Download Impacts of Climate-carbon Cycle Feedbacks on

Impact of climate-carbon cycle feedbacks on
emissions scenarios to achieve stabilisation
Chris Jones (1)
Peter Cox (2), Chris Huntingford (3)
1. Hadley Centre, Met Office, Exeter
2. Centre for Ecology and Hydrology, Dorset
3. Centre for Ecology and Hydrology, Wallingford
© Crown copyright 2004
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Outline
 Climate-Carbon cycle feedbacks
Uncertainties/intercomparisons
Implications for stabilisation emissions
 Results
GCM experiments
Simple “reduced form” model results
 Discussion
Uncertainties – between and within models
Reducing uncertainty? Model validation
Defining “optimal” pathways to stabilisation?
© Crown copyright 2004
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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
( WRE550 )
No feedbacks
With feedbacks
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Permissible Emissions
Total emissions, WRE
2000-2300
without
feedbacks
with
feedbacks
Stabilisation at
550 ppm
1355 GtC
1010 GtC
1393 GtC
 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
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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
Without feedbacks
With feedbacks
 UVic model – courtesy of
Damon Matthews (in press at
GRL)
 Stabilisation at 1000ppm
 Significant reduction in
allowed emissions
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C4MIP models
Stabilise at 1000ppm by 2350
Cumulative
Emissions
Reductions
(GtC)
© Crown copyright 2004
UVic
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C4MIP models
Stabilise at 1000ppm by 2350
Cumulative
Emissions
Reductions
(GtC)
UVic
Hadley
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C4MIP models
Stabilise at 1000ppm by 2350
C4MIP-min (g=0.04)
Cumulative
Emissions
Reductions
(GtC)
UVic (g=0.2)
Hadley (g=0.31)
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Range over C4MIP models
Stabilise at 1000ppm by 2350
Cumulative
Emissions
Reductions
(GtC)
C4MIP-mean*
(g=0.14)
UVic
* = C4MIP results estimated from gain factors derived from C4MIP transient expts
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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.
© Crown copyright 2004
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
WRE550
WRE450
CO2 fert’n
Soil resp
NPP(T)
∆T2x, 1.5-4.5
∆T2x, 1.5-10
 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
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Multiple parameter perturbations
Low climate sensitivity
High climate sensitivity
 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
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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
GPP
RE
Temp
Temp
GPP
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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
 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
 “desirability” varies
with timescale to
stabilisation
“worse”
 How do climatecarbon cycle
feedbacks affect our
choice of “optimal”?
Shifted optimum?
“better”
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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
© Crown copyright 2004
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