Light Availability and Phytoplankton Growth Beneath Arctic Sea Ice: Integrating Observations and Modeling

Observations of the seasonal light field in the upper Arctic Ocean are critical to understanding the impacts of changing Arctic ice conditions on phytoplankton growth in the water column. Here we discuss data from a new sensor system, deployed in seasonal ice cover north‐east of Utqiaġvik, Alaska in March 2014. The system was designed to provide observations of light and phytoplankton biomass in the water column during the formation of surface melt ponds and the transition from ice to open water. Hourly observations of downwelling irradiance beneath the ice (at 2.9, 6.9, and 17.9 m depths) and phytoplankton biomass (at 2.9 m depth) were transmitted via Iridium satellite from 9 March to 10 November 2014. Evidence of an under‐ice phytoplankton bloom (Chl a ∼8 mg m−3) was seen in June and July. Increases in light intensity observed by the buoy likely resulted from the loss of snow cover and development of surface melt ponds. A bio‐optical model of phytoplankton production supported this probable trigger for the rapid onset of under‐ice phytoplankton growth. Once under‐ice light was no longer a limiting factor for photosynthesis, open water exposure almost marginally increased daily phytoplankton production compared to populations that remained under the adjacent ice. As strong effects of climate change continue to be documented in the Arctic, the insight derived from autonomous buoys will play an increasing role in understanding the dynamics of primary productivity where ice and cloud cover limit the utility of ocean color satellite observations

Long-term coastal-polynya dynamics in the southern Weddell Sea from MODIS thermal-infrared imagery

Based upon thermal-infrared satellite imagery in combination with ERA-Interim atmospheric reanalysis data, we derive long-term polynya characteristics such as polynya area, thin-ice thickness distribution, and ice-production rates for a 13-year investigation period (2002–2014) for the austral winter (1 April to 30 September) in the Antarctic southern Weddell Sea. All polynya parameters are derived from daily cloud-cover corrected thin-ice thickness composites. The focus lies on coastal polynyas which are important hot spots for new-ice formation, bottom-water formation, and heat/moisture release into the atmosphere. MODIS has the capability to resolve even very narrow coastal polynyas. Its major disadvantage is the sensor limitation due to cloud cover. We make use of a newly developed and adapted spatial feature reconstruction scheme to account for cloud-covered areas. We find the sea-ice areas in front of the Ronne and Brunt ice shelves to be the most active with an annual average polynya area of 3018 ± 1298 and 3516 ± 1420 km2 as well as an accumulated volume ice production of 31 ± 13 and 31 ± 12 km3, respectively. For the remaining four regions, estimates amount to 421 ± 294 km2 and 4 ± 3 km3 (Antarctic Peninsula), 1148 ± 432 km2 and 12 ± 5 km3 (iceberg A23A), 901 ± 703 km2 and 10 ± 8 km3 (Filchner Ice Shelf), as well as 499 ± 277 km2 and 5 ± 2 km3 (Coats Land). Our findings are discussed in comparison to recent studies based on coupled sea-ice/ocean models and passive-microwave satellite imagery, each investigating different parts of the southern Weddell Sea

Sea ice leads in the Arctic Ocean: Model assessment, interannual variability and trends

Sea ice leads in the Arctic are important features that give rise to strong localized atmospheric heating; they provide the opportunity for vigorous biological primary production, and predicting leads may be of relevance for Arctic shipping. It is commonly believed that traditional sea ice models that employ elastic-viscous-plastic (EVP) rheologies are not capable of properly simulating sea ice deformation, including lead formation, and thus, new formulations for sea ice rheologies have been suggested. Here we show that classical sea ice models have skill in simulating the spatial and temporal variation of lead area fraction in the Arctic when horizontal resolution is increased (here 4.5 km in the Arctic) and when numerical convergence in sea ice solvers is considered, which is frequently neglected. The model results are consistent with satellite remote sensing data and discussed in terms of variability and trends of Arctic sea ice leads. It is found, for example, that wintertime lead area fraction during the last three decades has not undergone significant trends

Comparisons of passive microwave remote sensing sea ice concentrations with ship-based visual observations during the CHINARE Arctic summer cruises of 2010-2018

In order to apply satellite data to guiding navigation in the Arctic more effectively, the sea ice concentrations (SIC) derived from passive microwave (PM) products were compared with ship-based visual observations (OBS) collected during the Chinese National Arctic Research Expeditions (CHINARE). A total of 3 667 observations were collected in the Arctic summers of 2010, 2012, 2014, 2016, and 2018. PM SIC were derived from the NASA-Team (NT), Bootstrap (BT) and Climate Data Record (CDR) algorithms based on the SSMIS sensor, as well as the BT, enhanced NASA-Team (NT2) and ARTIST Sea Ice (ASI) algorithms based on AMSR-E/AMSR-2 sensors. The daily arithmetic average of PM SIC values and the daily weighted average of OBS SIC values were used for the comparisons. The correlation coefficients (CC), biases and root mean square deviations (RMSD) between PM SIC and OBS SIC were compared in terms of the overall trend, and under mild/normal/severe ice conditions. Using the OBS data, the influences of floe size and ice thickness on the SIC retrieval of different PM products were evaluated by calculating the daily weighted average of floe size code and ice thickness. Our results show that CC values range from 0.89 (AMSR-E/AMSR-2 NT2) to 0.95 (SSMIS NT), biases range from -3.96% (SSMIS NT) to 12.05% (AMSR-E/AMSR-2 NT2), and RMSD values range from 10.81% (SSMIS NT) to 20.15% (AMSR-E/AMSR-2 NT2). Floe size has a significant influence on the SIC retrievals of the PM products, and most of the PM products tend to underestimate SIC under smaller floe size conditions and overestimate SIC under larger floe size conditions. Ice thickness thicker than 30 cm does not have a significant influence on the SIC retrieval of PM products. Overall, the best (worst) agreement occurs between OBS SIC and SSMIS NT (AMSR-E/AMSR-2 NT2) SIC in the Arctic summer.Peer reviewe

Circulation in the vicinity of Mackenzie Canyon from a year-long mooring array

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Lin, P., Pickart, R. S., Fissel, D., Ross, E., Kasper, J., Bahr, F., Torres, D. J., O'Brien, J., Borg, K., Melling, H., & Wiese, F. K. Circulation in the vicinity of Mackenzie Canyon from a year-long mooring array. Progress in Oceanography, 187, (2020): 102396, doi:10.1016/j.pocean.2020.102396.Data from a five-mooring array extending from the inner shelf to the continental slope in the vicinity of Mackenzie Canyon, Beaufort Sea are analyzed to elucidate the components of the boundary current system and their variability. The array, part of the Marine Arctic Ecosystem Study (MARES), was deployed from October 2016 to September 2017. Four distinct currents were identified: an eastward-directed flow adjacent to the coast; a westward-flowing, surface-intensified current centered on the outer-shelf; a bottom-intensified shelfbreak jet flowing to the east; and a recirculation at the base of the continental slope within the canyon. The shelf current transports −0.120.03 Sv in the mean and is primarily wind-driven. The response is modulated by the presence of ice, with little-to-no signal during periods of nearly-immobile ice cover and maximum response when there is partial ice cover. The shelfbreak jet transports 0.030.02 Sv in the mean, compared to 0.080.02 Sv measured upstream in the Alaskan Beaufort Sea over the same time period. The loss of transport is consistent with a previous energetics analysis and the lack of Pacific-origin summer water downstream. The recirculation in the canyon appears to be the result of local dynamics whereby a portion of the westward-flowing southern limb of the Beaufort Gyre is diverted up the canyon across isobaths. This interpretation is supported by the fact that the low-frequency variability of the recirculation is correlated with the wind-stress curl in the Canada Basin, which drives the Beaufort gyre.The authors are indebted to Fisheries and Oceans Canada for building the logistics for MARES into the at-sea missions of the Integrated Beaufort Observatory. We are grateful to the captain and crew of the CCGS Sir Wilfred Laurier for ably deploying and recovering the MARES array. Marshall Swartz assisted with the cruise preparation logistics. We thank the two anonymous reviewers for their input which helped improve the paper. This project was funded by the US Bureau of Ocean Energy Management (BOEM), on behalf of the National Ocean Partnership Program. The Canadian contribution was supported by the Environmental Studies Research Fund (ESRF Project 2014-02N). MARES publication 003

Design of Soil Moisture Sensor for Validation of Passive Microwave Remote Sensed Soil Moisture Data

Soil Moisture is an important parameter that is of immense importance in the field of civil engineering, agriculture and ecology. Development of weather patterns and intake of nutrient by plants depend on soil moisture. In this paper, the design of sensor is described that uses the electrical resistance attribute of soil moisture. The soil moisture product of Advanced Microwave Scanning Radiometer-2 on board GCOM satellite of Japan Aerospace Exploration Agency (JAXA) is then compared with the soil moisture obtained from the designed sensor. Analysis shows the variability of the soil moisture values measured by both the satellite as well as the actual soil moisture measured by gravimetric method for the samples collected from different locations. The designed sensor shows similar variations in its output. Hence, the designed sensor can be used for checking the variations happening in soil moisture values instantaneously and can be used to validate the soil moisture product of remote sensing satellites for different location