40000
35000
30000
25000
20000
15000
10000
5000
0
Alta Floresta ratio black carbon to fine mode aerosol 1992-2001
30
25
20
15
10
5
0
Natural biogenic aerosol particles
EPMA photos from Gunther Helas, MPIC
Role of Tropical Convection
in Long Range Transport
Role of convection on long range transport
Aerosol Optical Thickness in
Amazonia
Optical Thickness AERONET
Santarem 500 nm Aerosol Optical Thickness Oct 99 - Jul 01
Rio Branco 500 nm Aerosol Optical Thickness Aug 00 - Jul 01
2.0
1.6
AOT 500 nm
1.2
0.8
1.2
0.8
0.4
0.4
Alta Floresta AOT Jan 99 -Jul 01
Balbina 500 nm Aerosol Optical Thickness Oct 99 - Jul 01
3.5
3.0
AOT 500 nm
1.0
0.6
0.4
2.5
2.0
1.5
1.0
0.5
0.2
0.0
l
n
n
n
n
v
y
g
p
ul
ar
ar
ct
pr
ov Dec Feb Apr May -Ju
-Ja 8-M -Ma 6-Ju -Au 4-O -No 2-Ja 2-M 1-A 0-Ju 30-J -Se 7-N
6- 2615
7
27
2
1
1
1
2
15
15
23
18
27
ov
ec
20
-J
an
19
-F
eb
20
-M
ar
19
-A
pr
19
-M
ay
18
-J
un
18
-J
u
17 l
-A
ug
16
-S
ep
16
-O
ct
15
-N
ov
15
-D
ec
14
-J
an
13
-F
eb
15
-M
ar
14
-A
pr
14
-M
ay
13
-J
un
13
-J
ul
21
-D
Rondonia Aerosol Optical Thickness Jan 1999 - Jul 2001
22
-J
ul
22
-S
ep
22
-N
ov
22
-J
an
22
-M
ar
22
-M
ay
22
-J
ul
22
-S
ep
22
-N
ov
22
-J
an
22
-M
ar
22
-M
ay
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
22
-J
an
22
-M
ar
22
-M
ay
AOT at 500 nm
21
-N
ct
0.0
22
-O
AOT 500 nm
0.8
26
-J
ul
1Ju
l
31
-J
ul
30
-A
ug
29
-S
ep
29
-O
ct
28
-N
ov
28
-D
ec
27
-J
an
26
-F
eb
28
-M
ar
27
-A
pr
27
-M
ay
26
-J
un
1Ju
n
2Ap
r
2M
ay
3M
ar
5O
ct
4N
ov
8Ju
l
8Ju
n
8M
ay
8Ap
r
eb
8M
ar
8F
ec
8Ja
n
ov
8D
8N
8O
ct
8Se
p
4D
ec
3Ja
n
2Fe
b
0.0
0.0
8Au
g
AOT at 500 nm
1.6
Aerosols and radiation in Amazonia
Instantaneous shortfall of 150-350 w/m2
Photosynthetic Active Radiation
Fraction of total irradiance and energy shortfall
Amazonia
avg.
África avg.
500
Shortfall (W/m²)
Amazonia
avg.
África avg.
0.5
0.91
0.87
81
121
1.0
0.84
0.78
145
210
2.0
0.72
0.63
254
353
3.0
0.63
-
337
-
Single Scattering albedo 0
0
400
-2
Fraction of background
W.m
AOT (500 nm)
300
200
Model (Background)
Alta Floresta
Rondonia
Zambia
0.86  0.04
0.87  0.03
0.79  0.03
Model (Smoke)
100
Observed
0
10
12
14
16
Time (GMT)
18
20
22
PAR modelling and measurements in Alta Floresta,
04/09/1999. Red line: no aerosols. Dashed line modelled
with AERONET measurements.
Joel Schaffer et al., 2002
Joel Schaffer et al., JGR 2001
Cloud-aerosol-mediated feedback loop on forest carbon uptake
Adapted from Jose Fuentes, 2002
Clear day
Visibility ~ ??? km
NCN ~ 500 cm-3
BC ~ 0.2 mg m-3
Smoke haze
Visibility ~ 800 m
NCN ~ 10000 cm-3
BC ~ 7 mg m-3
Ji-Paraná clean air (SW Amazon)
Ji-Paraná haze layer (SW Amazon)
25 SEP 2002
Ji-Paraná haze layer
Aerosol surface forcing - Rondônia
(Aline Procópico, 2003)
AOT(500nm)=3.5 =>~ -380W/m2
AOT(500nm)=2.0 =>~ -250W/m2
*Data from Hazemeter network NASA-GSFC
Summary of CCN Spectra
**
1000
**
**
**
-3
N (cm )
**
100
Wet Season
Pasture site
Jaru Tower
(120 - 132)
CLAIRE '98
Dry season
Pasture site
Transition
Jaru Tower
(134 - 143)
10
0.0
0.2
0.4
0.6
0.8
Sc (%)
1.0
1.2
1.4
1.6
The atmosphere has more aerosol, and more
Cloud Condensation Nuclei, during easterlies
Easterlies
Westerlies
Dry to Wet
Williams et al, 2002
Easterlies and Westerlies are two
modes of the continental scale
circulation and are part of the
intraseasonal oscillations
Wet Seasons
1980-1999
Jones and Carvalho, 2002
E
E
More lightning during the
easterlies = deep convective
systems with large Ice Water
Content
Petersen & Rutledge, 2000
E
Blakeslee,2000
E
Blakeslee, 2000
Diurnal cycle more pronounced
during easterlies, systems are larger
and with larger convective rain
fraction
Solid line= total rain, dotted line
= convective dashed line =
stratiform
Westerlies
Easterlies
Rickenbach et al. 2002
Westerly case
“maritime”
Convective
systems are
deeper during
easterlies
Easterly case
“continental”
S-pol
Rondônia
Petersen, Rutledge 2000
> 5000 fires
GOES-8 WF_ABBA
Local smoke plume
(deforestation fires)
(picture from A. Andreae)
smoke
smoke
smoke
Regional smoke plume
~5 millions km2
(Prins et al. 1998)
Freitas et al, 2005,
2007 Modelo de
transporte dos
procutos de queima
de biomassa (CATTBRAMS)
www.cptec.inpe.br/
meio_ambiente
n99
-9
9
M
ay
-9
9
Ju
l- 9
9
Se
p99
No
v99
Ja
n00
M
ar
-0
0
M
ay
-0
0
Ju
l- 0
0
Se
p00
No
v00
Ja
n01
M
ar
-0
1
M
ay
-0
1
Ju
l- 0
1
Se
p01
No
v01
Ja
n02
M
ar
-0
2
M
ay
-0
2
Ju
l- 0
2
Se
p02
No
v02
M
ar
Ja
01No
v -9
8
21De
c -9
8
09Fe
b- 9
9
31Ma
r-9
9
20Ma
y -9
9
09Ju l
- 99
28Au
g -9
9
17Oc
t- 9
9
06De
c -9
9
25Ja n
-0 0
15Ma
r-0
0
04Ma
y -0
0
23Ju n
-0 0
12Au
g -0
0
01Oc
t- 0
0
20No
v -0
0
09Ja n
-0 1
28Fe
b- 0
1
19Ap
r-0
1
08Ju n
-0 1
28Ju l
- 01
16Se
p -0
1
05No
v -0
1
25De
c -0
1
13Fe
b- 0
2
04Ap
r-0
2
24Ma
y -0
2
13Ju l
- 02
01Se
p -0
2
21Oc
t- 0
2
10De
c -0
2
Aerosol optical thickness 500 nm
AOT 500 nm
Aeronet measurements in Alta Floresta and Rondonia 1999-2002
Artaxo et al
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Alta Floresta AERONET Aerosol Optical Thickness Jan 99 - Nov 2002
1999
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
1999
2000
2001
2000
2001
2002
Aerosol Optical Thickness Aeronet Abracos Hill Rondonia 500 nm
2002
Aerossóis e Precipitação na transição da estação seca para a
estação chuvosa: medidas em Rondônia Setembro a
Dezembro de 2002
100
90
80
70
60
50
40
30
20
10
0
9-Sep
15-Sep
21-Sep
27-Sep
3-Oct
9-Oct
15-Oct
21-Oct
27-Oct
2-Nov
8-Nov
14-Nov
Date
29/12/2002
22/12/2002
15/12/2002
8/12/2002
1/12/2002
24/11/2002
17/11/2002
10/11/2002
3/11/2002
27/10/2002
20/10/2002
6/10/2002
13/10/2002
0
29/9/2002
50
22/9/2002
100
15/9/2002
150
1/9/2002
Concentration (ug/m³)
200
Cacoal
Gov,J,Teix,
Jaru
Mir,Serra
NovaVida
Pres,Med,
RolMoura
Average
8/9/2002
PM10
PM25
3-Sep
Daily Rainfall (mm)
SMOCC PM10 versus PM25 FNS Pasture Artaxo et al
250
Aerosol-cloud-precipitation feedbacks
AEROSOLS
CCN Activation
Cloud/Aerosol
Radiative
Transfer
Cloud Dynamics
IN Activation
Cloud Microphysics
PRECIPITATION
Aerosol Wet
Removal
Conceptual model:
HAIL
Graphics by Robert Simmon, NASA
TRMM-PR IWC (Z-M)
vs. LIS Flash Density
Wet Season (1998-2000): LIS Flash Density vs. IWC (7-9 km)
LIS Flash Density vs. 7-9 km IWC
4.50
5.00
y = 39.976x - 5.0843
R2 = 0.8008
4.00
Flashes/km /month
2
Flash Density (FL/km /mo)
6.00
y = 213.84x2 - 30.779x + 0.5822
R2 = 0.8164
2
3.00
2.00
1.00
4.00
=Ocean
3.50
=Ocean/Coast
=Land
0.05
0.10
0.15
IWC (g/m3)
0.20
0.25
0.30
Florida
3.00
AMZ (SON)
2.50
Sub-Congo Africa
2.00
Cntrl. Brazil
1.50
0.50
Gulf Stream
N. India
S. India
1.00
0.00
0.00
Congo
=AMZ/Monsoon
AMZ
WP Warm-pool
0.00
0.10
AMZ (DJF)
EPIC
0.12
0.14
0.16
0.18
3
IWC (g/m )
0.20
0.22
0.24
TRMM-LIS
Estação(DJF,
chuvosa
Seasonal
JJA, SON)
12/97 - 1/00
Transição estação seca
para a chuvosa
Petersen et al, 2000
Interações Aerossóis-Precipitação
1. Efeito direto da chuva em remover aerossóis
no início da estação chuvosa
2. Efeito combinado dos aerossóis na
microfísica de nuvem com a mudança na
dinâmica e termodinâmica da grande escala
e o efeito radiativo alterando a estabilidade
termodinâmica
Aerosol effects
Longo et al, 2003
• Radiation
– Colder surface, warmer
lower troposphere
=stabilizing effect – less
clouds → less rainfall?
• Cloud microphysics
Rainfall
accumulation
– Inhibit rainfall in warm
clouds
– Deep clouds?
Scheme of aerosol effects on precipitation
Warm
clouds
Accumulated rain
Khain et al, 2003
Maritime & moderate (wet)
continental clouds
(like GATE and
PRESTORM)
Cold
clouds
Dry unstable situation
(like Texas clouds)
Aerosol
Concentration
Aerosol concentration
Aerosol effects on precipitation
Aerosols (not including GCCN)
inhibit precipitation:
Aerosols can increase
precipitation:
Kaufman and Nakajima 1993;
Borys et al, 1998; 2000;
Rosenfeld and Lensky 1998;
Rosenfeld 1999, 2000;
Givati and Rosenfeld 2004
Ohashi and Kida 2002
Shepherd and Burian 2003
Amiranashvili et al 2004
Filho et al (2004)
Khain et al (2004a,b)
Khain et al (2005-QJRMS)
Tao et al (1995), Ferrier et al (1996(
Khain et al, 2001;
Axel et al (2004);
Axel and Beheng (2004)
Khain and Pokrovsky (2004);
Tao et al (2004)
Khain et al (2004a,b)
Van den Heever et al (2004)
Axel et al (2004); Tao et al (2004)
Lynn et al (2005a,b)
Teller and Levin(2006)
Lynn et al (2006)
Margaritz et al (2006)
Khain et al (2005, 2006)
Lynn et al (2006)
Jirak and Cotton (2006)
Khain and Pokrovsky (2006)
Observations in the Amazon (1)
• Koren et al (2004): scattered cumulus cloud
cover decrease with increase in biomass
burning derived aerosols
• Andreae et al (2004): biomass burning aerosol
reduce cloud droplet size and reduce rainfall
in low levels.
Flight region: SW Amazon
RACCI/LBA & SMOCC/LBA
Microphysics measurements
with aircraft:
CCN spectrum, cloud and
rain drop spectra, cloud
liquid water content,
temperature, dew point
temperature, pressure
and GPS positioning.
Flights from Sept 21 to Oct
13, 2002
Day
21
23
24
26
27
28
30a
30b
01a
01b
04a
04b
Time
(LT)
13:24
15:30
14:07
16:16
14:43
17:04
14:02
16:18
14:27
16:03
13:37
15:38
11:14
12:17
13:49
16:40
10:16
12:25
16:47
17:20
11:06
12:25
13:54
15:49
Day
04c
05a
05b
06a
06b
08
09
11a
11b
12
13
Time
(LT)
16:48
18:36
12:13
14:18
15:24
17:01
11:12
13:15
14:19
16:45
14:53
16:30
13:32
15:27
10:43
12:10
13:21
14:46
11:45
13:38
13:20
15:50
Andreae et al 2004 Smoking rain clouds over the Amazon Science
Equatorial Atlantic Ocean
Pristine Western Amazon
Smoky Thailand
Clean NW Thai Monsoon
Smoky Amazon
Pyro Cu Amazon
Pyro Cb Amazon
Height above cloud base, m
8000
7000
6000
Andreae, M. O.,
D. Rosenfeld,
P. Artaxo et al., 2004:
Smoking rain clouds
over the Amazon.
Science, 303, 13371342.
5000
4000
3000
2000
1000
0
5
10
15
20
25
30
Drop size of modal LWC, mm
35
40
Aircraft measured Modal LWC cloud drop diameter as a function of
height above cloud base, for the various aerosol regimes in Brazil
[Thailand].
Observations in the Amazon (2)
• Lin et al (2006): high values of aerosol optical thickness in the
Amazon (September-October 2000, 2003) are associated with
– Increase in rainfall rate – change in PDF
– Increase in Water Path (liquid + ice)
– Increase in high level cloud cover
– Lower values of cloud top temperatures
liquid +ice
Lin et al
2006
TRMM
Precipitation
Radar
vs
MODIS AOT
CWF ~ CAPE
• Martins, Silva Dias, Gonçalves (2007):Modeling the impacts of
biomass burning aerosols on precipitation in the Amazonian
region using BRAMS: a case study for 23 September 2002 – To
be submitted
• Martins et al 2007 : Cloud condensation nuclei from biomass
burning during the Amazonian dry-to-wet transition season –
Meteo. Atmos . Phys. (submitted)
• Gonçalves, Martins, Silva Dias, 2007 – Shape parameter
analysis usning cloud spectra and gamma functions in
numerical modelling during LBA in Amazon – accepted with
minor revisions Atmos Research.
Shape parameter in
cloud droplet
diameter
distribution
n( D ) 
Nt  D

( )  Dn
 1



 D
1

exp  
Dn
 Dn 
Frequency of shape parameters for all flights
50
LIMPO
CLEAN
POLUÍDO
POLLUTED
FREQUÊNCIA (%)
40
30
20
10
0
1
2
3
4
5
6
7
PARÂMETRO DE FORMA
8
9
10
Visible images from GOES satellite
showing the daily evolution of cloud
cover on 23 September 2002
Grid specifications for the numerical experiments with BRAMS
(Brazilian modifications to RAMS – Freitas et al 2007)
The Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional
Atmospheric Modeling System. 1: model description and evaluation .S. R. Freitas, K. M. Longo, M.
A. F. Silva Dias,,R. Chatfield, P. Silva Dias, P. Artaxo, M. O. Andreae, G. Grell, L. F. Rodrigues, A.
Fazenda, J. Panetta – Accepted Atmos.Chemis. Physics.
Grid 1
Starting and end time
Grid 2
Grid 3
Grid 4
00Z, 23 September 2002 – 00Z, 24 September 2002
Number of grid points (x, y, z) (62,62,43) (62,62,43)
(122,122,43) (42,42,43)
Horizontal grid spacing (x, y)
(64, 64 km) (16, 16 km)
(4, 4 km)
Vertical grid spacing
43 levels with variable stretching factor
Time step
120
Grid center
(10,92 ºS 62,41ºW)
30
7,5
(1, 1 km)
1,875
Observed shape parameter x CCN concentration
numerical experiments
Shape parameter
DE FORMA
PARÂMETRO
7.00
R = 0.74
6.00
5.00
4.00
3.00
2.00
300
600
900
1200
-3
CONCENTRAÇÃO
DE GOTÍCULAS
Cloud
droplet concentration
(cm-3) (cm )
1500
Microphysics parameters used in
numerical experiments
CCN300
CCN450
CCN600
CCN900
CCN900R
CCN concentration
300
450
600
900
900
Shape parameter –
2
3
4
5
5
1
1
1
1
1
None
None
None
None
Absorbing
cloud droplet and
pristine ice
Shape parameter –
all remaining
water categories
Radiative effect
and
reflecting
Parameterized radiative effect of aerosol from
biomass burning in the Amazon
Procópio et al (2004)
indicate 20% extinction
of solar radiation during
dry season in Amazon
due to aerosol
- 4% reflected
- 16% absorption in
lower layers: adjusted
according to observed
CCN profile
Vertical profile of CCN
concentration measurements on
23 September 2003
Control simulation CCN300
Visible Satelite and Radar
Total Condensate CCN300
Vertical structure of cloud and ice
water mixing rate observed at the time
of maximum liquid water path
CCN300
CCN450
CCN600
CCN900
ccn300
ccn450
ccn600
ccn900
CCN 900 & CCN 900R
MICROPHYSICS VARIABLE
UNIT
CCN300
CCN450
CCN600
CCN900
CCN900R
MAX. CLOUD DROPLET CONC.
cm-3
192.092
295.961
424.282
645.883
641.302
0.008
0.008
0.159
0.562
0.594
0.271
0.193
0.372
2.723
2.229
0.009
0.009
0.024
0.090
0.079
PRISTINE SPTA
PRISTINE STA
kg/m2
g/m
2
2
SNOW SPTA
kg/m
SNOW STA
g/m2
0.282
0.354
0.529
2.018
1.345
AGGREGATES SPTA
kg/m2
0.002
0.014
0.393
1.193
1.186
AGGREGATES STA
g/m2
0.006
0.009
0.538
7.353
6.412
0.005
0.016
0.412
1.273
1.238
0.004
0.006
0.662
3.181
2.780
0.058
0.052
0.355
0.581
0.592
0.190
0.019
0.562
1.064
1.020
0.082
0.100
1.343
3.700
3.690
0.753
0.581
2.664
16.340
13.785
1.013
1.286
2.293
2.693
2.475
64.600
56.273
61.452
52.061
51.359
1.948
1.931
4.222
4.899
4.946
50.217
33.677
36.968
28.597
30.105
2.965
3.229
7.011
9.251
9.349
2
GRAUPEL SPTA
kg/m
GRAUPEL STA
g/m2
HAIL SPTA
kg/m
2
2
HAIL STA
g/m
TOTAL ICE SPTA
kg/m2
TOTAL ICE STA
CLOUD WATER SPTA
g/m
2
kg/m
2
2
CLOUD WATER STA
g/m
RAIN WATER SPTA
kg/m2
RAIN WATER STA
g/m
2
2
TOTAL WATER SPTA
kg/m
TOTAL WATER STA
g/m2
115.569
90.532
101.083
96.998
95.249
%
33.280
27.974
24.012
18.965
17.589
RAIN RATE SPTA
mm/h
4.972
5.441
15.571
19.340
20.169
RAIN RATE STA
mm/h
0.126
0.091
0.134
0.119
0.125
MAX. AC. PRECIPITATION
mm
16.936
22.154
53.744
87.658
100.883
AVE. AC. PRECIPITATION
mm
0.964
0.685
1.032
0.943
0.991
MAX. VERTICAL VELOCITY
m/s
2.250
2.390
5.160
7.830
8.370
1.38
1.57
1.63
1.75
1.52
MAX. CLOUD COVER
SURF. ENERGY GAIN
MJ/m
2
STA –
Space &
Time
Average
SPTA –
Space
Peak &
Time
Average
Time evolution of precipitation fields in terms of
maximum and average values
10.0
120.0
100.0
120.0
60.0
4.0
40.0
2.0
20.0
0.0
0.0
300
450
600
900
900R
100.0
1.0
80.0
0.8
60.0
0.6
40.0
0.4
20.0
0.2
0.0
0.0
2
TOTAL WATER STA (g/m )
6.0
MAX. AC. RAIN (mm)
80.0
1.2
TOTAL WATER STA
AVER. AC. RAIN
SCENARIO
300
450
600
SCENARIO
900
900R
AVER. AC. RAIN (mm)
8.0
2
TOTAL WATER SPTA (kg/m )
TOTAL WATER SPTA
MAX. AC. RAIN
Partitioning of the precipitation according to the
intensity of averaged rain rate – change in PDF
RAIN RATE STA (mm/h)
0.16
PREC. > 5 mm/h
1 mm/h < PREC. < 5 mm/h
PREC. < 1 mm/h
0.12
0.08
0.04
0
CCN300
CCN450
CCN600
SCENARIO
CCN900
CCN900R
Time evolution of rain rate in terms of maximum values on grid
60.0
CCN300
RAIN RATE (mm/h)
CCN450
45.0
CCN600
CCN900
CCN900R
30.0
15.0
0.0
9
11
13
15
TIME (h)
17
19
21
Time evolution of ice path in terms of maximum values on grid
16.0
MAX. ICE PATH (kg/m2)
CCN300
CCN450
12.0
CCN600
CCN900
CCN900R
8.0
4.0
0.0
12
14
16
TIME (h)
18
20
Time evolution of ice path in terms of average values on grid
0.100
AVER. ICE PATH (kg/m2)
CCN300
CCN450
0.075
CCN600
CCN900
CCN900R
0.050
0.025
0.000
12
14
16
TIME (h)
18
20
Time evolution of
(a) max updraft
(b)max
downdraft
0.0
10.0
CCN450
8.0
VERT. VELOCITY (m/s)
VERT. VELOCITY (m/s)
CCN300
CCN600
CCN900
6.0
CCN900R
4.0
2.0
0.0
-0.4
-0.8
-1.2
-1.6
-2.0
9
12
15
TIME (h)
18
21
9
12
15
TIME (h)
18
21
Conclusions from numerical experiments
Amazon aerosol from biomass burning and rainfall
interactions
• Enhanced CCN concentration increase maximum
precipitation, maximum water content and maximum ice
content but eventually reduce average precipitation (basically
due to enhanced evaporation effects)
• Warm rain is suppressed and ice content enhanced with
increased CCN
• Total cloud cover decreases with increase of CCN (dominated
by shallow cumulus; increase in ice content increase high
level cover
• In this particular case, radiation absorption by aerosol delay
precipitation but higher rainfall amounts are obtained due to
decrease in low level thermodynamic stability
Próximos passos (1)
• Uso de constelações de satélites em
combinações que permitem identificar
relações empíricas entre aerossóis e
precipitação
Próximos passos
(2)
Definição de novas campanhas
de medidas para obtenção de
dados de validação com
enfoque na microfísica de
nuvens
Próximos passos (3)
• Modelos de alta resolução para estudo dos
processos microfísicos
• Parametrizações simplificadas do efeito dos
aerossóis na precipitação para uso em
modelos de clima
Obrigada!