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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Leo-Juhani Meriö; Anssi Rauhala; Pertti Ala-aho; Anton Kuzmin; +4 Authors

    Detailed information on seasonal snow cover and depth is essential to the understanding of snow processes, to operational forecasting, and as input for hydrological models. Recent advances in uncrewed or unmanned aircraft systems (UASs) and structure from motion (SfM) techniques have enabled low-cost monitoring of spatial snow depth distribution in resolutions of up to a few centimeters. Here, we study the spatiotemporal variability in snow depth and interactions between snow and vegetation in different subarctic landscapes consisting of a mosaic of conifer forest, mixed forest, transitional woodland/shrub, and peatland areas. To determine the spatiotemporal variability in snow depth, we used high-resolution (50 cm) snow depth maps generated from repeated UAS–SfM surveys in the winter of 2018/2019 and a snow-free bare-ground survey after snowmelt. Due to poor subcanopy penetration with the UAS–SfM method, tree masks were utilized to remove canopy areas and the area (36 cm) immediately next to the canopy before analysis. Snow depth maps were compared to the in situ snow course and a single-point continuous ultrasonic snow depth measurement. Based on the results, the difference between the UAS–SfM survey median snow depth and single-point measurement increased for all land cover types during the snow season, from +5 cm at the beginning of the accumulation to −16 cm in coniferous forests and −32 cm in peatland during the melt period. This highlights the poor representation of point measurements in selected locations even on the subcatchment scale. The high-resolution snow depth maps agreed well with the snow course measurement, but the spatial extent and resolution of maps were substantially higher. The snow depth range (5th–95th percentiles) within different land cover types increased from 17 to 42 cm in peatlands and from 33 to 49 cm in the coniferous forest from the beginning of the snow accumulation to the melt period. Both the median snow depth and its range were found to increase with canopy density; this increase was greatest in the conifer forest area, followed by mixed forest, transitional woodland/shrub, and open peatlands. Using the high-spatial-resolution data, we found a systematic increase (2–20 cm) and then a decline in snow depth near the canopy with increasing distance (from 1 to 2.5 m) of the peak value through the snow season. This study highlights the applicability of the UAS–SfM in high-resolution monitoring of snow depth in multiple land cover types and snow–vegetation interactions in subarctic and remote areas where field data are not available or where the available data are collected using classic point measurements or snow courses.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ The Cryosphere (TC)arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    The Cryosphere (TC)
    Article . 2023
    License: CC BY
    Data sources: Crossref
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    The Cryosphere (TC)
    Other literature type . 2023
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ The Cryosphere (TC)arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      The Cryosphere (TC)
      Article . 2023
      License: CC BY
      Data sources: Crossref
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      The Cryosphere (TC)
      Other literature type . 2023
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Anssi Rauhala; Leo-Juhani Meriö; Anton Kuzmin; Pasi Korpelainen; +4 Authors

    Abstract. Snow conditions in the Northern Hemisphere are rapidly changing, and information on snow depth is critical for decision-making and other societal needs. Uncrewed or unmanned aircraft systems (UASs) can offer data resolutions of a few centimeters at a catchment-scale and thus provide a low-cost solution to bridge the gap between sparse manual probing and low-resolution satellite data. In this study, we present a series of snow depth measurements using different UAS platforms throughout the winter in the Finnish subarctic site Pallas, which has a heterogeneous landscape. We discuss the different platforms, the methods utilized, difficulties working in the harsh northern environment, and the UAS snow depth results compared to in situ measurements. Generally, all UASs produced spatially representative estimates of snow depth in open areas after reliable georeferencing by using the structure from motion (SfM) photogrammetry technique. However, significant differences were observed in the accuracies produced by the different UASs compared to manual snow depth measurements, with overall root mean square errors (RMSEs) varying between 13.0 and 25.2 cm, depending on the UAS. Additionally, a reduction in accuracy was observed when moving from an open mire area to forest-covered areas. We demonstrate the potential of low-cost UASs to efficiently map snow surface conditions, and we give some recommendations on UAS platform selection and operation in a harsh subarctic environment with variable canopy cover.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ The Cryosphere (TC)arrow_drop_down
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    The Cryosphere (TC)
    Other literature type . 2023
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    The Cryosphere (TC)
    Article . 2023
    License: CC BY
    Data sources: Crossref
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ The Cryosphere (TC)arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      The Cryosphere (TC)
      Other literature type . 2023
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      The Cryosphere (TC)
      Article . 2023
      License: CC BY
      Data sources: Crossref
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Hélène Seroussi; Vincent Verjans; Sophie Nowicki; Antony J. Payne; +45 Authors

    Abstract. The Antarctic Ice Sheet represents the largest source of uncertainty in future sea level rise projections, with a contribution to sea level by 2100 ranging from -5 to 43 cm of sea level equivalent under high carbon emission scenarios estimated by the recent Ice Sheet Model Intercomparison for CMIP6 (ISMIP6). ISMIP6 highlighted the different behaviors of the East and West Antarctic ice sheets, as well as the possible role of increased surface mass balance in offsetting the dynamic ice loss in response to changing oceanic conditions in ice shelf cavities. However, the detailed contribution of individual glaciers, as well as the partitioning of uncertainty associated with this ensemble, have not yet been investigated. Here, we analyze the ISMIP6 results for high carbon emission scenarios, focusing on key glaciers around the Antarctic Ice Sheet, and we quantify their projected dynamic mass loss, defined here as mass loss through increased ice discharge into the ocean in response to changing oceanic conditions. We highlight glaciers contributing the most to sea level rise as well as their vulnerability to changes in oceanic conditions. We then investigate the different sources of uncertainty and their relative role in projections, for the entire continent and for key individual glaciers. We show that, in addition to Thwaites and Pine Island glaciers in West Antarctica, Totten and Moscow University glaciers in East Antarctica present comparable future dynamic mass loss and high sensitivity to ice shelf basal melt. The overall uncertainty in additional dynamic mass loss in response to changing oceanic conditions, compared to a scenario with constant oceanic conditions, is dominated by the choice of ice sheet model, accounting for 52 % of the total uncertainty of the Antarctic dynamic mass loss in 2100. Its relative role for the most dynamic glaciers varies between 14 % for MacAyeal and Whillans ice streams and 56 % for Pine Island Glacier at the end of the century. The uncertainty associated with the choice of climate model increases over time and reaches 13 % of the uncertainty by 2100 for the Antarctic Ice Sheet, but varies between 4 % for Thwaites glacier and 53 % for Whillans ice stream. The uncertainty associated with the ice-climate interaction, which captures different treatments of oceanic forcings such as the choice of melt parameterization, its calibration, and simulated ice shelf geometries, accounts for 22 % of the uncertainty at the ice sheet scale, but reaches 36 and 39 % for Institute ice stream and Thwaites Glacier, respectively, by 2100. Overall, this study helps inform future research by highlighting the sectors of the ice sheet most vulnerable to oceanic warming over the 21st century and by quantifying the main sources of uncertainty.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Vrije Universiteit B...arrow_drop_down
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    The Cryosphere (TC)
    Article . 2023
    License: CC BY
    Data sources: Crossref
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    https://doi.org/10.5194/tc-202...
    Preprint . 2023
    License: CC BY
    Data sources: Crossref
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    The Cryosphere (TC)
    Other literature type . 2023
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    The Cryosphere (TC)
    Other literature type . 2023
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Vrije Universiteit B...arrow_drop_down
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      The Cryosphere (TC)
      Article . 2023
      License: CC BY
      Data sources: Crossref
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      https://doi.org/10.5194/tc-202...
      Preprint . 2023
      License: CC BY
      Data sources: Crossref
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      The Cryosphere (TC)
      Other literature type . 2023
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      The Cryosphere (TC)
      Other literature type . 2023
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Jari-Pekka Nousu; Matthieu Lafaysse; Giulia Mazzotti; Pertti Ala-aho; +8 Authors

    The snowpack has a major influence on the land surface energy budget. Accurate simulation of the snowpack energy budget is challenging due to e.g. vegetation and topography that complicate the radiation budget, and limitations in theoretical understanding of turbulent transfer in the stable boundary layer. Studies that evaluate snow, hydrology and land surface models (LSMs) against detailed observations of all surface energy components at high latitudes are scarce. In this study, we compared different configurations of SURFEX LSM model against surface energy flux, snow depth and soil temperature observations from four eddy covariance stations in Finland. The sites cover two different climate and snow conditions, representing the southern and northern subarctic zones, and the contrasting forest and peatland ecosystems typical for the boreal landscape. We tested the sensitivity of surface energy fluxes to different process parameterizations implemented in the Crocus snowpack model. In addition, we examined common alternative approaches to conceptualize soil and vegetation, and assess their performance in simulating surface energy fluxes, snow conditions and soil thermal regime. Our results show that using a stability correction function that increases the turbulent exchange under stable atmospheric conditions is imperative to simulate sensible and latent heat fluxes over snow. For accurate simulations of surface heat fluxes and snow/soil conditions in forests, an explicit vegetation representation is necessary. Moreover, we found the peat soil temperature profile simulations to be greatly improved with realistic soil texture (soil organic carbon) parameterization. Although we focused on models within the SURFEX LSM platform, the results have broader implications for choosing suitable turbulent flux parameterization and model structures depending on the potential use cases. METEOROLOGICAL DATA.zip contains meteorological data csv-files for each site. Data come from the Finnish Meteorological Institute (FMI) open database (FMI, 2021) (Station IDs: Lompolojänkkä: 778135, Kenttärova: 101317, Hyytiälä: 101987, https://www.ilmatieteenlaitos.fi/havaintoasemat). Meteorological data at Siikaneva come from the SMEAR database (Alekseychik et al. 2022a) (https://smear.avaa.csc.fi/). At Siikaneva and Hyytiälä, the shortwave and longwave radiation were obtained from the SMEAR database, while at Lompolojänkkä and Kenttärova data from FMI stations were used. The meteorological data gaps were first filled by the contiguous sites and the remaining gaps by other nearby meteorological stations (IDs: Sodankylä: 101932, Ähtäri: 101520). The missing radiation observations were first filled by the contiguous sites, and the remaining gaps by ERA5 reanalysis data (Hersbach et al. 2020). Meteorological data is accompanied with metadata (METEO_metadata) and summary of flag counts (METEO_flags). EVALUATION DATA.zip contains evaluation data csv-files for each site. The surface energy fluxes of Lompolojänkkä and Kenttärova come from FMI while data of Hyytiälä and Siikaneva were downloaded from SMEAR open database. These data have been detailed in original site and data publications by Aurela et al. (2015) (Lompolojänkkä and Kenttärova) and Mammarella et al. (2016, 2019); Alekseychik et al. (2022b) (Siikaneva and Hyytiälä). Automated height of snow observations come from FMI and SMEAR open databases. Manual snow measurements at Lompolojänkkä, Kenttärova and Hyytiälä are described by Marttila et al. (2021) and Aalto et al. (2022). Soil temperature measurements are detailed in Aurela et al. (2015) and Aalto et al. (2022). Evaluation data is accompanied with metadata (EVALUATION_metadata). FORCING DATA.zip and NAMELIST FILES.zip contain SURFEX model specific forcing nc-files and parameter txt-files. These are needed for a SURFEX simulation. Documentation can be found at http://www.umr-cnrm.fr/surfex/spip.php?rubrique88 Aalto, J., Aalto, P., Keronen, P., Kolari, P., Rantala, P., Taipale, R., Kajos, M., Patokoski, J., Rinne, J., Ruuskanen, T., and others: SMEAR II Hyytiälä forest meteorology, greenhouse gases, air quality and soil, \url{https://doi.org/10.23729/62f7ad2c-7fe0-4f66-b0a4-8d57c80524ec}, 2022 Alekseychik, P., Kolari, P., Rinne, J., Haapanala, S., Laakso, H., Taipale, R., Matilainen, T., Salminen, T., Levula, J., Tuittila, E.-S., and others: SMEAR II Siikaneva 1 wetland meteorology and soil, \url{https://doi.org/10.23729/7d205559-3ef9-4f34-8e08-ea24316f50c8}, 2022a Alekseychik, P., Peltola, O., Li, X., Aurela, M., Hatakka, J., Pihlatie, M., Rinne, J., Haapanala, S., Laakso, H., Taipale, R., and others: SMEAR II Siikaneva 1 wetland eddy covariance, \url{https://doi.org/10.23729/f6455f02-905b-4bf7-a870-743bd3788bf6}, 2022b Aurela, M., Lohila, A., Tuovinen, J. P., Hatakka, J., Penttilä, T., and Laurila, T.: Carbon dioxide and energy flux measurements in four northern-boreal ecosystems at Pallas, Boreal Environment Research, 20, 455–473, 2015. FMI: Finnish Meteorological Institute past weather observations, available at: https://en.ilmatieteenlaitos.fi/download-observations, 2021. Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Sim-mons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren,P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J.,Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5 global reanalysis, Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. Mammarella, I., Peltola, O., Nordbo, A., and Järvi, L.: Quantifying the uncertainty of eddy covariance fluxes due to the use of different software packages and combinations of processing steps in two contrasting ecosystems, Atmospheric Measurement Techniques, 9, 4915–4933, https://doi.org/10.5194/amt-9-4915-2016, 2016. Mammarella, I., Rannik, , Launiainen, S., Alekseychik, P., Peltola, O., Keronen, P., Kolari, P., Laakso, H., Matilainen, T., Salminen, T., andothers: SMEAR II Hyytiälä forest eddy covariance, \url{https://doi.org/10.23729/40f64739-11d1-4e5f-8dc2-da931512c91c}, 2019. Marttila, H., Lohila, A., Ala-Aho, P., Noor, K., Welker, J. M., Croghan, D., Mustonen, K., Meriö, L., Autio, A., Muhic, F., Bailey, H., Aurela, M., Vuorenmaa, J., Penttilä, T., Hyöky, V., Klein, E., Kuzmin, A., Korpelainen, P., Kumpula, T., Rauhala, A., and Kløve, B.: Subarctic catchment water storage and carbon cycling – Leading the way for future studies using integrated datasets at Pallas, Finland, Hydrological Processes, 35, 1–19, https://doi.org/10.1002/hyp.14350, 2021.

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    Authors: Leo-Juhani Meriö; Anssi Rauhala; Pertti Ala-aho; Anton Kuzmin; +4 Authors

    Abstract. Detailed information on seasonal snow cover and depth is essential to the understanding of snow processes, operational forecasting, and as input for hydrological models. Recent advances in unmanned aircraft systems (UASs) and structure from motion (SfM) techniques have enabled low-cost monitoring of spatial snow depth distribution in resolutions up to a few centimeters. Here, we study the spatiotemporal variability of snow depth and interactions between snow and vegetation in different subarctic landscapes consisting of a mosaic of conifer forest, mixed forest, transitional woodland/shrub, and peatland areas. To determine the spatiotemporal variability of snow depth, we used high-resolution (50 cm) snow depth maps generated from repeated UAS-SfM surveys in the winter of 2018/2019 and a snow-free bare ground survey after snowmelt. Due to poor sub-canopy penetration with the UAS-SfM method, tree masks were utilized to remove canopy areas and the area (36 cm) immediately next to the canopy before analysis. Snow depth maps were compared to the in-situ snow course and a single-point continuous ultrasonic snow depth measurement. Based on the results, the difference between the UAS-SfM survey median snow depth and single-point measurement increased for all land cover types during the snow season, from +5 cm at the beginning of the accumulation to -16 cm in coniferous forests and -32 cm in peatland during the melt period. This highlights the poor representation of point measurements even on the sub-catchment scale. The high-resolution snow depth maps agreed well with the snow course measurement, but the spatial extent and resolution of maps were substantially higher. The snow depth variability (5–95 percentiles) within different land cover types increased from 17 cm to 42 cm in peatlands and from 33 cm to 49 cm in the coniferous forest from the beginning of the snow accumulation to the melt period. Both the median snow depth and its variability were found to increase with canopy density; this increase was greatest in the conifer forest area, followed by mixed forest, transitional woodland/shrub, and open peatlands. Using the high spatial resolution data, we found a systematic increase (2–20 cm), then a decline of snow depth near the canopy with increasing distance (from 1 m to 2.5 m) of the peak value through the snow season. This study highlights the potential of the UAS-SfM in high-resolution monitoring of snow depth in multiple land cover types and snow-vegetation interactions in subarctic and remote areas where field data is not available.

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    https://doi.org/10.5194/tc-202...
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      https://doi.org/10.5194/tc-202...
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    Authors: Anssi Rauhala; Leo-Juhani Meriö; Anton Kuzmin; Pasi Korpelainen; +4 Authors

    Abstract. Snow conditions in the northern hemisphere are rapidly changing, and information on snow depth is critical for decision-making and other societal needs. Unmanned aircraft systems (UASs) can offer data resolutions of a few centimeters at a catchment-scale, and thus provide a low-cost solution to bridge the gap between sparse manual probing and low-resolution satellite data. In this study, we present a series of snow depth measurements using different UAS platforms throughout the winter in the Finnish subarctic site Pallas, which has a heterogeneous landscape. We discuss the different platforms, the methods utilized, difficulties working in the harsh northern environment, and the results and their accuracy compared to in situ measurements. Generally, all UASs produced spatially representative estimates of snow depth in open areas after reliable georeferencing by using the Structure from Motion (SfM) photogrammetry technique. However, significant differences were observed in the accuracies produced by the different UASs compared to manual snow depth measurements, with overall RMSEs varying between 13.0 to 25.2 cm, depending on the UAS. Additionally, a reduction in accuracy was observed when moving from an open mire area to forest covered areas. We demonstrate the potential of low-cost UASs to efficiently map snow surface conditions, and we give some recommendations on UAS platform selection and operation in a harsh subarctic environment with variable canopy cover.

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    https://doi.org/10.5194/tc-202...
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    Authors: Verdonen, Mariana; Störmer, Alexander; Lotsari, Eliisa; Korpelainen, Pasi; +3 Authors

    Funding Information: This research has been supported by the Vilho, Yrjö and Kalle Väisälä Foundation of the Finnish Academy of Science and Letters, EU Horizon 2020 Research and Innovation Programme (grant no. 869471), the Academy of Finland (grant no. 330319, 338480, and 346602), and the Erasmus+ staff mobility programme. Publisher Copyright: © Author(s) 2023. Palsas and peat plateaus are expected to disappear from many regions, including Finnish Lapland. However, detailed long-Term monitoring data of the degradation process on palsas are scarce. Here, we present the results of the aerial photography time series analysis (1959-2021), annual real-Time kinematic (RTK) GNSS and active layer monitoring (2007-2021), and annual unoccupied aerial system surveys (2016-2021) at two palsa sites (Peera and Laassaniemi, 68gN) located in north-west Finland. We analysed temporal trends of palsa degradation and their relation to climate using linear regression. At both sites, the decrease in palsa area by-77g€¯% to-90g€¯% since 1959 and height by-16g€¯% to-49g€¯% since 2007 indicate substantial permafrost degradation throughout the study periods. The area loss rates are mainly connected to winter air temperature changes at Peera and winter precipitation changes at Laassaniemi. The active layer thickness (ALT) has varied annually between 2007 and 2021 with no significant trend andis related mainly to the number of very warm days during summer, autumn rainfall of previous year, and snow depths at Peera. At Laassaniemi, the ALT is weakly related to climate and has been decreasing in the middle part of the palsa during the past 8 years despite the continuous decrease in palsa volume. Our findings imply that the ALT in the inner parts of palsas do not necessarily reflect the overall permafrost conditions and underline the importance of surface position monitoring alongside the active layer measurements. The results also showed a negative relationship between the ALT and snow cover onset, indicating the complexity of climate-permafrost feedbacks in palsa mires. Peer reviewed

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    The Cryosphere (TC)
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    https://doi.org/10.15488/14141...
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      https://doi.org/10.15488/14141...
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    Authors: Wang, Xiaoqiao; Zhang, Zhaoru; Dinniman, Michael S.; Uotila, Petteri; +2 Authors

    Abstract. Coastal polynyas in the Ross Sea are important source regions of high salinity shelf water (HSSW) – the precursor of Antarctic Bottom Water that supplies the lower limb of the thermohaline circulation. Here, the response of sea ice production and HSSW formation to synoptic- and meso-scale cyclones were investigated for the Ross Ice Shelf Polynya (RISP) using a coupled ocean-sea ice-ice shelf model targeted on the Ross Sea. When synoptic-scale cyclones prevailed over RISP, sea ice production (SIP) increased rapidly by 20–30 % over the entire RISP. During the passage of mesoscale cyclones, SIP increased by about 2 times over the western RISP but decreased over the eastern RISP, resulting respectively from enhancement in the offshore and onshore winds. HSSW formation mainly occurred in the western RISP and was enhanced responding to the SIP increase under both types of cyclones. Promoted HSSW formation could persist for 12–48 hours after the decay of the cyclones. The HSSW export across the Drygalski Trough was negatively correlated with the meridional wind speed, while the export across the Glomar Challenger Trough was positively correlated with the meridional wind. Such correlations are mainly controlled by variations in geostrophic ocean currents that result from sea surface elevation change.

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    The Cryosphere (TC)
    Article . 2023
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    The Cryosphere (TC)
    Other literature type . 2022
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    https://doi.org/10.5194/tc-202...
    Preprint . 2022
    License: CC BY
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    The Cryosphere (TC)
    Other literature type . 2023
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    ZENODO
    Article . 2023
    License: CC BY
    Data sources: ZENODO
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      The Cryosphere (TC)
      Article . 2023
      License: CC BY
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      The Cryosphere (TC)
      Other literature type . 2022
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      https://doi.org/10.5194/tc-202...
      Preprint . 2022
      License: CC BY
      Data sources: Crossref
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      The Cryosphere (TC)
      Other literature type . 2023
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      ZENODO
      Article . 2023
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Shevnina, Elena; Potes, Miguel; Vihma, Timo; Naakka, Tuomas; +2 Authors

    Abstract. The study provides estimates of summertime evaporation over a glacial lake located in the Schirmacher oasis, Dronning Maud Land, East Antarctica. Lake Zub (alternately named Lake Priyadarshini and referred to throughout as Lake Zub/Priyadarshini) is the second-largest lake in the oasis, and its maximum depth is 6 m. The lake is also among the warmest glacial lakes in the oasis, and it is free of ice during almost 2 summer months. The summertime evaporation over the ice-free lake was measured using the eddy covariance method and estimated on the basis of five indirect methods (bulk-aerodynamic method and four combination equations). We used meteorological and hydrological measurements collected during a field experiment carried out in 2018. The eddy covariance method was considered the most accurate, and the evaporation was estimated to be 114 mm for the period from 1 January to 7 February 2018 (38 d) on the basis of this method. The average daily evaporation was 3.0 mm d−1 in January 2018. During the experiment period, the largest changes in daily evaporation were driven by synoptic-scale atmospheric processes rather than local katabatic winds. The bulk-aerodynamic method suggests the average daily evaporation is 2.0 mm d−1, which is 32 % less than the results based on the eddy covariance method. The bulk-aerodynamic method is much better in producing the day-to-day variations in evaporation compared to the combination equations. All selected combination equations underestimated the evaporation over the lake by 40 %–72 %. The scope of the uncertainties inherent in the indirect methods does not allow us to apply them to estimate the daily evaporation over Lake Zub/Priyadarshini. We suggested a new combination equation to evaluate the summertime evaporation over the lake's surface using meteorological observations from the nearest site. The performance of the new equation is better than the performance of the indirect methods considered. With this equation, the evaporation over the period of the experiment was 124 mm, which is only 9 % larger than the result according to the eddy covariance method.

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    The Cryosphere (TC)
    Article . 2022
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    ZENODO
    Article . 2023
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      The Cryosphere (TC)
      Article . 2022
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      ZENODO
      Article . 2023
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Elena Shevnina; Miguel Potes; Timo Vihma; Tuomas Naakka; +2 Authors

    The water cycle in glacier hydrological networks is not well known in Antarctica. We present the first evaluations of evaporation over a glacial lake located in the Schirmacher oasis, Dronning Maud Land, East Antarctica. Lake Zub/Priyadarshini is a shallow lake of the epiglacial type, and it is ice free for almost two months in summer (December–February). We evaluated evaporation over the ice free surface of Lake Zub/Priyadarshini using various methods including the eddy covariance (EC) method, the bulk aerodynamic method, and Dalton type empirical equations. The evaporation was estimated on the basis of data collected during a field experiment in December–February, 2017–2018, and regular observations at the nearest meteorological site. The EC was considered as the most accurate method providing the reference estimates for the evaporation over the lake surface. The EC method suggests that the mean daily evaporation was 3.0 mm day−1 in January, 2018. The bulk-aerodynamic method, based on observations at the lake shore as an input, yielded a mean daily evaporation of 2.3 mm day−1 for January. One of the Dalton type equations was better in estimating the summer mean evaporation, but the bulk aerodynamic method was much better in producing the day-to-day variations in evaporation. The summer evaporation over the ice-free Lake Zub/Priyadarshini exceeded the summer precipitation by a factor of 10. Hence, evaporation is a major term of the water balance of glacial lakes. Evaluation of the evaporation products of ERA5 reanalysis clearly demonstrated the need to add glacial lakes in the surface scheme of ERA5. Presently the area-averaged evaporation of ERA5 is strongly underestimated in the lake-rich region studied here.

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    https://doi.org/10.5194/tc-202...
    Preprint . 2021
    License: CC BY
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    The Cryosphere (TC)
    Other literature type . 2021
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      https://doi.org/10.5194/tc-202...
      Preprint . 2021
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      The Cryosphere (TC)
      Other literature type . 2021
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Leo-Juhani Meriö; Anssi Rauhala; Pertti Ala-aho; Anton Kuzmin; +4 Authors

    Detailed information on seasonal snow cover and depth is essential to the understanding of snow processes, to operational forecasting, and as input for hydrological models. Recent advances in uncrewed or unmanned aircraft systems (UASs) and structure from motion (SfM) techniques have enabled low-cost monitoring of spatial snow depth distribution in resolutions of up to a few centimeters. Here, we study the spatiotemporal variability in snow depth and interactions between snow and vegetation in different subarctic landscapes consisting of a mosaic of conifer forest, mixed forest, transitional woodland/shrub, and peatland areas. To determine the spatiotemporal variability in snow depth, we used high-resolution (50 cm) snow depth maps generated from repeated UAS–SfM surveys in the winter of 2018/2019 and a snow-free bare-ground survey after snowmelt. Due to poor subcanopy penetration with the UAS–SfM method, tree masks were utilized to remove canopy areas and the area (36 cm) immediately next to the canopy before analysis. Snow depth maps were compared to the in situ snow course and a single-point continuous ultrasonic snow depth measurement. Based on the results, the difference between the UAS–SfM survey median snow depth and single-point measurement increased for all land cover types during the snow season, from +5 cm at the beginning of the accumulation to −16 cm in coniferous forests and −32 cm in peatland during the melt period. This highlights the poor representation of point measurements in selected locations even on the subcatchment scale. The high-resolution snow depth maps agreed well with the snow course measurement, but the spatial extent and resolution of maps were substantially higher. The snow depth range (5th–95th percentiles) within different land cover types increased from 17 to 42 cm in peatlands and from 33 to 49 cm in the coniferous forest from the beginning of the snow accumulation to the melt period. Both the median snow depth and its range were found to increase with canopy density; this increase was greatest in the conifer forest area, followed by mixed forest, transitional woodland/shrub, and open peatlands. Using the high-spatial-resolution data, we found a systematic increase (2–20 cm) and then a decline in snow depth near the canopy with increasing distance (from 1 to 2.5 m) of the peak value through the snow season. This study highlights the applicability of the UAS–SfM in high-resolution monitoring of snow depth in multiple land cover types and snow–vegetation interactions in subarctic and remote areas where field data are not available or where the available data are collected using classic point measurements or snow courses.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ The Cryosphere (TC)arrow_drop_down
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    The Cryosphere (TC)
    Article . 2023
    License: CC BY
    Data sources: Crossref
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    The Cryosphere (TC)
    Other literature type . 2023
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      The Cryosphere (TC)
      Article . 2023
      License: CC BY
      Data sources: Crossref
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      The Cryosphere (TC)
      Other literature type . 2023
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Anssi Rauhala; Leo-Juhani Meriö; Anton Kuzmin; Pasi Korpelainen; +4 Authors

    Abstract. Snow conditions in the Northern Hemisphere are rapidly changing, and information on snow depth is critical for decision-making and other societal needs. Uncrewed or unmanned aircraft systems (UASs) can offer data resolutions of a few centimeters at a catchment-scale and thus provide a low-cost solution to bridge the gap between sparse manual probing and low-resolution satellite data. In this study, we present a series of snow depth measurements using different UAS platforms throughout the winter in the Finnish subarctic site Pallas, which has a heterogeneous landscape. We discuss the different platforms, the methods utilized, difficulties working in the harsh northern environment, and the UAS snow depth results compared to in situ measurements. Generally, all UASs produced spatially representative estimates of snow depth in open areas after reliable georeferencing by using the structure from motion (SfM) photogrammetry technique. However, significant differences were observed in the accuracies produced by the different UASs compared to manual snow depth measurements, with overall root mean square errors (RMSEs) varying between 13.0 and 25.2 cm, depending on the UAS. Additionally, a reduction in accuracy was observed when moving from an open mire area to forest-covered areas. We demonstrate the potential of low-cost UASs to efficiently map snow surface conditions, and we give some recommendations on UAS platform selection and operation in a harsh subarctic environment with variable canopy cover.

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    The Cryosphere (TC)
    Other literature type . 2023
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    The Cryosphere (TC)
    Article . 2023
    License: CC BY
    Data sources: Crossref
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ The Cryosphere (TC)arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      The Cryosphere (TC)
      Other literature type . 2023
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      The Cryosphere (TC)
      Article . 2023
      License: CC BY
      Data sources: Crossref