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

Yosuke Niwa^*1,^*2 (orcid.org/0000-0002-7600-9816)
^*1 Center for Global Environmental Research (CGER), National Institute for Environmental Studies (NIES)
^*2 Meteorological Research Institute

2020/11/27

1990/01/01 - 2019/12/31

NIES
Email: cgerdb_admin(at)nies.go.jp

77 MB

ver.2020.1 (Last updated: 2020/11/27)

English

Global

Monthly

1.0 x 1.0 degree (the grid interval of the simulation model is ca. 223 km)

[ver.2020.1]
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 had been derived from GCP-GridFEDv2020.1 (Jones et al.), were fixed and the other components were optimized. 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), 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) 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 1 deg. x 1 deg. flux data were produced by linearly interpolating the data of the original hexagonal or pentagonal model (NICAM) grid.

[GCMD_Platform]
[GCMD_Science]
[Free keywords]

[2020/11/27]

Ray Langenfelds^*1, Paul Krummel^*1, Zoe Loh^*1, Doug Worthy^*2, Juha Hatakka^*3, Tuula Aalto^*3, Michel Ramonet^*4, Marc Delmotte^*4, Martina Schmidt^*4, Francois Gheusi^*5, N. Mihalopoulos^*6, J.A. Morgui^*7, Arlyn Andrews^*8, Ed Dlugokencky^*8, John Lee^*9, Colm Sweeney^*8, Kirk Thoning^*8, Pieter Tans^*8, Stephan De Wekker^*10, Marc L. Fischer^*11, Dan Jaffe^*12, Kathryn McKain^*8, Brian Viner^*13, John B. Miller^*8, Anna Karion^*8, Charles Miller^*14, Christopher D. Sloop^*15, Kazuyuki Saito^*16, Shuji Aoki^*17, Shinji Morimoto^*17, Daisuke Goto^*18, Martin Steinbacher^*19, Cathrine Lund Myhre^*20, Ove Hermanssen^*20, Britton Stephens^*21, Ralph Keeling^*22, Sara Afshar^*22, Bill Paplawsky^*22, Adam Cox^*22, Stephen Walker^*22, Kenneth Schuldt^*8, Hitoshi Mukai^*23, Toshinobu Machida^*24, Motoki Sasakawa^*24, Shohei Nomura^*24
*1 Commonwealth Scientific and Industrial Research Organization, Oceans & Atmosphere
^*2 Environment and Climate Change Canada
^*3 Finnish Meteorological Institute
^*4 Laboratoire des Sciences du Climat et de l’Environnement, LSCE
^*5 Observatoire Midi-Pyrenees
^*6 Environmental and Chemical Processes Laboratory
^*7 Institut de Ciencia i Tecnologia Ambientals, Universitat Autonoma de Barcelona
^*8 National Oceanic and Atmospheric Administration
^*9 University of Maine
^*10 University of Virginia
^*11 Lawrence Berkeley National Laboratory
^*12 University of Washington
^*13 Atmospheric Technologies Group Savannah River National Laboratory
^*14 NASA Jet Propulsion Laboratory
^*15 Earth Networks
^*16 Japan Meteorological Agency
^*17 Tohoku University
^*18 National Institute of Polar Research
^*19 Swiss Federal Laboratories for Materials Science and Technology
^*20 Norwegian Institute for Air Research
^*21 National Center for Atmospheric Research
^*22 Scripps Institution of Oceanography
^*23 Center for Climate Change Adaptation, NIES
^*24 CGER, NIES

Akihiko Ito^*1, Yosuke Iida^*2, Matthew W. Jones^*3
*1 CGER, 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 NOAA, CSIRO, ECCC, FMI, LSCE, JMA, NCAR, SIO, TU, NIPR, EMPA, and NILU, which are archived in obspack_co2_1_GLOBALVIEWplus_v5.0_2019-08-12 (doi:10.25925/20190812), obspack_co2_1_NRT_v5.2_2020-06-03 (doi:10.25925/20200601). The observational data provided by LSCE is from the French monitoring network SNO-ICOS-France-Atmosphere. In addition, NIES observational data, available from NIES GED, were also used in the inverse analysis. The inverse analysis system used for this dataset was developed under the support of the Environment Research and Technology Development Fund of the Ministry of the Environment, Japan (JPMEERF20142001 and JPMEERF20172001). 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).

inversion_description.ver.2020.1.pdf
observation_list.ver.2020.1.csv

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.

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