thomas gasser (lsce/ispl)
TRANSCRIPT
Climate change: the physical aspects
ATHENS Programme AgroParisTech; Nov. 17, 2014
Thomas Gasser (LSCE/ISPL) ([email protected])
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.