Interações entre Clima e Vegetação na Interações entre Clima e Vegetação na Amazônia: do último período glacial Amazônia: do último período glacial

até o clima do futuroaté o clima do futuro

Carlos A. NobreCarlos A. NobreCPTEC/INPECPTEC/INPE

III CONFERÊNCIA CIENTÍFICA DO LBAIII CONFERÊNCIA CIENTÍFICA DO LBABrasília, 27-29 Julho 2004Brasília, 27-29 Julho 2004

Interações entre Clima e Vegetação na Interações entre Clima e Vegetação na Amazônia: do último período glacial Amazônia: do último período glacial

até o clima do futuroaté o clima do futuro

Carlos A. NobreCarlos A. NobreCPTEC/INPECPTEC/INPE

III CONFERÊNCIA CIENTÍFICA DO LBAIII CONFERÊNCIA CIENTÍFICA DO LBABrasília, 27-29 Julho 2004Brasília, 27-29 Julho 2004

Vegetation-Climate Interactions

Climate Vegetation

Bidirectional on various times scales

Atlantic rainforest

Map of dry season length (DSL) (data after Sombroek, 2001), expressed as the number of months with <100 mm of rain.

Steege et al., Biodiversity and Conservation 12 (in press), © 2003 Kluwer Academic Publishers

Sternberg, 2001, Global Ecology & Biogeography, 10, 369–378

Vegetation = f (climate)

Climate = f (vegetation)

Savanna

Forest

Simple Model of Biome (Savanna and Tropical Forest)-Climate Equilibrium States

Annualprecipitation

Meanclimaticequator

Arid Savanna Rainforest Savanna Arid

Growing season

Gro

win

g s

easo

n le

ng

th in

mo

nth

s

Mea

n a

nn

ual

pre

cip

itat

ion

in m

m

South Equator North Latitude

A scheme of the relationship between mean annual precipitation and growing season length in tropical climates (from Newman, 1977) 

Tmean > 24 C13 C < Tcoldest month < 18 CP (3 driest months) < 50 mmP (6 wettest months) > 600 mm1000 mm < Pannual < 1500 mm

Climatic Conditions forSavannas (Nix (1983)

Nobre et al. 1991, J. Climate

Modeling Deforestation and Biogeography in AmazoniaModeling Deforestation and Biogeography in Amazonia

Current Biomes Post-deforestation

“1” Tropical Forest“6” Savanna

Geog

rafi

a

Ecolo

gia

Modelagem de Distribuição Geográfica de Biomas

Área de Ocorrência

Algoritmo Variável Ambiental AVar

iáve

l am

bien

tal B

Modelo de Biomas

Previsão da

Distribuição

Five climate parameters drive theFive climate parameters drive the potential vegetation model potential vegetation model

Oyama and Nobre, 2002

Monthly values of precipitation and temperature

Water Balance Model

Potential Vegetation Model

SSiB Biomes

Visual Comparison of CPTEC-PBM Visual Comparison of CPTEC-PBM versus Natural Vegetation Mapversus Natural Vegetation Map

CPTEC-PBM

SiB BiomeClassification

Oyama and Nobre, 2002

62% agreement on a global 2 deg x 2 deg grid

Visual Comparison of CPTEC-PBM versus Natural Vegetation Map

SiB BiomeClassification

NATURAL VEGETATION POTENTIAL VEGETATION

Oyama and Nobre, 2002

Searching for Multiple

Biome-Climate Equilibria

Vegetation = f1 (climate variables) = f1 (g0, g5, Tc, h, s)

g0 = degree-days above 0 Cg5 = degree-days above 5 CTc = mean temperature of the coldest monthh = aridity index s = sesonality index

f1 is a highly nonlinear function

Climate = f2 (vegetation) = f2 (AGCM coupled to vegetated land surface scheme)

f2 is also a nonlinear function

IC: All land points covered by desert IC: All land points covered by tropical forest

I T

E

R

A

T

I

O

N

S

Results of CPTEC-DBM for two different Initial Conditons: all land areas covered by

desert (a) and forest (b)

Oyama, 2002

Biome-climate equilibrium solution with IC as forest (a) is similar to current natural vegetation (c); when the IC is desert (b), the final equilibrium solution is different for Tropical South America

a

b

c

Initial Conditions

Oyama and Nobre, 2003

Two Biome-Climate Equilibrium States found for South America!

Soil Moisture

Rainfallanomalies

-- current state (a)-- second state (b)

Unconditional probability of a wet day. a) Threshold of 1 mm, b) Weak rainfall (rainy days: 1 mm - 5 mm) and, c) Moderate rainfall (rainy days: 5 mm - 25 mm). The daily data spans 1979 to 1993.

P > 1 mm 1 mm < P < 5 mm 5 mm < P < 25 mm

Obregon 2001

Unconditional Probability of a Wet Day

Obregon 2001

1 mm < P < 5 mm

SACZ

Sea BreezesInstability lines

Annual Precipitation

Sensitivity Analysis to ‘Savannization’ of AmazoniaSensitivity Analysis to ‘Savannization’ of Amazonia

Resolution: ~ 2ºx2º

Control 2033 All Savanna

2033 All Savanna

JJA 5,4% -21,7%

JJAS 1,9% -21,9%

Dry Season Precipitation*

* 12°S-3°N / 50°W-75°WOliveira et al., 2004

Paleovegetation Reconstructions as Validation for the Second Stable

Equilibrium?

Application of CPTEC-PBM for Past Climate Changes

Oyama, 2002

(a) PBM results with uniform cooling of 6 C and drying of 3 mm/day to emulate climate conditions of the LGM (21 ka BP);

(b) vegetation reconstruction for LGM;

a

b

Last glacial maximum: GENESIS+IBISThe importance of vegetation feedbacks

Reference: Foley, J. A.; Levis, S.; Costa, M. H., Cramer, W.; Pollard, D. 2000: Incorporating dynamic vegetation cover within global climate models. Ecological Applications, v. 10, n. 6, p. 1620-1632.

R = radiation of 21 ka BP, fixed modern vegetation, [CO2] = 180 ppmvRPV = radiation of 21 ka BP + dynamic vegetation + physiology at [CO2] = 180 ppmv

Decrease of AmazoniaRainfall!

Results for Amazonia

What are the likely biome changes in Tropical South America due to Global

Warming?

Change in Amazon Climate and Hydrology in HadCM3LC

Lat: 15oS - 0oNLon: 70oW - 50oW

Amazon forest die-back!

Cox et al., 2000

Change in Amazon Carbon Balancein HadCM3LC

Lat: 15oS - 0oNLon: 70oW - 50oW

Amazon forest die-back!

Cox et al., 2000

Change in Global Climate in HadCM3LC

Interactive CO2 and Dynamic Vegetation

2090s - 1990s

Cox et al., 2000

Geog

rafi

a

Ecolo

gia

Análise de Redistribuição de Biomas em face a Mudanças Climáticas

Área de Ocorrência

Algoritmo Precipitação

Tem

pera

tura

Modelo de Biomas

Previsão daDistribuição

Projeção de Biomas comMudançaClimática

Projeção considerando alterações climáticas

A2 High GHG Emissions Scenario B2 Low GHG Emissions Scenario

Temperature Anomalies (deg C) for 2091-2100

Nobre et al., 2004

A2 High GHG Emissions Scenario B2 Low GHG Emissions Scenario

Precipitation Anomalies (mm/day) for 2091-2100

Nobre et al., 2004

Projected Biome Distributions for South America for 2091-2100

Natural Vegetation Natural Vegetation

A2 High GHG Emissions Scenario B2 Low GHG Emissions Scenario

Nobre et al., 2004

ConclusionsThe future of biome distribution in Amazonia

in face of land cover and climate changes

• Natural ecosystems in Amazonia have been under increasing land use change pressure.

• Large-scale land cover changes could cause warming and a reduction of rainfall by themselves in Amazonia.

• The synergistic combination of regional climate changes caused by global warming and land cover change over the next several decades could tip the biome-climate state to a new stable equilibrium with ‘savannization’ of parts of Amazonia (and ‘desertification’ of parts of Northeast Brazil).

Possible stability landscape for Tropical South America. Valleys (X1, X2 and Y) and hills correspond to stable and unstable equilibrium states, respectively. Arrows represent climate state (depicted as a black circle) perturbations. State X1 refers to present-day stable equilibrium. For small (large) excursions from X1, state X2 (Y) can be found. It is suggested that the new alternative stable equilibrium state found in this work corresponds to X2. Notice that it is necessary to reach X2 before reaching state Y.

Resilience Stochastic Perturbations Gradual Perturbations affect Resilience (e.g., deforestation, fragmentation, etc.)

Biome-Climate Bi-Stability for the SahelBiome-Climate Bi-Stability for the Sahel

Current State Second State

SCHEFFER EL AL., NATURE | VOL 413 | 11 OCTOBER 2001

The second equilibriun state depend mostly on vegetation(albedo) feedback and secondarily on ocean feedbacks

Pr: rain

Ps: snow

T: sfc air temperature

Ts: soil temperature

S: soil water storage

N: overland snow storage

E: evapotranspiration

R: runoff

M: snowmelt

Pr: rain

Ps: snow

T: sfc air temperature

Ts: soil temperature

S: soil water storage

N: overland snow storage

E: evapotranspiration

R: runoff

M: snowmelt

Simple Land Surface Model

Oyama and Nobre, 2002