This document discusses several key issues related to the Large Scale Biosphere Atmosphere Experiment in Amazonia (LBA), including carbon cycling and controls, atmospheric chemistry interactions, aerosol-cloud interactions, land use change effects, and the role of droughts. It provides background on topics like tropical deforestation drivers over time, the focus of the LBA experiment, carbon emissions from land use change, and forest cover changes in Amazonia from 2000-2005. Graphs and figures show data on deforestation rates in Brazil, greenhouse gas emissions in Brazil, precipitation and temperature trends, and aerosol distributions and impacts on clouds and radiation in Amazonia.

2. Instituto de Estudos Avançados,
USP Ribeirão Preto 26/Agosto/2011
Prof. Paulo Artaxo
Institute of Physics,
University of São Paulo, Brazil
artaxo@if.usp,br

3. Focus of the Large Scale Biosphere
Atmosphere Experiment in Amazonia
Some key issues that are important from the scientific, public
policies and conservation in Amazonia:
• Carbon cycling and the physiological and climatic controls
• Atmospheric chemistry in terms of oxidants and
biosphere-atmosphere interactions (O3, VOCs, NOx, etc)
• Aerosol-clouds interactions and aerosol radiative forcing
• Land Use Change and its effects, including carbon cycling,
biomass burning emissions, modeling and social drivers.
• Role of disturbances (droughts of 2005 and 2010)
• Effects of climate change in Amazonia

4. 26 years ago…

5. Tropical deforestation drivers

6. Net CO2 Emissions from LUC in Tropical Countries
600
2000-2005 RA Houghton 2009,
CO2 emissions (TgC y-1)
500 Brazil 60%
400
Indonesia
300
Cameroon Colombia
Venezuela
200 Nicaragua
Peru
Rep.Dem.Congo
India
100 Nigeria Philippines Nepal
0
4-2% 2-1% <1%
Land use change was responsible for estimated net emissions of 1.5 PgC per year over the
last 15 years.
This is 12% of total emissions in 2008, down from 20% in the 1990´s

7. Forest clearing and forest cover in the humid tropical
forest biome, 2000–2005
Forest loss in Brazil accounts for 48% of total biome clearing, nearly four times that
of the next highest country, Indonesia, which accounts for 13%.
Hansen M. C. et.al. PNAS 2008

8. Deforestation was reduced from
27,000 Km² in 2004 to 7,000 Km² in
2010.
A very dynamical system, and we
need to know what effects on the
ecosystem these changes have
produced
Deforestation in Amazonia 1977-2009 in km² per year
35000
30000 27.000 Km² in 2004
Desflorestation (km² per year)
25000
20000
15000 7.000 Km²
in 2010
10000
5000
0
88/89
89/90
90/91
91/92
92/94
94/95
95/96
96/97
97/98
98/99
99/00
00/01
01/02
02/03
03/04
04/05
05/06
06/07
07/08
08/09
77/88*
* annual average per decade Data from INPE, 2009
What public policies are needed to sustain this reduction?

9. Brazilian Greenhouse Gases Emission Inventory 2005
24
56 12
5
3
Deforestation Agrobusines Energy+Transport
Industry Landfills
MCT Feb 2010
Copenhagen Commitment: Reduction in 80% emissions from deforestation
in 2015 from 2004. Same target in the Brazilian law passed in Congress.

10. Current pyrogeography
on Earth, illustrated by
(A) net primary
productivity (NPP, g C NPP
m-2 year-1) from 2001 to
2006, and (B) annual
average number of
fires observed by
satellite
Fires
Bowman et al., Science, 2009

11. Global Deforestation Fires:
Responsible for 19% of
global radiative forcing
Estimated contribution of fire
associated with deforestation
to changes in radiative forcing
compared to 1750, assuming a
steady state for other fire
emissions.
Bowman et al., Science, 2009

12. Global Distribution of Carbon Monoxide (CO) from MOPPIT

13. (mm) (b) Santarem k67 (oC)
(mm) (a) Manaus k34 (oC) 28
28 300 27
300
26
27 200
25
200
26 24
100
100 23
25
0 22
Jan Mar May Jul Sep Nov
0 24
Jan Mar May Jul Sep Nov
(mm) (c) Santarem k83 (oC)
28
300 27
26
200
25
(d) Jarú (JRU)
(mm) (oC) 24
100
28 23
300 27 0 22
Jan Mar May Jul Sep Nov
26
200
25
24
100
23
0 22 (mm) (e) Javaés (JAV) (oC)
Jan Mar May Jul Sep Nov
29
300
28
200 27
(mm) (f) Sinop (SIN) (oC)
400 27
26
LBA Flux Towers 100
26
25
300
0 24
25 Jan Mar May Jul Sep Nov
200
24
100
23 Climatological
0 22 (mm) (g) Pé deGigante (oC) precipitation (mm mo-1)
Jan Mar May Jul Sep Nov
(PEG) 26 Top tower
300
24
precipitation (mm mo-1)
200 22 Climatological
temperature (oC)
20
100 Top tower
18 temperature (oC)
0 16
Jan Mar May Jul Sep Nov Rocha et al. 2010

14. 8
7.5
7
6.5
LBA/RAINFOR - Aboveground 6
Soil pH
5.5
5
wood production for 97 sites
4.5
4
3.5
3
0 500 1000 1500 2000 2500
Malhi et al, 2010 Distance from Andes (km)
Above-Ground Wood Production (t C ha-1 year-1)
6
Ecuador
5
S Peru
4 N Peru
Growth
Bolivia
Venezuela
3 Guyanas
Brazil
2
1
0
PAK-03
PAK-02
BNT-07
BNT-04
BDF-01
BDF-10
BDF-14
BDF-09
BDF-13
BDF-12
BNT-05
HCC-21
RIO-01
LSL-02
LSL-01
TAP-02
TAP-01
TAP-03
TIP-03
CEL-15
ELD-03
TAM-04
SUC-02
TAM-05
TAM-07
LIN-01
CRP-01
TAM-02
SCR-02
SCR-01
SCR-03
MNU-04
MNU-03
MNU-06
MNU-01
JEN-06
JEN-09
ALP-22
JEN-10
CYB-01
YAN-01
CAX-02
CUZ-04
CUZ-03
BCI-50
NOR-01
BCI-01
JAS-03
JAS-02
Site
Forest in Amazonia are accumulating carbon at a rate of about 0.7 tC/ha/year from 1998-2010

15. WP4 EOS project
Amazonica
WP3 Biomass
WP2 Ecosystem
WP1 Atmospheric
approach touse
fluxesmeasure
inventories
land
concentrations
regional carbon balance

16. CO2 flux – tropical forest Santarem (k83)
CO2 fluxes: annual sum is prone to Reco ~ Respiration
uncertainties (nighttime flux)
Miller 2004, Ecol Appl; Goulden 2004 Ecol Appl, 2006 JGR
Saleska 2003, Science; Hutyra 2007 JGR
GPP ~ daytime flux – Reco
High numbers are observed in the
tropics (Miller 2004, Ecol Appl)
Reco u*filtered
Reco
Dry season Wet season GPP
sink loss
... but leads to a reasonable (Humberto Rocha, USP, 2011)
interpretation of seasonality

17. 320 meters tall tower in Amazonia for long term monitoring of trace gases and aerosols

18. Two plots with rain exclusion
(drought experiments) in Amazonia
Intact forests seem resilient to substantial
seasonal drought, but begin to die back after
several successive years of drought
Nepstad et al (2007), Ecology, Fisher et al. (2007), Global Change Biology, Brando et al
(2008), Philosophical Transactions of the Royal Society B, Sotta et al. (2008), Global
Change Biology

19. Two strong droughts in 2005 and 2010: Variability of Rio Negro during drought years
Rio Negro mean water levels (m) at Manaus-AM during drought years
1963 2005
2010
Lowest levels at Manaus

20. Response to interannual drought
Model-Predicted Response Empirical Test: the 2005 drought
Hadley modeled GPP & precip in central Tropical Rainfall Measuring Mission (TRMM)
Amazonia in years relative to El Nino drought satellite precip anomalies in 3rd quarter 2005
Forest Photosynthesis
30
(Mg C ha-1 yr-1)
20 El Nino Drought
10 Precip (mm mo-1)
(Jones et al.,
2001) 200
100
0
1 2 3 4 5 6 7 8
Years: -3 -2 -1 0 1 2 3 4 (Saleska et al., Science, 2007)

21. Drought sensitivity of
the Amazon Rainforest
Annual aboveground biomass change
during the 2005 interval.
Effect of the 2005
drought in the
carbon balance
in Amazonia
Phillips et al. 2009 Science

22. Drought of 2010 in Amazonia
Manaus river level for 2005 and 2010
Spatial patterns of standardized anomalies of
normalized difference vegetation index (NDVI)
and enhanced vegetation index (EVI).
Xu et al., GRL 2011

23. Aerosol-clouds interactions and aerosol radiative forcing
• Optical, physical properties and chemical
composition of biomass burning aerosols
• Properties of natural biogenic aerosols
• Cloud Condensation Nuclei (CCN) properties
• Long term measurements of ground, vertical
distribution and column integrated optical
properties
• Clouds physical properties and distribution
coupled with cloud droplets microphysical
properties.

24. Aerosol Particles: Coupling of Terrestrial Ecosystems
and the Hydrologic Cycle
Energy and Water Exchange and Processing

25. Large scale aerosol distribution in
Amazonia
• Severe health effects on the Amazonian
population (about 20 million people)
• Climatic effects, with strong effects on
cloud physics and radiation balance.
• Changes in carbon uptake and
ecosystem functioning

26. Amazonia - Average aerosol forcing clear sky
Top: - 10 w/m²
Atmosphere: + 28 w/m²
Surface: - 38 w/m²
Conditions: surface: forest vegetation AOT (=0.95 at 500nm); 24 hour average
7 years (93-95, 99-02 dry season Aug-Oct)

27. Hydrological cycle critical for Amazonia.
Variety of cloud structure caused
by different CCN amounts and
other cloud dynamic issues
Pyrocumulus Clouds
“Green Ocean Clouds“

28. Aerosol-cloud-precipitation feedbacks
CCN = cloud condensation nuclei and IN = ice nuclei.
AEROSOLS
CCN Activation Ice Nuclei Activation
Cloud/Aerosol Cloud Microphysics
Radiative
Transfer
Cloud Dynamics PRECIPITATION Aerosol Wet
Removal

29. Cloud Physics in the Pristine Atmosphere

30. Suppression of low cloud formation by aerosols in Amazonia
Cloud fraction as function of aerosol optical depth (OD)..
On average, the cloud fraction decreases to less than 1/8 of the cloud fraction in clean conditions when
OD = 1. (Koren and Kaufman, 2003)

31. Relationships between cloud properties and aerosol loading in Amazonia
Microphysics absorption effects
Aerosol Optical Thickness
Koren et al., Science 2008

32. Ice nuclei from biogenic
emissions and Sahara dust in
Central Amazonia
Dust relation to ice-nucleus measurements. Dust
concentrations during AMAZE-08. a, GEOS-Chem
simulated dust from 2–6 March at 18 UTC. The field
site, shown as a black diamond, typically fell near the
edge of the plumes. Fine-dust concentrations from
PIXE measurements (black rectangles; µg/m³,
dp<2µm.

33. Precipitação na Amazônia em mm/mês

34. Rainfall trends in the Brazilian Amazon 1925-2008:
Decreasing at Pará and Amazon states?
Annual
Wet
Dry
Satyamurty et al., 2010

35. Rainfall trends in the Brazilian Amazon 1925-2008:
increasing?
Satyamurty et al., 2010

36. Rainfall trends in the Brazilian Amazon 1925-2008: whole region
No biomass burning smoke Heavy biomass burning smoke
Annual
Wet
Dry
Satyamurty et al., 2010

37. Aerosol effects on
the Net Plant Productivity
CO2 Concentration Aerosol Concentration
+ -
+ Temperature +
+ +
Photosynthesis
+? BVOC emissions
Kulmala et al., 2004

38. Strong aerosol effect on forest photosynthesis diffuse radiation
have a large effect on CO2 fluxes
Amazonia Rondonia Forest site 2000-2001
Dry Season - NEE increase: 46 %
0
Wet Season - NEE increase: 24 %
NEE (µmolm s )
-2 -1
-10
-20
-30
Increase in aerosol loading
0.0 0.2 0.4 0.6 0.8 1.0
Relative Irradiance

39. Amazon shortwave aerosol radiative forcing (SWARF) at the top of
the atmosphere (TOA) from 2000 to 2009 using shortwave (SW)
flux at the TOA from the CERES sensor and AOD from MODIS.
Table 1 – Shortwave aerosol radiative
forcing for Amazon region during the
biomass burning season of the years
2000 to 2009.
Year Valid Cells SWARF (W/m2)
2000 1163 -12.3 + 12.5
2001 1492 -8.1 + 13.3
2002 1447 -12.8 + 11.8
2003 1392 -12.0 + 12.5
2004 185 -13.4 + 17.6
2005 1799 -15.0 + 13.4
2006 1654 -9.5 + 12.9
2007 1731 -13.9 + 17.1
AERONET time series of the aerosol optical depth at
2008 1665 -8.2 + 15.9
500 nm from 2000 to 2009 over two Amazon sites:
2009 1405 -4.7 + 11.0 Alta Floresta and Rio Branco.
Average -10.6 + 4.2

40. Large scale radiative forcing in Amazonia from 2000 to 2007
CERES (Clouds and the Earth's Radiant Energy System) and MODIS

41. Effects of climate change in Amazonia
Complex Earth System Models are needed to study
all these interacting and simultaneous drivers
LUCC
Climate
Fire Change
Climate Nobre et al., 2011
Extremes
Ecosystems of Amazonia - environmental drivers of change

42. Warming of 0.8°C in Amazonia (Victoria et al., Total deforested area (clear-cutting) is 730,000 km2
2004. J Climate); IPCC AR4: 3°C to > 5°C in 2100! in Brazilian Amazonia (18%) (INPE, 2008)
GLOBAL WARMING DEFORESTATION
Anthropogenic and Natural Drivers of Environmental
Change in Amazonia
DROUGHTS FOREST FIRES
Droughts (e.g., 2005) can become frequent
(Cox et al., 2008 Nature) Forest fire frequency ↑ (Nepstad et al., 2006)

43. What direction the Brazilian agriculture will take?
The socio-economic drivers matters a lot!!!

44. Impacto das Queimadas na saúde da população amazônica
Os primeiros estudos tiveram início em 1992 com medidas
de material particulado e Hg gasoso com o objetivo de
identificar a composição físico química, concentrações,
tamanho da partícula e as propriedades toxicológicas
da fumaça.

45. Quais os riscos da exposição humana à fumaça?
 Quais poluentes ?
Qual a magnitude da exposição?
O risco é o mesmo para todos ?
 Qual o custo-benefício do controle?

46. Efeitos significativos
Período chuvoso sobre a saúde humana
8 a 10 µg.m³ Exposição de elevada
100 a 300 partículas cm³ magnitude
Periodo seco
100 a 300 µg.m-³
15.000 a 30.000
partículas cm-³
Combustão de partículas,
Brônquio
s
Cabelo humano compostos orgânicos, etc Bronquíolos
Bronquíolos
respiratóri
Poeira, pólen, etc os
Alvéolos
Areia fina de praia

47. Mass concentration (μg/m³)
23
-A
100
200
300
400
500
600
0
08 ug
-S -9
25 ep 2
-S -9
31 ep 2
-O -9
12 ct- 2
-J 9
urbana
20 a n 2
-A -9
p 3
13 r-9
n=735
31 -Ju 3
-A l-9
08 ug 3
-S -9
26 ep 3
-N -9
23 ov 3
-M -9
21 ar 3
- -9
19 Ju n 4
-A -9
15 ug - 4
-O 9
07 ct 4
-F -9
25 eb 4
-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 -9
01 ug 6
-S -9
09 ep 6
-S -9
04 ep 6
-O -9
30 ct 6
-M -96
28 ar-
9
16 -Ju 7
-A l-9
29 ug 7
-S -97
07 ep
-N -9
03 ov 7
-J -9
19 a n 7
-A -9
p 8
20 r-9
24 -Ju 8
-A l-9
03 ug 8
-S -98
09 ep
- -9
12 Oct 8
-N -9
28 ov 8
-J -9
29 a n 8
-M -9
Amazônia Subequatorial
07 ar 9
-J -99
u
14 n-9
19 -Ju 9
-A l-9
16 ug 9
Alta Floresta Aerosol Mass Concentration 1992-2001
-S -99
25 ep
-O -9
Exposição humana não necessariamente
ocorre no local da queima. Efeito na área
02 ct 9
-F -9
24 eb 9
-A -00
p
11 r-0
- 0
Fine Mode
21 Ju l
-O -00
22 ct
-A -0
Coarse Mode
15 pr- 0
-S 01
ep
-0
1
Queimadas e Doenças na Amazonia
AF
2000 - 2005
apresentou os piores
mortalidade por doenças
Amazônia Subequatorial
respiratórias no período de
indicadores de morbidade e

48. Poluição do Ar – Efeitos na Saúde
Mortalidade
Hospitalização
Visitas de emergência (PS)
gravidade do
Visitas médicas
efeito
Redução da atividade física
Uso de medicação
Sintomas respiratórios
Alteração na função pulmonar
Efeitos sub-clínicos
Proporção da população afetada

49. Efeitos das Queimadas na Saúde
Média das taxas de internação por asma em menores de
cinco anos (por 10.000) dos municípios maiores de 25
mil habitantes do estado de Mato Grosso: 2000 - 2005
média asma 2000-2005
Alta Floresta 349,7
Colíder 265,3
Juína 173,0
Sorriso 120,4
Sinop 82,1
Cuiabá 21,2
Tangará da Serra 11,2
0,0 50,0 100,0 150,0 200,0 250,0 300,0 350,0 400,0

50. Média das taxas de internação por pneumonia em menores
de cinco anos (por 10.000) dos municípios < de 25 mil
habitantes em MT 2000 - 2005
média_pneumonia
Tangará da Serra 1578,0
Tangará da Serra
Alta Floresta 757,0
Colíder 736,7
Sinop 363,0
Juína 261,5
Sorriso 189,7
Cuiabá 189,1
0,0 500,0 1000,0 1500,0 2000,0

51. RESULTADOS DOS ESTUDOS
Estudo de Asma and Alergias em escolares
(ISAAC – fase I) na região de Alta Floresta
e Tangara da Serra
6370 estudantes
Maior prevalência de asma na região foi em meninos
(6-7 anos) > 20%

52. Estimativas da redução do fluxo expiratorio - peak flow
( l/min) para cada aumento de 10 μg/m3 PM2.5 para todos
os estudantes
Redução do fluxo de 0.31 and 0.34 l/min para a exposição ao PM2.5 no mesmo dia e de
0.18 - 0.21 l/min para efeitos acumulados de dois dias.

53. São Paulo State sugar cane
biomass burning:
Also large atmospheric impacts
Significant health impacts
Change in nutrient deposition
Change in the hydrological cycle.

54. Obrigado pela atenção!!!

55. Examples of the spatial distribution of the SWARF at TOA
2005 2005
SWARF (W/m2) AOD
2008 2008
SWARF (W/m2) AOD
The higher the AOD the higher is the correlation between SWARF
and AOD. For lower AOD values the influence of other parameters
such as the surface reflectance also become important.

56. Impact of Manaus City on the Amazon Green Ocean atmosphere: aerosol and
ozone production, precursor sensitivity and transport
Kuhn et al., ACPD 2010

57. Potential Vegetation Simulated by the PVM2.0Reg (50 km)
Figure 1. Natural vegetation reference map [Salazar, 2009] and actual potential vegetation simulated by
CPTEC•PVM2.0Reg model under the 1961–1990 mean climate. The division of the Amazon domain is
-
indicated by the continuous box in the natural vegetation map. Region 1: Southeast (5.25°S–13.75°S;
50.75°W–63.75°W); Region 2: Northeast (4.75°N–5.25°S; 50.75°W–63.75°W); Region 3: Northwest
(4.75°N–5.25°S; 63.75°W; 75.25°W); Region 4: Southwest (5.25°S–13.75°S; 63.75°W–75.25°W).
Salazar and Nobre, 2010 GRL

58. Potential Dominant Biome in Response to ∆T, ∆P and CO2 “fertilization” effect
Figure 2. Potential dominant
biome simulated by CPTEC• -
PVM2.0Reg for different
temperature anomalies,
precipitation changes, and
fertilization effects (0%, 25%
and 100%) for SRES A2 climate
scenario for the period 2070–
2099, and for the regions of
Amazonia (indicated in Figure
1): (a–c) southeast, (d–f)
northeast, (g–i) northwest and
(j–l) southwest Amazonia.
The climate anomalies
projected by regional (ETA CCS,
RegCM3 and HadRM3P) and
selected global (GISS• ]ER,
ECHAM5, HadCM3 and M:
average of fifteen global models
from IPCC) models plotted for
each region.
Salazar and Nobre, 2010 GRL