# Thirty-Nine-Year Wave Hindcast, Storm Activity, and Probability Analysis of Storm Waves in the Kara Sea, Russia

^{1}

^{2}

^{3}

^{4}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Wave Modeling

_{0}is the zero-order moment of the wave spectrum, approximately SWH is the mean value from 1/3 of the highest waves), the wave propagation direction, the mean wave period (WP) Tm02=($2\pi \sqrt{\overline{{\sigma}^{2}}}$), and mean wavelength (WL)= ($2\pi \overline{{k}^{-1}}$). Also, the wave heights of 1% and 3% probability of exceedance (it mean that 1% of the single waves are higher than 99% other waves during the 15 min period) were used for the data analysis. These values were calculated as 1.51 × SWH and 1.32 × SWH, respectively [49,50]. SWH and wave height with other probability calculated in the model for 15 min integration interval. The maximum and long-term SWH were calculated based on these data. When the Kara Sea was ice covered the wave parameters were equal to zero in model results. The mean long-term characteristics were performed for the ice-free period when the wave parameters were nonzero.

#### 2.2. Quality Assessment of the Wave Model Results

_{i}—is the model value, O

_{i}—is the observed value, $\overline{\mathrm{P}}$—is the mean model value, $\overline{\mathrm{O}}$—is the mean observed value.

#### 2.3. Recurrence of the Storm Wave Events

## 3. Wave Climate

#### 3.1. Mean and Extreme Wave Parameters

#### 3.2. Seasonal Variability of Wave Characteristics

#### 3.3. Interannual Variability of Storm Wave Events

## 4. Probability Analysis of Storm Waves

#### 4.1. Probability Functions of the Storm Recurrence in Different Sectors of the Kara Sea

^{th}percentile for the sample for the ice-free period. In this catalog, each member of the series is a separate storm event. It is a necessary condition for the independence of the members of the series according to the method of “independent storms” [56]. The length of the data series is sufficient for statistical analysis. Series consists of 450–750 values depending on the sector.

_{th}= σ / k, and shape parameter γ = 1 / k. We tested the abovementioned distributions and got the following result: R

^{2}= 0.67 for the Gumbel distribution, R

^{2}= 0.75 for the Weibull distribution and R

^{2}= 0.96 for the Pareto distribution. A comparison of the functions with the empirical data showed that the best approximations for the storm recurrence was the Pareto distribution

_{th}= 3 m and γ = 4.8 and a determination coefficient of R

^{2}≈0.98 in sector 6. This approximation is used as base distribution. A similar pattern of distribution functions is observed for all six sectors.

#### 4.2. Interannual Analysis of Extreme Events (“Dragons”)

^{2}is more than 0.9 and significant in most cases), which indicates they could obey the other law and physical processes. However, other sectors showed strong deviations of “dragons” approximation parameters from the “swans” ones. Exceptions are marked outliers (sectors 2 and 5), which do not fit any version of Pareto distribution. The γ values (starting with some values of H) begin to increase rapidly. Particularly, the marked outliers in sectors 2 and 5 corresponds simultaneously to absolute maxima in the Kara Sea (above 9 m) and does not fit to the “dragons” sample. Thus, the “dragons” pattern could indicate a certain natural limit observed for maxima wave height that differs from the base distribution. When several cases from the data set do not match the base distribution, it could indicate a chaotic behavior of the most extreme waves in these sectors, and this pattern has some similarities with the definition of freak waves in the article [62]. Freak waves are unique anomalous individual waves that do not correspond to the general distribution. In our case, we have a similar picture on the synoptic scale, where specific storms with a certain SWH maximum defined as “dragons”.

## 5. Discussion and Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- De Leo, F.; Solari SBesio, G. Extreme wave analysis based on atmospheric pattern classification: An application along the Italian coast. Nat. Hazards Earth Syst. Sci.
**2020**, 20, 1233–1246. [Google Scholar] [CrossRef] - Menéndez, M.; Méndez, F.; Losada, I.; Graham, N. Variability of extreme wave heights in the northeast Pacific Ocean based on buoy measurements. Geophys. Res. Lett.
**2008**, 35, L22607. [Google Scholar] [CrossRef] - Meucci, A.; Young, I.; Aarnes, O.; Breivik, Ø. Comparison of wind speed and wave height trends from twentieth-century models and satellite altimeters. J. Clim.
**2020**, 33, 611–624. [Google Scholar] [CrossRef] - Young, I.; Ribal, A. Multiplatform evaluation of global trends in wind speed and wave height. Science
**2019**, 364, 548–552. [Google Scholar] [CrossRef] - Liu, Q.; Babanin, A.; Zieger, S.; Young, I.; Guan, C. Wind and wave climate in the Arctic Ocean as observed by altimeters. J. Clim.
**2016**, 29, 7957–7975. [Google Scholar] [CrossRef] - Bertin, X.; Prouteau, E.; Letetrel, C. A significant increase in wave height in the North Atlantic Ocean over the 20th century. Glob. Planet Change
**2013**, 106, 77–83. [Google Scholar] [CrossRef] - Dobrynin, M.; Murawski, J.; Baehr, J.; Ilyina, T. Detection and attribution of climate change signal in ocean wind waves. J. Clim.
**2015**, 28, 1578–1591. [Google Scholar] [CrossRef][Green Version] - Kumar, P.; Min, S.-K.; Weller, E.; Lee, H.; Wang, X. Influence of climate variability on extreme ocean surface wave heights assessed from ERA-Interim and ERA-20C. J. Clim.
**2016**, 29, 4031–4046. [Google Scholar] [CrossRef] - Semedo, A.; Sušelj, K.; Rutgersson, A.; Sterl, A. A global view on the wind sea and swell climate and variability from ERA-40. J. Clim.
**2011**, 24, 1461–1479. [Google Scholar] [CrossRef] - Wang, X.; Swail, V. Changes of extreme wave heights in Northern Hemisphere oceans and related atmospheric circulation regimes. J. Clim.
**2001**, 14, 2204–2221. [Google Scholar] [CrossRef] - Weisse, R.; Von Storch, H.; Feser, F. Northeast Atlantic and North Sea storminess as simulated by a regional climate model during 1958–2001 and comparison with observations. J. Clim.
**2005**, 18, 465–479. [Google Scholar] [CrossRef][Green Version] - Khon, V.; Mokhov, I.; Pogarskiy, F.; Babanin, A.; Dethloff, K.; Rinke, A.; Matthes, H. Wave heights in the 21st century Arctic Ocean simulated with a regional climate model. Geophys. Res. Lett.
**2014**, 41, 2956–2961. [Google Scholar] [CrossRef][Green Version] - Fedele, F.; Arena, F. Long-Term Statistics and Extreme Waves of Sea Storms. J. Phys. Oceanogr.
**2010**, 40, 1106–1117. [Google Scholar] [CrossRef] - Adekunle, O.; Xiaopei, L.; Dongliang, Z.; Zhifeng, W. Long-term variability of extreme significant wave height in the South China Sea. Adv. Meteorol.
**2016**, 2016, 2419353. [Google Scholar] [CrossRef][Green Version] - Lopatukhin, L.I. (Ed.) Wind and Wave Climate Handbook. Kara Sea and Sea of Japan; Russian Maritime, Register of Shipping: St. Petersburg, Russia, 2009. [Google Scholar]
- Diansky, N.; Fomin, V.; Kabatchenko, I.; Gruzinov, V. Simulation of circulation of the Kara and Pechora Seas through the system of express diagnosis and prognosis of marine dynamics. Arct. Ecol. Econom.
**2014**, 1, 57–73. [Google Scholar] - Stopa, J.; Ardhuin, F.; Girard-Ardhuin, F. Wave climate in the Arctic 1992–2014: Seasonality and trends. Cryosphere
**2016**, 10, 1605–1629. [Google Scholar] [CrossRef][Green Version] - Li, J.; Ma, Y.; Liu, Q.; Zhang, W.; Guan, C. Growth of wave height with retreating ice cover in the Arctic. Cold Reg. Sci. Technol.
**2019**, 164, 102790. [Google Scholar] [CrossRef] - Duan, C.; Dong, S.; Wang, Z. Wave climate analysis in the ice-free waters of Kara Sea Regio. Stud. Mar. Sci.
**2019**, 30, 100719. [Google Scholar] - Waseda, T.; Webb, A.; Sato, K.; Inoue, J.; Kohout, A.; Penrose, B. Correlated increase of high ocean waves and winds in the ice-free waters of the Arctic Ocean. Sci. Rep.
**2018**, 8, 1–9. [Google Scholar] [CrossRef][Green Version] - Young, I.; Zieger, S.; Babanin, A. Global trends in wind speed and wave height. Science
**2011**, 332, 451–455. [Google Scholar] [CrossRef] [PubMed] - Kislov, A.; Matveeva, T. The Monsoon over the Barents Sea and Kara Sea. Atmos. Clim. Sci.
**2020**, 10, 339–356. [Google Scholar] [CrossRef] - Serreze, M.; Stroeve, J. Arctic sea ice trends, variability and implications for seasonal ice forecasting. Philos. Trans. R. Soc. Lond.
**2015**, 373, 20140159. [Google Scholar] [CrossRef] [PubMed][Green Version] - Caian, M.; Koenigk, T.; Döscher, R.; Devasthale, A. An interannual link between Arctic sea-ice cover and the North Atlantic Oscillation. Clim. Dyn.
**2018**, 50, 423–441. [Google Scholar] [CrossRef][Green Version] - Shalina, E. Arctic sea ice decline from satellite passive microwave observations. Sovremennye Probl. Distantsionnogo Zondirovaniya Zemli iz Kosm.
**2013**, 10, 328–336. [Google Scholar] - Semenov, E.; Sokolikhina, N.; Tudriy, K.; Shchenin, M. Synoptic mechanisms of winter warming in the Arctic. Rus. Meteorol. Hydrol.
**2015**, 40, 576–583. [Google Scholar] [CrossRef] - Semenov, V.; Cherenkova, E. Evaluation of the Atlantic multidecadal oscillation impact on large-scale atmospheric circulation in the Atlantic region in summer. Dokl. Earth Sc.
**2018**, 478, 263–267. [Google Scholar] [CrossRef] - Ivanov, V.; Repina, I. The effect of seasonal variability of Atlantic water on the Arctic sea ice cover. Izv. Atmos. Ocean. Phys.
**2018**, 54, 65–72. [Google Scholar] [CrossRef] - Tilinina, N.; Gulev, S.; Bromwich, D. New view of Arctic cyclone activity from the Arctic System reanalysis. Geophys. Res. Lett.
**2014**, 43, 1766–1772. [Google Scholar] [CrossRef][Green Version] - Zhang, X.; Walsh, J.; Zhang, J.; Bhatt, U.; Ikeda, M. Climatology and interannual variability of Arctic cyclone activity: 1948–2002. J. Climate
**2004**, 17, 2300–2317. [Google Scholar] [CrossRef] - Surkova, G.; Sokolova, L.; Chichev, A. Long-term regime of extreme winds in the Barents and Kara seas. Vestn. Mosk. Univ. Ser. 5 Geogr.
**2015**, 5, 53–58. [Google Scholar] - Stocker, T.F.; Qin, D.; Plattner, G.K.; Tignor, M.; Allen, S.K.; Boschung, J.; Nauels, A.; Xia, Y.; Bex, V.; Midgley, P.M. (Eds.) IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2013. [Google Scholar]
- Reistad, M.; Breivik, Ø.; Haakenstad, H.; Aarnes, O.J.; Furevik, B.R. A high-resolution hindcast of wind and waves for the North Sea, the Norwegian Sea and the Barents Sea. J. Geophys. Res.
**2011**, 116, C05019. [Google Scholar] [CrossRef][Green Version] - Tolman, H. The WAVEWATCH III Development Group User Manual and System Documentation of WAVEWATCH III Version 4.18. Tech. Note 316, NOAA/NWS/NCEP/MMAB. 2014. Available online: https://www.researchgate.net/publication/282672355_User_manual_and_system_documentation_of_WAVEWATCH_III_R_version_418 (accessed on 18 December 2020).
- Tolman, H. The WAVEWATCH III Development Group User Manual and System Documentation of WAVEWATCH III Version 6.07. Tech. Note 333, NOAA/NWS/NCEP/MMAB 2019. Available online: https://www.researchgate.net/publication/336069899_User_manual_and_system_documentation_of_WAVEWATCH_III_R_version_607 (accessed on 18 December 2020).
- Snyder, R.L.; Dobson, F.W.; Elliott, J.A.; Long, R.B. Array measurements of atmospheric pressure fluctuations above surface gravity waves. J. Fluid Mech.
**1981**, 102, 1–59. [Google Scholar] [CrossRef] - Komen, G.J.; Hasselmann, S.; Hasselmann, K. On the existence of a fully developed wind-sea spectrum. J. Phys. Oceanogr.
**1984**, 14, 1271–1285. [Google Scholar] [CrossRef] - WAMDIG. The WAM model—A third generation ocean wave prediction model. J. Phys. Oceanogr.
**1988**, 18, 1775–1810. [Google Scholar] [CrossRef][Green Version] - Rogers, W.E.; Babanin, A.V.; Wang, D.W. Observation consistent input and white capping dissipation in a model for wind generated surface waves: Description and simple calculations. J. Atmos. Ocean. Technol.
**2012**, 29, 1329–1346. [Google Scholar] [CrossRef] - Zieger, S.; Babanin, A.V.; Rogers, W.E.; Young, I.R. Observation based source terms in the third-generation wave model WAVEWATCH. Ocean Mod.
**2015**, 96, 2–25. [Google Scholar] [CrossRef][Green Version] - Hasselmann, S.; Hasselmann, K. Computations and parameterizations of the nonlinear energy transfer in a gravity-wave spectrum, Part I: A new method for efficient computations of the exact nonlinear transfer integral. J. Phys. Oceanogr.
**1985**, 15, 1369–1377. [Google Scholar] [CrossRef][Green Version] - Hasselmann, K.; Barnett, T.P.; Bouws, E.; Carlson, H.; Cartwright, D.E.; Enke, K.; Ewing, J.A.; Gienapp, H.; Hasselmann, D.E.; Kruseman, P.; et al. Measurements of wind-wave growth and swell decay during the Joint North Sea Wave Project (JONSWAP), Erganzungsheft zur Deutschen Hydrographischen Zeitschrift. Reihe A
**1973**, 8, 12. [Google Scholar] - Myslenkov, S.; Arkhipkin, V.; Koltermann, K. Evaluation of swell height in the Barents and White Seas. Mosc. Univ. Bull. Ser. 5 Geogr.
**2015**, 5, 59–66. [Google Scholar] - Saha, S.; Moorthi, S.; Pan, H.L.; Wu, X.; Wang, J.; Nadiga, S.; Goldberg, M. The NCEP climate forecast system reanalysis. Bull. Am. Meteorol. Soc.
**2010**, 91, 1015–1057. [Google Scholar] [CrossRef] - Saha, S.; Moorthi, S.; Wu, X.; Wang, J.; Nadiga, S.; Tripp, P.; Becker, E. The NCEP Climate Forecast System Version 2. J. Clim.
**2014**, 27, 2185–2208. [Google Scholar] [CrossRef] - Myslenkov, S.; Markina, M.; Arkhipkin, V.; Tilinina, N. Frequency of storms in the Barents Sea under modern climate conditions. Vestnik Moskovskogo Universiteta Seriya 5 Geografiya
**2019**, 2, 45–54. [Google Scholar] - Myslenkov, S.A.; Markina, M.Y.; Kiseleva, S.V.; Stoliarova, E.V.; Arkhipkin, V.S.; Umnov, P.M. Estimation of Available Wave Energy in the Barents Sea. Therm. Eng.
**2018**, 65, 411–419. [Google Scholar] [CrossRef] - Myslenkov, S.; Medvedeva, A.; Arkhipkin, V.; Markina, M.; Surkova, G.; Krylov, A.; Dobrolyubov, S.; Zilitinkevich, S.; Koltermann, P. Long-term statistics of storms in the Baltic, Barents and White Seas and their future climate projections. Geogr. Environ. Sustain.
**2018**, 11, 93–112. [Google Scholar] [CrossRef][Green Version] - Sawaragi, T. (Ed.) Coastal Engineering—Waves, Beaches, Wave-Structure Interactions; Elsevier: Amsterdam, The Netherlands, 1995; Volume 78, pp. 1–479. [Google Scholar]
- Lopatoukhin, L.; Rozhkov, V.; Ryabinin, V.; Swail, V.; Boukhanovsky, A.; Degtyarev, A. Estimation of Extreme Wind Wave Heights; JCOMM Technical Report WMO/TD-No. 1041; WMO & IOC: Geneva, Switzerland, 2000. [Google Scholar]
- Atlas of Hydrometeorological and Ice Conditions of the Seas of the Russian Arctic; Neftyanoe khozyaistvo: Moscow, Rrussia, 2015; 128 p.
- Ribal, A.; Young, I.R. 33 years of globally calibrated wave height and wind speed data based on altimeter observations. Sci. Data
**2019**, 6, 77. [Google Scholar] [CrossRef] [PubMed][Green Version] - Cavalieri, D.; Parkinson, C. Arctic sea ice variability and trends, 1979–2010. Cryosphere
**2012**, 6, 881–889. [Google Scholar] [CrossRef][Green Version] - Comiso, J.; Meier, W.; Gersten, R. Variability and trends in the Arctic Sea ice cover: Results from different techniques. J. Geophys. Res. Oceans
**2017**, 122, 6883–6900. [Google Scholar] [CrossRef] - Maslanik, J.; Stroeve, J.; Fowler, C.; Emery, W. Distribution and trends in Arctic sea ice age through spring 2011. Geophys. Res. Lett.
**2011**, 38, L13502. [Google Scholar] [CrossRef] - Cook, N. Towards better estimation of wind speeds. J. Wind Eng. Ind. Aerodyn.
**1982**, 9, 295–323. [Google Scholar] [CrossRef] - Taleb, N.N. Black swans and the domains of statistics. Am. Stat.
**2007**, 198–200. [Google Scholar] [CrossRef] - Sornette, D. Dragon-kings, black swans and the prediction of crises. Int. J. Terrasp. Sci. Eng.
**2009**, 2, 1–18. [Google Scholar] [CrossRef] - Kislov, A.; Platonov, V. Analysis of observed and modelled near-surface wind extremes over the sub-arctic northeast Pacific. Atmos. Clim. Sci.
**2019**, 9, 146–158. [Google Scholar] [CrossRef][Green Version] - Kislov, A.; Matveeva, T. An extreme value analysis of wind speed over the European and Siberian parts of Arctic region. Atmos. Clim. Sci.
**2016**, 6, 205–223. [Google Scholar] [CrossRef][Green Version] - Platonov, V.; Kislov, A. Spatial distribution of extreme wind speeds statistics over the Sakhalin island based on observations and high-resolution modelling data. In Proceedings of the IOP Conference Series, International Young Scientists School and Conference on Computational Information Technologies for Environmental Sciences, Moscow, Russia, 27 May–6 June 2019; p. 386. [Google Scholar] [CrossRef]
- Bühler, O. Large deviation theory and extreme waves. In Proceedings of the Aha Hulikoa Hawaiian Winter Workshop University of Hawaii, Manoa, HI, USA, 23–26 January 2007; pp. 9–18. [Google Scholar]
- Janssen, P.; Abdalla, S.; Hersbsch, H.; Bidlot, J.-R. Error estimation of buoy, satellite, and model wave height data. J. Atm. Ocean. Tech.
**2006**, 24, 1665. [Google Scholar] [CrossRef][Green Version] - Chiranjivi, J.; Saurabh, B.; Sai Krishnaveni, A.; Neethu Chacko, V.M.; Chowdary, D.; Dutta, K.H.; Rao, C.B.; Dutt, S.; Sharma, J.R.; Dadhwal, V.K. Evaluation of SARAL/AltiKa measured significant wave height and wind speed in the Indian ocean region. J. Indian Soc. Rem. Sens.
**2016**, 44, 225–231. [Google Scholar] - Taylor, P.K.; Yelland, M.J. The dependence of sea surface roughness on the height and steepness of the waves. J. Phys. Ocean.
**2001**, 31, 572–590. [Google Scholar] [CrossRef][Green Version] - Yang, X.; Yuan, X.; Ting, M. Dynamical link between the Barents-Kara sea ice and the Arctic Oscillation. J. Clim.
**2016**, 29, 5103–5122. [Google Scholar] [CrossRef]

**Figure 1.**Unstructured computational grid of the WWIII model for the North Atlantic and the Kara Sea.

**Figure 2.**The measured and simulated significant wave height (SWH) for mooring station in the Kara Sea, location of the wave measurement station marked on insert map.

**Figure 3.**Scatter diagram of measured on the mooring station and simulated SWH for two model implementations: (

**a**) ST1, (

**b**) ST6.

**Figure 4.**Scatter diagram of simulated SWH and Sentinel data for two model implementation: (

**a**) ST1, (

**b**) ST6.

**Figure 5.**The long-term mean (

**a**), maximum (

**b**), significant wave heights, maximum wave height of 3% probability of exceedance (

**c**), and maximum wave height of 1% probability of exceedance (

**d**) according to the modeled data in the Kara Sea for the 1979–2017 period.

**Figure 6.**The long-term average probability of the ice presence of with a concentration more than 50% in the Kara Sea according to reanalysis data from 1979 to 2017 (in 0–1 unit). T1 and T2 are a points where the ice probability and wind events analyses provided.

**Figure 7.**The long-term mean period (

**a**), the maximum period (

**b**), the long-term mean wavelength (

**c**), and the maximum wavelength (

**d**) in the Kara Sea according to modeling data for the period from 1979 to 2017.

**Figure 8.**The maximum SWH in the Kara Sea according to the model data (from 1979 to 2017) for the periods: March–April–May (MAM) (

**a**), June–July–August (JJA) (

**b**), September–October–November (SON) (

**c**), December–January–February (DJF) (

**d**).

**Figure 9.**The probability of the presence of ice with a concentration of more than 50% in the Kara Sea according to reanalysis data (in 0–1 unit) for the periods: MAM (

**a**), JJA (

**b**), SON (

**c**), DJF (

**d**).

**Figure 10.**The number of storms with different SWH thresholds per year and its linear trends for 1979 to 2017.

**Figure 11.**The probability of the ice presence with a concentration of more than 50% for two points in the Kara Sea by years.

**Figure 12.**Recurrence of wind speed of more than 10 m/s and 2 consecutive days at T1 point, the number of storms with a SWH threshold 4 m, and probability of the ice presence at T1 point (opposite scale).

**Figure 13.**The SWH maximum and segmentation of the Kara Sea: Six sectors with different wave conditions.

**Figure 14.**The empirical probability distribution of storms with different wave heights for each of the six sectors, presented in the Ppareto logarithmic coordinates. The determination coefficient of determination and regression equations are given for each sector.

Sat/Parameter | R | Bias, m | RMSE, m | SI | N | Years |
---|---|---|---|---|---|---|

ST1 | ||||||

Cryosat | 0.89 | −0.07 | 0.39 | 0.3 | ~83,000 | 2010–2017 |

Saral | 0.92 | 0.05 | 0.32 | 0.24 | ~74,000 | 2013–2017 |

Sentinel | 0.91 | 0.07 | 0.37 | 0.27 | ~34,000 | 2016–2017 |

ST6 | ||||||

Cryosat | 0.89 | −0.03 | 0.38 | 0.28 | ~83,000 | 2010–2017 |

Saral | 0.93 | 0.11 | 0.33 | 0.24 | ~74,000 | 2013–2017 |

Sentinel | 0.92 | 0.14 | 0.37 | 0.26 | ~34,000 | 2016–2017 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Myslenkov, S.; Platonov, V.; Kislov, A.; Silvestrova, K.; Medvedev, I.
Thirty-Nine-Year Wave Hindcast, Storm Activity, and Probability Analysis of Storm Waves in the Kara Sea, Russia. *Water* **2021**, *13*, 648.
https://doi.org/10.3390/w13050648

**AMA Style**

Myslenkov S, Platonov V, Kislov A, Silvestrova K, Medvedev I.
Thirty-Nine-Year Wave Hindcast, Storm Activity, and Probability Analysis of Storm Waves in the Kara Sea, Russia. *Water*. 2021; 13(5):648.
https://doi.org/10.3390/w13050648

**Chicago/Turabian Style**

Myslenkov, Stanislav, Vladimir Platonov, Alexander Kislov, Ksenia Silvestrova, and Igor Medvedev.
2021. "Thirty-Nine-Year Wave Hindcast, Storm Activity, and Probability Analysis of Storm Waves in the Kara Sea, Russia" *Water* 13, no. 5: 648.
https://doi.org/10.3390/w13050648