From Bright Windows to Dark Spots: Snow Cover Controls Melt Pond Optical Properties During Refreezing

Anhaus, Philipp; Katlein, Christian; Nicolaus, Marcel; Hoppmann, Mario; Haas, Christian

doi:10.1029/2021gl095369

Cited by 6 publications

(2 citation statements)

References 58 publications

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“…The geographical locations for the scientific activities ranged from the marginal ice zone (MIZ, ∼82° N) north of Svalbard to close to the North Pole (89° N), where the icebreaker was moored and drifting for nearly 5 weeks. The general goal of MOCCHA was to investigate potential links between marine microbiology, local aerosol emissions, and cloud formation in the central Arctic Ocean. ,,− …”

Section: Methodsmentioning

confidence: 99%

Using Novel Molecular-Level Chemical Composition Observations of High Arctic Organic Aerosol for Predictions of Cloud Condensation Nuclei

Siegel

Neuberger

Karlsson

et al. 2022

Environ. Sci. Technol.

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Predictions of cloud droplet activation in the late summertime (September) central Arctic Ocean are made using κ -Köhler theory with novel observations of the aerosol chemical composition from a high-resolution time-of-flight chemical ionization mass spectrometer with a filter inlet for gases and aerosols (FIGAERO-CIMS) and an aerosol mass spectrometer (AMS), deployed during the Arctic Ocean 2018 expedition onboard the Swedish icebreaker Oden . We find that the hygroscopicity parameter κ of the total aerosol is 0.39 ± 0.19 (mean ± std). The predicted activation diameter of ∼25 to 130 nm particles is overestimated by 5%, leading to an underestimation of the cloud condensation nuclei (CCN) number concentration by 4–8%. From this, we conclude that the aerosol in the High Arctic late summer is acidic and therefore highly cloud active, with a substantial CCN contribution from Aitken mode particles. Variability in the predicted activation diameter is addressed mainly as a result of uncertainties in the aerosol size distribution measurements. The organic κ was on average 0.13, close to the commonly assumed κ of 0.1, and therefore did not significantly influence the predictions. These conclusions are supported by laboratory experiments of the activation potential of seven organic compounds selected as representative of the measured aerosol.

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Section: Methodsmentioning

confidence: 99%

Using Novel Molecular-Level Chemical Composition Observations of High Arctic Organic Aerosol for Predictions of Cloud Condensation Nuclei

Siegel

Neuberger

Karlsson

et al. 2022

Environ. Sci. Technol.

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“…Melt ponds contribute to the ice-albedo feedback by lowering the surface albedo (e.g., Curry et al, 1995;Light et al, 2022). For autumn, Anhaus et al (2021) showed that melt ponds influence light transmission. The preconditioning of melt ponds can be partly explained by ice topography (e.g., Flocco et al, 2015;Polashenski et al, 2012), predominately for deformed second-year ice (SYI), or snow dunes and snow accumulations (Petrich et al, 2012;Polashenski et al, 2012), mainly on level first-year ice (FYI).…”

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confidence: 99%

Preconditioning of Summer Melt Ponds From Winter Sea Ice Surface Temperature

Thielke

Fuchs

Spreen

et al. 2023

Geophysical Research Letters

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Melt ponds on Arctic sea ice are an important component of the summer energy budget (e.g., Nicolaus et al., 2012). Melt water collects at the ice surface during summer melt in topographic lows. Melt ponds contribute to the ice-albedo feedback by lowering the surface albedo (e.g., Curry et al., 1995;Light et al., 2022). For autumn, Anhaus et al. (2021) showed that melt ponds influence light transmission. The preconditioning of melt ponds can be partly explained by ice topography (e.g., Flocco et al., 2015;Polashenski et al., 2012), predominately for deformed second-year ice (SYI), or snow dunes and snow accumulations (Petrich et al., 2012;Polashenski et al., 2012), mainly on level first-year ice (FYI). Additional factors for melt pond preconditioning are ice permeability and pond hydrology (Eicken et al., 2002(Eicken et al., , 2004. There are distinct differences between melt ponds on level or deformed ice. The melt pond location and size are controlled by the topography of deformed ice while on level ice melt ponds can cover large areas (Webster et al., 2022). The ice topography, induced by ridges or leads, are either remnant from the previous seasons' dynamic events or can be newly created due to ice dynamics and/or snow accumulation (Polashenski et al., 2012). Also, refrozen melt ponds can have a lower ice surface elevation and ice thickness compared to the surroundings. There are still large uncertainties in models to predict melt pond

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Spatiotemporal evolution of melt ponds on Arctic sea ice

Webster

Holland

Wright

et al. 2022

Elementa: Science of the Anthropocene

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Melt ponds on sea ice play an important role in the Arctic climate system. Their presence alters the partitioning of solar radiation: decreasing reflection, increasing absorption and transmission to the ice and ocean, and enhancing melt. The spatiotemporal properties of melt ponds thus modify ice albedo feedbacks and the mass balance of Arctic sea ice. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition presented a valuable opportunity to investigate the seasonal evolution of melt ponds through a rich array of atmosphere-ice-ocean measurements across spatial and temporal scales. In this study, we characterize the seasonal behavior and variability in the snow, surface scattering layer, and melt ponds from spring melt to autumn freeze-up using in situ surveys and auxiliary observations. We compare the results to satellite retrievals and output from two models: the Community Earth System Model (CESM2) and the Marginal Ice Zone Modeling and Assimilation System (MIZMAS). During the melt season, the maximum pond coverage and depth were 21% and 22 ± 13 cm, respectively, with distribution and depth corresponding to surface roughness and ice thickness. Compared to observations, both models overestimate melt pond coverage in summer, with maximum values of approximately 41% (MIZMAS) and 51% (CESM2). This overestimation has important implications for accurately simulating albedo feedbacks. During the observed freeze-up, weather events, including rain on snow, caused high-frequency variability in snow depth, while pond coverage and depth remained relatively constant until continuous freezing ensued. Both models accurately simulate the abrupt cessation of melt ponds during freeze-up, but the dates of freeze-up differ. MIZMAS accurately simulates the observed date of freeze-up, while CESM2 simulates freeze-up one-to-two weeks earlier. This work demonstrates areas that warrant future observation-model synthesis for improving the representation of sea-ice processes and properties, which can aid accurate simulations of albedo feedbacks in a warming climate.

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From Bright Windows to Dark Spots: Snow Cover Controls Melt Pond Optical Properties During Refreezing

Cited by 6 publications

References 58 publications

Using Novel Molecular-Level Chemical Composition Observations of High Arctic Organic Aerosol for Predictions of Cloud Condensation Nuclei

Using Novel Molecular-Level Chemical Composition Observations of High Arctic Organic Aerosol for Predictions of Cloud Condensation Nuclei

Preconditioning of Summer Melt Ponds From Winter Sea Ice Surface Temperature

Spatiotemporal evolution of melt ponds on Arctic sea ice

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