Observational needs for global carbon
cycle modelling
Chris Jones
Met Office Hadley Centre
ESA CCI CMUG Fourth Integration Meeting, Exeter, June 2014
Introduction
• Importance of carbon cycle in climate
models and projections
• Large Uncertainty
• Better evaluation needed
• Role of EO and ESA-CCI
• Requirements for CMIP6
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Motivation – why are carbon cycle projections important?
• Carbon cycle key new element in CMIP5 modelling
• Makes projections more relevant and useful
• “TCRE” – critical new outcome of AR5
• What emissions (reductions) required to achieve given pathway?
• But large uncertainty hinders usefulness
compatible emissions pathways
for the RCPs. Fig 6.25;
Jones et al., 2013
Warming link to cumulative emissions
AR5, WG1, SPM.10
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But what are the key processes and uncertainties?
• ANOVA decomposition of spread between models and scenarios
• Scenario differences dominate compatible fossil emissions
After mid-century
emissions pathways
separate almost
completely by scenario
Hewitt et al., 2013 submitted
But what are the key processes and uncertainties?
• ANOVA decomposition of spread between models and scenarios
• Scenario differences dominate compatible fossil emissions
• Similar for ocean uptake, but not for land
“very high confidence,
ocean carbon uptake of
anthropogenic CO2
emissions will continue”
ocean spread
largely due to
scenarios
Land
uncertainty
large in
models
through 21st
century
Hewitt et al., 2013 submitted
“Low confidence on the magnitude
of modelled future land carbon
changes”
But what are the key processes and uncertainties?
• ANOVA decomposition of spread between models and scenarios
• Scenario differences dominate compatible fossil emissions
• Similar for ocean uptake, but not for land
• Caveat – not true regionally for ocean…
Global ocean
N. Atlantic
S. ocean
Hewitt et al., 2013 submitted
Missing processes in CMIP5 models?
●
Permafrost carbon
Permafrost thaw “virtually certain”
[Ch. 12]
● “low confidence” on the magnitude
of carbon losses
N-cycle: “very likely, …, that nutrient
shortage will limit ... future land
carbon sinks”
Wetlands: “ [CH4 emissions] likely
to increase... low confidence in
magnitude”
Land-management
fire
●
●
●
●
●
Fig 6.36; O'Connor et
al., 2013
Evaluation background
• Model development has moved towards
greater complexity
• Carbon-cycle, chemistry, more interactive aerosols
now common place in CMIP5-class models
• Evaluation not necessarily kept apace
Ocean
Atmos
AOIL well
evaluated
Ice
Land
Aerosol
Ecosystems
Chemistry
ESM
less well
evaluated
Evaluation
• Taken here in its widest sense
• Understanding the system and implementing
improvements in the models
• Goes far beyond simple beauty context of comparing
datasets side-by-side
• Top-down
• Need to look at whole-system outputs. “get the right
answer…”
• Bottom-up
• Process understanding and evaluation. “…for the right
reason”
• Emergent constraints
• A posterior constraint on outputs – determining which
observations matter
CMIP5 Biogeochemistry
Evaluation
• Anav et al. (2013, J. Clim) began an
activity to systematically evaluate
carbon cycle in CMIP5 models
Global soil and biomass carbon stores
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Anav et al, 2013
Global soil and biomass carbon stores
Model spread in biomass
540 ± 220 PgC
N. Hemi model
spread: factor 4
© Crown copyright Met
tropics model
spread: factor 2
Anav et al, 2013
Global soil and biomass carbon stores
Model spread in soil carbon
1510 ± 790 PgC
N. Hemi model
spread: factor 10
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tropics model
spread: factor 5
Anav et al, 2013
EO requirements
• Long list
• LAI/NDVI
• Phenology, seasonal cycle and trends
• Land cover
• Especially for land-use/change
• Biomass
• Evaluating/monitoring stock changes, land use emissions
• Atmospheric Composition
• CO2, CH4
• Soil moisture, fire
• Drivers of terrestrial carbon changes
• Ocean colour
• Biological activity, location of nutrients
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CCI example: Land-cover
•
ESA CCI land-cover project and new dataset coming out of this
•
Being used to evaluate new PFTs map
•
Example of working directly with EO community to influence
format/quality of products
courtesy Anna Harper, Andy Hartley
Emergent Constraints
 First coined in the context of climate projections by Allen &
Ingram (2002) (?)
 Emergent Constraint : a relationship between an Earth
System sensitivity to anthropogenic forcing and an
observable (or already observed) feature of the ES.
 Emergent because it emerges from the ensemble of ESMs.
 Constraint because it enables an observation to constrain
the estimate of the ES sensitivity in the real world.
 Fluctuation Dissipation Theorem – so we think we can
trust links across timescales from variability to sensitivity...
Archetypal Example of an Emergent Constraint
Slide courtesy Peter Cox
Hall & Qu (2006)
Relationship between CO2 Growth-rate
and Tropical Temperature - Observations
Slide courtesy Peter Cox
Constrained distribution of tropical land carbon
After IAV
Constraint
Prior C4MIP
PDF
Slide courtesy Peter Cox
Emergent Constraints:
caveats and potential
• Not a silver bullet
• Not intended to replace “traditional” evaluation
• But fine balance of carbon processes leads to high
risk that model improvement won't narrow
uncertainty...
• c.f. Cloud feedbacks and climate sensitivity
• EMCs provide a complimentary approach
• But Carbon IAV only uses 1 data point!
• Mauna Loa CO2 site
• Spatial information may allow regional constraints
Also apply
© Crown• copyright
Metto
CH4 IAV to assess future sensitivity
Future requirements of
ESM Evaluation
• CMIP6
•
Idea of satellite “MIP”s around a smaller core
•
Each MIP to be responsible for own set of process
experiments
•
Must all have strong evaluation focus
© Crown copyright courtesy
Met Eyring & Stouffer
Requirements and priorities for CMIP6
• CMIP6 will devolve experiment design/evaluation activities back to
component communities
• Crude history:
• 2000-2009: “carbon cycle is important”
• 2009-2014: “included in CMIP models. Large spread”
• 2015-2020: “must improve”
• Not just make progress
• But be able to demonstrate/quantify progress
OCMIP
C4MIP
LUMIP
MIP activities
Ocean
colour
GHGs
Land
cover
CCI datasets
Biomass
© Crown copyright Met
Future datasets
Conclusions
• Carbon cycle crucial in current / next-generation
climate models
• But only if we can make demonstrable progress in
evaluation and improvement
• Evaluation need to keep pace with added
complexity
• Vision for CMIP6
• Leading role of MIPs in ensuring evaluation focus
• Multiple carbon-related MIP activities
• EO / CCI data will prove invaluable
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