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Damian Wilson, 12 th October 2005

Assessment of model performance and potential improvements using CloudNet data. Damian Wilson, 12 th October 2005. Summary. CloudNet data has allowed a long period comparison of cloud properties between models and observations

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Damian Wilson, 12 th October 2005

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  1. Assessment of model performance and potential improvements using CloudNet data Damian Wilson, 12th October 2005

  2. Summary • CloudNet data has allowed a long period comparison of cloud properties between models and observations • There is a generally good agreement between the models (including the Met Office) and observations, better than is generally thought • However, there are some specific differences which we can address (for the Met Office) • Microphysical information is also available which can guide future parametrization development

  3. Mid-level mean cloud fractions • The mid-level cloud fractions are much reduced compared to observations • Direct modification of the diagnostic ice cloud fraction to better improve the mid-level cloud fractions, as in HadGAM

  4. Low level cloud • The low level cloud peak is at too low an altitude • A boundary layer problem? • The model has a large amount of fog and very low cloud in stable boundary layers • Possibly due to inaccuracies in the stable boundary layer turbulent flux profiles JJA: Downwards, stable Observations HadGAM PC2 m

  5. Thick liquid water contents • There are not enough of the highest liquid water contents • Possibly from not including the convective cloud. The representation of liquid water in convection schemes should be investigated • Possibly a poor representation of the drizzle process, with too ready autoconversion

  6. Supercooled liquid water content • The mesoscale model (and to a lesser extent, the global model) has significantly less supercooled liquid water than the observations • Possible adjustment of the overlap between liquid and ice in mixed phase clouds

  7. Cloud water PDFs • The histogram of cloud fraction and liquid water contents for low level cloud suggests not enough cloud fractions of 1 • Tuning the PDF shape and critical relative humidity values should improve the model

  8. Other uses of CloudNet data • CloudNet has provided more detailed information which could be used in developing better parametrizations in the future. These include: • Ice particle size distributions • Inhomogenerity information and cloud overlaps to inform the radiation scheme assumptions • Diagnostic area cloud fraction representation • Drizzle parametrization improvements

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