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Sea ice export through the Baffin Bay plays a vital role in modulating the sea ice cover variability in the Labrador Sea. In this study, satellite-derived sea ice products are used to obtain the sea ice area flux (SIAF) through the three passages in the Baffin Bay (referred to as A, B, and C for the north, middle, and south passages, respectively). The spatial variability of the monthly sea ice drift in the Baffin Bay is presented. The interannual variability and trends in SIAF via the three passages are outlined. The connection to several large-scale atmospheric circulation modes is assessed. Over the period of 1988–2015, the average annual (October to the following September) SIAF amounts to 555×103 km2, 642×103 km2, and 551×103 km2 through Passages A, B, and C, respectively. These quantities are less than that observed through the Fram Strait (FS, 707×103 km2) of the corresponding period. The positive trends in annual SIAF, on the order of 53.1×103 km2/(10 a) and 43.2×103 km2/(10 a) (significant at the 95% confidence level), are identified at Passages A and B, respectively. The trend of the south passage (C), however, is slightly negative (−13.3×103 km2/(10 a), not statistically significant). The positive trends in annual SIAF through the Passages A and B are primarily attributable to the significant increases after 2000. The connection between the Baffin Bay sea ice export and the North Atlantic Oscillation is not significant over the studied period. By contrast, the association with the cross-gate sea level pressure difference is robust in the Baffin Bay (R equals 0.69 to 0.71, depending on the passages considered), but relatively weaker than that over FS (R=0.74). [ABSTRACT FROM AUTHOR]

The spatial structure of the Arctic sea ice concentration (SIC) variability and the connection to atmospheric as well as radiative forcing during winter and summer for the 1979–2017 period are investigated. The interannual variability with different spatial characteristics of SIC in summer and winter is extracted using the empirical orthogonal function (EOF) analysis. The present study confirms that the atmospheric circulation has a strong influence on the SIC through both dynamic and thermodynamic processes, as the heat flux anomalies in summer are radiatively forced while those in winter contain both radiative and "circulation-induced" components. Thus, atmospheric fluctuations have an explicit and extensive influence to the SIC through complex mechanisms during both seasons. Moreover, analysis of a variety of atmospheric variables indicates that the primary mechanism about specific regional SIC patterns in Arctic marginal seas are different with special characteristics. [ABSTRACT FROM AUTHOR]

In comparison with seasonal sea ice (first-year ice, FY ice), multiyear (MY) sea ice is thicker and has more opportunity to survive through the summer melting seasons. Therefore, the variability of wintertime MY ice plays a vital role in modulating the variations in the Arctic sea ice minimum extent during the following summer. As a response, the ice-ocean-atmosphere interactions may be significantly affected by the variations in the MY ice cover. Satellite observations are characterized by their capability to capture the spatiotemporal changes of Arctic sea ice. During the recent decades, many active and passive sensors onboard a variety of satellites (QuikSCAT, ASCAT, SSMIS, ICESat, CryoSat-2, etc.) have been used to monitor the dramatic loss of Arctic MY ice. The main objective of this study is to outline the advances and remaining challenges in monitoring the MY ice changes through the utilization of multiple satellite observations. We summarize the primary satellite data sources that are used to identify MY ice. The methodology to classify MY ice and derive MY ice concentration is reviewed. The interannual variability and trends in the MY ice time series in terms of coverage, thickness, volume, and age composition are evaluated. The potential causes associated with the observed Arctic MY ice loss are outlined, which are primarily related to the export and melting mechanisms. In addition, the causes to the MY ice depletion from the perspective of the oceanic water inflow from Pacific and Atlantic Oceans and the water vapor intrusion, as well as the roles of synoptic weather, are analyzed. The remaining challenges and possible upcoming research subjects in detecting the rapidly changing Arctic MY ice using the combined application of multisource remote sensing techniques are discussed. Moreover, some suggestions for the future application of satellite observations on the investigations of MY ice cover changes are proposed. [ABSTRACT FROM AUTHOR]

Arctic sea ice cover has decreased dramatically over the last three decades. This study quantifies the sea ice concentration (SIC) trends in the Arctic Ocean over the period of 1979–2016 and analyzes their spatial and temporal variations. During each month the SIC trends are negative over the Arctic Ocean, wherein the largest (smallest) rate of decline found in September (March) is -0.48%/a (-0.10%/a). The summer (-0.42%/a) and autumn (-0.31%/a) seasons show faster decrease rates than those of winter (-0.12%/a) and spring (-0.20%/a) seasons. Regional variability is large in the annual SIC trend. The largest SIC trends are observed for the Kara (-0.60%/a) and Barents Seas (-0.54%/a), followed by the Chukchi Sea (-0.48%/a), East Siberian Sea (-0.43%/a), Laptev Sea (-0.38%/a), and Beaufort Sea (-0.36%/a). The annual SIC trend for the whole Arctic Ocean is -0.26%/a over the same period. Furthermore, the infl uences and feedbacks between the SIC and three climate indexes and three climatic parameters, including the Arctic Oscillation (AO), North Atlantic Oscillation (NAO), Dipole anomaly (DA), sea surface temperature (SST), surface air temperature (SAT), and surface wind (SW), are investigated. Statistically, sea ice provides memory for the Arctic climate system so that changes in SIC driven by the climate indices (AO, NAO and DA) can be felt during the ensuing seasons. Positive SST trends can cause greater SIC reductions, which is observed in the Greenland and Barents Seas during the autumn and winter. In contrast, the removal of sea ice (i.e., loss of the insulating layer) likely contributes to a colder sea surface (i.e., decreased SST), as is observed in northern Barents Sea. Decreasing SIC trends can lead to an in-phase enhancement of SAT, while SAT variations seem to have a lagged infl uence on SIC trends. SW plays an important role in the modulating SIC trends in two ways: by transporting moist and warm air that melts sea ice in peripheral seas (typically evident inthe Barents Sea) and by exporting sea ice out of the Arctic Ocean via passages into the Greenland and Barents Seas, including the Fram Strait, the passage between Svalbard and Franz Josef Land (S-FJL), and the passage between Franz Josef Land and Severnaya Zemlya (FJL-SZ). [ABSTRACT FROM AUTHOR]

The Fram Strait (FS) is the primary region of sea ice export from the Arctic Ocean and thus plays an important role in regulating the amount of sea ice and fresh water entering the North Atlantic seas. A 5 a (2011-2015) sea ice thickness record retrieved from CryoSat-2 observations is used to derive a sea ice volume flux via the FS. Over this period, a mean winter accumulative volume flux (WAVF) based on sea ice drift data derived from passivemicrowave measurements, which are provided by the National Snow and Ice Data Center (NSIDC) and the Institut Francais de Recherche pour d’Exploitation de la Mer (IFREMER), amounts to 1 029 km3 (NSIDC) and 1 463 km3 (IFREMER), respectively. For this period, a mean monthly volume flux (area flux) difference between the estimates derived from the NSIDC and IFREMER drift data is-62 km3 per month (-18×106 km2 per month). Analysis reveals that this negative bias is mainly attributable to faster IFREMER drift speeds in comparison with slower NSIDC drift data. NSIDC-based sea ice volume flux estimates are compared with the results from the University of Bremen (UB), and the two products agree relatively well with a mean monthly bias of (5.7±45.9) km3 per month for the period from January 2011 to August 2013. IFREMER-based volume flux is also in good agreement with previous results of the 1990s. Compared with P1 (1990/1991-1993/1994) and P2 (2003/2004-2007/2008), the WAVF estimates indicate a decline of more than 600 km3 in P3 (2011/2012-2014/2015). Over the three periods, the variability and the decline in the sea ice volume flux are mainly attributable to sea ice motion changes, and second to sea ice thickness changes, and the least to sea ice concentration variations. [ABSTRACT FROM AUTHOR]

Sea ice is a quite sensitive indicator in response to regional and global climate changes. Based on monthly mean Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) sea ice thickness fields, we computed the conductive heat flux (CHF) in the Arctic Ocean in the four winter months (November-February) for a long period of 36 years (1979-2014). The calculated results for each month manifest the increasing extension of the domain with high CHF values since 1979 till 2014. In 2014, regions of roughly 90% of the central Arctic Ocean have been dominated by the CHF values larger than 18 W m (November-December) and 12 W m (January-February), especially significant in the shelf seas around the Arctic Ocean. Moreover, the population distribution frequency (PDF) patterns of the CHF with time show gradually peak shifting toward increased CHF values. The spatiotemporal patterns in terms of the trends in sea ice thickness and other three geophysical parameters, surface air temperature (SAT), sea ice thickness (SIT), and CHF, are well coupled. This suggests that the thinner sea ice cover preconditions for the more oceanic heat loss into atmosphere (as suggested by increased CHF values), which probably contributes to warmer atmosphere which in turn in the long run will cause thinner ice cover. This represents a positive feedback mechanism of which the overall effects would amplify the Arctic climate changes. [ABSTRACT FROM AUTHOR]

By combing satellite-derived ice motion and concentration with ice thickness fields from a popular model PIOMAS we obtain the estimates of ice volume flux passing the Fram Strait over the 1979-2012 period. Since current satellite and field observations for sea ice thickness are limited in time and space, the use of PIOMAS is expected to fill the gap by providing temporally continued ice thickness fields. Calculated monthly volume flux exhibits a prominent annual cycle with the peak record in March (roughly 145 km/month) and the trough in August (10 km/month). Annual ice volume flux (1 132 km) is primarily attributable to winter (October through May) outflow (approximately 92%). Uncertainty in annual ice volume export is estimated to be 55 km (or 5.7%). Our results also verified the extremely large volume flux appearing between late 1980s and mid- 1990s. Nevertheless, no clear trend was found in our volume flux results. Ice motion is the primary factor in the determination of behavior of volume flux. Ice thickness presented a general decline trend may partly enhance or weaken the volume flux trend. Ice concentration exerted the least influences on modulating trends and variability in volume flux. Moreover, the linkage between winter ice volume flux and three established Arctic atmospheric schemes were examined. Compared to NAO, the DA and EOF3 mechanism explains a larger part of variations of ice volume flux across the strait. [ABSTRACT FROM AUTHOR]

The Yellow River delta, the second largest oil and gas base of China, has been subsiding due to the combination effects of human and natural factors. Increasing anthropogenic activities, like hydrocarbon exploitation, accelerate the subsidence, gradually threatening the stability of infrastructures and causing the inhabitants more vulnerable to natural hazards. Interferometric synthetic aperture radar (InSAR) techniques can measure ground movements in high resolution. The purpose of this study is to map the spatial and temporal variations in surface deformation over the Yellow River delta using a Small Baseline Subset InSAR technique and to assess the role of hydrocarbon exploitation in subsidence. The Stanford Method for Persistent Scatterers/Multi-Temporal InSAR (StaMPS/MTI) package is employed to process ENVISAT ASAR images collected from 2007 to 2010. InSAR-derived surface deformation measurements are then compared to geological and petroleum geologic data, oil field data and hydrocarbon reservoir inversion information to address the causes of the observed subsidence. Spirit leveling data and standard deviation maps are used to verify the InSAR results and measurement accuracy. Consistent results of the two descending tracks indicate subsidence up to 40 mm/yr over oil field. The time-series deformation manifests that subsidence area and peak broaden over time and subsidence rate are approximately constant in Shikou oil field, while the rate decreases in Dongying oil field since 2009. Moreover, our results indicate that the land subsidence pattern is concentrated in hydrocarbon exploration field and has a good consistency with faults distributions. In addition, a multiple finite prolate ellipsoid sources model is implemented to model the InSAR-derived deformation in Shikou oil field. The model manifests that land subsidence and hydrocarbon exploitation can be quantitatively linked to each other. The intimate connection of surface deformation with reservoir change suggests that land subsidence over oil field in the Yellow River delta is primarily caused by hydrocarbon exploitation. The results provide new insights into the land subsidence mechanism in the Yellow River delta. [ABSTRACT FROM AUTHOR]

In this study, changes in Arctic sea ice thickness for each ice age category were examined based on satellite observations and modelled results. Interannual changes obtained from Ice, Cloud, and Land Elevation Satellite (ICESat)-based results show a thickness reduction over perennial sea ice (ice that survives at least one melt season with an age of no less than 2 year) up to approximately 0.5-1.0 m and 0.6-0.8 m (depending on ice age) during the investigated winter and autumn ICESat periods, respectively. Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS)-based results provide a view of a continued thickness reduction over the past four decades. Compared to 1980s, there is a clear thickness drop of roughly 0.50 m in 2010s for perennial ice. This overall decrease in sea ice thickness can be in part attributed to the amplified warming climate in north latitudes. Besides, we figure out that strongly anomalous southerly summer surface winds may play an important role in prompting the thickness decline in perennial ice zone through transporting heat deposited in open water (primarily via albedo feedback) in Eurasian sector deep into a broader sea ice regime in central Arctic Ocean. This heat source is responsible for enhanced ice bottom melting, leading to further reduction in ice thickness. [ABSTRACT FROM AUTHOR]

The culture of suspended kelp, such as Laminaria japonica Aresch, has arisen in nearshore areas for approximately 30 years since the 1980s. This long-term activity has significant impact on the regional hydrodynamic and sedimentary environments. In this study the impact was investigated, based on synchronized multi-station data from continuous observations made within and around the culture area. In total, three current velocity profiles were identified inside and on the landward side of the culture area. Based on the current velocity profiles we calculated the boundary layer parameters, the fluxes of erosion/deposition, and the rate of sediment transport in different times at each observation site. Comparison between culture and non-culture periods showed that the presence of suspended kelp caused the reduction in the average flow velocity by approximately 49.5%, the bottom friction velocity by 24.8%, the seabed roughness length by 62.7%, and the shear stress and the flux of resuspended sediment by approximately 50%. From analyses in combination with the corresponding vertical variation of the suspended sediment distribution, it is revealed that the lifted sediments by resuspension is mixed with the upper suspended material, which will modify the regional distribution of suspended sediment. These changes in flow structure and sediment movement will accelerate seabed siltation, which corresponds to the changes in seabed erosion/deposition. However, under the influences of the seasonal changes in kelp growth the magnitude of change with the seabed siltation was not obvious inside the culture area, but a fundamental change was apparent around the culture area. [ABSTRACT FROM AUTHOR]

A modified algorithm taking into account the first year (FY) and multiyear (MY) ice densities is used to derive a sea ice thickness from freeboard measurements acquired by satellite altimetry ICESat (2003-2008). Estimates agree with various independent in situ measurements within 0.21 m. Both the fall and winter campaigns see a dramatic extent retreat of thicker MY ice that survives at least one summer melting season. There were strong seasonal and interannual variabilities with regard to the mean thickness. Seasonal increases of 0.53 m for FY the ice and 0.29 m for the MY ice between the autumn and the winter ICESat campaigns, roughly 4-5 month separation, were found. Interannually, the significant MY ice thickness declines over the consecutive four ICESat winter campaigns (2005-2008) leads to a pronounced thickness drop of 0.8 m in MY sea ice zones. No clear trend was identified from the averaged thickness of thinner, FY ice that emerges in autumn and winter and melts in summer. Uncertainty estimates for our calculated thickness, caused by the standard deviations of multiple input parameters including freeboard, ice density, snow density, snow depth, show large errors more than 0.5 m in thicker MY ice zones and relatively small standard deviations under 0.5 m elsewhere. Moreover, a sensitivity analysis is implemented to determine the separate impact on the thickness estimate in the dependence of an individual input variable as mentioned above. The results show systematic bias of the estimated ice thickness appears to be mainly caused by the variations of freeboard as well as the ice density whereas the snow density and depth brings about relatively insignificant errors. [ABSTRACT FROM AUTHOR]

The traditional method of Synthetic Aperture Radar (SAR) wind field retrieval is based on an empirical relation between the near surface winds and the normalized radar backscatter cross section to estimate wind speeds, where this relation is called the geophysical model function (GMF). However, the accuracy rapidly decreases due to the impact of rainfall on the measurement of SAR and the saturation of backscattered intensity under the condition of tropical cyclone. Because of no available instrument synchronously monitoring rain rate on the satellite platform of SAR, we have to derive the precipitation of the SAR observation time from non-simultaneous passive microwave observations of rain in combination with geostationary IR images, and then use the model of rain correction to remove the impact of rain on SAR wind field measurements. For the saturation of radar backscatter cross section in high wind speed conditions, we develop an approach to estimate tropical cyclone parameters and wind fields based on the improved Holland model and the SAR image features of tropical cyclone. To retrieve the low-to-moderate wind speed, the wind direction of tropical cyclone is estimated from the SAR image using wavelet analysis. And then the maximum wind speed and the central pressure of tropical cyclone are calculated by a least square minimization of the difference between the improved Holland model and the low-to-moderate wind speed retrieved from SAR. In addition, wind fields are estimated from the improved Holland model using the above-mentioned parameters of tropical cyclone as input. To evaluate the accuracy of our approach, the SAR images of typhoon Aere, typhoon Khanun, and hurricane Ophelia are used to estimate tropical cyclone parameters and wind fields, which are compared with the best track data and reanalyzed wind fields of the Joint Typhoon Warning Center (JTWC) and the Hurricane Research Division (HRD). The results indicate that the tropical cyclone center, maximum wind speed, and central pressure are generally consistent with the best track data, and wind fields agree well with reanalyzed data from HRD. [ABSTRACT FROM AUTHOR]

Sea-ice concentration is a key item in global climate change research. Recent progress in remotely sensed sea-ice concentration product has been stimulated by the use of a new sensor, advanced microwave scanning radiometer for EOS (AMSR-E), which offers a spatial resolution of 6 km×4 km at 89GHz. A new inversion algorithm named LASI (linear ASI) using AMSR-E 89GHz data was proposed and applied in the antarctic sea areas. And then comparisons between the LASI ice concentration products and those retrieved by the other two standard algorithms, ASI (arctic radiation and turbulence interaction study sea-ice algorithm) and bootstrap, were made. Both the spatial and temporal variability patterns of ice concentration differences, LASI minus ASI and LASI minus bootstrap, were investigated. Comparative data suggest a high result consistency, especially between LASI and ASI. On the other hand, in order to estimate the LASI ice concentration errors introduced by the tie-points uncertainties, a sensitivity analysis was carried out. Additionally an LASI algorithm error estimation based on the field measurements was also completed. The errors suggest that the moderate to high ice concentration areas (>70%) are less affected (never exceeding 10%) than those in the low ice concentration. LASI and ASI consume 75 and 112 s respectively when processing the same AMSR-E time series thourghout the year 2010. To conclude, by using the LASI algorithm, not only the seaice concentration can be retrieved with at least an equal quality as that of the two extensively demonstrated operational algorithms, ASI and bootstrap, but also in a more efficient way than ASI. [ABSTRACT FROM AUTHOR]

The affiliations of the authors of this article unfortunately contained a mistake. [ABSTRACT FROM AUTHOR]