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Motivation

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Motivation

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  1. Cloud fraction in SCMs: Representation of clouds constitutes a major source of uncertainty in climate models and thus future climate change projections. This motivates the quantitative evaluations of clouds in climate models against in situ and remote-sensing observational data. Using the single-column model (SCM) and evaluating SCM performances against measurements is a useful tool to diagnose the parameterization schemes in climate models. To make a statistically meaningful comparison and evaluation on modeled cloud fraction, three-year-long SCM simulations of seven GCMs participating in the FASTER project at the ARM SGP site have been carried out byDOE scientists at the Brookhaven National Laboratory and the other FASTER investigators. The results show that compared with the ARM cloud observations, most of the seven SCMs overestimate upper-level cloud fraction but underestimate lower-level cloud fraction. Further analyses reveal that the underestimation of lower-level cloud fraction in most SCMs is mainly due to their larger RH threshold used in cloud scheme, while biases in the upper-level cloud fraction for different SCMs exist for quite different reasons. These results shed new light on the challenge of accurately representing cloud fraction in climate models. Reference:Song, H., W. Lin, Y. Lin, A. B. Wolf, L. J. Donner, A. D. Del Genio, R. Neggers, S. Endo and Y. Liu (2014), Evaluation of Cloud Fraction Simulated by Seven SCMs against the ARM Observations at the SGP Site, J. Climate, accepted. Contact: Dorothy Koch, SC23.1, 301-903-0105.

  2. Evaluation of Cloud Fraction Simulated by Seven SCMs against the ARM Observations at the SGP Site • Motivation • This study is a companion paper on SCM simulated precipitation published in J. Climate in 2013, aiming to reveal cloud fraction parameterization problems by evaluating key features of cloud fraction simulated by seven SCMs against the ARM observations. Stratiform and non-stratiform partitioning analysis are performed and relationships of cloud fraction to relative humidity are investigated iconnectionwith model parameterizations. • Results • Most of the seven SCMs underestimate lower-level cloud fraction and overestimate upper-level cloud fraction. • Both frequency distribution and partitioning analysis of cloud fraction reveal large discrepancies between the SCMs with prognostic cloud fraction schemes and those with diagnostic cloud fraction schemes. • Underestimation of lower-level cloud fraction in most SCMs is mainly due to their larger threshold RH used in cloud scheme, while biases in the upper-level cloud fraction for different SCMs exist for quite different reasons. Fig1. Vertical profiles of 3-yr mean cloud fraction in the ARM observations and 7 SCMs Fig2. Vertical profiles of threshold RH in the ARM observations and 7 SCMs Fig3. Model-observation differences in RH frequency (shading) and mean cloud fraction (contours) sorted by RH bins Song, H., W. Lin, Y. Lin, A. B. Wolf, L. J. Donner, A. D. Del Genio, R. Neggers, S. Endo and Y. Liu (2014), Evaluation of Cloud Fraction Simulated by Seven SCMs against the ARM Observations at the SGP Site, J. Climate, accepted.

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