Title | The CO2 Human Emissions (CHE) global nature run |
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Description | This dataset contains atmospheric mixing ratios of carbon dioxide (CO2), methane (CH4) and carbon monoxide (CO), CO2 and CH4 fluxes at the surface, CO2 tagged tracers to monitor the atmospheric enhancement associated with anthropogenic emissions and natural fluxes, and meteorological parameters from very high resolution (9km) global simulations with the Integrated Forecasting System of the European Weather Centre for Medium Range Weather Forecasts (ECMWF). The high-resolution simulation - also known as nature run - provides realistic variability of carbon tracers associated with the weather for 2015, the year of the Paris Agreement. It was produced by the CO2 Human Emissions (CHE) project (https://che-project.eu) to provide boundary conditions for regional models, as a reference truth for assessing the impact of proposed designs of new satellites and in situ networks (e.g. with Observing System Simulation Experiments) and to study atmospheric variability of the tracers modulated by the weather, among other scientific applications. |
DOI | 10.21957/w4wq-sd03 |
Experiment ID | ha58 |
Experiment class | ECMWF Research Department |
Examples |
Carbon dioxide and methane mass mixing ratios on the lowest (level 1 is the top of the atmosphere) model level for all the forecast steps for 1 July 2015 interpolated to 0.1 x 0.1 deg regular latitude-longitude grid
Carbon dioxide and methane mass mixing ratios on all the pressure levels for all the forecast steps for 1 July 2015 interpolated to 0.1 x 0.1 deg regular latitude-longitude grid
Carbon dioxide and methane column mean molar fractions for all the forecast steps for 1 January 2015 interpolated to 0.1 x 0.1 deg regular latitude-longitude grid
Natural biosphere flux and anthropogenic emissions of carbon dioxide for all the forecast steps for 31 December 2015 interpolated to 0.1 x 0.1 deg regular latitude-longitude grid
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The CO2 Human Emissions (CHE) global nature run
To retrieve the data described in this experiment, you will need to use the ECMWF Web API with the example(s) given on this page. Please note that when accessing the data you are bound by the ECMWF terms of use.