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Most radiation schemes in weather and climate models use the "correlated k-distribution" (CKD) method to treat gas absorption, which approximates the integration over hundreds of thousands of spectral lines by N pseudo-monochromatic radiative transfer calculations, where N is in the range tens to hundreds. In principle, there is a trade-off to make: larger N means more accuracy and a wider range of atmospheric conditions can be simulated, but at greater computational cost. Unfortunately most CKD schemes are a black box: there is no way for the user to adjust N according to their application. The purpose of CKDMIP is to address these issues, and specifically:

• To use benchmark line-by-line calculations to evaluate the accuracy of existing CKD models for applications spanning short-range weather forecasting to climate modelling, and to explore how accuracy varies with number of g-points in individual CKD schemes.

• To understand how different choices in way that CKD models are generated affects their accuracy for the same number of g-points.

• To provide freely available datasets and software to facilitate the development of new gas-optics models, with the ultimate aim of producing a community tool to allow users to generate their own gas-optics models targeted at specific applications.

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More information on the design of the experimental protocol is here:

• Hogan and Matricardi: draft GMD protocol paper

Table 3 of Hogan and Matricardi defines four datasets consisting of a number of gas-concentration profiles:

• Evaluation-1 - 50 realistic profiles for training and evaluation: ckdmip_evaluation1_concentrations.nc
• Evaluation-2 - 50 further realistic profiles for independent evaluation (not yet available)
• MMM - profiles of median, minimum and maximum temperature, H2O and O3: ckdmip_mmm_concentrations.nc
• Idealized - idealistic profiles of temperature, pressure and humidity: ckdmip_idealized_concentrations.nc

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