modelling the influence of regional co fluxes at surface ... · modelling the influence of regional...

Modelling the Influence of Regional CO2 fluxes at Surface and Column Observing Sites

Sara Mikaloff Fletcher1, Vanessa Sherlock1, Nicholas Deutscher2*, David Griffith2, Brian Connor3

Solar FTS: John Robinson1, Ronald Macatangay2, Hisako Shiona1, Clare Murphy2, Nicholas Jones2, Caltech collaborators @ Darwin: Paul

Wennberg, Debra Wunch, Geoffrey Toon In Situ FTS: Dan Smale1, Gordon Brailsford1, Britt Stephens4, Rowena

Moss1, Mike Kotkamp1, Antony Gomez1, Graham Kettlewell2, Martin Riggenbach2

1.  NIWA, NZ 2.  University of Wollongong

3.  BC Consulting, NZ 4.  National Center for Atmospheric Research (NCAR), USA

* Now at University of Bremen, Inst. Of Environmental Physics.

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Overview

•  Challenges for integrating data from new measurements platforms into models

•  Total Column Carbon observing Network (TCCON)

•  Puzzles in the TCCON model-data comparison •  Model simulations to interpret fluxes driving

seasonal variability at these stations •  A case for SH TCCON data as a new window onto

carbon sources and sinks from South America and Africa

2

Inferring fluxes from observations

•  Traditionally done using a network of ~100 surface sites •  Strongly limited by the observing network, especially in the

tropics and Southern Hemisphere

Figure Courtesy of WMO 3

Much recent work has focused on integrating data from new platforms

•  Continuous analysers •  Ocean carbon data (ΔpCO2, Dissolved Inorganic

Carbon) •  Tall towers

•  Aircraft •  Flux towers

•  Remote sensing data (Satellite, Ground based)

These data present new challenges to the atmospheric models and inverse techniques

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What can continuous analysers see at Baring Head?

5

TCCON overview •  Total column CO2, CH4, N2O, CO, and other gases

•  Direct-sun solar absorption spectroscopy in the near IR •  Currently 15 sites •  Derive column average dry air mole fractions (e.g. XCO2)

–  Using column O2 as internal standard •  Calibrated against NOAA/WMO in situ scales

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Column v.s. In Situ Data

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TCCON CO2

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Column FTS Measurements at Darwin

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Column FTS Measurements at Lauder

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Comparison of TCCON sites with models

11 Houweling et al., 2010

CarbonTracker Tagged Tracer Simulations

•  CarbonTracker fluxes optimized against the surface network –  Tagged forward simulations with optimized 2009 CT fluxes

•  Separate tracer tags for: –  each of the 22 Transcom regions + AU/NZ split –  fossil fuel, biomass burning, terrestrial biosphere, ocean flux

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Contribution of source processes to the seasonal cycle at Darwin

X C

O2 (

ppm

)

13

Regions contributing to the land seasonal cycle at Darwin

X C

O2 (

ppm

)

14

Bias in the Model Transport?

•  Houweling et al. [2010] compared TCCON data to four atmospheric models using the CT fluxes as boundary conditions

•  They found similar seasonal biases at Darwin for all of the models

•  However, there could be biases common to all the models or the the reanalysis fields forcing them

•  Comparisons with aircraft data may provide a degree of independent validation

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Comparisons with Aircraft Data January 2009

January 2009 Model Mean

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Comparisons with Aircraft Data November 2009

November 2009 Model Mean

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Comparisons with Aircraft Data March 2010

March 2009 Model Mean

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Contribution of source processes to the seasonal cycle at Darwin

X C

O2 (

ppm

)

19

Regions contributing to the biomass burning seasonal cycle at Darwin

X C

O2 (

ppm

)

20

Evolution of the Vertical Profile Footprint from Biomass Burning From South East Asia

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Contribution of source processes to the seasonal cycle at Lauder

X C

O2 (

ppm

)

22

Regions contributing to the land seasonal cycle at Lauder

X C

O2 (

ppm

) X

CO

2 (pp

m)

23

Regions contributing to the biomass burning seasonal cycle at Lauder

X C

O2 (

ppm

) X

CO

2 (pp

m)

24

Zonal Mean Biomass Burning Emissions From South America and Africa

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Conclusions and Outlook

•  There is substantial variability in the seasonal cycle at the SH TCCON stations, with larger amplitude cycles in 2007-2008

•  This variability is not captured by model simulations •  Seasonal variability at Darwin is largely controlled by land

fluxes, particularly from the Northern Hemisphere, Tropical Asia, and Australia, and biomass burning from Southern Hemisphere regions.

•  Seasonal variability at Lauder is primarily driven by land fluxes in South America, Africa, and the Northern hemisphere, with biomass burning from South America and Africa also playing a substantial role

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Thanks to: •  The CarbonTracker-North America Team •  For funding:

–  NIWA: FRST, ISAT –  UoW: ARC

–  NIES

–  NASA, CalTech

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