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:
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
It’s all about trajectories. . . .
How hot is it going to get?
Climate projections are sensitive to human decisions and carbon cycle feedbacks. . .
[IPCC, 2007]
. . . and human decisions are sensitive to scientific knowledge feedbacks
How much can we burn?
[IPCC, 2007]
3 or 7?
6 or 12?
[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
How well can we predict climate feedbacks?
Arctic summer sea ice levels have already exceeded the lowest model estimates
[Stroeve, et al., GRL, 2007]
[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
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 %
Global atmospheric inverse models and surface data can be used to make regional flux estimates
Forward: Flux + Transport = [CO2]
Inverse: [CO2] – Transport = Flux
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
[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
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
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
Bottom-up estimates have generally failed to find large uptake in northern ecosystems and large net sources in the tropics
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
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).
12 Airborne Sampling Programs from 6 International Laboratories
Vertical CO2 profiles for different seasonal intervals
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
• 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
[figure courtesy of Scott Denning]
Seasonal vertical mixing
[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
ppm
pres
sure
N S N S N S N S
Transcom3 Fossil Fuel Response
ppm
latitude
TransCom3 Seasonal Ocean O2 Amplitude
[T. Blaine, SIO Dissertation, 2005]
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
Southern Ocean Air-Sea CO2 Fluxes
solubility
biology
anthropogenic
solubility
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
Air-Sea Flux ComparisonContemporary Fluxes 1992-6
[courtesy A. Jacobsen]
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
Solubility (thermal) and biological processes have discernable effects on atmospheric O2 and CO2
Southern Ocean Air-Sea CO2 and O2 Fluxes
SeaWiFS Summer Chlorophyll a
SIO Gradient =
PSA – (CGO+SPO)/2
SPO
CGO
PSA
[PCTM runs courtesy David Baker]
SIO Stations
Fuel-cell technique can be used on ships to greatly increase data coverage in Southern Ocean
R/V Lawrence M. Gould
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]
Fraser Experimental Forest Noctural Respiration Signals
Ecosystem respiration decreases, because reduction in autotrophic respiration is greater than increase in heterotrophic respiration
So what is the ideal form of modeler - observationalist interaction?
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