Effect of melt ponds fraction on sea ice anomalies in the Arctic Ocean

Feng, Jiajun; Zhang, Yuanzhi; Cheng, Qiuming; Wong, Kapo; Liu, Yu; Tsou, Jin Yeu

doi:10.1016/j.jag.2021.102297

Cited by 6 publications

(9 citation statements)

References 52 publications

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“…This decrease in the surface albedo can lead to more absorption of solar radiation, which can further amplify the climate change in the Arctic via the sea-ice feedback mechanism [18]. It has also been reported that the MPF is considered to be a good predictor for the Arctic sea ice extent in September and can be used to improve forecasts of seasonal sea ice changes and climate models [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Generating a Long-Term Spatiotemporally Continuous Melt Pond Fraction Dataset for Arctic Sea Ice Using an Artificial Neural Network and a Statistical-Based Temporal Filter

Peng

Ding

et al. 2022

Remote Sensing

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The melt pond fraction (MPF) is an important geophysical parameter of climate and the surface energy budget, and many MPF datasets have been generated from satellite observations. However, the reliability of these datasets suffers from short temporal spans and data gaps. To improve the temporal span and spatiotemporal continuity, we generated a long-term spatiotemporally continuous MPF dataset for Arctic sea ice, which is called the Northeast Normal University-melt pond fraction (NENU-MPF), from Moderate Resolution Imaging Spectroradiometer (MODIS) data. First, the non-linear relationship between the MODIS reflectance/geometries and the MPF was constructed using a genetic algorithm optimized back-propagation neural network (GA-BPNN) model. Then, the data gaps were filled and smoothed using a statistical-based temporal filter. The results show that the GA-BPNN model can provide accurate estimations of the MPF (R2 = 0.76, root mean square error (RMSE) = 0.05) and that the data gaps can be efficiently filled by the statistical-based temporal filter (RMSE = 0.047; bias = −0.002). The newly generated NENU-MPF dataset is consistent with the validation data and with published MPF datasets. Moreover, it has a longer temporal span and is much more spatiotemporally continuous; thus, it improves our knowledge of the long-term dynamics of the MPF over Arctic sea ice surfaces.

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Section: Introductionmentioning

confidence: 99%

Generating a Long-Term Spatiotemporally Continuous Melt Pond Fraction Dataset for Arctic Sea Ice Using an Artificial Neural Network and a Statistical-Based Temporal Filter

Peng

Ding

et al. 2022

Remote Sensing

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“…SA changes are mostly confined to sea ice borders in winter (Figures 2c and 2d). The MDR also has an increased melt pond fraction (not shown; Feng et al, 2021). Both sea ice shrinkage and increased melt ponds further decrease the Arctic regional SA as part of the surface albedo feedback (Massonnet et al, 2018;Serreze & Barry, 2011;Zhang et al, 2019).…”

Section: Differences In Surface Albedo and Sea Ice Concentrationmentioning

confidence: 96%

Sensitivity of Arctic Surface Temperature to Including a Comprehensive Ocean Interior Reflectance to the Ocean Surface Albedo Within the Fully Coupled CESM2

Wei,

Ren,

Yang

et al. 2023

J Adv Model Earth Syst

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Almost all current climate models simplify the ocean surface albedo (OSA) by assuming the reflected solar energy without the ocean interior contribution. In this study, an improved ocean surface albedo scheme is incorporated into the Community Earth System Model version 2 (CESM2) to assess the sensitivity of Arctic surface temperature to including ocean interior reflectance to the OSA. Fully coupled CESM2 simulations with and without ocean interior reflectance are subsequently performed, we focus on the analysis of Arctic surface temperature responses. Incorporating ocean interior reflectance increases absorbed solar radiation and warms the ocean, enhancing seasonal heat storage and release across the Arctic Ocean, and increasing sea ice reduction and positive climate feedbacks that elevates Arctic surface temperature. Seasonal variations in air‐surface temperature differences induce changes in turbulent heat flux patterns, concurrently modifying dynamic advection and moisture processes that affect boundary layer humidity and low clouds, especially in winter. Based on partitioning physical processes in the thermodynamic energy equation, surface air warming is induced primarily through positive heating anomalies of vertical advection, latent heat release, and longwave radiative forcing. Through an examination of the surface energy budget, skin temperature warming is driven predominantly by increased downward longwave radiation, positive surface albedo feedback in summer, and increased conductive heat transport from the ocean particularly in winter. Significant effects of ocean interior reflectance on the Arctic Ocean, including sea surface warming and sea ice reduction, justify the importance of ocean interior reflectance in climate models for better understanding of ongoing Arctic climate changes.

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“…Since then, optical sensors have been used to study melt ponds for two main reasons: to monitor their shape, dynamics and spatial distribution (e.g. Tschudi et al, 2008;Wang et al, 2020;Feng et al, 2021); and for surface energy balance studies (Istomina et al, 2016;Lu et al, 2018). Optical satellite data with medium to low resolution (i.e., between 30 m to 1 km), such as the Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), the ENVISAT Medium Resolution Imaging Spectrometer (MERIS) and the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) multispectral images have been used to observe melt ponds in the Arctic (Markus et al, 2003;Tschudi et al, 2008;Rösel et al, 2011Istomina et al, 2015;Zege et al, 2015)(Fig.…”

Section: Optical and Sar Technical Abilities For Melt Pond Discrimina...mentioning

confidence: 99%

Review Article: Earth observations of Melt Ponds on Sea Ice

Aparício¹

2023

Preprint

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Abstract. Melt ponds are pools of open water that form during summer on sea ice surface, playing a key role in the Arctic climate and sea ice energy budget. Due to their lower albedo, melt ponds absorb more radiation than un-ponded ice, spurring further ice melt and enhancing the positive ice-albedo feedback. This feedback is expected to increase as the Arctic ice cover is moving towards a regime where multiyear ice (MYI) is being replaced by first-year ice (FYI), which presents wider melt pond coverage as well as more energy absorption with profound consequences from an energy balance perspective. Nevertheless, the lack of knowledge or inclusion of melt pond fraction (MPF) on global climate and sea ice models, is pointed as their main source of uncertainty and disparity of predictions results. This, along with the recent conclusions on the potential of MPF for enhancing the forecasting ability of summer sea ice extent, underscores the importance of accurately obtaining large and spatiotemporal scale of MPF, across the Arctic. However, observations of melt ponds are far from adequate for the Arctic ocean and for both MYI and FYI, and on a large scale this is only possible through satellite-based Earth observations (EO). This paper provides an overview of efforts in EO remote sensing studies of melt ponds, for both optical sensors and radar-based approaches. The main algorithms used for melt pond identification and the different methods for MPF retrievals are outlined, ranging from the early traditional techniques to the increasingly prevalent use of Artificial intelligence (AI), namely machine and deep learning. The current large-scale optical-based pan-Arctic MPF datasets are intercompared along with the main advantages and disadvantages of various optical and radar data-based methods for MPF retrievals. The potential of radar, namely Synthetic Aperture Radar (SAR) technical abilities to the enhancement of reliability is analysed, since optical approaches, despite being more used, are hampered by cloud cover, spectral representativeness and resolution. Finally, current gaps in melt pond knowledge and MPF retrievals are discussed and summarised leading to the outline of further directions of research development.

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Effect of melt ponds fraction on sea ice anomalies in the Arctic Ocean

Cited by 6 publications

References 52 publications

Generating a Long-Term Spatiotemporally Continuous Melt Pond Fraction Dataset for Arctic Sea Ice Using an Artificial Neural Network and a Statistical-Based Temporal Filter

Generating a Long-Term Spatiotemporally Continuous Melt Pond Fraction Dataset for Arctic Sea Ice Using an Artificial Neural Network and a Statistical-Based Temporal Filter

Sensitivity of Arctic Surface Temperature to Including a Comprehensive Ocean Interior Reflectance to the Ocean Surface Albedo Within the Fully Coupled CESM2

Review Article: Earth observations of Melt Ponds on Sea Ice

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