Impact of climate-carbon cycle feedbacks on emissions scenarios to achieve stabilisation
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Transcript of Impact of climate-carbon cycle feedbacks on emissions scenarios to achieve stabilisation
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Impact of climate-carbon cycle feedbacks on emissions scenarios to achieve stabilisation
1. Hadley Centre, Met Office, Exeter2. Centre for Ecology and Hydrology, Dorset3. Centre for Ecology and Hydrology, Wallingford
Chris Jones (1)Peter Cox (2), Chris Huntingford (3)
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Outline Climate-Carbon cycle feedbacks
Uncertainties/intercomparisonsImplications for stabilisation emissions
ResultsGCM experimentsSimple “reduced form” model results
DiscussionUncertainties – between and within modelsReducing uncertainty? Model validationDefining “optimal” pathways to stabilisation?
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Climate Carbon Cycle feedbacks
Well known that climate-carbon cycle models predict a positive feedback
Climate change will reduce the carbon cycle’s ability to sequester CO2
Models have consensus on sign (+ve), but not magnitude of feedback (i.e. C4MIP)
Uncertainties in the feedback strength mean large uncertainty in:
Future CO2 levels given an emissions scenario Permissible emissions to stabilise CO2 at a given level
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Climate Carbon Cycle feedbacks
If climate change weakens natural carbon sinks then we must reduce emissions by more than previously thought to stabilise atmospheric CO2
Passing mention in TAR but needs to be brought out more TAR showed range of permissible emissions but didn’t stress
impact of climate feedbacks in reducing these Huge political implications Plea to AR4 authors – Needs to be given more prominence.
Instead of “managing the carbon cycle” this comes under “being managed by the carbon cycle”
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WRE scenarios
“WRE” is a family of scenarios of CO2 level, stabilising at 450, 550, 650, 750 and 1000ppm Wigley, Richels and Edmonds. ‘Economic and
environmental choices in the stabilisation of atmospheric CO2 concentrations’. Nature, 1996
We run the carbon cycle GCM with the prescribed 550 CO2 scenario and infer the emissions required to achieve it
Results shown in detail for 550ppm Summary of results for all levels
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WRE550 CO2 emissions
Climate feedbacks imply reduced permissible emissions
Lower peak Earlier peak
Reduced integral
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WRE550 cumulative emissions
Similar to previous experiments
Ocean continues to uptake carbon, but at reduced rate
Terrestrial sink saturates and reverses
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Reduced Form “simple” model
GCM prohibitively expensive!
Simple model has: Global means climate in terms of T
Responds instantly to CO2
Carbon cycle calibrated to follow GCM from transient run of Cox et al 2000.
Does good job at matching WRE550 GCM run
Aim is to give broad idea of response – don’t trust exact details…
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WRE550 CO2 emissions – simple model
No feedbacks
With feedbacks
( WRE550 )
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Permissible Emissions
Without feedbacks, we get close to the WRE result
Climate-Carbon cycle feedbacks significantly reduce the permissible emissions for stabilisation
This is true for stabilisation at any level
Total emissions, 2000-2300
WRE without feedbacks
with feedbacks
Stabilisation at 550 ppm
1393 GtC 1355 GtC 1010 GtC
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Other stabilisation levels
Greater reductions at higher stabilisation levels
Not surprising given greater level of climate change
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Uncertainties
Large uncertainties undermine political impact of results
Do we understand them? Can we reduce them?
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Sources of uncertainty
The impact of carbon-cycle feedbacks on permissible emissions will depend on:
“Political” uncertainties: Chosen level of stabilisation (and hence climate change)
Scientific uncertainties: Climate sensitivity: Greater sensitivity will mean stronger
feedbacks for given CO2 level carbon-cycle parameters
vegetation sensitivity to warming/CO2 Soil sensitivity Ocean response to climate/circulation changes
All climate-carbon cycle studies to date show future weakening of the natural carbon sink in response to climate change
But significant uncertainty in strength of feedback
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Other models
UVic model – courtesy of Damon Matthews (in press at GRL)
Stabilisation at 1000ppm
Significant reduction in allowed emissions
Without feedbacks
With feedbacks
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C4MIP models
CumulativeEmissionsReductions(GtC)
UVic
Stabilise at 1000ppm by 2350
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C4MIP models
CumulativeEmissionsReductions(GtC)
UVic
Hadley
Stabilise at 1000ppm by 2350
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C4MIP models
CumulativeEmissionsReductions(GtC)
UVic (g=0.2)
Hadley (g=0.31)
C4MIP-min (g=0.04)
Stabilise at 1000ppm by 2350
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Range over C4MIP models
CumulativeEmissionsReductions(GtC)
C4MIP-mean*
UVic
* = C4MIP results estimated from gain factors derived from C4MIP transient expts
(g=0.14)
Stabilise at 1000ppm by 2350
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Implications of uncertainty
2 main implications of the C4MIP uncertainty
Uncertainty does not span zero All models agree on positive feedback and hence
some degree of reduction in permissible emissions
Required emissions vary greatly Reductions due to climate feedbacks uncertain by
almost an order of magnitude
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Reducing that uncertainty?
To what extent does the historical record constrain future behaviour?
Climate sensitivity? No – can’t be well constrained observationally Causes large spread in future climate and hence in future
feedback strength
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Climate sensitivity
Uncertainty in historical forcing – especially from aerosols – means large uncertainty in climate sensitivity
TAR shows GCM range from 1.5-4.5, but values up to 8-10K can’t be ruled out completely from observations.
Andreae et al, Nature, 2005
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Reducing that uncertainty?
To what extent does the historical record constrain future behaviour?
Climate sensitivity? No – can’t be well constrained observationally Causes large spread in future climate and hence in future feedback
strength Carbon cycle parameters?
Not directly from observations – CO2 record can’t distinguish strong fertilisation/strong respiration from weak fertilisation/weak respiration.
But give different future behaviour
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Single parameter perturbations
Large ensemble of simple model runs with perturbed parameters In these runs, NPP sensitivity to climate is most important carbon-cycle parameter
More sensitivity than CO2 fertilisation strength or soil respiration sensitivity to temperature
Similar conclusion to Matthews et al., GRL, 2005.
Climate sensitivity outweighs carbon cycle uncertainty
CO2 fert’n
Soil resp
NPP(T)
∆T2x, 1.5-4.5 ∆T2x, 1.5-10
WRE550
WRE450
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Multiple parameter perturbations
Varying all these parameters, but still fitting historical emissions, gives only very weak constraint on future permissible emissions
High climate sensitivities lead to requirement for significant NEGATIVE emissions
Low climate sensitivity
High climate sensitivity
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Reducing that uncertainty?
To what extent does the historical record constrain future behaviour?
Climate sensitivity? No – can’t be well constrained observationally Causes large spread in future climate and hence in future feedback
strength Carbon cycle parameters?
Not directly from observations – CO2 record can’t distinguish strong fertilisation/strong respiration from weak fertilisation/weak respiration.
But give different future behaviour Model validation?
Maybe – recreating observed behaviour is necessary but not sufficient test of a model
C4MIP phase 1 is essential step!
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C4MIP phase 1 - validation
Atmosphere only model with observed 20th century SSTs Just simulate terrestrial carbon cycle Validate against range of obs:
Site-specific from flux towers Regional estimates from inversion studies Interannual variability – e.g. ENSO
Validation is important if we are to know which C4MIP models to trust But, ability to get these right doesn’t constrain future
feedback size – merely gives us clues about how to interpret the models
See Jones & Warnier report on HadCM3LC at: http://www.metoffice.com/research/hadleycentre/pubs/HCTN/index.html
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C4MIP phase 1 - validation
Flux tower validation from CarboEurope data Assess model sensitivity of GPP, Resp against T, P
RE
GPP
GPP
TempTemp
precip
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C4MIP phase 1 - validation
Comparison with TransCom inversions study (Gurney et al, Nature, 2002)
Regional carbon flux estimates from 1992-96
black = transcom pink = Hadley C4MIP
experiment
Agrees pretty well in most places
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Other potential issues
How important is time to stabilisation? Emit soon and reduce strongly? Or more gradual? Can we define an “optimal” pathway?
Sensitivity studies for stabilisation at 550ppm at different rates: Idealised profiles with asymptotic approach to stabilisation:
CO2 = a0 + a1 * tanh (a2 + a3.τ) Match CO2 level and rate of change at 2000 τ =time to (95%) stabilisation. Range from 20-150 years.
Not attempted to quantify likelihood – more illustrative
How do climate-carbon cycle feedbacks affect resulting emissions profiles?
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‘Optimal’ pathways to stabilisation
“fast” (τ=30) and “slow” (τ=80) emissions profiles to 550 ppm
Carbon cycle feedbacks reduce emissions in all cases
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‘Optimal’ pathways to stabilisation
Total 21st century emissions (higher may be seen as “desirable”)
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‘Optimal’ pathways to stabilisation
Max rate of required emissions reductions (higher may be seen as “undesirable”)
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‘Optimal’ pathways to stabilisation
“worse”
“better”
Open Questions: Can we convert this into “desirability” somehow?
E.g. Linearly combine “total emissions” and “max rate of reduction” deliberately simplistic – clearly many more factors to consider
Shifted optimum?
“desirability” varies with timescale to stabilisation
How do climate-carbon cycle feedbacks affect our choice of “optimal”?
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Conclusions
Climate feedbacks on the carbon cycle will reduce future natural carbon uptake
Hence, to stabilise CO2, significantly greater emissions reductions may be required
This is true regardless of: Stabilisation level
But higher levels see greater reduction Model
But large spread of feedback strength between models Timescale to stabilise
Strength of feedback may alter “optimal” shape of trajectory as well as magnitude
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Conclusions
Large uncertainties between/within models
Observational record directly offers only weak constraint on future behaviour
Validation of complex carbon cycle models against all available data is lacking Will prove vital to reducing uncertainty