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Climate models accurately represent the average diurnal cycle of clouds (DCC) but struggle with its amplitude and phase. These inconsistencies lead to inaccurate radiation estimates, potentially increasing climate prediction uncertainty.

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Area of Science:

  • Atmospheric Science
  • Climate Modeling
  • Remote Sensing

Background:

  • The diurnal cycle of clouds (DCC) significantly influences Earth's energy balance by modulating solar and terrestrial radiation.
  • While mean cloud properties are well-studied for climate projections, the DCC's characteristics remain less understood.
  • Accurate representation of DCC in climate models is crucial for reliable climate projections.

Purpose of the Study:

  • To quantify and compare the mean, amplitude, and phase of the DCC in climate models against satellite observations and reanalysis data.
  • To identify inconsistencies in DCC representation across climate models.
  • To assess the impact of DCC errors on radiation estimates and climate projections.

Main Methods:

  • Analysis of DCC mean, amplitude, and phase using climate model outputs.
  • Comparison of model-derived DCC characteristics with satellite observations.
  • Validation against reanalysis data to assess model performance.

Main Results:

  • Climate models show reliable representation of the mean DCC.
  • Significant inconsistencies were found in the amplitude and phase of the DCC across models.
  • These DCC inconsistencies lead to overestimation of radiation in most climate models, with some models exhibiting shifts over oceans potentially masking land-based errors.

Conclusions:

  • While model tuning may mitigate some DCC errors, significant amplitude and phase inconsistencies persist.
  • The limited response of DCC to global warming suggests current climate projections may not be invalidated.
  • However, these DCC discrepancies could increase the uncertainty in future climate predictions.