Source: Journal of Geophysical Research: Atmospheres
The water that makes up a cloud can exist as liquid droplets, ice crystals, or a mixture of both phases. Cloud phase affects how much radiation from the Sun reaches the ground, stays in the atmosphere, or makes its way back into space; all three influence Earth’s temperature. However, inadequate tools and data have made it challenging for scientists to accurately incorporate cloud phase into predictions of future climate.
In a new study, Matus and L’Ecuyer present a recent update to an algorithm for processing satellite data that could make such predictions more accurate. They used the algorithm to determine the influence of different cloud phases on solar radiation. The results confirm that the mixture of liquid and ice in a cloud can significantly influence how the cloud affects its environment.
The new version is an update of 2B-FLXHR-LIDAR, which is designed to use satellite data to estimate the amount of radiation passing through a given part of the atmosphere. The 2B-FLXHR-LIDAR algorithm uses data from CloudSat and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite, which fly in formation to gather data on the structure of clouds, including their liquid and ice particles. It also incorporates cloud imaging data from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite.
The latest version of 2B-FLXHR-LIDAR features improved representation of cloud phase. Specifically, it is now better at realistically representing supercooled liquid water clouds, thin ice clouds, and clouds composed of a mixture of liquid and ice, yielding improved estimates of the ability of a cloud to reflect solar radiation and trap emitted thermal radiation.
To evaluate the strength of the new update, the team compared its outputs with measurements made by NASA’s Clouds and the Earth’s Radiant Energy System Information and Data (CERES) instrument that also flies aboard Aqua. The researchers used the updated algorithm to estimate radiation fluxes at the top of the atmosphere; they found that these estimates agreed better with CERES observations than the outputs of previous versions of 2B-FLXHR-LIDAR did.
The researchers then used the newly updated algorithm to calculate, for the first time, the radiative effects of clouds of different phases seen in satellite data collected from 2007 to 2010. For each type of cloud phase, they calculated the net effect on the exchange of solar and thermal radiation at the top of the atmosphere. A negative number yielded by the calculation indicated that more radiation was reflected into space than was retained through the cloud greenhouse effect, resulting in a net cooling effect, whereas a positive number indicated net warming.
The algorithm showed that clouds cool the Earth on average by −17.1 watts over every square meter (W/m2). Warm liquid clouds have a net cooling effect of –11.8 W/m2, whereas ice clouds have a warming effect of 3.5 W/m2. For the first time, the study found that clouds consisting entirely of liquid or ice account for nearly half of the total cooling effect of clouds on the climate. Clouds containing a mixture of ice crystals and water droplets (mixed-phase clouds) cool the globe by −3.4 W/m2, and clouds consisting of multiple distinct ice and liquid layers cause an additional cooling of −5.4 W/m2.
The large variation in these effects by region and season highlights the importance of accurately simulating cloud phase when making climate predictions. Although further improvements and better data will be needed to reduce uncertainty, the newly updated algorithm could prove especially helpful in discerning the effects of mixed-phase clouds, which are known to play an important yet not fully understood role in the global energy budget. (Journal of Geophysical Research: Atmospheres, https://doi.org/10.1002/2016JD025951, 2017)
—Sarah Stanley, Freelance Writer
Stanley, S. (2017), Better estimates of clouds’ climate effects are on the horizon, Eos, 98, https://doi.org/10.1029/2017EO071189. Published on 14 April 2017.
Text © 2017. The authors. CC BY-NC-ND 3.0
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