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Band-by-band spectral radiative kernels based on the ERA5 reanalysis.

Han HuangYi HuangQiang WeiYongyun Hu
Published in: Scientific data (2024)
Radiative kernel is a widely adopted method for diagnosing radiation variability and climate feedback. However, most of the existing radiative kernels are broadband flux kernels and lack the spectral information. Motivated by the growing interest in the spectral changes of the Earth radiation budget, we generate a new set of band-by-band radiative kernels based on the fifth generation European Center for Medium-Range Weather Forecasts (ERA5) reanalysis, which can be used for analyzing the spectrally decomposed changes in the top of atmosphere, surface and atmospheric radiation. The radiative sensitivity quantified by the ERA5 band-by-band kernel is compared to another spectral kernel and validated in a spectral radiation closure test. The use and benefits of the new ERA5 kernels are then demonstrated in an analysis of spectral feedbacks of an ensemble of global climate models (GCMs).
Keyphrases
  • optical coherence tomography
  • dual energy
  • climate change
  • radiation induced
  • computed tomography
  • magnetic resonance imaging
  • machine learning
  • convolutional neural network
  • high efficiency