The Determinants of Currency Hedging : Evidence from the Finnish Markets

The purpose of the thesis is to investigate the determinants of currency hedging in the Finnish markets. The sample consists of 106 non-financial firms listed on Nasdaq Helsinki in the year 2018. Logit regressions are estimated as the dependent variable is dichotomous. Different estimations are carried out to find the differences between currency hedgers and non-hedgers and to test robustness of the results. Five different models are estimated. Results imply that economies of scale, investment opportunity set, and foreign currency exposure are all important factors in hedging decision. Some evidence is provided for the relation between hedging and costs of financial distress, but the evidence is weak. Further, liquidity is an insignificant factor and leverage has some support behind it after excluding other hedgers from the non-hedging group. Currency hedgers are larger in terms of asset size and have higher foreign sales ratio. Hedgers spend more on research and development and distribute higher dividend yields than non-hedgers. Excluding other hedgers from the non-hedging sample results in stronger estimates. These results are robust through variety of tests with alterations on model specifications

3D-modeling of snow in the Saariselkä region during the winter 2015-2016

Studying and modelling the snow distribution processes is important because snow influences the ground, flora, and fauna by affecting among other things the energy balance both in large and small scales and the near-surface atmospheric conditions due to its highly reflective and insulating properties. The aim of the study was to use the spatially distributed high-resolution snow-evolution modelling system SnowModel to simulate the snow conditions in winter 2015-2016 in the Saariselkä region in Northern Finland and assess the model's performance. SnowModel has not been used to study a domain in Finland before, and the model gives information about variables that are hardly measured in Finland, such as snow sublimation. The simulations were first run without snow water equivalent assimilation and then assimilating the available snow water equivalent (SWE) observations. The simulation results show that in the default mode the model needs assimilation and SWE observations, preferably more frequent observations towards the spring, to produce physically sensible results. The domain averaged simulated end-of-winter maximum SWE value of 220 mm was reached on 21 April 2016. The simulated SWE patterns match with known elevation and vegetation dependencies. Timing of the first snow, the beginning of the snow season and the end-of-winter SWE are simulated well, whereas the melt and the snowfree date depend on the amount of snow. The assimilation run suggests that the needed summed precipitation is as much as 18 % larger than the observed increasing towards the northeast. Similarly, the simulated summed melt reaches locally up to 70 % larger values compared to the non-assimilation run. Blowing-snow sublimation takes place in open areas and its simulated summed value is up to 27 mm. Simulated static-surface sublimation varies between 4-22 mm. The simulated sublimation from the canopy-intercepted snow peaks at 110 mm. Up to 16 % of the precipitation is returned to the atmosphere by sublimation. The simulation results could be improved by utilizing more detailed data of the study domain and modifying the hard-coded variables to suit the surroundings, which could in turn decrease the need for assimilating SWE observations.Lumen alueelliseen jakautumiseen liittyvien prosessien tutkiminen ja mallintaminen on tärkeää, sillä lumi vaikuttaa suuren heijastavuutensa ja pienen lämmönjohtokykynsä avulla maankamaraan, kasveihin ja eläimiin muun muassa suuren ja pienen skaalan energiataseen kautta sekä muokkaamalla pinnanläheisen ilmakehän olosuhteita. Tutkimuksen tavoitteena oli tarkkaa spatiaalista SnowModel-lumimallisysteemiä käyttämällä simuloida talven 2015-2016 lumioloja Saariselän alueella Pohjois-Suomessa ja arvioida mallin sopivuutta kyseisiin olosuhteisiin. SnowModelia ei ole aiemmin käytetty Suomessa sijaitsevan tutkimusalueen simulointiin, mutta malli antaa tietoja sellaisista suureista, joita Suomessa harvoin mitataan, kuten lumen sublimaatiosta. Ensimmäinen simulaatio tehtiin ilman lumen vesiarvojen assimilointia ja toinen lumen vesiarvohavainnot assimiloiden. Tulokset osoittavat että oletusarvoisia käyttäjän määrittämiä muuttujia käytettäessä malli tarvitsee assimilointia ja lumen vesiarvohavaintoja, mieluiten tihenevästi kevättä kohti, jotta tulokset olisivat fysikaalisesti mielekkäitä. Tutkimusalueella simuloidun lumen vesiarvon aluekeskiarvo saavutti maksiminsa 220 mm 21.4.2016. Lumen vesiarvon jakautuminen noudattaa tunnettuja maastonkorkeus- ja kasvillisuusriippuvuuksia. Ensilumen, pysyvän lumen alkamisen ja lumen vesiarvon maksimin ajoitus mallintuu hyvin, mutta sulaminen riippuu lumen määrästä. Assimilaatiomallinnuksen mukaan jopa 18 % suurempi vuosisadanta tarvitaan havaittujen lumiolosuhteiden toistamiseksi, ja sadanta kasvaa kohti tutkimusalueen koillisosia. Vastaavasti vuosisulanta on assimilaatiomallinnuksessa enintään 70 % suurempi kuin ilman assimilaatiota. Lumituiskusta sublimoituu lunta aukeilla alueilla yhteensä enintään 27 mm vuoden aikana, kun taas lumipeitteen pinnalta simulaation mukaan sublimoituu 4-22 mm. Puihin interseptoituneesta lumesta simulaatiossa sublimoituu yhteensä jopa 110 mm. Yhteensä sadannasta enintään 16 % sublimoituu takaisin ilmakehään. Mallin tuloksia voisi parantaa keräämällä yksityiskohtaisempaa dataa tutkimusalueelta ja muokkaamalla malliin koodattuja muuttujia ja vakioita olosuhteisiin sopiviksi, jolloin tarve assimiloida lumen vesiarvoja voisi vuorostaan vähentyä

IceBird Winter 2023 Campaign Report

IceBird Winter 2023 is part of a long-term sea ice observation program within the IceBird aircraft campaign series. IceBird was initiated in 2018 with the objective to ensure the long-term availability of a unique data record of direct sea-ice thickness observations to understand the role of the sea ice component for the causes and consequences of Arctic change, but is built on the heritage of airborne sea-ice thickness observations that date back to 2004. Compared to earlier airborne programs, IceBird has been enhanced with an improved sensor setup that also allows measuring snow depth on sea ice, fully collocated with sea-ice thickness and surface roughness at high resolution. The objectives of IceBird Winter 2023 include the continued quantification of trends, the separation of variability and extreme events of sea ice thickness and its snow cover in the Western Seas of the Arctic Ocean. The continuation of airborne sea-ice observation programs fulfils the requirement of consistent and long-term observations of key climate parameters. The data will be used to improve understanding of the response of sea ice and its snow cover to the ongoing warming of the Arctic and to improve snow models. Airborne data of snow and sea-ice thickness are also critically needed for the evaluation of sea-ice remote sensing products as well as for the evolution of algorithms for current and future satellite missions. Surveys from IceBird Winter 2023 will target the validation of sea-ice freeboard and snow depth estimates from CryoSat-2, ICESat-2, Sentinel-3A/B and AltiKa altimeters

IceBird 2019 Winter: ICESat-2 Validation Data Acquisition Report

This technical report provides information on validation activities of ICESat-2 sea ice products in April 2019 by airborne observations within the IceBird aircraft campaign series of the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research. The airborne data acquisition includes direct observations of snow freeboard, snow depth on sea ice and sea-ice thickness by a set of sensors. The following sections specify the extent of airborne data collocated with an ICESat-2 orbit, the specification of the sensors and the file format of the validation data

Measurements of light transfer through drift ice and landfast ice in the northern Baltic Sea

The aim of this study was to investigate the light transfer through sea ice with a focus on bio-optical substances both in fast ice and in the drift ice zones in the northern Baltic Sea. The measurements included snow and ice structure, spectral irradiance and photo-synthetically active radiation below the sea ice. We also measured the concentrations of the three main bio-optical substances which are chlorophyll-a, suspended particulate matter, and coloured dissolved organic matter (CDOM). These bio-optical substances were determined for melted ice samples and for the underlying sea water. The present study provides the first spectral light transfer data set for drift ice in the Baltic Sea. We found high CDOM absorption values typical to the Baltic Sea waters also within sea ice. Our results showed that the transmittance through bare ice was lower for the coastal fast ice than for the drift ice sites. Bio-optical substances, in particular CDOM, modified the spectral distribution of light penetrating through the ice cover. Differences in crystal structure and the amount of gas inclusions in the ice caused variation in the light transfer. Snow cover on ice was found to be the dominant factor influencing the light field under ice, confirming previous studies. In conclusion, snow cover dominated the amount of light under the ice, but did not modify its spectral composition. CDOM in the ice absorbs strongly in the short wavelengths. As pure water absorbs most in the long wavelengths, the light transfer through ice was highest in the green (549-585 nm). (C) 2020 Institute of Oceanology of Polish Academy of Sciences. Published by Elsevier B.V.Peer reviewe

Sea ice surface temperatures from helicopter-borne thermal infrared imaging during the MOSAiC expedition

The sea ice surface temperature is important to understand the Arctic winter heat budget. We conducted 35 helicopter flights with an infrared camera in winter 2019/2020 during the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The flights were performed from a local, 5 to 10 km scale up to a regional, 20 to 40 km scale. The infrared camera recorded thermal infrared brightness temperatures, which we converted to surface temperatures. More than 150000 images from all flights can be investigated individually. As an advanced data product, we created surface temperature maps for every flight with a 1 m resolution. We corrected image gradients, applied an ice drift correction, georeferenced all pixels, and corrected the surface temperature by its natural temporal drift, which results in time-fixed surface temperature maps for a consistent analysis of one flight. The temporal and spatial variability of sea ice characteristics is an important contribution to an increased understanding of the Arctic heat budget and, in particular, for the validation of satellite products

Small-scale variability of snow properties on sea ice: from snow pits to the SnowMicroPen

Snow on sea ice alters the properties of the underlying ice cover as well as associated exchange processes at the interfaces between atmosphere, sea ice, and ocean. It contributes significantly to the sea-ice mass and energy budgets due to comprehensive seasonal transition processes within the snowpack. Therefore, several studies have shown the importance of comprehensive understanding of snow properties for large-scale estimates in the ice-covered oceans. However, field studies reveal not only a strong seasonality but especially spatial variations on floe-size scales. It is therefore necessary to locate and quantify seasonal snow processes, such as internal snowmelt, snow metamorphism, and snow-ice formation in the Arctic and Antarctic snowpack on small scales. Doing so, we present here in-situ observations of physical snow properties from point measurements (snow pits) and transect lines (SnowMicroPen, SMP) during recent expeditions in the Weddell Sea and off the northeastern coast of Ellesmere Island, Canada, from 2013 to 2019, covering summer and winter conditions. Results from a case study of snow pit analyses in the Weddell Sea during austral winter reveal a high variability of snow parameters throughout the snowpack. It is shown that snow grain size dominates the spatial variability of the snow pack while snow density variability can be neglected. The additional use of the SMP allows to even quantify length-scale variabilities of snow properties in different ice regimes in both hemispheres. Overall, results will improve our understanding of seasonal processes in the snowpack and will guide us towards upscaling approaches of vertical snow layers on Arctic and Antarctic sea ice

Kilometer-scale digital elevation models of the sea ice surface with airborne laser scanning during MOSAiC

An integrated sensor platform including an inertial navigation system (INS) and a commercial airborne laser scanner (ALS) among other sensor was mounted in the cargo compartment in one of the Polarstern helicopters during MOSAiC. ALS data was acquired from more than 60 flights between October 2019 and September 2020 with a range of survey types intended to map changes of the sea ice surface during the full annual cycle at high spatial resolution and coverage. Here, we provide an overview of the collected data, the challenge of achieving centimeter elevation accuracy with a helicopter platform at high polar latitudes as well as the content and specifications of ALS data products. The high spatial resolution and repeated coverage of the larger area around Polarstern allow studying various surface features (e.g. pressure ridges, floes, melt ponds, snow drifts, etc.), their seasonal evolution, and their impact on atmosphere and ocean. Finally, we outline methods for planned applications, such as identifying individual floes and surface types using both measured freeboard and surface reflectance. Collocated helicopter-based optical and infrared imagery allow analyzing sea ice properties in further applications and to upscale comparable in-situ observations

Relation between sea ice freeboard and draft and its seasonal evolution in the Central Arctic

Relating the sea-ice surface to the under-ice topography is a timely scientific effort in the Arctic. This relation is crucial for estimating the ice thickness distribution for large‐scale modelling, for assessing the mechanical force that ships need to overcome, for risk evaluation of offshore structures, for determining roughness characteristics to derive wind and water drag coefficients for dynamics modelling, for sound scattering, and for the confinement of under-ice oil spills. Existing relations are based on numerical modelling assuming estimates of snow depth, snow density, and ice density or are based on field observations confined to specific areas and short time periods. MOSAiC provided the first year-long, high-resolution dataset of sea-ice draft derived from a multibeam echosounder. In combination with co-located freeboard estimates from airborne mapping of the surface, we construct the 3D sea-ice topography to study the evolution of sea-ice geometry both at the surface and underside. We can obtain direct and high precision relations between draft and freeboard on an almost weekly basis for an ice floe continuously drifting from the North Pole to Fram Strait during winter, spring, and summer. A precise evaluation of total ice thickness, ice density, freeboard, draft and their respective relations on small scales is crucial information to future satellite remote sensing ice thickness retrievals, a key asset of climate monitoring in the Arctic

Spectral Light Transmittance of Arctic Sea Ice

Light transmittance through Arctic sea ice has an important impact on both the ocean heat content and the ice associated ecosystem. Thus, it is crucial to investigate the optical properties of sea ice to assess the role of the surface energy budget and its change due to climate change. Measurements of spectral transmittance can be used to investigate the influence of surface and ice properties regulating radiative transfer, especially on a larger horizontal scale. Here, we concentrate on categorizing snow and sea ice based on spectral transmittance data. Transmitted radiance and irradiance are measured at the underside of sea ice using a remotely operated vehicle (ROV). The scientific payload also includes CTD, fluorometer, pH-, nitrate-, oxygen-, attenuation sensor, upward-looking single-beam sonar, and periodically a surface and under ice trawl for assessing the spatio-temporal variability of sea ice algae. Thus, data for all disciplines in sea ice research can be recorded. The main benefits using the ROV compared to point measurements are the larger spatial coverage in comparably short times and the undisturbed sampling even under very thin sea ice, with parameters all collected during the same time. Snow depth is derived from a combination of terrestrial laser scanner data and manual measurements, while ice draft is measured using the single-beam sonar. Here, we present first data from the Last Ice campaign off Alert in May 2018. This region is dominated by sea ice with a larger thickness due to dynamic thickening. We investigated different ice regimes, such as First Year Ice with a continuous thickness of about 1.5 m and structured Multi Year Ice with thicknesses up to 6 m over the duration of four weeks to study the differences between various ice types