multiple biomemultiple biome-climate equilibria in ... · multiple biome-climate equilibria in...

MULTIPLE BIOME-CLIMATE EQUILIBRIA IN AMAZONIA AND PERSPECTIVES FOR THE FUTURE OF THE

RAINFOREST

MULTIPLE BIOMEMULTIPLE BIOME--CLIMATE EQUILIBRIA IN AMAZONIA CLIMATE EQUILIBRIA IN AMAZONIA AND PERSPECTIVES FOR THE FUTURE OF THE AND PERSPECTIVES FOR THE FUTURE OF THE

RAINFORESTRAINFOREST

Carlos A. NobreCarlos A. Nobre

CPTEC/INPE, Cachoeira Paulista, SPCPTEC/INPE, Cachoeira Paulista, SP-- BrazilBrazil

Environmental Changes in Environmental Changes in Amazonia Amazonia and the and the Hypothesis of Hypothesis of ‘‘SavannizationSavannization’’

Carlos A Nobre, Marcos Oyama, Manoel Cardoso, Gilvan Sampaio, Luis Salazar, David Lapola, and Marcos Costa

Slide courtesy: IPAM

Focus on Amazonia

Vegetation-Climate Interactions

Climate Vegetation

Bidirectional on various times scales

Is vegetation distribution a fingerprint of climate ?or

Does vegetation distribution influenceand participate to climate (state and changes) ?

Does vegetation matter for the Earth System ?

Does vegetation matter for the Earth System ?

• The impacts of human activity on the Amazon rainforest could result in the collapse of large portions of the rainforest and significant loss of biodiversity within 30 to 50 years.

• A comparison is made with similar events in the Saharan ecosystem, which was once a region of richer vegetation, before its abrupt collapse about 6000 years ago

Kleidon et al. (2000)

Remove vegetation from the continents

large changes will happen in the water cycle




Land=desert Land=forest





ATMOSPHERE

OCEANS CONTINENTS





ATMOSPHERE

OCEANS CONTINENTS37 000 km3

421 000 km3 464 000 km3 71 000 km3 31 000 km3





ATMOSPHERE

OCEANS CONTINENTS37 000 km3

421 000 km3 464 000 km3 71 000 km3 31 000 km3

28 000 km3

137 000 km3 108 000 km3410 000 km3 443 000 km3

(figure taken from Kabat et al.: Vegetation, Water, Humans, and the Climate, IGBP BAHC)

Vegetation partitions net radiation into more latent and less sensible heat

The ecosystems of Amazonia are subjected to a suite of environmental drivers of change

LUCC

FireClimateChange

ClimateExtremes

PE

C

Balanço HídricoP = E + C

P = PrecipitaçãoE = EvapotranspiraçãoC = Convergência de Umidade

Nobre et al. 1991, J. Climate

ModelingModeling DeforestationDeforestation and Biogeographyand Biogeography in in AmazoniaAmazoniaCurrent Biomes Post-deforestation

“1” Tropical Forest“6” Savanna

BiomeBiome--Climate BiClimate Bi--Stability for the Stability for the SahelSahel

Current State Second State

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

The second equilibriun statedepend mostly on vegetation(albedo) feedback andsecondarily on ocean feedbacks

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

Figure 6 Over the past 9,000 years, average Northern Hemisphere summer insolation(upper panel) has varied gradually owing to subtle variation in the Earth's orbit. About 5,000 years before present (yr BP), this change in solar radiation triggered an abrupt shift in climate and vegetation cover over the Sahara, as reffected in the contribution of terrigenous(land-eroded) dust to oceanic sediment at a sample site near the African coast (lower panel). Modified from ref. 61.

Figure 2 Two ways to shift between alternative stable states. a, If the system is on the upper branch, but closeto the bifurcation point F2, a slight incremental change in conditions may bring it beyond the bifurcation and induce a catastrophic shift to the lower alternative stable state (`forward shift'). If one tries to restore the state on the upper branch by means of reversing the conditions, the system shows hysteresis. A backward shift occurs only if conditions are reversed far enough to reach the other bifurcation point, F1. b, A perturbation (arrow) may also induce a shift to the alternative stable state, if it is suf®ciently large to bring the system over the border of the attraction basin (see also Fig. 3).

Figure 3 External conditions affect the resilience of multi-stable ecosystems to perturbation. The bottom plane shows the equilibrium curve as in Fig. 2. The stability landscapes depict the equilibria and their basins of attraction at five different conditions. Stable equilibria correspond to valleys; the unstable middle section of the folded equilibrium curve corresponds to a hill. If the size of the attraction basin is small, resilience is small and even a moderate perturbation may bring the system into the alternative basin of attraction.

Externally driven equilibrium change

Amazonian Vegetation: 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

Atlantic rainforest

The Holdridge Life-Zone Classification System (Holdridge, 1947; 1964)

Savanna?

Holdridge life zones (Holdridge 1967)

Data courtesy of D. Skole

drying

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

Annualprecipitation

Meanclimaticequator

Arid Savanna Rainforest Savanna Arid

Growing season

Gro

win

g se

ason

leng

th in

mon

ths

Mea

n an

nual

pre

cipi

tatio

n in

mm

South Equator NorthLatitude

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

Fig. 1 Regions of the Amazon basin that can potentially be converted to savanna after some deforestation. Black regions represent regions in the Amazon basin with tropical forest and having d.s. precipitation > 100 mm. Dark grey regions represent regions having tropical forest with d.s. precipitation ≤ 100 mm. Thisregion could potentially be converted to savanna, given enough deforestation. Light grey regions represent other types of vegetation but mainly savannas having precipitation during the dry season ≤ 100 mm. The dry season precipitation isoline was derived from Nix (1983).

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

Area of Study

VegetationA, Aa, Ab, AsC, CsD, Da, Db, Dm, DsF, Fa, FsLO, La, Ld, LgONP, Pa, PfS, SM, SN, SO, ST, Sa, Sd, Sg, SpTd, TpWATERrm

N

EW

S

-45,-15

-45,-10

-45,0

-50,5-55,5-60,5-65,5-70,5

-70,0

VegetationTypes in Brazilian an AmazoniaRadam

200 100 0 200 M

100 100 0 200 km

Scale 1:16.000.000Projection

longitude of central meridian - 57 00 00

Sombroek 2001, Ambio

Map. no. 1Annual rainfall (mm)

DEZ-FEV

SET-NOVJUN-AGO

MAR-MAI

Precipitação (mm)

>900

600-900

300-599

<300

Nilo and Nobre, 1991, Climanálise

Amazon River Discharge ( mAmazon River Discharge ( m33/s)/s)station: station: ÓbidosÓbidos (01 S, 55 W)(01 S, 55 W)

Year

mon

th

Large interannual variability in the hydrological cycle

The Hypothesis of ‘Savannization’

• Nobre et al. (1991) proposed that a post-deforestation climate in Southern Amazonia would be warmer, drier and with longer dry season, typical of the climate envelope of the tropical savanna (Cerrado) domain of Central South America.

• ‘Savannization’ in this context is a statement on regional climate change and not intended to describe complex ecological processes of vegetation replacement.

Biomes of tropical south America and precipitation seasonality


Number of consecutive months with less than 50 mm rainfall

Annual Rainfall

Biomes of Brazil

The importance of rainfall seasonality

(short dry season) for maintaining tropical

forests all over Amazonia

Tropical Forest Shrubland

Savanna

Tropical Forest-SavannaBoundary

Evapotranspirationseasonality in the Amazon tropical forest and savannaSource: Rocha (2004)

Cerrado s.s. SP

Floresta trop RO

Floresta trop Manaus

Floresta trop Santarém

Forest

Savanna

Forest

Savanna

Late

ntH

eatf

lux

(W m

-2)

Net

Rad

iatio

n(W

m-2

)

mm

day-1

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

TRO

PIC

AL

FOR

EST

CO

VER

CLIMATE STATE(ANNUAL PRECIPITATION AND LENGTH OF DRY SEASON)

BIOME DISTRIBUTION BIOME DISTRIBUTION RESPONDS TO CLIMATE !RESPONDS TO CLIMATE !

SAVANNASAVANNA

TRO

PIC

AL

FOR

EST

CO

VER



SAVANNASAVANNA

FORESTFORESTTR

OPI

CA

L FO

RES

T C

OVE

R



PREC

IPIT

ATI

ON

TROPICAL FOREST COVER

CLIMATE RESPONDS CLIMATE RESPONDS TO VEGETTION !TO VEGETTION !

PREC

IPIT

ATI

ON


PREC

IPIT

ATI

ON


FocusFocus of LBA of LBA FieldField ResearchResearch


PREC

IPIT

ATI

ON




ON

PREC

IPIT

ATI

ON




PREC

IPIT

ATI

ON


Deforestation Deforestation SimulationsSimulations

PREC

IPIT

ATI

ON



PPpresentpresent

0.8 0.8 PPpresentpresent

P present ≅ 2 to 2.5m

PREC

IPIT

ATI

ON



SimpleSimple ClimateClimate -- BiomeBiome InteractionInteraction ModelModel

CLI

MA

TE S

TATE


FOREST COVER = fFOREST COVER = f (CLIMATE)(CLIMATE)


CLI

MA

TE S

TATE


FOREST COVER = fFOREST COVER = f (CLIMATE)(CLIMATE)FOREST COVER FOREST COVER


CLI

MA

TE S

TATE



CLIMATECLIMATE= f (FOREST= f (FORESTCOVER)COVER)


CLI

MA

TE S

TATE




“STABLE”“STABLE”


CLI

MA

TE S

TATE




“STABLE”“STABLE”

“UNSTABLE”“UNSTABLE”


CLI

MA

TE S

TATE


Fig. 3 Establishment of relative forest area in a savanna region as a function of precipitation.



Vegetation = f (climate)

Climate = f (vegetation)

Savanna

Forest

1.1 Mudanças globais: aspectos climato-ecológicos

Como os biomas da América do Sul seriam afetados?R: (i) Experimentos in loco, e.g. FACE (caros e difíceis de controlar)

(ii) Modelos de Vegetação Potencial (MVPot; captam interações bioma-clima)

BIOME3 (Haxeltine & Prentice 1996)

BIOME (Prentice et al. 1992)

TRIFFID (Cox 2001)

Simple TRIFFID (Huntingford et al. 2000)

MAPSS (Neilson 1995)

CPTEC PVM (Oyama & Nobre 2004)

2. CPTEC PVM (Oyama & Nobre 2004)

→ entradas: Temperatura, Precipitação

→ saída: um bioma (através de 5 variáveis ambientais)

1. G0 (°C dia mês-1) : growing degree-days (em T basal 0°C)

2. G5 (°C dia mês-1) : growing degree-days (em T basal 5°C)

3. Tc (°C): temperatura do mês mais frio

4. H (adimensional): índice hídrico (mod. balanço hídrico)

5. D (adimensional): índice de sazonalidade (mod. balanço hídrico)

algoritmo identifica bioma em equilíbrio climático

MVPot com melhor desempenho na Am. Sul!

Geo

gra

fia

Eco

log

iaModelagem de Distribuição

Geográfica de Espécies

Pontos de Ocorrência

Algoritmo Precipitação

Tem

pera

tura

Modelo do Nicho Ecológico

Previsão daDistribuição

Geo

gra

fia

Eco

log

iaModelagem de Distribuição

Geográfica de Biomas

Área de Ocorrência

Algoritmo Variável Ambiental AVar

iáve

lam

bien

taql

B

Modelo de Biomas

Previsão daDistribuição

• A Potential Biome Model that uses 5 climate parameters to represent the (SiB) biome classification was developed (CPTEC-PBM).

• CPTEC-PBM is able to represent quite well the world’s biome distribution. A dynamical vegetation model was constructed by coupling CPTEC-PBM to the CPTEC Atmospheric GCM (CPTEC-DBM).

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

Pi, i+1

Mi, i+1

Ei, i+1Ri, i+1Pri,, i+1

Psi,, i+1

Ti, i+1 Tsi, i+1 (soil freezing)

Ni Ni+1

Si Si+1

FiveFive climateclimate parametersparameters drivedrive thethepotentialpotential vegetationvegetation modelmodel


Monthly values of precipitation and temperature

Water Balance Model

Potential Vegetation Model

SSiB Biomes

Figure 6. Environmental variables used in CPTEC PVM: growing degree-days on 0oC base (a), growing degree-days on 5oC base (b), mean temperature of the coldest month (c), wetness index (d), seasonality index (e). Growing degree-days in oC day month-1, and temperature in oC.

growing degree-days on 5oC base


growing degree-days on 0oC base

Wetness index

mean temperature of the coldest month



seasonality index

The potentialvegetation modelalgorithm


Tropical Forest

Visual Visual ComparisonComparison of CPTECof CPTEC--PBM PBM versus Natural versus Natural VegetationVegetation MapMap

CPTEC-PBM

SiB BiomeClassification


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


Statistic κ (Monserud e Leemans 1992)

good agreement

poor agreement


agrement

perfect

excel.

v. good

good

regular

poor

v.poor

none

Objective verification of CPTEC-PBM

bioma nome p0 (%) κ concordância

1 floresta tropical 71 0,73 muito boa

2 floresta temperada 52 0,49 regula

3 floresta mista 26 0,26 pouca

4 floresta de coníferas 55 0,56 boa

5 lariços 70 0,65 boa

6 savana 56 0,60 boa

7 campos extratropicais 76 0,50 regular

8 caatinga 50 0,40 regular

9 semi-deserto 57 0,55 boa

10 tundra 62 0,67 boa

11 deserto 70 0,74 muito boa

média global 62 0,58 boa

literatura ~ 40 0,40 - 0,50 regular


Global Mean GoodVery Good

Good

Good

Good

Good

Good

Very Good

RegularPoor

Regular

Regular

agreement

Tropical Forest

Temperate Forest

Mixed Forest

Boreal Forest

Larch

Savannas

Grasslands

Dry shrubland

Semi-desert

Desert

Literature

Searching for Multiple Biome-Climate Equilibria

Climate Equilibrium States

Oyama, 2002

Vegetation = f (climate)

Climate = f (vegetation)

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

38 níveis verticais

1ºlat 1º

long

100 km

100 km

Modelo Atmosférico Global para Previsão de Tempo:Código computacional (centenas de milhares de linhas de código) que representa aproximações numéricas de equações matemáticas, equações estas representativas das Leis Físicas que regem os movimentos da atmosfera e as interações com a superfície; o cálculo é feito para até 10 dias de previsão.

Interações laterais

Interações com a superfície

Interações entre camadas

Número de elementos:400 x 200 x 28= 2,24 milhõesE-W N-S Vertical

Calcula-se para cada um destes volumes:Temperatura, umidade, direção e velocidade do vento, altura geopotencial.


Calcula-se para cada um destes volumes:Temperatura, umidade, direção e velocidade do vento, altura geopotencial.

Domínio Geográfico

www.cptec.inpe.br


1ºlat 1º

long

20 km

20 km

Modelo Atmosférico Regional para Previsão de Tempo:Semelhante ao Modelo Global, porém para um domínio geográfico limitado; o cálculo é feito para 3 dias de previsão.

Interações laterais

Interações com a superfície

Interações entre camadas

Número de elementos:330 x 330 x 46= 5 milhõesE-W N-S Vertical

Calcula-se para cada um destes volumes:Temperatura, umidade, direção e velocidadedo vento, altura geopotencial.

Número de elementos:330 x 330 x 46= 5 milhõesE-W N-S Vertical

Calcula-se para cada um destes volumes:Temperatura, umidade, direção e velocidadedo vento, altura geopotencial.

globo

Domínio Geográfico

www.cptec.inpe.br



1,8lat 1,8

long

125 km

200 km

-5km de profundidade

200 km

Atmosfera

Oceano

Modelo Acoplado Atmosfera-Oceano Global para Previsão Climática: Código computacional (centenas de milhares de linhas de código) que representa aproximações numéricas de equações matemáticas, equações estas representativas das Leis Físicas que regem os movimentos da atmosfera, dos oceanos e as interações entre estes dois fluídos e entre a superfície dos continentes e a atmosfera; o cálculo é feito para um período de poucos meses a anos.


Calcula-se para cada um dos volumes atmosféricos:Temperatura, umidade, direção e velocidade dovento, altura geopotencial.Calcula-se para cada um dos volumes atmosféricos:Temperatura, salinidade, direção e velocidade dacorrente, pressão.


Calcula-se para cada um dos volumes atmosféricos:Temperatura, umidade, direção e velocidade dovento, altura geopotencial.Calcula-se para cada um dos volumes atmosféricos:Temperatura, salinidade, direção e velocidade dacorrente, pressão.

El Niño

How to find numerically MultipleVegetation-Climate Equilibrium States?

Oyama, 2002

Results of CPTEC-DBM for two differentInitial 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 currentnatural vegetation (c); when the IC is desert (b), the final equilibrium solution is different for Tropical South America

a

b

c

Initial Conditions

Is the current Climate-Biome equilibrium in Amazonia the only

possible one?


Two Biome-Climate Equilibrium States found for South America!

Soil Moisture

Rainfallanomalies

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

Source: Obregon, 2001

1 mm < P < 5 mm

SACZ

Sea BreezesInstability lines

Annual Precipitation

Unconditional probability of a wet day. The daily data spans 1979 to 1993

5 mm < P < 25 mm

Precipitation mechanism in the Amazon18 UTC

03 UTC

Source: Bruno et al., 2005 – Tropical forest data in Santarem km83

Ecological adaptation I: Deep rooting

Wet season

Fraction of water extracted by roots

Dep

th (m

)

68% Wet season

84%



Wet season


Dep

th (m

)Dry season

68% Wet season

84%



Wet season


Dep

th (m

)Dry season

multiple biomemultiple biome-climate equilibria in ... · multiple biome-climate equilibria in...

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