thomas gasser (lsce/ispl)

Climate change: the physical aspects

ATHENS Programme AgroParisTech; Nov. 17, 2014

Thomas Gasser (LSCE/ISPL) (tgasser@lsce.ipsl.fr)

Overview 2

1. Physical basis

• What evidence?

• Temperature of a planet

• The case of Earth

• Radiative forcings

• Climate response

2. Climate models

• Fluid dynamics

• Model complexity

• The IPSL model

• Basic performance

3. Climate projections

• The scenarios

• Temperature

• Water cycle

• Oceans

• Carbon cycle

• Uncertainty

4. Attribution of climate change

1. Physical basis

Expected changes of the climate system

1. Physical basis. What evidence? 4

IPCC AR5, WG1 Ch2

Changes actually observed

1. Physical basis. What evidence? 5

IPCC AR5, WG1 Ch2

Changes actually observed

1. Physical basis. What evidence? 6

IPCC AR5, WG1 Ch2

Also by statistical analysis of meteorological data.

Reminder: Meteorology is the weather at a given time and place; Climate is the statistical aspect of it.

More and more observations available

1. Physical basis. What evidence? 7

IPCC AR5, WG1 Ch1

Comparable to past changes?

1. Physical basis. What evidence? 8

IPCC AR4, WG1 Ch6

Comparable to past changes?

1. Physical basis. What evidence? 9

IPCC AR4, WG1 Ch6

Planet without an atmosphere

1. Physical Basis. Temperature of a planet. 10 Su

rfac

e

Sun

Stefan-Boltzman law: FS = σT4

F0 αF0 FS

Notation: F0 = insolation α = surface albedo FS = surface radiation

Equilibrium: (1-α)F0 = FS = σT4

What’s IR temperature?

1. Physical Basis. Temperature of a planet. 11

Image: nature.com/nature_education

Planet with an atmosphere

1. Physical Basis. Temperature of a planet. 12 A

tmo

sph

ere

aerosols, clouds

GHGs, clouds

F0 αF0

FS

Surf

ace

Su

n

Stefan-Boltzman law: FS = σT4

Notation: F0 = insolation α = planetary albedo FS = surface IR radiation ε = atmospheric ‘opacity’

Equilibrium: (1-α)F0 = (1-ε)FS = (1-ε)σT4 εFS

Greenhouse effect: G = εFS = εσT4

Greenhouse effect in the solar system

1. Physical Basis. Temperature of a planet. 13

* nssdc.gsfc.nasa.gov/planetary

Mercury Venus Earth Mars

Insolation (W/m2)*§

F0

2282 654 342 147

Planetary albedo* α

0.07 0.90 0.31 0.25

Black body temperature (K) TBB = [(1-α)F0/σ] 1/4

440 184 254 (-19 °C)

210

Observed temperature* (K) Tobs

440 737 287 (14 °C)

210

Atmospheric ‘opacity’ ε = 1-TBB/Tobs

0 0.75 0.11 0

Greenhouse effect (W/m2) G = εσTobs

4

0 319 149 0

§ Insolation is a quarter the irradiance reported by NASA. This is the ratio between the cross-section of a sphere and its surface area.

Greenhouse effect on Earth

1. Physical Basis. The case of Earth. 14

IPCC TAR

H2O 60%

CO2 26%

O3 8%

N2O+CH4 6%

The GH effect on Earth can be computed from radiation theory and knowledge of atmospheric distributed composition:

(clear sky)

Images: wikipedia.org; periodni.com

Earth’s energy budget…

1. Physical Basis. The case of Earth. 15

IPCC AR4, WG1 Ch1

Earth’s energy budget… easily disturbed

1. Physical Basis. The case of Earth. 16

IPCC AR5, WG1 Ch1

Orbital and solar forcings

1. Physical Basis. Radiative forcings. 17

IPCC AR5, WG1 Ch8

Milankovich’s theory: Past changes of climate triggered by orbit-induced changes in solar influx.

Orbital and solar forcings

1. Physical Basis. Radiative forcings. 18

IPCC AR5, WG1 Ch8

Short-term cycles of the solar activity:

Long-lived greenhouse gases

1. Physical Basis. Radiative forcings. 19

IPCC AR4, WG1 Ch6

Long-lived greenhouse gases

1. Physical Basis. Radiative forcings. 20

IPCC AR5, WG1 Ch6

Long-lived greenhouse gases

1. Physical Basis. Radiative forcings. 21

IPCC AR5, WG1 Ch2

Long-lived greenhouse gases

1. Physical Basis. Radiative forcings. 22

Source: D. Hauglustaine (LSCE/IPSL)

Long-lived greenhouse gases

1. Physical Basis. Radiative forcings. 23

Source: D. Hauglustaine (LSCE/IPSL)

CO2 and the carbon cycle

1. Physical Basis. Radiative forcings. 24

IPCC AR5, WG1 Ch6

About 50% of CO2 absorbed by the ocean and the vegetation:

Image: esrl.noaa.gov/gmd/ccgg/trends

CO2 and the carbon cycle

1. Physical Basis. Radiative forcings. 25

Canadell et al., 2007

CO2 and the carbon cycle

1. Physical Basis. Radiative forcings. 26

IPCC AR5, WG1 Ch6

CO2 is not the only GHG with a global biogeochemical cycles:

Methane and atmospheric chemistry

1. Physical Basis. Radiative forcings. 27

Image: ds.data.jma.go.jp/ghg/info_ghg_e.html

Methane and atmospheric chemistry

1. Physical Basis. Radiative forcings. 28

Source: H. Le Treut (LMD/IPSL)

Emission of NOx and CO

Emission of CH4 OH

O3

Tropospheric and stratospheric ozone

1. Physical Basis. Radiative forcings. 29

IPCC AR5, WG1 Ch2

2 effects: Increase of tropospheric O3 (emission of oxydants); Decrease of stratospheric O3 (emission of halogenated species).

Image: H. Le Treut (LMD/IPSL)

(model)

Aerosols and clouds

1. Physical Basis. Radiative forcings. 30

IPCC AR5, WG1 Ch2

Aerosols and clouds

1. Physical Basis. Radiative forcings. 31

IPCC AR5, WG1 Ch7

(sulfates, nitrate, particulate organic matter)

(black carbon)

Aerosols and clouds

1. Physical Basis. Radiative forcings. 32

IPCC AR5, WG1 Ch7

Aerosols and clouds

1. Physical Basis. Radiative forcings. 33

Image: cyriljackson.wa.edu.au

And yet another global biogeochemical cycle:

Land surface albedo

1. Physical Basis. Radiative forcings. 34

IPCC AR4, WG1 Ch2; IPCC AR5, WG1, Ch8

Land-cover change (cooling)

Black carbon deposition on snow (warming)

Assessed by the latest IPCC report

1. Physical Basis. Radiative forcings. 35

IPCC AR5, WG1 Ch8

Climate sensitivity

1. Physical Basis. Climate response. 36

IPCC AR5, WG1 Ch10

Radiative theory gives: 2x CO2 increases GH effect by about 3.7 W/m2 which increases surface T° by about 1.2 °C

But, there are feedbacks:

T° increases water vapor (positive feedback);

T° decreases ice cover (positive feedback);

T° changes cloudiness (positive or negative feedback).

Models are needed to study this complex system!

1. Physical Basis. Climate response. 37

UK MetOffice

2. Climate models

Navier-Stokes differential equation

2. Climate models. Fluid dynamics 39

Source: H. Le Treut (LMD/IPSL)

Old equation (1845):

It is still analytically unsolved…

It concerns two stratified fluids in climate science:

Navier-Stokes differential equation

2. Climate models. Fluid dynamics 40

Source: T. Dubos (LMD/IPSL)

It requires discretization; to avoid chaotic behavior:

and dimensioning;

Rossby nb. for geostrophic eq. (Ro < 1) Froude nb. for hydrostatic eq. (Fr < 1)

Resolution improves

2. Climate models. Model complexity. 41

IPCC AR5, WG1 Ch1

1990 1995 2001 2007

AR5 (2013)

AR6?

Processes improve

2. Climate models. Model complexity. 42

IPCC AR5, WG1 Ch1; IPCC AR4, WG1 Ch1

From climate models to Earth system models

2. Climate models. Model complexity. 43

Image: nature.com/nature_education

Coupling of several other models

2. Climate models. The IPSL model. 44

Source: J.-L. Dufresne (LMD/IPSL)

Requiring heavy computation

2. Climate models. The IPSL model. 45

Source: H. Le Treut (LMD/IPSL)

Atmospheric circulation

2. Climate models. Basic performance. 46

Source: H. Le Treut (LMD/IPSL)

Some simulated trends

2. Climate models. Basic performance. 47

Mean daily precipitation over 1979-1999

Minimum daily temperature in summer over 1971-2000

Source: H. Le Treut (LMD/IPSL)

CNRM: Obs:

Back to the FAR in 1990

2. Climate models. Basic performance. 48

observations 3 models

Source: H. Le Treut (LMD/IPSL)

IPCC’s projections versus observations

2. Climate models. Basic performance. 49

IPCC AR5, WG1 Ch1

Concluding words

2. Climate models. Basic performance. 50

Henri Atlan : «Il y a un problème de crédibilité des modèles de changements climatiques et des prédictions qui en sont déduites. Ces modèles concernent en effet un domaine - le climat - où le nombre de données disponibles est petit par rapport au nombre de variables qui sont prises en compte dans leur construction, sans parler des variables encore inconnues. Cela implique qu'il existe un grand nombre de bons modèles, capables de rendre compte des observations disponibles, alors même qu'ils reposent sur des hypothèses explicatives différentes et conduisent aussi à des prédictions différentes, voire opposées. Il s'agit là d'une situation dite "des modèles par les observations« , cas particulier de "sous-détermination des théories par les faits", bien connue des chercheurs engagés dans la construction de modèles de systèmes complexes naturels, où le nombre de données ne peut pas être multiplié à l'envi par des expérimentations répétées et reproductibles. Conséquence : les modèles sur les changements climatiques ne peuvent être que des hypothèses, mises en formes informatiques très sophistiquées mais pleines d'incertitudes quant à leur relation à la réalité ; et il en va de même des prédictions qui en

sont déduites.»

« La religion de la catastrophe », Le Monde, 27 mars 2010

Voir réponse: « Un étonnant effet collatéral du changement climatique », Le Monde, 6 avril 2010

Concluding words

2. Climate models. Basic performance. 51

George E. P. Box :

« Essentially, all models are wrong, but some are useful. »

« […] all models are wrong; the practical question is how wrong do they have to be to not be useful. »

Empirical Model-Building and Response Surfaces (1987)

3. Climate projections

Creating scenarios

3. Climate projections. The scenarios. 53

SRES (2000) RCP (2013)

Representative Concentration Pathways

3. Climate projections. The scenarios. 54

+ emissions of short-lived pollutants + trajectories of natural forcings

IPCC AR5, WG1 Ch11

Temperature projections

3. Climate projections. Temperature. 55

IPCC AR5, WG1 TS

Understanding temperature change

3. Climate projections. Temperature. 56

last glacial era (about -5°C; equilibrium)

wikipedia.org

Understanding temperature change

3. Climate projections. Temperature. 57

climate analogues (about +4°C, one model)

Hallegatte et al., 2007

Understanding temperature change

3. Climate projections. Temperature. 58

climate analogues (about +4°C, another model)

Hallegatte et al., 2007

Understanding temperature change

3. Climate projections. Temperature. 59

IPCC AR4, WG2 Ch8, Ch12

2003 heat wave in Europe

Understanding temperature change

3. Climate projections. Temperature. 60

IPCC AR4, WG2 Ch8 Ch12

2003 heat wave in Europe

Precipitations projections

3. Climate projections. Water cycle. 61

IPCC AR5, WG1 TS

Sea-ice cover projections

3. Climate projections. Water cycle. 62

IPCC AR5, WG1 TS

Acidity projections and biological activity

3. Projections. Oceans. 63

IPCC AR5, WG1 TS; Bopp et al., 2013

Acidity projections and biological activity

3. Projections. Oceans. 64

IPCC AR5, WG1 TS, Ch13

Compatible emissions

3. Projections. Carbon cycle. 65

IPCC AR5, WG1 Ch6

Source of the spread in projections

3. Climate projections. Uncertainties. 66

IPCC AR5, WG1 Ch1

The longest timescale

3. Climate projections. Uncertainties. 67

IPCC AR5, WG1 Ch12

The inertia of the system implies several timescales:

Is the system linear?

3. Climate projections. Uncertainties. 68

IPCC AR5, WG1 TS

What about tipping points?

3. Climate projections. Uncertainties. 69

Lenton et al., 2008

4. Attribution of climate change

Using models to test different assumptions

4. Attribution of climate change 71

IPCC AR5, WG1 Ch10

Detection is a matter of natural variability

4. Attribution of climate change 72

IPCC AR5, WG1 Ch10

Thank you for your attention

References: IPCC reports available at http://www.ipcc.ch Canadell et al. (2007). “Contributions to accelerating atmospheric CO2 growth from economic

activity, carbon intensity, and efficiency of natural sinks”, PNAS, 104(47): 18866–18870.

Hallegatte et al. (2007). “Using climate analogues for assessing climate change economic impacts in urban areas”, Climatic Change, 82:47–60.

Bopp et al., (2013). “Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models”, Biogeosciences, 10:6225–6245.

Lenton et al. (2008). “Tipping elements in the Earth’s climate system”, PNAS, 105(6): 1786–1793.