there’s a rectifier in my closet: vertical co 2 transport and latitudinal flux partitioning
DESCRIPTION
THERE’S A RECTIFIER IN MY CLOSET: Vertical CO 2 Transport and Latitudinal Flux Partitioning. [illustrations from There’s a Nightmare in my Closet by Mercer Mayer]. Britton Stephens, National Center for Atmospheric Research TransCom Meeting, Purdue 2007. Aircraft Data Providers: - PowerPoint PPT PresentationTRANSCRIPT
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THERE’S A RECTIFIER IN MY CLOSET: Vertical CO2 Transport and Latitudinal Flux Partitioning
Britton Stephens, National Center for Atmospheric ResearchTransCom Meeting, Purdue 2007
[illustrations from There’s a Nightmare in my Closet by Mercer Mayer]
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TransCom3 Modelers:Kevin R. Gurney, Rachel M. Law, Scott Denning, Peter J. Rayner, David Baker, Philippe Bousquet, Lori Bruhwiler, Yu-Han Chen, Philippe Ciais, Inez Y. Fung, Martin Heimann, Jasmin John, Takashi Maki, Shamil Maksyutov, Philippe Peylin, Michael Prather, Bernard C. Pak, Shoichi Taguchi
Aircraft Data Providers:Pieter P. Tans, Colm Sweeney, Philippe Ciais, Michel Ramonet, Takakiyo Nakazawa, Shuji Aoki, Toshinobu Machida, Gen Inoue, Nikolay Vinnichenko, Jon Lloyd, Armin Jordan, Martin Heimann, Olga Shibistova, Ray L. Langenfelds, L. Paul Steele, Roger J. Francey
Additional Modeling:Wouter Peters, Philippe Ciais, Philippe Bousquet, Lori Bruhwiler
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[figure courtesy of Scott Denning]
Seasonal vertical mixing
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Annual mean accumulation near surface and depletion aloft
[Denning et al., Nature, 1995]
Observed
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Transcom3 neutral biosphere flux response
349.5
350.0
350.5
351.0
351.5
352.0
352.5
353.0
353.5
354.0
-90 -70 -50 -30 -10 10 30 50 70 90
CSU.gurney
GISS.fung
GISS.prather
GISS.prather2
GISS.prather3
J MA-CDTM.maki
MATCH.bruhwiler
MATCH.chen
MATCH.law
NIES.maksyutov
NIRE.taguchi
RPN.yuen
SKYHI.fan
TM2.lsce
TM3.heimann
GCTM.baker
Latitude
ppm
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Gurney et al, Nature, 2002
TransCom3 model results show a large transfer of carbon from tropical to northern land regions.
Level 1 (annual mean)Level 2 (seasonal)
Gurney et al, GBC, 2004
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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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Model Model NameNorthern
Total Flux (1)Tropical
Total Flux (1)Northern
Land Flux (1)Tropical
Land Flux (1)
1 CSU -4.4 (0.2) 3.7 (0.6) -3.6 (0.3) 3.3 (0.7)
2 GCTM -3.4 (0.2) 2.3 (0.7) -2.0 (0.3) 2.7 (0.8)
3 UCB -4.4 (0.3) 3.7 (0.6) -3.1 (0.3) 4.0 (0.7)
4 UCI -2.6 (0.3) 0.5 (0.7) -1.5 (0.3) -0.1 (0.8)
5 JMA -1.4 (0.3) -0.5 (0.8) -0.9 (0.4) -0.5 (0.9)
6 MATCH.CCM3 -3.0 (0.2) 2.2 (0.6) -2.1 (0.3) 2.3 (0.7)
7 MATCH.NCEP -4.0 (0.2) 3.2 (0.5) -4.0 (0.3) 3.4 (0.7)
8 MATCH.MACCM2 -3.7 (0.3) 3.1 (0.8) -3.0 (0.3) 2.5 (0.9)
9 NIES -4.0 (0.3) 2.2 (0.6) -3.5 (0.3) 2.7 (0.8)
A NIRE -4.5 (0.3) 1.6 (0.7) -2.8 (0.3) 1.2 (0.8)
B TM2 -1.6 (0.3) -1.4 (0.7) -0.5 (0.3) -1.0 (0.8)
C TM3 -2.4 (0.2) 1.4 (0.6) -2.2 (0.3) 1.0 (0.8)
TransCom 3 Level 2 annual-mean model fluxes (PgCyr-1)
Study N. Total T. Total N. Land T. Land
Jacobson et al., 2006 ('92-'96) -3.9 5.0 -2.9 4.2
Baker et al., 2006 ('91-'00) -3.7 2.7 -2.6 1.9
Gurney et al., 2004 ('92-'96) -3.3 1.8 -2.4 1.8
CarbonTracker, 2007 ('01-'05) -2.8 1.1 -1.8 0.1
Rödenbeck et al., 2003 ('92-'96) -2.3 -0.1 -0.7 -1.0
Rödenbeck et al., 2003 ('96-'99) -2.1 0.3 -0.4 -0.8
Comparison to other studies
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TransCom3 predicted rectifier explains most of the variability in estimated fluxes
Impact on predicted fluxes
349.5
350.0
350.5
351.0
351.5
352.0
352.5
353.0
353.5
354.0
-90 -70 -50 -30 -10 10 30 50 70 90
CSU.gurney
GISS.fung
GISS.prather
GISS.prather2
GISS.prather3
J MA-CDTM.maki
MATCH.bruhwiler
MATCH.chen
MATCH.law
NIES.maksyutov
NIRE.taguchi
RPN.yuen
SKYHI.fan
TM2.lsce
TM3.heimann
GCTM.baker
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
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ppm
pressure
N S N S N S N S
Transcom3 neutral biosphere flux response
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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).
Airborne flask sampling locations
Collaborating Institutions: USA: NOAA GMD, CSU, France: LSCE, Japan: Tohoku Univ., NIES, Nagoya Univ., Russia: CAO, SIF, England: Univ. of Leeds, Germany: MPIB, Australia: CSIRO MAR
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Airborne flask sampling data
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Altitude-time CO2 contour plots for all sampling locations
20
-15
10
-10
10
-10
0
-5
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Model-predicted NH Average CO2 Contour Plots
Observed NH Average CO2 Contour Plot
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Vertical CO2 profiles for different seasonal intervals
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Observed and predicted NH average profiles
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• 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
Estimated fluxes versus predicted 1 km – 4 km gradients
Observed value
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
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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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Estimated fluxes versus predicted 1 km – 4 km gradients for different seasonal intervals
Observed values
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
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Should annual-mean or seasonal gradients be used to evaluate model fluxes?
• Annual-mean fluxes are of most interest because they are relevant to annual ecosystem budgeting, to policy makers, and to projections of future greenhouse gas levels.
• No model does well at all times of year, but do not want to reject all models.
• Errors in seasonal timing of fluxes make selection of seasonal criteria problematic.
• Seasonal (rectifier) effects are inherently cumulative, such that a model with large seasonal errors that offset will do better in annual-mean that one with small seasonal errors that compound.
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HIPPO ’08-’11 (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
Fossil fuel CO2 gradients over the PacificUCI UCIs
JMA MATCH.CCM3
ppm
pres
sure
pres
sure
S N S N S N
N S
N S
N S
N S
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• Models with large tropical sources and large northern uptake are inconsistent with observed annual-mean vertical gradients.
• A global budget with less tropical-to-north carbon transfer is more consistent with bottom-up estimates and does not conflict with independent global 13C and O2 constraints.
• Simply adding airborne data into the inversions will not necessarily lead to more accurate flux estimates
• Models’ seasonal vertical mixing must be improved to produce flux estimates with high confidence
• There is value in leaving some data out of the inversions to look for systematic biases
Conclusions:
And of course, watch out for the next monster. . . .
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Representativeness