Prof. Paulo Artaxo

Institute of Physics,

University of São Paulo, Brazil

artaxo@if.usp,br

Instituto de Estudos Avançados,

USP Ribeirão Preto 26/Agosto/2011

Focus of the Large Scale Biosphere Atmosphere Experiment 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

Some key issues that are important from the scientific, public

policies and conservation in Amazonia:

26 years ago…

Tropical deforestation drivers

Net CO2 Emissions from LUC in Tropical Countries

2000-2005

0

100

200

300

400

500

600

Brazil

Indonesia

CO

2 em

issi

ons

(TgC

y-1

) RA Houghton 2009,

60%

Venezuela

Rep.Dem.Congo

Nigeria

4-2%

Cameroon

Peru

Philippines

2-1%

Colombia

Nicaragua

Nepal

India

<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

Forest clearing and forest cover in the humid tropical forest biome, 2000–2005

Hansen M. C. et.al. PNAS 2008

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%.

0

5000

10000

15000

20000

25000

30000

35000

77/8

8*

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

Desflore

sta

tion (

km

² per

year)

Deforestation in Amazonia 1977-2009 in km² per year

* annual average per decade Data from INPE, 2009

27.000 Km² in 2004

7.000 Km² in 2010

What public policies are needed to sustain this reduction?

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

Copenhagen Commitment: Reduction in 80% emissions from deforestation

in 2015 from 2004. Same target in the Brazilian law passed in Congress.

56

24

12

5

3

Deforestation Agrobusines Energy+Transport

Industry Landfills

Brazilian Greenhouse Gases Emission Inventory 2005

MCT Feb 2010

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

Bowman et al., Science, 2009

NPP

Fires

Estimated contribution of fire associated with deforestation to changes in radiative forcing compared to 1750, assuming a steady state for other fire emissions.

Global Deforestation Fires: Responsible for 19% of global radiative forcing

Bowman et al., Science, 2009

Global Distribution of Carbon Monoxide (CO) from MOPPIT

Climatological precipitation (mm mo-1)

Top tower precipitation (mm mo-1)

Climatological temperature (oC)

Top tower temperature (oC)

Jan Mar May Jul Sep Nov

0

100

200

300

(mm)

24

25

26

27

28

(oC)(a) Manaus k34

Jan Mar May Jul Sep Nov

0

100

200

300

(mm)

22

23

24

25

26

27

28

(oC)

(d) Jarú (JRU)

Jan Mar May Jul Sep Nov

0

100

200

300

(mm)

22

23

24

25

26

27

28

(oC)(c) Santarem k83

Jan Mar May Jul Sep Nov

0

100

200

300

(mm)

22

23

24

25

26

27

28

(oC)(b) Santarem k67

Jan Mar May Jul Sep Nov

0

100

200

300

(mm)

24

25

26

27

28

29

(oC)(e) Javaés (JAV)

Jan Mar May Jul Sep Nov

0

100

200

300

(mm)

16

18

20

22

24

26

(oC)(g) Pé deGigante (PEG)Jan Mar May Jul Sep Nov

0

100

200

300

400

(mm)

22

23

24

25

26

27

(oC)(f) Sinop (SIN)

Rocha et al. 2010

LBA Flux Towers

0

1

2

3

4

5

6

JEN

-06

CY

B-0

1

JEN

-09

HC

C-2

1

MN

U-0

4

CU

Z-0

4

JAS

-03

YA

N-0

1

CU

Z-0

3

PA

K-0

3

TA

M-0

4

BC

I-50

MN

U-0

3

JAS

-02

LS

L-0

2

SU

C-0

2

ALP

-22

CE

L-15

ELD

-03

TA

M-0

5

MN

U-0

6

TA

M-0

7

LIN

-01

NO

R-0

1

MN

U-0

1

RIO

-01

PA

K-0

2

CR

P-0

1

TA

P-0

2

BN

T-0

7

TIP

-03

BN

T-0

5

TA

M-0

2

TA

P-0

1

BN

T-0

4

TA

P-0

3

JEN

-10

SC

R-0

2

LS

L-0

1

BC

I-01

BD

F-0

1

BD

F-1

0

BD

F-1

4

BD

F-0

9

CA

X-0

2

BD

F-1

3

BD

F-1

2

SC

R-0

1

SC

R-0

3

Gro

wth

Site

Above-Ground Wood Production (t C ha-1 year-1)

Venezuela

Brazil

N PeruS Peru

Bolivia

Guyanas

Ecuador

LBA/RAINFOR - Aboveground

wood production for 97 sites

Malhi et al, 2010

3

3.5

4

4.5

5

5.5

6

6.5

7

7.5

8

0 500 1000 1500 2000 2500

Distance from Andes (km)

So

il p

H

Forest in Amazonia are accumulating carbon at a rate of about 0.7 tC/ha/year from 1998-2010

WP1 Atmospheric

concentrations

WP2 Ecosystem

fluxes

WP3 Biomass

inventories

WP4 EOS

land use

Amazonica project

approach to measure

regional carbon balance

(Humberto Rocha, USP, Emanuel Gloor, Leeds, 2011)

CO2 fluxes: annual sum is prone to uncertainties Miller 2004, Ecol Appl; Goulden 2004 Ecol Appl, 2006 JGR Saleska 2003, Science; Hutyra 2007 JGR

Reco

GPP

Reco ~ Respiration (nighttime flux)

GPP ~ daytime flux – Reco

High numbers are observed in the tropics (Miller 2004, Ecol Appl)

... but leads to a reasonable interpretation of seasonality

Reco u*filtered

Dry season

sink

Wet season

loss

CO2 flux – tropical forest Santarem (k83)

(Humberto Rocha, USP, 2011)

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

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

Two plots with rain exclusion (drought experiments) in Amazonia

Rio Negro mean water levels (m) at Manaus-AM during drought years

1963

2010

2005

Lowest levels at Manaus

Two strong droughts in 2005 and 2010: Variability of Rio Negro during drought years

Response to interannual drought

1 2 3 4 5 6 7 8

10

20

30

0

100

200

For

est

Phot

osynt

hesis

(Mg

C h

a-1 y

r-1)

El Nino Drought

Hadley modeled GPP & precip in central Amazonia in years relative to El Nino drought

Model-Predicted Response

(Jones et al., 2001)

Years: -3 -2 -1 0 1 2 3 4

Precip

(mm m

o-1)

Empirical Test: the 2005 drought

Tropical Rainfall Measuring Mission (TRMM) satellite precip anomalies in 3rd quarter 2005

(Saleska et al., Science, 2007)

Annual aboveground biomass change during the 2005 interval.

Effect of the 2005

drought in the

carbon balance

in Amazonia

Phillips et al. 2009 Science

Drought sensitivity of the Amazon Rainforest

Spatial patterns of standardized anomalies of normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI).

Drought of 2010 in Amazonia Manaus river level for 2005 and 2010

Xu et al., GRL 2011

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.

Aerosol Particles: Coupling of Terrestrial Ecosystems and the Hydrologic Cycle

Energy and Water Exchange and Processing

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

Conditions: surface: forest vegetation AOT (=0.95 at 500nm); 24 hour average 7 years (93-95, 99-02 dry season Aug-Oct)

Top: - 10 w/m²

Atmosphere: + 28 w/m²

Surface: - 38 w/m²

Amazonia - Average aerosol forcing clear sky

Pyrocumulus Clouds

“Green Ocean Clouds“

Hydrological cycle critical for Amazonia. Variety of cloud structure caused by different CCN amounts and other cloud dynamic issues

Cloud Microphysics

PRECIPITATION

CCN Activation

Cloud/Aerosol Radiative Transfer

AEROSOLS

Ice Nuclei Activation

Aerosol Wet Removal

Aerosol-cloud-precipitation feedbacks

Cloud Dynamics

CCN = cloud condensation nuclei and IN = ice nuclei.

Cloud Physics in the Pristine Atmosphere

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)

Relationships between cloud properties and aerosol loading in Amazonia

Koren et al., Science 2008

Microphysics absorption effects

Aerosol Optical Thickness

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.

Ice nuclei from biogenic emissions and Sahara dust in Central Amazonia

Precipitação na Amazônia em mm/mês

Satyamurty et al., 2010

Rainfall trends in the Brazilian Amazon 1925-2008: Decreasing at Pará and Amazon states?

Annual

Wet Dry

Satyamurty et al., 2010

Rainfall trends in the Brazilian Amazon 1925-2008: increasing?

Rainfall trends in the Brazilian Amazon 1925-2008: whole region

Satyamurty et al., 2010

No biomass burning smoke Heavy biomass burning smoke

Annual

Wet

Dry

Temperature

CO2 Concentration

Photosynthesis BVOC emissions

Aerosol Concentration

+

+

+

+ +

+?

-

Aerosol effects on the Net Plant Productivity

Kulmala et al., 2004

0.0 0.2 0.4 0.6 0.8 1.0

-30

-20

-10

0Wet Season - NEE increase: 24 %

NE

E (

µm

olm

-2s

-1)

Relative Irradiance

Dry Season - NEE increase: 46 %

Amazonia Rondonia Forest site 2000-2001

Strong aerosol effect on forest photosynthesis diffuse radiation have a large effect on CO2 fluxes

Increase in aerosol loading

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

2008 1665 -8.2 + 15.9

2009 1405 -4.7 + 11.0

Average -10.6 + 4.2

AERONET time series of the aerosol optical depth at

500 nm from 2000 to 2009 over two Amazon sites:

Alta Floresta and Rio Branco.

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.

Large scale radiative forcing in Amazonia from 2000 to 2007

CERES (Clouds and the Earth's Radiant Energy System) and MODIS

Ecosystems of Amazonia - environmental drivers of change

LUCC

Fire Climate Change

Climate Extremes

Complex Earth System Models are needed to study all these interacting and simultaneous drivers

Nobre et al., 2011

Effects of climate change in Amazonia

Total deforested area (clear-cutting) is 730,000 km2 in Brazilian Amazonia (18%) (INPE, 2008)

Anthropogenic and Natural Drivers of Environmental

Change in Amazonia

DROUGHTS FOREST FIRES

DEFORESTATION GLOBAL WARMING

Warming of 0.8°C in Amazonia (Victoria et al., 2004. J Climate); IPCC AR4: 3°C to > 5°C in 2100!

Forest fire frequency ↑ (Nepstad et al., 2006) Droughts (e.g., 2005) can become frequent (Cox et al., 2008 Nature)

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

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.

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?

Efeitos significativos sobre a saúde humana

Exposição de elevada magnitude

Período chuvoso 8 a 10 µg.m³ 100 a 300 partículas cm³ Periodo seco 100 a 300 µg.m-³ 15.000 a 30.000 partículas cm-³

Brônquios Bronquíolos

Bronquíolos respiratóri

os

Alvéolos

(diâmetros em micrômetros) Areia fina de praia

Cabelo humano

Combustão de partículas, compostos orgânicos, etc

Poeira, pólen, etc

AF

Amazônia Subequatorial

Amazônia Subequatorial apresentou os piores

indicadores de morbidade e mortalidade por doenças

respiratórias no período de 2000 - 2005

Exposição humana não necessariamente ocorre no local da queima. Efeito na área urbana

Alta Floresta Aerosol Mass Concentration 1992-2001

0

100

200

300

400

500

600

23-A

ug-9

2

08-S

ep-9

2

25-S

ep-9

2

31-O

ct-9

2

12-J

an-93

20-A

pr-9

3

13-J

ul-93

31-A

ug-9

3

08-S

ep-9

3

26-N

ov-9

3

23-M

ar-9

4

21-J

un-94

19-A

ug-9

4

15-O

ct-9

4

07-F

eb-9

5

25-M

ay-9

5

05-A

ug-9

5

24-A

ug-9

5

05-N

ov-9

5

12-M

ar-9

6

24-A

ug-9

6

01-S

ep-9

6

09-S

ep-9

6

04-O

ct-9

6

30-M

ar-9

7

28-J

ul-97

16-A

ug-9

7

29-S

ep-9

7

07-N

ov-9

7

03-J

an-98

19-A

pr-9

8

20-J

ul-98

24-A

ug-9

8

03-S

ep-9

8

09-O

ct-9

8

12-N

ov-9

8

28-J

an-99

29-M

ar-9

9

07-J

un-99

14-J

ul-99

19-A

ug-9

9

16-S

ep-9

9

25-O

ct-9

9

02-F

eb-0

0

24-A

pr-0

0

11-J

ul-00

21-O

ct-0

0

22-A

pr-0

1

15-S

ep-0

1

Mass c

on

cen

trati

on

(μ

g/m

³)

Coarse Mode

Fine Mode

n=735

Queimadas e Doenças na Amazonia

Mortalidade

Hospitalização

Visitas de emergência (PS)

Visitas médicas

Redução da atividade física

Uso de medicação

Sintomas respiratórios

Alteração na função pulmonar

Efeitos sub-clínicos

Proporção da população afetada

gravidade do efeito

Poluição do Ar – Efeitos 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

11,2

21,2

82,1

120,4

173,0

265,3

349,7

0,0 50,0 100,0 150,0 200,0 250,0 300,0 350,0 400,0

Tangará da Serra

Cuiabá

Sinop

Sorriso

Juína

Colíder

Alta Floresta

Efeitos das Queimadas na Saúde

Tangará da Serra

média_pneumonia

189,1

189,7

261,5

363,0

736,7

757,0

1578,0

0,0 500,0 1000,0 1500,0 2000,0

Cuiabá

Sorriso

Juína

Sinop

Colíder

Alta Floresta

Tangará da Serra

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

Estudo de Asma and Alergias em escolares (ISAAC – fase I) na região de Alta Floresta

e Tangara da Serra

Maior prevalência de asma na região foi em meninos (6-7 anos) > 20%

6370 estudantes

RESULTADOS DOS ESTUDOS

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.

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

Obrigado pela atenção!!!

Examples of the spatial distribution of the SWARF at TOA

SWARF (W/m2)

2005

AOD

2005

SWARF (W/m2)

2005

AOD

2005

SWARF (W/m2)

2008

AOD

2008

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.

Impact of Manaus City on the Amazon Green Ocean atmosphere: aerosol and ozone production, precursor sensitivity and transport

Kuhn et al., ACPD 2010

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

Potential Vegetation Simulated by the PVM2.0Reg (50 km)

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

Potential Dominant Biome in Response to ∆T, ∆P and CO2 “fertilization” effect