Flores, Hauke

Gerland, Sebastian

Hudson, Stephen R.

Kauko, Hanna M.

Lange, Benjamin

Nicolaus, Marcel

Pavlov, Alexey

Perovich, Donald K.

Wang, Caixin

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Seasonality	of	light	transmittance	through	Arctic	sea	ice	during	spring	and	summer	13 Dec 2017M. Nicolaus, H. Flores, S. Hudson, S. Gerland, M. Granskog, H. M. Kauko,C. Katlein, B Lange, A. Pavlov, D. Perovich, C. WangSunlight and Sea IceNicolaus et al. (2010, CRST)Seasonality from Tara 2007385%15%85%15%65%35%50%50%70%30%20%80%Nicolaus et al. (2010, JGR)• Prototype	transmittance	and	albedo	time	series• Multi	Year	Ice	conditions	(ice:	2.0m,	snow	0.2m)• Strong	spatial	variabilityAutonomous Drift 2012Wang et al. (2014, JGR)From North Pole to Fram StraitIce thickness: 1.20 mSnow depth: 0.40 mFreeboard: -0.06 mTransmittance ResultsEnergy budgets• 2/3 of annual flux during melt season• 2/3 of energy for observed bottom melt• Max. integrated fluxes: 15 (Tara) 35 (Barneo) W/m2• “Interruption” by snow fall eventsTara 2007Barneo 2012Ecosystem interaction• Reduced transmittance• Ocean warmingNicolaus	et	al.	(2010,	JGR)Wang	et	al.	(2014,	JGR)Arctic-wide Up-scalingResults • Transmitted short wave is of same order as ocean heat flux• 96% of annual flux from May to August • Highest fluxes in June• Large uncertainties during melt season: 14 days => 25%Apr May JunJul Aug Sep01#Jan# EMO# MO# MO+14d# EFO# FO# FO+60d#31#Dec#0   0.020.040.060.080.1 0.120.140.160.180.2 Transmittance  FYImelting FYInew FYInew MYImelting MYIMYII" II" III" IV" V" VI" I"Pond"covered"sea"ice"0 0.020.040.060.080.1 0.120.140.160.180.2 Transmittance  F Ielting F Ine  F Ine  Ielting IIArndt	&	Nicolaus (2014,	Cryosphere)Characterize the variability of ice conditions- New and thin ice, ridges, seasonal ice, melt ponds - Towards distribution functions => spatial variabilityFocus on key season: spring-summer transition- Snow melt and melt pond formation- Light transmission: atmosphere > snow > ice > oceanPhysical snow, ice and water properties- Light transmission: atmosphere > snow > ice > ocean- Scattering and radiative transfer- Spectral properties and analysisEcosystem studies- Biomass estimates- Habitat conditionsObjectivesConditions- Drift north of Svalbard- 24 May to 03 June 2015Frozen lead (3 weeks old)- Ice thickness 25 cm- Snow depth 2 cm5-Sensor Setup- Surface albedo- @ 1 m irradiance- @ 10 m radiance- @ 10 m irradianceN-ICE 2015Main features- Differences in 1m and 10m transmittance- Bloom and snow fall…- …N-ICE 2015Effects of Phytoplankton Bloom- Changing fractions of more direct and scattered light- Decaying bloom after 30 MayPS106 Buoy Station16 June 2017 12 July 2017Radiation Station PS106Pond formationNew snow and freezingEarly meltfeaturesRadiation Station PS106ROV divesTowards	MOSAiCMissionRealizationFull	annual	cycle- Seasonality- Spatial	variability	of	all	ice	typesInterdisciplinary	projectsImproving	modelsRepeated	spatial	transects	(ROV)Distributed	networkMOSAiC: Central ObservatoryNicolaus et al. (2010, CRST)MOSAiC: Distributed NetworkResults and Next StepsImproved understanding of seasonalityTechnology: From prototypes to monitoring systems (distributed networks)Interruptions by snow fall and freezing eventsRole of different ice conditions (new/thin and old ice, ridges and ponds)Improve up-scaling and model parameterizationReduce uncertainties in key seasons (spring-summer transition)Quantifying spatial variabilityInclude ROV transects and select different ice typesImportance of thin ice for aggregate scale studies => needs more focusInclude aerial data sets, e.g. photography => upscalingAdvanced studies of bio-physical interactionDiurnal cycles and spectral featuresBiomass estimates and habitat conditions

Seasonality of light transmittance through Arctic sea ice during spring and summer

Seasonality of light transmittance through Arctic sea ice during spring and summer

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