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Mechanisms for the link between onset and duration of open water in the Kara Sea
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Chunming Dong1, Hongtao Nie1, *, Xiaofan Luo1, Hao Wei1, Wei Zhao1
Acta Oceanologica Sinica | 2021, 40(11) : 119 - 128
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Acta Oceanologica Sinica | 2021, 40(11): 119-128
• Physical Oceanography, Marine Meteorology and Marine Physics •
Mechanisms for the link between onset and duration of open water in the Kara Sea
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Chunming Dong1, Hongtao Nie1, *, Xiaofan Luo1, Hao Wei1, Wei Zhao1
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  • 1 School of Marine Science and Technology, Tianjin University, Tianjin 300072, China
Published: 2021-11-25 doi: 10.1007/s13131-021-1767-5
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The sea ice conditions in the Kara Sea have important impacts on Arctic shipping, oil and gas production, and marine environmental changes. In this study, sea ice coverage (CR) less than 30% is considered as open water, its onset and end dates are defined as Topen and Tclose, respectively. The sea ice melt onset (Tmelt) is defined as the date when ice-sea freshwater flux initially changes from ice into the ocean. Satellite-based sea ice concentration (SIC) from 1989 to 2019 shows a negative correlation between Topen and Tclose (r = –0.77, p < 0.01) in the Kara Sea. This phenomenon is also obtained through analyzing the hindcast simulation from 1994 to 2015 by a coupled ocean and sea-ice model (NAPA1/4). The model results reveal that thermodynamics dominate the sea ice variations, and ice basal melt is greater than the ice surface melt. Heat budget estimation suggests that the heat flux is significant correlated with Topen (r = –0.95, p < 0.01) during the melt period (the duration of multi-year averaged Tmelt to Topen) influenced by the sea ice conditions. Additionally, this heat flux is also suggested to dominate the interannual variation of the heat input during the whole heat absorption process (r = 0.81, p < 0.01). The more heat input during this process leads to later Tclose (r = 0.77, p < 0.01). This is the physical basis of the negative correlation between Topen and Tclose. Therefore, the duration of open water can be predicted by Topen and thence support earlier planning of marine activities.

sea ice  /  open water onset  /  duration of open water  /  heat budget  /  Kara Sea
Chunming Dong, Hongtao Nie, Xiaofan Luo, Hao Wei, Wei Zhao. Mechanisms for the link between onset and duration of open water in the Kara Sea[J]. Acta Oceanologica Sinica, 2021 , 40 (11) : 119 -128 . DOI: 10.1007/s13131-021-1767-5
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1 Introduction
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Arctic sea ice extent has decreased dramatically in the past 40 years (Cavalieri and Parkinson, 2012; Lindsay and Schweiger, 2015; Stroeve and Notz, 2018). The summer sea ice has decreased significantly, especially in the marginal seas of the Arctic (Onarheim et al., 2018; Simmonds, 2015). Continued sea ice loss will make the Arctic Ocean increasingly accessible for oil and natural gas exploration and development, marine transportation, tourism, and other activities (Navy’s Task Force Climate Change, 2014). Therefore, it is very important to understand the regularity of the onset and duration of open water every year to arrange production in advance and to ensure the safety of marine activities. The Kara Sea (Fig. 1) is rich in oil and gas resources and is a key sea area of the Northeast Passage (Aksenov et al., 2017; Bird et al., 2008; Gautier et al., 2009). Meanwhile, the Kara Sea is one of the regions that most dramatically impacted by global warming in the Arctic Ocean (Kim et al., 2016). The prediction of open water duration in this region is urgently required.
Previous studies have shown that seasonal changes in sea ice cover not only represent the response of the surface energy budget to climate (Blanchard-Wrigglesworth et al., 2011; Markus et al., 2009) but also result from ocean dynamic and thermodynamic effects (Maslanik et al., 1996; Wang et al., 2019b). The average annual melt and freeze onset dates of the Arctic Ocean are significantly correlated with the Arctic Oscillation (AO) index. During the high-index AO, sea ice in most parts of the Arctic began to melt earlier and freeze later (Belchansky et al., 2004; Johnson and Eicken, 2016; Lebrun et al., 2019; Stammerjohn et al., 2012; Stroeve et al., 2016). Winter cyclone wind field responding to high-index AO enhances the divergence of ice in the eastern Arctic (Rigor et al., 2002). These processes result in more open water, thin ice or leads, leading to decreased surface albedo and increased heat flux into the sea (Lei et al., 2016; Perovich et al., 2007a). Additionally, the increased sea surface air humidity and temperature trigger the efficient feedback of the atmosphere-ocean-sea ice system (Ikeda et al., 2001; Mysak and Venegas, 1998), favoring a longer duration of open water in summer (Curry et al., 1995; Flanner et al., 2011; Screen and Simmonds, 2010; Serreze and Barry, 2011).
However, the sea ice melt onset in the marginal seas of the Arctic Ocean responds differently to AO. Earlier melt onset in the Laptev, East Siberian, and the Chukchi Sea regions was found to be associated with a positive AO index (Belchansky et al., 2004; Bliss and Anderson, 2014), while the melt onset in the Beaufort Sea and the Barents Sea is primarily controlled by ocean circulation heat transport (Lien et al., 2017; Serreze et al., 2016). Owing to the strong reliance of melt onset on local weather conditions, the Kara Sea is the most highly variable sub-region of the Arctic Ocean (Bliss and Anderson, 2014). Melt onset in the Kara Sea does not reveal an obvious relationship with climate factors, making it difficult to predict. Therefore, determining an independent indicator from melt onset for open water evolution is significant to meet practical demands.
It is difficult to distinguish the contributions of dynamic and thermodynamic processes to water column heat changes only through the correlations among wind, runoff, sea surface temperature, and sea ice (Chen and Zhao, 2017; Duan et al., 2019a; Leifer et al., 2018). The underlying mechanism linking sea ice melting and freezing is required to be analyzed with a coupled ocean and sea-ice model that includes the feedback process. Some questions regarding the causes of ice variation remain unanswered; for example, does the atmosphere-forced ice surface melt regulate the primary features of ice variation? Which period is more crucial for interannual variations of heat budget and thence the ice volume variations? Additionally, in the Chukchi Sea, Pacific heat influx through the Bering Strait is equivalent to the solar radiation (Perovich et al., 2007b; Woodgate et al., 2015), indicating that the lateral heat flux has a significant impact on the interannual variations in sea ice (Wang et al., 2019a). In the Kara Sea, the role of lateral heat flux, including runoff from the Ob and Yenisei rivers and the inflow from the Barents Sea remains unclear. Quantitative analysis based on the combination of sea ice and heat budget is helpful for further understanding the physical mechanism of the atmosphere-ocean-sea ice feedback in different periods of the year.
Using satellite-based sea ice concentration (SIC) and a coupled ocean and sea-ice model simulations, this study explores interannual variations in the open water onset and end date in the Kara Sea. Afterwards, heat budgets during the ice melt and open water period are estimated. Finally, the underlying mechanism linking sea ice melting and freezing is clarified and the prediction scheme for open water duration is obtained.
2 Data and methods
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2.1 Data sources
The daily satellite remote sensing data set from 1989 to 2019, with a horizontal resolution of 25 km, is the Climate Data Record (CDR) from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Sea Ice Concentration, created by the National Snow and Ice Data Centre (NSIDC) of the USA National Oceanic and Atmospheric Administration (NOAA) (Meier et al., 2017). The CDR algorithm output is a rule-based combination of ice concentration estimates from two well-established algorithms: the National Aeronautics and Space Administration (NASA) Team (NT) algorithm (Cavalieri et al., 1984) and NASA Bootstrap (BT) algorithm (Comiso, 1986).
The coupled ocean and sea-ice model (NAPA1/4) is version 3.6 of the Nucleus for European Modelling of the Ocean (NEMO 3.6) (Madec, 2008; NEMO book, available at https://www.nemo-ocean.eu/wp-content/uploads/NEMO_book.pdf) and version 3 of Louvain-la-Neuve Sea Ice Model (LIM3) (Rousset et al., 2015; Vancoppenolle et al., 2009). The horizontal model grid is derived from the ORCA global three-pole grid (Madec and Imbard, 1996) (DRAKKAR, https://www.drakkar-ocean.eu/what-is-drakkar). The model covers the North Atlantic-North Pacific-Arctic Oceans (NAPA) with a nominal horizontal resolution of (1/4)° in latitude/longitude and reproduces the main observed features of ocean and sea ice variations during 1994–2015. The configuration and evaluation of the coupled ocean and sea-ice model are detailed in Luo et al. (2019) and Zhang et al. (2020).
LIM3 in the NAPA1/4 model is a C-grid dynamic-thermodynamic sea ice model with thickness, enthalpy, salinity, and age distributions. It includes a series of ice thickness categories, that can better simulate the rapid formation and loss of thin ice and the physical transition process from thin ice to thick ice caused by sea ice overlap and ridge formation (Uotila et al., 2017). The melting of sea ice is divided into two parts: surface and basal melt. The widely used LIM3 in the Arctic does not explicitly consider the lateral melt, but some implicit lateral melting is accounted for by the melt of thin ice (Bitz et al., 2001).
2.2 Evaluation of model simulated sea ice concentration
The NAPA1/4 model reproduces the consistent variation in sea ice in the Kara Sea with that displayed by the CDR data set. The spatial distribution of SIC from May to October is based on the CDR data set (Figs 2a-f) and the NAPA1/4 simulation (Figs 2g-l) averaged over 1994 to 2015. Both data sets suggested that sea ice first melted in the regions of warm water inflow, including the estuaries, the Kara Strait, and the northern part of Novaya Zemlya. Subsequently, the ice melting area gradually expanded, and the SIC in the southern and southwestern regions gradually decreased. In July, the ice-free area first appeared in the estuaries, and the ice edge retreated from south to north and from west to east. By September, the sea ice covered area reached the minimum value, and the focus area bounded by the Kara Strait, the estuaries, and the northern side of the Novaya Zemlya-Taymyr Peninsula, corresponding to KG, ES, NT sections, is completely ice-free. In October, sea ice expanded from high latitudes to low latitudes and then extended to the southwestern Kara Sea. Seasonal variations in sea ice concentration indicate that compared to freezing, melting is a relatively slow process in the Kara Sea. Further statistical analysis shows that the modeled seasonal variation in the regional averaged SIC in each year from 1994 to 2015 has a significant correlation coefficient of greater than 0.95 with the CDR data set (Fig. 2m).
2.3 Methods
In this study, sea ice coverage is used to analyze the changes in sea ice and is calculated through the method introduced by Duan et al. (2019b).
${\rm {SIA}} = \int_S {{e_i}{s_i}{c_i}{\rm d}S} {\rm{, }}\left\{ \begin{split} & {{e_i} = 0\;{\rm{ }}{{{c}}_i} < 15\% }\\& {{e_i} = 1\;{\rm{ }}{{{c}}_i} \geqslant 15\% }\end{split} \right.,\\$
$ {C_R} = {\rm{SIA}}/S,$
where CR is the sea ice coverage (%); SIA is the sea ice cover area; ci, si, and ei are the SIC, area, and weight coefficient in each grid; S is the total area of the focus region. The ice concentration of 15% is considered as the sea ice edge (Comiso and Nishio, 2008; Ogi et al., 2008).
To quantify the interannual variations in the open water evolution in the Kara Sea (Fig.1), CR = 30% is used as the threshold of the start and end dates of open water (Stroeve et al., 2016; Serreze et al., 2016; Wang et al., 2019a). The date when CR reaches and remains below the threshold is defined as the start date of the open water (Topen); the earliest date when CR is larger than and remains above the threshold after Topen is defined as the end date of the open water (Tclose). The period between Topen and Tclose is defined as the duration of open water (DOW).
For the heat balance of the water column, the vertical heat flux at the sea surface (including the surfaces of open water and the leads, and the ice-sea interface) in the study area is the sum of net solar radiation, net longwave radiation, and sensible and latent heat fluxes. The ice-sea freshwater flux, sea surface net heat flux, the ice surface/basal melt rates are included in the model output. The calculation method is clearly described in the LIM3 instruction (available at http://www.climate.be/users/lecomte/LIM3_users_guide _2012.pdf).
The lateral net heat flux includes the heat exchange through KG, ES, and NT sections in Fig. 1, and is calculated following the method proposed by Woodgate (2018):
${{Q}_{\rm{lateral}}}=\rho {{C}_{\rm{w} }}\iint{(\theta -{{\theta }_{\rm{ref}}})v{\rm d}x{\rm d}z+}\rho {{C}_{\rm{w} }}\iint{(\theta -{{\theta }_{{\rm{ref}}}})u{\rm d}y{\rm d}z},$
where Qlateral is defined as the lateral heat flux; θref is the referential temperature (–1.9°C); θ (°C) is the seawater temperature; ρ is the density of seawater (1023 kg/m3); Cω is the specific heat capacity of water which is the function of temperature, salinity and pressure; and v (m/s) and u (m/s) are the north–south and east–west components of the velocity, respectively.
3 Results and discussion
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3.1 Interannual variations of ${\boldsymbol T}_{\bf{open}}$ and ${\boldsymbol T}_{\bf{close}}$
Based on the CDR data set, the daily CR overlapped with Topen and Tclose from 1989 to 2019 is shown in Fig. 3a. A strong correlation was found between Topen and Tclose with the coefficient r = –0.77 (p < 0.01). While Tclose in the previous year showed a weak correlation with the following Topen (r = –0.43, p < 0.01). This suggests a significant impact of Topen on Tclose in one year, i.e., early (late) opening and late (early) closing (Lei et al., 2015), while Tclose has a limited impact on the next year Topen. Satellite data shows that Topen and Tclose of the sea ice have a linear trend of approximately –1.0 d/a and 1.0 d/a, respectively, and the DOW has an increasing trend of interannual change. In 2011 and 2012, Topen was relatively earlier and together with a longer duration of open water. In 1998 and 1999, heavy ice existed in the Kara Sea, the Topen was delayed, and the duration of open water was shorter.
NAPA1/4 reproduced similar interannual variations in open water evolution from 1994 to 2015 in this region (Fig. 3b). Overall, Topen derived from NAPA1/4 is slightly earlier (three days on average) than that derived from the CDR data set. The two have a correlation of r = 0.92 (p < 0.01). In terms of Tclose, the two time series also matches well (r = 0.89, p < 0.01). Topen and Tclose based on NAPA1/4 show a correlation of r = –0.67 (p < 0.01), indicating that Topen arrives earlier and Tclose becomes later. Both the NAPA1/4 and CDR data set reveal that the duration of open water is gradually extended with time. This agrees with the phenomenon that the sea ice cover in the Arctic Ocean has fallen sharply and the duration of the ice-free season has increased (Barnhart et al., 2016).
3.2 Controls on sea ice variations
The variations in sea ice are influenced by dynamic and thermodynamic processes (Hu et al., 2018; Lindsay and Zhang, 2005; Rigor et al., 2002). The thermodynamic process directly affects the freezing and melting of sea ice. The dynamic process mainly affects the sea ice movement, breakage, ridge, leads, and then affects the sea ice distribution and thermal changes. Based on NAPA1/4, the contributions of thermodynamic and dynamic processes on the ice volume averaged over 1994–2015 were estimated (Fig. 4). From January to May, the Kara Sea is ice covered, the thermodynamics leads to the increase of sea ice volume, while the dynamics make the sea ice volume decrease. At the beginning of June, thermodynamic effects shift from positive to negative and induce ice decay. The total ice decay, driven by thermodynamics, peaks in early July. In early October, sea ice grows gradually, and the contribution of thermodynamics changes from negative to positive. During the warm months (June–October), the total ice variation (–1.08 ± 0.93) cm/d matches well with thermodynamics indicated ice variation (–1.09 ± 0.97) cm/d. In the other months, thermodynamics remains prevalent (1.21 ± 0.59) cm/d, but the dynamic effect increases by 24% (–0.39 ± 0.26) cm/d as compared to warm months (0.02 ± 0.39) cm/d. During the cold months, the dynamic processes play a role in ice export, which is consistent with the finding from Kern et al. (2005). This is likely accountable for the weak correlation between Tclose and the following Topen. The contribution ratios of dynamic and thermodynamic processes were quantitatively analyzed through the sea ice growth and decay rates. Using the dynamics as an example, the contribution ratio is calculated as follows:
${\rm{contribution\;ratio}}{\rm{ = }}\frac{{\left| {{\rm{dynamics }}} \right|}}{{\left| {{\rm{dynamics }}} \right| + \left| {{\rm{thermodynamics }}} \right|}} \times {\rm{100\% }}.$
During June–October and the remaining months, the contribution ratios of dynamics are 2% and 24%, respectively. Annually, the contribution ratios of thermodynamics and dynamics are 63% and 37%, respectively, suggesting that thermodynamics are the principal forces causing ice variations in the Kara Sea (Polyakov et al., 2003).
Hence, sea ice volume changes due to thermodynamics are focused on. Figure 5a shows the multi-year averaged surface, basal melting rate, and ice-sea freshwater flux in the Kara Sea in 1994–2015. The ice-sea freshwater flux can be regarded as the change of sea ice volume caused by the thermodynamic process, and its size and variation are consistent with the total melting rate of sea ice. The negative (positive) values of ice-sea freshwater flux denote the total ice melting (formation). The sea ice melt onset (Tmelt) is defined as the date when ice-sea freshwater flux initially changes from ice into the ocean. The duration of multi-year averaged Tmelt to Topen is defined as the ice melt period (May 21 to July 15) and the duration from Topen to Tclose is defined as the open water period (July 16 to October 22), based on the multi-year averaged result. Before the melt period, ice basal melt (< –0.01 cm/d) already exists (Fig. 5b). In early July, the ice-sea freshwater flux reaches its peak value of approximately –2.65 cm/d. This phenomenon occurs in the melt period. During this period, the mean basal melt rate is (–1.13 ± 0.54) cm/d, 79% larger than surface melt (–0.63 ± 0.37) cm/d . Averaged over the open water period, the basal melt accounts for 82% of the total melt.
Overall, the total ice melting during the two periods is primarily contributed by the ice basal melt (76%). Ice surface melt plays a minor role in ice variation. This indicates that atmosphere influences on the ice surface processes alone cannot explain ice variation, while oceanic heat fluxes that are influenced by atmosphere forcing and oceanic transport play a key role in ice basal processes and ice variation. Thus, more attention should be paid to oceanic heat fluxes.
3.3 Heat budget in the ice melt and open water periods
The heat budget in the Kara Sea includes the vertical heat flux through the sea surface and the lateral heat flux through the NT, KG and ES sections. Figure 6 shows the annual-cycles of the sea surface temperature (SST) and heat fluxes in the study region. Before the melt period, the lateral heat flux entering the Kara Sea is principally used in the ice/water phase transition and does not make SST rise. In early May, the ocean absorbs vertical heat flux through the sea surface to promote ice basal melt (Fig. 5b). During the melt period, the vertical heat flux through the ice surface increases rapidly, further accelerating the melting of the ice basal (Fig. 5a) and the increase in SST. During the open water period, the lateral heat flux continues to increase, and the vertical heat flux decreases gradually from August owing to seasonal solar radiation changes. At the end of August, the SST reaches a peak value higher than 3°C. In September, the vertical heat flux changes from positive to negative, the ocean surface begins to release heat to the atmosphere and the decreases. Until late October, the open water is covered by ice.
In general, when sea ice melts to form thin ice or open water, it is difficult to distinguish the influence of lateral inflow on sea ice from that of local solar radiation, temperature, wind, and other factors (Stroeve et al., 2014; Wang et al., 2019b). Although the Kara Sea is more closely connected with rivers and the Barents Sea (Dmitrenko et al., 2015; Osadchiev et al., 2017), the lateral flux plays a minor role in the heat budget. This is quite different from the lateral inflow controlled by continental shelf sea, such as the Chukchi Sea (Wang et al., 2019a). Compared with lateral heat flux, vertical heat flux contributes 96% and 79% of the heat budget in the water column during the melt and open water period, respectively.
3.4 Linkage of interannual variations in ${\boldsymbol T}_{\bf{open}} $ and ${\boldsymbol T}_{\bf{close}} $
The total heat flux, i.e., the summation of net vertical and lateral heat fluxes is essential in the ice melt and open water periods. The SIC during the melt period can reflect the macro effects of the local weather process. The SIC and total heat flux during the melt period have opposite interannual variations with Topen as shown in Fig. 7, with correlation coefficients of r = –0.91 (p < 0.01) and r = –0.95 (p < 0.01), respectively. The results show that sea ice conditions influence the variations in heat absorption during the melt period. More leads and thin ice enhance the heat flux, decrease surface albedo, and accelerate the melting through positive feedbacks (Markus et al., 2009; Rigor et al., 2002; Zhang et al., 2000), making the Topen earlier. Conversely, higher SIC delays the date of sea ice retreat, leading to a later Topen. Therefore, the net heat flux accumulated during the melt period has a great effect on the formation of the open water under the influences of sea ice conditions.
Figure 8 shows the interannual variations between total net heat flux and Tclose during the melt period and the open water period. The mean value and standard deviation of the heat budget during the two periods are (1.47 ± 0.44) × 1020 J and (1.10 ± 0.36) × 1020 J, respectively. Tclose is significantly correlated with net heat flux during the melt period (r = 0.66, p < 0.01), but without an obvious relationship with that during the open water period (p > 0.05). While during the first half of the open water period, the water column keeps gaining heat (Fig. 6). The accumulated heat during the whole heat absorption process (including the melt period and the first half of the open water period) shows significant correlations with Tclose (r = 0.77, p < 0.01), indicating that more heat input during this process results in the later freezing date. In addition, the heat flux during the melt period is also correlated (r = 0.81, p < 0.01) with and dominates the interannual variation of the heat input during the whole heat absorption process, and therefore can be a good indicator of DOW afterwards.
4 Linkage between DOW and Topen
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Sea ice conditions during the melt period influence the sea ice albedo positive feedback mechanism, resulting in an interannual variation in seawater heat absorption, which in turn affects the formation and DOW. Least-squares regression analysis shows the two time series Topen and DOW have a coefficient of determination (R2) as high as 0.89 significance at the 95% confidence level (Fig. 9a). During 1989–2019, there is a lengthening of DOW, as well as earlier Topen. The relationship between Tclose and DOW shows a significant correlation coefficient of r = 0.94 (p < 0.01). The empirical formula for the prediction of DOW in the Kara Sea is obtained as follows:
${\rm{DOW}} = - 1.72{T_{{\rm{open}}}} + {\rm{ }}177.15.$
The earliest open date of the Kara Sea is June 13; thus, the prediction formula considers June 1 as the first day of Topen, and Topen represents the number of days relative to the end of May. The relationship between Topen and DOW based on the CDR data set has been detrended by removing the long-term linear trend in the Kara Sea (Fig. 9b) still presents a significant correlation coefficient of r = –0.91 (p < 0.01). Using this regression relationship, the predicted DOW agrees well with CDR derived DOW. Assuming the latter as is accurate, a prediction using Topen and the above regression relationship provides a maximum absolute error of 9 days; the mean predicted DOW during 1989–2019 is equal to the observation values. This prediction scheme is practical and helpful for supporting the planning of marine activities in the Kara Sea.
5 Conclusions
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Satellite sea ice concentration in 1989–2019 identifies a strong negative correlation (r = –0.77, p < 0.01) between open water onset and reclose (denoted as Topen and Tclose, respectively) in the Kara Sea. A coupled ocean and sea-ice model (NAPA1/4) reproduces the interannual variations. Based on NAPA1/4, this study estimated the relative contributions of thermodynamics and dynamics to sea ice variation. The results suggest that thermodynamics are the prevalent forces influencing the sea ice variations in the Kara Sea. We further analyzed the sea ice volume changes due to thermodynamics and separated the evolution of sea ice into two periods: the melt period and the open water period. The results suggest that the total ice melting is primarily contributed by the ice basal melt (76%). Ice surface melt plays a minor role in ice variation. This indicates that atmosphere influences on the ice surface processes alone cannot explain ice variation, while oceanic heat fluxes that are influenced by atmosphere forcing and oceanic transport play a key role in ice basal processes and ice variation.
The heat budget estimation in the Kara Sea suggests that compared with lateral heat flux, vertical heat flux primarily regulates 96% and 79% of the heat budget in the water column during the melt and open water period, respectively. During the melt period, heat flux presents a significant correlation with Topen and is also suggested to dominate the interannual variation of the whole heat absorption process (r = 0.81, p < 0.01). The more heat input during this process leads to later Tclose (r = 0.77, p < 0.01). Hence the heat flux during the melt period is crucial to the evolution of open water and can be a good indicator of DOW afterwards.
A link between Topen and DOW, i.e., DOW = –1.72Topen + 177.15, is revealed based on satellite data. Topen is the number of days relative to the end of May. This prediction scheme for DOW via Topen supports earlier planning of marine activities. This study provides a reference for a profound understanding of the coupling effects of the positive feedback mechanism on the ocean-sea ice-air interaction in the marginal continental shelf where seasonal sea ice changes. With the future climate change in the Arctic Ocean, the prediction scheme is also sustainable if the seasonal sea ice still exists with ice coverage higher than 30% in the Kara Sea.
Acknowledgements
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We acknowledge NSIDC (National Snow and Ice Data Centre of the USA National Oceanic and Atmospheric Administration) for providing the dataset of Climate Data Record of Passive Microwave Sea Ice Concentration ( https://nsidc.org/data/G02202/versions/3). We are grateful to the NEMO development team for providing the state-of-the-art model. We also thank two anonymous reviewers for the very insightful and constructive comments that guided the revision of the manuscript.
Funding
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  • The National Key Research and Development Program of China under contract No. 2016YFC1401401; the National Natural Science Foundation of China under contract Nos 41630969, 41941013 and 41806225.
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Appendix
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doi: 10.1007/s13131-021-1767-5
  • Receive Date：2020-10-27
  • Online Date：2026-03-06
  • Published：2021-11-25
Article Data
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  • Received：2020-10-27
  • Accepted：2020-11-28
Funding
The National Key Research and Development Program of China under contract No. 2016YFC1401401; the National Natural Science Foundation of China under contract Nos 41630969, 41941013 and 41806225.
Affiliations
    1 School of Marine Science and Technology, Tianjin University, Tianjin 300072, China

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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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