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Diurnal cloud cycle biases in climate models

Author

Listed:
  • Jun Yin

    (Princeton University
    Princeton University)

  • Amilcare Porporato

    (Princeton University
    Princeton University)

Abstract

Clouds’ efficiency at reflecting solar radiation and trapping the terrestrial radiation is strongly modulated by the diurnal cycle of clouds (DCC). Much attention has been paid to mean cloud properties due to their critical role in climate projections; however, less research has been devoted to the DCC. Here we quantify the mean, amplitude, and phase of the DCC in climate models and compare them with satellite observations and reanalysis data. While the mean appears to be reliable, the amplitude and phase of the DCC show marked inconsistencies, inducing overestimation of radiation in most climate models. In some models, DCC appears slightly shifted over the ocean, likely as a result of tuning and fortuitously compensating the large DCC errors over the land. While this model tuning does not seem to invalidate climate projections because of the limited DCC response to global warming, it may potentially increase the uncertainty of climate predictions.

Suggested Citation

  • Jun Yin & Amilcare Porporato, 2017. "Diurnal cloud cycle biases in climate models," Nature Communications, Nature, vol. 8(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-02369-4
    DOI: 10.1038/s41467-017-02369-4
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    Cited by:

    1. Junying Fang & Yu Du, 2022. "A global survey of diurnal offshore propagation of rainfall," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    2. Abhnil Amtesh Prasad & Merlinde Kay, 2020. "Assessment of Simulated Solar Irradiance on Days of High Intermittency Using WRF-Solar," Energies, MDPI, vol. 13(2), pages 1-22, January.

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