scholarly journals Surface Freshwater Fluxes in the Arctic and Subarctic Seas during Contrasting Years of High and Low Summer Sea Ice Extent

2021 ◽  
Vol 13 (8) ◽  
pp. 1570
Author(s):  
Sarah B. Hall ◽  
Bulusu Subrahmanyam ◽  
Ebenezer S. Nyadjro ◽  
Annette Samuelsen
Keyword(s):  
Sea Ice ◽  
Global Climate ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Spatial And Temporal Variability ◽  
Polar Regions ◽  
Surface Salinity ◽  
Sea Ice Concentration ◽  
Sea Ice Extent

Freshwater (FW) flux between the Arctic Ocean and adjacent waterways, predominantly driven by wind and oceanic currents, influences halocline stability and annual sea ice variability which further impacts global circulation and climate. The Arctic recently experienced anomalous years of high and low sea ice extent in the summers of 2013/2014 and 2012/2016, respectively. Here we investigate the interannual variability of oceanic surface FW flux in relation to spatial and temporal variability in sea ice concentration (SIC), sea surface salinity (SSS), and sea surface temperature (SST), focusing on years with summer sea–ice extremes. Our analysis between 2010–2018 illustrate high parameter variability, especially within the Laptev, Kara, and Barents seas, as well as an overall decreasing trend of FW flux through the Fram Strait. We find that in 2012, a maximum average FW flux of 0.32 × 103 ms−1 in October passed over a large portion of the Northeast Atlantic Ocean at 53°N. This study highlights recent changes in the Arctic and Subarctic Seas and the importance of continued monitoring of key variables through remote sensing to understand the dynamics behind these ongoing changes. Observations of FW fluxes through major Arctic routes will be increasingly important as the polar regions become more susceptible to warming, with major impacts on global climate.

scholarly journals Consistent Comparison of Remotely Sensed Sea Ice Concentration Products with ERA-Interim Reanalysis Data in Polar Regions

2020 ◽  
Vol 12 (18) ◽  
pp. 2880
Author(s):  
Shuang Liang ◽  
Jiangyuan Zeng ◽  
Zhen Li ◽  
Dejing Qiao ◽  
Ping Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
Sea Ice ◽  
Global Climate ◽  
Remotely Sensed ◽  
The Arctic ◽  
Polar Regions ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Climate Change Research ◽  
Ice Concentration

Sea ice concentration (SIC) plays a significant role in climate change research and ship’s navigation in polar regions. Satellite-based SIC products have become increasingly abundant in recent years; however, the uncertainty of these products still exists and needs to be further investigated. To comprehensively evaluate the consistency of the SIC derived from different SIC algorithms in long time series and the whole polar regions, we compared four passive microwave (PM) satellite SIC products with the ERA-Interim sea ice fraction dataset during the period of 2015–2018. The PM SIC products include the SSMIS/ASI, AMSR2/BT, the Chinese FY3B/NT2, and FY3C/NT2. The results show that the remotely sensed SIC products derived from different SIC algorithms are generally in good consistency. The spatial and temporal distribution of discrepancy among satellite SIC products for both Arctic and Antarctic regions are also observed. The most noticeable difference for all the four SIC products mostly occurs in summer and at the marginal ice zone, indicating that large uncertainties exist in satellite SIC products in such period and areas. The SSMIS/ASI and AMSR2/BT show relatively better consistency with ERA-Interim in the Arctic and Antarctic, respectively, but they exhibit opposite bias (dry/wet) relative to the ERA-Interim data. The sea ice extent (SIE) and sea ice area (SIA) derived from PM and ERA-Interim SIC were also compared. It is found that the difference of PM SIE and SIA varies seasonally, which is in line with that of PM SIC, and the discrepancy between PM and ERA-Interim data is larger in Arctic than in Antarctic. We also noticed that different algorithms have different performances in different regions and periods; therefore, the hybrid of multiple algorithms is a promising way to improve the accuracy of SIC retrievals. It is expected that our findings can contribute to improving the satellite SIC algorithms and thus promote the application of these useful products in global climate change studies.

Comparison of sea ice concentrations from ASI, BT and NT2 algorithms with ERA-Interim dataset in the Arctic and Antarctic regions

2020 ◽  
Author(s):  
Shuang Liang ◽  
Jiangyuan Zeng ◽  
Zhen Li
Keyword(s):  
Sea Ice ◽  
Temporal Distribution ◽  
Arctic Region ◽  
The Arctic ◽  
Polar Regions ◽  
Significant Discrepancy ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Long Time ◽  
Mean Square Difference

<p>Evaluating the performance and consistency of passive microwave (PM) sea ice concentration (SIC) products derived from different algorithms is critical since a good knowledge of the quality of the satellite SIC products is essential for their application and improvement. To comprehensively evaluate the performance of satellite SIC in long time series and the whole polar regions (both Arctic and Antarctic), in the study we examined the spatial and temporal distribution of the discrepancy between four PM satellite SIC products with the ERA-Interim sea ice fraction dataset (ERA SIC) during the period of 2015-2018. The four PM SIC products include the DMSP SSMIS with Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) algorithm (SSMIS/ASI), the GCOM-W AMSR2 with NASA Bootstrap (BT) algorithm (AMSR2/BT), the Chinese Feng Yun-3B with enhanced NASA Team (NT2) sea ice algorithm (FY3B/NT2), and the Chinese Feng Yun-3C with NT2 (FY3C/NT2) at a spatial resolution of 12.5 km.</p><p>The results show the spatial patterns of PM SIC products are generally in good agreement with ERA SIC. The comparison of monthly and annual SIC shows that the largest bias and root mean square difference (RMSD) for the PM SIC products mainly occur in summer and the marginal ice zone, indicating that there are still many uncertainties in PM SIC products in such period and region. Meanwhile, the daily sea ice extent (SIE) and sea ice area (SIA) derived from the four PM SIC products can generally well reflect the variation trend of SIE and SIA in Arctic and Antarctic. The largest bias of SIE and SIA are above 4×10<sup>6</sup> km<sup>2</sup> when the sea ice reaches the maximum and minimum value, and the daily bias of SIE and SIA vary seasonally and regionally, which is mainly concentrated from June to October in Arctic. In general, among the four PM SIC products, the SSMIS/ASI product performs the best compared with ERA SIC though it usually underestimates SIC with a negative bias. The FY3B/NT2 and FY3C/NT2 products show more significant discrepancy with higher RMSD and bias in Arctic and Antarctic compared with the SSMIS/ASI and AMSR2/BT. The AMSR2/BT product performs much better in Antarctic than in Arctic and it always overestimates ERA SIC with a positive bias. The consistency of the four PM products concerning ERA SIC in the Antarctic region is generally superior to that in Arctic region.</p>

scholarly journals September Arctic Sea Ice minimum prediction – a new skillful statistical approach

2018 ◽  
Author(s):  
Monica Ionita ◽  
Klaus Grosfeld ◽  
Patrick Scholz ◽  
Renate Treffeisen ◽  
Gerrit Lohmann
Keyword(s):  
Sea Ice ◽  
Statistical Model ◽  
Climate Models ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Polar Regions ◽  
Sea Ice Extent ◽  
Important Indicator ◽  
Predictive Skill

Abstract. Sea ice in both Polar Regions is an important indicator for the expression of global climate change and its polar amplification. Consequently, a broad interest exists on sea ice coverage, variability and long term change. However, its predictability is complex and it depends on various atmospheric and oceanic parameters. In order to provide insights into the potential development of a monthly/seasonal signal of sea ice evolution, we developed a robust statistical model based on oceanic and different atmospheric variables to calculate an estimate of the September sea ice extent (SSIE) on monthly time scale. Although previous statistical attempts of monthly/seasonal SSIE forecasts show a relatively reduced skill, when the trend is removed, we show here that the September sea ice extent has a high predictive skill, up to 4 months ahead, based on previous months' atmospheric and oceanic conditions. Our statistical model skillfully captures the interannual variability of the SSIE and could provide a valuable tool for identifying relevant regions and atmospheric parameters that are important for the sea ice development in the Arctic and for detecting sensitive and critical regions in global coupled climate models with focus on sea ice formation.

scholarly journals Hemispheric sea ice extent dynamics as observed from MSMR

MAUSAM ◽  
2021 ◽  
Vol 60 (3) ◽  
pp. 295-308
Author(s):  
NILAY SHARMA ◽  
M. K. DASH ◽  
P. C. PANDEY ◽  
N. K. VYAS
Keyword(s):  
Sea Ice ◽  
Global Climate ◽  
Ice Cover ◽  
The Arctic ◽  
Ice Formation ◽  
Spatial And Temporal Variation ◽  
Polar Regions ◽  
Sea Ice Extent ◽  
Year 2000 ◽  
Formation Phase

The ice covered regions of the polar seas influence the global climate in several ways. Any perturbation in the polar oceanic cryosphere affects the local weather and the global climate through modulation of the radiative forcing, the bottom water formation and the mass & the momentum transfer between Atmosphere-Cryosphere-Ocean System. The cold, harsh and inhospitable conditions in the polar regions prohibit the collection of extensive in situ data with sufficient spatial and temporal variation. However, satellite remote sensing is an ideal technique for studying the areas like the polar regions with synoptic and repetitive coverage.  This paper discusses the analysis of the data obtained over the polar oceanic regions during the period June 1999 – September 2001 through the use of Multi-channel Scanning Microwave Radiometer (MSMR), onboard India’s first oceanographic satellite Oceansat-1. The MSMR observation shows that all the sectors in the Antarctic behave differently to the melting and formation of the sea ice. Certain peculiar features like the increase in sea ice extent during the melt season of 1999 – 2000 in the Indian Ocean sector, 15 – 20% decrease in the sea ice extent in the western Pacific sector during the ice formation period for the year 2000, melting spell within the formation phase of sea ice in B & A sector in the year 2000 were observed. On the other hand the northern polar sea ice extent is seen to be more dominated by the land characteristics. The ice formation in Kara and the Barent Sea sector is dominated by the ocean currents, where as the ice covered in the Japan and the Okhotsk Sea is dominated by the land processes. The sea ice extent in the Arctic Ocean show fluctuations from July to October and remain almost steady over other months. The global sea ice cover shows a formation phase from March to June and melting phase from November to February. In other months, i.e., from July – October the global sea ice cover is dominated by the hemispheric asymmetry of the ice growth and retreat.

scholarly journals Sea Surface Salinity and Wind Speed Retrievals Using GNSS-R and L-Band Microwave Radiometry Data from FMPL-2 Onboard the FSSCat Mission

2021 ◽  
Vol 13 (16) ◽  
pp. 3224
Author(s):  
Joan Francesc Munoz-Martin ◽  
Adriano Camps
Keyword(s):  
Wind Speed ◽  
Microwave Radiometer ◽  
Sea Surface ◽  
Third Party ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Weekly Schedule ◽  
Surface Salinity ◽  
Sea Ice Concentration ◽  
L Band

The Federated Satellite System mission (FSSCat), winner of the 2017 Copernicus Masters Competition and the first ESA third-party mission based on CubeSats, aimed to provide coarse-resolution soil moisture estimations and sea ice concentration maps by means of the passive microwave measurements collected by the Flexible Microwave Payload-2 (FMPL-2). The mission was successfully launched on 3 September 2020. In addition to the primary scientific objectives, FMPL-2 data are used in this study to estimate sea surface salinity (SSS), correcting for the sea surface roughness using a wind speed estimate from the L-band microwave radiometer and GNSS-R data themselves. FMPL-2 was executed over the Arctic and Antarctic oceans on a weekly schedule. Different artificial neural network algorithms have been implemented, combining FMPL-2 data with the sea surface temperature, showing a root-mean-square error (RMSE) down to 1.68 m/s in the case of the wind speed (WS) retrieval algorithms, and RMSE down to 0.43 psu for the sea surface salinity algorithm in one single pass.

Dynamics and drivers of extreme seasons in the Arctic region

2021 ◽  
Author(s):  
Katharina Hartmuth ◽  
Lukas Papritz ◽  
Maxi Boettcher ◽  
Heini Wernli
Keyword(s):  
Climate Change ◽  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface ◽  
The Arctic ◽  
Polar Regions ◽  
Sea Surface Temperatures ◽  
Sea Ice Concentration ◽  
Surface Temperatures ◽  
Ice Concentration

<p>Single extreme weather events such as intense storms or blocks can have a major impact on polar surface temperatures, the formation and melting rates of sea-ice, and, thus, on minimum and maximum sea-ice extent within a particular year. Anomalous weather conditions on the time scale of an entire season, for example resulting from an unusual sequence of storms, can affect the polar energy budget and sea-ice coverage even more. Here, we introduce the concept of an extreme season in a distinct region using an EOF analysis in the phase space spanned by anomalies of a set of surface parameters (surface temperature, precipitation, surface solar and thermal radiation and surface heat fluxes). To focus on dynamical instead of climate change aspects, we define anomalies as departures of the seasonal mean from a transient climatology. The goal of this work is to study the dynamical processes leading to such anomalous seasons in the polar regions, which have not yet been analysed. Specifically, we focus here on a detailed analysis of Arctic extreme seasons and their underlying atmospheric dynamics in the ERA5 reanalysis data set.</p><p>We find that in regions covered predominantly by sea ice, extreme seasons are mostly determined by anomalies of atmospheric dynamical features such as cyclones and blocking. In contrast, in regions including large areas of open water the formation of extreme seasons can also be partially due to preconditioning during previous seasons, leading to strong anomalies in the sea ice concentration and/or sea surface temperatures at the beginning of the extreme season.</p><p>Two particular extreme season case studies in the Kara-Barents Seas are discussed in more detail. In this region, the winter of 2011/12 shows the largest positive departure of surface temperature from the background warming trend together with a negative anomaly in the sea ice concentration. An analysis of the synoptic situation shows that the strongly reduced frequency of cold air outbreaks compared to climatology combined with several blocking events and the frequent occurrence of cyclones transporting warm air into the region favored the continuous anomalies of both parameters. In contrast, the winter of 2016/17, which shows a positive precipitation anomaly and negative anomaly in the surface energy balance, was favored by a strong surface preconditioning. An extremely warm summer and autumn in 2016 caused strongly reduced sea ice concentrations and increased sea surface temperatures in the Kara-Barents Seas at the beginning of the winter, favoring increased air-sea fluxes and precipitation during the following months.</p><p>Our results reveal a high degree of variability of the processes involved in the formation of extreme seasons in the Arctic. Quantifying and understanding these processes will also be important when considering climate change effects in polar regions and the ability of climate models in reproducing extreme seasons in the Arctic and Antarctica.</p>

scholarly journals The Antarctic Circumpolar Wave in a global, high-resolution, coupled ice-ocean model

2001 ◽  
Vol 33 ◽  
pp. 539-544 ◽  
Author(s):  
Yuxia Zhang ◽  
Albert J. Semtner
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Phase Speed ◽  
Ocean Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Sea Ice Extent ◽  
Antarctic Circumpolar Wave ◽  
The Antarctic

AbstractThe Antarctic Circumpolar Wave (ACW) is identified by White and Peterson (1996) as anomalies in sea-level pressure, meridional wind stress (MWS), sea-surface temperature (SST) and sea-ice extent (SIE) propagating eastward over the Southern Ocean. In this study, the ACW is examined using a global coupled ice-ocean model with an average horizontal grid size of 1/4°. The model is forced with 1979−93 daily average atmospheric data from the European Centre for Medium-range Weather Forecasts (ECMWF) re-analysis (ERA). The sea-ice model includes both dynamics and thermodynamics, and the ocean model is a primitive-equation, free-surface, z-coordinate model. Both standing and propagating oscillations are present in ERA surface net heat-flux (NHF) and MWS anomalies. The ocean and ice respond to such atmospheric forcing with similar standing and propagating oscillations. For the propagating mode, SIE, SST and sea-surface salinity anomalies propagate eastward with a period of about 4−5 years and take about 8−9 years to encircle the Antarctic continent. Thus, the simulated ACW is a wavenumber-2 phenomenon which agrees with the ACW identified by White and Peterson (1996). The correctly simulated strength of the Antarctic Circumpolar Current, which governs the phase speed of oceanic anomalies, in our high-resolution model is essential for obtaining the observed wavenumber-2 ACW mode in the ocean. The ACW signature is also present in ocean temperature and salinity anomalies down to about 1000 m depth with similar eastward-propagating speed. The anomalies in the interior ocean are more coherent and intense over the Pacific and Atlantic sectors than over the Indian sector. Northward (southward) MWS anomalies, northward (southward) SIE anomalies, cold (warm) SST anomalies and saltier (fresher) than normal salinity anomalies are in phase, while less (more) than normal NHF is 90° out of phase with them, indicating the ACW in sea ice and ocean is a response to that in the atmosphere.

scholarly journals The Potential of Space-Based Sea Surface Salinity on Monitoring the Hudson Bay Freshwater Cycle

2020 ◽  
Vol 12 (5) ◽  
pp. 873 ◽  
Author(s):  
Wenqing Tang ◽  
Simon H. Yueh ◽  
Daqing Yang ◽  
Ellie Mcleod ◽  
Alexander Fore ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Sea Ice ◽  
River Discharge ◽  
Open Water ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Hudson Bay ◽  
Surface Salinity ◽  
Ice Melt

Hudson Bay (HB) is the largest semi-inland sea in the Northern Hemisphere, connecting with the Arctic Ocean through the Foxe Basin and the northern Atlantic Ocean through the Hudson Strait. HB is covered by ice and snow in winter, which completely melts in summer. For about six months each year, satellite remote sensing of sea surface salinity (SSS) is possible over open water. SSS links freshwater contributions from river discharge, sea ice melt/freeze, and surface precipitation/evaporation. Given the strategic importance of HB, SSS has great potential in monitoring the HB freshwater cycle and studying its relationship with climate change. However, SSS retrieved in polar regions (poleward of 50°) from currently operational space-based L-band microwave instruments has large uncertainty (~ 1 psu) mainly due to sensitivity degradation in cold water (<5°C) and sea ice contamination. This study analyzes SSS from NASA Soil Moisture Active and Passive (SMAP) and European Space Agency (ESA) Soil Moisture and Ocean Salinity(SMOS) missions in the context of HB freshwater contents. We found that the main source of the year-to-year SSS variability is sea ice melting, in particular, the onset time and places of ice melt in the first couple of months of open water season. The freshwater contribution from surface forcing P-E is smaller in magnitude comparing with sea ice contribution but lasts on longer time scale through the whole open water season. River discharge is comparable with P-E in magnitude but peaks before ice melt. The spatial and temporal variations of freshwater contents largely exceed the remote sensed SSS uncertainty. This fact justifies the use of remote sensed SSS for monitoring the HB freshwater cycle.

scholarly journals Arctic Ocean CO<sub>2</sub> uptake: an improved multi-year estimate of the air–sea CO<sub>2</sub> flux incorporating chlorophyll-a concentrations

2017 ◽  
Author(s):  
Sayaka Yasunaka ◽  
Eko Siswanto ◽  
Are Olsen ◽  
Mario Hoppema ◽  
Eiji Watanabe ◽  
...  
Keyword(s):  
Arctic Ocean ◽  
Chlorophyll A ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Co2 Uptake ◽  
Self Organizing Map ◽  
Surface Salinity ◽  
Sea Ice Concentration ◽  
Chl A

Abstract. We estimated monthly air–sea CO2 fluxes in the Arctic Ocean and its adjacent seas north of 60° N from 1997 to 2014, after mapping partial pressure of CO2 in the surface water (pCO2w) using a self-organizing map (SOM) technique incorporating chlorophyll-a concentration (Chl-a), sea surface temperature, sea surface salinity, sea ice concentration, atmospheric CO2 mixing ratio, and geographical position. The overall relationship between pCO2w and Chl-a is negative in most regions when Chl-a ≤ 1 mg m−3, whereas there is no significant relationship when Chl-a > 1 mg m−3. In the Kara Sea and the East Siberian Sea and the Bering Strait, however, the relationship is typically positive in summer. The addition of Chl-a as a parameter in the SOM process enabled us to improve the estimate of pCO2w via better representation of its decline in spring, which resulted from biologically mediated pCO2w reduction. Mainly as a result of the inclusion of Chl-a, the uncertainty in the CO2 flux estimate was reduced, and a net annual Arctic Ocean CO2 uptake of 180 ± 130 TgC y−1 was determined to be significant.

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