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The microstructure of the uppermost portions of a melting Arctic sea ice cover has a disproportionately large influence on how incident sunlight is reflected and absorbed in the ice/ocean system. The surface scattering layer (SSL) effectively backscatters solar radiation and keeps the surface albedo of melting ice relatively high compared to ice with the SSL manually removed. Measurements of albedo provide information on how incoming shortwave radiation is partitioned by the SSL and have been pivotal to improving climate model parameterizations. However, the relationship between the physical and optical properties of the SSL is still poorly constrained. Until now, radiative transfer models have been the only way to infer the microstructure of the SSL. During the MOSAiC expedition of 2019–2020, we took samples and, for the first time, directly measured the microstructure of the SSL on bare sea ice using X-ray micro-computed tomography. We show that the SSL has a highly anisotropic, coarse, and porous structure, with a small optical diameter and density at the surface, increasing with depth. As the melting surface ablates, the SSL regenerates, maintaining some aspects of its microstructure throughout the melt season. We used the microstructure measurements with a radiative transfer model to improve our understanding of the relationship between physical properties and optical properties at 850 nm wavelength. When the microstructure is used as model input, we see a 10–15% overestimation of the reflectance at 850 nm. This comparison suggests that either a) spatial variability at the meter scale is important for the two in situ optical measurements and therefore a larger sample size is needed to represent the microstructure or b) future work should investigate either i) using a ray-tracing approach instead of explicitly solving the radiative transfer equation or ii) using a more appropriate radiative transfer model.

Remote sensing of snow is a method to measure snow cover characteristics without direct physical contact with the target from airborne or space-borne platforms. Reliable estimates of snow cover extent and snow properties are vital for several applications including climate change research and weather and hydrological forecasting. Optical remote sensing methods detect the extent of snow cover based on its high reflectivity compared to other natural surfaces. A universal challenge for snow cover mapping is the high spatiotemporal variability of snow properties and heterogeneous landscapes such as the boreal forest biome. The optical satellite sensor’s footprint may extend from tens of meters to a kilometer; the signal measured by the sensor can simultaneously emerge from several target categories within individual satellite pixels. By use of spectral unmixing or inverse model-based methods, the fractional snow cover (FSC) within the satellite image pixel can be resolved from the recorded electromagnetic signal. However, these algorithms require knowledge of the spectral reflectance properties of the targets present within the satellite scene and the accuracy of snow cover maps is dependent on the feasibility of these spectral model parameters. On the other hand, abrupt changes in land cover types with large differences in their snow properties may be located within a single satellite image pixel and complicate the interpretation of the observations. Ground-based in-situ observations can be used to validate the snow parameters derived by indirect methods, but these data are affected by the chosen sampling. This doctoral thesis analyses laboratory-based spectral reflectance information on several boreal snow types for the purpose of the more accurate reflectance representation of snow in mapping method used for the detection of fractional snow cover. Multi-scale reflectance observations representing boreal spectral endmembers typically used in optical mapping of snow cover, are exploited in the thesis. In addition, to support the interpretation of remote sensing observations in boreal and tundra environments, extensive in-situ dataset of snow depth, snow water equivalent and snow density are exploited to characterize the snow variability and to assess the uncertainty and representativeness of these point-wise snow measurements applied for the validation of remote sensing observations. The overall goal is to advance knowledge about the spectral endmembers present in boreal landscape to improve the accuracy of the FSC estimates derived from the remote sensing observations and support better interpretation and validation of remote sensing observations over these heterogeneous landscapes. The main outcome from the work is that laboratory-controlled experiments that exclude disturbing factors present in field circumstances may provide more accurate representation of wet (melting) snow endmember reflectance for the FSC mapping method. The behavior of snow band reflectance is found to be insensitive to width and location differences between visible satellite sensor bands utilized in optical snow cover mapping which facilitates the use of various sensors for the construction of historical data records. The results also reveal the high deviation of snow reflectance due to heterogeneity in snow macro- and microstructural properties. The quantitative statistics of bulk snow properties show that areal averages derived from in-situ measurements and used to validate remote sensing observations are dependent on the measurement spacing and sample size especially over land covers with high absolute snow depth variability, such as barren lands in tundra. Applying similar sampling protocol (sample spacing and sample size) over boreal and tundra land cover types that represent very different snow characteristics will yield to non-equal representativeness of the areal mean values. The extensive datasets collected for this work demonstrate that observations measured at various scales can provide different view angle to the same challenge but at the same time any dataset individually cannot provide a full understanding of the target complexity. This work and the collected datasets directly facilitate further investigation of uncertainty in fractional snow cover maps retrieved by optical remote sensing and the interpretation of satellite observations in boreal and tundra landscapes. Lumen kaukokartoitus on menetelmä, jolla mitataan lumen ominaisuuksia ilmasta tai avaruudesta käsin ilman fyysistä kontaktia kohteeseen. Luotettavat arviot lumipeitteen laajuudesta ja lumen ominaisuuksista ovat elintärkeitä useille menetelmille mukaan lukien ilmastonmuutoksen tutkimus sekä hydrologinen ennustaminen ja sään ennustaminen. Optiset kaukokartoitusmenetelmät havaitsevat lumipeitteen laajuuden lumen korkean heijastavuuden perusteella. Lumen ominaisuuksien korkea ajallinen ja alueellinen vaihtelu sekä heterogeeniset maastotyypit ovat yleinen haaste lumipeitteen laajuuden kaukokartoitukselle. Satelliitin optisen sensorin jalanjälki voi ulottua muutamista kymmenistä metreistä kilometriin; sensorin mittaama signaali voi samanaikaisesti nousta useista eri kohteista saman satelliittipikselin sisällä. Käyttämällä metodeja, joissa pyritään ratkaisemaan erilaisten kohdetyyppien osuus mitatussa signaalissa tai käänteismallintamalla, lumen osuus satelliittipikselin sisällä voidaan ratkaista mitatusta elektromagneettisesta signaalista. Nämä menetelmät kuitenkin vaativat tietoa pikselissä olevien kohteiden – mallimuuttujien – spektraalisista ominaisuuksista. Tuotetun lumipeitekartan tarkkuus on suoraan riippuvainen näille muuttujille asetettujen arvojen käyttökelpoisuudesta. Toisaalta saman satelliittipikselin sisällä lumipeitteen ominaisuuksissa voi olla jyrkkiäkin muutoksia, jotka vaikeuttavat satelliittihavaintojen tulkintaa. Epäsuorilla menetelmillä havaittuja lumen estimaatteja voidaan varmentaa hyödyntämällä maanpinnalla kerättyjä maastohavaintoja, mutta myös nämä aineistot sisältävät epätarkkuutta ja virhettä. Tämä väitöskirja analysoi laboratoriossa useista boreaalisista lumityypeistä kerättyjä spektraalisia mittauksia, joiden tarkoitus on tarjota tarkempia lumen heijastusarvoja hyödynnettäväksi menetelmässä, jota käytetään lumipeitteen laajuuden kartoituksessa. Boreaalisella metsävyöhykkeellä olevia spektraalisia mallimuuttujia, joita tyypillisesti käytetään optisissa lumen kartoitusmenetelmissä, kuvataan väitöstyössä usean eri mittakaavan havainnoilla. Lisäksi mittavaa lumensyvyyden, lumen vesiarvon sekä lumen tiheyden maastomittausaineistoa hyödynnetään kaukokartoitushavaintojen tulkinnan tukemiseksi boreaalisella vyöhykkeellä sekä tundralla. Aineiston avulla kuvataan lumen ominaisuuksien alueellista ja ajallista vaihtelua sekä tutkitaan pistemäisesti kerättyjen maastohavaintojen epätarkkuutta sekä edustavuutta, kun niitä käytetään kaukokartoitushavaintojen validoinnissa. Väitöstyön yleisenä tarkoituksena on edistää tietoutta boreaalisen vyöhykkeen spektraalisista mallimuuttujista, jotta optisella kaukokartoituksella tuotettujen lumipeitehavaintojen tarkkuus paranee ja tukea kaukokartoitushavaintojen parempaa tulkintaa ja validointia epähomogeenisissa satelliittipikseleissä. Väitöstyön pääasiallinen viesti on, että laboratorio-olosuhteissa kerätyillä mittauksilla voidaan tuottaa tarkempia arvoja lumipeitteen kaukokartoitushavaintojen tulkinta-algoritmeille, koska maastomittauksissa läsnä olevia häiritseviä tekijöitä voidaan sulkea pois. Lumipeitteen kaukokartoituksessa hyödynnettävien sensorien hieman toisistaan poikkeavat optiset kaistat eivät näytä merkittävästi vaikuttavan lumen heijastusarvoon. Tämä tukee historiallisten aineistojen rakentamista eri sensoreilla kerätyistä havainnoista. Tulokset myös paljastavat, että lumen heijastusarvoissa on suurta hajontaa, joka liittyy lumen makro- ja mikrostruktruuristen ominaisuuksien vaihteluun. Lisäksi tulokset osoittivat, että maastomittauksista saadut alueelliset lumensyvyyden keskiarvot, joita usein käytetään karkeamman resoluution kaukokartoitushavaintojen validoinnissa, ovat riippuvaisia mittausten välisestä etäisyydestä sekä mittausten lukumäärästä. Näin on erityisesti maanpeiteluokissa, joilla lumensyvyyden vaihtelu on erityisen suurta, kuten paljakat tundralla. Soveltamalla samaa mittausprotokollaa boreaalisiin ja tundran maanpeiteluokkiin, jotka edustavat hyvin erilaisia lumiolosuhteita, saadaan keskenään eriävästi edustavia alueellisia keskiarvoja. Tässä työssä kerätyt laajamittaiset havaintoaineistot osoittavat, että eri mittakaavoilla kerätyt havainnot voivat tarjota eri näkökulman samaan ongelmaan, mutta samaan aikaan yksittäinen havaintoaineisto on riittämätön tarjotakseen täyden ymmärryksen tiettyyn haasteeseen, kuten epähomogeenisen satelliittipikselin tulkintaan. Tämä väitöstyö ja siinä kerätyt aineistot hyödyttävät suoraan tutkimusta, joka koskee lumipeitteen laajuuden optisen kaukokartoituksen epätarkkuuksia sekä satelliittihavaintojen tulkintaa boreaalisella metsävyöhykkeellä sekä tundralla.

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.

This document - Final implementation: Data delivery and report on results of the distributed observing systems for ocean and sea ice - describes autonomous components of the Arctic observing system for ocean and sea ice measurements that were developed, tested, and implemented during the INTAROS field work seasons. Instruments and platforms described in D3.13 drifted freely on the sea ice or in the water column (ice tethered platforms, ice buoys and floats), moved along preprogrammed tracks (gliders) or measured autonomously at fixed locations (deep ocean moorings). An autonomous sensor package (FerryBox) and drone-based sensors were used to collect observations from the ships of opportunity or during large observing campaigns like MOSAiC. This document is intended to: − Review status and performance of autonomous mobile and fixed observing platforms and sensors used for collecting ocean and sea ice observations in the Arctic during the INTAROS field work seasons. − Describe performance and data obtained from the ice-tethered IAOOS-Equipex platform used for combined physical, atmospheric and sea ice measurements in the central Arctic Ocean. − Describe performance and data collected with the deep ocean mooring deployed for the second INTAROS field season in the deep Nansen Basin. − Describe performance and data collected by SIMBA (Snow and Ice Mass Balance Array) platforms for sea ice measurements, deployed with INTAROS contribution in the Arctic Ocean. − Describe performance and data collected with new biogeochemical sensors for the FerryBox (pH/carbonate sensor, spectral absorption sensor and microplastic sampler) developed under INTAROS. − Describe the performance of gliders and results from new endurance glider lines established under INTAROS in the northern Fram Strait. − Describe INTAROS contribution to an array of BGC Argo floats in the Baffin Bay observatory, their performance, and collected data. − Assess performance and fitness-to-purpose of the platforms, sensors and systems implemented under Task 3.4 for a future sustained Arctic observing system.

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice-ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.

Small-scale surface roughness (on an area of about 0.5 m x 0.5 m) affects remote sensing retrieval algorithms and radiative transfer models, yet data of snow and ice surface roughness over sea ice is scarce. Microwave emissivity, important for satellite retrievals of sea ice concentration, type, thickness, etc., is a function of ice/snow and snow/air interface roughness (amongst other factors). During MOSAiC, we collected data to determine the small-scale surface roughness of the surface (snow and surface scattering layer) and the snow/sea-ice interface using a rapid photogrammetric method. Surface roughness data was collected with most snow pits as well as on some transects to account for temporal and spatial variability. Here, we present the surface roughness evolution for the year-long snow pit observations and discuss the applicability and relevance of this dataset for remote sensing and radiative transfer studies. We will consider the spatial variability and anisotropy of surface roughness for different snow and surface types. Furthermore, we will compare this small-scale surface roughness to ∼100m-scale surface roughness obtained from UAVs to determine relevant roughness scales for different applications. We will also discuss other potential applications for this dataset, such as correlations between the small-scale surface roughness and specific surface area (from near-infrared photography, SnowMicroPen, and microCT), shear strength (from SnowMicroPen), or aerodynamic roughness for turbulent fluxes.