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The ISLAS2020 field campaign: studying the near-surface exchange process of stable water isotopes during the arctic wintertime

Seidl, Andrew W.; Johannessen, Aina; Dekhtyareva, Alena; Huss, Jannis M.; Jonassen, Marius Opsanger; Schulz, Alexander; Hermansen, Ove; Thomas, Christoph K.; Sodemann, Harald

The ISLAS2020 field campaign during February and March 2020 set out to obtain a unique dataset describing the Arctic water cycle using stable water isotope (SWI) observations. Our observation strategy focused on measuring evaporation, deposition, and precipitation, all of which are commonly sub-grid scale processes in numerical weather and climate models. Uncertain parameterizations for these processes can lead to compensating errors, which can go unnoticed; however, evaporation and precipitation can also be investigated with SWIs, as they are an integrated tracer for processes that atmospheric moisture has undergone. The campaign can be divided into two efforts: a localised field experiment in Ny-Ålesund focused on evaporation and deposition, and a larger precipitation collection network distributed around the Nordic Seas. The Ny-Ålesund field experiment lasted three weeks, from 23 February to 15 March 2020, with temperatures reaching below −30 °C. During these weeks, we obtained near-surface, high-resolution (approx. 20 cm) SWI profiles at two deployment sites. Using a newly developed profiling system, we measured SWI gradients in the lowermost 5 and 2 m over fjord water and snow-covered tundra, respectively. These profiles are complemented by fiber-optic distributed sensing (FODS) columns and ambient conditions from nearby meteorological stations. The FODS columns supply continuous, high-resolution (2 cm or finer) temperature profiles above both locations, whereas the meteorological stations provide information on wind speed and direction. We also made a short deployment to the Zeppelin mountain observatory (472 ma.s.l.) for measurements of the isotopic signal in the free-troposphere. Additionally, numerous water samples from the snowpack in and around Ny-Ålesund were taken, in addition to daily fjord water samples from Kongsfjorden. These samples provide the context for the surface conditions under which profiles were collected. Isotopic connections on the synoptic scale are achieved by linking Ny-Ålesund observations with precipitation sampling at locations across the European Arctic, namely Longyearbyen, Tromsø, Andenes, Ålesund, and Bergen. The resulting dataset provides comprehensive insight into the Arctic hydrological cycle and can facilitate the study of phase change processes and transport of water vapour into and out of the Svalbard region. Datasets from the field campaign are publicly available at the PANGAEA data repository (https://doi.org/10.1594/PANGAEA.971241, Seidl et al., 2024).

2026

The Kongsfjorden system - a flagship programme for Ny-Ålesund. A concluding document from Workshop 28-31 March, 2008. Kortrapport 11/2009

Gabrielsen, G.W.; Hop, H.; Hübner, C.; Kallenborn, R.; Weslawski, J.M.; Wiencke, C. (eds.)

2009

The Kyoto Protocol: climate change.

Reimann, S.; Stordal, F. with contrib. from Ciais, P.; Goede, A.; Lazaridis, M.; Mazière, M. De, Zander, R.

2004

The Lagrangian particle dispersion model FLEXPART version 10.

Pisso, I.; Sollum, E.; Grythe, H.; Kristiansen, N.; Cassiani, M.; Eckhardt, S.; Thompson, R.; Zwaaftnik, C. G.; Evangeliou, N.; Hamburger, T.; Sodemann, H.; Haimberger, L.; Henne, S.; Brunner, D.; Burkhart, J.; Fouilloux, A.; Fang, X.; Phillip, A.; Seibert, P.; Stohl, A.

2017

The Lagrangian particle dispersion model FLEXPART version 10.4

Pisso, Ignacio; Sollum, Espen; Grythe, Henrik; Kristiansen, Nina Iren; Cassiani, Massimo; Eckhardt, Sabine; Arnold, Delia; Morton, Don; Thompson, Rona Louise; Zwaaftink, Christine Groot; Evangeliou, Nikolaos; Sodemann, Harald; Haimberger, Leopold; Henne, Stephan; Brunner, Dominik; Burkhart, John; Fouilloux, Anne Claire; Brioude, Jerome; Philipp, Anne; Seibert, Petra; Stohl, Andreas

The Lagrangian particle dispersion model FLEXPART in its original version in the mid-1990s was designed for calculating the long-range and mesoscale dispersion of hazardous substances from point sources, such as those released after an accident in a nuclear power plant. Over the past decades, the model has evolved into a comprehensive tool for multi-scale atmospheric transport modeling and analysis and has attracted a global user community. Its application fields have been extended to a large range of atmospheric gases and aerosols, e.g., greenhouse gases, short-lived climate forcers like black carbon and volcanic ash, and it has also been used to study the atmospheric branch of the water cycle. Given suitable meteorological input data, it can be used for scales from dozens of meters to global. In particular, inverse modeling based on source–receptor relationships from FLEXPART has become widely used. In this paper, we present FLEXPART version 10.4, which works with meteorological input data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) and data from the United States National Centers of Environmental Prediction (NCEP) Global Forecast System (GFS). Since the last publication of a detailed FLEXPART description (version 6.2), the model has been improved in different aspects such as performance, physicochemical parameterizations, input/output formats, and available preprocessing and post-processing software. The model code has also been parallelized using the Message Passing Interface (MPI). We demonstrate that the model scales well up to using 256 processors, with a parallel efficiency greater than 75 % for up to 64 processes on multiple nodes in runs with very large numbers of particles. The deviation from 100 % efficiency is almost entirely due to the remaining nonparallelized parts of the code, suggesting large potential for further speedup. A new turbulence scheme for the convective boundary layer has been developed that considers the skewness in the vertical velocity distribution (updrafts and downdrafts) and vertical gradients in air density. FLEXPART is the only model available considering both effects, making it highly accurate for small-scale applications, e.g., to quantify dispersion in the vicinity of a point source. The wet deposition scheme for aerosols has been completely rewritten and a new, more detailed gravitational settling parameterization for aerosols has also been implemented. FLEXPART has had the option of running backward in time from atmospheric concentrations at receptor locations for many years, but this has now been extended to also work for deposition values and may become useful, for instance, for the interpretation of ice core measurements. To our knowledge, to date FLEXPART is the only model with that capability. Furthermore, the temporal variation and temperature dependence of chemical reactions with the OH radical have been included, allowing for more accurate simulations for species with intermediate lifetimes against the reaction with OH, such as ethane. Finally, user settings can now be specified in a more flexible namelist format, and output files can be produced in NetCDF format instead of FLEXPART's customary binary format. In this paper, we describe these new developments. Moreover, we present some tools for the preparation of the meteorological input data and for processing FLEXPART output data, and we briefly report on alternative FLEXPART versions.

2019

The Lagrangian particle dispersion model FLEXPART version 10.4

Pisso, Ignacio; Sollum, Espen; Grythe, Henrik; Kristiansen, Nina Iren; Cassiani, Massimo; Eckhardt, Sabine; Arnold, Delia; Morton, Don; Thompson, Rona Louise; Zwaaftink, Christine Groot; Evangeliou, Nikolaos; Sodemann, Harald; Haimberger, Leopold; Henne, Stephan; Brunner, Dominik; Burkhart, John; Fouilloux, Anne Claire; Brioude, Jerome; Philipp, Anne; Seibert, Petra; Stohl, Andreas

2020

The Lagrangian particle dispersion model FLEXPART-WRF version 3.1.

Brioude, J.; Arnold, D.; Stohl, A.; Cassiani, M.; Morton, D.; Seibert, P.; Angevine, W.; Evan, S.; Dingwell, A.; Fast, J. D.; Easter, R. C.; Pisso, I.; Burkhart, J.; Wotawa, G.

2013

The libRadtran software package for radiative transfer calculations (version 2.0.1).

Emde, C.; Buras-Schnell, R.; Kylling, A.; Mayer, B.; Gasteiger, J.; Hamann, U.; Kylling, J.; Richter, B.; Pause, C.; Dowling, T.; Bugliaro, L.

2016

The link between springtime total ozone and summer UV radiation in Northern Hemisphere extratropics.

Karpechko, A.Yu.; Backman, L.; Thölix, L.; Ialongo, I.; Andersson, M.; Fioletov, V.; Heikkilä, A.; Johnsen, B.; Koskela, T.; Kyrölä, E.; Lakkala, K.; Myhre, C.L.; Rex, M.; Sofieva, V.F.; Tamminen, J.; Wohltmann, I.

2013

The magnitude, trend and drivers of the global nitrous oxide budget: a new assessment

Tian, Hanqin; Thompson, Rona Louise; Xu, Rongting; Canadell, Josep G.; Davidson, Eric A.; Ciais, Philippe; Jackson, Robert B.; Winiwarter, Wilfried; Suntharalingam, Parvadha; Regnier, Pierre; Zhou, Feng; Janssens-Maenhout, Greet; Arneth, Almut; Li, Wei; Pan, Naiqing; Pan, Shufen; Prather, Michael J.; Raymond, Peter A.; Shi, Hao; Team, * GCP/INI Synthesis

2019

The mass flow and proposed management of bisphenol A in selected Norwegian waste streams.

Arp, H. P. H.; Morin, N. A. O.; Hale, S. E.; Okkenhaug, G.; Breivik, K.; Sparrevik, M.

2017

The MEMORI dosimeter - a user friendly tool for evaluation of indoor air quality for cultural heritage. NILU OR

Grøntoft, T.; Wittstadt, K.; Bellendorf, P.; Dahlin, E.; Håland, S.; Bernardo, C.; Ødegård, R.; Røen, H.V.; Heltne, T.

2012

The MEMORI dosimeter - a user friendly tool for evaluation of indoor air quality for cultural heritage. NILU F

Grøntoft, T.; Wittstadt, K.; Bellendorf, P.; Dahlin, E.; Håland, S.; Bernardo, C.; Ødegård, R.; Røen, H.V.; Heltne, T.

2012

The MEMORI dosimeter for indoor environment. NILU PP

Dahlin, E.; Grøntoft, T.; Lopez-Aparicio, S.; Bellendorf, P.; Wittstadt, Schieweck, A.; Drda-Kühn, K.; Perla Colombini, M.; Bonaduce, I.; Vandenabeele, P.; Larsen, R.; Poulsen Sommer, D.V.; Potthast, A.; Marincas, O.; Thickett, D.; Andrade, G.; Tabuenca, A.; Odlyha, M.; Hackney, S.; Laurenson, P.; McDonagh, C.; Ackerman, J.J.

2012

The MEMORI dosimeter for indoor environment.

Dahlin, E.; Grøntoft, T.; Lopez-Aparicio, S.; Bellendorf, P.; Wittstadt, Schieweck, A.; Drda-Kühn, K.; Perla Colombini, M.; Bonaduce, I.; Vandenabeele, P.; Larsen, R.; Poulsen Sommer, D.V.; Potthast, A.; Marincas, O.; Thickett, D.; Andrade, G.; Tabuenca, A.; Odlyha, M.; Hackney, S.; Laurenson, P.; McDonagh, C.; Ackerman, J.J.

2012

The MEMORI technology - an innovative tool for the protection of movable cultural assets. Lecture Notes in Computer Science, 7616

Grøntoft, T.; Dahlin, E.

2012

The MEMORI technology for movable cultural assets.

Dahlin, E.; Grøntoft, T.; Lopez-Aparicio, S.; Bellendorf, P.; Schieweck, A.; Drda-Kühn, K.; Colombini, M.P.; Bonaduce, I.; Vandenabeele, P.; Larsen, R.; Potthast, A.; Marincas, O.; Thickett, D.; Odlyha, M.; Andrade, G.; Hackney, S.; McDonagh, C.; Ackerman, J.J.

2011

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