Estimating Arctic Sea Ice Melt Pond Fraction And Assessing Ice Type Separability During Advanced Melt
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| Author | : Sasha Nasonova |
| Publisher | : |
| Total Pages | : |
| Release | : 2018 |
| Genre | : |
| ISBN | : |
Arctic sea ice is rapidly declining in extent, thickness, volume and age, with the majority of the decline in extent observed at the end of the melt season. Advanced melt is a thermodynamic regime and is characterized by the formation of melt ponds on the sea ice surface, which have a lower surface albedo (0.2-0.4) than the surrounding ice (0.5-0.7) allowing more shortwave radiation to enter the system. The loss of multiyear ice (MYI) may have a profound impact on the energy balance of the system because melt ponds on first-year ice (FYI) comprise up to 70% of the ice surface during advanced melt, compared to 40% on MYI. Despite the importance of advanced melt to the ocean-sea ice-atmosphere system, advanced melt and the extent to which winter conditions influence it remain poorly understood due to the highly dynamic nature of melt pond formation and evolution, and a lack of reliable observations during this time. In order to establish quantitative links between winter and subsequent advanced melt conditions, and assess the effects of scale and choice of aggregation features on the relationships, three data aggregation approaches at varied spatial scales were used to compare high resolution satellite GeoEye-1 optical images of melt pond covered sea ice to winter airborne laser scanner surface roughness and electromagnetic induction sea ice thickness measurements. The findings indicate that winter sea ice thickness has a strong association with melt pond fraction (fp) for FYI and MYI. FYI winter surface roughness is correlated with fp, whereas for MYI no association with fp was found. Satellite-borne synthetic aperture radar (SAR) data are heavily relied upon for sea ice observation; however, during advanced melt the reliability of observations is reduced. In preparation for the upcoming launch of the RADARSAT Constellation Mission (RCM), the Kolmogorov-Smirnov (KS) statistical test was used to assess the ability of simulated RCM parameters and grey level co-occurrence matrix (GLCM) derived texture features to discriminate between major ice types during winter and advanced melt, with a focus on advanced melt. RCM parameters with highest discrimination ability in conjunction with optimal GLCM texture features were used as input parameters for Support Vector Machine (SVM) supervised classifications. The results indicate that steep incidence angle RCM parameters show promise for distinguishing between FYI and MYI during advanced melt with an overall classification accuracy of 77.06%. The addition of GLCM texture parameters improved accuracy to 85.91%. This thesis provides valuable contributions to the growing body of literature on fp parameterization and SAR ice type discrimination during advanced melt.
| Author | : Anja Rösel |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 120 |
| Release | : 2013-05-23 |
| Genre | : Law |
| ISBN | : 3642370330 |
The Arctic sea ice is characterized by profound changes caused by surface melting processes and the formation of melt ponds in summer. Melt ponds contribute to the ice-albedo feedback as they reduce the surface albedo of sea ice, and hence accelerate the decay of Arctic sea ice. To quantify the melting of the entire Arctic sea ice, satellite based observations are necessary. Due to different spectral properties of snow, ice, and water, theoretically, multi-spectral optical sensors are necessary for the analysis of these distinct surface types. This study demonstrates the potential of optical sensors to detect melt ponds on Arctic sea ice. For the first time, an Arctic-wide, multi-annual melt pond data set for the years 2000-2011 has been created and analyzed.
| Author | : Niels Fuchs |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2023 |
| Genre | : |
| ISBN | : |
Sea ice plays a fundamental role in Polar climate and ecosystems. Melt ponds, forming routinely on Arctic sea ice during summer, can cover and impact a considerable fraction of the ice area. However, data that allow a comprehensive understanding of pond evolution processes remain scarce. Consequently, we cannot yet predict how ponds will develop on the increasingly prevalent young ice in the future. Previous studies have drawn a very heterogeneous picture of pond coverage on young ice, which we can only improve with more detailed measurement data and analysis tools that allow the derivation of properties possibly driving pond evolution. The existence of over ten years of high-resolution aerial image data from AWI aircraft campaigns in the Arctic has motivated me to develop and refine evaluation methods for this dataset, the one-year drift campaign MOSAiC, and future measurement campaigns. I created a customized classification algorithm to classify images into sea ice surface classes with minimal manual intervention. By implementing cutting-edge photogrammetry tools and developing a spatially high-resolution albedo and pond depth retrieval method, I draw an unprecedented multidimensional picture of melt ponds. From this, I derived properties of the sea ice cover that favor and limit pond coverage. I found that within the observed areas, melt pond coverage was more constant than expected, ranging between 15% to 25%. The first-ever tracking of the evolution of the entire pond bathymetry shows that we have so far overlooked the deformability of the pond bottom ice. The multidimensional, high-resolution approach for long-range airborne measurements allowed me to make general recommendations for representative ground measurements. The tools presented, together with the refined insights into pond properties and evolution, will improve our understanding of summer sea ice and can help better assess the role and fate of ponds in the future Polar climate and ecosystems.
| Author | : Hannah Niehaus |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2024 |
| Genre | : |
| ISBN | : |
The presence of melt ponds on Arctic summer sea ice strongly alters the absorption of solar radiation by the sea ice-ocean system and thereby the Arctic energy budget. Therefore, melt ponds are key to the positive sea ice-albedo feedback, which is one of the main drivers of the amplified Arctic warming observed in recent decades, and even affects the global climate. To analyze the mechanisms of melt pond evolution and their implications on the sea ice state, and to improve their representation in climate models, comprehensive observational data are needed. This dissertation presents a new approach to retrieve melt pond, sea ice and open ocean fractions at pan-Arctic scales from Sentinel-3 optical satellite data. The newly developed Melt Pond Detection 2 (MPD2) algorithm is the first fully physical retrieval that can distinguish these three surface types at the spatial resolution of 1.2 km. Because multiple combinations of surface type fractions result in similar observations at this coarse resolution, prior information are required for retrieval. As part of the development process, a reference data set of 33 local melt pond fraction maps with a spatial resolution of 10 m has been created from Sentinel-2 satellite data. Parts of these data were then used to calibrate an empirical pre-retrieval to provide preliminary estimates of surface type fractions. In addition, the correlation between sea ice optical properties and air temperature history has been investigated using measurement data from field campaigns. This correlation and the results of the pre-retrieval are used to initialize and constrain the physical retrieval. The results are validated against the full extent of the reference data set, leading to an uncertainty estimate of 7.8 % and 9 % for the melt pond and open ocean fractions, respectively. The MPD2 algorithm has been applied to seven years of Sentinel-3 observations from 2017 to 2023. This data set can be continued for future years and expanded by the application to previous satellite sensors. Finally, the newly produced data set has been used to study regional differences in melt pond evolution: the lowest melt pond fractions are found in the Central Arctic with low seasonal variability, and the highest fractions are observed in the landfast ice-dominated Canadian Archipelago; the highest seasonal and interannual variability are observed in the Beaufort Sea. Additionally, a pan-Arctic analysis correlating the melt pond fraction product with sea ice surface roughness data has been carried out: this showed that flat sea ice features higher melt pond fractions at the beginning of the melt season, while later in the season melt pond fractions tend to be higher on deformed sea ice.
| Author | : Gunnar Spreen |
| Publisher | : GRIN Verlag |
| Total Pages | : 194 |
| Release | : 2008 |
| Genre | : Science |
| ISBN | : 3640130642 |
Doctoral Thesis / Dissertation from the year 2008 in the subject Geography / Earth Science - Physical Geography, Geomorphology, Environmental Studies, grade: 1,0, University of Hamburg (Institut für Meereskunde), language: English, abstract: The sea ice export out of the Arctic Ocean through Fram Strait into the Greenland Sea is the single largest source of freshwater in the Nordic Seas and therefore of spezial importance for the hydrological cycle of the North Atlantic. On its way south, the exported sea ice melts and thereby modifies the stratification of the ocean surface mixed layer, which in turn influences oceanic deep convection and water mass transformation processes in the Nordic Seas and thus impact global ocean thermohaline circulation. The lack of spatial sea ice thickness information has been one of the weaknesses for previous existing methods to determine the sea ice export. In this study a new method to obtain the sea ice volume flux exclusively from satellite measurements is presented. Previous estimates of the sea ice volume flux relayed on ice draft measurements of a single Upward Looking Sonar (ULS) in the Greenland Sea. The GLAS laser altimeter onboard the ICESat satellite launched in 2003 offers for the first time the opportunity to obtain the spatial sea ice thickness distribution up to 86°N latitude. In this study a method to determine the sea ice freeboard from ICESat altimeter data is developed and applied to nine ICESat measurement periods between 2003 and 2007. Assuming hydrostatic balance and by utilization of further satellite, in situ and climatological data these sea ice freeboard measurements are converted to sea ice thickness maps of the Fram Strait region. The satellite-based ice thickness estimates are combined with sea ice area and sea ice drift, as retrieved from AMSR-E microwave radiometer measurements at 89GHz, to obtain the sea ice volume flux. The errors of the input quantities and the final sea ice volume flux are assessed.
| Author | : Angela C. Bliss |
| Publisher | : |
| Total Pages | : 198 |
| Release | : 2015 |
| Genre | : |
| ISBN | : 9781321695649 |
The timing of snowmelt onset (MO) on Arctic sea ice derived from passive microwave satellite data is examined by determining the melting area (in km 2) on a daily basis for the spring and summer melt season months over the 1979 -- 2012 data record. The date of MO on Arctic sea ice has important implications for the amount of total solar energy absorbed by the ice-ocean system in a given year. Increasingly early mean MO dates have been recorded over the 34-year data record. Statistically significant trends indicate that MO is occurring 6.6 days decade-1 earlier in the year over all Arctic sea ice extent. Larger trends exist in sub-regions of the Arctic Ocean including the Barents, Kara, Laptev, East Siberian, Chukchi, and Beaufort Seas and in the Central Arctic region. The Bering Sea is the only sub-region of the Arctic that has a positive trend in mean MO date indicating that melting is occurring later in the year. Temporal and spatial variability in melting events are examined in the time series of daily MO areas via the identification of several types of melting events. These melting events are characterized based on the magnitude of area melted and duration of the event. Daily maps of MO during melting events are compared with the atmospheric conditions from reanalysis data to investigate the nature of spatial variability in melting area. The occurrence of transient cyclones tends to produce large, contiguous areas of melting on sea ice located in the warm sector of the cyclone. By contrast, high pressure and attendant clear sky conditions tend to produce sporadic, discontinuous areas of melting area. Interannual variability in daily MO area is assessed using an annual accumulation of daily MO area for each melt season. Trends in mean MO dates are evident in the annual accumulations, however, regional variability is high and outlier events can occur. This work illustrates the need for a better understanding of the synoptic weather conditions leading to specific patterns in MO area to improve the predictability of early season Arctic sea ice response to a changing climate.
| Author | : Tom Carrieres |
| Publisher | : Cambridge University Press |
| Total Pages | : 263 |
| Release | : 2017-10-05 |
| Genre | : Science |
| ISBN | : 1108417426 |
A comprehensive overview of the science involved in automated prediction of sea ice, for sea ice analysts, researchers, and professionals.
| Author | : |
| Publisher | : |
| Total Pages | : 15 |
| Release | : 2015 |
| Genre | : Melt ponds |
| ISBN | : |
| Author | : Lee E. Collins |
| Publisher | : |
| Total Pages | : 61 |
| Release | : 2013 |
| Genre | : Albedo |
| ISBN | : |
The albedo of Arctic sea ice depends greatly on the formation of melt ponds. These ponds form in depressions on the ice as surface snow melts during the summer months, and their location is determined mainly by the initial snow topography. Using a high resolution sea ice model forced with data taken from the Atmospheric Radiation Measurement (ARM) site in Barrow, AK, we investigate how specific factors, both internal model parameters and initial conditions, affect the evolution of melt ponds on Arctic sea ice. We also use forcing data taken from output of the Community Earth Systems Model (CESM) to investigate the differences in melt pond parametrization between our model and CESM. The resolved model uses a unique and innovative approach in pond modeling, the "trigger depth" method, to initiate pond drainage. Results from sensitivity analysis on the trigger depth show the validity of this new approach, suggesting it could be useful in other ice models. The initial snowpack has a very large role in pond formation and extent. We use surface topography gathered from LiDAR scans from the ARM site to provide a realistic snowpack surface. For our sensitivity analysis of the total initial amount of snow in the model, we alter only the minimum thickness of the snow on top of the ice, retaining a consistent surface topography for each simulation. The LiDAR topography from the ARM site provides a more realistic approach to the pond model, as opposed to a randomly generated method of creating snow topography. Large initial snowpack inhibits the formation of deep channels in the ice, reducing pond fraction at the end of the melt season. Finally, we force the resolved model simulations with data from CESM and compare the pond behavior to that of CESM. CESM does an unrealistic job of representing melt ponds, partially due to the way melt ponds are parametrized in the model, using a "thickness-class" method for creating and categorizing melt ponds. CESM pond formation occurs over a much broader time span compared to observations and our resolved model. Results from this work will be used to investigate and possibly improve the melt pond parametrization in CESM.
| Author | : D. FLOCCO |
| Publisher | : |
| Total Pages | : |
| Release | : 2007 |
| Genre | : |
| ISBN | : |
