Model Simulations

Flux Estimation

Top-down Approach

Long-term global CO2 fluxes estimated by NICAM-based Inverse Simulation for Monitoring CO2
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.

Bottom-up Approach

Output data of greenhouse gas budget and carbon cycle simulated by the VISIT terrestrial ecosystem model
This dataset contains a set of simulation output by a terrestrial ecosystem model, the Vegetation Integrative SImulator for Trace gases (VISIT). The simulation was conducted globally for the historical period from 1901 to 2020, using an observation-based climate data and land-use data.
Fossil-fuel CO2 emission estimates at 1x1 km in Tokyo
The product includes annual total fossil-fuel CO2 emissions from point, line, and area sources at 1x1 km spatial resolution in Tokyo in 2014. The emissions were estimated using a bottom-up approach with detailed activity data including the operating ratios of power plants, load factors of vessels, fossil-carbon contents of waste, emission factors for fossil-fueled power generation, aircraft movements, navigation, and combustion processes.
Global 1km Fossil Fuel Carbon Dioxide (CO2) Emission Dataset
This dataset (ODIAC CO2 emission dataset) indicates global distributions of man-made CO2 emissions due to fossil fuel (coal, oil and gas) combustion at 1x1km spatial resolution. The emission spatial distributions are estimated using an emission model that utilizes information from a power plant database and satellite-observed nighttime lights. The emission estimates are updated on annual basis using the latest fuel statistical data. In addition to the dataset in the native 1x1km resolution (in binary format), the 1x1 degree resolution version of the dataset (in netCDF) is also available.

Machine Learning etc.

Data based estimate of global ocean CO2 flux for 1980-2020
This product extends the flux estimate of “Global surface ocean CO2 concentration and uptake estimated using a neural network (doi:10.17595/20201020.001)” to 1980-2020 using the grided data of CO2 observations of SOCAT 2021. We made two small changes in the input variables for training a neural network: (1) the log scaled mixed layer depth was used and (2) observed CO2 values over 1000 µatm were excluded.
Global surface ocean CO2 concentration and uptake estimated using a neural network
This dataset includes monthly distribution of CO2 fugacity and ocean-atmosphere CO2 flux in 1x1 degree grids after 1985 estimated using a neural network.
A Data-driven Upscale Product of Global GPP, NEE and ER
The product includes 10-day means of global GPP (gross primary production), NEE (net ecosystem exchange), and ER (ecosystem respiration) in 0.1x0.1 degree spatial resolution. Random Forest was used to upscale observations of FLUXNET 2015 to the globe.

Climate Model

100-member ensemble simulation output from NIES Chemistry-Climate Models to investigate the effect of unregulated HFC increase on the ozone laye
To investigate the effect of unregulated HFC abundance increases on ozone and temperature at the end of the 21st Century, 100 ensemble simulations with increased HFCs were performed by Chemistry-Climate Models (CCMs) assuming the atmosphere with the constituent concentrations in 2095 under the RCP2.6 and the WMO-A1 scenarios.
Bias corrected climate scenarios based on CMIP6
This dataset is bias corrected climate scenarios over Japan based on CMIP6. The spatial and temporal resolutions are 1km and daily. This data can be used for various impact studies.
Bias corrected climate scenarios based on CMIP5
This dataset is bias corrected climate scenarios over Japan based on CMIP5. The spatial and temporal resolutions are 1km and daily. This data can be used for various impact studies.