scholarly journals Bottom-to-top decomposition of time series by smoothness-controlled cubic splines: Uncovering distinct freezing-melting dynamics between the Arctic and the Antarctic

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Imre M. Jánosi ◽  
Ágnes Baki ◽  
Marcus W. Beims ◽  
Jason A. C. Gallas
Keyword(s):  
Time Series ◽  
Cubic Splines ◽  
The Arctic ◽  
Melting Dynamics ◽  
Bottom To Top ◽  
The Antarctic

scholarly journals Antarctic sea ice types from active and passive microwave remote sensing

2022 ◽  
Author(s):  
Christian Melsheimer ◽  
Gunnar Spreen ◽  
Yufang Ye ◽  
Mohammed Shokr
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Microwave Remote Sensing ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Distribution Data ◽  
Synthetic Aperture Radar Data ◽  
Current Time ◽  
Antarctic Sea Ice ◽  
The Antarctic

Abstract. Polar sea ice is one of the Earth’s climate components that has been significantly affected by the recent trend of global warming. While the sea ice area in the Arctic has been decreasing at a rate of about 4 % per decade, the multi-year ice (MYI), also called perennial ice, is decreasing at a faster rate of 10 %–15 % per decade. On the other hand, the sea ice area in the Antarctic region was slowly increasing at a rate of about 1.5 % per decade until 2014 and since then it has fluctuated without a clear trend. However, no data about ice type areas are available from that region, particularly of MYI. Due to differences in physical and crystalline structural properties of sea ice and snow between the two polar regions, it has become difficult to identify ice types in the Antarctic. Until recently, no method has existed to monitor the distribution and temporal development of Antarctic ice types, particularly MYI throughout the freezing season and on decadal time scales. In this study, we have adapted a method for retrieving Arctic sea ice types and partial concentrations using microwave satellite observations to fit the Antarctic sea ice conditions. The first circumpolar, long-term time series of Antarctic sea ice types; MYI, first-year ice and young ice is being established, so far covering years 2013–2019. Qualitative comparison with synthetic aperture radar data, with charts of the development stage of the sea ice, and with Antarctic polynya distribution data show that the retrieved ice types, in particular the MYI, are reasonable. Although there are still some shortcomings, the new retrieval for the first time allows insight into the evolution and dynamics of Antarctic sea ice types. The current time series can in principle be extended backwards to start in the year 2002 and can be continued with current and future sensors.

scholarly journals Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data

2021 ◽  
Vol 13 (11) ◽  
pp. 2174
Author(s):  
Lijian Shi ◽  
Sen Liu ◽  
Yingni Shi ◽  
Xue Ao ◽  
Bin Zou ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Ocean Circulation ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Atmospheric Effects ◽  
Observation Data ◽  
Long Time Series ◽  
Ice Concentration ◽  
Long Time

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites.

scholarly journals Time-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine

2021 ◽  
Vol 256 ◽  
pp. 112318
Author(s):  
Dong Liang ◽  
Huadong Guo ◽  
Lu Zhang ◽  
Yun Cheng ◽  
Qi Zhu ◽  
...  
Keyword(s):  
Time Series ◽  
Google Earth ◽  
Sar Images ◽  
Google Earth Engine ◽  
The Antarctic

scholarly journals Metagenomic Analysis of Stress Genes in Microbial Mat Communities from Antarctica and the High Arctic

2011 ◽  
Vol 78 (2) ◽  
pp. 549-559 ◽  
Author(s):  
Thibault Varin ◽  
Connie Lovejoy ◽  
Anne D. Jungblut ◽  
Warwick F. Vincent ◽  
Jacques Corbeil
Keyword(s):  
High Arctic ◽  
Environmental Stresses ◽  
The Arctic ◽  
Functional Responses ◽  
Protein Coding ◽  
Content Type ◽  
Stress Genes ◽  
Cyanobacterial Genes ◽  
Shock Proteins ◽  
The Antarctic

ABSTRACTPolar and alpine microbial communities experience a variety of environmental stresses, including perennial cold and freezing; however, knowledge of genomic responses to such conditions is still rudimentary. We analyzed the metagenomes of cyanobacterial mats from Arctic and Antarctic ice shelves, using high-throughput pyrosequencing to test the hypotheses that consortia from these extreme polar habitats were similar in terms of major phyla and subphyla and consequently in their potential responses to environmental stresses. Statistical comparisons of the protein-coding genes showed similarities between the mats from the two poles, with the majority of genes derived fromProteobacteriaandCyanobacteria; however, the relative proportions differed, with cyanobacterial genes more prevalent in the Antarctic mat metagenome. Other differences included a higher representation ofActinobacteriaandAlphaproteobacteriain the Arctic metagenomes, which may reflect the greater access to diasporas from both adjacent ice-free lands and the open ocean. Genes coding for functional responses to environmental stress (exopolysaccharides, cold shock proteins, and membrane modifications) were found in all of the metagenomes. However, in keeping with the greater exposure of the Arctic to long-range pollutants, sequences assigned to copper homeostasis genes were statistically (30%) more abundant in the Arctic samples. In contrast, more reads matching the sigma B genes were identified in the Antarctic mat, likely reflecting the more severe osmotic stress during freeze-up of the Antarctic ponds. This study underscores the presence of diverse mechanisms of adaptation to cold and other stresses in polar mats, consistent with the proportional representation of major bacterial groups.

scholarly journals The influence of the lunar nodal cycle on Arctic climate

2006 ◽  
Vol 63 (3) ◽  
pp. 401-420 ◽  
Author(s):  
Harald Yndestad
Keyword(s):  
Time Series ◽  
Signal To Noise Ratio ◽  
Phase Relationship ◽  
Harmonic Spectrum ◽  
Arctic Climate ◽  
The Arctic ◽  
Major Influence ◽  
Sea Temperature ◽  
Arctic Ice

Abstract The Arctic Ocean is a substantial energy sink for the northern hemisphere. Fluctuations in its energy budget will have a major influence on the Arctic climate. The paper presents an analysis of the time-series for the polar position, the extent of Arctic ice, sea level at Hammerfest, Kola section sea temperature, Røst winter air temperature, and the NAO winter index as a way to identify a source of dominant cycles. The investigation uses wavelet transformation to identify the period and the phase in these Arctic time-series. System dynamics are identified by studying the phase relationship between the dominant cycles in all time-series. A harmonic spectrum from the 18.6-year lunar nodal cycle in the Arctic time-series has been identified. The cycles in this harmonic spectrum have a stationary period, but not stationary amplitude and phase. A sub-harmonic cycle of about 74 years may introduce a phase reversal of the 18.6-year cycle. The signal-to-noise ratio between the lunar nodal spectrum and other sources changes from 1.6 to 3.2. A lunar nodal cycle in all time-series indicates that there is a forced Arctic oscillating system controlled by the pull of gravity from the moon, a system that influences long-term fluctuations in the extent of Arctic ice. The phase relation between the identified cycles indicates a possible chain of events from lunar nodal gravity cycles, to long-term tides, polar motions, Arctic ice extent, the NAO winter index, weather, and climate.

scholarly journals Relationships between the Arctic and the Antarctic cyanobacteria; three Phormidium-like strains evaluated by a polyphasic approach

2007 ◽  
Vol 59 (2) ◽  
pp. 366-376 ◽  
Author(s):  
Katia Comte ◽  
Marie Å Abacká ◽  
Alyssa Carré-Mlouka ◽  
Josef Elster ◽  
Jiří Komárek
Keyword(s):  
Polyphasic Approach ◽  
The Arctic ◽  
The Antarctic

Meridional Tripole Mode of Winter Precipitation over the Arctic and Continental North Africa–Eurasia

2021 ◽  
pp. 1
Author(s):  
Xiaolin Liu ◽  
Jianhua Lu ◽  
Yimin Liu ◽  
Guoxiong Wu
Keyword(s):  
Time Series ◽  
North Africa ◽  
North Atlantic ◽  
Hadley Cell ◽  
The Arctic ◽  
Positive Phase ◽  
The North ◽  
Ferrel Cell ◽  
The North Atlantic ◽  
Westerly Jets

AbstractWintertime precipitation is vital to the growth of glaciers in the northern hemisphere. We find a tripole mode of precipitation (PTM), with each pole of the mode extending zonally over the eastern hemisphere roughly between 30°W and 120°E, and the positive/negative/positive structure for its positive phase extending meridionally from the Arctic to the continental North Africa–Eurasia. The large-scale dynamics associated with the PTM is explored. The positive phase of the PTM is associated with the negative while eastward-shifted phase of the North Atlantic Oscillation (NAO) and a zonal band of positive SST anomaly in the tropics, together with a narrowed Hadley cell and weakened Ferrel cell. While being north-eastward tilted and separated from their North Africa-Eurasia counterpart in the climatological mean, the upper-tropospheric westerly jets over the east Pacific and north Atlantic become extending zonally and shifting southward and hence form a circumpolar subtropical jet as a whole by connecting with the westerly jets over the North Africa-Eurasia. The enhanced zonal winds over the north Atlantic promote more synoptic-scale transient eddies which are waveguided by the jet streams. The polar vortex weakens and cold air dips southward from the North Pole. Further diagnosis of the E-vectors suggests that transient eddies have a positive feedback on the weakening of Ferrel cell. Opposite features are associated with the negative phase of the PTM. The reconstructed time series using multiple linear regression on the NAO index and the tropical SST averaged over 20°S– 20°N, can explain 62.4% of the variance of the original the original precipitation time series.

Remote sensing of multiyear ice in the Antarctic

2021 ◽  
Author(s):  
Christian Melsheimer ◽  
Gunnar Spreen
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Global Climate ◽  
Ice Cover ◽  
Cold Season ◽  
Arctic Sea Ice ◽  
Weddell Sea ◽  
Detailed Knowledge ◽  
The Antarctic ◽  
Multiyear Ice

<p>The changing sea ice cover of polar seas is of key importance for the exchange of heat and moisture between atmosphere and ocean and hence for weather and climate, and in addition, the sea ice and its long-term changes are  an indicator for global change.  In order to properly understand and model the evolution of the sea ice cover and its interaction with the global climate system, we need detailed knowledge about sea ice, i.e., not only its extent, but also, e.g., its thickness and its type.</p> <p>We can broadly distinguish a few different sea ice types that have different dynamic and thermodynamic properties, namely: young ice (YI, thin/smooth new ice), first-year ice (FYI, formed during one cold season), and multiyear ice (MYI, which has survived at least one melt season). The  latter is of particular interest as it is usually thicker than other ice types (thus, takes more time to melt), much less saline, and may accommodate a unique ecosystem. Sea ice types in the Antarctic, until recently, have not been monitored much because of the lack of appropriate remote  sensing methods. While the Antarctic sea ice is greatly dominated by FYI, there are, nevertheless, considerable amounts of MYI, in particular in the Weddell Sea.</p> <p>We have recently adapted an algorithm for the detection of Arctic sea ice types for application in the Antarctic. The algorithm uses data from space-borne microwave radiometers and scatterometers as input. So far we have compiled a time series of daily Antarctic MYI data (and also an estimate of YI and FYI) data at a spatial resolution of 12.5 km, starting in 2013, but excluding the melt seasons when the algorithm does not work. Here give an overview of the data, showing, e.g., the quite large interannual variability of MYI and its evolution in the Weddell Sea, and discuss shortcomings of the algorithm and possible ways forward. The time series of daily Antarctic MYI data can in principle be extended backwards to the year 2000, when the used satellite data first became available, and with planned future satellite missions, it can be continued for years to come.</p>

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