greening and browning in response to the effects of rising co2 - … · 2020. 5. 2. · alexander...

Alexander J. Winkler*, Ranga B. Myneni, Alexis Hannart, and Victor Brovkin

May 5, 2020

Greening and Browning in Response to the Effects of Rising CO2Driver Attribution Using Causal Theory

* [email protected]

Winkler, 2020 DOI: 10.17617/2.3190373

What is driving the observed changes in natural vegetation?

�2

• Human dominated land surface is greening due to land management (Chen et al., 2019).

• Rising CO2 as driver of changes in natural vegetation is debated:

– CO2 fertilization explain 70% of the global greening trend (Zhu et al., 2016).

– However, no significant increase in leaf area under elevated CO2 in field studies (e.g. Norby et al. 2003).

• A rigorous regional attribution at biome-level of what is behind the observed changes is currently lacking.

Our approach:

• Analyze multi-decadal satellite observations of natural vegetation

• Disentangle the effects of CO2 in idealized model simulations

• Apply Causal Theory for driver attribution on biome scale*

* here only tropical forests and northern ecosystems are shown

http://hdl.handle.net/21.11116/0000-0005-9396-B

Winkler, 2020 DOI: 10.17617/2.3190373

How Rising CO2 Affects Plant Productivity

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Radiative effect (RE): Climatic changesPhysiological effect (PE): CO2 fertilization

1. Stimulation of carbon fixation

radiation (cloudiness)

precipitationtemperature

2. Enhancement of water-use efficiency

↓H2O

↑CO2

RuBisCO enzyme

↑CO2O2

↑Carbon assimilationPhotorespiration

Stomata

Plant productivity can be affected either way:

↑ Increase due to e.g. warming or increased rainfall ↓ Decline due to e.g. drying or disturbances


Winkler, 2020 DOI: 10.17617/2.3190373

Observed Patterns of Greening and Browning

• CO2 increased by 65 ppm throughout the satellite era (1982 to 2017).

• Satellite observations of leaf area index (LAI, leaf area per unit ground area).

• 54% of Earth’s natural vegetation exhibit significant* trends:

- Greening: 40%

- Browning: 14%

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AVHRR LAI3g

* Mann-Kendall test ( p < 0.1 )


Winkler, 2020 DOI: 10.17617/2.3190373

Driver Attribution Using Causal Theory

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Probability of Necessary Causation

Probability of Sufficient Causation

Probability of Necessary and Sufficient Causation

EG

results,andstreamliningthecommunicationofcausal

claims—inparticularwhenitcomestothequantification

ofuncertainty,thatis,oftheprobabilitythatagiven

forcinghascausedagivenobservedphenomenon.

Here,weadaptsomeformaldefinitionsofcausality

andprobabilityofcausationtothecontextofclimate

changeattribution.Then,wedetailtechnicalimple-

mentationunderstandardassumptionsusedinD&A.

Themethodisfinallyillustratedonthewarmingob-

servedoverthetwentiethcentury.

2.Causalcounterfactualtheory

Whileanoverviewofcausaltheorycannotberepeated

here,itisnecessaryforclarityandself-containednessto

highlightitskeyideasandmostrelevantconceptsforthe

presentdiscussion.

Letusfirstrecalltheso-calledcounterfactualdefini-

tionofcausalitybyquotingtheeighteenth-century

ScottishphilosopherDavidHume:‘‘Wemaydefinea

causetobeanobjectfollowedbyanother,where,ifthe

firstobjecthadnotbeen,thesecondneverhadexisted.’’

Inotherwords,aneventE(Estandsforeffect)iscaused

byaneventC(Cstandsforcause)ifandonlyifEwould

notoccurwereitnotforC.Notethatthewordeventis

usedhereinitsgeneral,mathematicalsenseofasubset

ofasamplespaceV.Accordingtothisdefinition,evi-

dencingcausalityrequiresacounterfactualapproachby

whichoneinquireswhetherornottheeventEwould

haveoccurredinahypotheticalworld,termedcoun-

terfactual,inwhichtheeventCwouldnothaveoc-

curred.Thefundamentalapproachofcausalitythatis

impliedbythisdefinitionisstillentirelyrelevantin

thestandardcausaltheory.Itmayalsoarguablybe

connectedtotheguidanceprinciplesoftheconventional

climatechangeattributionframeworkandtotheopti-

malfingerprintingmodels,inaqualitativemanner.The

mainvirtueofthestandardcausalitytheoryofPearl

consistsinourviewinformalizingpreciselytheabove

qualitativedefinition,thusallowingforsoundquantita-

tivedevelopments.Aprominentfeatureofthistheory

consistsinfirstrecognizingthatcausationcorresponds

toratherdifferentsituationsandthatthreedistinct

facetsofcausalityshouldbedistinguished:(i)necessary

causation,wheretheoccurrenceofErequiresthatofC

butmayalsorequireotherfactors;(ii)sufficientcausa-

tion,wheretheoccurrenceofCdrivesthatofEbutmay

notberequiredforEtooccur;and(iii)necessaryand

sufficientcausation,where(i)and(ii)bothhold.The

fundamentaldistinctionbetweenthesethreefacetscan

bevisualizedbyusingthesimpleillustrationshownin

Fig.1.

Whilethecounterfactualdefinitionaswellasthe

threefacetsofcausalitydescribedabovemaybeun-

derstoodatfirstinafullydeterministicsense,perhaps

themainstrengthofPearl’sformalizationistopropose

anextensionofthesedefinitionsunderaprobabilistic

setting.Theprobabilitiesofcausationaretherebyde-

finedasfollow:

PS(C/

E)5PhEjdo(C),C,Ei,

(3a)

PN(C/

E)5PhEjdo(C),C,Ei,

(3b)

PNS(C/

E)5PhEjdo(C),Ejdo(C)i,

(3c)

whereCandEarethecomplementariesofCandE,and

wherethenotationdo(.)meansthataninterventionis

appliedtothesystemundercausalinvestigation.For

instancePS,theprobabilityofsufficientcausation,reads

fromtheabovethattheprobabilitythatEoccurswhen

Cisinterventionallyforcedtooccur,conditionalonthe

factthatneitherCnorEwasoccurringinthefirstplace.

ConverselyPN,theprobabilityofnecessarycausation,is

definedastheprobabilitythatEwouldnotoccurwhenC

isinterventionallyforcedtonotoccur,conditionalon

thefactthatbothCandEwereoccurringinthefirst

place.Whileweomitheretheformaldefinitionofthe

interventiondo(.)forbrevity,thelattercanbeunder-

stoodmerelyasexperimentation:applyingthesedefi-

nitionsthusrequirestheabilitytoexperiment.Real

experimentation,whetherinsituorinvivo,isoftenac-

cessibleinmanyfieldsbutitisnotinclimateresearchfor

obviousreasons.Inthiscase,onecanthusonlyrelyon

numericalinsilicoexperimentation:theimplicationsof

thisconstraintarediscussedfurther.

Whiletheprobabilitiesofcausationarenoteasily

computableingeneral,theirexpressionfortunately

FIG.1.Thethreefacetsofcausality.(a)BulbEcanneverbelit

unlessswitchC1ison,yetactivatingC1doesnotalwaysresultin

lightingEasthisalsorequiresturningonC2:turningonC1isthus

anecessarycauseofElighting,butnotasufficientone.(b)Eislit

anytimeC1isturnedon,yetifC1isturnedoffEmaystillbelitby

activatingC2:turningonC1isthusasufficientcauseofElighting,

butnotanecessaryone.(c)TurningonC1alwayslightsE,andE

maynotbelightedunlessC1ison:turningonC1isthusanecessary

andsufficientcauseofElighting.

15JULY2018

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C1

C2CPE

CRE

EG















presentdiscussion.



































Fig.1.







finedasfollow:

PS(C/

E)5PhEjdo(C),C,Ei,

(3a)

PN(C/

E)5PhEjdo(C),C,Ei,

(3b)

PNS(C/


(3c)
































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presentdiscussion.



































Fig.1.







finedasfollow:

PS(C/

E)5PhEjdo(C),C,Ei,

(3a)

PN(C/

E)5PhEjdo(C),C,Ei,

(3b)

PNS(C/


(3c)
































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CRE

CPE

What is the probability that a given forcing has caused a long-term change in the system?

Event E: occurrence of long-term change in the system, e.g. greening (EG)

Cause C: presence of given forcing, e.g. radiative (CRE) or physiological (CPE) effect of CO2

References: Pearl, 2009 Hannart & Naveau, 2018

PNS

EG















presentdiscussion.



































Fig.1.







finedasfollow:

PS(C/

E)5PhEjdo(C),C,Ei,

(3a)

PN(C/

E)5PhEjdo(C),C,Ei,

(3b)

PNS(C/


(3c)
































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CRE CPE

High PNS reflects evidence for the existence of a causal relationship.















presentdiscussion.



































Fig.1.







finedasfollow:

PS(C/

E)5PhEjdo(C),C,Ei,

(3a)

PN(C/

E)5PhEjdo(C),C,Ei,

(3b)

PNS(C/


(3c)
































15JULY2018

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presentdiscussion.



































Fig.1.







finedasfollow:

PS(C/

E)5PhEjdo(C),C,Ei,

(3a)

PN(C/

E)5PhEjdo(C),C,Ei,

(3b)

PNS(C/


(3c)
































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flip switch

light bulb


Winkler, 2020 DOI: 10.17617/2.3190373

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Overview of experiments:

1. Control run at preindustrial conditions

2. Historical simulation with all forcings

All historical forcings except the

3. physiological effect of CO2 (No PE)

4. radiative effect of CO2 (No RE)

5. combined effect of CO2 (No CO2)

Multi-Model Ensemble: TRENDYv7

• Stand-alone land surface models

• Driven with observed climate 1900-2017

• Ensemble of 13 different models

Model SimulationsSingle Model Ensemble: MPI-ESM1.2

• Fully-coupled Earth system model

• Historical simulations 1850-2013

• Ensemble of 6 realizations


Winkler, 2020 DOI: 10.17617/2.3190373

Radiative Effect is Dominant Driver in the North

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PNS MPI-ESM TRENDYv7Combined 99 % 99 %

Physiological 98 % 61 %

Radiative 86 % 87 %

PNS MPI-ESM TRENDYv7Combined 98 % 99 %

Physiological 59 % 47 %

Radiative 84 % 99 %

TundraBoreal Forests1985 1990 1995 2000 2005 2010 2015Time, yr

1.00

1.05

1.10

1.15

1.20

1.25

Ann

ualav

erag

eLA

I,m

2m

�2

a

Positive ⇤ (40%)

Negative ⇤ (19%)

Total signal

1985 1990 1995 2000 2005 2010 2015Time, yr

0

2

4

6

8

10

12

14

16

Rel

ativ

ech

ange

inLA

I,%

b

MPI-ESM (r=0.36, p=0.029)

TRENDYv7 (r=0.5, p=0.0018)

OBS

OBS IV

0.0

0.5

1.0

1.5

2.0

2.5

Rel

ativ

etr

end

inLA

I,%

deca

de�

1

c

ALL No PE No RE No CO2

1982–2017

MPI-ESM TRENDYv7

PE RE Both0.0

0.2

0.4

0.6

0.8

1.0

PN

S

d1982–2017

MPI-ESM

TRENDYv7

OBS IV

0.0

0.5

1.0

1.5

2.0

2.5

Rel

ativ

etr

end

inLA

I,%

deca

de�

1

e


2000–2017

MPI-ESM TRENDYv7

PE RE Both0.0

0.2

0.4

0.6

0.8

1.0

PN

S

f2000–2017

MPI-ESM

TRENDYv7

Rela

tive

trend

in L

AI, %

dec

ade-

1

OBS IV* ALL No PE No RE No CO2

Rela

tive

trend

in L

AI, %

dec

ade-

1

1985 1990 1995 2000 2005 2010 2015Time, yr

0.40

0.45

0.50

0.55

0.60

0.65

0.70

Ann

ualav

erag

eLA

I,m

2m

�2

a

Positive ⇤ (41%)

Negative ⇤ (14%)

Total signal

1985 1990 1995 2000 2005 2010 2015Time, yr

0

5

10

15

20

Rel

ativ

ech

ange

inLA

I,%

b

MPI-ESM (r=0.35, p=0.036)

TRENDYv7 (r=0.55, p=0.0005)

OBS

OBS IV

0

1

2

3

4

Rel

ativ

etr

end

inLA

I,%

deca

de�

1

c


1982–2017

MPI-ESM TRENDYv7

PE RE Both0.0

0.2

0.4

0.6

0.8

1.0

PN

S

d1982–2017

MPI-ESM

TRENDYv7

OBS IV

0

1

2

3

4

Rel

ativ

etr

end

inLA

I,%

deca

de�

1

e


2000–2017

MPI-ESM TRENDYv7

PE RE Both0.0

0.2

0.4

0.6

0.8

1.0

PN

S

f2000–2017

MPI-ESM

TRENDYv7

OBS IV* ALL No PE No RE No CO2

* Internal Variability


Winkler, 2020 DOI: 10.17617/2.3190373

Tropical Forests are Browning with Rising CO2

�8

• No attribution possible due to high uncertainty.

• Models show compensatory effects of rising CO2.

• Observed trend switches sign in recent decades.

1982

-201

7

1985 1990 1995 2000 2005 2010 2015Time, yr

3.60

3.80

4.00

4.20

4.40

Ann

ualav

erag

eLA

I,m

2m

�2

a

Positive ⇤ (28%)

Negative ⇤ (16%)

Total signal

1985 1990 1995 2000 2005 2010 2015Time, yr

0

2

4

6

8

10

12

Rel

ativ

ech

ange

inLA

I,%

b

MPI-ESM (r=-0.06, p=0.73)

TRENDYv7 (r=0.18, p=0.28)

OBS

OBS IV

�2.0

�1.5

�1.0

�0.5

0.0

0.5

1.0

1.5

Rel

ativ

etr

end

inLA

I,%

deca

de�

1

c


1982–2017

MPI-ESM TRENDYv7

PE RE Both0.0

0.2

0.4

0.6

0.8

1.0

PN

S

d1982–2017

MPI-ESM

TRENDYv7

OBS IV

�2.0

�1.5

�1.0

�0.5

0.0

0.5

1.0

1.5

Rel

ativ

etr

end

inLA

I,%

deca

de�

1

e


2000–2017

MPI-ESM TRENDYv7

PE RE Both0.0

0.2

0.4

0.6

0.8

1.0

PN

S

f2000–2017

MPI-ESM

TRENDYv7

OBS IV ALL No PE No RE No CO2

Rela

tive

trend

in L

AI, %

dec

ade-

1Re

lativ

e tre

nd in

LAI

, % d

ecad

e-1

2000

-201

7

1985 1990 1995 2000 2005 2010 2015Time, yr

3.60

3.80

4.00

4.20

4.40

Ann

ualav

erag

eLA

I,m

2m

�2

a

Positive ⇤ (28%)

Negative ⇤ (16%)

Total signal

1985 1990 1995 2000 2005 2010 2015Time, yr

0

2

4

6

8

10

12

Rel

ativ

ech

ange

inLA

I,%

b

MPI-ESM (r=-0.06, p=0.73)

TRENDYv7 (r=0.18, p=0.28)

OBS

OBS IV

�2.0

�1.5

�1.0

�0.5

0.0

0.5

1.0

1.5

Rel

ativ

etr

end

inLA

I,%

deca

de�

1

c


1982–2017

MPI-ESM TRENDYv7

PE RE Both0.0

0.2

0.4

0.6

0.8

1.0

PN

S

d1982–2017

MPI-ESM

TRENDYv7

OBS IV

�2.0

�1.5

�1.0

�0.5

0.0

0.5

1.0

1.5

Rel

ativ

etr

end

inLA

I,%

deca

de�

1

e


2000–2017

MPI-ESM TRENDYv7

PE RE Both0.0

0.2

0.4

0.6

0.8

1.0

PN

S

f2000–2017

MPI-ESM

TRENDYv7

OBS IV ALL No PE No RE No CO2


Winkler, 2020 DOI: 10.17617/2.3190373

Conclusions

�9

• Northern ecosystems are mainly greening due to the radiative effect of CO2.

• Tropical forests enter browning phase which is not captured by models.

•☞ Rising CO2 drives divergent responses of the natural vegetation: Persistent leaf area loss in tropical forests and gain in northern ecosystems.

• This development indicates the respective contributions of these ecosystems to the recent terrestrial carbon sink.

greening and browning in response to the effects of rising co2 - … · 2020. 5. 2. · alexander...

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