Observation-Based Radiative Kernels From CloudSat/CALIPSO

Ryan J. Kramer, Alexander V. Matus, Brian J Soden, Tristan S. L'Ecuyer

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Radiative kernels describe the differential response of radiative fluxes to small perturbations in state variables and are widely used to quantify radiative feedbacks on the climate system. Radiative kernels have traditionally been generated using simulated data from a global climate model, typically sourced from the model's base climate. Consequently, these radiative kernels are subject to model bias from the climatological fields used to produce them. Here, we introduce the first observation-based temperature, water vapor, and surface albedo radiative kernels, developed from CloudSat's fluxes and heating rates data set, 2B-FLXHR-LIDAR, which is supplemented with cloud information from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). We compare the radiative kernels to a previously published set generated from the Geophysical Fluid Dynamics Laboratory (GFDL) model and find general agreement in magnitude and structure. However, several key differences illustrate the sensitivity of radiative kernels to the distribution of clouds. The radiative kernels are used to quantify top-of-atmosphere and surface cloud feedbacks in an ensemble of global climate models from the Climate Model Intercomparison Project Phase 5, showing that biases in the GFDL low clouds likely cause the GFDL kernel to underestimate longwave surface cloud feedback. Since the CloudSat kernels are free of model bias in the base state, they will be ideal for future analysis of radiative feedbacks and forcing in both models and observations and for evaluating biases in model-derived radiative kernels.

Original languageEnglish (US)
Pages (from-to)5431-5444
Number of pages14
JournalJournal of Geophysical Research: Atmospheres
Volume124
Issue number10
DOIs
StatePublished - May 27 2019

Fingerprint

CloudSat
lidar
satellite observation
Optical radar
optical radar
geophysical fluids
Satellites
Infrared radiation
Climate models
climate models
fluid dynamics
Fluid dynamics
seeds
Feedback
climate modeling
climate
global climate
fluid mechanics
Fluxes
top of atmosphere

Keywords

  • cloud distribution
  • cloud feedback
  • cloud masking
  • radiative kernel
  • remote sensing

ASJC Scopus subject areas

  • Geophysics
  • Forestry
  • Oceanography
  • Aquatic Science
  • Ecology
  • Water Science and Technology
  • Soil Science
  • Geochemistry and Petrology
  • Earth-Surface Processes
  • Atmospheric Science
  • Earth and Planetary Sciences (miscellaneous)
  • Space and Planetary Science
  • Palaeontology

Cite this

Observation-Based Radiative Kernels From CloudSat/CALIPSO. / Kramer, Ryan J.; Matus, Alexander V.; Soden, Brian J; L'Ecuyer, Tristan S.

In: Journal of Geophysical Research: Atmospheres, Vol. 124, No. 10, 27.05.2019, p. 5431-5444.

Research output: Contribution to journalArticle

Kramer, Ryan J. ; Matus, Alexander V. ; Soden, Brian J ; L'Ecuyer, Tristan S. / Observation-Based Radiative Kernels From CloudSat/CALIPSO. In: Journal of Geophysical Research: Atmospheres. 2019 ; Vol. 124, No. 10. pp. 5431-5444.
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