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 CO 2 and latitudinal flux partitioning 4.Ocean example: seasonal O 2 and CO 2 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; 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 CO 2, ± 6 PgC/yr = ± $700 Billion/yr C 4 MIP 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 = [CO 2 ] Inverse: [CO 2 ] – 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 ModelModel Name 1CSU 2GCTM 3UCB 4UCI 5JMA 6MATCH.CCM3 7MATCH.NCEP 8MATCH.MACCM2 9NIES ANIRE BTM2 CTM3 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 ModelModel Name 1CSU 2GCTM 3UCB 4UCI 5JMA 6MATCH.CCM3 7MATCH.NCEP 8MATCH.MACCM2 9NIES ANIRE BTM2 CTM3 Systematic trade off is related to vertical mixing biases in the models Tropical Land and Northern Land fluxes plotted versus vertical CO 2 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 CO 2 profiles for different seasonal intervals

Comparing the Observed and Modeled Gradients ModelModel Name 1CSU 2GCTM 3UCB 4UCI 5JMA 6MATCH.CCM3 7MATCH.NCEP 8MATCH.MACCM2 9NIES ANIRE BTM2 CTM3 Most of the models overestimate the annual-mean vertical CO 2 gradient Observed value 3 models that most closely reproduce the observed annual-mean vertical CO 2 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, ] 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 CO 2 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 CO 2 transport remain 1 1 Faraday, 1855

ppm pressure NSNSNSNS Transcom3 Fossil Fuel Response ppm latitude

TransCom3 Seasonal Ocean O 2 Amplitude [T. Blaine, SIO Dissertation, 2005]

HIPPO (PIs: Harvard, NCAR, Scripps, and NOAA): A global and seasonal survey of CO 2, O 2, CH 4, CO, N 2 O, H 2, SF 6, COS, CFCs, HCFCs, O 3, H 2 O, and hydrocarbons HIAPER Pole-to-Pole Observations of Atmospheric Tracers 5 loops over next 3 years, starting in January 2009 NCAR Airborne O 2 Instrument

Southern Ocean Air-Sea CO 2 Fluxes solubility biology anthropogenic solubility

The Southern Ocean will play a key role in future anthropogenic CO 2 uptake, mediated by strong opposing solubility and biological influences Global Warming Simulation [Sarmiento et al., Nature 1998 ] Solubility Pump Biological Pump

Air-Sea Flux Comparison Contemporary Fluxes [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 O 2 and CO 2 Southern Ocean Air-Sea CO 2 and O 2 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?