Building on the Phase II experiments this effort will support the interpolation of consolidated flight track points from high-temporal resolution model output to minimise the large sampling biases that would otherwise be present.

Note, we are now only requesting a single year of simulation for the mandatory Tier 1 submissions. Tier 2 submissions are also welcome.

Recent dedicated aircraft measurement campaigns and data collection efforts have delivered a large amount of in-situ aerosol measurements of great value to AeroCom modellers. The Global Aerosol Synthesis and Science Project (GASSP) dataset brings 1000s of separate aircraft measurement flights across 10s of campaigns into a single consistent database. Combining this with data from recent campaigns such as CLARIFY, ORACLES, AToM and ACE-ENA provides a unique opportunity to evaluate AeroCom model aerosol distributions across a wide range of regions and meteorological conditions.

Each campaign includes different measurements of aerosol properties such as size distributions and speciation, and each focuses on different regions or phenomena; however, they all provide valuable model constraints and all require similar sampling considerations. Some campaign or region focussed analyses build on the baseline experiment with their own sensitivity experiments or specialist diagnostics, such as the ATom experiment. Please refer to each extension analysis for further details.

For this experiment the flight track points will be provided in a single CF-conformant NetCDF format with time, latitude, longitude, altitude and pressure coordinates. A post-processing script can also be provided allowing interpolation from high-temporal resolution output (at least 3 hourly) using the CIS tool to output in the same CF-compliant NetCDF format as the sample data, and then deletion of the full output fields. Vertical interpolation will automatically be performed by height or pressure as required. Some models have implemented a ‘flight-track simulator’ to allow on-line interpolation of these spatially sparse measurement points, thus avoiding significant output storage requirements.

The CIS commands required are very simple and the syntax is described in the documentation documentation. An example command to extract the tracers from a set of model outputs for March 2009 would be:

cis col <tracer_vars>:model_file_2009_03_??.nc all_points_200903??.nc -o tracers_200903.nc

A python interface is also available if preferred.