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A Correlated K-Distribution (CKD) tool generates CKD gas-optics models in a number of steps, some which may require human intervention. One of the most interesting parts of the CKDMIP project will be to understand how differences in how each step is performed feed through to differences in the accuracy of fluxes and heating rates. The page is an attempt to gather the necessary information about the CKD tools participating in CKDMIP, specifically:

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Computing incoming solar radiation for each shortwave g point: The incoming solar spectral flux is accounted for in computing g as a weighting function of the spectral absorption coefficients.

KBIN

Reference: Doppler et al. (2014)

Implementation details: Code in Python (BSD license)

Selecting band boundaries: There are no restrictions on the width of a band.

Line-by-line model: LBLRTM version 12.8; in fact we use only the LBL calculations available from the CKDMIP FTP site. In-house version is CGASA (Doppler et al., 2014)

Reordering spectrum: The spectra of the “median” CKDMIP present-day profile are reordered. For the “climate” application we reorder the major gases H2O, O3, CO2, CH4, N2O and a composite gas of 02+N2. The mapping is unique for all pressure levels and done for all gases together. We use uncorrelated k-binning, where the mapping function is found by iteratively optimizing the reordering of a “layer and atmosphere absorption coefficient” (average of total atmosphere and layer absorption coefficient) at each layer by comparing the user-defined error tolerance for total atmosphere and layer transmission. This ensures that the final mapping function is a optimal mapping for total and layer transmission error tolerance.

Choosing number of g points: The number of g points is increased iteratively until either the specified accuracy conditions are met or a specified maximum number of g points is reached.

Partitioning g space for one gas: The partitioning of g space is optimized in our k-binning algorithm in order to satisfy the accuracy conditions.

Partitioning g space for multiple gases: Binning is done for all gases together. Same g points for all gases.

Computing absorption of one gas: The averaging per gas and g point can be specified by the user, default is to average the optical thickness/cross sections.

Computing combined absorption of multiple gases: The optical thicknesses of the individual gases are added according to their relative abundance. Thus the amount of foreign continuum absorption is related to the profile which is used for the k-binning.

Computing Planck function for each longwave g point: We are currently working on solar spectral range only, the longwave part is in revision. The plan is to calculate the Planck function exactly at the high resolution points and then average the radiance in the g space. But this not yet finalized.

Computing incoming solar radiation for each shortwave g point: Can be specified by the user. Default method is to convolve the band with the solar irradiance spectra (Chance and Kurucs, 2010) and normalize after. This is equivalent to a band average over the g point. During the optimization of the number and width of g points the relative solar spectral irradiance is considered (wavenumbers with low intensities do contribute less).

RRTMG

Reference(s): Mlawer et al. (1997).  Also Iacono et al. (2008) has some useful information.

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