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  • Geophysics  (1)
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  • 1
    Publication Date: 2019-07-13
    Description: Reanalysis data sets are widely used to understand atmospheric processes and past variability, and are often used to stand in as observations for comparisons with climate model output. Because of the central role of water vapor (WV) and ozone (O3) in climate change, it is important to understand how accurately and consistently these species are represented in existing global reanalyses. In this paper, we present the results of WV and O3 intercomparisons that have been performed as part of the SPARC (Stratosphere-troposphere Processes and their Role in Climate) Reanalysis Intercomparison Project (SRIP). The comparisons cover a range of timescales and evaluate both inter-reanalysis and observation-reanalysis differences. We also provide a systematic documentation of the treatment of WV and O3 in current reanalyses to aid future research and guide the interpretation of differences amongst reanalysis fields.The assimilation of total column ozone (TCO) observations in newer reanalyses results in realistic representations of TCO in reanalyses except when data coverage is lacking, such as during polar night. The vertical distribution of ozone is also relatively well represented in the stratosphere in reanalyses, particularly given the relatively weak constraints on ozone vertical structure provided by most assimilated observations and the simplistic representations of ozone photochemical processes in most of the reanalysis forecast models. However, significant biases in the vertical distribution of ozone are found in the upper troposphere and lower stratosphere in all reanalyses.In contrast to O3, reanalysis estimates of stratospheric WV are not directly constrained by assimilated data. Observations of atmospheric humidity are typically used only in the troposphere, below a specified vertical level at or near the tropopause. The fidelity of reanalysis stratospheric WV products is therefore mainly dependent on the reanalyses representation of the physical drivers that influence stratospheric WV, such as temperatures in the tropical tropopause layer, methane oxidation, and the stratospheric overturning circulation. The lack of assimilated observations and known deficiencies in the representation of stratospheric transport in reanalyses result in much poorer agreement amongst observational and reanalysis estimates of stratospheric WV. Hence, stratospheric WV products from the current generation of reanalyses should generally not be used in scientific studies.
    Keywords: Geophysics
    Type: GSFC-E-DAA-TN46784 , Atmospheric Chemistry and Physics (ISSN 1680-7316) (e-ISSN 1680-7324); 17; 20; 12,743-12,778
    Format: application/pdf
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  • 2
    Publication Date: 2023-09-01
    Description: Abstract
    Description: These data files contain the results of Evolved Gas Analysis (EGA) on a Mettler-Toledo TGA-DSC 3+ attached to a Pfeiffer Vacuum GSD 320 gas mass spectrometer on ash samples from the 15 January 2022 eruption of Hunga volcano, Tonga. Samples were heated in Nitrogen at a rate of 5 °C/min from 30 to 150 °C for dehydration purposes and then at a rate of 30 °C/min from 150 °C to 1300 °C. During heating, the relative amount of different volatile species (H2O, CO2, SO2, HCl, and H2S, which are extracted from mass-to-charge ratios of 18, 44, 64, 36, and 34, respectively) were recorded by the gas mass spectrometer. Natural samples collected for the analysis were bulk ash material or correspond to a given ash fraction. Particles were unwashed and dried at 40°C before analysis. We compared the signals for natural samples with (i) a mixture of salt-free dense glassy particles, white and dark pumice (HT10), (ii) pure halite, anhydrite, gypsum, calcite and pyrit, (iii) mix between pure magmatic particles (HT10) from (i) and manually added components from (iii).
    Keywords: Volcano ; Submarine eruption ; ash aggregation ; salt formation ; ash_and_lapilli ; Composition ; EARTH SCIENCE 〉 ATMOSPHERE 〉 ATMOSPHERIC CHEMISTRY ; EARTH SCIENCE 〉 OCEANS 〉 MARINE VOLCANISM ; Scanning Electrone Microscope
    Type: Dataset , Dataset
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