The microwave emissivity variability of snow covered first-year sea ice from late winter to early summer: a model study

Willmes, Sascha; Nicolaus, Marcel; Haas, Christian

doi:10.5194/tc-8-891-2014

Cited by 42 publications

(48 citation statements)

References 49 publications

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“…The variation amounts to approximately 20-30 K, which corresponds to about 8-12 % of the average value, and the peaks in the variation occur in summer. Thus, increased variability in late spring/early summer connected to melt onset and consequent snow metamorphoses, reported by Willmes et al (2014), is confirmed in our study.…”

Section: Dynamic Tie Pointssupporting

confidence: 78%

“…Effect of diurnal, regional and inter-annual variability of atmospheric forcing on surface microwave emissivity was also reported in a model study of Willmes et al (2014). This means that not only sea ice area has a climatic trend, but atmospheric and surface parameters affecting the microwave emission may also have a trend.…”

Section: Introductionmentioning

confidence: 96%

“…This is of importance because both sea ice area/extent and the geophysical noise parameters (sea ice emissivity, atmospheric parameters) have climatic trends. Also, as model study by Willmes et al (2014) showed, emissivity of FYI covered by snow is characterised by seasonal and regional variations caused by atmospherically driven snow metamorphism. Secondly, the dynamical tie points are needed when accurately quantifying the SIC uncertainties.…”

Section: Dynamic Tie Pointsmentioning

confidence: 99%

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Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations

Ivanova¹,

et al. 2015

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Abstract. Sea ice concentration has been retrieved in polar regions with satellite microwave radiometers for over 30 years. However, the question remains as to what is an optimal sea ice concentration retrieval method for climate monitoring. This paper presents some of the key results of an extensive algorithm inter-comparison and evaluation experiment. The skills of 30 sea ice algorithms were evaluated systematically over low and high sea ice concentrations. Evaluation criteria included standard deviation relative to independent validation data, performance in the presence of thin ice and melt ponds, and sensitivity to error sources with seasonal to inter-annual variations and potential climatic trends, such as atmospheric water vapour and water-surface roughening by wind. A selection of 13 algorithms is shown in the article to demonstrate the results. Based on the findings, a hybrid approach is suggested to retrieve sea ice concentration globally for climate monitoring purposes. This approach consists of a combination of two algorithms plus dynamic tie points implementation and atmospheric correction of input brightness temperatures. The method minimizes inter-sensor calibration discrepancies and sensitivity to the mentioned error sources.

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Section: Dynamic Tie Pointssupporting

confidence: 78%

Section: Introductionmentioning

confidence: 96%

Section: Dynamic Tie Pointsmentioning

confidence: 99%

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Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations

Ivanova¹,

et al. 2015

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“…In addition, ice-snow interface flooding, formation of meteoric ice and snow metamorphism all impact sea ice concentrations, which have not been quantified yet for Antarctic sea ice, and trends in brightness temperatures found in the Weddell Sea may reflect increased melt rates or changes in the melt season (Willmes et al, 2014). The advantage of the Bootstrap algorithm is that the ice concentration can be derived without an a priori assumption about ice type, though consolidated ice data points are sometimes difficult to distinguish from mixtures of ice and open ocean due to the presence of snow cover, flooding or roughness effects.…”

Section: Discussionmentioning

confidence: 99%

“…However, another complication is that seasonal variations in sea ice and snow emissivity can be very large, leading to seasonal biases in either algorithm (e.g., Andersen et al, 2007;Willmes et al, 2014;Gloersen and Cavalieri, 1986). In addition, ice-snow interface flooding, formation of meteoric ice and snow metamorphism all impact sea ice concentrations, which have not been quantified yet for Antarctic sea ice, and trends in brightness temperatures found in the Weddell Sea may reflect increased melt rates or changes in the melt season (Willmes et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Mapping and assessing variability in the Antarctic marginal ice zone, pack ice and coastal polynyas in two sea ice algorithms with implications on breeding success of snow petrels

et al. 2016

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Abstract. Sea ice variability within the marginal ice zone (MIZ) and polynyas plays an important role for phytoplankton productivity and krill abundance. Therefore, mapping their spatial extent as well as seasonal and interannual variability is essential for understanding how current and future changes in these biologically active regions may impact the Antarctic marine ecosystem. Knowledge of the distribution of MIZ, consolidated pack ice and coastal polynyas in the total Antarctic sea ice cover may also help to shed light on the factors contributing towards recent expansion of the Antarctic ice cover in some regions and contraction in others. The long-term passive microwave satellite data record provides the longest and most consistent record for assessing the proportion of the sea ice cover that is covered by each of these ice categories. However, estimates of the amount of MIZ, consolidated pack ice and polynyas depend strongly on which sea ice algorithm is used. This study uses two popular passive microwave sea ice algorithms, the NASA Team and Bootstrap, and applies the same thresholds to the sea ice concentrations to evaluate the distribution and variability in the MIZ, the consolidated pack ice and coastal polynyas. Results reveal that the seasonal cycle in the MIZ and pack ice is generally similar between both algorithms, yet the NASA Team algorithm has on average twice the MIZ and half the consolidated pack ice area as the Bootstrap algorithm. Trends also differ, with the Bootstrap algorithm suggesting statistically significant trends towards increased pack ice area and no statistically significant trends in the MIZ. The NASA Team algorithm on the other hand indicates statistically significant positive trends in the MIZ during spring. Potential coastal polynya area and amount of broken ice within the consolidated ice pack are also larger in the NASA Team algorithm. The timing of maximum polynya area may differ by as much as 5 months between algorithms. These differences lead to different relationships between sea ice characteristics and biological processes, as illustrated here with the breeding success of an Antarctic seabird.

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Timing and regional patterns of snowmelt on Antarctic sea ice from passive microwave satellite observations

et al. 2016

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An improved understanding of the temporal variability and the spatial distribution of snowmelt on Antarctic sea ice is crucial to better quantify atmosphere-ice-ocean interactions, in particular sea-ice mass and energy budgets. It is therefore important to understand the mechanisms that drive snowmelt, both at different times of the year and in different regions around Antarctica. In this study, we combine diurnal brightness temperature differences (dT B (37 GHz)) and ratios (T B (19 GHz)/T B (37 GHz)) to detect and classify snowmelt processes. We distinguish temporary snowmelt from continuous snowmelt to characterize dominant melt patterns for different Antarctic sea-ice regions from 1988/1989 to 2014/2015. Our results indicate four characteristic melt types. On average, 38.9 6 6.0% of all detected melt events are diurnal freeze-thaw cycles in the surface snow layer, characteristic of temporary melt (Type A). Less than 2% reveal immediate continuous snowmelt throughout the snowpack, i.e., strong melt over a period of several days (Type B). In 11.7 6 4.0%, Type A and B take place consecutively (Type C), and for 47.8 6 6.8% no surface melt is observed at all (Type D). Continuous snowmelt is primarily observed in the outflow of the Weddell Gyre and in the northern Ross Sea, usually 17 days after the onset of temporary melt. Comparisons with Snow Buoy data suggest that also the onset of continuous snowmelt does not translate into changes in snow depth for a longer period but might rather affect the internal stratigraphy and density structure of the snowpack. Considering the entire data set, the timing of snowmelt processes does not show significant temporal trends.

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The microwave emissivity variability of snow covered first-year sea ice from late winter to early summer: a model study

Cited by 42 publications

References 49 publications

Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations

Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations

Mapping and assessing variability in the Antarctic marginal ice zone, pack ice and coastal polynyas in two sea ice algorithms with implications on breeding success of snow petrels

Timing and regional patterns of snowmelt on Antarctic sea ice from passive microwave satellite observations

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