comparing global carbon cycle models to observations is hard but better than the alternative
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Comparing Global Carbon Cycle Models to Observations is Hard but Better Than the Alternative. Britton Stephens, National Center for Atmospheric Research. [illustration by Mercer Mayer]. Outline:. Why model-data comparisons are important Why model-data comparisons are hard - PowerPoint PPT PresentationTRANSCRIPT
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Comparing Global Carbon Cycle Models to Observations is Hard but Better Than the Alternative
Britton Stephens, National Center for Atmospheric Research
[illustration by Mercer Mayer]
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Outline:
1. Why model-data comparisons are important
2. Why model-data comparisons are hard
3. Atmosphere example: vertical profiles of CO2 and latitudinal flux partitioning
4. Ocean example: seasonal O2 and CO2 cycles and Southern Ocean ventilation
5. Land example: Ecosystem respiration response to widespread beetle outbreak
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It’s all about trajectories. . . .
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How hot is it going to get?
Climate projections are sensitive to human decisions and carbon cycle feedbacks. . .
[IPCC, 2007]
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. . . and human decisions are sensitive to scientific knowledge feedbacks
How much can we burn?
[IPCC, 2007]
3 or 7?
6 or 12?
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[Raupach et al. 2007, PNAS; www.globalcarbonproject.org]
How well can we predict human factors?
Fossil-fuel emissions have already exceeded highest scenario used for IPCC projections
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How well can we predict climate feedbacks?
Arctic summer sea ice levels have already exceeded the lowest model estimates
[Stroeve, et al., GRL, 2007]
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[Friedlingstein, et al., J. Climate, 2006]
How well can we predict carbon-cycle feedbacks?
In 2050, combined anthropogenic offsets have a range of 3 to 15 PgC/yr
At $32/ton CO2, ± 6 PgC/yr = ± $700 Billion/yr
C4MIP Projections
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Annual fluxes are small relative to balanced seasonal exchanges and to standing pools
The global carbon cycle for the 1990s, showing the main annual fluxes in GtC yr –1. [IPCC, 2007]
Annual residuals
Land-Based Sink
Net Oceanic Sink
Pools and flows
Uncertainties on natural annual-mean ocean and land fluxes are +/- 25 to 75 %
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Global atmospheric inverse models and surface data can be used to make regional flux estimates
Forward: Flux + Transport = [CO2]
Inverse: [CO2] – Transport = Flux
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Model-data fusion is hard because:
1. Models often don’t predict something that can be measured
2. Observations don’t measure something that can be predicted
3. A cultural divide
360 m
120 m
800 m
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[Gurney et al, Nature, 2002]
Annual mean TransCom3 Level 1 Results
“For most regions, the between-model uncertainties are of similar or smaller magnitude than the within-model uncertainties. This suggests that the choice of transport model is not the critical determinant of the inferred fluxes.”
Measurement uncertainty ≈ 0.2 ppm
Continental site “Data error” ≈ 2.2 ppm
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12 Model Results from the TransCom 3 Level 2 Study
Model Model Name
1 CSU
2 GCTM
3 UCB
4 UCI
5 JMA
6 MATCH.CCM3
7 MATCH.NCEP
8 MATCH.MACCM2
9 NIES
A NIRE
B TM2
C TM3
Systematic trade off between northern and tropical land fluxes
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Regional land flux uncertainties are very large
• All model average and standard deviations:
Northern Land = -2.4 ± 1.1 PgCyr-1
Tropical Land = +1.8 ± 1.7 PgCyr-1
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Bottom-up estimates have generally failed to find large uptake in northern ecosystems and large net sources in the tropics
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A helpful discovery about the nature of the model disagreements
Model Model Name
1 CSU
2 GCTM
3 UCB
4 UCI
5 JMA
6 MATCH.CCM3
7 MATCH.NCEP
8 MATCH.MACCM2
9 NIES
A NIRE
B TM2
C TM3
Systematic trade off is related to vertical mixing biases in the models
Tropical Land and Northern Land fluxes plotted versus vertical CO2 gradient
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12 Airborne Sampling Programs from 6 International Laboratories
Northern Hemisphere sites include Briggsdale, Colorado, USA (CAR); Estevan Point, British Columbia, Canada (ESP); Molokai Island, Hawaii, USA (HAA); Harvard Forest, Massachusetts, USA (HFM); Park Falls, Wisconsin, USA (LEF); Poker Flat, Alaska, USA (PFA); Orleans, France (ORL); Sendai/Fukuoka, Japan (SEN); Surgut, Russia (SUR); and Zotino, Russia (ZOT). Southern Hemisphere sites include Rarotonga, Cook Islands (RTA) and Bass Strait/Cape Grim, Australia (AIA).
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12 Airborne Sampling Programs from 6 International Laboratories
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Vertical CO2 profiles for different seasonal intervals
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Comparing the Observed and Modeled Gradients
Model Model Name
1 CSU
2 GCTM
3 UCB
4 UCI
5 JMA
6 MATCH.CCM3
7 MATCH.NCEP
8 MATCH.MACCM2
9 NIES
A NIRE
B TM2
C TM3
Most of the models overestimate the annual-mean vertical CO2 gradient
Observed value
• 3 models that most closely reproduce the observed annual-mean vertical CO2 gradients (4, 5, and C):
Northern Land = -1.5 ± 0.6 PgCyr-1
Tropical Land = +0.1 ± 0.8 PgCyr-1
• All model average:
Northern Land = -2.4 ± 1.1 PgCyr-1
Tropical Land = +1.8 ± 1.7 PgCyr-1
Northern Land
Tropical Land
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• Interlaboratory calibration offsets and measurement errors
• Diurnal biases
• Interannual variations and long-term trends
• Flight-day weather bias
• Spatial and Temporal Representativeness
Observational and modeling biases evaluated:
All were found to be small or in the wrong direction to explain the observed annual-mean discrepancies
[Schulz et al., Environ. Sci. Technol. 2004, 38, 3683-3688]
WLEF Diurnal Cycle Observations
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[figure courtesy of Scott Denning]
Seasonal vertical mixing
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[Stephens et al., Science, 2007]
Airborne measurements suggest:
• Northern forests, including U.S. and Europe, are taking up much less CO2 than previously thought
• Intact tropical forests are strong carbon sinks and are playing a major role in offsetting carbon emissions
However, large (O ~ 2 PgCyr-1) flux uncertainties associated with modeling atmospheric CO2 transport remain
1
1Faraday, 1855
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ppm
pres
sure
N S N S N S N S
Transcom3 Fossil Fuel Response
ppm
latitude
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TransCom3 Seasonal Ocean O2 Amplitude
[T. Blaine, SIO Dissertation, 2005]
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HIPPO (PIs: Harvard, NCAR, Scripps, and NOAA): A global and seasonal survey of CO2, O2, CH4, CO, N2O, H2, SF6, COS, CFCs, HCFCs, O3, H2O, and hydrocarbons
HIAPER Pole-to-Pole Observations of Atmospheric Tracers
5 loops over next 3 years, starting in January 2009
NCAR Airborne O2 Instrument
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Southern Ocean Air-Sea CO2 Fluxes
solubility
biology
anthropogenic
solubility
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The Southern Ocean will play a key role in future anthropogenic CO2 uptake, mediated by strong opposing solubility and biological influences
2056-65 Global Warming Simulation [Sarmiento et al., Nature 1998 ]
Solubility Pump
Biological Pump
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Air-Sea Flux ComparisonContemporary Fluxes 1992-6
[courtesy A. Jacobsen]
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ORCA-PISCES-T
UVic-ESCM
Direction and magnitude of response to increased circumpolar winds is uncertain
[Le Quéré et al., Science 2007] [Zickfeld et al., Science 2008]
sea to air = positive
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Solubility (thermal) and biological processes have discernable effects on atmospheric O2 and CO2
Southern Ocean Air-Sea CO2 and O2 Fluxes
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SeaWiFS Summer Chlorophyll a
SIO Gradient =
PSA – (CGO+SPO)/2
SPO
CGO
PSA
[PCTM runs courtesy David Baker]
SIO Stations
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Fuel-cell technique can be used on ships to greatly increase data coverage in Southern Ocean
R/V Lawrence M. Gould
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Effects of large-scale Mountain Pine Beetle outbreaks on ecosystem carbon fluxes
Fraser Experimental Forest, Colorado
Model for British Columbia
[Kurz et al., Nature, 2008]
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Fraser Experimental Forest Noctural Respiration Signals
Ecosystem respiration decreases, because reduction in autotrophic respiration is greater than increase in heterotrophic respiration
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So what is the ideal form of modeler - observationalist interaction?