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Diagnosing the radiation biases in global climate models using radiative kernels
  • Han Huang,
  • Yi Huang
Han Huang
Department of Atmospheric and Oceanic Sciences, McGill University
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Yi Huang
McGill University

Corresponding Author:yi.huang@mcgill.ca

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Radiation energy balance at the top of the atmosphere (TOA) is a critical boundary condition for the Earth climate. It is essential to validate it in the global climate models (GCM) on both global and regional scales. However, the comparison of overall radiation field is known to conceal compensating errors. Here we use a new set of radiative kernels to diagnose the radiation biases by different geophysical variables in the latest GCMs. We find although clouds remain a primary cause of radiation biases, the radiation biases caused by non-cloud variables are of comparable magnitudes. Many GCMs tend to have a cold bias in the air temperature and a moist bias in the tropospheric humidity, which lead to considerable biases in TOA radiation budget but are compensated by cloud biases. These findings signify the importance of validating the GCM-simulated radiation fields, with respect to both the overall and component radiation biases.
17 Mar 2023Submitted to ESS Open Archive
26 Mar 2023Published in ESS Open Archive