From space emerge large decks of closely spaced stratocum clouds, like bright cotton balls hovering over the ocean. They cover vast areas – literally thousands of kilometers of subtropical oceans – and last for weeks to months.
Because these sea clouds reflect more solar radiation than the surface of the ocean, cooling the Earth’s surface, the lifespan of cumulus clouds is an important component of the Earth’s radiation balance. Therefore, it is necessary to accurately represent the lifetime of clouds in ground system (ESM) models used to predict future climatic conditions. Turbulence – air movements that occur on a small scale – are primarily responsible for the longevity of marine layered clouds.
The cap – rain containing water droplets less than half a millimeter in diameter – is constantly present inside and below these marine cloud systems. Because these small droplets affect and affect turbulence beneath sea clouds, scientists need to know more about how rain affects turbulence in those clouds to allow for more accurate climate forecasts.
A team led by Virendra Ghate, an atmospheric scientist, and Maria Cadeddu, chief atmospheric research engineer in the Department of Environmental Science at the U.S. Department of Energy’s Argonne National Laboratory, have been studying the impact of rain in sea clouds since 2017. Their unique dataset caught the attention of researchers from DOE’s Lawrence Livermore National Laboratory.
About three years ago, an associate from Livermore, who led national efforts to improve cloud representation in climate models, called for observational studies focused on rain-turbulence interactions. Such studies did not exist at the time due to the limited set of observations and the lack of techniques to derive all the troubling geophysical properties.
“The analysis of the developed data set allowed us to show that rain reduces turbulence under the streaked cloud – something that only model simulations have shown in the past,” Ghate said. “The wealth of data developed will allow us to address several fundamental issues regarding rain-turbulence interactions in the future.”
The Argonne team set out to characterize cloud properties using observations at the eastern North Atlantic Atmospheric Measurement Site (ARM), a user facility of the DOE’s Office of Science, and instrumental data on geostationary satellites and satellites around polar orbit. The instruments collect engineering variables, such as voltage and temperature. The team combined measurements from different instruments to derive water vapor properties and dew in and under clouds.
Ghate and Cadingdu were interested in geophysical variables, such as cloud water content, rainwater particle size, and others. Thus, they developed a new algorithm that synergistically found all the necessary parameters involved in rain-turbulence interactions. The algorithm uses data from several ARM instruments – including radar, lidar and radiometer – to derive geophysical variables of interest: precipitation size (or diameter), amount of running water corresponding to cloud falls, and precipitation. Using data from ARM, Ghate and Cadeddu derived these parameters, then published three observational studies focusing on two different spatial stratocum cloud organizations, to characterize rain-turbulence interactions in these cloud systems.
Their results led to a joint effort with the Livermore modelers. In this effort, the team used observations to improve the display of rain-turbulence interactions in the DOE model of the Earth System for Exact Energy System (E3SM).
“Observation references from the Ghate and Cadeddu discovery techniques helped us determine that version 1 of the E3SM produces unrealistic rain processes. Our collaborative study implies that current climate models require comprehensive testing of modeled cloud and rainwater processes with observation references,” Xue Zheng said. , an expert scientist in the Department of Atmosphere, Earth and Energy in Livermore.
Said Cadeddu: “In general, the unique expertise here in the lab is attributed to our ability to move from raw data to physical parameters and from there to physical processes in the clouds. The data and the instruments themselves are very difficult to use because they are mostly remote sensors that do not measure directly what we need (eg rain speed or running water path), but measures electromagnetic properties such as backscattering, Doppler spectrum and radiation.In addition, it often affects the raw signal by artifacts, noise, aerosols and precipitation.Raw data is either directly related to physical quantities that we want to measure through well-defined sets of equations or are indirectly related.In the latter case, deriving physical quantities means solving mathematical equations called ‘inverse problems’ which are inherently complex.The fact that we have managed to develop new ways to quantify physical properties of clouds and extract reliable information about them is a great achievement.And that of us has put research into these types of clouds at the forefront. “
Having focused on only a few aspects of the complex rain-turbulence interactions, Ghate and Cadeddu plan to continue their research. They also intend to focus on other regions such as the North Pacific and South Atlantic, where the properties of clouds, rain and turbulence differ greatly from those in the North Atlantic.
Explosive origin of “secondary” ice and snow
X. Zheng et al. Cloud oscillation of the boundary layer of the planet formed by combining turbulence with precipitation in climate simulations, Journal of Progress in Modeling Earth Systems (2017). DOI: 10.1002 / 2017MS000993
Virendra P. Ghate et al. Rainfall and turbulence below the closed cellular marine layers of the stracuma, Journal of Geophysical Research: Atmospheres (2019). DOI: 10.1029 / 2018JD030141
Virendra P. Ghate et al. Currents of rain, turbulence and density below the post-cold frontal open cellular marine stratocumulus clouds, Journal of Geophysical Research: Atmospheres (2020). DOI: 10.1029 / 2019JD031586
X. Zheng et al. Estimation of marine stratocumulus cloud deposition in the E3SMv1 atmospheric model: A case study from the ARM MAGIC field campaign, Monthly time overview (2020). DOI: 10.1175 / MWR-D-19-0349.1
Provided by the Argonne National Laboratory
Citation: Algorithm for capturing rain-turbulence interactions could improve predictions of future climate conditions (2021, March 25) downloaded March 25, 2021 from https://phys.org/news/2021-03-algorithm-capture-drizzle-turbulence -interactions- future.html
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