4.7 Article

Instantaneous Linkages between Clouds and Large-Scale Meteorology over the Southern Ocean in Observations and a Climate Model

Journal

JOURNAL OF CLIMATE
Volume 30, Issue 23, Pages 9455-9474

Publisher

AMER METEOROLOGICAL SOC
DOI: 10.1175/JCLI-D-17-0156.1

Keywords

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Funding

  1. Regional and Global Climate Modeling Program of the Office of Science of the U.S. Department of Energy [DE-SC0012580]
  2. U.S. Department of Energy, Office of Science (BER), Regional and Global Climate Modeling program
  3. Department of Energy [DE-AC05-76RL01830]
  4. U.S. Department of Energy (DOE) [DE-SC0012580] Funding Source: U.S. Department of Energy (DOE)

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Instantaneous, coincident, footprint-level satellite observations of cloud properties and radiation taken during austral summer over the Southern Ocean are used to study relationships between clouds and large-scale meteorology. Cloud properties are very sensitive to the strength of vertical motion in the midtroposphere, and low-cloud properties are sensitive to estimated inversion strength, low-level temperature advection, and sea surface temperature. These relationships are quantified. An index for the meteorological anomalies associated with midlatitude cyclones is presented, and it is used to reveal the sensitivity of clouds to the meteorology within the warm and cold sectors of cyclones. The observed relationships between clouds and meteorology are compared to those in the Community Atmosphere Model, version 5 (CAM5), using satellite simulators. Low clouds simulated by CAM5 are too few, are too bright, and contain too much ice. In the cold sector of cyclones, the low clouds are also too sensitive to variations in the meteorology. When CAM5 is coupled with an updated boundary layer parameterization known as Cloud Layers Unified by Binormals (CLUBB), bias in the ice content of low clouds is dramatically reduced. More generally, this study demonstrates that examining the instantaneous time scale is a powerful approach to understanding the physical processes that control clouds and how they are represented in climate models. Such an evaluation goes beyond the cloud climatology and exposes model bias under various meteorological conditions.

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