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The impact of atmospheric river events in preserved stable water isotope signature in the snow pack in Finse, Southern Norway Evelien van Dijk Thesis submitted for the degree of Master in Physical Geography, Hydrology and Geomatics 60 credits Department of Geoscience Faculty of mathematics and natural sciences UNIVERSITY OF OSLO Spring 2018 The impact of atmospheric river events in preserved stable water isotope signature in the snow pack in Finse, Southern Norway Evelien van Dijk © 2018 Evelien van Dijk The impact of atmospheric river events in preserved stable water isotope signature in the snow pack in Finse, Southern Norway http://www.duo.uio.no/ Printed: Reprosentralen, University of Oslo Abstract Stable water isotopes have become a key element in hydrological and atmospheric research topics. Hydrologists aim to close the water cycle, and the improvement of measurement techniques and models makes that they come closer every time. Especially the atmospheric part of the cycle is not yet fully understood, and this study aims to get an increased understanding about the isotopic signals in the snow pack, and if the snow pack record may be used to derive an improved understanding of the importance of atmospheric river events for the Norwegian snow cover. The Finse area has been chosen as a proxy site for Norway, and 215 samples have been taken to analyze for stable water isotopes. 11 different snow profiles that were computed from snow pits that were excavated during the fieldwork were compared with the isotope values for δ18O and d-excess, and with meteorological data provided by the meteorological institute of Norway. The snow pack from the same winter season (2016-2017) was simulated with the model CROCUS, using forecast data from AROME. One of the precipitation events, an atmospheric river event, was picked out and modeled with FLEXPART, to simulate the moisture uptake areas. This was backed up by satellite data and compared to the isotopic signals from the snow pits. The isotopic signal from this event was found in two of the snow pits, in February and May. Other isotopic signals were connected to other precipitation events, and by comparing the signals with the relative humidity and air temperature at the time of deposition the moisture source conditions were interpreted. Most precipitation events seem to have a southerly source, with the signal from the atmospheric river period having its source furthest south. Two of the signals appear to be from a more local (northern) source, and one isotopic signal was concluded not to have been preserved. Further research is needed to get an understanding about the role of different atmospheric and snow processes altering the isotopic signal, and multi-year studies on the isotope signal in seasonal snow is desirable as well, to get an understanding about different atmospheric circulation patterns. i List of Names and Abbreviations AR Atmospheric River AROME numerical weather prediction model CROCUS snow pack model δ18O oxygen isotope 18 δD deuterium d-excess deuterium excess ECMWF European Center for Medium-range Weather Forecast FLEXPART langrangian particle dispersion model IVT vertical integrated horizontal water vapor flux IWV integrated water vapor MET meteorological institute in Norway Metop polar orbiting meteorological satellites MSG Meteosat Second Generation NOAA satellites from the National Oceanic and Atmospheric Administration RH Relative Humidity SEVIRI Spinning Enhanced Visual and Infrared Imager SST Sea Surface Temperature SURFEX land surface model SWE Snow Water Equivalent SWI Stable Water Isotopes TPW Total Precipitable Water WV Water Vapor ii Contents 1 Introduction 1 2 Background 3 2.1 Stable water isotopes . .3 2.2 Stable water isotopes in the atmosphere . .5 2.3 Stable water isotopes in snow . .6 2.4 Snow metamorphism . .7 2.5 Atmospheric rivers . .9 3 Study site: The Finse Alpine Research Center and Regional Characteristics 11 3.1 Climate Southern Norway . 11 3.2 Finse . 12 3.3 Climate . 13 4 Methods 17 4.1 Fieldwork . 17 4.2 Labwork . 18 4.3 Modeling . 19 4.3.1 CROCUS . 19 4.3.2 FLEXPART . 21 5 Results 25 5.1 Snow profile data . 25 5.1.1 Meteorological data . 34 6 Discussion 41 6.1 Snow pit data . 41 6.2 Model sensitivity . 52 6.3 Atmospheric river case . 53 6.3.1 IWV and IVT . 57 6.3.2 TPW . 57 6.3.3 Water Vapor imagery . 62 iii 6.3.4 Isotopic signals . 63 6.3.5 Challenges . 67 7 Conclusion 69 8 Acknowledgements 73 8.1 MET data . 79 8.2 Isotope- and other field data . 79 iv List of Figures 2.1 Schematic drawing of stable water isotopes in the hydrological cycle (Yoshimura, 18 2015). H2 O and HDO are heavier than H216O, what makes that the latter will evaporate first. The heavier isotopes in turn will condensate first (left part). On the right part the mixing and parting of isotopes is shown in the hydrological cycle.4 2.2 Different types of snow crystals described and showed by Colbeck (1982); McClung & Schaerer (2006); Dingman (2008). .9 2.3 Constructive metamorphism as visualized by McClung & Schaerer (2006). 10 3.1 The normalized temperature and precipitation for Southern Norway. The values are normalized over the period 1971-2000. Figures adapted from senorge.no . 12 3.2 Location of Finse. Modified from Norgeskart.no . 13 3.3 Locations of the samples taken for stable water isotopes. The three main locations are the glacier Middalsbreen, the Thomas station, which is located on a slope and the marshlands, an area around a braided river system. The map used for the foundation was obtained from Kartverket. 14 3.4 Wind rose with the wind direction at Finse. The colour indicates the wind speed and the size of the bar indicates how often the wind comes from that direction. SOURCE . 15 4.1 Example of a snow pit excavation in the field. 18 4.2 Schematic drawing of CROCUS with all its main physical components and variables (Vionnet et al., 2012). 21 4.3 a) The differentiation between dendricity and sphericity of snow crystals. The grain parameter is based on wind speed. b) The density of fresh snow for different wind speeds. Figure adapted from Vionnet et al. (2012). 22 v 4.4 Sketch of the method for identifying uptakes along a backward trajectory of an air parcel from the Atlantic ocean to Greenland (black line). The time before arrival is given at the top (t). q (dashed line) is the specific humidity in the air parcel [gkg−1]. ∆q° is the changes in specific humidity of an air parcel between two time intervals and BLH is the boundary layer height. The thick blue sections along the trajectory represent sections of moisture increase, where the red arrows are identified evaporation locations (Sodemann, Schwierz & Wernli, 2008). Figure adapted from Sodemann, Schwierz & Wernli (2008). 24 5.1 The density-, temperature curves, isotopic values and the stratigraphy for the snow pits from the marsh, Thomas station and Middalsbreen. The density- and temperature curves from the modeled simulation are given for comparison. The numbers represent the precipitation events that were identified and the letters correspond to the warm events, which will be described further down and in the next section. 30 5.2 δ18O and d-excess values for different snow profiles from December til May. 32 5.3 The a) temperature and b) density profiles from all snow pits taken in the marshlands with normalized depth. 33 5.4 a) shows meteorological data (precipitation, wind speed and air temperature) from the Finse station. The highlighted time periods represent warm episodes during the winter season, where ice layers could have formed. Each episode is given a letter for referencing, as will be described in the section Discussion.b) shows meteorological data (precipitation, wind speed and air temperature) from the Finse station. The beginning of different precipitation events described in the discussion is given by a number. These are derived from the model output and are placed at the time where a significant build up of the snow pack was simulated (figure 6.1). 35 5.5 Meteorological time series for air pressure, air temperature, relative humidity and precipitation for December. 36 5.6 Meteorological time series for air pressure, air temperature, relative humidity and precipitation for the snow season 2016-2017. 37 5.7 The normalized temperature and precipitation for Southern Norway and the annual temperature and precipitation for 2017. Figures adapted from senorge.no . 38 5.8 The precipitation from the forcing data (top) and the meteorological data, both measured (middle) and wind-corrected (bottom). The meteorological data is from eKlima (n.d.). In the lower two plots the wind speed (middle), with the 7m/s indicated by the straight line, and the temperature (bottom), with the 0 °C line, are given. Note that the biggest differences between the non-windcorrected data and the windcorrected data are when the precipitation falls as snow (<0°C) and the wind speed is high (>7m/s). 40 vi 6.1 CROCUS output compared with observed precipitation. The different increases in snow height (precipitation events) are each labeled with a number. The legend for the grain type plot uses abbreviations from the international classification for snow on the ground by UNESCO (Fierz et al., 2009). The most important for this study are MF(meltform), FC(faceted crystals) and DH(depth hoar). The average desity was used to calculate the SWE plot. 43 6.2 Integrated water vapor flux and integrated water vapor for precipitation event 5 on the 30th of December 2016. Especially in the IWV the AR is visible as a long narrow band of water vapor that reaches all the from the south to the west coast of Norway.......................................

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