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  • 01. Atmosphere::01.01. Atmosphere::01.01.02. Climate  (2)
  • Flow-through  (1)
  • American Chemical Society  (1)
  • American Institute of Physics  (1)
  • American Meterological Society  (1)
  • 1
    Publication Date: 2020-11-16
    Description: The double-intertropical convergence zone (DI) systematic error, affecting state-of-the-art coupled general circulation models (CGCM) is examined in the multi-model Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) ensemble of simulations of the twentieth-century climate. Aim of this study is to quantify the DI error on precipitation in the tropical Pacific, with a specific focus on the relationship between the DI error and the representation of large-scale vertical circulation regimes in climate models. The DI rainfall signal is analysed using a regime sorting approach for the vertical circulation regimes. Through the use of this compositing technique, precipitation events are regime-sorted based on the large scale vertical motions, as represented by the mid-tropospheric lagrangian pressure tendency omega500 dynamical proxy. This methodology allows the partition of the precipitation signal into deep and shallow convective components. Following the regime-sorting diagnosis, the total DI bias is split into an error affecting the magnitude of precipitation associated with individual convective events and an error affecting the frequency of occurrence of single convective regimes. It is shown that, despite the existing large intra-model differences, CGCMs can be ultimately grouped into a few homegenous clusters, each featuring a well defined rainfall-vertical circulation relationship in the DI region. Three major behavioural clusters are identified within the AR4 models ensemble: two unimodal distributions, featuring maximum precipitation under subsidence and deep convection regimes, respectively, and one bimodal distribution, displaying both components. Extending this analysis to both coupled and uncoupled (atmosphere-only) AR4 simulations reveals that the DI bias in CGCMs is mainly due to the overly frequent occurrence of deep convection regimes, whereas the error on rainfall magnitude associated with individual convective events is overall consistent with errors already present in the corresponding atmosphere stand-alone simulations. A critical parameter controlling the strength of the DI systematic error is identified in the model-dependent sea surface temperature (SST) threshold leading to the onset of deep convection (THR), combined with the average SST in the south-eastern Pacific.
    Description: Published
    Description: 1127–1145
    Description: 4A. Oceanografia e clima
    Description: JCR Journal
    Description: open
    Keywords: double ITCZ ; climate models ; 01. Atmosphere::01.01. Atmosphere::01.01.02. Climate
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 2
    Publication Date: 2020-12-14
    Description: In this study we present an intercomparison of measurements of very low water vapor column content obtained with a Ground-Based Millimeter-wave Spectrometer (GBMS), Vaisala RS92k radiosondes, a Raman Lidar, and an IR Fourier Transform Spectrometer. These sets of measurements were carried out during the primary field campaign of the ECOWAR (Earth COoling by WAter vapor Radiation) project which took place on the Western Italian Alps from 3 to 16 March, 2007.
    Description: Published
    Description: 135-138
    Description: 1.8. Osservazioni di geofisica ambientale
    Description: N/A or not JCR
    Description: open
    Keywords: Precipitable Water Vapor ; ECOWAR ; IR and Millimeter-Wave Spectroscopy ; 01. Atmosphere::01.01. Atmosphere::01.01.01. Composition and Structure ; 01. Atmosphere::01.01. Atmosphere::01.01.02. Climate
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 3
    Publication Date: 2022-10-21
    Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Colson, B. C., & Michel, A. P. M. Flow-through quantification of microplastics using impedance spectroscopy. ACS Sensors, 6(1), (2021): 238–244, doi:10.1021/acssensors.0c02223.
    Description: Understanding the sources, impacts, and fate of microplastics in the environment is critical for assessing the potential risks of these anthropogenic particles. However, our ability to quantify and identify microplastics in aquatic ecosystems is limited by the lack of rapid techniques that do not require visual sorting or preprocessing. Here, we demonstrate the use of impedance spectroscopy for high-throughput flow-through microplastic quantification, with the goal of rapid measurement of microplastic concentration and size. Impedance spectroscopy characterizes the electrical properties of individual particles directly in the flow of water, allowing for simultaneous sizing and material identification. To demonstrate the technique, spike and recovery experiments were conducted in tap water with 212–1000 μm polyethylene beads in six size ranges and a variety of similarly sized biological materials. Microplastics were reliably detected, sized, and differentiated from biological materials via their electrical properties at an average flow rate of 103 ± 8 mL/min. The recovery rate was ≥90% for microplastics in the 300–1000 μm size range, and the false positive rate for the misidentification of the biological material as plastic was 1%. Impedance spectroscopy allowed for the identification of microplastics directly in water without visual sorting or filtration, demonstrating its use for flow-through sensing.
    Description: The authors thank the Richard Saltonstall Charitable Foundation and the National Academies Keck Futures Initiative (NAKFI DBS13) for their funding support.
    Keywords: Microplastics ; Plastics ; Impedance spectroscopy ; Dielectric properties ; Instrumentation ; Particle detection ; Flow-through ; Environmental sensing
    Repository Name: Woods Hole Open Access Server
    Type: Article
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