A tool for generating fast k ‐distribution gas‐optics models for weather and climate applications

Hogan, R. J. ORCID: https://orcid.org/0000-0002-3180-5157 and Matricardi, M. ORCID: https://orcid.org/0000-0001-7514-9473 (2022) A tool for generating fast k ‐distribution gas‐optics models for weather and climate applications. Journal of Advances in Modeling Earth Systems, 14 (10). e2022MS003033. ISSN 1942-2466 doi: 10.1029/2022MS003033

Abstract/Summary

One of the most important components of an atmospheric radiation scheme is its treatment of gas optical properties, which determines not only the accuracy of its radiative forcing calculations fundamental to climate prediction, but also its computational cost. This paper describes a free software tool “ecCKD” for generating fast gas-optics models by optimally dividing the spectrum into pseudo-monochromatic spectral intervals (known as k-terms) according to a user-specified error tolerance and the range of greenhouse-gas concentrations that needs to be simulated. The models generated use the correlated k-distribution method in user-specified bands, but can also generate accurate “full-spectrum correlated-k” models that operate on the entire longwave or near-infrared (NIR) parts of the spectrum. In the NIR, the large spectral variation in cloud absorption is represented by partitioning the parts of the spectrum where gases are optically thin into 2–6 sub-bands, while allowing k-terms for the optically thicker parts of the spectrum (where clouds and surface reflectance are less important) to span the entire NIR spectrum. Candidate models using only 16 and 32 k-terms in each of the shortwave and longwave are evaluated against line-by-line calculations on clear and cloudy profiles. The 32-term models are able to accurately capture the radiative forcing of varying greenhouse gases including CO2 concentrations spanning a factor of 12, and heating rates at pressures down to 1 Pa.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/110544
Identification Number/DOI	10.1029/2022MS003033
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	American Geophysical Union
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