CarbonTracker Documentation
CT2019B release

Nov 12, 2020

Andrew R. Jacobson^1,2, Kenneth N. Schuldt^1,2, John B. Miller², Tomohiro Oda^3,4,
Pieter Tans², Arlyn Andrews², John Mund^1,2, Lesley Ott³,
George J. Collatz³, Tuula Aalto⁵, Sara Afshar⁶, Ken Aikin⁷,
Shuji Aoki⁸, Francesco Apadula⁹, Bianca Baier^1,2, Peter Bergamaschi¹⁰,
Andreas Beyersdorf¹¹, Sebastien C. Biraud¹², Alane Bollenbacher⁶, David Bowling¹³,
Gordon Brailsford¹⁴, James Brice Abshire¹⁵, Gao Chen¹⁶, Huilin Chen¹⁷,
Lukasz Chmura¹⁸, Sites Climadat¹⁹, Aurelie Colomb²⁰, Sébastien Conil²¹,
Adam Cox⁶, Paolo Cristofanelli²², Emilio Cuevas²³, Roger Curcoll¹⁹,
Christopher D. Sloop²⁴, Ken Davis²⁵, Stephan De Wekker²⁶, Marc Delmotte²⁷,
Joshua P. DiGangi¹⁶, Ed Dlugokencky², Jim Ehleringer²⁸, James W. Elkins²,
Lukas Emmenegger²⁹, Marc L. Fischer¹², Grant Forster³⁰, Arnoud Frumau³¹,
Michal Galkowski¹⁸, Luciana V. Gatti³², Emanuel Gloor³³, Tim Griffis³⁴,
Samuel Hammer³⁵, László Haszpra^36,37, Juha Hatakka⁵, Michal Heliasz³⁸,
Arjan Hensen³¹, Ove Hermanssen³⁹, Eric Hintsa², Jutta Holst⁴⁰,
Dan Jaffe⁴¹, Anna Karion², Stephan Randolph Kawa¹⁵, Ralph Keeling⁶,
Petri Keronen⁴², Pasi Kolari⁴², Katerina Kominkova⁴³, Eric Kort⁴⁴,
Paul Krummel⁴⁵, Dagmar Kubistin⁴⁶, Casper Labuschagne⁴⁷, Ray Langenfelds⁴⁵,
Olivier Laurent⁴⁸, Tuomas Laurila⁵, Thomas Lauvaux²⁵, Bev Law⁴⁹,
John Lee⁵⁰, Irene Lehner³⁸, Markus Leuenberger⁵¹, Ingeborg Levin³⁵,
Janne Levula⁴², John Lin²⁸, Matthias Lindauer⁴⁶, Zoe Loh⁴⁵,
Morgan Lopez²⁷, Ingrid T. Luijkx^52,53, Cathrine Lund Myhre³⁹, Toshinobu Machida⁵⁴,
Ivan Mammarella⁵⁵, Giovanni Manca¹⁰, Alistair Manning⁵⁶, Andrew Manning⁵⁷,
Michal V. Marek⁴³, Per Marklund⁵⁸, Melissa Yang Martin¹⁶, Hidekazu Matsueda⁵⁹,
Kathryn McKain^1,2, Harro Meijer¹⁷, Frank Meinhardt⁶⁰, Natasha Miles²⁵,
Charles E. Miller⁶¹, Meelis Mölder⁴⁰, Stephen Montzka², Fred Moore²,
Josep-Anton Morgui¹⁹, Shinji Morimoto⁸, Bill Munger⁶², Jaroslaw Necki¹⁸,
Sally Newman⁶³, Sylvia Nichol¹⁴, Yosuke Niwa⁵⁴, Simon O'Doherty⁶⁴,
Mikaell Ottosson-Löfvenius⁵⁸, Bill Paplawsky⁶, Jeff Peischl⁷, Olli Peltola⁴²,
Jean-Marc Pichon²⁰, Steve Piper⁶, Christian Plass-Dölmer⁴⁶, Michel Ramonet²⁷,
Enrique Reyes-Sanchez²³, Scott Richardson²⁵, Haris Riris¹⁵, Thomas Ryerson⁷,
Kazuyuki Saito⁶⁵, Maryann Sargent⁶², Motoki Sasakawa⁵⁴, Yousuke Sawa⁵⁹,
Daniel Say⁶⁴, Bert Scheeren¹⁷, Martina Schmidt²⁷, Andres Schmidt⁴⁹,
Marcus Schumacher⁴⁶, Paul Shepson⁶⁶, Michael Shook¹⁶, Kieran Stanley⁶⁴,
Martin Steinbacher²⁹, Britton Stephens⁶⁷, Colm Sweeney², Kirk Thoning²,
Margaret Torn¹², Jocelyn Turnbull^68,1, Kjetil Tørseth³⁹, Pim van den Bulk³¹,
Danielle van Dinther³¹, Alex Vermeulen⁴⁰, Brian Viner⁶⁹, Gabriela Vitkova⁴³,
Stephen Walker⁶, Dietmar Weyrauch⁷⁰, Steve Wofsy⁶², Doug Worthy⁷¹,
Dickon Young⁶⁴ and Miroslaw Zimnoch¹⁸

¹CIRES, University of Colorado, Boulder, Colorado, USA
²NOAA Earth System Research Laboratory, Global Monitoring Division, Boulder, Colorado, USA
³Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
⁴Universities Space Research Association, Columbia, Maryland, USA
⁵Finnish Meteorological Institute, Climate System Research, Helsinki, Finland
⁶Scripps Institution of Oceanography, University of California, La Jolla, California, USA
⁷NOAA Earth System Research Laboratory, Chemical Sciences Division, Boulder, Colorado, USA
⁸Tohoku University, Sendai, Japan
⁹Ricerca sul Sistema Energetico, Milano, Italy
¹⁰European Commission, Joint Research Centre, Ispra, Italy
¹¹California State University, San Bernardino, California, USA
¹²Lawrence Berkeley National Laboratory, Berkeley, California, USA
¹³School of Biological Sciences, University of Utah, Salt Lake City, Utah, USA
¹⁴National Institute of Water and Atmospheric Research, Wellington, New Zealand
¹⁵NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
¹⁶NASA Langley Research Center, Hampton, Virginia, USA
¹⁷Centre for Isotope Research, University of Groningen, Groningen, Netherlands
¹⁸University of Science and Technology (AGH), Krakow, Poland
¹⁹Institut de Ciencia i Tecnologia Ambientals, Universitat Autonoma de Barcelona, Barcelona, Spain
²⁰Observatoire de Physique du Globe de Clermont Ferrand, Aubiere, France
²¹Agence Nationale pour la Gestion des Déchets Radioactifs, France
²²Institute of Atmospheric Sciences and Climate (CNR-ISAC), Bologna, Italy
²³Agencia Estatal Meteorologia, Santa Cruz de Tenerife, Spain
²⁴Earth Networks, Earth Networks, Inc., Germantown, Maryland, USA
²⁵Penn State University, Department of Meteorology, University Park, Pennsylvania, USA
²⁶University of Virginia, Charlottesville, Virginia, USA
²⁷Laboratoire des Sciences du Climat et de l'Environnement, Gif sur Yvette, France
²⁸Department of Atmospheric Sciences, University of Utah, Salt Lake City, Utah, USA
²⁹Empa, Swiss Federal Laboratories for Materials Science and Technology, Laboratory for Air Pollution/Environmental Technology, Duebendorf, Switzerland
³⁰National Centre for Atmospheric Sciences, University of East Anglia, Norwich, Norfolk, United Kingdom
³¹Netherlands Organisation for Applied Scientific Research (TNO), Petten, The Netherlands
³²National Institute for Space Research (INPE), Sao Paulo, Brazil
³³University of Leeds,School of Geography, Leeds, United Kingdom
³⁴University of Minnesota,Department of Soil, Water, and Climate, St. Paul, Minnesota, USA
³⁵Universität Heidelberg, Institut für Umweltphysik, Heidelberg, Germany
³⁶Research Centre for Astronomy and Earth Sciences, Sopron, Hungary
³⁷Hungarian Meteorological Service, Budapest, Hungary
³⁸Lund University, Centre for Environmental and Climate Research, Lund, Sweden
³⁹NILU - Norwegian Institute for Air Research, Kjeller, Norway
⁴⁰Lund University, Dept. Phys. Geography and Ecosystem Science, Lund, Sweden
⁴¹University of Washington, Seattle, Washington, USA
⁴²University of Helsinki, Helsinki, Finland
⁴³CzechGlobe Global Change Research Institute CAS, Brno, Czech Republic
⁴⁴University of Michigan, Ann Arbor, Michigan, USA
⁴⁵Commonwealth Scientific and Industrial Research Organization, Oceans and Atmosphere, Aspendale, Victoria, Australia
⁴⁶Hohenpeissenberg Meteorological Observatory, Hohenpeissenberg, Germany
⁴⁷South African Weather Service,Cape Point, South Africa
⁴⁸ICOS Atmospheric Thematic Center, France
⁴⁹Oregon State University, Corvallis, Oregon, USA
⁵⁰University of Maine, Orono, Maine, USA
⁵¹Climate and Environmental Physics, University of Bern, Bern, Switzerland
⁵²Wageningen University, Wageningen, Netherlands
⁵³ ICOS Carbon Portal, Lund University, Lund, Sweden
⁵⁴National Instiute for Environmental Studies, Tsukuba, Japan
⁵⁵Institute for Atmospheric and Earth System Research/Physics, Faculty of Sciences, University of Helsinki, Finland
⁵⁶Met Office Exeter, Devon , United Kingdom
⁵⁷Centre for Ocean and Atmospheric Sciences, University of East Anglia, Norfolk, United Kingdom
⁵⁸Unit for Field-based Forest Research, Swedish University of Agricultural Sciences, Vindeln, Sweden
⁵⁹Meteorological Research Institute, Tsukuba, Japan
⁶⁰Umweltbundesamt, Oberried-Hofsgrund, Germany
⁶¹Jet Propulsion Laboratory, California Institute of Technology, Pasadena California, USA
⁶²Harvard University, Department of Earth and Planetary Science, Cambridge, Massachusetts, USA
⁶³California Institute of Technology, Pasadena, California, USA
⁶⁴University of Bristol, Bristol, United Kingdom
⁶⁵Japan Meteorological Agency, Tokyo, Japan
⁶⁶Purdue University, West Lafayette, Indiana, USA
⁶⁷National Center for Atmospheric Research, Boulder, Colorado, USA
⁶⁸GNS Science,National Isotope Centre, Lower Hutt, New Zealand
⁶⁹Savannah River National Laboratory, Aikin, South Carolina, USA
⁷⁰Hohenpeissenberg Meteorological Observatory, Germany
⁷¹Environment and Climate Change Canado, Ontario, Canada

Contents

1  Introduction
    1.1  A tool for science, and policy
    1.2  A community effort
    1.3  The role of other atmospheric species in constraining the atmospheric carbon budget
    1.4  Updates
    1.5  Citation and usage policy
        1.5.1  Usage Policy
        1.5.2  Citing our results
2  Terrestrial biosphere module
    2.1  CASA model
    2.2  Temporal downscaling
        2.2.1  Smooth month-to-month variations
    2.3  GFED4.1s and GFED_CMS
3  Fire module
    3.1  Global Fire Emissions Database (GFED)
    3.2  GFED_CMS: Fluxes from the NASA Carbon Monitoring System
4  Fossil fuel module
    4.1  The "Miller" emissions dataset
    4.2  The "ODIAC" emissions dataset
    4.3  Uncertainties
5  Oceans module
    5.1  Air-sea gas exchange
    5.2  OIF: the Ocean Inversion Fluxes prior
    5.3  pCO₂-Clim: Takahashi climatology prior
    5.4  Gas-transfer velocity and ocean surface properties
    5.5  Specifics of the inversion methodology related to air-sea CO₂ fluxes
6  Atmospheric transport
    6.1  TM5 offline tracer transport model
    6.2  Convective flux fix
7  Observations
    7.1  The CarbonTracker observational network
    7.2  Adaptive model-data mismatch
    7.3  Statistical performance of CT2019B
8  Ensemble data assimilation
    8.1  Parameterization of unknowns
        8.1.1  Optimization regions
        8.1.2  Assimilation window
        8.1.3  Ensemble size and localization
        8.1.4  Dynamical model
    8.2  Covariance structure
    8.3  Multiple prior models
        8.3.1  Posterior uncertainties in CarbonTracker
9  Ecoregions in CarbonTracker
    9.1  What are ecoregions?
    9.2  Why use ecoregions?
    9.3  Ecosystems within Transcom regions
10  Resources and References

1  Introduction

The goal of the CarbonTracker program is to produce quantitative estimates of atmospheric carbon uptake and release at the Earth's surface that are consistent with observed patterns of CO₂ in the atmosphere. CarbonTracker is an inverse model of atmospheric CO₂, which means that it attempts to model atmospheric CO₂ measurements by adjusting inputs and removals of CO₂ at the Earth surface until they best agree with those observational constraints. CarbonTracker is updated on a approximately-annual basis. The current release, CT2019B, provides results from 2000 through the end of 2018. A "near-real" time model product, CT-NRT, extends these results through 2019 and later.

1.1  A tool for science, and policy

CarbonTracker is made possible by the long-term monitoring of atmospheric CO₂ conducted by many academic and governmental programs around the world (see Sec. 7). These data help improve our understanding of how the land and ocean are responding to Earth's changing climate. The uptake and release of CO₂ by these ecosystems is changing due to chemical and physical responses to increased atmospheric CO₂ concentrations, to human management of lands and oceans, and to changes in temperature, precipitation, and winds. CarbonTracker is a completely open product. All results, including graphics and tabular data, may be freely used without restriction, although we do request the favor of appropriate acknowledgment (see Sec. 1.5 and https://www.esrl.noaa.gov/ccgg/carbontracker/citation.php). The unrestricted access to all CarbonTracker results means that anyone can scrutinize our work, suggest improvements, and profit from our efforts. We hope this scrutiny will help guide further development of our methods, and improve our ability to monitor, diagnose, and possibly predict the behavior of the global carbon cycle. CarbonTracker also can be relevant for helping to inform carbon policy. Its ability to accurately quantify natural and anthropogenic emissions and uptake at regional scales is currently limited by a sparse observational network. With enough observations however, CarbonTracker and systems like it will be able to monitor regional emissions, including those from fossil fuel use. This will provide an independent check on emissions accounting, including estimates of fossil fuel use based on economic inventories. It can thus provide feedback to policies aimed at limiting greenhouse gas emissions. This independent evaluation of the effectiveness of carbon policy is the bottom line in any mitigation strategy. It has the added advantage of being a constraint provided by the atmosphere itself, where CO₂ levels matter most.

1.2  A community effort

CarbonTracker is intended to be a tool for the community, and we welcome feedback and collaboration from anyone interested. Our ability to accurately track carbon with more spatial and temporal detail is fundamentally dependent on our collective ability to make enough measurements to characterize variability present in the atmosphere. For example, estimates suggest that observations from tall communication towers (taller than 200m) can tell us about carbon uptake and emission over a radius of only several hundred kilometers. The shows how sparse the current network is. One way to join this effort is by contributing measurements. Regular air samples collected from the surface, towers or aircraft are needed. It would also be very fruitful to expand use of continuous measurements like the ones now being made on very tall (more than 200m) communications towers. Another way to join this effort is by volunteering flux estimates from your own work, to be run through CarbonTracker and assessed against atmospheric CO₂ measurements. We also encourage collaborations focused on use of the CarbonTracker model as a tool for scientific analysis. Please contact us if you would like to get involved and collaborate with us.

1.3  The role of other atmospheric species in constraining the atmospheric carbon budget

Many laboratories making high accuracy CO₂ observations also make many other measurements of the same air, typically other greenhouse gases such as methane (CH₄), nitrous oxide (N₂O), sulfur hexafluoride (SF₆), as well as carbon monoxide (CO) and isotopic ratios of CO₂ and CH₄. These measurements are usually made as mole fractions, for reasons explained here. These trace gases are relevant for the study of climate change and interesting in their own right, but the additional measurements can also help in identifying sources and sinks of carbon or in understanding carbon cycle processes. For this reason, many air samples are now analyzed for a suite of halocompounds and hydrocarbons. Several of these species can be useful for monitoring air quality, but they can also help with better source apportionment of the greenhouse gases. In addition, the estimation of the source strengths of a number of pollutants could be greatly improved if we were able to quantify fossil fuel CO₂ emissions from air measurements for specified regions. The best tracer for quantifying the component of atmospheric CO₂ that has been recently added to an air mass through the burning of fossil fuels is the decrease of the carbon-14 (¹⁴C) content of CO₂. Cosmic rays produce ¹⁴C, a radioactive form of carbon, in the higher regions of the atmosphere. It is present in the atmosphere and oceans and in all living organisms and their remains, but coal, oil, and natural gas contain no ¹⁴C because it has long decayed away. Currently, ¹⁴CO₂ measurements are made on only a small subset of the air samples because of higher analysis costs. None of these other data and their relationships have been used directly in this release of CarbonTracker. We expect them to be incorporated gradually at later stages. CarbonTracker is a NOAA contribution to the North American Carbon Program.

1.4  Updates

CarbonTracker is updated about once per year to include new data and model improvements. CT2019B provides results from 2000 through 2018, the most recent complete year of observations. Previous versions of CarbonTracker and our CT-NRT (CarbonTracker Near-Real Time) releases are available at the CarbonTracker website. Important revisions of our methods for CT2019B include the following:

• Correction of a minor bug in CT2019
• Extension through the end of 2018.
• Revision of fossil fuel emissions to include 2018 increase of about 2%
• Use of reduced prior covariance scaling based on L-curve analysis
• New land and wildfire priors
• Cross-evaluation using withheld assimilation data
• Revised model-data mismatch errors on GLOBALVIEW+ measurements

1.5  Citation and usage policy

1.5.1  Usage Policy

CarbonTracker is an open product of NOAA's Earth System Research Laboratory using data from the NOAA ESRL greenhouse gas observational network and collaborating institutions. Results, including figures and tabular material found on the CarbonTracker website may be used for non-commercial purposes without restriction. We kindly ask you to acknowledge, cite, and/or reference CarbonTracker as described below.

1.5.2  Citing our results

• We ask that scientific work that relies heavily on CarbonTracker products is discussed with us before publication, to ensure proper representation of our work and co-authorship if appropriate.
• Please cite as Jacobson et al. (2020)
• The DOI for CT2019B and all its associated products and results is http://dx.doi.org/10.25925/20201008.
• Please use "CT2019B" as the shorthand to refer to our product, not "CT". This identifies both the product and the release version. It is vital to identify the version of the product you are using.
• Note that the product is called "CarbonTracker" without a space character, not "Carbon ␣ Tracker".
• Please include our suggested acknowledgment text in your acknowledgments section.
• Boilerplate model description text provided upon request.

Example   ...we compare our results to NOAA's CarbonTracker, version CT2019B (Jacobson et al., 2020). In this work, CT2019B is ... Acknowledgments   CarbonTracker CT2019B results provided by NOAA ESRL, Boulder, Colorado, USA from the website at http://carbontracker.noaa.gov. Reference   A. R. Jacobson, K. N. Schuldt, J. B. Miller, T. Oda, P. Tans, A. Andrews, J. Mund, L. Ott, G. J. Collatz, T. Aalto, S. Afshar, K. Aikin, S. Aoki, F. Apadula, B. Baier, P. Bergamaschi, A. Beyersdorf, S. C. Biraud, A. Bollenbacher, D. Bowling, G. Brailsford, J. B. Abshire, G. Chen, H. Chen, L. Chmura, Sites Climadat, A. Colomb, S. Conil, A. Cox, P. Cristofanelli, E. Cuevas, R. Curcoll, C. D. Sloop, K. Davis, S. D. Wekker, M. Delmotte, J. P. DiGangi, E. Dlugokencky, J. Ehleringer, J. W. Elkins, L. Emmenegger, M. L. Fischer, G. Forster, A. Frumau, M. Galkowski, L. V. Gatti, E. Gloor, T. Griffis, S. Hammer, L. Haszpra, J. Hatakka, M. Heliasz, A. Hensen, O. Hermanssen, E. Hintsa, J. Holst, D. Jaffe, A. Karion, S. R. Kawa, R. Keeling, P. Keronen, P. Kolari, K. Kominkova, E. Kort, P. Krummel, D. Kubistin, C. Labuschagne, R. Langenfelds, O. Laurent, T. Laurila, T. Lauvaux, B. Law, J. Lee, I. Lehner, M. Leuenberger, I. Levin, J. Levula, J. Lin, M. Lindauer, Z. Loh, M. Lopez, I. T. Luijkx, C. Lund Myhre, T. Machida, I. Mammarella, G. Manca, A. Manning, A. Manning, M. V. Marek, P. Marklund, M. Y. Martin, H. Matsueda, K. McKain, H. Meijer, F. Meinhardt, N. Miles, C. E. Miller, M. Mölder, S. Montzka, F. Moore, J.-A. Morgui, S. Morimoto, B. Munger, J. Necki, S. Newman, S. Nichol, Y. Niwa, S. O’Doherty, M. Ottosson-Löfvenius, B. Paplawsky, J. Peischl, O. Peltola, J.-M. Pichon, S. Piper, C. Plass-Dölmer, M. Ramonet, E. Reyes-Sanchez, S. Richardson, H. Riris, T. Ryerson, K. Saito, M. Sargent, M. Sasakawa, Y. Sawa, D. Say, B. Scheeren, M. Schmidt, A. Schmidt, M. Schumacher, P. Shepson, M. Shook, K. Stanley, M. Steinbacher, B. Stephens, C. Sweeney, K. Thoning, M. Torn, J. Turnbull, K. Tørseth, P. V. D. Bulk, D. V. Dinther, A. Vermeulen, B. Viner, G. Vitkova, S. Walker, D. Weyrauch, S. Wofsy, D. Worthy, D. Young, and M. Zimnoch. Carbontracker CT2019B, Model published 2020 by NOAA Earth System Research Laboratory, Global Monitoring Division, DOI: 10.25925/20201008 Acknowledgment text   "CarbonTracker CT2019B results provided by NOAA ESRL, Boulder, Colorado, USA from the website at http://carbontracker.noaa.gov." Suggested Website Citation   "CarbonTracker CT2019B, http://carbontracker.noaa.gov"

2  Terrestrial biosphere module

The biospheric component of the terrestrial carbon cycle consists of all the carbon stored in `biomass' around us. This includes trees, shrubs, grasses, carbon within soils, dead wood, and leaf litter. Such reservoirs of carbon can exchange CO₂ with the atmosphere. Exchange starts when plants take up CO₂ during their growing season through the process called photosynthesis (uptake). Most of this carbon is released back to the atmosphere throughout the year through the process of respiration (release). This includes both the decay of dead wood and litter and the metabolic respiration of living plants. Of course, plants can also return carbon to the atmosphere when they burn, as described in Section 3. Even though the yearly sum of uptake and release of carbon amounts to a relatively small number, a few petagrams (one Pg=10¹⁵ g)) of carbon per year), the flow of carbon each way is as large as 120 PgC each year. This is why the net result of these flows needs to be monitored in a system such as ours. It is also the reason we need a good physical description (model) of these flows of carbon. After all, from the atmospheric measurements of CO₂ we can only see the relatively small net sum of the much larger two-way streams (gross fluxes). Information on what the biospheric fluxes are doing in each season, and in every location on Earth is derived from specialized biosphere models, and fed into our system as a first guess, to be refined by our assimilation procedure.

2.1  CASA model

Two biosphere models currently provide first-guess terrestrial fluxes for CT2019B. Both models are versions of the Carnegie-Ames Stanford Approach (CASA) biogeochemical model introduced by Potter et al. (1993). CASA calculates global carbon fluxes using input from weather models to drive biophysical processes, and satellite observed Normalized Difference Vegetation Index (NDVI) to track plant phenology. The models are driven by year-specific weather and satellite observations, and include the effects of fires on photosynthesis and respiration (van der Werf et al., 2003), (van der Werf et al., 2006), (Giglio et al., 2006). Both simulations provide 0.5^°×0.5^° global fluxes with a monthly time resolution. CASA models directly simulate monthly-mean Net Primary Production (NPP) and heteotrophic respiration (R_H) for each terrestrial grid cell being simulated. NPP is the difference in photosynthetic carbon uptake (Gross Primary Production, GPP) and the carbon release by the same plants due to "maintenance respiration", which is also called autotrophic respiration, R_A. The carbon uptake represented by NPP and carbon release represented by R_H can be differenced to provide Net Ecosystem Exchange (NEE) of CO₂. Throughout this discussion, we use the convention that fluxes carry algebraic signs and we adopt the "atmospheric perspective" for those signs. Thus carbon uptake by the terrestrial biosphere is a negative flux to the atmosphere, and release of CO₂ back to the atmosphere is a positive flux. This means that we represent all respiration fluxes as positive and GPP as negative, so NEE = NPP + R_H. This stands in contrast to convention in the terrestrial carbon community, where all fluxes are generally non-negative.

2.2  Temporal downscaling

Use of monthly-mean terrestrial fluxes to simulate atmospheric CO₂ is not sufficient to resolve the variability observed at measurement sites. Instead, higher-frequency variations, including the diurnal cycle and effects of passing weather systems must be imposed on the CASA monthly fluxes. Following the logic laid out by Olsen and Randerson (2004), we transform the CASA-supplied monthly-mean NPP and R_H fluxes into GPP and total ecosystem respiration, R_E = R_A + R_H. To estimate sub-monthly variations, including diurnal and synoptic variability, the Olsen and Randerson (2004) strategy is to model GPP as a linear function of incoming surface solar radiation and total ecosystem respiration as a function of near-surface temperature. The fundamental assumption needed to apply this scheme is that we can resolve CASA-simulated NPP into GPP and R_A. We apply the assumption that GPP is twice NPP, which further implies that R_A is the same size as NPP (but of opposite sign):

GPP = 2*NPP,

(1)

NPP = GPP + RA,

(2)

and

RA = −1*NPP.

(3)

We use meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-interim reanalysis to supply temperature and shortwave radiation. Fluxes are generated with 90-minute variability using a simple temperature Q₁₀ relationship for respiration, assuming a global Q₁₀ value of 1.5, and a linear scaling of photosynthesis with solar radiation. The procedure is very similar, but NOT identical to the procedure in Olsen and Randerson (2004). Note that the introduction of 90-minute variability conserves the monthly mean NEE from the CASA model. Instantaneous NEE for each 90-minute interval is created as:

NEE(t) = GPP(t) + RE(t),

(4)

where

GPP(t) = GPPmean ( I(t) / Imean )

(5)

RE(t) = RE,mean ( Q10(t) / Q10,mean ),

(6)

and Q₁₀ is computed as

Q10(t) = 1.5(T2m(t)−273.15)/10.0 ,

(7)

where T_2m is temperature at 2 meters above the land surface in Kelvin, I is surface incoming solar radiation, t is time in 90-minute intervals, and x_mean represents the monthly mean of quantity x, including the monthly-mean fluxes derived from the CASA model.

2.2.1  Smooth month-to-month variations

While the scheme outlined above imposes realistic diurnal- and synoptic-scale variations on monthly-mean GPP and R_E, it still allows for abrupt changes from one month to the next. For CT2019B, we add a further processing step designed to remove such unrealistic step changes. We fit smooth curves to the monthly GPP and R_E using the piecewise integral quadratic splines (PIQS) of Rasmussen (1991). These PIQS fits are continuous in the first and second derivatives, and have the property of preserving monthly mean flux. We use a similar scheme to smooth over year-to-year step changes in fossil fuel emissions. The final smoothed GPP is

GPPF(t) = GPP(t) − GPPmean + GPPPIQS(t),

(8)

and the final smoothed ecosystem respiration is

RE,F(t) = RE(t) − RE,mean + RE,PIQS(t).

(9)

Together, these form the terrestrial NEE imposed as a first-guess flux in CT2019B:

NEEF(t) = GPPF(t) + RE,F(t).

(10)

2.3  GFED4.1s and GFED_CMS

CarbonTracker uses fluxes from CASA runs from two models associated with the GFED project as its first guess for terrestrial biosphere fluxes. We have found a significantly better match to observations when using this output compared to the fluxes from a neutral biosphere simulation. Both of the CASA simulations used in CT2019B (GFED 4.1s and GFED_CMS) are driven by AVHRR NDVI. This satellite driver tends to produce a larger-amplitude annual cycle of NEE compared to the alternative driver, MODIS fPAR. As one of the robust results of atmospheric invrsions is a deeper annual cycle of terrestrial NEE, inversions using NDVI-driven first-guess fluxes perform slightly better than those with a MODIS fPAR driver. The record of atmospheric CO₂ calls for a deeper terrestrial biosphere sink than that generally simulated by terrestrial biosphere models like CASA. Inverse models manifesting such a sink generally simulate a larger annual cycle of terrestrial biosphere fluxes, and in particular a deeper boreal summer uptake of carbon dioxide, in the posterior optimized fluxes compared to the prior models (See Fig. 2). We call upon the atmospheric CO₂ observations to make this change, and in order to handle these prior model differences the ensemble Kalman filter's prior covariance model has to be appropriately tuned. In short, this prior uncertainty needs to comfortably span differences among the terrestrial biosphere priors, the fossil fuel emissions estimates, and adjustments to fluxes required to bring model predictions into agreement with observations. CT2019B prior covariances have been adjusted compared to prior releases, and details on this adjustment can be found in Section 8. CarbonTracker CT2019B is a full reanalysis of the 2000-2018 period using new fossil fuel emissions, CASA-GFED v4.1s and GFED_CMS fire emissions, and first-guess biosphere model fluxes derived from CASA-GFED v4.1s for the first of our inversions, and from CASA GFED_CMS for the second inversion. Due to the inclusion of fires, inter-annual variability in weather and NDVI, the fluxes for North America start with a small net flux even before optimizing the fluxes. This first-guess flux ranges from neutral exchange to about 0.5 PgC yr⁻¹ of uptake.

3  Fire module

Vegetation fires are an important part of the carbon cycle and have been so for many millennia. Even before human civilization began to use fires to clear land for agricultural purposes, most ecosystems were subject to natural wildfires that would rejuvenate old forests and bring important minerals to the soils. When fires consume part of the landscape in either controlled or natural burning, carbon dioxide (amongst many other gases and aerosols) is released in large quantities. Each year, vegetation fires emit around 2 PgC as CO₂ into the atmosphere, mostly in the tropics. Currently, a large fraction of wildfire is started by humans. This is mostly intentional to clear land for agriculture, or to re-fertilize soils before a new growing season. This important component of the carbon cycle is monitored mostly from space, while sophisticated `biomass burning' models are used to estimate the amount of CO₂ emitted by each fire. Such estimates are then used in CarbonTracker to prescribe the emissions. These emissions are not modified in the optimization (inverse modeling) process. In CT2019B we use two fire emissions datasets, each with at least daily temporal resolution. The GFED4.1s emissions are modeled at 3-hourly intervals, and GFED_CMS emissions are available at daily resolution.

3.1  Global Fire Emissions Database (GFED)

CT2019B uses GFED4.1s (Giglio et al., 2013), (van der Werf et al., 2017) as one of the fire modules to estimate biomass burning. GFED4.1s is a variant of the CASA biogeochemical model as described in the terrestrial biosphere model documentation to estimate the carbon fuel in various biomass pools. The dataset consists of 1^° × 1^° gridded monthly burned area, fuel loads, combustion completeness, and fire emissions (Carbon, CO₂, CO, CH₄, NMHC, H₂, NO_x, N₂O, PM2.5, Total Particulate Matter, Total Carbon, Organic Carbon, Black Carbon) for the time period spanning January 1997 - December 2018, of which we currently only use CO₂. The GFED burned area is based on MODIS satellite observations of fire counts. These, together with detailed vegetation cover information and a set of vegetation specific scaling factors, allow predictions of burned area over the time span that active fire counts from MODIS are available. The relationship between fire counts and burned area is derived, for the specific vegetation types, from a `calibration' subset of 500m resolution burned area from MODIS in the period 2001-2004. Once burned area has been estimated globally, emissions of trace gases are calculated using the CASA biosphere model. The seasonally changing vegetation and soil biomass stocks in the CASA model are combusted based on the burned area estimate, and converted to atmospheric trace gases using estimates of fuel loads, combustion completeness, and burning efficiency. For CT2019B, we also apply temporal scaling factors updated from Mu et al. (2011) to downscale the GFED4.1s CO₂ emissions from monthly averages to emissions with 3-hourly resolution.

3.2  GFED_CMS: Fluxes from the NASA Carbon Monitoring System

The NASA GFED_CMS team uses a variant of the GFED4 system to produce alternative fire emissions. This model uses GIMSS NDVI, the GFEDv3 fire model and GFEDv4 burned area. Fire emissions are available on a daily basis from 2003-2017. For 2000-2002, and for 2018-2019, we apply the climatology of GFED_CMS fire emissions, computed from its 2003-2017 mean. Note that the GFED_CMS team produces temporally-downscaled GPP, heterotrophic respiration, and fires with 3-hourly resolution. This is done using MERRA meteorology using a scheme similar to Olsen and Randerson (2004). We do not use this downscaled product, in part because the MERRA meteorology is different from the ECMWF meteorology, and in part because the spatial resolution of the MERRA meteorology is different from our 1^° × 1^° flux grid. This means that we are limited to daily resolution of GFED_CMS fire emissions: unlike the GFED4.1s fire emissions, these have no diurnal cycle.

4  Fossil fuel module

Human beings first influenced the carbon cycle through land-use change. Early humans used fire to control animals and later cleared forests for agriculture. Over the last two centuries, following the industrial and technical revolutions and continuing global population increase, fossil fuel combustion has become the largest anthropogenic source of CO₂. Coal, oil and natural gas combustion are the most common energy sources in both developed and developing countries. Important sectors of the economy-power generation, transportation, residential & commercial building heating, and industrial processes-rely on fossil fuels. The continued growth of fossil fuel combustion has led to a steady increase of global CO₂ emissions to the atmosphere (Fig. 4). According to the Carbon Dioxide Information and Analysis Center (CDIAC) as reported in Boden et al. (2017), world emissions of CO₂ from fossil fuel burning, cement manufacturing, and flaring reached 5 billion metric tons of carbon per year (PgC yr⁻¹) in the decade of the 1970s. Estimates extrapolated by the CarbonTracker team indicate that global total emissions exceeded 10 PgC yr⁻¹ for the first time in 2018. One petagram of carbon, PgC, is equal to 10¹⁵ grams of carbon, or one billion metric tons of carbon. To convert to mass of CO₂ emitted, one would multiply by the factor [44/12], representing the molecular weight of CO₂ compared to the atomic weight of carbon. U.S. input of CO₂ to the atmosphere from fossil fuel burning in 2015 was 1.4 PgC, representing 14% of the global total. North American emissions have remained nearly constant since 2000, with a slight decrease after the economic slowdown of 2008. On the other hand, emissions from developing economies such as the People's Republic of China have been increasing. Emissions from China in 2015 were 2.6 PgC yr⁻¹, representing 27% of the global total. In most global and regional carbon flux estimation systems, including CarbonTracker, fossil fuel CO₂ emissions are not optimized. Instead, these emissions are imposed and are not subject to revision by the inverse modeling framework. Global mass balance requires that any errors in fossil fuel emissions be compensated by opposing errors in land and ocean CO₂ exchange. Thus it is vital that fossil fuel CO₂ emissions are prescribed accurately, so that flux estimates for the land biosphere and oceans are robust. The fossil fuel emissions source data we use are available on an annually-integrated global and national basis. This aggregate information needs to be gridded before being incorporated into CarbonTracker. The major uncertainty in this process is distributing the national-annual emissions spatially across a nation and temporally into hourly contributions. In CT2019B, two different fossil fuel CO₂ emissions datasets were used to help assess the uncertainty in this mapping process. These two emissions products are called the "Miller" and "ODIAC" emissions datasets. These two datasets have very similar global and national emissions for each year, but differ in how those emissions are distributed spatially and temporally. Whereas previous CarbonTracker releases used monthly-constant fossil fuel emissions, in CT2015 we introduced the use of temporal scaling factors to simulate day-of-week and diurnal variability for those emissions. These "Temporal Improvements for Modeling Emissions by Scaling" (TIMES) scaling factors, introduced by Nassar et al. (2013), are again applied to both the Miller and ODIAC emissions modules for CT2019B. The scaling factors consist of seven day-of-week global scaling factor maps, and 24 hourly global scaling factor maps to represent the diurnal cycle. For use in TM5, the hourly scaling factors were aggregated to three-hourly factors to accommodate the time step of the model.

4.1  The "Miller" emissions dataset

• Global Totals The Miller fossil fuel emission inventory is derived from independent global total and spatially-resolved inventories. Annual global total fossil fuel CO₂ emissions are from the Carbon Dioxide Information and Analysis Center (Boden et al., 2017) which extend through 2014. In order to estimate these fluxes through 2018, we extrapolate using the percentage increase or decrease for each fuel type (solid, liquid, and gas) in each country from the British Petroleum (2019). To estimate emissions for the first few months of 2019 (required by CarbonTracker's 12-week assimilation window), no increase is applied to 2018 values (British Petroleum, 2019).
• Spatial Distribution Miller fossil-fuel CO₂ fluxes are spatially distributed in two steps: First, the coarse-scale country totals through 2014 from Boden et al. (2017) are mapped onto a 1^° × 1^° grid according to the spatial patterns from the EDGAR v4.2 inventories (European Commission, 2011). The spatial pattern varies by year up until the end of the EDGAR v4.2 product in 2008. After this, the trends estimated in each pixel are linearly extrapolated. Note that while EDGAR provides annual emissions estimates at 1^° × 1^° resolution, their totals do not agree with those from CDIAC (Boden et al., 2017). Thus, only the spatial patterns in EDGAR are used. The CDIAC country-by-country totals sum to about 95% of the global total emissions; the remaining 5% is mapped to global shipping routes according to EDGAR, which we treat as a proxy for bunker fuel emissions.
• Temporal Distribution For North America between 30 and 60^°N, the Miller system imposes a seasonal cycle derived from the first and second harmonics (Thoning et al., 1989) of the Blasing et al. (2004) analysis for the United States. The Blasing analysis has 10% higher emissions in winter than in summer. This scheme defines a fixed fraction of emissions for each month, so while the shape of the annual cycle is invariant, the amplitude of that cycle scales with the annual total emissions. For Eurasia, a set of seasonal emissions factors from EDGAR distributed by emissions sector is used to define fossil fuel seasonality. As in North America, this seasonality is imposed only from 30-60^°N. The Eurasian seasonal amplitude is about 25%, significantly larger than that in North America, owing to the absence of a secondary summertime maximum due to air conditioning. See Figure 6 for the resulting time series of fossil fuel emissions. In order to avoid discontinuities in the fossil fuel emissions between consecutive years, a spline curve that conserves annual totals (Rasmussen, 1991) is fit to seasonal emissions in each 1^° × 1^° grid cell.

4.2  The "ODIAC" emissions dataset

• Global Totals We use the ODIAC2018 fossil fuel emission inventory (Oda et al., 2018), (Oda and Maksyutov, 2011), (Oda and Maksyutov, 2015) as one of our fossil fuel emissions estimates. ODIAC is also derived from independent global and country emission estimates from CDIAC, but national emission estimates used were taken from the year 2018 edition of CDIAC estimates (Boden et al., 2018). Differences between the Boden et al. (2018) global total and country-by-country totals were ascribed to the entire emissions field. Annual country total fossil fuel CO₂ emissions for 2014-2017 were extrapolated using the BP Statistical Review of World Energy (British Petroleum, 2019). Emissions for 2018 were estimated by applying a global scaling factor representing the reported  ∼ 2.1% increase compared to 2017 emissions (British Petroleum, 2019). Emissions for 2019 were set to those of 2018.
• Spatial Distribution ODIAC emissions are spatially distributed using many available "proxy data" that explain spatial extent of emissions according to emission types (emissions over land, gas flaring, aviation and marine bunker). Emissions over land were distributed in two steps: First, emissions attributable to power plants were mapped using geographical locations (latitude and longitude) provided by the global power plant dataset CARbon Monitoring and Action, CARMA. Next, the remaining land emissions (i.e. land total minus power plant emissions) were distributed using nightlight imagery collected by U.S. Air Force Defense Meteorological Satellite Project (DMSP) satellites. Emissions from gas flaring were also mapped using nightlight imagery. Emissions from aviation were mapped using flight tracks adopted from UK AERO2k air emission inventory. It should be noted that currently, air traffic emissions are emitted at ground level within CarbonTracker. Emissions from marine bunker fuels are placed entirely in the ocean basins along shipping routes according to patterns from the EDGAR database.
• Temporal Distribution The CDIAC estimates used for mapping emissions in ODIAC only describe how much CO₂ was emitted in a given year. To present seasonal changes in emissions, we used the CDIAC 1^° × 1^° monthly fossil fuel emission inventory (Andres et al., 2011). The CDIAC monthly data utilizes the top 20 emitting countries' fuel (coal, oil and gas) consumption statistics available to estimate seasonal change in emissions. Monthly emission numbers at each pixel were divided by annual total and then a fraction to annual total was obtained. Monthly emissions in the ODIAC inventory were derived by multiplying this fraction by the emission in each grid cell.

4.3  Uncertainties

Marland (2008) attached an uncertainty of about 5% (95% confidence interval; approximately 2-σ) to the global total fossil fuel source. Recent estimates by Andres et al. (2014) put a larger uncertainty of 8.4% (2-σ) on the CDIAC global total. Uncertainties for individual regions of the world, and for sub-annual time periods are likely to be larger. Additional uncertainties are introduced when the emissions are distributed in space and time. In the Miller dataset, the overall Eurasian seasonality is based on scaling factors derived only from Western Europe and thus highly uncertain, but most likely a better representation than assuming no emission seasonality at all. Similarly, the use of the CDIAC monthly emission dataset for modeling seasonality introduces additional uncertainty in ODIAC. The additional uncertainty for the global total in the monthly CDIAC emission, which is solely due to the method for estimating seasonality, is reported as 6.4% (Andres et al., 2011). As mentioned earlier, fossil fuel emissions are not optimized in the current CarbonTracker system, similar to nearly all carbon data analysis systems. Spatial and temporal atmospheric CO₂ gradients arise from terrestrial biosphere and fossil-fuel sources. These gradients, which are interpreted by CarbonTracker, are difficult to attribute to one or the other cause. This is because atmospheric sampling sites have historically been established in locations remote from biospheric and anthropogenic sources, especially in the temperate Northern Hemisphere. Given that surface CO₂ flux due to biospheric activity and oceanic exchange is much more uncertain compared to fossil fuel emissions, CarbonTracker, like most current carbon dioxide data assimilation systems, does not attempt to optimize fossil fuel emissions. That is, the contribution of CO₂ from fossil fuel burning to observed CO₂ mole fractions is considered known. As detailed above, however, in CarbonTracker an effort is made to account for some aspects of fossil fuel uncertainty by using two different fossil fuel estimates. From a technical point of view, extra land biosphere prior flux uncertainty is included in the system to represent the random errors in fossil fuel emissions. Eventually, fossil fuel emissions could be optimized within CarbonTracker, especially with the addition of ¹⁴CO₂ observations as constraints (Basu et al., 2016).

5  Oceans module

The oceans play an important role in the Earth's carbon cycle. They are the largest long-term sink for carbon and have an enormous capacity to store and redistribute CO₂ within the Earth system. Oceanographers estimate that about 48% of the CO₂ from fossil fuel burning has been absorbed by the ocean (Sabine et al., 2004). The dissolution of CO₂ in seawater shifts the balance of the ocean carbonate equilibrium towards a more acidic state with a lower pH. This effect is already measurable (Caldeira and Wickett, 2003), and is expected to become an acute challenge to shell-forming organisms over the coming decades and centuries. Although the oceans as a whole have been a relatively steady net carbon sink, CO₂ can also be released from oceans depending on local temperatures, biological activity, wind speeds, and ocean circulation. These processes are all considered in CarbonTracker, since they can have significant effects on the ocean sink. Improved estimates of the air-sea exchange of carbon in turn help us to understand variability of both the atmospheric burden of CO₂ and terrestrial carbon exchange. The initial release of CarbonTracker (CT2007) used climatogical estimates of CO₂ partial pressure in surface waters (pCO₂) from Takahashi et al. (2002) to compute a first-guess air-sea flux. This air-sea pCO₂ disequilibrium was modulated by a surface barometric pressure correction before being multiplied by a gas-transfer coefficient to yield a flux. Starting with CT2007B and continuing through the CT2011_oi release, the air-sea pCO₂ disequilibrium was imposed from analysis of ocean inversions (Jacobson et al., 2007),"OIF" results, with short-term flux variability derived from the atmospheric model wind speeds via the gas transfer coefficient. The barometric pressure correction was removed so that climatological high- and low-pressure cells did not bias the long-term means of the first guess fluxes. In CT2019B, two models are used to provide prior estimates of air-sea CO₂ flux. The OIF scheme provides one of these flux priors, and the other is an updated version of the Takahashi et al. (2009) pCO₂ climatology.

5.1  Air-sea gas exchange

Oceanic uptake of CO₂ in CarbonTracker is computed using air-sea differences in partial pressure of CO₂ inferred either from ocean inversions (called "OIF" henceforth), or from a compilation of direct measurements of seawater pCO₂ (called "pCO₂-clim" henceforth). These air-sea partial pressure differences are combined with a gas transfer velocity computed from wind speeds in the atmospheric transport model to compute fluxes of carbon dioxide across the sea surface. In either method, the first-guess fluxes have no interannual variability (IAV) other than a smooth trend. IAV in oceanic CO₂ flux is due to anomalies in surface pCO₂, such as those that occur in the tropical eastern Pacific during an El Niño, and to associated variability in winds, ocean circulation, and sea-surface properties. In CarbonTracker, only the surface winds (and hence gas transfer), manifest these interannual anomalies; the remaining IAV of flux must be inferred from atmospheric CO₂ signals. In the following sections we describe the two ocean flux prior models. We then describe the air-sea gas transfer velocity parameterization and discuss detais of the inversion methodology specific to oceanic exchange of CO₂.

5.2  OIF: the Ocean Inversion Fluxes prior

For the OIF prior, long-term mean air-sea fluxes and the uncertainties associated with them are derived from the ocean interior inversions reported in Jacobson et al. (2007). These ocean inversion flux estimates are composed of separate preindustrial (natural) and anthropogenic flux inversions based on the methods described in Gloor et al. (2003) and biogeochemical interpretations of Gruber et al. (1996). The uptake of anthropogenic CO₂ by the ocean is assumed to increase in proportion to atmospheric CO₂ levels, consistent with estimates from ocean carbon models. OIF contemporary pCO₂ fields were computed by summing the preindustrial and anthropogenic flux components from inversions using five different configurations of the Princeton/GFDL MOM3 ocean general circulation model (Pacanowski and Gnanadesikan, 1998), then dividing by a gas transfer velocity computed from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA40 reanalysis. There are two small differences in first-guess fluxes in this computation from those reported in Jacobson et al. (2007). First, the five OIF estimates all used Takahashi et al. (2002) pCO₂ estimates to provide high-resolution patterning of flux within inversion regions (the alternative "forward" model patterns were not used). To good approximation, this choice only affects the spatial and temporal distribution of flux within each of the 30 ocean inversion regions, not the magnitude of the estimated flux. Second, wind speed differences between the ERA40 product used in the offline analysis and the ECMWF operational model used in the online CarbonTracker analysis result in small deviations from the OIF estimates. Other than the smooth trend in anthropogenic flux assumed by the OIF results, interannual variability (IAV) in the first guess ocean flux comes entirely from wind speed effects on the gas transfer velocity. This is because the ocean inversions retrieve only a long-term mean and smooth trend.

5.3  pCO₂-Clim: Takahashi climatology prior

The pCO₂-Clim prior is derived from the Takahashi et al. (2009) climatology of seawater pCO₂. This climatology was created from about 3 million direct observations of seawater pCO₂ around the world between 1970 and 2007. With the exception of measurements in the Bering Sea, these observations were all linearly extrapolated to the corresponding month of the year 2000 by assuming a constant trend of 1.5  μatm yr⁻¹. This set of global monthly measurements corrected to the reference year 2000 was then interpolated onto a regular grid using a modeled surface current field. The Takahashi et al. (2009) product goes beyond providing this estimate of surface water pCO₂. They also compute climatological air-sea exchange of CO₂ by using the GLOBALVIEW-CO₂ atmospheric carbon dioxide product to compute air-sea ∆pCO₂, sea surface properties inferred from ocean climatologies, and winds from atmospheric reanalysis to estimate gas-transfer velocity. Unlike many other atmospheric analyses, we have chosen not to use the climatological fluxes as our prior, nor to use the climatological ∆pCO₂. Instead, we take only the seawater pCO₂ distribution from the Takahashi et al. (2009) climatology-our atmospheric model provides both pCO₂ in the air at the sea surface and the winds needed to estimate gas transfer. Seawater pCO₂ is extrapolated from 2000 to the actual year of the CarbonTracker simulation using a presumed increase of 1.5  μatm yr⁻¹ at every point in the global ocean. This is the same trend used in Takahashi et al. (2009) to normalize observations from many years to the reference year of the analysis (2000).

5.4  Gas-transfer velocity and ocean surface properties

Both priors use CO₂ solubilities and Schmidt numbers computed from World Ocean Atlas 2009 (WOA09) climatological fields of sea surface temperature and sea surface salinity fields (Levitus et al., 2010). Gas transfer velocity in CarbonTracker is parameterized as a quadratic function of wind speed following Wanninkhof (1992), using the formulation for instantaneous winds. Gas exchange is computed every 3 hours using wind speeds from the ECMWF operational model as represented by the atmospheric transport model. Air-sea transfer is inhibited by the presence of sea ice, and for this work fluxes are scaled by the daily sea ice fraction in each gridbox provided by the ECMWF forecast data.

5.5  Specifics of the inversion methodology related to air-sea CO₂ fluxes

The first-guess fluxes described here are subject to scaling during the CarbonTracker optimization process, in which atmospheric CO₂ mole fraction observations are combined with transport simulated by the atmospheric model to infer flux signals. Prior air-sea fluxes are adjusted within each of of the 30 ocean inversion regions. In this process, signals of terrestrial flux in atmospheric CO₂ distribution can be erroneously interpreted as being caused by oceanic fluxes. This flux "aliasing" or "leakage" is evident in some regions as a change in the shape of the seasonal cycle of air-sea flux. Prior uncertainties for the OIF and pCO₂-clim models are specified as uncertainties on scaling factors multiplying net CO₂ flux in each of the 30 ocean inversion regions. The pCO₂-clim prior has independent regional uncertainties (a diagonal prior covariance matrix), with the uncertainty standard deviation on each region set to 40%. The OIF prior uncertainty has a fully-covariate covariance matrix with off-diagonal elements representing the results of the ocean inversion of Jacobson et al. (2007). The pre-industrial flux uncertainty is time-independent, but the anthropogenic flux uncertainty grows in time as anthropogenic flux uptake increases. The latter is scaled to the simulation date, then added to the former. Total uncertainties are consistent with the Jacobson et al. (2007) results.

6  Atmospheric transport

The link between observations of CO₂ in the atmosphere and the exchange of CO₂ at the Earth's surface is transport in the atmosphere: storm systems, cloud complexes, and weather of all sorts cause winds that transport CO₂ around the world. As a result, local surface CO₂ exchange events like fires, forest growth, and ocean upwelling can have impacts at remote locations. To simulate the winds and the weather, CarbonTracker uses sophisticated numerical models that are driven by the daily weather forecasts from the specialized meteorological centers of the world. Since CO₂ does not decay or react in the lower atmosphere, the influence of emissions and uptake in locations such as North America and Europe are ultimately seen in our measurements even at the South Pole. Getting the transport of CO₂ just right is an enormous challenge, and costs us almost all of the computer resources for CarbonTracker. To represent the atmospheric transport, we use the Transport Model 5 (TM5). This is a community-supported model whose development is shared among many scientific groups with different areas of expertise. TM5 is used for many applications other than CarbonTracker, including forecasting air-quality, studying the dispersion of aerosols in the tropics, tracking biomass burning plumes, and predicting pollution levels that future generations might have to deal with.

6.1  TM5 offline tracer transport model

TM5 is an offline global chemical transport model with two-way nested grids. In this global model, regions for which high-resolution simulations are desired can be nested in the coarser global grid. The advantage to this approach is that transport simulations can be performed with a regional focus without the need for boundary conditions. Further, this approach allows measurements outside the "zoom" domain to constrain regional fluxes in the data assimilation, and ensures that regional estimates are consistent with global constraints. TM5 is based on a predecessor model TM3, with improvements in the advection scheme, vertical diffusion parameterization, and meteorological preprocessing of the wind fields (Krol et al., 2005). The model is developed and maintained jointly by the Institute for Marine and Atmospheric Research Utrecht (IMAU, The Netherlands), the Joint Research Centre (JRC, Italy), the Royal Netherlands Meteorological Institute (KNMI), the Netherlands Institude for Space Research (SRON), and the NOAA Earth System Research Laboratory (ESRL). In CarbonTracker, TM5 separately simulates advection, deep and shallow convection, and vertical diffusion in both the planetary boundary layer and free troposphere. The carbon dioxide concentrations predicted by CarbonTracker do not feed back onto these predictions of winds. Prior to use in TM5, ECMWF meteorological data are preprocessed into coarser grids, with attention to retrieving a flow that conserves tracer mass. Like most numerical weather prediction models, advection in the parent ECMWF model is not strictly mass-conserving, so this step is crucial. In CarbonTracker, TM5 is currently run at a global 3^° longitude × 2^° latitude resolution with a nested regional grid over North America at 1^° × 1^° resolution (Figure 11). TM5 uses a dynamically-variable time step with a maximum length of 90 minutes. This overall timestep is dynamically reduced to maintain numerical stability, generally during times of high wind speeds. The timestep is divided in half and individual advection, diffusion, convection, and chemistry operators are applied symmetrically in each half step. Furthermore, transport operators in nested grids are modeled at shorter timesteps, so processes at the finest scales are conducted at an effective timestep of one-quarter the overall timestep. See Krol et al. (2005) for details. The winds which drive TM5 come from the ERA-interim reanalysis implemented in the European Centre for Medium-Range Weather Forecasts (ECMWF) modeling system. The ERA-Interim reanalysis uses Cy31r2 version of the ECMWF Integrated Forecast System (IFS) model, which was used for the operational forecasts up until June 2007. That model uses a 30-minute time step and a a spectral T255 horizontal resolution, which corresponds to approximately 79 km spacing on a reduced Gaussian grid. This version of the IFS has 60 model layers in the vertical, of which TM5 uses a 25-layer subset. These levels are listed in Table 1.

Model Level	Mean Height (m)	Model Level	Mean Height (m)
1	25	14	9114
2	103	15	10588
3	247	16	12184
4	480	17	13928
5	814	18	15843
6	1259	19	17983
7	1822	20	20412
8	2508	21	24433
9	3317	22	30003
10	4248	23	35895
11	5300	24	43210
12	6467	25	123622
13	7741

6.2  Convective flux fix

Until recently, TM5 was known to have difficulties representing the global surface distribution of sulfur hexafluoride (SF₆, see Figure 12 and Peters et al. (2004). SF₆ is a nearly inert tracer in the atmosphere, with very small surface and atmospheric sinks and an atmospheric lifetime of about 1,000 years. Consequently, its global budget is very well known from observations alone. It is thought to be released mainly via leakage from electrical transformers. Since the electrical distribution system is closely tied to fossil fuel consumption, SF₆ is often considered an analog for fossil fuel CO₂ in the atmosphere. It is useful for understanding the rate at which Northern Hemisphere land surfaces are ventilated to the free troposphere, and the rate of interhemispheric exchange in models (Patra et al., 2011). As a result of more than a decade's worth of work on understanding the apparently sluggish mixing in TM5 as revealed by SF₆ simulations, a fault in one of the vertical mixing parameterizations of the model was discovered. When it was originally created, TM5 implemented the same planetary boundary layer (PBL) mixing and convection schemes as the parent ECMWF model. Recent comparisons between TM5, the ECMWF parent model, and radiosonde profile data show that the PBL scheme in TM5 performs similarly to that of the parent ECMWF model. The convective scheme, however, does not produce similar results in TM5 as compared to the ECMWF model. In a previous configuration of TM5, the convective entrainment and detrainment mass fluxes of the parent ECMWF model were re-diagnosed within TM5 using other meteorological information. The ECMWF model is used to produce both operational forecasts and the ERA-interim reanalysis, but the convective fluxes are stored for the ERA-interim product only. Thus, using ERA-interim meteorology, a direct comparison is possible. This comparison revealed that the TM5 internal rediagnosis of convective fluxes was faulty. TM5 was subsequently modified to use parent model ERA-interim convective fluxes directly. Using the parent model convective fluxes result in a significantly better SF₆ simulations. Simulations with these parent-model convective fluxes are said to use the "convective flux fix". Simulations with the convective flux fix show significantly improved agreement with SF₆ observations (see Figure 12). Since the parent-model convective fluxes are only available for the ERA-interim product, CT2019B uses only ERA-interim transport with the convective flux fix. Previous releases of CarbonTracker also used the ECMWF operational model transport, for which parent-model convective fluxes are not available. We believe that TM5 simulations without the parent-model convective fluxes are faulty and should not be included in our product. When the convective flux fix was instituted in CT2013B, it resulted in the largest realignment of surface CO₂ fluxes in the history of the CarbonTracker program (Schuh et al., 2019). This is a prominent example of the sensitive reliance of atmospheric inversions on accurate atmospheric transport.

7  Observations

The observations of atmospheric CO₂ mole fraction made by NOAA ESRL and partner laboratories are at the heart of CarbonTracker. They inform us on changes in the carbon cycle, whether those changes are regular (such as the annual cycle of growth and decay of leaves and other plant matter), or irregular (such as the release of tons of carbon by a wildfire). The results in CarbonTracker depend directly on the quality, location, and frequency of avaiable observations. The level of detail at which we can retrieve information on the carbon cycle increases strongly with the density of the CO₂ observing network.

7.1  The CarbonTracker observational network

Observations simulated by CT2019B are suppplied by the GLOBALVIEW+ data product version 5.0 (Cooperative Global Atmospheric Data Integration Project, 2019), available at the NOAA ESRL ObsPack web site. This study uses measurements of air samples collected at 460 sites around the world by 55 laboratories:

• Commonwealth Scientific and Industrial Research Organization, Oceans & Atmosphere Flagship - GASLAB (CSIRO)
• Instituto de Pesquisas Energeticas e Nucleares (IPEN)
• Environment and Climate Change Canada (ECCC)
• Finnish Meteorological Institute (FMI)
• Laboratoire des Sciences du Climat et de l'Environnement - UMR8212 CEA-CNRS-UVSQ (LSCE)
• University of Heidelberg, Institut für Umweltphysik (UHEI-IUP)
• Umweltbundesamt, Station Schauinsland (UBA-SCHAU)
• Hungarian Meteorological Service (HMS)
• Center for Atmospheric and Oceanic Studies, Tohoku University (TU)
• Meteorological Research Institute (MRI)
• Japan Meteorological Agency (JMA)
• National Institute for Environmental Studies (NIES)
• Comprehensive Observation Network for TRace gases by AirLiner (CONTRAIL)
• University of Groningen (RUG), Centre for Isotope Research (CIO) (RUG)
• Energy Research Centre of the Netherlands (ECN)
• National Institute of Water and Atmospheric Research (NIWA)
• University of Science and Technology (AGH)
• South African Weather Service (SAWS)
• Izana Atmospheric Research Center, Meteorological State Agency of Spain (AEMET)
• Swiss Federal Laboratories for Materials Science and Technology (EMPA)
• World Meteorological Organization/Global Atmosphere Watch (WMO/GAW)
• University of Bern, Physics Institute, Climate and Environmental Physics (KUP)
• University of East Anglia (UEA)
• NOAA Global Monitoring Division (NOAA)
• National Center For Atmospheric Research (NCAR)
• Scripps Institution of Oceanography (SIO)
• Harvard University (HU)
• Lawrence Berkeley National Laboratory and ARM Climate Research Facility (LBNL-ARM)
• HIAPER Pole-to-Pole Observations project (HIPPO)
• University of Wisconsin (UOFWI)
• Savannah River National Laboratory (SRNL)
• Lawrence Berkeley National Laboratory (LBNL)
• National Institute for Space Research (INPE)
• University of Helsinki (UHELS)
• Hohenpeissenberg Meteorological Observatory (HPB)
• Ricerca sul Sistema Energetico (RSE)
• Institut de Ciencia i Tecnologia Ambientals, Universitat Autonoma de Barcelona (ICTA-UAB)
• Lund University - Centre for Environmental and Climate Research (LUND-CEC)
• CarboCount-CH (CARBOCOUNT-CH)
• AVOCET Group @ NASA LaRC (NASA-LARC)
• Penn State University (PSU)
• Oregon State University (OSU)
• NOAA ESRL Halocarbons and Other Atmospheric Trace Species (NOAA-HATS)
• AmeriFlux Network (AMERIFLUX)
• University of Minnesota (UOFMN)
• NOAA Chemical Sciences Division (NOAA-CSD)
• California Institute of Technology, Division of Geological and Planetary Science (CALTECH)
• University of Virginia (UOFVA)
• Indianapolis Flux Experiment (INFLUX)
• Carbon in Arctic Reservoirs Vulnerability Experiment, NASA Earth Ventures (CARVE)
• Utah Atmospheric Trace gas & Air Quality (U-ATAQ)
• NASA Goddard Space Flight Center (NASA-GSFC)

The CO₂ measurement data assimilated in CT2019B are freely available for download from the ESRL ObsPack web portal or from partner websites. The bulk of assimilated measurements come from GLOBALVIEWplus v5.0 (2019) and from the NRT ObsPack v5.0 product. Additional observations were gathered from specialized ObsPack products as detailed in Table 2.

Source	Online availability	Comment
GLOBALVIEW+ v5.0	ObsPack download	Main source of observations
NRT v5.0	ObsPack download	Measurements in 2019, after end of GLOBALVIEW+ 5.0
NIES shipboard observations	Available from NIES	No permission to redistribute
NIES JR-STATION Siberia towers	Download from NIES website or Available upon request to Motoki Sasakawa; ask for access to JR-STATION restricted ObsPack	No permission to redistribute. See Sasakawa et al. (2010), Sasakawa et al. (2013)
AirCore	Available upon request to Colm Sweeney; ask for access to AirCore restricted ObsPack	No permission to redistribute
INPE (Brazil) aircraft data	Available upon request to Luciana Gatti; ask for access to INPE restricted ObsPack	No permission to redistribute

We also make available an ObsPack containing the simulated values of all measurement data considered by CT2019B. This CT2019B ObsPack contains most, but not all, of the measured values. Measured values are only distributed directly when we have permission to do so. Users are encouraged to review the usage requirements for these data products, and to contact the measurement laboratories directly for details about the observations. With the advent in 2015 of GLOBALVIEW+, data are now presented to CarbonTracker with a higher temporal frequency than in past observational products. At sites with quasi-continuous monitoring, CT2019B now assimilates hourly average CO₂ concentrations. In the past, a single daily assimilation value was constructed at these sites, generally a four-hour average during well-mixed background conditions. At continental sites, this four-hour period was generally from local noon to 4pm; at many mountain sites background conditions are met at nighttime when upslope winds are uncommon. With GLOBALVIEW+, CarbonTracker now assimilates each hourly average during these background conditions independently. For many sites, all available hourly averages throughout the day are assimilated. Details vary by dataset, but can be checked at the interactive data plotting page. Note that all of these observations are calibrated against the same world CO₂ standard (WMO-X2007). Starting with GLOBALVIEW+, we generally use the recommendations of data providers as to which observations are appropriate for assimilation. Such observations are identified by a variable in the ObsPack distribution, obs_flag. Only observations with obs_flag = 1 are identified for assimilation by data providers. We modify the designation of assimilation data for Environment and Climate Change Canada quasi-continuous sampling sites. For these data, obs_flag is set to 1 by the data provider for times when they represent the daily minimum CO₂ concentration. This is generally later in the day than our standard scheme of local noon-4pm used to represent times of well-mixed PBLs. For these datasets, we have changed obs_flag to indicate assimilation only for the local noon - 4pm time period. These selected observations are further filtered based on the CCG curve fitting routine of Thoning et al. (1989). This filter fits a smooth curve to the selected observations, and measurements more than 3 standard deviations away from this curve are excluded from assimilation. At mountain-top sites (e.g. MLO, NWR, and SPL), it is usually nighttime hours that are selected for assimilation, as these tend to be the most stable time period. Nighttime hours also avoid periods of upslope flows that contain local vegetative and/or anthropogenic influence. Data from the Sutro tower (STR) and the Boulder (Erie, Colorado) tower (BAO) are strongly influenced by local urban emissions, which CarbonTracker is unable to resolve. At these two sites, pollution events have been identified using co-located measurements of carbon monoxide. In this study, measurements thought to be affected by pollution events have been excluded. This technique is under active refinement. With CT2019B, we have begun to assimilate CO₂ measurements from NOAA light aircraft profiling time series, from intakes at multiple levels on NOAA tall towers, and from extensive shipboard and Siberian tower measurements collected by our partners at NIES. These datasets can be explored at the interactive data plotting page. We apply a further selection criterion during the assimilation to exclude non-marine boundary layer (MBL) observations that are very poorly forecasted in our framework. We use the so-called model-data mismatch in this process, which is the random error ascribed to each observation to account for measurement errors as well as modeling errors of that observation. We interpret an observed-minus-forecasted mole fraction that exceeds 3 times the prescribed model-data mismatch as an indicator that our modeling framework fails. This can happen for instance when an air sample is representative of local exchange not captured well by our 1^° × 1^° fluxes, when local meteorological conditions are not captured by our offline transport fields, but also when large-scale CO₂ exchange is suddenly changed (e.g. fires, pests, droughts) to an extent that can not be accommodated by our flux modules. This last situation would imply an important change in the carbon cycle and has to be recognized by the researchers when analyzing the results. In accordance with the 3-sigma rejection criterion, about 0.2% of the observations are discarded through this mechanism in our assimilations.

7.2  Adaptive model-data mismatch

The statistical optimization method we use to constrain surface CO₂ fluxes requires that each assimilation constraint is assigned a "model-data mismatch" (MDM) error value. This is meant to express the statistics of simulated-minus-observed CO₂ observations we could expect if CarbonTracker were using perfect surface fluxes. Such deviations arise from many sources, including random noise in the measurement system, in situ variability that we do not expect to resolve in our model, and faults with the atmospheric transport model. Generally, transport and inverse model faults are the dominant terms in MDM values. The MDM is one of two major "tuning knobs" used to adjust the performance of our ensemble Kalman filter. The other is also an error quantity, meant to represent the expected error on our first-guess fluxes. Discussion of this prior covariance error can be found in section 8.2. Prior to CT2015, CarbonTracker used a single MDM value for each assimilation dataset. The NOAA continuous observations at the 396m level of the WLEF tower in northern Wisconsin, for example, were assigned a MDM of 3.0 ppm, meaning that the residuals between model-forecasted measurements and the actual observed concentrations are expected to be unbiased (i.e., have a mean of zero) and have a standard deviation of 3 ppm. In practice, however, we have found that it is far easier to simulate wintertime observations than those during summer. This is mainly due to higher ambient variability of CO₂ in the summer. Starting with CT2016, we began to use an empirical scheme to assign MDM values, exploiting statistics of model performance from indenpendently-configured preliminary inversions. The posterior residuals for each dataset are classified into relevant bins, and then statistics of model performance are analyzed within each of those bins. For every dataset, these bins include equally-spaced intervals of one-tenth of a year. For analyzers collecting data throughout the day, we also classify the measurements into 4-hour intervals of local time. For aircraft datasets, we further classify measurements into vertical levels of 1000m thickness (0-1000 m ASL, 1000-2000 m ASL, etc.). For each of these bins, bias and random error are combined to form total deviation from observed values as a root-mean square error (RMSE). The assigned MDM is set to a constant fraction of this total RMSE. This scaling is meant to force the assimilation scheme to extract as much information as possible from available observations. We use two different scaling factors to convert RMSE to MDM, depending on whether the preliminary inversions actually assimilated the measurements in the relevant bin, or merely simulated those measurements. For measurements assimilated by the preliminary inversions, the MDM is 0.95 * RMSE; for measurements not assimilated in the preliminary inversions, the MDM is 0.85 * RMSE. The adaptive MDM scheme performs well in terms of average χ², which in an optimally-tuned system should be close to 1.0 for each dataset (see Table 3). Notably, the seasonal variations of MDM successfully compensate for the higher ambient variability of CO₂ at continental sites during the growing season. It is, however, an iterative process, requiring that we conduct a previous inversion. For various reasons, this previous inversion performed before CT2019B differs in significant aspects from the actual CT2019B inversions. These differences have led to MDM values which are slightly too large and thus average χ² values which are generally smaller than the target of 1.0 (in some cases, as low as 0.2 or 0.3). The next iteration of CarbonTracker will be able to use the more recent CT2019B inversions to refine the adaptive MDM scheme. Duplicate observations are identified as those within 50 minutes temporally, 10m vertically, and 0.05 degrees of latitude and longitude laterally (nominally, about 5km). The MDM for such observations is inflated by √n, where n is the number of duplicates.

7.3  Statistical performance of CT2019B

Starting with CT2019B, we reserved about 5% of available assimilation data for a cross-validation exercise. To the extent possible, withheld measurements were chosen to be independent from other observations. For surface flask observations, which are generally collected on a weekly time basis, all samoples are considered independent from one another, so withheld data were selected randomly. Aircraft flasks collected during a profile are considered co-dependent, so entire profiles were randomly selected for withholding. Finally, for in situ analyzers with quasi-continuous sampling (towers, observatories), 24-hour periods were randomly chosen and that entire day's worth of data were withheld. Shipboard quasi-continuous datasets, which account for more than a quarter of assimilation data for CT2019B were inadvertently excluded from this cross-validation exercise. As a result, about 3% (115,756 measurements out of about 4.2 million) were withheld. Each residual from the withheld measurements was normalized by its prescribed model-data mismatch (MDM) error, to form a set of χ values. The mean χ², which for a perfect set of independent, normally-distributed variates should approach 1.0, was found to be 0.92. Table 3 summarizes the datasets assimilated in CarbonTracker, and the performace of the assimilation scheme for each dataset. These diagnostics are useful for evaluating how well CarbonTracker does in simulating observed CO₂.

Dataset	Lab.	Location	Latitude	Longitude	Elev.	Used	Rej.	Unsampled	R	χ2	Bias	SE
					(m)				(ppm)		(ppm)	(ppm)
co2_abp_surface-flask_1_representative	NOAA	Arembepe, Bahia, Brazil	12.77°S	38.17°W	1	88	0	0	1.0 - 2.9	0.47	-1.03	0.96
co2_abp_surface-flask_26_marine	IPEN	Arembepe, Bahia, Brazil	12.77°S	38.17°W	1	88	3	0	0.5 - 4.6	0.66	-2.88	12.89
co2_acg_aircraft-pfp_1_allvalid_0-1000masl	NOAA	Alaska Coast Guard, United States	57.74°N	152.50°W	440	413	22	0	0.2 - 4.9	1.10	-0.41	2.62
co2_acg_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	Alaska Coast Guard, United States	57.74°N	152.50°W	1490	229	7	0	0.5 - 4.9	1.10	-0.03	1.79
co2_acg_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	Alaska Coast Guard, United States	57.74°N	152.50°W	2533	147	1	0	0.5 - 3.2	1.05	0.08	1.36
co2_acg_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	Alaska Coast Guard, United States	57.74°N	152.50°W	3530	136	1	0	0.5 - 4.9	1.00	0.31	1.33
co2_acg_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	Alaska Coast Guard, United States	57.74°N	152.50°W	4424	124	1	0	0.5 - 4.9	0.81	-0.05	1.25
co2_acg_aircraft-pfp_1_allvalid_5000-6000masl	NOAA	Alaska Coast Guard, United States	57.74°N	152.50°W	5494	133	2	0	0.5 - 4.9	0.70	0.09	1.29
co2_acg_aircraft-pfp_1_allvalid_6000-7000masl	NOAA	Alaska Coast Guard, United States	57.74°N	152.50°W	6443	131	3	0	0.5 - 4.9	0.89	0.22	1.33
co2_acg_aircraft-pfp_1_allvalid_7000-8000masl	NOAA	Alaska Coast Guard, United States	57.74°N	152.50°W	7495	156	1	1	0.3 - 3.2	0.84	0.03	1.36
co2_acg_aircraft-pfp_1_allvalid_8000-9000masl	NOAA	Alaska Coast Guard, United States	57.74°N	152.50°W	8342	24	0	0	0.7 - 3.0	0.74	0.45	0.93
co2_ah2_shipboard-insitu_20_allvalid	NIES	Alligator Hope (M/S Alligator Hope of Mitsui O.S.K. Lines, Ltd.)	variable		Surface	29521	2764	14	0.2 - 100.6	0.97	-0.34	2.91
co2_alf_aircraft-pfp_26_representative_0-1000masl	IPEN	Alta Floresta, Brazil	8.92°S	56.79°W	677	159	0	0	1.3 - 6.9	0.95	-1.20	4.22
co2_alf_aircraft-pfp_26_representative_1000-2000masl	IPEN	Alta Floresta, Brazil	8.92°S	56.79°W	1526	171	2	0	1.3 - 6.1	1.01	-0.28	2.81
co2_alf_aircraft-pfp_26_representative_2000-3000masl	IPEN	Alta Floresta, Brazil	8.92°S	56.79°W	2379	105	1	0	0.8 - 4.4	0.95	-0.37	1.68
co2_alf_aircraft-pfp_26_representative_3000-4000masl	IPEN	Alta Floresta, Brazil	8.92°S	56.79°W	3504	179	3	0	0.6 - 3.1	1.03	-0.06	1.63
co2_alf_aircraft-pfp_26_representative_4000-5000masl	IPEN	Alta Floresta, Brazil	8.92°S	56.79°W	4420	54	1	0	0.7 - 2.4	1.16	0.35	1.42
co2_alt_surface-flask_1_representative	NOAA	Alert, Nunavut, Canada	82.45°N	62.51°W	190	956	30	0	0.2 - 5.1	0.67	0.02	0.87
co2_alt_surface-flask_2_representative	CSIRO	Alert, Nunavut, Canada	82.45°N	62.51°W	190	608	38	0	0.2 - 1.9	1.08	0.08	0.97
co2_alt_surface-flask_4_representative	SIO	Alert, Nunavut, Canada	82.45°N	62.51°W	190	397	25	0	0.4 - 4.4	0.76	0.17	0.84
co2_alt_surface-flask_426_representative	SIO_CO2	Alert, Nunavut, Canada	82.45°N	62.51°W	190	584	11	0	0.5 - 5.9	0.65	0.00	0.96
co2_alt_surface-insitu_6_allvalid	EC	Alert, Nunavut, Canada	82.45°N	62.51°W	190	21929	1384	0	0.5 - 4.1	1.01	-0.00	0.83
co2_ams_surface-insitu_11_representative	LSCE	Amsterdam Island, France	37.80°S	77.54°E	55	126717	2302	0	0.3 - 1.0	0.78	-0.10	0.57
co2_amt_surface-pfp_1_allvalid-107magl	NOAA	Argyle, Maine, United States	45.03°N	68.68°W	53	1401	7	0	1.5 - 13.2	0.41	0.34	3.42
co2_amt_tower-insitu_1_allvalid-107magl	NOAA	Argyle, Maine, United States	45.03°N	68.68°W	53	18481	420	0	1.5 - 8.5	0.74	0.27	3.54
co2_amt_tower-insitu_1_allvalid-12magl	NOAA	Argyle, Maine, United States	45.03°N	68.68°W	53	18047	455	0	1.5 - 6.6	0.83	0.47	4.02
co2_amt_tower-insitu_1_allvalid-30magl	NOAA	Argyle, Maine, United States	45.03°N	68.68°W	53	12289	344	0	1.5 - 6.7	0.87	0.81	3.85
co2_ara_surface-flask_2_representative	CSIRO	Arcturus, Queensland, Australia	23.86°S	148.47°E	175	11	0	0	1.7 - 10.0	0.18	0.08	2.41
co2_asc_surface-flask_1_representative	NOAA	Ascension Island, United Kingdom	7.97°S	14.40°W	85	1524	0	0	0.5 - 1.0	1.49	0.10	0.78
co2_ask_surface-flask_1_representative	NOAA	Assekrem, Algeria	23.26°N	5.63°E	2710	814	10	0	0.2 - 1.0	0.99	-0.12	0.62
co2_azr_surface-flask_1_representative	NOAA	Terceira Island, Azores, Portugal	38.77°N	27.38°W	19	465	12	0	0.4 - 2.5	0.99	0.18	1.37
co2_azv_tower-insitu_20_allvalid-29magl	NIES	Azovo, Russia	54.70°N	73.03°E	110	10113	220	0	1.9 - 6.0	0.93	-0.77	3.70
co2_azv_tower-insitu_20_allvalid-50magl	NIES	Azovo, Russia	54.70°N	73.03°E	110	9758	211	0	1.9 - 6.0	0.93	-0.65	3.69
co2_bal_surface-flask_1_representative	NOAA	Baltic Sea, Poland	55.35°N	17.22°E	3	907	13	0	0.4 - 11.3	0.73	-1.97	5.57
co2_bao_surface-pfp_1_allvalid-300magl	NOAA	Boulder Atmospheric Observatory, Colorado, United States	40.05°N	105.00°W	1584	2136	2	0	2.5 - 29.7	0.39	-1.77	2.98
co2_bao_tower-insitu_1_allvalid-100magl	NOAA	Boulder Atmospheric Observatory, Colorado, United States	40.05°N	105.00°W	1584	9931	309	0	2.3 - 9.5	0.82	-3.44	6.60
co2_bao_tower-insitu_1_allvalid-22magl	NOAA	Boulder Atmospheric Observatory, Colorado, United States	40.05°N	105.00°W	1584	10045	292	0	2.4 - 12.6	0.80	-4.02	8.23
co2_bao_tower-insitu_1_allvalid-300magl	NOAA	Boulder Atmospheric Observatory, Colorado, United States	40.05°N	105.00°W	1584	65994	1103	0	2.2 - 78.3	0.77	0.29	5.65
co2_bck_surface-insitu_6_allvalid	EC	Behchoko, Northwest Territories, Canada	62.80°N	115.92°W	160	8692	385	0	1.7 - 9.2	0.78	0.05	2.37
co2_bcs_surface-flask_426_representative	SIO_CO2	Baja California Sur, Mexico	23.30°N	110.20°W	4	112	2	0	0.6 - 6.5	0.53	0.66	1.97
co2_bgi_aircraft-pfp_1_allvalid_0-1000masl	NOAA	Bradgate, Iowa, United States	42.82°N	94.41°W	612	8	0	0	2.2 - 8.8	0.60	-2.34	5.09
co2_bgi_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	Bradgate, Iowa, United States	42.82°N	94.41°W	1565	53	4	0	0.5 - 6.9	0.79	-1.11	2.62
co2_bgi_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	Bradgate, Iowa, United States	42.82°N	94.41°W	2548	16	0	0	0.5 - 1.9	1.06	-0.16	1.11
co2_bgi_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	Bradgate, Iowa, United States	42.82°N	94.41°W	3524	50	2	0	0.2 - 3.0	1.27	0.24	1.23
co2_bgi_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	Bradgate, Iowa, United States	42.82°N	94.41°W	4569	26	0	0	0.2 - 1.4	1.37	0.28	0.69
co2_bgi_aircraft-pfp_1_allvalid_5000-6000masl	NOAA	Bradgate, Iowa, United States	42.82°N	94.41°W	5506	36	6	0	0.0 - 1.8	1.28	0.22	0.89
co2_bgi_aircraft-pfp_1_allvalid_6000-7000masl	NOAA	Bradgate, Iowa, United States	42.82°N	94.41°W	6474	38	0	0	0.5 - 1.9	1.15	0.14	1.19
co2_bgi_aircraft-pfp_1_allvalid_7000-8000masl	NOAA	Bradgate, Iowa, United States	42.82°N	94.41°W	7469	39	0	0	0.2 - 1.3	1.19	0.09	0.68
co2_bgi_aircraft-pfp_1_allvalid_8000-9000masl	NOAA	Bradgate, Iowa, United States	42.82°N	94.41°W	8050	3	0	0	0.8 - 0.8	0.85	-0.08	1.19
co2_bhd_surface-flask_1_representative	NOAA	Baring Head Station, New Zealand	41.41°S	174.87°E	85	222	3	0	0.2 - 2.8	0.78	0.08	1.28
co2_bhd_surface-flask_426_representative	SIO_CO2	Baring Head Station, New Zealand	41.41°S	174.87°E	85	130	3	0	0.3 - 5.7	1.01	1.08	1.26
co2_bhd_surface-insitu_15_baseline	NIWA	Baring Head Station, New Zealand	41.41°S	174.87°E	85	595	20	0	0.4 - 2.7	1.02	0.65	1.24
co2_bir_surface-insitu_56_allvalid	NILU	Birkenes Observatory, Norway	58.39°N	8.25°E	219	1291	29	0	1.6 - 5.9	0.76	0.75	3.08
co2_bkt_surface-flask_1_representative	NOAA	Bukit Kototabang, Indonesia	0.20°S	100.32°E	845	441	1	0	4.6 - 6.6	0.98	4.36	3.73
co2_bme_surface-flask_1_representative	NOAA	St. Davids Head, Bermuda, United Kingdom	32.37°N	64.65°W	12	213	7	0	0.7 - 2.9	0.98	0.53	1.58
co2_bmw_surface-flask_1_representative	NOAA	Tudor Hill, Bermuda, United Kingdom	32.26°N	64.88°W	30	589	24	0	0.4 - 2.1	1.06	0.64	1.30
co2_bne_aircraft-pfp_1_allvalid_0-1000masl	NOAA	Beaver Crossing, Nebraska, United States	40.80°N	97.18°W	634	73	1	0	2.6 - 10.8	0.75	-0.21	4.54
co2_bne_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	Beaver Crossing, Nebraska, United States	40.80°N	97.18°W	1393	183	4	0	0.6 - 8.7	0.99	-0.31	2.62
co2_bne_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	Beaver Crossing, Nebraska, United States	40.80°N	97.18°W	2296	110	3	0	0.3 - 9.8	0.92	-0.54	2.02
co2_bne_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	Beaver Crossing, Nebraska, United States	40.80°N	97.18°W	3393	147	3	0	0.2 - 10.0	1.05	-0.23	1.71
co2_bne_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	Beaver Crossing, Nebraska, United States	40.80°N	97.18°W	4280	81	2	0	0.4 - 12.8	1.03	-0.33	2.36
co2_bne_aircraft-pfp_1_allvalid_5000-6000masl	NOAA	Beaver Crossing, Nebraska, United States	40.80°N	97.18°W	5392	118	3	0	0.3 - 14.8	0.95	-0.16	2.20
co2_bne_aircraft-pfp_1_allvalid_6000-7000masl	NOAA	Beaver Crossing, Nebraska, United States	40.80°N	97.18°W	6362	113	2	0	0.2 - 15.2	1.24	-0.12	2.26
co2_bne_aircraft-pfp_1_allvalid_7000-8000masl	NOAA	Beaver Crossing, Nebraska, United States	40.80°N	97.18°W	7657	86	2	0	0.1 - 23.3	1.15	0.28	0.74
co2_bne_aircraft-pfp_1_allvalid_8000-9000masl	NOAA	Beaver Crossing, Nebraska, United States	40.80°N	97.18°W	8072	24	1	0	0.4 - 1.2	0.70	-0.21	0.80
co2_bra_surface-insitu_6_allvalid	EC	Bratt's Lake Saskatchewan, Canada	50.20°N	104.71°W	595	8611	191	0	2.3 - 8.6	0.80	0.06	2.74
co2_brw_surface-flask_1_representative	NOAA	Barrow Atmospheric Baseline Observatory, United States	71.32°N	156.61°W	11	989	10	3	0.8 - 10.9	0.46	-0.23	1.77
co2_brw_surface-flask_426_representative	SIO_CO2	Barrow Atmospheric Baseline Observatory, United States	71.32°N	156.61°W	11	655	7	0	0.7 - 16.2	0.73	-0.01	1.75
co2_brw_surface-insitu_1_allvalid	NOAA	Barrow Atmospheric Baseline Observatory, United States	71.32°N	156.61°W	11	31993	951	0	1.4 - 7.4	0.65	-0.08	1.41
co2_brz_aircraft-insitu_20_allvalid_0-1000masl	NIES	Berezorechka, Russia	56.15°N	84.33°E	636	32953	99	0	1.2 - 156.0	0.25	-0.63	3.32
co2_brz_aircraft-insitu_20_allvalid_1000-2000masl	NIES	Berezorechka, Russia	56.15°N	84.33°E	1501	44112	113	0	1.2 - 88.5	0.17	-0.56	2.73
co2_brz_aircraft-insitu_20_allvalid_2000-3000masl	NIES	Berezorechka, Russia	56.15°N	84.33°E	2408	20179	52	0	1.2 - 114.8	0.14	-0.57	2.43
co2_brz_aircraft-insitu_20_allvalid_3000-4000masl	NIES	Berezorechka, Russia	56.15°N	84.33°E	3085	2750	1	0	1.2 - 62.2	0.14	-0.52	2.61
co2_brz_tower-insitu_20_allvalid-20magl	NIES	Berezorechka, Russia	56.15°N	84.33°E	168	13338	312	0	2.3 - 24.6	0.80	-0.22	3.79
co2_brz_tower-insitu_20_allvalid-40magl	NIES	Berezorechka, Russia	56.15°N	84.33°E	168	13325	280	0	2.2 - 23.8	0.82	-0.43	3.84
co2_brz_tower-insitu_20_allvalid-5magl	NIES	Berezorechka, Russia	56.15°N	84.33°E	168	13493	290	0	2.3 - 25.0	0.81	-0.24	3.84
co2_brz_tower-insitu_20_allvalid-80magl	NIES	Berezorechka, Russia	56.15°N	84.33°E	168	8392	204	0	1.9 - 24.3	0.89	-0.69	3.94
co2_bsc_surface-flask_1_representative	NOAA	Black Sea, Constanta, Romania	44.18°N	28.66°E	0	405	4	0	1.8 - 20.7	0.99	-5.92	8.11
co2_car_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	Briggsdale, Colorado, United States	40.63°N	104.33°W	1788	34	0	0	1.3 - 5.1	1.20	-0.95	2.49
co2_car_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	Briggsdale, Colorado, United States	40.63°N	104.33°W	2441	917	25	0	0.7 - 7.3	0.78	0.32	2.07
co2_car_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	Briggsdale, Colorado, United States	40.63°N	104.33°W	3460	1090	42	0	0.2 - 3.9	1.12	0.20	1.01
co2_car_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	Briggsdale, Colorado, United States	40.63°N	104.33°W	4500	1008	27	0	0.2 - 1.9	1.13	0.25	0.86
co2_car_aircraft-pfp_1_allvalid_5000-6000masl	NOAA	Briggsdale, Colorado, United States	40.63°N	104.33°W	5484	785	16	0	0.2 - 1.8	1.21	0.15	0.85
co2_car_aircraft-pfp_1_allvalid_6000-7000masl	NOAA	Briggsdale, Colorado, United States	40.63°N	104.33°W	6446	779	33	0	0.1 - 1.5	1.26	0.31	0.78
co2_car_aircraft-pfp_1_allvalid_7000-8000masl	NOAA	Briggsdale, Colorado, United States	40.63°N	104.33°W	7477	793	17	0	0.3 - 1.4	1.22	0.30	0.80
co2_car_aircraft-pfp_1_allvalid_8000-9000masl	NOAA	Briggsdale, Colorado, United States	40.63°N	104.33°W	8217	170	2	0	0.2 - 1.7	1.39	0.22	0.71
co2_car_aircraft-pfp_1_allvalid_9000-10000masl	NOAA	Briggsdale, Colorado, United States	40.63°N	104.33°W	9140	2	0	0	0.7 - 0.7	1.09	-0.29	0.93
co2_car_aircraft-pfp_1_allvalid_11000-12000masl	NOAA	Briggsdale, Colorado, United States	40.63°N	104.33°W	11869	4	0	0	0.5 - 0.8	0.45	-0.28	0.45
co2_cba_surface-flask_1_representative	NOAA	Cold Bay, Alaska, United States	55.21°N	162.72°W	21	1243	26	0	0.8 - 4.6	0.97	-0.95	1.72
co2_cba_surface-flask_4_representative	SIO	Cold Bay, Alaska, United States	55.21°N	162.72°W	21	347	24	0	0.5 - 6.6	0.93	-0.59	2.11
co2_cby_surface-insitu_6_allvalid	EC	Cambridge Bay, Nunavut Territory, Canada	69.13°N	105.06°W	35	6851	229	0	1.1 - 4.3	0.61	0.00	1.30
co2_cdl_surface-insitu_6_allvalid	EC	Candle Lake, Saskatchewan, Canada	53.99°N	105.12°W	600	8650	161	0	2.0 - 8.1	0.68	0.34	2.61
co2_cfa_surface-flask_2_representative	CSIRO	Cape Ferguson, Queensland, Australia	19.28°S	147.06°E	2	318	3	0	0.3 - 2.4	0.74	-0.44	1.04
co2_cgo_surface-flask_1_representative	NOAA	Cape Grim, Tasmania, Australia	40.68°S	144.69°E	94	628	0	0	0.3 - 4.3	0.63	-0.07	0.71
co2_cgo_surface-flask_2_representative	CSIRO	Cape Grim, Tasmania, Australia	40.68°S	144.69°E	94	839	4	0	0.2 - 3.3	0.41	-0.19	0.64
co2_cgo_surface-flask_4_representative	SIO	Cape Grim, Tasmania, Australia	40.68°S	144.69°E	94	372	4	0	0.3 - 3.5	0.61	0.22	0.88
co2_chl_surface-insitu_6_allvalid	EC	Churchill, Manitoba, Canada	58.74°N	93.82°W	29	5988	144	0	1.7 - 6.2	0.57	-0.17	1.85
co2_chm_surface-insitu_6_allvalid	EC	Chibougamau, Quebec, Canada	49.69°N	74.34°W	393	3469	116	0	2.3 - 5.5	0.95	0.01	2.43
co2_chr_surface-flask_1_representative	NOAA	Christmas Island, Republic of Kiribati	1.70°N	157.15°W	0	564	0	0	0.4 - 1.7	0.79	-0.20	0.60
co2_chr_surface-flask_426_representative	SIO_CO2	Christmas Island, Republic of Kiribati	1.70°N	157.15°W	0	261	5	0	0.4 - 1.5	0.93	-0.41	0.86
co2_cib_surface-flask_1_representative	NOAA	Centro de Investigacion de la Baja Atmosfera (CIBA), Spain	41.81°N	4.93°W	845	390	4	0	2.0 - 6.2	0.85	0.43	3.34
co2_cma_aircraft-pfp_1_allvalid_0-1000masl	NOAA	Offshore Cape May, New Jersey, United States	38.83°N	74.32°W	634	478	9	0	1.7 - 7.4	0.90	-0.41	3.15
co2_cma_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	Offshore Cape May, New Jersey, United States	38.83°N	74.32°W	1537	285	11	0	0.6 - 6.0	0.83	-0.47	2.30
co2_cma_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	Offshore Cape May, New Jersey, United States	38.83°N	74.32°W	2299	358	7	0	0.6 - 4.3	0.86	-0.35	2.00
co2_cma_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	Offshore Cape May, New Jersey, United States	38.83°N	74.32°W	3443	333	8	0	0.4 - 2.5	1.24	0.22	1.25
co2_cma_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	Offshore Cape May, New Jersey, United States	38.83°N	74.32°W	4196	166	13	0	0.1 - 3.0	1.13	0.17	1.27
co2_cma_aircraft-pfp_1_allvalid_5000-6000masl	NOAA	Offshore Cape May, New Jersey, United States	38.83°N	74.32°W	5343	320	10	0	0.3 - 2.6	1.17	0.18	1.04
co2_cma_aircraft-pfp_1_allvalid_6000-7000masl	NOAA	Offshore Cape May, New Jersey, United States	38.83°N	74.32°W	6266	262	7	0	0.4 - 1.6	1.15	0.09	1.00
co2_cma_aircraft-pfp_1_allvalid_7000-8000masl	NOAA	Offshore Cape May, New Jersey, United States	38.83°N	74.32°W	7724	239	6	0	0.3 - 1.8	1.20	0.35	0.92
co2_cma_aircraft-pfp_1_allvalid_8000-9000masl	NOAA	Offshore Cape May, New Jersey, United States	38.83°N	74.32°W	8043	24	1	0	0.6 - 1.4	1.06	0.52	1.03
co2_con_aircraft-flask_20_allvalid_0-1000masl	NIES	CONTRAIL (Comprehensive Observation Network for TRace gases by AIrLiner)	variable		824	2	0	0	0.7 - 0.7	0.01	0.00	0.03
co2_con_aircraft-flask_20_allvalid_1000-2000masl	NIES	CONTRAIL (Comprehensive Observation Network for TRace gases by AIrLiner)	variable		1385	6	0	0	0.3 - 0.6	0.82	0.21	0.27
co2_con_aircraft-flask_20_allvalid_2000-3000masl	NIES	CONTRAIL (Comprehensive Observation Network for TRace gases by AIrLiner)	variable		2509	3	0	0	0.3 - 0.5	3.84	-0.04	1.00
co2_con_aircraft-flask_20_allvalid_3000-4000masl	NIES	CONTRAIL (Comprehensive Observation Network for TRace gases by AIrLiner)	variable		3484	4	2	0	0.3 - 0.9	1.79	0.57	1.33
co2_con_aircraft-flask_20_allvalid_4000-5000masl	NIES	CONTRAIL (Comprehensive Observation Network for TRace gases by AIrLiner)	variable		4494	3	0	0	0.3 - 0.6	1.13	-0.37	0.16
co2_con_aircraft-flask_20_allvalid_5000-6000masl	NIES	CONTRAIL (Comprehensive Observation Network for TRace gases by AIrLiner)	variable		5618	8	0	0	0.3 - 1.5	0.86	-0.10	0.48
co2_con_aircraft-flask_20_allvalid_6000-7000masl	NIES	CONTRAIL (Comprehensive Observation Network for TRace gases by AIrLiner)	variable		6398	9	0	0	0.3 - 1.7	0.64	0.36	0.95
co2_con_aircraft-flask_20_allvalid_7000-8000masl	NIES	CONTRAIL (Comprehensive Observation Network for TRace gases by AIrLiner)	variable		7625	5	1	0	0.4 - 1.2	0.41	-0.35	0.83
co2_con_aircraft-flask_20_allvalid_8000-9000masl	NIES	CONTRAIL (Comprehensive Observation Network for TRace gases by AIrLiner)	variable		8577	14	0	0	0.2 - 2.1	0.66	-0.10	0.48
co2_con_aircraft-flask_20_allvalid_9000-10000masl	NIES	CONTRAIL (Comprehensive Observation Network for TRace gases by AIrLiner)	variable		9634	268	5	1	0.1 - 2.1	1.29	0.07	0.81
co2_con_aircraft-flask_20_allvalid_10000-11000masl	NIES	CONTRAIL (Comprehensive Observation Network for TRace gases by AIrLiner)	variable		10676	1550	30	0	0.1 - 2.4	1.05	0.06	0.67
co2_con_aircraft-flask_20_allvalid_11000-12000masl	NIES	CONTRAIL (Comprehensive Observation Network for TRace gases by AIrLiner)	variable		11534	822	23	0	0.2 - 1.7	1.07	0.21	0.76
co2_con_aircraft-flask_20_allvalid_12000-13000masl	NIES	CONTRAIL (Comprehensive Observation Network for TRace gases by AIrLiner)	variable		12262	168	5	0	0.3 - 1.7	1.02	0.11	0.98
co2_cps_surface-insitu_6_allvalid	EC	Chapais,Quebec, Canada	49.82°N	74.98°W	381	7760	273	0	1.4 - 5.0	0.89	0.35	2.50
co2_cpt_surface-flask_1_representative	NOAA	Cape Point, South Africa	34.35°S	18.49°E	230	177	1	0	0.1 - 1.8	0.33	0.20	0.42
co2_cpt_surface-insitu_36_marine	SAWS	Cape Point, South Africa	34.35°S	18.49°E	230	118821	1306	0	0.4 - 1.8	0.63	0.04	0.50
co2_crv_aircraft-pfp_1_allvalid_0-1000masl	NOAA	Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE), United States	64.99°N	147.60°W	315	1431	57	3	0.5 - 44.9	0.80	-2.41	5.67
co2_crv_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE), United States	64.99°N	147.60°W	1438	85	9	0	0.5 - 9.3	0.85	0.10	2.37
co2_crv_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE), United States	64.99°N	147.60°W	2555	79	3	1	0.3 - 12.8	0.64	0.38	1.74
co2_crv_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE), United States	64.99°N	147.60°W	3390	65	2	0	1.1 - 9.3	0.23	0.23	1.16
co2_crv_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE), United States	64.99°N	147.60°W	4518	39	1	0	0.3 - 12.8	1.01	0.34	1.35
co2_crv_aircraft-pfp_1_allvalid_5000-6000masl	NOAA	Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE), United States	64.99°N	147.60°W	5269	300	2	0	0.6 - 31.8	0.23	0.44	1.44
co2_crv_surface-pfp_1_allvalid-32magl	NOAA	Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE), United States	64.99°N	147.60°W	611	1168	18	0	0.9 - 12.6	0.67	-0.39	3.00
co2_crv_tower-insitu_1_allvalid-17magl	NOAA	Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE), United States	64.99°N	147.60°W	611	8478	360	0	1.6 - 10.5	0.86	-0.24	3.26
co2_crv_tower-insitu_1_allvalid-32magl	NOAA	Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE), United States	64.99°N	147.60°W	611	8895	368	0	1.6 - 18.1	0.83	-0.30	2.79
co2_crv_tower-insitu_1_allvalid-5magl	NOAA	Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE), United States	64.99°N	147.60°W	611	8656	357	0	1.7 - 10.9	0.86	-0.19	3.15
co2_crz_surface-flask_1_representative	NOAA	Crozet Island, France	46.43°S	51.85°E	197	674	0	0	0.1 - 0.5	0.75	0.02	0.30
co2_cya_surface-flask_2_representative	CSIRO	Casey, Antarctica, Australia	66.28°S	110.52°E	47	393	0	0	0.1 - 0.5	0.88	-0.06	0.23
co2_dem_tower-insitu_20_allvalid-45magl	NIES	Demyanskoe, Russia	59.79°N	70.87°E	63	12932	289	0	2.0 - 7.3	0.90	-0.31	3.29
co2_dem_tower-insitu_20_allvalid-63magl	NIES	Demyanskoe, Russia	59.79°N	70.87°E	63	12050	260	0	2.1 - 7.3	0.88	-0.46	3.32
co2_dnd_aircraft-pfp_1_allvalid_0-1000masl	NOAA	Dahlen, North Dakota, United States	47.50°N	99.24°W	743	218	9	0	0.8 - 11.1	0.74	-0.21	3.72
co2_dnd_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	Dahlen, North Dakota, United States	47.50°N	99.24°W	1510	303	15	0	0.3 - 7.5	0.79	-0.36	2.48
co2_dnd_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	Dahlen, North Dakota, United States	47.50°N	99.24°W	2470	349	14	0	0.2 - 5.7	0.90	-0.30	1.42
co2_dnd_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	Dahlen, North Dakota, United States	47.50°N	99.24°W	3530	262	14	0	0.5 - 3.3	1.32	0.05	1.19
co2_dnd_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	Dahlen, North Dakota, United States	47.50°N	99.24°W	4466	201	4	0	0.4 - 3.3	1.15	0.10	1.28
co2_dnd_aircraft-pfp_1_allvalid_5000-6000masl	NOAA	Dahlen, North Dakota, United States	47.50°N	99.24°W	5508	234	5	0	0.4 - 2.5	1.17	0.19	1.00
co2_dnd_aircraft-pfp_1_allvalid_6000-7000masl	NOAA	Dahlen, North Dakota, United States	47.50°N	99.24°W	6481	210	3	0	0.3 - 1.9	1.36	0.21	0.96
co2_dnd_aircraft-pfp_1_allvalid_7000-8000masl	NOAA	Dahlen, North Dakota, United States	47.50°N	99.24°W	7474	209	2	0	0.2 - 2.5	1.23	0.23	0.95
co2_dnd_aircraft-pfp_1_allvalid_8000-9000masl	NOAA	Dahlen, North Dakota, United States	47.50°N	99.24°W	8053	4	0	0	0.7 - 1.9	0.78	-1.42	1.20
co2_drp_shipboard-flask_1_representative	NOAA	Drake Passage	variable		Surface	201	11	0	0.1 - 1.1	0.68	-0.01	0.38
co2_dsi_surface-flask_1_representative	NOAA	Dongsha Island, Taiwan	20.70°N	116.73°E	3	306	7	0	0.5 - 5.9	1.00	1.75	3.02
co2_egb_surface-insitu_6_allvalid	EC	Egbert, Ontario, Canada	44.23°N	79.78°W	251	12547	90	0	3.1 - 7.3	0.57	-0.17	3.57
co2_eic_surface-flask_1_representative	NOAA	Easter Island, Chile	27.16°S	109.43°W	47	534	4	0	0.6 - 2.3	1.01	0.37	0.99
co2_esp_aircraft-pfp_1_allvalid_0-1000masl	NOAA	Estevan Point, British Columbia, Canada	49.38°N	126.54°W	536	726	11	0	0.6 - 11.6	0.65	-0.77	3.03
co2_esp_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	Estevan Point, British Columbia, Canada	49.38°N	126.54°W	1541	923	37	0	0.2 - 3.3	1.13	-0.13	1.33
co2_esp_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	Estevan Point, British Columbia, Canada	49.38°N	126.54°W	2551	758	32	0	0.3 - 2.9	1.18	-0.03	1.25
co2_esp_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	Estevan Point, British Columbia, Canada	49.38°N	126.54°W	3551	732	31	0	0.4 - 2.5	1.45	0.11	1.19
co2_esp_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	Estevan Point, British Columbia, Canada	49.38°N	126.54°W	4506	639	22	0	0.4 - 2.2	1.25	0.12	1.19
co2_esp_aircraft-pfp_1_allvalid_5000-6000masl	NOAA	Estevan Point, British Columbia, Canada	49.38°N	126.54°W	5383	406	11	0	0.5 - 2.0	1.14	0.02	1.12
co2_esp_surface-flask_2_representative	CSIRO	Estevan Point, British Columbia, Canada	49.38°N	126.54°W	7	21	0	0	0.4 - 7.8	0.53	-0.76	2.74
co2_esp_surface-insitu_6_allvalid	EC	Estevan Point, British Columbia, Canada	49.38°N	126.54°W	7	10874	121	0	2.0 - 9.9	0.56	-0.30	2.42
co2_est_surface-insitu_6_allvalid	EC	Esther, Alberta, Canada	51.67°N	110.21°W	707	10589	237	0	2.0 - 6.8	0.87	0.03	2.87
co2_etl_aircraft-pfp_1_allvalid_0-1000masl	NOAA	East Trout Lake, Saskatchewan, Canada	54.35°N	104.99°W	909	195	15	0	1.0 - 5.8	0.99	-0.30	2.18
co2_etl_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	East Trout Lake, Saskatchewan, Canada	54.35°N	104.99°W	1513	734	46	0	0.5 - 6.7	0.96	-0.31	1.83
co2_etl_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	East Trout Lake, Saskatchewan, Canada	54.35°N	104.99°W	2483	797	27	0	0.8 - 8.0	1.21	-0.26	1.46
co2_etl_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	East Trout Lake, Saskatchewan, Canada	54.35°N	104.99°W	3500	283	7	0	0.6 - 3.0	1.20	-0.02	1.26
co2_etl_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	East Trout Lake, Saskatchewan, Canada	54.35°N	104.99°W	4580	274	9	0	0.5 - 4.8	1.23	0.11	1.60
co2_etl_aircraft-pfp_1_allvalid_5000-6000masl	NOAA	East Trout Lake, Saskatchewan, Canada	54.35°N	104.99°W	5639	255	11	0	0.6 - 2.3	1.27	0.14	1.24
co2_etl_aircraft-pfp_1_allvalid_6000-7000masl	NOAA	East Trout Lake, Saskatchewan, Canada	54.35°N	104.99°W	6740	135	0	0	0.5 - 1.8	1.51	0.44	1.00
co2_etl_aircraft-pfp_1_allvalid_7000-8000masl	NOAA	East Trout Lake, Saskatchewan, Canada	54.35°N	104.99°W	7151	94	2	0	0.5 - 2.4	1.28	0.15	1.74
co2_etl_surface-insitu_6_allvalid	EC	East Trout Lake, Saskatchewan, Canada	54.35°N	104.99°W	492	16166	396	0	1.8 - 7.2	0.73	-0.05	2.54
co2_fsd_surface-insitu_6_allvalid	EC	Fraserdale, Canada	49.88°N	81.57°W	210	21072	318	0	2.1 - 7.1	0.65	-0.02	2.74
co2_ftl_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	Fortaleza, Brazil	3.52°S	38.28°W	1782	13	0	0	0.6 - 1.1	1.43	0.04	0.98
co2_ftl_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	Fortaleza, Brazil	3.52°S	38.28°W	2498	25	1	0	0.3 - 1.8	1.59	-0.28	1.38
co2_ftl_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	Fortaleza, Brazil	3.52°S	38.28°W	3479	40	1	0	0.2 - 1.7	1.38	0.12	1.38
co2_ftl_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	Fortaleza, Brazil	3.52°S	38.28°W	4267	7	0	0	0.4 - 2.4	2.01	0.63	1.67
co2_ftw_shipboard-insitu_20_allvalid	NIES	Fujitrans World (M/S Fujitrans World of Kagoshima Shipping Co., Ltd.)	variable		Surface	74257	1836	11	0.1 - 19.6	0.78	-0.21	1.51
co2_ftws_shipboard-insitu_20_allvalid	NIES	Fujitrans World - Southeast Asia Route (M/S Fujitrans World of Kagoshima Shipping Co., Ltd.)	variable		Surface	240244	5154	70	0.2 - 30.8	0.64	-0.77	2.82
co2_fwi_aircraft-pfp_1_allvalid_0-1000masl	NOAA	Fairchild, Wisconsin, United States	44.66°N	90.96°W	623	10	0	0	1.8 - 11.0	0.32	-2.31	5.77
co2_fwi_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	Fairchild, Wisconsin, United States	44.66°N	90.96°W	1562	58	1	0	0.3 - 6.2	0.95	-0.35	3.05
co2_fwi_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	Fairchild, Wisconsin, United States	44.66°N	90.96°W	2535	18	0	0	0.4 - 5.0	0.63	-0.43	1.61
co2_fwi_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	Fairchild, Wisconsin, United States	44.66°N	90.96°W	3518	58	1	0	0.3 - 3.5	1.20	-0.14	1.19
co2_fwi_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	Fairchild, Wisconsin, United States	44.66°N	90.96°W	4570	29	2	0	0.2 - 1.6	2.32	0.44	1.10
co2_fwi_aircraft-pfp_1_allvalid_5000-6000masl	NOAA	Fairchild, Wisconsin, United States	44.66°N	90.96°W	5523	39	5	0	0.2 - 10.3	1.35	0.80	2.36
co2_fwi_aircraft-pfp_1_allvalid_6000-7000masl	NOAA	Fairchild, Wisconsin, United States	44.66°N	90.96°W	6504	34	4	0	0.1 - 2.4	1.63	0.34	1.06
co2_fwi_aircraft-pfp_1_allvalid_7000-8000masl	NOAA	Fairchild, Wisconsin, United States	44.66°N	90.96°W	7487	39	0	0	0.5 - 2.3	1.28	0.19	1.10
co2_gmi_surface-flask_1_representative	NOAA	Mariana Islands, Guam	13.39°N	144.66°E	0	972	27	0	0.3 - 1.2	0.92	0.12	0.80
co2_gw_shipboard-insitu_20_allvalid	NIES	Golden Wattle (M/S Alligator Hope of Mitsui O.S.K. Lines, Ltd.)	variable		Surface	14179	507	6	0.1 - 72.3	0.89	-0.20	1.47
co2_haa_aircraft-pfp_1_allvalid_0-1000masl	NOAA	Molokai Island, Hawaii, United States	21.23°N	158.95°W	871	32	1	0	0.2 - 1.0	1.54	0.42	0.67
co2_haa_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	Molokai Island, Hawaii, United States	21.23°N	158.95°W	1582	224	19	0	0.0 - 1.0	1.21	0.24	0.67
co2_haa_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	Molokai Island, Hawaii, United States	21.23°N	158.95°W	2524	207	17	0	0.2 - 1.0	1.33	0.11	0.71
co2_haa_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	Molokai Island, Hawaii, United States	21.23°N	158.95°W	3488	227	11	0	0.2 - 1.0	1.19	0.12	0.74
co2_haa_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	Molokai Island, Hawaii, United States	21.23°N	158.95°W	4532	256	15	0	0.1 - 1.3	1.24	0.17	0.79
co2_haa_aircraft-pfp_1_allvalid_5000-6000masl	NOAA	Molokai Island, Hawaii, United States	21.23°N	158.95°W	5440	196	9	0	0.1 - 1.0	1.27	0.19	0.74
co2_haa_aircraft-pfp_1_allvalid_6000-7000masl	NOAA	Molokai Island, Hawaii, United States	21.23°N	158.95°W	6490	209	6	0	0.2 - 1.5	1.20	0.30	0.91
co2_haa_aircraft-pfp_1_allvalid_7000-8000masl	NOAA	Molokai Island, Hawaii, United States	21.23°N	158.95°W	7461	111	4	0	0.3 - 2.0	1.16	0.45	1.29
co2_haa_aircraft-pfp_1_allvalid_8000-9000masl	NOAA	Molokai Island, Hawaii, United States	21.23°N	158.95°W	8044	41	0	0	0.2 - 1.8	1.02	0.07	0.93
co2_hba_surface-flask_1_representative	NOAA	Halley Station, Antarctica, United Kingdom	75.61°S	26.21°W	30	711	0	0	0.1 - 0.5	0.95	0.05	0.19
co2_hdp_surface-insitu_3_nonlocal	NCAR	Hidden Peak (Snowbird), Utah, United States	40.56°N	111.65°W	3351	55708	1595	0	0.7 - 2.5	1.08	-0.36	1.19
co2_hfm_aircraft-pfp_1_allvalid_0-1000masl	NOAA	Harvard Forest, Massachusetts, United States	42.54°N	72.17°W	766	158	2	0	0.4 - 9.1	0.62	-0.40	3.24
co2_hfm_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	Harvard Forest, Massachusetts, United States	42.54°N	72.17°W	1533	283	2	0	1.3 - 12.3	0.70	-0.22	2.70
co2_hfm_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	Harvard Forest, Massachusetts, United States	42.54°N	72.17°W	2452	189	5	0	0.5 - 10.1	0.90	-0.15	1.79
co2_hfm_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	Harvard Forest, Massachusetts, United States	42.54°N	72.17°W	3432	163	4	0	0.4 - 6.5	1.17	0.16	1.30
co2_hfm_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	Harvard Forest, Massachusetts, United States	42.54°N	72.17°W	4563	196	1	0	0.2 - 2.5	1.41	0.18	1.22
co2_hfm_aircraft-pfp_1_allvalid_5000-6000masl	NOAA	Harvard Forest, Massachusetts, United States	42.54°N	72.17°W	5483	207	7	0	0.2 - 1.5	1.38	0.24	1.06
co2_hfm_aircraft-pfp_1_allvalid_6000-7000masl	NOAA	Harvard Forest, Massachusetts, United States	42.54°N	72.17°W	6425	150	5	0	0.3 - 2.6	1.44	0.42	1.04
co2_hfm_aircraft-pfp_1_allvalid_7000-8000masl	NOAA	Harvard Forest, Massachusetts, United States	42.54°N	72.17°W	7390	168	9	0	0.1 - 6.6	1.10	0.24	1.25
co2_hfm_aircraft-pfp_1_allvalid_8000-9000masl	NOAA	Harvard Forest, Massachusetts, United States	42.54°N	72.17°W	8031	2	0	0	1.7 - 1.7	0.66	-0.99	2.14
co2_hil_aircraft-pfp_1_allvalid_0-1000masl	NOAA	Homer, Illinois, United States	40.07°N	87.91°W	614	156	9	0	0.7 - 10.4	1.06	-0.57	3.54
co2_hil_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	Homer, Illinois, United States	40.07°N	87.91°W	1549	390	8	0	0.7 - 7.8	0.82	-0.71	2.49
co2_hil_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	Homer, Illinois, United States	40.07°N	87.91°W	2537	226	6	0	0.1 - 5.6	0.98	-0.67	1.71
co2_hil_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	Homer, Illinois, United States	40.07°N	87.91°W	3501	399	7	0	0.4 - 3.3	1.16	-0.29	1.45
co2_hil_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	Homer, Illinois, United States	40.07°N	87.91°W	4542	259	3	0	0.2 - 2.7	1.24	-0.20	1.28
co2_hil_aircraft-pfp_1_allvalid_5000-6000masl	NOAA	Homer, Illinois, United States	40.07°N	87.91°W	5512	338	5	0	0.2 - 1.9	1.17	-0.14	1.19
co2_hil_aircraft-pfp_1_allvalid_6000-7000masl	NOAA	Homer, Illinois, United States	40.07°N	87.91°W	6517	292	6	0	0.3 - 2.3	1.17	-0.04	1.20
co2_hil_aircraft-pfp_1_allvalid_7000-8000masl	NOAA	Homer, Illinois, United States	40.07°N	87.91°W	7482	301	7	0	0.3 - 2.3	1.09	-0.06	1.18
co2_hil_aircraft-pfp_1_allvalid_8000-9000masl	NOAA	Homer, Illinois, United States	40.07°N	87.91°W	8043	28	0	0	0.5 - 3.9	1.03	-0.78	1.67
co2_hpb_surface-flask_1_representative	NOAA	Hohenpeissenberg, Germany	47.80°N	11.02°E	936	530	3	0	3.7 - 8.4	0.93	1.19	5.64
co2_hun_surface-flask_1_representative	NOAA	Hegyhatsal, Hungary	46.95°N	16.65°E	248	855	7	0	2.4 - 10.9	0.55	-1.78	5.59
co2_ice_surface-flask_1_representative	NOAA	Storhofdi, Vestmannaeyjar, Iceland	63.40°N	20.29°W	118	471	16	0	0.4 - 4.2	1.08	-0.38	1.10
co2_igr_tower-insitu_20_allvalid-24magl	NIES	Igrim, Russia	63.19°N	64.42°E	9	10672	236	0	3.4 - 7.2	0.77	-1.89	4.35
co2_igr_tower-insitu_20_allvalid-47magl	NIES	Igrim, Russia	63.19°N	64.42°E	9	10559	216	0	4.0 - 8.9	0.57	-1.73	5.77
co2_inu_surface-insitu_6_allvalid	EC	Inuvik,Northwest Territories, Canada	68.32°N	133.53°W	113	8769	236	0	2.5 - 6.5	0.61	-0.14	2.57
co2_inx_aircraft-pfp_1_allvalid_0-1000masl	NOAA	INFLUX (Indianapolis Flux Experiment), United States	39.58°N	86.42°W	654	170	9	0	1.4 - 7.6	1.00	-2.14	3.29
co2_inx_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	INFLUX (Indianapolis Flux Experiment), United States	39.58°N	86.42°W	1354	60	0	0	1.4 - 6.5	0.61	-0.67	2.15
co2_inx_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	INFLUX (Indianapolis Flux Experiment), United States	39.58°N	86.42°W	2501	23	0	0	1.4 - 4.2	0.37	0.25	1.41
co2_inx_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	INFLUX (Indianapolis Flux Experiment), United States	39.58°N	86.42°W	3226	12	0	0	1.4 - 6.5	0.11	0.10	0.81
co2_izo_surface-insitu_27_allvalid	AEMET	Izana, Tenerife, Canary Islands, Spain	28.31°N	16.50°W	2373	67486	2638	0	0.5 - 1.3	1.13	0.11	0.78
co2_jfj_surface-insitu_442_allvalid-5magl	ICOS-ATC	Jungfraujoch, Switzerland	46.55°N	7.99°E	3570	2927	10	0	1.4 - 5.0	0.26	0.11	1.55
co2_jfj_surface-insitu_49_allvalid	KUP	Jungfraujoch, Switzerland	46.55°N	7.99°E	3570	16294	336	0	1.5 - 5.0	0.71	0.07	2.01
co2_jfj_surface-insitu_5_allvalid	EMPA	Jungfraujoch, Switzerland	46.55°N	7.99°E	3570	1259	7	0	2.0 - 5.0	0.30	-0.03	1.86
co2_key_surface-flask_1_representative	NOAA	Key Biscayne, Florida, United States	25.67°N	80.16°W	1	604	1	0	1.1 - 4.3	0.46	0.54	1.60
co2_krs_tower-insitu_20_allvalid-35magl	NIES	Karasevoe, Russia	58.25°N	82.42°E	76	12564	373	0	1.9 - 7.3	0.89	-0.43	3.64
co2_krs_tower-insitu_20_allvalid-67magl	NIES	Karasevoe, Russia	58.25°N	82.42°E	76	11957	333	0	1.9 - 7.3	0.90	-0.48	3.60
co2_kum_surface-flask_1_representative	NOAA	Cape Kumukahi, Hawaii, United States	19.74°N	155.01°W	0	1069	25	0	0.2 - 2.4	0.75	-0.13	0.93
co2_kum_surface-flask_4_representative	SIO	Cape Kumukahi, Hawaii, United States	19.74°N	155.01°W	0	596	16	0	0.3 - 1.9	0.85	-0.07	1.06
co2_kum_surface-flask_426_representative	SIO_CO2	Cape Kumukahi, Hawaii, United States	19.74°N	155.01°W	0	670	33	0	0.1 - 1.9	0.95	-0.18	1.25
co2_kzd_surface-flask_1_representative	NOAA	Sary Taukum, Kazakhstan	44.08°N	76.87°E	595	415	1	0	0.7 - 11.7	0.62	-1.23	3.88
co2_kzm_surface-flask_1_representative	NOAA	Plateau Assy, Kazakhstan	43.25°N	77.88°E	2519	361	4	0	1.4 - 5.0	0.92	-0.37	2.65
co2_lef_aircraft-pfp_1_allvalid_0-1000masl	NOAA	Park Falls, Wisconsin, United States	45.95°N	90.27°W	774	599	17	0	1.0 - 8.7	0.76	-0.49	2.90
co2_lef_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	Park Falls, Wisconsin, United States	45.95°N	90.27°W	1522	1148	29	0	0.5 - 7.6	0.73	-0.41	2.63
co2_lef_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	Park Falls, Wisconsin, United States	45.95°N	90.27°W	2465	879	26	0	0.7 - 5.8	0.78	-0.34	2.05
co2_lef_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	Park Falls, Wisconsin, United States	45.95°N	90.27°W	3501	980	24	0	0.5 - 3.2	0.94	-0.13	1.65
co2_lef_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	Park Falls, Wisconsin, United States	45.95°N	90.27°W	4012	3	0	0	1.9 - 1.9	0.86	-0.84	2.13
co2_lef_tower-insitu_1_allvalid-11magl	NOAA	Park Falls, Wisconsin, United States	45.95°N	90.27°W	472	10638	229	0	2.2 - 7.7	0.69	0.01	3.32
co2_lef_tower-insitu_1_allvalid-122magl	NOAA	Park Falls, Wisconsin, United States	45.95°N	90.27°W	472	23173	649	0	2.2 - 6.9	0.75	0.09	3.35
co2_lef_tower-insitu_1_allvalid-244magl	NOAA	Park Falls, Wisconsin, United States	45.95°N	90.27°W	472	64322	1298	0	2.1 - 25.9	0.71	-0.24	3.48
co2_lef_tower-insitu_1_allvalid-30magl	NOAA	Park Falls, Wisconsin, United States	45.95°N	90.27°W	472	23166	600	0	2.3 - 7.4	0.74	0.21	3.55
co2_lef_tower-insitu_1_allvalid-396magl	NOAA	Park Falls, Wisconsin, United States	45.95°N	90.27°W	472	139509	3808	0	2.1 - 14.2	0.79	-0.10	3.32
co2_lef_tower-insitu_1_allvalid-76magl	NOAA	Park Falls, Wisconsin, United States	45.95°N	90.27°W	472	10549	235	0	2.2 - 7.6	0.68	-0.20	3.10
co2_lew_surface-pfp_1_allvalid-95magl	NOAA	Lewisburg, Pennsylvania, United States	40.94°N	76.88°W	161	528	16	0	3.3 - 8.1	0.73	-1.72	5.69
co2_ljo_surface-flask_426_representative	SIO_CO2	La Jolla, California, United States	32.87°N	117.26°W	10	389	18	0	0.9 - 11.7	1.25	3.81	3.88
co2_llb_surface-flask_1_representative	NOAA	Lac La Biche, Alberta, Canada	54.95°N	112.47°W	540	139	6	0	2.0 - 15.5	0.94	-0.80	4.76
co2_llb_surface-insitu_6_allvalid	EC	Lac La Biche, Alberta, Canada	54.95°N	112.47°W	540	11398	136	0	2.2 - 10.1	0.75	-0.29	3.56
co2_lmp_surface-flask_1_representative	NOAA	Lampedusa, Italy	35.52°N	12.62°E	45	482	4	0	1.0 - 3.3	0.84	0.03	1.64
co2_lut_surface-insitu_44_allvalid	RUG	Lutjewad, Netherlands	53.40°N	6.35°E	1	13312	289	0	3.8 - 14.0	0.63	-1.77	5.87
co2_maa_surface-flask_2_representative	CSIRO	Mawson Station, Antarctica, Australia	67.62°S	62.87°E	32	425	0	0	0.1 - 0.6	0.95	-0.04	0.23
co2_mbo_surface-pfp_1_allvalid-11magl	NOAA	Mt. Bachelor Observatory, United States	43.98°N	121.69°W	2731	1152	21	0	0.9 - 2.4	0.93	-0.32	1.48
co2_mex_surface-flask_1_representative	NOAA	High Altitude Global Climate Observation Center, Mexico	18.98°N	97.31°W	4464	338	3	0	0.8 - 3.1	1.06	0.85	1.42
co2_mhd_surface-flask_1_representative	NOAA	Mace Head, County Galway, Ireland	53.33°N	9.90°W	5	707	13	0	0.4 - 5.3	0.67	-0.34	1.18
co2_mhd_surface-insitu_11_representative	LSCE	Mace Head, County Galway, Ireland	53.33°N	9.90°W	5	34770	1628	0	0.6 - 2.8	1.18	-0.01	0.89
co2_mid_surface-flask_1_representative	NOAA	Sand Island, Midway, United States	28.21°N	177.38°W	11	793	20	0	0.4 - 1.7	1.24	0.39	1.02
co2_mkn_surface-flask_1_representative	NOAA	Mt. Kenya, Kenya	0.06°S	37.30°E	3644	127	0	0	0.5 - 3.4	1.12	1.68	1.97
co2_mlo_surface-flask_1_representative	NOAA	Mauna Loa, Hawaii, United States	19.54°N	155.58°W	3397	1237	45	0	0.3 - 1.1	0.94	0.12	0.53
co2_mlo_surface-flask_2_representative	CSIRO	Mauna Loa, Hawaii, United States	19.54°N	155.58°W	3397	576	11	0	0.3 - 2.9	0.83	0.20	0.57
co2_mlo_surface-flask_4_representative	SIO	Mauna Loa, Hawaii, United States	19.54°N	155.58°W	3397	664	20	0	0.2 - 1.4	1.01	0.29	0.58
co2_mlo_surface-flask_426_representative	SIO_CO2	Mauna Loa, Hawaii, United States	19.54°N	155.58°W	3397	855	42	0	0.2 - 1.4	1.16	0.20	0.56
co2_mlo_surface-insitu_1_allvalid	NOAA	Mauna Loa, Hawaii, United States	19.54°N	155.58°W	3397	35558	0	0	0.4 - 0.7	1.79	0.18	0.53
co2_mqa_surface-flask_2_representative	CSIRO	Macquarie Island, Australia	54.48°S	158.97°E	6	484	0	1	0.1 - 0.9	0.74	0.15	0.37
co2_mvy_surface-insitu_1_allvalid	NOAA	Marthas Vineyard, Massachusetts, United States	41.33°N	70.57°W	0	62937	0	0	2.4 - 9.6	1.31	0.11	4.49
co2_mwo_surface-pfp_1_allvalid-46magl	NOAA	Mt. Wilson Observatory, United States	34.22°N	118.06°W	1728	3094	69	0	1.4 - 15.8	0.73	-2.86	5.47
co2_nat_surface-flask_1_representative	NOAA	Farol De Mae Luiza Lighthouse, Brazil	5.80°S	35.19°W	50	280	1	0	0.9 - 1.7	0.86	-0.61	1.06
co2_nat_surface-flask_26_marine	IPEN	Farol De Mae Luiza Lighthouse, Brazil	5.80°S	35.19°W	50	191	0	0	0.8 - 1.9	0.84	-0.82	1.15
co2_nha_aircraft-pfp_1_allvalid_0-1000masl	NOAA	Offshore Portsmouth, New Hampshire (Isles of Shoals), United States	42.95°N	70.63°W	610	697	35	0	1.3 - 6.6	1.00	0.00	2.77
co2_nha_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	Offshore Portsmouth, New Hampshire (Isles of Shoals), United States	42.95°N	70.63°W	1489	576	19	0	0.6 - 5.0	0.94	-0.09	2.17
co2_nha_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	Offshore Portsmouth, New Hampshire (Isles of Shoals), United States	42.95°N	70.63°W	2410	550	11	0	0.6 - 4.7	0.96	-0.24	1.92
co2_nha_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	Offshore Portsmouth, New Hampshire (Isles of Shoals), United States	42.95°N	70.63°W	3472	452	11	0	0.5 - 4.4	1.15	0.08	1.41
co2_nha_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	Offshore Portsmouth, New Hampshire (Isles of Shoals), United States	42.95°N	70.63°W	4415	277	9	0	0.3 - 2.4	1.29	0.08	1.19
co2_nha_aircraft-pfp_1_allvalid_5000-6000masl	NOAA	Offshore Portsmouth, New Hampshire (Isles of Shoals), United States	42.95°N	70.63°W	5378	325	9	0	0.3 - 2.9	1.12	-0.01	1.34
co2_nha_aircraft-pfp_1_allvalid_6000-7000masl	NOAA	Offshore Portsmouth, New Hampshire (Isles of Shoals), United States	42.95°N	70.63°W	6320	238	5	0	0.3 - 3.8	1.15	-0.04	1.50
co2_nha_aircraft-pfp_1_allvalid_7000-8000masl	NOAA	Offshore Portsmouth, New Hampshire (Isles of Shoals), United States	42.95°N	70.63°W	7664	245	2	0	0.5 - 2.1	1.33	0.30	1.08
co2_nha_aircraft-pfp_1_allvalid_8000-9000masl	NOAA	Offshore Portsmouth, New Hampshire (Isles of Shoals), United States	42.95°N	70.63°W	8040	5	0	0	0.5 - 0.7	0.25	-0.21	0.45
co2_nmb_surface-flask_1_representative	NOAA	Gobabeb, Namibia	23.58°S	15.03°E	456	411	5	0	0.5 - 3.6	0.82	0.04	0.97
co2_noy_tower-insitu_20_allvalid-21magl	NIES	Noyabrsk, Russia	63.43°N	75.78°E	108	8761	238	0	1.9 - 8.9	0.78	-0.11	3.21
co2_noy_tower-insitu_20_allvalid-43magl	NIES	Noyabrsk, Russia	63.43°N	75.78°E	108	9143	221	0	1.9 - 8.9	0.74	-0.23	3.13
co2_nwr_surface-flask_1_representative	NOAA	Niwot Ridge, Colorado, United States	40.05°N	105.59°W	3523	820	1	0	0.6 - 7.1	0.56	0.33	1.20
co2_nwr_surface-insitu_3_nonlocal	NCAR	Niwot Ridge, Colorado, United States	40.05°N	105.59°W	3523	63448	1603	0	0.8 - 4.6	0.89	-0.07	1.40
co2_nwr_surface-pfp_1_allvalid-3magl	NOAA	Niwot Ridge, Colorado, United States	40.05°N	105.59°W	3523	2904	50	0	0.6 - 8.5	0.58	0.19	1.79
co2_obn_surface-flask_1_representative	NOAA	Obninsk, Russia	55.11°N	36.60°E	183	124	1	0	1.8 - 13.8	0.76	-0.73	5.74
co2_ofr_surface-insitu_68_allhours	OSU	Fir, Oregon, United States	44.65°N	123.55°W	263	58178	15	0	3.6 - 60.2	0.16	-0.97	6.80
co2_omp_surface-insitu_68_allhours	OSU	Marys Peak, Oregon, United States	44.50°N	123.55°W	1249	104785	987	0	1.2 - 21.1	0.56	0.71	2.92
co2_omt_surface-insitu_68_allhours	OSU	Metolius, Oregon, United States	44.45°N	121.56°W	1255	78534	539	0	1.9 - 18.4	0.41	-0.24	3.76
co2_ong_surface-insitu_68_allhours	OSU	Burns, Oregon, United States	43.47°N	119.69°W	1398	85276	454	0	1.3 - 30.8	0.35	-0.08	3.41
co2_owa_surface-insitu_68_allhours	OSU	Walton, Oregon, United States	44.07°N	123.63°W	715	39357	496	0	2.2 - 7.4	0.82	-1.06	3.35
co2_oxk_surface-flask_1_representative	NOAA	Ochsenkopf, Germany	50.03°N	11.81°E	1022	456	6	0	0.9 - 6.7	0.86	-0.97	3.90
co2_oyq_surface-insitu_68_allhours	OSU	Yaquina Head, Oregon, United States	44.67°N	124.07°W	116	35231	18	0	2.5 - 33.9	0.20	-1.71	4.30
co2_pal_surface-flask_1_representative	NOAA	Pallas-Sammaltunturi, GAW Station, Finland	67.97°N	24.12°E	565	686	2	0	0.8 - 18.6	0.44	-0.38	2.48
co2_pal_surface-insitu_30_marine	FMI	Pallas-Sammaltunturi, GAW Station, Finland	67.97°N	24.12°E	565	22915	152	0	1.1 - 6.0	0.59	-0.14	1.14
co2_pal_surface-insitu_30_nonlocal	FMI	Pallas-Sammaltunturi, GAW Station, Finland	67.97°N	24.12°E	565	96286	719	0	1.6 - 9.4	0.55	-0.17	1.96
co2_pfa_aircraft-pfp_1_allvalid_0-1000masl	NOAA	Poker Flat, Alaska, United States	64.90°N	148.76°W	550	483	23	0	0.7 - 10.9	0.82	-0.68	3.20
co2_pfa_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	Poker Flat, Alaska, United States	64.90°N	148.76°W	1510	555	22	0	0.6 - 6.2	0.98	-0.31	1.63
co2_pfa_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	Poker Flat, Alaska, United States	64.90°N	148.76°W	2508	644	20	0	0.3 - 3.6	1.05	-0.30	1.17
co2_pfa_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	Poker Flat, Alaska, United States	64.90°N	148.76°W	3471	641	18	0	0.3 - 2.6	1.11	-0.03	1.12
co2_pfa_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	Poker Flat, Alaska, United States	64.90°N	148.76°W	4509	568	36	0	0.2 - 2.3	1.20	0.07	1.06
co2_pfa_aircraft-pfp_1_allvalid_5000-6000masl	NOAA	Poker Flat, Alaska, United States	64.90°N	148.76°W	5443	513	17	0	0.3 - 2.4	1.17	0.02	1.17
co2_pfa_aircraft-pfp_1_allvalid_6000-7000masl	NOAA	Poker Flat, Alaska, United States	64.90°N	148.76°W	6449	488	15	0	0.5 - 2.5	1.28	0.19	1.27
co2_pfa_aircraft-pfp_1_allvalid_7000-8000masl	NOAA	Poker Flat, Alaska, United States	64.90°N	148.76°W	7205	206	3	0	0.3 - 2.8	1.26	0.17	1.58
co2_poc_shipboard-flask_1_representative	NOAA	Pacific Ocean	variable		Surface	1997	105	11	0.1 - 3.3	1.01	-0.04	0.70
co2_prs_surface-insitu_21_allvalid	RSE	Plateau Rosa Station, Italy	45.93°N	7.70°E	3480	12689	331	0	1.2 - 3.2	0.93	0.10	1.59
co2_psa_surface-flask_1_representative	NOAA	Palmer Station, Antarctica, United States	64.92°S	64.00°W	10	843	0	0	0.1 - 0.8	0.59	-0.08	0.28
co2_psa_surface-flask_4_representative	SIO	Palmer Station, Antarctica, United States	64.92°S	64.00°W	10	380	11	0	0.2 - 1.1	0.57	0.07	0.33
co2_pta_surface-flask_1_representative	NOAA	Point Arena, California, United States	38.95°N	123.74°W	17	374	2	0	2.8 - 11.4	0.56	-1.88	5.18
co2_px_shipboard-insitu_20_allvalid	NIES	Pyxis (M/S Pyxis of Toyofuji Shipping Co., Ltd.)	variable		Surface	337437	21443	93	0.0 - 55.7	0.96	0.02	1.52
co2_rba-b_aircraft-pfp_26_representative_0-1000masl	IPEN	Rio Branco, Brazil	9.36°S	67.60°W	616	153	2	0	0.9 - 13.3	0.66	-0.53	5.27
co2_rba-b_aircraft-pfp_26_representative_1000-2000masl	IPEN	Rio Branco, Brazil	9.36°S	67.60°W	1522	171	5	0	0.4 - 7.5	0.85	-0.75	2.72
co2_rba-b_aircraft-pfp_26_representative_2000-3000masl	IPEN	Rio Branco, Brazil	9.36°S	67.60°W	2368	108	1	0	0.6 - 5.9	1.00	-0.49	1.68
co2_rba-b_aircraft-pfp_26_representative_3000-4000masl	IPEN	Rio Branco, Brazil	9.36°S	67.60°W	3501	165	7	0	0.4 - 2.0	0.98	-0.29	1.32
co2_rba-b_aircraft-pfp_26_representative_4000-5000masl	IPEN	Rio Branco, Brazil	9.36°S	67.60°W	4420	47	4	0	0.5 - 1.9	1.06	-0.15	1.23
co2_rk1_surface-flask_426_representative	SIO_CO2	Kermadec Island, Raoul Island	29.20°S	177.90°W	2	76	2	0	0.3 - 1.4	0.95	-0.14	0.64
co2_rpb_surface-flask_1_representative	NOAA	Ragged Point, Barbados	13.16°N	59.43°W	15	828	26	0	0.3 - 1.2	1.02	0.09	0.68
co2_rta_aircraft-pfp_1_allvalid_0-1000masl	NOAA	Rarotonga, Cook Islands	21.25°S	159.83°W	644	106	1	1	0.4 - 1.2	0.53	0.19	0.46
co2_rta_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	Rarotonga, Cook Islands	21.25°S	159.83°W	1641	304	2	2	0.2 - 0.7	1.13	-0.04	0.46
co2_rta_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	Rarotonga, Cook Islands	21.25°S	159.83°W	2557	307	3	1	0.3 - 0.8	1.19	-0.08	0.50
co2_rta_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	Rarotonga, Cook Islands	21.25°S	159.83°W	3482	416	6	0	0.2 - 0.9	1.23	-0.12	0.56
co2_rta_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	Rarotonga, Cook Islands	21.25°S	159.83°W	4547	321	5	0	0.3 - 0.7	1.30	0.01	0.57
co2_rta_aircraft-pfp_1_allvalid_5000-6000masl	NOAA	Rarotonga, Cook Islands	21.25°S	159.83°W	5474	326	5	0	0.2 - 0.8	1.27	-0.06	0.57
co2_rta_aircraft-pfp_1_allvalid_6000-7000masl	NOAA	Rarotonga, Cook Islands	21.25°S	159.83°W	6319	182	6	0	0.2 - 1.2	1.21	0.02	0.70
co2_san_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	Santarem, Brazil	2.85°S	54.95°W	1702	10	0	0	0.8 - 3.5	1.18	-1.95	2.18
co2_san_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	Santarem, Brazil	2.85°S	54.95°W	2527	52	0	0	0.3 - 2.8	0.98	-0.10	1.45
co2_san_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	Santarem, Brazil	2.85°S	54.95°W	3424	76	0	0	0.4 - 1.8	1.34	0.34	1.07
co2_san_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	Santarem, Brazil	2.85°S	54.95°W	4600	6	0	0	1.6 - 1.7	0.25	0.11	0.89
co2_sca_aircraft-pfp_1_allvalid_0-1000masl	NOAA	Offshore Charleston, South Carolina, United States	32.77°N	79.55°W	648	284	3	0	1.2 - 4.9	1.02	-0.62	2.61
co2_sca_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	Offshore Charleston, South Carolina, United States	32.77°N	79.55°W	1538	220	5	0	0.5 - 3.2	1.03	0.12	1.86
co2_sca_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	Offshore Charleston, South Carolina, United States	32.77°N	79.55°W	2491	492	10	0	0.4 - 2.4	0.95	0.12	1.32
co2_sca_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	Offshore Charleston, South Carolina, United States	32.77°N	79.55°W	3519	314	6	0	0.2 - 3.0	1.21	0.30	1.10
co2_sca_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	Offshore Charleston, South Carolina, United States	32.77°N	79.55°W	4455	433	10	0	0.1 - 2.3	1.21	0.20	1.04
co2_sca_aircraft-pfp_1_allvalid_5000-6000masl	NOAA	Offshore Charleston, South Carolina, United States	32.77°N	79.55°W	5496	266	7	0	0.3 - 2.0	1.22	0.20	0.84
co2_sca_aircraft-pfp_1_allvalid_6000-7000masl	NOAA	Offshore Charleston, South Carolina, United States	32.77°N	79.55°W	6414	297	13	0	0.2 - 1.5	1.20	0.22	0.83
co2_sca_aircraft-pfp_1_allvalid_7000-8000masl	NOAA	Offshore Charleston, South Carolina, United States	32.77°N	79.55°W	7452	375	15	0	0.1 - 1.5	1.15	0.20	0.76
co2_sca_aircraft-pfp_1_allvalid_8000-9000masl	NOAA	Offshore Charleston, South Carolina, United States	32.77°N	79.55°W	8113	85	2	0	0.3 - 1.8	0.98	0.30	1.31
co2_sca_aircraft-pfp_1_allvalid_9000-10000masl	NOAA	Offshore Charleston, South Carolina, United States	32.77°N	79.55°W	9362	17	0	0	0.3 - 4.8	1.47	-0.21	2.24
co2_sca_aircraft-pfp_1_allvalid_10000-11000masl	NOAA	Offshore Charleston, South Carolina, United States	32.77°N	79.55°W	10432	18	0	0	0.1 - 5.0	1.30	-0.31	1.94
co2_sca_aircraft-pfp_1_allvalid_11000-12000masl	NOAA	Offshore Charleston, South Carolina, United States	32.77°N	79.55°W	11160	7	0	0	0.6 - 5.4	0.82	-1.01	3.20
co2_sca_aircraft-pfp_1_allvalid_12000-13000masl	NOAA	Offshore Charleston, South Carolina, United States	32.77°N	79.55°W	12636	15	0	0	0.3 - 1.8	1.45	0.43	0.89
co2_sca_aircraft-pfp_1_allvalid_13000-14000masl	NOAA	Offshore Charleston, South Carolina, United States	32.77°N	79.55°W	13145	4	0	0	0.8 - 0.8	0.90	0.16	0.86
co2_sct_surface-pfp_1_allvalid-305magl	NOAA	Beech Island, South Carolina, United States	33.41°N	81.83°W	115	2067	1	0	2.5 - 12.9	0.28	-0.81	3.57
co2_sct_tower-insitu_1_allvalid-305magl	NOAA	Beech Island, South Carolina, United States	33.41°N	81.83°W	115	82658	1209	0	2.7 - 17.4	0.71	-0.30	4.92
co2_sct_tower-insitu_1_allvalid-31magl	NOAA	Beech Island, South Carolina, United States	33.41°N	81.83°W	115	13622	186	0	3.3 - 6.2	0.61	-0.34	4.15
co2_sct_tower-insitu_1_allvalid-61magl	NOAA	Beech Island, South Carolina, United States	33.41°N	81.83°W	115	13699	184	0	3.2 - 5.5	0.63	-0.52	4.01
co2_sey_surface-flask_1_representative	NOAA	Mahe Island, Seychelles	4.68°S	55.53°E	2	758	0	0	0.4 - 1.2	1.21	-0.03	0.75
co2_sgp_aircraft-pfp_1_allvalid_0-1000masl	NOAA	Southern Great Plains, Oklahoma, United States	36.61°N	97.49°W	683	1049	8	0	1.0 - 19.8	0.80	-0.03	2.83
co2_sgp_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	Southern Great Plains, Oklahoma, United States	36.61°N	97.49°W	1572	1327	26	0	0.8 - 6.8	0.79	0.07	2.13
co2_sgp_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	Southern Great Plains, Oklahoma, United States	36.61°N	97.49°W	2460	1192	24	0	0.6 - 3.2	0.84	-0.25	1.42
co2_sgp_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	Southern Great Plains, Oklahoma, United States	36.61°N	97.49°W	3465	1002	18	0	0.6 - 2.1	0.94	-0.16	1.05
co2_sgp_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	Southern Great Plains, Oklahoma, United States	36.61°N	97.49°W	4676	538	7	0	0.4 - 1.9	1.07	-0.13	0.94
co2_sgp_aircraft-pfp_1_allvalid_5000-6000masl	NOAA	Southern Great Plains, Oklahoma, United States	36.61°N	97.49°W	5395	242	6	0	0.4 - 3.2	1.24	0.01	0.91
co2_sgp_aircraft-pfp_1_allvalid_6000-7000masl	NOAA	Southern Great Plains, Oklahoma, United States	36.61°N	97.49°W	6464	8	0	0	0.8 - 0.8	1.15	0.04	0.59
co2_sgp_aircraft-pfp_1_allvalid_8000-9000masl	NOAA	Southern Great Plains, Oklahoma, United States	36.61°N	97.49°W	8062	2	0	0	0.9 - 0.9	1.02	-0.14	1.90
co2_sgp_aircraft-pfp_1_allvalid_9000-10000masl	NOAA	Southern Great Plains, Oklahoma, United States	36.61°N	97.49°W	9686	6	2	0	0.6 - 0.6	2.06	-0.47	0.65
co2_sgp_aircraft-pfp_1_allvalid_11000-12000masl	NOAA	Southern Great Plains, Oklahoma, United States	36.61°N	97.49°W	11321	2	0	0	0.5 - 0.5	0.65	-0.21	0.91
co2_sgp_aircraft-pfp_1_allvalid_12000-13000masl	NOAA	Southern Great Plains, Oklahoma, United States	36.61°N	97.49°W	12858	4	0	0	0.4 - 0.4	1.86	-0.24	0.52
co2_sgp_surface-flask_1_representative	NOAA	Southern Great Plains, Oklahoma, United States	36.61°N	97.49°W	314	723	7	0	1.9 - 12.0	0.53	-0.80	3.47
co2_sgp_surface-insitu_64_allvalid-60magl	LBNL-ARM	Southern Great Plains, Oklahoma, United States	36.61°N	97.49°W	314	20998	303	0	2.7 - 16.9	0.67	-0.21	4.31
co2_shm_surface-flask_1_representative	NOAA	Shemya Island, Alaska, United States	52.71°N	174.13°E	23	619	13	0	0.8 - 4.2	1.06	-0.41	1.76
co2_sis_surface-flask_2_representative	CSIRO	Shetland Islands, Scotland	60.09°N	1.25°W	30	83	4	0	0.2 - 2.2	1.49	0.53	1.13
co2_smo_surface-flask_1_representative	NOAA	Tutuila, American Samoa	14.25°S	170.56°W	42	1410	15	2	0.2 - 1.7	0.68	-0.06	0.41
co2_smo_surface-flask_4_representative	SIO	Tutuila, American Samoa	14.25°S	170.56°W	42	513	2	1	0.1 - 6.1	0.43	0.02	0.60
co2_smo_surface-flask_426_representative	SIO_CO2	Tutuila, American Samoa	14.25°S	170.56°W	42	608	13	0	0.2 - 6.0	0.99	-0.04	0.53
co2_smo_surface-insitu_1_allvalid	NOAA	Tutuila, American Samoa	14.25°S	170.56°W	42	38415	0	0	0.2 - 0.9	1.44	0.01	0.33
co2_spl_surface-insitu_3_nonlocal	NCAR	Storm Peak Laboratory (Desert Research Institute), United States	40.45°N	106.73°W	3210	66974	1387	0	1.0 - 2.8	0.98	-0.62	1.59
co2_spo_surface-flask_1_representative	NOAA	South Pole, Antarctica, United States	89.98°S	24.80°W	2810	910	3	1	0.1 - 0.9	0.45	0.10	0.14
co2_spo_surface-flask_4_representative	SIO	South Pole, Antarctica, United States	89.98°S	24.80°W	2810	434	1	0	0.1 - 1.1	0.40	0.11	0.36
co2_spo_surface-flask_426_representative	SIO_CO2	South Pole, Antarctica, United States	89.98°S	24.80°W	2810	379	1	1	0.1 - 1.1	0.44	0.10	0.22
co2_spo_surface-insitu_1_allvalid	NOAA	South Pole, Antarctica, United States	89.98°S	24.80°W	2810	49375	2	0	0.1 - 0.4	1.28	0.01	0.09
co2_stm_surface-flask_1_representative	NOAA	Ocean Station M, Norway	66.00°N	2.00°E	0	790	11	0	0.3 - 5.7	0.86	-0.23	1.26
co2_sum_surface-flask_1_representative	NOAA	Summit, Greenland	72.60°N	38.42°W	3210	747	34	0	0.4 - 1.6	1.21	-0.04	0.78
co2_svv_tower-insitu_20_allvalid-27magl	NIES	Savvushka, Russia	51.33°N	82.13°E	495	7753	64	0	1.8 - 5.9	0.75	-0.60	3.38
co2_svv_tower-insitu_20_allvalid-52magl	NIES	Savvushka, Russia	51.33°N	82.13°E	495	7330	70	0	1.8 - 5.8	0.80	-0.55	3.44
co2_syo_surface-flask_1_representative	NOAA	Syowa Station, Antarctica, Japan	69.01°S	39.59°E	14	407	0	0	0.0 - 0.3	1.01	-0.04	0.17
co2_tab_aircraft-pfp_26_representative_0-1000masl	IPEN	Tabatinga, Brazil	5.95°S	70.07°W	681	100	3	0	0.8 - 26.7	0.79	-1.30	6.54
co2_tab_aircraft-pfp_26_representative_1000-2000masl	IPEN	Tabatinga, Brazil	5.95°S	70.07°W	1519	123	1	0	1.0 - 17.8	0.85	-0.39	2.33
co2_tab_aircraft-pfp_26_representative_2000-3000masl	IPEN	Tabatinga, Brazil	5.95°S	70.07°W	2394	71	1	0	0.4 - 8.7	0.74	-0.30	1.36
co2_tab_aircraft-pfp_26_representative_3000-4000masl	IPEN	Tabatinga, Brazil	5.95°S	70.07°W	3499	121	0	0	0.5 - 2.0	0.68	-0.12	1.04
co2_tab_aircraft-pfp_26_representative_4000-5000masl	IPEN	Tabatinga, Brazil	5.95°S	70.07°W	4420	35	0	0	0.6 - 2.3	0.85	0.10	0.80
co2_tap_surface-flask_1_representative	NOAA	Tae-ahn Peninsula, Republic of Korea	36.74°N	126.13°E	16	728	14	0	1.4 - 15.1	0.85	-0.87	4.81
co2_tf1_shipboard-insitu_20_allvalid	NIES	Trans Future 1 (M/S Trans Future 1 of the Toyofuji Shipping Co., Ltd)	variable		Surface	80437	1253	25	0.4 - 68.3	0.56	-0.82	3.35
co2_tf5_shipboard-insitu_20_allvalid	NIES	Trans Future 5 (M/S Trans Future 5 of Toyofuji Shipping Co., Ltd.)	variable		Surface	362308	7298	100	0.2 - 69.5	0.55	-0.05	1.85
co2_tgc_aircraft-pfp_1_allvalid_0-1000masl	NOAA	Offshore Corpus Christi, Texas, United States	27.73°N	96.86°W	686	186	5	0	0.5 - 6.0	0.64	-0.09	2.03
co2_tgc_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	Offshore Corpus Christi, Texas, United States	27.73°N	96.86°W	1554	200	5	0	0.4 - 3.4	0.92	0.17	1.47
co2_tgc_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	Offshore Corpus Christi, Texas, United States	27.73°N	96.86°W	2518	425	6	0	0.4 - 2.8	0.94	0.06	1.05
co2_tgc_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	Offshore Corpus Christi, Texas, United States	27.73°N	96.86°W	3488	243	6	0	0.3 - 1.5	1.05	0.09	0.78
co2_tgc_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	Offshore Corpus Christi, Texas, United States	27.73°N	96.86°W	4460	418	6	0	0.3 - 1.3	1.08	0.04	0.76
co2_tgc_aircraft-pfp_1_allvalid_5000-6000masl	NOAA	Offshore Corpus Christi, Texas, United States	27.73°N	96.86°W	5548	211	6	0	0.2 - 1.0	1.13	0.15	0.67
co2_tgc_aircraft-pfp_1_allvalid_6000-7000masl	NOAA	Offshore Corpus Christi, Texas, United States	27.73°N	96.86°W	6412	235	5	0	0.3 - 1.1	1.24	0.22	0.64
co2_tgc_aircraft-pfp_1_allvalid_7000-8000masl	NOAA	Offshore Corpus Christi, Texas, United States	27.73°N	96.86°W	7438	319	8	0	0.1 - 1.1	1.19	0.20	0.64
co2_tgc_aircraft-pfp_1_allvalid_8000-9000masl	NOAA	Offshore Corpus Christi, Texas, United States	27.73°N	96.86°W	8056	72	0	0	0.3 - 1.1	1.54	0.39	0.77
co2_thd_aircraft-pfp_1_allvalid_0-1000masl	NOAA	Trinidad Head, California, United States	41.05°N	124.15°W	621	347	2	0	1.8 - 12.4	0.57	-1.62	5.57
co2_thd_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	Trinidad Head, California, United States	41.05°N	124.15°W	1533	199	7	0	0.4 - 2.3	1.32	0.11	1.38
co2_thd_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	Trinidad Head, California, United States	41.05°N	124.15°W	2475	348	17	0	0.3 - 2.5	1.21	0.06	1.27
co2_thd_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	Trinidad Head, California, United States	41.05°N	124.15°W	3517	239	18	0	0.1 - 3.9	1.23	0.22	1.40
co2_thd_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	Trinidad Head, California, United States	41.05°N	124.15°W	4444	282	12	0	0.1 - 4.6	1.09	0.23	1.40
co2_thd_aircraft-pfp_1_allvalid_5000-6000masl	NOAA	Trinidad Head, California, United States	41.05°N	124.15°W	5484	189	7	0	0.2 - 2.1	1.36	0.14	1.21
co2_thd_aircraft-pfp_1_allvalid_6000-7000masl	NOAA	Trinidad Head, California, United States	41.05°N	124.15°W	6454	235	11	0	0.3 - 2.1	1.28	0.14	1.01
co2_thd_aircraft-pfp_1_allvalid_7000-8000masl	NOAA	Trinidad Head, California, United States	41.05°N	124.15°W	7470	234	7	0	0.2 - 2.0	1.12	0.14	1.03
co2_thd_aircraft-pfp_1_allvalid_8000-9000masl	NOAA	Trinidad Head, California, United States	41.05°N	124.15°W	8043	19	0	0	0.7 - 2.0	0.91	0.12	1.29
co2_thd_surface-flask_1_representative	NOAA	Trinidad Head, California, United States	41.05°N	124.15°W	107	629	3	0	1.6 - 26.3	0.49	-2.05	6.07
co2_tik_surface-flask_1_representative	NOAA	Hydrometeorological Observatory of Tiksi, Russia	71.60°N	128.89°E	19	286	5	0	0.6 - 8.9	0.80	0.16	3.74
co2_ulb_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	Ulaanbaatar, Mongolia	47.40°N	106.00°E	1678	128	6	0	0.4 - 5.7	0.96	-0.21	1.74
co2_ulb_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	Ulaanbaatar, Mongolia	47.40°N	106.00°E	2471	149	3	0	0.4 - 5.6	1.19	-0.11	1.54
co2_ulb_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	Ulaanbaatar, Mongolia	47.40°N	106.00°E	3477	155	5	0	0.2 - 3.6	1.06	-0.32	1.44
co2_ulb_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	Ulaanbaatar, Mongolia	47.40°N	106.00°E	4209	58	0	0	0.2 - 1.8	1.18	-0.23	1.16
co2_ulb_aircraft-pfp_1_allvalid_5000-6000masl	NOAA	Ulaanbaatar, Mongolia	47.40°N	106.00°E	5718	2	0	0	0.4 - 0.4	1.72	0.46	0.15
co2_ush_surface-flask_1_representative	NOAA	Ushuaia, Argentina	54.85°S	68.31°W	12	289	1	0	0.3 - 1.4	0.51	-0.26	0.51
co2_uta_surface-flask_1_representative	NOAA	Wendover, Utah, United States	39.90°N	113.72°W	1327	787	5	0	0.9 - 7.2	0.86	0.60	2.45
co2_uum_surface-flask_1_representative	NOAA	Ulaan Uul, Mongolia	44.45°N	111.10°E	1007	818	8	0	2.2 - 5.8	0.84	-0.29	3.28
co2_vgn_tower-insitu_20_allvalid-42magl	NIES	Vaganovo, Russia	54.50°N	62.32°E	192	9524	232	0	2.2 - 6.3	0.80	0.03	3.28
co2_vgn_tower-insitu_20_allvalid-85magl	NIES	Vaganovo, Russia	54.50°N	62.32°E	192	9306	225	0	2.3 - 6.4	0.81	-0.03	3.51
co2_wbi_aircraft-pfp_1_allvalid_0-1000masl	NOAA	West Branch, Iowa, United States	41.72°N	91.35°W	630	136	3	0	1.4 - 17.8	0.84	-0.73	4.07
co2_wbi_aircraft-pfp_1_allvalid_1000-2000masl	NOAA	West Branch, Iowa, United States	41.72°N	91.35°W	1554	342	10	0	0.7 - 7.9	0.81	-0.71	2.86
co2_wbi_aircraft-pfp_1_allvalid_2000-3000masl	NOAA	West Branch, Iowa, United States	41.72°N	91.35°W	2552	194	8	0	0.4 - 8.3	0.89	-0.53	1.93
co2_wbi_aircraft-pfp_1_allvalid_3000-4000masl	NOAA	West Branch, Iowa, United States	41.72°N	91.35°W	3514	345	8	0	0.4 - 2.5	0.94	-0.04	1.15
co2_wbi_aircraft-pfp_1_allvalid_4000-5000masl	NOAA	West Branch, Iowa, United States	41.72°N	91.35°W	4544	225	10	0	0.2 - 2.6	1.17	0.00	1.08
co2_wbi_aircraft-pfp_1_allvalid_5000-6000masl	NOAA	West Branch, Iowa, United States	41.72°N	91.35°W	5518	295	8	0	0.4 - 2.1	1.09	0.07	1.13
co2_wbi_aircraft-pfp_1_allvalid_6000-7000masl	NOAA	West Branch, Iowa, United States	41.72°N	91.35°W	6506	265	3	0	0.2 - 2.1	1.22	-0.04	1.03
co2_wbi_aircraft-pfp_1_allvalid_7000-8000masl	NOAA	West Branch, Iowa, United States	41.72°N	91.35°W	7487	273	6	0	0.2 - 1.9	1.23	0.07	0.99
co2_wbi_aircraft-pfp_1_allvalid_8000-9000masl	NOAA	West Branch, Iowa, United States	41.72°N	91.35°W	8051	28	1	0	0.5 - 1.5	1.14	-0.09	0.99
co2_wbi_surface-pfp_1_allvalid-379magl	NOAA	West Branch, Iowa, United States	41.72°N	91.35°W	242	2376	5	0	3.1 - 30.3	0.39	-0.76	4.61
co2_wbi_tower-insitu_1_allvalid-31magl	NOAA	West Branch, Iowa, United States	41.72°N	91.35°W	242	14464	291	0	3.5 - 11.1	0.74	-0.18	5.32
co2_wbi_tower-insitu_1_allvalid-379magl	NOAA	West Branch, Iowa, United States	41.72°N	91.35°W	242	87436	2424	0	2.7 - 16.8	0.85	-0.51	4.76
co2_wbi_tower-insitu_1_allvalid-99magl	NOAA	West Branch, Iowa, United States	41.72°N	91.35°W	242	14579	285	0	3.4 - 10.8	0.74	-0.28	5.18
co2_wgc_tower-insitu_1_allvalid-30magl	NOAA	Walnut Grove, California, United States	38.27°N	121.49°W	0	14682	259	0	3.4 - 12.2	0.75	-2.43	7.81
co2_wgc_tower-insitu_1_allvalid-483magl	NOAA	Walnut Grove, California, United States	38.27°N	121.49°W	0	88734	1291	0	2.6 - 14.1	0.65	0.73	5.65
co2_wgc_tower-insitu_1_allvalid-91magl	NOAA	Walnut Grove, California, United States	38.27°N	121.49°W	0	14800	241	0	3.2 - 11.8	0.72	-2.58	7.42
co2_wis_surface-flask_1_representative	NOAA	Weizmann Institute of Science at the Arava Institute, Ketura, Israel	29.96°N	35.06°E	151	851	6	0	0.9 - 4.8	0.94	-0.63	2.19
co2_wkt_tower-insitu_1_allvalid-122magl	NOAA	Moody, Texas, United States	31.31°N	97.33°W	251	18976	411	0	2.6 - 4.4	0.63	-0.81	3.31
co2_wkt_tower-insitu_1_allvalid-244magl	NOAA	Moody, Texas, United States	31.31°N	97.33°W	251	15185	248	0	2.3 - 6.1	0.82	-1.02	3.88
co2_wkt_tower-insitu_1_allvalid-30magl	NOAA	Moody, Texas, United States	31.31°N	97.33°W	251	19395	380	0	2.8 - 4.7	0.62	-0.58	3.45
co2_wkt_tower-insitu_1_allvalid-457magl	NOAA	Moody, Texas, United States	31.31°N	97.33°W	251	112506	2548	0	2.4 - 5.4	0.76	-0.55	3.22
co2_wkt_tower-insitu_1_allvalid-62magl	NOAA	Moody, Texas, United States	31.31°N	97.33°W	251	2491	41	0	2.3 - 5.6	0.69	-0.81	3.24
co2_wkt_tower-insitu_1_allvalid-9magl	NOAA	Moody, Texas, United States	31.31°N	97.33°W	251	2880	55	0	2.7 - 7.3	0.76	-0.69	4.11
co2_wlg_surface-flask_1_representative	NOAA	Mt. Waliguan, Peoples Republic of China	36.29°N	100.90°E	3810	723	4	1	1.4 - 5.3	0.67	-0.24	2.41
co2_wpc_shipboard-flask_1_representative	NOAA	Western Pacific Cruise	variable		Surface	176	3	0	0.1 - 1.7	1.16	-0.15	0.68
co2_wsa_surface-insitu_6_allvalid	EC	Sable Island, Nova Scotia, Canada	43.93°N	60.01°W	5	14888	312	0	1.9 - 4.2	0.76	0.02	2.23
co2_yak_tower-insitu_20_allvalid-11magl	NIES	Yakutsk, Russia	62.09°N	129.36°E	264	4968	94	0	2.5 - 8.8	0.93	-0.78	5.73
co2_yak_tower-insitu_20_allvalid-77magl	NIES	Yakutsk, Russia	62.09°N	129.36°E	264	5076	94	0	2.5 - 8.2	0.91	-0.23	5.39
co2_zep_surface-flask_1_representative	NOAA	Ny-Alesund, Svalbard, Norway and Sweden	78.91°N	11.89°E	474	871	35	0	0.2 - 2.6	0.99	0.04	0.98
co2_zep_surface-insitu_442_allvalid-15magl	ICOS-ATC	Ny-Alesund, Svalbard, Norway and Sweden	78.91°N	11.89°E	474	2178	70	0	0.3 - 1.8	0.77	0.07	0.92
co2_zep_surface-insitu_56_allvalid	NILU	Ny-Alesund, Svalbard, Norway and Sweden	78.91°N	11.89°E	474	1128	126	0	0.3 - 1.8	1.24	0.39	1.06

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File translated from T_EX by T_TH, version 4.04.
On 12 Nov 2020, 15:45.