impact of climate-carbon cycle feedbacks on emissions scenarios to achieve stabilisation

© Crown copyright 2004

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)


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?


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


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”


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


WRE550 CO2 emissions

Climate feedbacks imply reduced permissible emissions

Lower peak Earlier peak

Reduced integral


WRE550 cumulative emissions

Similar to previous experiments

Ocean continues to uptake carbon, but at reduced rate

Terrestrial sink saturates and reverses


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…


WRE550 CO2 emissions – simple model

No feedbacks

With feedbacks

( WRE550 )


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


Other stabilisation levels

Greater reductions at higher stabilisation levels

Not surprising given greater level of climate change


Uncertainties

Large uncertainties undermine political impact of results

Do we understand them? Can we reduce them?


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


Other models

UVic model – courtesy of Damon Matthews (in press at GRL)

Stabilisation at 1000ppm

Significant reduction in allowed emissions

Without feedbacks

With feedbacks


C4MIP models

CumulativeEmissionsReductions(GtC)

UVic

Stabilise at 1000ppm by 2350


C4MIP models


UVic

Hadley



C4MIP models


UVic (g=0.2)

Hadley (g=0.31)

C4MIP-min (g=0.04)



Range over C4MIP models


C4MIP-mean*

UVic

* = C4MIP results estimated from gain factors derived from C4MIP transient expts

(g=0.14)



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


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


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




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


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


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




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!


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



Flux tower validation from CarboEurope data Assess model sensitivity of GPP, Resp against T, P

RE

GPP

GPP

TempTemp

precip



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


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?


‘Optimal’ pathways to stabilisation

“fast” (τ=30) and “slow” (τ=80) emissions profiles to 550 ppm

Carbon cycle feedbacks reduce emissions in all cases



Total 21st century emissions (higher may be seen as “desirable”)



Max rate of required emissions reductions (higher may be seen as “undesirable”)



“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”?


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


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

impact of climate-carbon cycle feedbacks on emissions scenarios to achieve stabilisation

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