Variance and Vulnerability in Amazonian Forests:Effects of climatic variability and extreme events on the

structure and survival of tropical forests

Steven C. Wofsy, (Presenting), Lucy Hutyra, Scott R. Saleska, J. W. Munger, Amy Rice, Greg Santoni, V.Y. Chow, Bruce C. Daube, John W. Budney, Alfram V. Bright, Harvard University; Michael M. Keller, Michael William Palace, Patrick Michael Crill, Hudson Silva, University of New Hampshire, Michael L. Goulden, Scott Miller, U. California, Irvine, Humberto Ribeiro da Rocha, USP, Plinio Barbosa de Camargo, Simone Aparecida Vieira, USP/CENA, Volker Kirchhoff, INPE, David Fitzjarrald, Ricardo Sakai, SUNY Albany, Osvaldo Luiz Leal de Moraes, UFFM LBA Science Meeting, Brasilia, July 2004.

A synthesis study based on LBA science

Jul Jan Jul Jan Jul Jan Jul

-3

-2

-1

0

1

2

3

4

5

6

0

100

200

prec

ip

(mm

/wee

k)

upta

kelo

ss to

at

mos

pher

e

Cum

ulat

ive

MgC

ha-1

eddyflux metry

Bio-

STM: Eddy flux and Biometry C Balance

Annual rates MgC/ha/yr

2001 2002 2003 2004 (end: 11/04?)

0

10

20

30

40

50

60

70

80

90

10-2020-30

30-4040-50

50-6060-70

70-8080-90

90-100

100-110> 110

DBH Class

Bio

mas

s (M

g.ha

-1)

Manaus

Rio Branco

Santarém

Bio

mas

s (M

g ha

-1)

Tree diameter class (cm)

Not all forests in the Amazon are equal

Vieira et al., 2004

Manaus has more biomass overall, in smaller trees, than Santarém and Rio Branco with longer dry seasons

net

C lo

ss |

net

C u

ptak

e Carbon fluxes to live and dead biomass, 1999-2001

Live Biomass(145-160 MgC/ha)

Dead Wood(30–45 MgC/ha)

whole-forestnet:

-1.9 1.0 (loss)

Eddy Flux,

u* cor-rected:

-1.3 0.9(loss)

grow

th/lo

ss r

ate

(Mg

C h

a-1 y

r-1)

-6-4

-20

24

mortality

growth

recruit

mortality

decomp-osition

live wood change:

+1.4 0.6 MgC ha-1 yr-1

dead wood change:

-3.3 1.1 (loss)

Rice et al. 2004&

Saleska et al. 2003

Small stems

Cf. Phillips et al. 2004

Why is FLONA Tapajós losing C (or in balance)

while recruiting and growing rapidly?

•There are more large trees, faster turnover, and more recruitment and growth in smaller trees, than in comparable forests with shorter dry seasons. Dead wood stocks are notably large all over Tapajos, with different ages in different locations. Decay of dead wood nullifies growth. The forest looks in many ways like the 104 plots of Phillips, Malhi et al.

•The Tapajos appears to be subject to frequent, relatively small scale disturbance. Disturbance is evidently a major factor in structuring the ecosystem.

•Tapajos has a long dry season and is subject to sporadic droughts. Perhaps the disturbance is associated with drought or dry season severe storms.

Science questions:

•Climate variations operate in concert with factors such as soils, land use, and hydrology, but at least conceptually climate effects can be studied independently.

Why focus on variability and extreme events?

•Climate and weather events represent a principal mechanism for disturbance of ecosystems, and disturbance is a major factor structuring ecosystems [Connell’s “Intermediate Disturbance” hypothesis, et seq.].

•Transitions to flammability in particular can cause dramatic shifts (degradation) of moist tropical forest systems [e.g. Nepstad et al. 2003].

•Despite their importance, extreme events (droughts) are rarely considered in vegetation change studies because variance is poorly known (data limitations) and poorly represented by atmospheric models . This is especially true for climate change simulations [e.g. Cox et al. 2001; Oyama & Nobre, 2003].

•How does climate variability affect Amazônian forests?•How might extreme climatic events control the structure of Amazonian forests, in particular, the transition between tropical forest and savanna?

Holdridge life zones (Holdridge 1967)

lon

lat

-80 -70 -60 -50 -40

-20

-10

010

Data courtesy of D. Skole

drying

Holdridge Life Zones and potential vegetation: the way most models deal with climatic effects on vegetation cover.

AmazonianVegetation: Multiple Equilibria, Persistence & Climate

After Wang & Eltahir 2000

B

A

Vegetation, like climate, can have more than one state that is persistent and resilient, in analogy with movement of a ball on a landscape. Small disturbances lead to adjustments and return to the initial state. Large disturbances may cause the system to change to a new stable state, possibly to revert at a later time (cf. C. Nobre).

A complication: How does the system get to one or the other?

C

Climate change shifts equilibria

A shift in climate, due to natural or anthropogenic causes, can change the landscape, as well as the frequency and magnitude of disturbance. The change in relative system stability might make a vegetation change irreversible (e.g. Cox et al, 2001), but it might take a disturbance for the shift to occur. Leads to the concept of instability.

Another complication

Our approach: Assess the influence of climatic variability and extreme events

(droughts) on forests based on data for: climate, vegetation structure, vegetation

cover, atmosphere-biosphere exchange. We will use a statistical simulation approach.

This synthesis draws on key aspects of the pre-LBA and LBA data sets and science results. The issues raised by climate-vegetation feedback studies bring into sharp focus the importance of understanding major factors regulating vegetation change.

Amazon Precip Anomaly

Mm

/yr

-600

0

6

001900 2000

New et al. (2000, CRU/East Anglia):

•Separated global station data into mean and anomaly (deviance) fields,

•Interpolated and combined them.

•Product: global monthly precipitation for 1900-1995, gridded (0.5o x 0.5o)

Density of reporting stations (0.5o grid)

Figures from New, Hulme & Jones, J. Climate. 2000

Lon

Lat

-70 -60 -50 -40

-20

-10

010

-70 -60 -50 -40

-20

-10

010

Mean Precipitation (mm)

1050 1250 1450 1650 1850 2050 2250 2450 2650 2850 3050 3250 3450

J J J D

0200

400 Mean = 2020 mm

J J J D

0200

400 Mean = 2091 mm

J J J D

0200

400 Mean = 2279 mm

J J J D

0200

400

Mean = 1373 mm

Precipitation data from: New et al. 2000Lon

Lat

Precipitation (mm/month)

Eva

p.

(FP

ET

mm

/mo

nth

)

0

1

00

20

0

130

115

0 100 200 300

From Eddy flux data: Shuttleworth (ABRACOS); da Rocha et al. & Saleska et al. (LBA);

Simulate a 2500 year time series of Net Evaporation, using observed deviances and autocorrelation.

Drought yr = 12 months with Net Evap > 0.

Net evaporation = FPET - precipitation

Forest Potential Evapotranspiration = ET if a forest were present at all pixels.

From CRU [New et al.]

95 year time series of Net Evap, gridded 0.5x0.5 degrees.

Amazon precipitation data New et al. (2000)

Auto-regressive fit tomonthly deviances

Simulate Net Evaporation =

(FPET –Precip)means & variance

Examine spatial distribution of droughts: frequency and intensity. Compare extreme dry events to Skole’s 1980 vegetation map

q

titt XX1

Lag

ACF

0.0 1.0 2.0

0.00.2

0.40.6

0.81.0

Series : tts

Lag

ACF

0.0 1.0 2.0

-0.5

0.00.5

1.0

Series : dum

Lag (years)

Deseasonalized &

detrended

Aut

ocor

rela

tion

Lag

ACF

0.0 1.0 2.0

0.00.2

0.40.6

0.81.0

Series : tts

Lag

ACF

0.0 1.0 2.0

-0.5

0.00.5

1.0

Series : dum

Summary of conceptual framework

Auto Correlation Coef.

0.0 0.5 1.0 1.5 2.0

0.0

0.2

0.4

0.6

0.8

1.0SANTAREM TAP. -2.4 -54.3 63yrs

0.0 0.5 1.0 1.5 2.0 2.5

0.0

0.2

0.4

0.6

0.8

1.0CRU, Lat= -2.25 , lon= -54.25

0.0 0.5 1.0 1.5 2.0 2.5

MANAUS -3.1 -60 93yrs

0.0 0.5 1.0 1.5 2.0 2.5

CRU, Lat= -3.25 , lon= -60.250.0 0.5 1.0 1.5 2.0 2.5

CUIABA -15.6 -56.1 102yrs

0.0 0.5 1.0 1.5 2.0 2.5

CRU, Lat= -15.75 , lon= -56.25

Lag (years)

95% signif.

Are the statistics of the CRU Precip data reliable?Autocorrelation of precipitation time series:

Original station data (upper), New et al. reconstruction (CRU, lower)

lon

lat

-70 -60 -50 -40

-20

-10

010

-70 -60 -50 -40

-20

-10

010

CRU polymap of NTotalDr, 2500 yr sim.

0 1 5 10 20 50 100 500 10002000

Total number of years of drought (2500 year simulation)

0.3 3 33 per century

lon

lat

-70 -60 -50 -40

-20

-10

010

-70 -60 -50 -40

-20

-10

010

CRU polymap of D50yr, 2500 yr sim.

0 0.1 0.25 0.5 0.75 1 1.5 3 6 12 24

Longitude

La

titu

de

(m H2O)

‡ Deficit in the last year of a multi-year drought (50yr return interval in a 2500 yr simulation)

Water deficit from 50 year drought‡ event

lon

lat

-70 -60 -50 -40

-20

-15

-10

-50

5

-70 -60 -50 -40

-20

-10

05

savanafloresta decidualsemidecidual tropicalfloresta densa tropicalfloresta aberta tropicalcampinaranatensao--contato ecologicarefugioformaceos pioneiras

SavannaDeciduous Forest

Open tropical forest Dense tropical forest

The 1980 Amazônian vegetation distribution agrees well with the distribution of vegetation types.

Vegetation distribution data: D. Skole

Cut 1

Cut 2

50 yr drought=0.1 m water

0.01.0

2.0

04

12

0.01.0

2.0

04

12

50 year drought deficit (m H

2 O)

Num

ber of droughts per 100 yrs

key value: 1—3 big droughts/100 yr

Precip simulations -53.25 Longitudem

eanP

-15 -10 -5 0

1500

2000

2500

0.0

1.0

2.0

010

020

030

040

0

Precip simulations -8.25 Longitude

lon

mea

nP

-70 -65 -60 -55 -50 -45

1500

2000

2500

0.0

1.0

2.0

010

020

030

040

0

Cut 1, -53.2 W

Longitude

Cut 2, –8.3 S

88

-15 -10 -5 0Lat

150

0

2000

2500

Mea

n A

nn

ual

Pre

cip

itat

ion

(m

m)

150

0

2000

2500

-70 -65 -60 -55 -40 -45

Lon

Lat

-70 -60 -50 -40

-20

-10

010

-70 -60 -50 -40

-20

-10

010

Mean Precipitation (mm)

1050 1250 1450 1650 1850 2050 2250 2450 2650 2850 3050 3250 3450

Projections of regions converted to savanna for 10 and 25% reduction in precip. Note that this approach captures the effects of rainfall patterns (e.g. sea breeze front).

-25% -10%current

Manaus Precip Deviance

Decade +/-5yr

Dec

adal

mea

n of

mon

thly

dev

ianc

e (m

m^2

)

1920 1940 1960 1980 2000

4000

5000

6000

7000

8000

The variability of climate changes with time and can have important ecological impacts. Since variability is a second-order quantity and extreme events are rare, it is very difficult to assess the role of variability on ecosystems.

Summary and conclusions

•Disturbance is a major factor in structuring the primary forest at STM, likely others.

•The CRU reconstructions provide the basis for assessing the intensity and recurrence times for extreme droughts, a major mechanism for disturbance.

•We simulate long time series of net precipitation using an autoregressive model. As mean precipitation approaches a critical value (1600 mm/yr; dry season > 5 months), severe droughts (>2 consecutive years net positive evaporation) recur at 25-100 year intervals. This appears to be the threshold for replacement of tropical forests by savanna or woodland vegetation. Soils and topography are other major factors.

•The transition to savanna likely requires forests to ignite, and the presence of flammable savannas (or farmers) nearby are an important additional risk factor.

•A sizable fraction of Amazônian forests appear vulnerable to reduced precipitation, higher T increasing evaporation, or increased variance of rainfall.

Data sets/concepts used: CRU precip; Fizjarrald/NCAR climate data; Nepstad precip and flammability; Shuttleworth; da Rocha/Goulden; Saleska Eddy FPET; Skole 1970s veg.; Phillips/Malhi/Vieira/Camargo tree mortality & size dist.; Chambers and our own CWD; Nobre/Avissar/Eltahir multiple states.

Lucy, Scott, Steve, offer thanks to all! We have enjoyed LBA “beyond earth and sky”.

Long term data needed: phenology, rainfall (MODIS, TRMM, stations).

Having fire-prone woodlands at your back is a factor too (extreme event gradient is steep!)

2500 year simulation

mean precipitation (mm)

Nto

tal d

roug

hts

1600 1800 2000 2200

020

040

060

080

010

00

2500 year simulation

mean precipitation (mm)

Nto

tal d

roug

hts

1000 1500 2000 2500 3000 3500

050

010

0015

0020

0025

00

savanafloresta decidualsemidecidual tropicalfloresta densa tropicalfloresta aberta tropicalcampinaranatensao--contato ecologicarefugio

Does climate variability play the key role linking together climate change, edaphic factors, and human use factors?

Amazon soils map and potential flammability (Nepstad et al. 2004)

Multiple equilibria: coupled climateand vegetation (Oyama & Nobre 2003)

Before deforestation After deforestation

Potential Vegetation

Forest Cerrado Desert

-2 0 2

010

2030

savana

stuff$x

95%

ile f

or d

roug

ht

-2 0 2

010

2030

semidecidual tropical

stuff$x

95%

ile f

or

dro

ught

-2 0 2

010

20

30

floresta aberta tropical

stuff$x

95%

ile f

or

dro

ught

-2 0 2

010

20

30

floresta densa tropical

stuff$x

95%

ile f

or

dro

ught

-2 0 2

010

20

30 Floresta densa tropical

semidecidual tropical

savana

Floresta aberta tropical Quantiles of std. normal

Wat

er lo

ss f

rom

50

yr d

roug

ht (

m)

Tail of the distribution

Use Missy’s suggestion, add Skole’s map and a ref.

Lag

AC

F

0.0 1.0 2.0

0.0

0.2

0.4

0.6

0.8

1.0

Series : tts

Lag

AC

F

0.0 1.0 2.0

-0.5

0.0

0.5

1.0

Series : dum

These are for ~km 67 using the cru data.

Deseasonalized &

Detrended

Raw monthly net

evaporation

Lag (yrs) Lag (yrs)

Wet season

Dry season

MODIS

March 22, 2001

August 12, 2001

annual precipitation(mm; Nepstad data)

NE

E, M

gC

ha-1

yr-1

1500 1600 1700 1800 1900 2000 2100

-2-1

01

2 Dry 2002-wet 2003

Dry 2001-wet 2002

Dry 2003-wet 2004

STM: relationship between annual precipitation and NEE

Losses of CWD?

10 35 60 85 110 135 160 185

-20

-10

010

2030

40

size class, cm DBH

dens

ity

chan

ge, s

tem

s ha

-1

Recruitment

Net

Mortality

Outgrowth

Rice et al. 2004

Consistent with the results in Phillips et al. 2004, we observed also observed high rates of stem recruitment. However, our high recruitment was coupled with large stocks of coarse woody debris.

0 100 200 300

6080

100

120

Total monthly precip (mm) [Nepstad et al.]

Mea

n m

onth

ly w

ater

flu

x, m

m/m

onth

0 100 200 300

6080

100

120

Relationship between Evaporation and precipitation from Eddy Flux data (km 67, STM)