Sea ice concentration satellite retrievals influenced by surface changes due to warm air intrusions: A case study from the MOSAiC expedition.

Rückert, Janna; Rostosky, Philip; Huntemann, Marcus; Clemens‐Sewall, David; Ebell, Kerstin; Kaleschke, Lars; Lemmetyinen, Juha; Macfarlane, Amy R.; Naderpour, Reza; Stroeve, Julienne; Walbröl, Andreas; Spreen, Gunnar

doi:10.31223/x5vw85

Cited by 3 publications

(2 citation statements)

References 72 publications

(87 reference statements)

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“…(2023) and Rückert et al. (2023), are well represented in the retrieved data. Figure 4 shows the good correlation (squared Pearson coefficient: 0.7) and low bias (0.18 kg m −2 ) between satellite retrieved values of TWV and the ship‐based measurements.…”

Section: Resultssupporting

confidence: 53%

“…For TWV, the agreement is good. Special events like the two warm air intrusions in April 2020 (visible as peaks in the time series), described in Kirbus et al (2023) and Rückert et al (2023), are well represented in the retrieved data. Figure 4 shows the good correlation (squared Pearson coefficient: 0.7) and low bias (0.18 kg m −2 ) between satellite retrieved values of TWV and the ship-based measurements.…”

Section: Mosaic Time Seriesmentioning

confidence: 61%

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Modeling Snow and Ice Microwave Emissions in the Arctic for a Multi‐Parameter Retrieval of Surface and Atmospheric Variables From Microwave Radiometer Satellite Data

Rückert,

Huntemann,

Tonboe

et al. 2023

Earth and Space Science

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Monitoring surface and atmospheric parameters—like water vapor—is challenging in the Arctic, despite the daily Arctic‐wide coverage of spaceborne microwave radiometer data. This is mainly due to the difficulties in characterizing the sea ice surface emission: sea ice and snow microwave emission is high and highly variable. There are very few data sets combining relevant in situ measurements with co‐located remote sensing data, which further complicates the development of accurate retrieval algorithms. Here, we present a multi‐parameter retrieval based on the inversion of a forward model for both, atmosphere and surface, for non‐melting conditions. The model consists of a layered microwave emission model of snow and ice. Since snow scattering and emission effects, as well as temperature gradients, are taken into account, a high variability in brightness temperatures can be simulated. For ocean regions and the atmosphere existing parameterized forward models are used. By using optimal estimation, the forward model can be inverted allowing for the simultaneous and consistent retrieval of nine variables: integrated water vapor, liquid water path, sea ice concentration, multi‐year ice fraction, snow depth, snow‐ice interface temperature and snow‐air interface temperature as well as sea‐surface temperature and wind speed (over open ocean). In addition, the method provides retrieval uncertainty estimates for each retrieved parameter. To evaluate the forward model as well as the retrieval, we use the extensive data sets acquired during the year‐long Arctic expedition Multidisciplinary drifting Observatory for the Study of Arctic Climate (2019–2020) as a reference.

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Section: Resultssupporting

confidence: 53%

Section: Mosaic Time Seriesmentioning

confidence: 61%

Modeling Snow and Ice Microwave Emissions in the Arctic for a Multi‐Parameter Retrieval of Surface and Atmospheric Variables From Microwave Radiometer Satellite Data

Rückert,

Huntemann,

Tonboe

et al. 2023

Earth and Space Science

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Asymmetries in cloud microphysical properties ascribed to sea ice leads via water vapour transport in the central Arctic

Saavedra Garfias,

Kalesse-Los,

von Albedyll

et al. 2023

Atmos. Chem. Phys.

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Abstract. To investigate the influence of sea ice openings like leads on wintertime Arctic clouds, the air mass transport is exploited as a heat and humidity feeding mechanism which can modify Arctic cloud properties. Cloud microphysical properties in the central Arctic are analysed as a function of sea ice conditions during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2019–2020. The Cloudnet classification algorithm is used to characterize the clouds based on remote sensing observations and the atmospheric thermodynamic state from the observatory on board the research vessel (RV) Polarstern. To link the sea ice conditions around the observational site with the cloud observations, the water vapour transport (WVT) being conveyed towards RV Polarstern has been utilized as a mechanism to associate upwind sea ice conditions with the measured cloud properties. This novel methodology is used to classify the observed clouds as coupled or decoupled to the WVT based on the location of the maximum vertical gradient of WVT height relative to the cloud-driven mixing layer. Only a conical sub-sector of sea ice concentration (SIC) and the lead fraction (LF) centred on the RV Polarstern location and extending up to 50 km in radius and with an azimuth angle governed by the time-dependent wind direction measured at the maximum WVT is related to the observed clouds. We found significant asymmetries for cases when the clouds are coupled or decoupled to the WVT and selected by LF regimes. Liquid water path of low-level clouds is found to increase as a function of LF, while the ice water path does so only for deep precipitating systems. Clouds coupled to WVT are found to generally have a lower cloud base and larger thickness than decoupled clouds. Thermodynamically, for coupled cases the cloud-top temperature is warmer and accompanied by a temperature inversion at the cloud top, whereas the decoupled cases are found to be closely compliant with the moist adiabatic temperature lapse rate. The ice water fraction within the cloud layer has been found to present a noticeable asymmetry when comparing coupled versus decoupled cases. This novel approach of coupling sea ice to cloud properties via the WVT mechanism unfolds a new tool to study Arctic surface–atmosphere processes. With this formulation, long-term observations can be analysed to enforce the statistical significance of the asymmetries. Furthermore, our results serve as an opportunity to better understand the dynamic linkage between clouds and sea ice and to evaluate its representation in numerical climate models for the Arctic system.

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Relevance of warm air intrusions for Arctic satellite sea ice concentration time series

Rostosky,

Spreen

2023

The Cryosphere

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Abstract. Winter warm air intrusions entering the Arctic region can strongly modify the microwave emission of the snow-covered sea ice system due to temperature-induced snow metamorphism and ice crust formations, e.g., after melt–refreeze events. Common microwave radiometer satellite sea ice concentration retrievals are based on empirical models using the snow-covered sea ice emissivity and thus can be influenced by strong warm air intrusions. Here, we carry out a long-term study analyzing 41 years of winter sea ice concentration observations from different algorithms to investigate the impact of warm air intrusions on the retrieved ice concentration. Our results show that three out of four algorithms underestimate the sea ice concentration during (and up to 10 d after) warm air intrusions which increase the 2 m air temperature (daily maximum) above − 5 ∘C. This can lead to sea ice area underestimations in the order of 104 to 105 km2. If the 2 m temperature during the warm air intrusions crosses − 2 ∘C, all algorithms are impacted. Our analysis shows that the strength of these strong warm air intrusions increased in recent years, especially in April. With a further climate change, such warm air intrusions are expected to occur more frequently and earlier in the season, and their influence on sea ice climate data records will become more important.

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Sea ice concentration satellite retrievals influenced by surface changes due to warm air intrusions: A case study from the MOSAiC expedition.

Cited by 3 publications

References 72 publications

Modeling Snow and Ice Microwave Emissions in the Arctic for a Multi‐Parameter Retrieval of Surface and Atmospheric Variables From Microwave Radiometer Satellite Data

Modeling Snow and Ice Microwave Emissions in the Arctic for a Multi‐Parameter Retrieval of Surface and Atmospheric Variables From Microwave Radiometer Satellite Data

Asymmetries in cloud microphysical properties ascribed to sea ice leads via water vapour transport in the central Arctic

Relevance of warm air intrusions for Arctic satellite sea ice concentration time series

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