277 Where Clouds Warm: Global Observations of the Cloud Impact on Surface Radiation Ratio (CISRR)

Wednesday, 11 July 2018
Regency A/B/C (Hyatt Regency Vancouver)
Elin A. McIlhattan, Univ. of Wisconsin–Madison, Madison, WI; and T. S. L'Ecuyer

The amount of radiation that reaches Earth’s surface is strongly regulated by overlying cloud systems. Clouds reduce the amount of shortwave (SW) radiation received at the surface by reflecting some of the incident solar energy. Clouds simultaneously increase the downwelling longwave (LW) radiation absorbed at the surface by trapping some of the terrestrial LW that otherwise would have escaped to space. Cloud and surface properties together govern which of these competing effects dominates in a given scene. In this work, we utilize the satellite-derived data product, 2B-FLXHR-LIDAR, to quantify the ratio of SW to LW cloud radiative effect (CRE) and map the annual average observed Cloud Impact on Surface Radiation Ratio (CISRR).

During the period 2007-2010, the LWCRE dominates over the polar ice sheets (CISRR < 1) while the SWCRE is stronger over much of the open ocean (CISRR > 1). There is a straightforward explanation as to why the LWCRE is stronger over the ice sheets. The brightness of the ice does not change the surface LWCRE, however since the bright ice already reflects much of incident SW, an overlying cloud does not have a considerable impact on the total absorbed SW resulting in a small surface SWCRE. Similarly, it is logical that the tropical oceans are dominated by the SWCRE – bright clouds overlying dark water severely reduce the SW absorbed by the surface, while the LWCRE is not as large due to the already warm, humid air.

Harder to explain are the observed areas of clouds warming the surface (CISRR <1) outside of the high latitudes: the Himalayas, the deserts of north Africa, and the regions with persistent stratocumulus clouds off the western coasts of South America and southern Africa. In this work, we look in detail regions of CISRR < 1, leveraging spaceborne, ground based, and airborne observations. We also explore models’ ability to capture the large scale spatial features of observed CISRR, looking at both reanalysis data and the Community Earth System Model Large Ensemble (CESM-LE) project.

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