The impact of melt ponds on summertime microwave brightness temperatures and sea-ice concentrations

Sea-ice concentrations derived from satellite microwave brightness temperatures are less accurate during summer. In the Arctic Ocean the lack of accuracy is primarily caused by melt ponds, but also by changes in the properties of snow and the sea-ice surface itself. We investigate the sensitivity of eight sea-ice concentration retrieval algorithms to melt ponds by comparing sea-ice concentration with the melt-pond fraction. We derive gridded daily sea-ice concentrations from microwave brightness temperatures of summer 2009. We derive the daily fraction of melt ponds, open water between ice floes, and the ice-surface fraction from contemporary Moderate Resolution Spectroradiometer (MODIS) reflectance data. We only use grid cells where the MODIS sea ice concentration, which is the melt-pond fraction plus the ice-surface fraction, exceeds 90 %. For one group of algorithms, e.g., Bristol and Comiso bootstrap frequency mode (Bootstrap_f), sea-ice concentrations are linearly related to the MODIS melt-pond fraction quite clearly after June. For other algorithms, e.g., Near9OGHz and Comiso bootstrap polarization mode (Bootstrap_p), this relationship is weaker and develops later in summer. We attribute the variation of the sensitivity to the melt-pond fraction across the algorithms to a different sensitivity of the brightness temperatures to snow-property variations. We find an underestimation of the sea-ice concentration by between 14 % (Bootstrap_f) and 26 % (Bootstrap_p) for 100 % sea ice with a melt-pond fraction of 40 %. The underestimation reduces to 0 % for a melt pond fraction of 20 %. In presence of real open water between ice floes, the sea-ice concentration is overestimated by between 26 % (Bootstrap_f) and 14 % (Bootstrap_p) at 60 % sea-ice concentration and by 20 % across all algorithms at 80 % sea-ice concentration. None of the algorithms investigated performs best based on our investigation of data from summer 2009. We suggest that those algorithms which are more sensitive to melt ponds could be optimized more easily because the influence of unknown snow and sea-ice surface property variations is less pronounced

Sea ice segmentation using Tandem-X pursuit mono static and alternative bistatic modes

Accepted manuscript version. Source at https://doi.org/10.1109/IGARSS.2017.8126964In this paper we investigate interferometric pairs of SAR images acquired by Tandem-X with the monostatic pursuit and the alternative bistatic modes for sea ice segmentation. The individual SAR images are modelled as non-Gaussian, and from the modelled data different features are extracted, stacked together and clustered. The interferometric coherence is regarded as an additional feature and utilized for clustering. In addition to complementing the information extracted from the individual images, the interferometric coherence is found to be capable of discriminating between open water and sea ice, as well as between different ice types

Snow contribution to first-year and second-year Arctic sea ice mass balance north of Svalbard

The salinity and water oxygen isotope composition (δ18O) of 29 first-year (FYI) and second-year (SYI) Arctic sea ice cores (total length 32.0 m) from the drifting ice pack north of Svalbard were examined to quantify the contribution of snow to sea ice mass. Five cores (total length 6.4 m) were analyzed for their structural composition, showing variable contribution of 10–30% by granular ice. In these cores, snow had been entrained in 6–28% of the total ice thickness. We found evidence of snow contribution in about three quarters of the sea ice cores, when surface granular layers had very low δ18O values. Snow contributed 7.5–9.7% to sea ice mass balance on average (including also cores with no snow) based on δ18O mass balance calculations. In SYI cores, snow fraction by mass (12.7–16.3%) was much higher than in FYI cores (3.3–4.4%), while the bulk salinity of FYI (4.9) was distinctively higher than for SYI (2.7). We conclude that oxygen isotopes and salinity profiles can give information on the age of the ice and enables distinction between FYI and SYI (or older) ice in the area north of Svalbard

The Future of the Arctic: What Does It Mean for Sea Ice and Small Creatures?

The warming of our planet is changing the Arctic dramatically. The area covered by sea-ice is shrinking and the ice that is left is younger and thinner. We took part in an expedition to the Arctic, to study how these changes affect organisms living in and under the ice. Following this expedition, we found that storms can more easily break the thinner ice. Storms form cracks in the sea ice, allowing sunlight to pass into the water below, which makes algal growth possible. Algae are microscopic “plants” that grow in water or sea ice. Storms also brought thick heavy snow, which pushed the ice surface below the water. This flooded the snow and created slush. We discovered that this slush is another good habitat for algae. If Arctic sea ice continues to thin, and storms become more common, we expect that these algal habitats will become more important in the future

The seeding of ice algal blooms in Arctic pack ice: The multiyear ice seed repository hypothesis

Source at http://dx.doi.org/10.1002/2016JG003668 During the Norwegian young sea ICE expedition (N-ICE2015) from January to June 2015 the pack ice in the Arctic Ocean north of Svalbard was studied during four drifts between 83° and 80°N. This pack ice consisted of a mix of second year, fi rst year, and young ice. The physical properties and ice algal community composition was investigated in the three different ice types during the winter-spring-summer transition. Our results indicate that algae remaining in sea ice that survived the summer melt season are subsequently trapped in the upper layers of the ice column during winter and may function as an algal seed repository. Once the connectivity in the entire ice column is established, as a result of temperature-driven increase in ice porosity during spring, algae in the upper parts of the ice are able to migrate toward the bottom and initiate the ice algal spring bloom. Furthermore, this algal repository might seed the bloom in younger ice formed in adjacent leads. This mechanism was studied in detail for the dominant ice diatom Nitzschia frigida . The proposed seeding mechanism may be compromised due to the disappearance of older ice in the anticipated regime shift toward a seasonally ice-free Arctic Ocean

Leads in Arctic pack ice enable early phytoplankton blooms below snow-covered sea ice

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Scientific Reports 7 (2017): 40850, doi:10.1038/srep40850.The Arctic icescape is rapidly transforming from a thicker multiyear ice cover to a thinner and largely seasonal first-year ice cover with significant consequences for Arctic primary production. One critical challenge is to understand how productivity will change within the next decades. Recent studies have reported extensive phytoplankton blooms beneath ponded sea ice during summer, indicating that satellite-based Arctic annual primary production estimates may be significantly underestimated. Here we present a unique time-series of a phytoplankton spring bloom observed beneath snow-covered Arctic pack ice. The bloom, dominated by the haptophyte algae Phaeocystis pouchetii, caused near depletion of the surface nitrate inventory and a decline in dissolved inorganic carbon by 16 ± 6 g C m−2. Ocean circulation characteristics in the area indicated that the bloom developed in situ despite the snow-covered sea ice. Leads in the dynamic ice cover provided added sunlight necessary to initiate and sustain the bloom. Phytoplankton blooms beneath snow-covered ice might become more common and widespread in the future Arctic Ocean with frequent lead formation due to thinner and more dynamic sea ice despite projected increases in high-Arctic snowfall. This could alter productivity, marine food webs and carbon sequestration in the Arctic Ocean.This study was supported by the Centre for Ice, Climate and Ecosystems (ICE) at the Norwegian Polar Institute, the Ministry of Climate and Environment, Norway, the Research Council of Norway (projects Boom or Bust no. 244646, STASIS no. 221961, CORESAT no. 222681, CIRFA no. 237906 and AMOS CeO no. 223254), and the Ministry of Foreign Affairs, Norway (project ID Arctic), the ICE-ARC program of the European Union 7th Framework Program (grant number 603887), the Polish-Norwegian Research Program operated by the National Centre for Research and Development under the Norwegian Financial Mechanism 2009–2014 in the frame of Project Contract Pol-Nor/197511/40/2013, CDOM-HEAT, and the Ocean Acidification Flagship program within the FRAM- High North Research Centre for Climate and the Environment, Norway

Leads in Arctic pack ice enable early phytoplankton blooms below snow-covered sea ic

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Melt ponds on Arctic sea ice determined from MODIS satellite data using an artificial neural network

Melt ponds on sea ice strongly reduce the surface albedo and accelerate the decay of Arctic sea ice. Due to different spectral properties of snow, ice, and water, the fractional coverage of these distinct surface types can be derived from multispectral sensors like the Moderate Resolution Image Spectroradiometer (MODIS) using a spectral unmixing algorithm. The unmixing was implemented using a multilayer perceptron to reduce computational costs. <br><br> Arctic-wide melt pond fractions and sea ice concentrations are derived from the level 3 MODIS surface reflectance product. The validation of the MODIS melt pond data set was conducted with aerial photos from the MELTEX campaign 2008 in the Beaufort Sea, data sets from the National Snow and Ice Data Center (NSIDC) for 2000 and 2001 from four sites spread over the entire Arctic, and with ship observations from the trans-Arctic HOTRAX cruise in 2005. The root-mean-square errors range from 3.8 % for the comparison with HOTRAX data, over 10.7 % for the comparison with NSIDC data, to 10.3 % and 11.4 % for the comparison with MELTEX data, with coefficient of determination ranging from <i>R</i><sup>2</sup>=0.28 to <i>R</i><sup>2</sup>=0.45. The mean annual cycle of the melt pond fraction per grid cell for the entire Arctic shows a strong increase in June, reaching a maximum of 15 % by the end of June. The zonal mean of melt pond fractions indicates a dependence of the temporal development of melt ponds on the geographical latitude, and has its maximum in mid-July at latitudes between 80° and 88° N. <br><br> Furthermore, the MODIS results are used to estimate the influence of melt ponds on retrievals of sea ice concentrations from passive microwave data. Results from a case study comparing sea ice concentrations from ARTIST Sea Ice-, NASA Team 2-, and Bootstrap-algorithms with MODIS sea ice concentrations indicate an underestimation of around 40 % for sea ice concentrations retrieved with microwave algorithms