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The frequent and persistent cloud cover in the Arctic limits the extent to which sea surface temperature (SST) can be retrieved from thermal infrared (IR) satellite sensors. Passive microwave (PMW) observations provide highly complementary information to IR, enabling measurements through non-precipitating clouds, although at a coarser spatial resolution. The differences in coverage, accuracy, footprint size, spatial resolution and error characteristics between IR and PMW SSTs require a systematic assessment of how to best combine IR and PMW SST retrievals. This is provided in this study on the basis of the ESA-CCI PMW SST climate data record (CDR) and an existing IR-based gap-free SST and sea ice surface temperature CDR covering the Arctic (> 58 ° N), where cloud cover is a serious limitation to IR sensors. An important step towards a combined IR and PMW SST CDR is to correct for systematic biases in the PMW and IR SST datasets relative to each other. The PMW SSTs show reduced biases against in situ SSTs compared to the IR SSTs, but for consistency with time periods when no Arctic PMW SSTs were available, the PMW SSTs have been adjusted to the IR SSTs in this study. This is done using a dynamic bias correction to generate a consistent combined IR and PMW Arctic SST CDR for the period 2002–2017. Including PMW SSTs reduces the standard deviations from 0.54 °C, 0.55 °C and 0.47 °C to 0.47 °C, 0.54 °C and 0.41 °C against drifters, moorings and Argo floats, respectively. The improved performance is seen in almost all regions (including those already covered by IR observations), with the largest improvement in IR data sparse regions. The average theoretical uncertainty reduces by 0.08 °C, which is in good agreement with the observed improvement in the standard deviation against drifters. The results are very promising and expected to improve even further in the future with the launch of the Copernicus Imaging Microwave Radiometer (CIMR), which will enable PMW SST retrievals with lower uncertainties and much closer to coasts and sea ice (where the largest uncertainties arise) than what is possible with previous and current PMW radiometers. • Optimal blending of infrared and microwave sea surface temperatures in the Arctic. • Improved spatial coverage when including microwave satellite observations. • Reduced standard deviations against in situ observations in almost all regions. [ABSTRACT FROM AUTHOR]

The article presents a study regarding the sea ice variability in the Antarctic. It states that ice concentrations were calculated using the data from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSMI), and Special Sensor Microwave Imager Sounder (SSMIS). The result of the study shows that highly complex nature of the Arctic climate system was reflected by the regional, seasonal, and monthly sea ice trend variabilities.

The article presents a study regarding the sea ice variability in Antarctic. It states that ice concentrations were calculated using the data from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSMI), and Special Sensor Microwave Imager Sounder (SSMIS). The result of the study shows that global warming was being tied to the decrease of sea ice in the Arctic.

The article reports on the use of satellite technology by researchers to determine when conditions in the Arctic become unfavorable enough to cause mass starvation of native animals such as musk oxen and reindeer. According to researcher Thomas Grenfeel, starvation happened over a period of many months in year 2003. Moreover, evidence of the rain-on-snow event came to light in passive satellite microwave imagery which could provide data to help detect such events in other locations.