Linearized Flux Evolution (LiFE): A technique for rapidly adapting fluxes from full-physics radiative transfer models

Tyler D. Robinson, David Crisp

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Solar and thermal radiation are critical aspects of planetary climate, with gradients in radiative energy fluxes driving heating and cooling. Climate models require that radiative transfer tools be versatile, computationally efficient, and accurate. Here, we describe a technique that uses an accurate full-physics radiative transfer model to generate a set of atmospheric radiative quantities which can be used to linearly adapt radiative flux profiles to changes in the atmospheric and surface state—the Linearized Flux Evolution (LiFE) approach. These radiative quantities describe how each model layer in a plane-parallel atmosphere reflects and transmits light, as well as how the layer generates diffuse radiation by thermal emission and by scattering light from the direct solar beam. By computing derivatives of these layer radiative properties with respect to dynamic elements of the atmospheric state, we can then efficiently adapt the flux profiles computed by the full-physics model to new atmospheric states. We validate the LiFE approach, and then apply this approach to Mars, Earth, and Venus, demonstrating the information contained in the layer radiative properties and their derivatives, as well as how the LiFE approach can be used to determine the thermal structure of radiative and radiative-convective equilibrium states in one-dimensional atmospheric models.

Original languageEnglish (US)
Pages (from-to)78-95
Number of pages18
JournalJournal of Quantitative Spectroscopy and Radiative Transfer
Volume211
DOIs
StatePublished - May 2018

ASJC Scopus subject areas

  • Radiation
  • Atomic and Molecular Physics, and Optics
  • Spectroscopy

Fingerprint

Dive into the research topics of 'Linearized Flux Evolution (LiFE): A technique for rapidly adapting fluxes from full-physics radiative transfer models'. Together they form a unique fingerprint.

Cite this