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Traversi, R.
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Traversi, R.
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- PublicationUnknownRelationships linking primary production, sea ice melting, and biogenic aerosol in the Arctic(2016)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;; ; ; ;This study examines the relationships linking methanesulfonic acid (MSA, arising from the atmospheric oxidation of the biogenic dimethylsulfide, DMS) in atmospheric aerosol, satellite-derived chlorophyll a (Chl-a), and oceanic primary production (PP), also as a function of sea ice melting (SIM) and extension of the ice free area in the marginal ice zone (IF-MIZ) in the Arctic. MSA was determined in PM10 samples collected over the period 2010–2012 at two Arctic sites, Ny Ålesund (78.9°N, 11.9°E), Svalbard islands, and Thule Air Base (76.5°N, 68.8°W), Greenland. PP is calculated by means of a bio-optical, physiologically based, semi-analytical model in the potential source areas located in the surrounding oceanic regions (Barents and Greenland Seas for Ny Ålesund, and Baffin Bay for Thule). Chl-a peaks in May in the Barents sea and in the Baffin Bay, and has maxima in June in the Greenland sea; PP follows the same seasonal pattern of Chl-a, although the differences in absolute values of PP in the three seas during the blooms are less marked than for Chl-a. MSA shows a better correlation with PP than with Chl-a, besides, the source intensity (expressed by PP) is able to explain more than 30% of the MSA variability at the two sites; the other factors explaining the MSA variability are taxonomic differences in the phytoplanktonic assemblages, and transport processes from the DMS source areas to the sampling sites. The taxonomic differences are also evident from the slopes of the correlation plots between MSA and PP: similar slopes (in the range 34.2–36.2 ng m−3of MSA/(gC m−2 d−1)) are found for the correlation between MSA at Ny Ålesund and PP in Barents Sea, and between MSA at Thule and PP in the Baffin Bay; conversely, the slope of the correlation between MSA at Ny Ålesund and PP in the Greenland Sea in summer is smaller (16.7 ng m−3of MSA/(gC m−2 d−1)). This is due to the fact that DMS emission from the Barents Sea and Baffin Bay is mainly related to the MIZ diatoms, which are prolific DMS producers, whereas in the Greenland Sea the DMS peak is related to an offshore pelagic bloom where low-DMS producer species are present. The sea ice dynamic plays a key role in determining MSA concentration in the Arctic, and a good correlation between MSA and SIM (slope = 39 ng m−3 of MSA/106 km2 SIM) and between MSA and IF-MIZ (slope = 56 ng m−3 of MSA/106 km2 IF-MIZ) is found for the cases attributable to bloomings of diatoms in the MIZ. Such relationships are calculated by combining the data sets from the two sites and suggest that PP is related to sea ice melting and to the extension of marginal sea ice areas, and that these factors are the main drivers for MSA concentrations at the considered Arctic sites.342 6 - PublicationRestrictedNew estimations of precipitation and surface sublimation in East Antarctica from snow accumulation measurements(2004-12)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ;Frezzotti, M.; Ente per le Nuove Tecnologie, l’Energia e l’Ambiente, ‘Progetto Clima Globale’, Rome, Italy ;Pourchet, M.; Laboratoire de Glaciologie et Géophysique de l’Environnement, CNRS, Saint Martin d’Hères, France ;Flora, O.; Dipartimento di Scienze Geologiche, Ambientali e Marine, University of Trieste, Trieste, Italy ;Gandolfi, S.; Dipartimento di Ingegneria delle Strutture, dei Trasporti, delle Acque, del Rilevamento, del Territorio, University of Bologna, Bologna, Italy ;Gay, M.; Laboratoire de Glaciologie et Géophysique de l’Environnement, CNRS, Saint Martin d’Hères, France ;Urbini, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma2, Roma, Italia ;Vincent, C.; Laboratoire de Glaciologie et Géophysique de l’Environnement, CNRS, Saint Martin d’Hères, France ;Becagli, S.; Dipartimento di Chimica, University of Florence, Florence, Italy ;Gragnani, R.; Ente per le Nuove Tecnologie, l’Energia e l’Ambiente, ‘Progetto Clima Globale’, Rome, Italy ;Proposito, M.; Ente per le Nuove Tecnologie, l’Energia e l’Ambiente, ‘Progetto Clima Globale’, Rome, Italy ;Severi, M.; Dipartimento di Chimica, University of Florence, Florence, Italy ;Traversi, R.; Dipartimento di Chimica, University of Florence, Florence, Italy ;Udisti, R.; Dipartimento di Chimica, University of Florence, Florence, Italy ;Fily, M.; Laboratoire de Glaciologie et Géophysique de l’Environnement, CNRS, Saint Martin d’Hères, France; ; ; ; ; ; ; ; ; ; ; ; ; Surface mass balance (SMB) distribution and its temporal and spatial variability is an essential input parameter in mass balance studies. Different methods were used, compared and integrated (stake farms, ice cores, snow radar, surface morphology, remote sensing) at eight sites along a transect from Terra Nova Bay (TNB) to Dome C (DC) (East Antarctica), to provide detailed information on the SMB. Spatial variability measurements show that the measured maximum snow accumulation (SA) in a 15 km area is well correlated to firn temperature. Wind-driven sublimation processes, controlled by the surface slope in the wind direction, have a huge impact (up to 85% of snow precipitation) on SMB and are significant in terms of past, present and future SMB evaluations. The snow redistribution process is local and has a strong impact on the annual variability of accumulation. The spatial variability of SMB at the kilometre scale is one order of magnitude higher than its temporal variability (20–30%) at the centennial time scale. This high spatial variability is due to wind-driven sublimation. Compared with our SMB calculations, previous compilations generally over-estimate SMB, up to 65% in some areas.388 31 - PublicationRestrictedSpatial and temporal variability of snow accumulation in East Antarctica from traverse data(2005-07-21)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ;Frezzotti, M.; Ente per le Nuove Tecnologie, l’Energia e l’Ambiente, ‘Progetto Clima Globale’, Rome, Italy ;Pourchet, M.; Laboratoire de Glaciologie et Géophysique de l’Environnement, CNRS, Saint Martin d’Hères, France ;Flora, O.; Dipartimento di Scienze Geologiche, Ambientali e Marine, University of Trieste, Trieste, Italy ;Gandolfi, S.; Dipartimento di Ingegneria delle Strutture, dei Trasporti, delle Acque, del Rilevamento, del Territorio, University of Bologna, Bologna, Italy ;Gay, M.; Laboratoire de Glaciologie et Géophysique de l’Environnement, CNRS, Saint Martin d’Hères, France ;Urbini, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma2, Roma, Italia ;Vincent, C.; Laboratoire de Glaciologie et Géophysique de l’Environnement, CNRS, Saint Martin d’Hères, France ;Becagli, S.; Dipartimento di Chimica, University of Florence, Florence, Italy ;Gragnani, R.; Ente per le Nuove Tecnologie, l’Energia e l’Ambiente, ‘Progetto Clima Globale’, Rome, Italy ;Proposito, M.; Ente per le Nuove Tecnologie, l’Energia e l’Ambiente, ‘Progetto Clima Globale’, Rome, Italy ;Severi, M.; Dipartimento di Chimica, University of Florence, Florence, Italy ;Traversi, R.; Dipartimento di Chimica, University of Florence, Florence, Italy ;Udisti, R.; Dipartimento di Chimica, University of Florence, Florence, Italy ;Fily, M.; Laboratoire de Glaciologie et Géophysique de l’Environnement, CNRS, Saint Martin d’Hères, France; ; ; ; ; ; ; ; ; ; ; ; ; Recent snow accumulation rate is a key quantity for ice core and mass balance studies. Several accumulation measurement methods (stake farm, fin core, snow-radar profiling, surface morphology, remote sensing) were used, compared and integrated at eight sites along a transect from Terra Nova Bay to Dome C (East Antarctica) to provide information about the spatial and temporal variability of snow accumulation. Thirty-nine cores were dated by identifying tritium/β marker levels (1965–66[AUTHOR: Please check dates, I don’t think this agrees with table 1]) and no-sea-salt (nss) SO4 raised to the power of 2– spikes of the Tambora volcanic event (1816) in order to provide information on temporal variability. Cores were linked by snow radar and GPS surveys to provide detailed information on spatial variability in snow accumulation. Stake farm and ice core accumulation rates are observed to differ significantly, but isochrones (snow radar) correlate well with ice core derived accumulation. The accumulation/ablation pattern from stake measurements suggests that the annual local noise (metre scale) in snow accumulation can approach 2 years of ablation and more than four times the average annual accumulation, with no accumulation or ablation for a 5-year period in up to 40% of cases. The spatial variability of snow accumulation at the kilometre scale is one order of magnitude higher than temporal variability at the multi-decadal/secular scale. Stake measurements and firn cores at Dome C confirm an approximate 30% increase in accumulation over the last two centuries, with respect to the average over the last 5000 years.253 30