Long-term global CO2 fluxes estimated by NICAM-based Inverse Simulation for Monitoring CO2 (NISMON-CO2)

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This dataset contains global CO2 fluxes estimated by NICAM-based Inverse Simulation for Monitoring CO2 (NISMON-CO2). Spatiotemporal variations of the CO2 fluxes at the Earth's surface are constrained by observations of atmospheric CO2 mole fractions. The analyzed (posterior) fluxes are derived by an optimization calculation of the four-dimensional variational method coupled with the atmospheric transport model NICAM-TM. The analysis period is set long-term so that one can investigate not only the seasonal cycles but also the interannual variations of CO2 fluxes.


Release date
Temporal coverage
1990/01/01 - 2022/12/31
Data provider
Email: cgerdb_admin(at)nies.go.jp
File format
Data volume
8.16 GB
ver.2023.1 (Last updated: 2023/12/19)
Publications using this dataset

Data Set

surface CO2 flux
Time resolution
Spatial resolution
1.0 x 1.0 degree (the grid interval of the simulation model is ca. 223 km)
Calculation method
A BFGS-based quasi-Newton method was employed for the optimization (Fujii, 2005; Niwa, Fujii et al., 2017). The NICAM-TM simulations were performed with a spatial resolution of glevel-5 (the mean grid interval is ca. 223 km). In the inverse analysis, deviations from the prior fluxes were optimized. Here, fossil fuel emissions, which are derived from GCP-GridFED (Jones et al., 2021), were fixed and the other components were opptimized. The version of the GCP-GridFED data used is v2023.1. Specifically, scaling factors applied to gross primary production (GPP), respiration (RE), fires, and land-use change (LUC) fluxes were optimized at a monthly interval, while annual scaling factors applied to long-term mean net fluxes were optimized for the oceans. For the prior terrestrial fluxes, GPP, RE, and LUC data of VISIT (Ito and Inatomi, 2012; Ito, 2019), and fire emission data of GFEDv4.1s (van der Werf et al., 2017) were used. Meanwhile, the JMA air-sea flux dataset (Iida et al., 2015, 2021) was used for the prior ocean fluxes. The period of the inverse calculation starts one year before and ends 3 months after the analysis period so that the spin-up and spin-down effects of the inversion are not included in the target period. Note that the fluxes were optimized on the 1° x 1° grids, instead of the atmospheric transport model grids.
Other > Models
Atmosphere > Atmospheric Chemistry > Carbon and Hydrocarbon Compounds > Carbon dioxide
[Free keywords]
Greenhouse gas, Carbon dioxide, Atmospheric transport model, Inverse analysis, Data assimilation
Update history
Version 2023.1 was released (ver.2023.1)
Version 2022.1 was released (ver.2022.1)
Version 2021.1 was released (ver.2021.1)
Version 2020.1 was released (ver.2020.1)


Model development
Kentaro Ishijima*1
*1 Meteorological Research Institute
Observational data (ver.2023.1)
Ray Langenfelds*1, Paul Krummel*1, Zoe Loh*1, Doug Worthy*2, Martin Steinbacher*3, Juha Hatakka*4, Tuula Aalto*4, Tuomas Laurila*4,Viktor Ivakhov*5,Luciana V. Gatti*6, Kazuyuki Saito*7, Michel Ramonet*8, Marc Delmotte*8, Gilles Bentz, Morgan Lopez*9, Francois Gheusi*10, N. Mihalopoulos*11, Josep-Anton Morgui*12, Olivier Laurent*13, Britton Stephens*14, Hitoshi Mukai*15, Toshinobu Machida*16, Motoki Sasakawa*16, Shohei Nomura*16, Yukio Terao*16, Shin-Ichiro Nakaoka*16, Yasunori Tohjima*16, Hiroshi Tanimoto*16,Cathrine Lund Myhre*17, Ove Hermanssen*17, Gordon Brailsford*18, Sylvia Nichol*18, Arlyn Andrews*19, Ed Dlugokencky*19, John Lee*20, Colm Sweeney*19, Kirk Thoning*19, Pieter Tans*19, David Munro*19,Stephan De Wekker*21, Marc L. Fischer*22, Dan Jaffe*23, Kathryn McKain*19, Brian Viner*24, John B. Miller*19, Anna Karion*25, Charles Miller*26, Christopher D. Sloop*27, Peter Bakwin, Ralph Keeling*28, Shane Clark*28, Bill Paplawsky*28, Adam Cox*28, Stephen Walker*28, Eric Morgan*28, Eric Hintsa*19, Shuji Aoki*29, Shinji Morimoto*29, Daisuke Goto*30, Kenneth Schuldt*19
*1 Commonwealth Scientific and Industrial Research Organization, Oceans & Atmosphere
*2 Environment and Climate Change Canada
*3 Empa, Swiss Federal Laboratories for Materials Science and Technology
*4 Finnish Meteorological Institute
*5 Voeikov Main Geophysical Observatory
*6 National Institute for Space Research (INPE), Center of Terrestrial System Science (CCST), Greenhouse Gas Laboratory (LaGEE)
*7 Japan Meteorological Agency
*8 Laboratoire des Sciences du Climat et de l'Environnement - UMR8212 CEA-CNRS-UVSQ
*9 Laboratoire des Sciences du Climat et de l’Environnement, Gif sur Yvette, France
*10 Observatoire Midi-Pyrenees
*11 Environmental and Chemical Processes Laboratory
*12 Institut de Ciencia i Tecnologia Ambientals, Universitat Autonoma de Barcelona
*13 ICOS Atmospheric Thematic Center
*14 National Center for Atmospheric Research
*15 Center for Climate Change Adaptation, NIES
*16 Earth System Division, NIES
*17 Norwegian Institute for Air Research, Kjeller, Norway
*18 National Institute of Water & Atmospheric Research
*19 National Oceanic and Atmospheric Administration
*20 University of Maine
*21 University of Virginia
*22 Lawrence Berkeley National Laboratory
*23 University of Washington
*24 Atmospheric Technologies Group Savannah River National Laboratory
*25 National Institute of Standards and Technology
*26 NASA Jet Propulsion Laboratory
*27 Earth Networks
*28 Scripps Institution of Oceanography
*29 Tohoku University
*30 National Institute of Polar Research
Prior flux data
Akihiko Ito*1, Yosuke Iida*2, Matthew W. Jones*3
*1 The University of Tokyo / Earth System Division, NIES
*2 Japan Meteorological Agency
*3 Tyndall Centre for Climate Change Research, University of East Anglia


The observational data of atmospheric CO2 mole fractions used in the inverse calculation are provided by CSIRO, ECCC, Empa, FMI, IPEN, JMA, LSCE, NCAR, NIES, NILU, NIWA, NOAA, SIO and TU/NIPR, which are archived in obspack_co2_1_GLOBALVIEWplus_v8.0_2022-08-27 (Shuldt, et al., 2022, doi:10.25925/20220808), obspack_co2_1_NRT_v8.2_2023-06-13 (Shuldt, et al., 2023, doi:10.25925/20230601). The observational data provided by LSCE is from the French monitoring network SNO-ICOS-France-Atmosphere. Empa's measurements at Jungfraujoch are part of the Swiss National Air Pollution Monitoring Network and the Integrated Carbon Observation System (ICOS) and are supported by the Swiss Federal Office for the Environment and ICOS Switzerland. In addition, NIES observational data, available from NIES GED, were also used in the inverse analysis. This inverse analysis is supported by the Environment Research and Technology Development Fund of the Ministry of the Environment provided by the Ministry of Environment of Japan (JPMEERF21S20810). Yosuke Niwa wishes to express his gratitude to the NICAM developers of the University of Tokyo, JAMSTEC, RIKEN, and NIES for maintaining and developing the NICAM.The model simulations in this inverse analysis were performed using the supercomputer systems of NIES (NEC SX-Aurora) and MRI (FUJITSU Server PRIMERGY CX2550M5).

Reference Information

Supplementary Materials
Niwa, Y., H. Tomita, M. Satoh, R. Imasu, Y. Sawa, K. Tsuboi, H. Matsueda, T. Machida, M. Sasakawa, B. Belan, N. Saigusa (2017) A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0) – Part 1: Offline forward and adjoint transport models, Geosci. Model Dev., 10, 1157–1174, doi:10.5194/gmd-10-1157-2017.
Niwa, Y., Y. Fujii, Y. Sawa, Y. Iida, A. Ito, M. Satoh, R. Imasu, K. Tsuboi, H. Matsueda, N. Saigusa (2017) A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0) – Part 2: Optimization scheme and identical twin experiment of atmospheric CO2 inversion, Geosci. Model Dev., 10, 2201-2219, doi:10.5194/gmd-10-2201-2017.
Niwa, Y., Y. Sawa, H. Nara, T. Machida, H. Matsueda, T. Umezawa, A. Ito, S.-I. Nakaoka, H. Tanimoto, Y. Tohjima (2021) Estimation of fire-induced carbon emissions from Equatorial Asia in 2015 using in situ aircraft and ship observations, Atmos. Chem. Phys., 21, 9455–9473, doi:10.5194/acp-21-9455-2021.
Niwa, Y., K. Ishijima, A. Ito, Y. Iida (2022) Toward a long-term atmospheric CO2 inversion for elucidating natural carbon fluxes: technical notes of NISMON-CO2 v2021.1, Prog. Earth Planet Sci., 9, 42, doi:10.1186/s40645-022-00502-6.

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When this data set is referred to in publications, it should be cited in the following format.
Yosuke Niwa (2020), Long-term global CO2 fluxes estimated by NICAM-based Inverse Simulation for Monitoring CO2 (NISMON-CO2), ver.xxxx.x*1, Earth System Division, NIES, DOI:10.17595/20201127.001, (Reference date*2: YYYY/MM/DD)
*1 The version number is indicated in the name of each data file.
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This dataset includes the fossil fuel emission data from GCP-GriFED. When this fossil fuel emission data are used, the following references should be cited.
Jones, et al. (2021), Gridded fossil CO2 emissions and related O2 combustion consistent with national inventories 1959–2018, Sci Data 8, 2, https://doi.org/10.1038/s41597-020-00779-6 (description article)
Jones, et al. (2022), Gridded fossil CO2 emissions and related O2 combustion consistent with national inventories 1959-2021, https://doi.org/10.5281/zenodo.7016360(v2022.2)
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