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Snow and sea ice temperature profiles from satellite data and ice mass balance buoys

Grönfeldt, Isabella LU (2016) In Student thesis series INES NGEM01 20151
Dept of Physical Geography and Ecosystem Science
Abstract
The sea ice covers approximately 5% of the Earth’s surface at any given time and it plays an important role in the polar climate system affecting the heat, mass and momentum exchange between the atmosphere and the ocean. The snow cover on top of the sea ice affects its insulating and reflective properties and thus key figures in the climate system feedback loop.

Sea ice and snow is of significant importance for our global climate system. However, it is difficult to effectively and accurately access data relating to snow and sea ice properties in the vast and remote Arctic region, especially during the winter, and snow is poorly constrained in current climate models. Improved information on snow and sea ice properties and thermodynamics... (More)
The sea ice covers approximately 5% of the Earth’s surface at any given time and it plays an important role in the polar climate system affecting the heat, mass and momentum exchange between the atmosphere and the ocean. The snow cover on top of the sea ice affects its insulating and reflective properties and thus key figures in the climate system feedback loop.

Sea ice and snow is of significant importance for our global climate system. However, it is difficult to effectively and accurately access data relating to snow and sea ice properties in the vast and remote Arctic region, especially during the winter, and snow is poorly constrained in current climate models. Improved information on snow and sea ice properties and thermodynamics from satellite observations could give valuable information in the process of validating, optimizing and improving these sea ice models and thereby the future predictions of sea ice growth and related climate variables.

This project examines the possibility of deriving the temperature profile through the snow and ice layers, from the surface down to 0.5 m into the ice, from a combination of available satellite data. Satellite data used are thermal infrared (TIR) and microwave radiation at different wavelengths and polarisations. The satellite data are compared with coincident data from ice mass balance buoys (IMB) and numerical weather prediction (NWP) data. This combined dataset are analysed for possible and theoretically derived relationships between the satellite measurements and different snow and ice parameters.

Different empirical models are used in this study to derive the mean snow temperature, snow density and snow and ice thickness, with various degree of success. It is clear that more advanced models are needed to accurately predict the observed variations of the snow and ice parameters. From the analysis it is clear that the satellite channels of lower frequencies are able to retrieve temperature measurements from deeper levels in the snow and ice than the higher frequencies. It is also clear that the satellite sensors are sensitive to changes in snow emissivity, associated with melting processes initiated by surface air temperatures around the freezing point, as the penetration depth is significantly decreased.

The models derived in the multiple regression analysis, performed on one of the four IMB buoys available, show a higher level of confidence for the deeper levels in the sea ice. When the models are tested on the remaining three IMB buoys the correlation for the lower levels in the sea ice are stronger. The comparisons between measured and theoretically derived temperatures show a generally strong correlation with R2-values ranging from 0.43 to 0.90. It is evident that the models without TIR are superior to those including TIR measurements. The differences in correlation between the IMB buoys indicate a spatial dependency, as well as a strong dependency on differences in snow and ice thickness. The models derived in this study are based on conditions with relatively thick snow and ice covers. Further studies would need to be conducted in order to improve and generalize the models derived in this project, in order to implement the empirical models in operating, global sea ice models. (Less)
Popular Abstract (Swedish)
Som minst är alltid 5% av jordens yta täckt av havsis. Det gör att isen har en avgörande roll i klimatet, särskilt vid polerna, då den påverkar utbytet av värme, massa och rörelse mellan havet och atmosfären. Ett snötäcke ovanpå isen förstärker ytterligare effekterna av isolering och reflektion.

Havsisen och snön är av stor betydelse för det globala klimatsystemet. Dock är det svårt att inhämta data angående isens och snöns egenskaper i det avlägsna Arktis, speciellt under vintern. Förbättrad informationen om havsisens och snöns egenskaper från satellitobservationer skulle ge värdefull information i processen att förbättra moderna havsismodeller och därmed framtida prognoser för utbredningen av havsisen och andra relaterade... (More)
Som minst är alltid 5% av jordens yta täckt av havsis. Det gör att isen har en avgörande roll i klimatet, särskilt vid polerna, då den påverkar utbytet av värme, massa och rörelse mellan havet och atmosfären. Ett snötäcke ovanpå isen förstärker ytterligare effekterna av isolering och reflektion.

Havsisen och snön är av stor betydelse för det globala klimatsystemet. Dock är det svårt att inhämta data angående isens och snöns egenskaper i det avlägsna Arktis, speciellt under vintern. Förbättrad informationen om havsisens och snöns egenskaper från satellitobservationer skulle ge värdefull information i processen att förbättra moderna havsismodeller och därmed framtida prognoser för utbredningen av havsisen och andra relaterade klimatvariabler.

Detta examensarbete undersöker möjligheten att återskapa temperaturprofilen ner genom snö- och isskikten, från ytan ner till 0.5 m i isen, från en kombination av tillgängliga satellitdata. Satellitdata som används i denna studie är från termisk infraröd och mikrovågsstrålning av olika frekvenser, detta är elektromagnetisk strålning som kontinuerligt skickas ut av jorden och sedan registreras av satelliter. Satellitdatan jämförs med sammanfallande data från särskilda driftbojar, kallade ismassbalansbojar (IMB), och data från numeriska väderprognoser. Detta kombinerade dataset analyseras för eventuella och teoretiskt härledda relationer mellan satellitmätningarna och olika snö och is parametrar.

Olika empiriska modeller används i denna studie för att härleda medelsnötemperaturen, snödensiteten samt snö- och istjockleken, med olika grad av framgång. Det är tydligt att mer avancerade modeller behövs för att exakt återskapa de observerade variationerna i snö- och isskikten. Av analysen står det klart att satellitdatan från lägre frekvenser ger information om de lägre nivåerna i snö- och isskikten och går djupare än de högre frekvenserna. Det är också klart att satellitsensorerna är känsliga för lufttemperaturer kring fryspunkten, vilka initierar smältprocesser i de övre snö- och isskikten och minskar djupet från vilket den elektromagnetiska strålningen skickas ut.

En multipel regressionsanalys utförs på en av de fyra tillgängliga IMB-bojarna i detta projekt och de resulterande temperaturmodellerna testas sedan på de tre kvarvarande IMB-bojarna. Modellerna visar en högre korrelation och större säkerhet för de lägre skikten i isen, där inte temperaturvariationerna är så kraftiga som vid ytan. Jämförelserna mellan de uppmätta och beräknade temperaturerna visar på en generellt hög korrelation med R2-värden mellan 0.43 och 0.90, där 1.00 är perfekt överrensstämmelse. Graden av korrelation varierar mellan de tre IMB-bojarna och detta indikerar att det finns ett rumsligt beroende, samt ett starkt beroende av tjockleken på snön och havsisen vid bojens mätposition. Modellerna som tagits fram i denna studie är baserade på förhållandena vid den första bojen, där både snö- och islagret är relativt tjocka.

Avslutningsvis konstateras det att vidare studier krävs för att förbättra, och möjliggöra generaliseringar av, de framtagna modellerna i detta projekt för att de ska kunna användas i globala havsismodeller. (Less)
Please use this url to cite or link to this publication:
author
Grönfeldt, Isabella LU
supervisor
organization
alternative title
Temperaturprofiler genom snö och is från satellitdata och driftbojar
course
NGEM01 20151
year
type
H2 - Master's Degree (Two Years)
subject
keywords
IMB, ice mass balance buoys, snow-ice interface, air-snow interface, temperature profile, snow, sea ice, Fram Strait, Greenland, Arctic Ocean, climate change, climate, physical geography and ecosystem analysis, remote sensing, satellite, sensor, SMOS, AMSR-2, TIR, numerical weather prediction, multiple regression analysis
publication/series
Student thesis series INES
report number
370
language
English
additional info
External supervisors: Gorm Dybkjær, Leif Toudal Pedersen, Rasmus Tonboe: Climate and Arctic, Danish Meteorological Institute, Copenhagen
id
8812383
date added to LUP
2016-02-29 12:45:16
date last changed
2016-03-02 09:31:36
@misc{8812383,
  abstract     = {The sea ice covers approximately 5% of the Earth’s surface at any given time and it plays an important role in the polar climate system affecting the heat, mass and momentum exchange between the atmosphere and the ocean. The snow cover on top of the sea ice affects its insulating and reflective properties and thus key figures in the climate system feedback loop.

Sea ice and snow is of significant importance for our global climate system. However, it is difficult to effectively and accurately access data relating to snow and sea ice properties in the vast and remote Arctic region, especially during the winter, and snow is poorly constrained in current climate models. Improved information on snow and sea ice properties and thermodynamics from satellite observations could give valuable information in the process of validating, optimizing and improving these sea ice models and thereby the future predictions of sea ice growth and related climate variables.

This project examines the possibility of deriving the temperature profile through the snow and ice layers, from the surface down to 0.5 m into the ice, from a combination of available satellite data. Satellite data used are thermal infrared (TIR) and microwave radiation at different wavelengths and polarisations. The satellite data are compared with coincident data from ice mass balance buoys (IMB) and numerical weather prediction (NWP) data. This combined dataset are analysed for possible and theoretically derived relationships between the satellite measurements and different snow and ice parameters.

Different empirical models are used in this study to derive the mean snow temperature, snow density and snow and ice thickness, with various degree of success. It is clear that more advanced models are needed to accurately predict the observed variations of the snow and ice parameters. From the analysis it is clear that the satellite channels of lower frequencies are able to retrieve temperature measurements from deeper levels in the snow and ice than the higher frequencies. It is also clear that the satellite sensors are sensitive to changes in snow emissivity, associated with melting processes initiated by surface air temperatures around the freezing point, as the penetration depth is significantly decreased.

The models derived in the multiple regression analysis, performed on one of the four IMB buoys available, show a higher level of confidence for the deeper levels in the sea ice. When the models are tested on the remaining three IMB buoys the correlation for the lower levels in the sea ice are stronger. The comparisons between measured and theoretically derived temperatures show a generally strong correlation with R2-values ranging from 0.43 to 0.90. It is evident that the models without TIR are superior to those including TIR measurements. The differences in correlation between the IMB buoys indicate a spatial dependency, as well as a strong dependency on differences in snow and ice thickness. The models derived in this study are based on conditions with relatively thick snow and ice covers. Further studies would need to be conducted in order to improve and generalize the models derived in this project, in order to implement the empirical models in operating, global sea ice models.},
  author       = {Grönfeldt, Isabella},
  keyword      = {IMB,ice mass balance buoys,snow-ice interface,air-snow interface,temperature profile,snow,sea ice,Fram Strait,Greenland,Arctic Ocean,climate change,climate,physical geography and ecosystem analysis,remote sensing,satellite,sensor,SMOS,AMSR-2,TIR,numerical weather prediction,multiple regression analysis},
  language     = {eng},
  note         = {Student Paper},
  series       = {Student thesis series INES},
  title        = {Snow and sea ice temperature profiles from satellite data and ice mass balance buoys},
  year         = {2016},
}