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?