The behavior of trade‐wind cloudiness in observations and models: The major cloud components and their variability

Journal of Advances in Modeling Earth Systems

Louise Nuijens¹  Brian Medeiros³  Irina Sandu² 

[1] Atmosphere in the Earth System Department, Max Planck Institute for Meteorology, Hamburg, Germany;Physical Aspects Section, European Centre for Medium-Range Weather Forecasts, Reading, UK;Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, Colorado, USA

关键词: clouds;    climate;   

DOI  :  10.1002/2014MS000390

来源: Wiley

【 摘 要 】

Abstract

Guided by ground-based radar and lidar profiling at the Barbados Cloud Observatory (BCO), this study evaluates trade-wind cloudiness in ECMWF's Integrated Forecast System (IFS) and nine CMIP5 models using their single-timestep output at selected grid points. The observed profile of cloudiness is relatively evenly distributed between two important height levels: the lifting condensation level (LCL) and the tops of the deepest cumuli near the trade-wind inversion (2–3 km). Cloudiness at the LCL dominates the total cloud cover, but is relatively invariant. Variance in cloudiness instead peaks at the inversion. The IFS reproduces the depth of the cloud field and its variability, but underestimates cloudiness at the LCL and the inversion. A few CMIP5 models produce a single stratocumulus-like layer near the LCL, but more than half of the CMIP5 models reproduce the observed cloud layer depth in long-term mean profiles. At single-time steps, however, half of the models do not produce cloudiness near cloud tops along with the (almost ever-present) cloudiness near the LCL. In seven models, cloudiness is zero at both levels 10 to 65% of the time, compared to 3% in the observations. Models therefore tend to overestimate variance in cloudiness near the LCL. This variance is associated with longer time scales than in observations, which suggests that modeled cloudiness is too sensitive to large-scale processes. To conclude, many models do not appear to capture the processes that underlie changes in cloudiness, which is relevant for cloud feedbacks and climate prediction.

【 授权许可】

CC BY-NC-ND   
© 2015. The Authors.

Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.