Annual Report 2017

Nansen International Environmental and Remote Sensing Centre St. Petersburg,

Non-profit international centre for environmental and climate research Founded in 1992 Founders of the Nansen Centre REPORT FROM THE GENERAL Bergen University Research Foundation (UNIFOB) Bergen, Norway MEETING OF FOUNDERS Max-Planck Society (MPS) Munich, Germany Nansen Environmental and Remote Sensing Centre (NERSC) Vision Bergen, Norway Northern Water Problems Institute of Russian Academy of Sciences (NWPI RAS) The Scientific Foundation “Nansen International Environ- Petrozavodsk, Republic of Karelia, Russia mental and Remote Sensing Centre” (Nansen Centre, Saint-Petersburg State University (SPbSU) Saint-Petersburg, Russia NIERSC) vision is to understand, monitor and predict Scientific Research Centre for Ecological Safety of RAS (SRCES RAS) climate and environmental changes in the high northern Saint-Petersburg, Russia latitudes for serving the Society. With the initial support from The Joint Research Centre of the European Commission (JRC EC) Major Research Areas Associate Partners of the Nansen Centre DLR Maritime Security Lab (DLR MSL) Climate Variability and Change in High Northern Latitudes Bremen, Germany Finnish Meteorological Institute (FMI) Aquatic Ecosystems in Response to Global Change Helsinki, Finland Global Climate Forum (GCF) Applied Meteorological and Oceanographic Research for Berlin, Germany Industrial Activities Helsinki University (HU) Helsinki, Finland Socioeconomic Impact of Climate Change Nansen Scientific Society (NSS) Bergen, Norway Organization Stockholm University (SU) Stockholm, Sweden NIERSC is an independent non-profit international Director research foundation established by Russian, Norwegian and Dr. Leonid P. Bobylev German research organizations. NIERSC conducts basic President and applied environmental and climate research funded by Prof. Nikolay N. Filatov the national and international governmental agencies, Corresponding Member of Russian Academy of Sciences (RAS); Northern Water research councils, space agencies and industry. Problems Institute of RAS (NWPI RAS) Petrozavodsk, Karelia, Russia Additionally, NIERSC receives basic funding from its Co-President Founder – Nansen Environmental and Remote Sensing Prof. Valentin P. Meleshko Centre. Main Scientist, Voeikov Main Geophysical Observatory (VMGO) St. Petersburg, Russia NIERSC has been established in 1992 and re-registered in the St. Petersburg Administration Registration Chamber Vice-Presidents into a non-profit scientific foundation in 2001. The Centre Prof. Hartmut Grassl Max Planck Institute for Meteorology and University of Hamburg (MPIM and UH) got accreditation at the Ministry of Industry, Science and Hamburg, Germany Technology of the Russian Federation as a scientific Prof. Stein Sandven institution in 2002 and has been re-registered in 2006 Project Leader, Nansen Environmental and Remote Sensing Centre (NERSC) Bergen, Norway according to a new legislation on Non-Commercial Organizations of the Russian Federation. Board of Guardians Chairman NIERSC got a license for conducting meteorological and Prof. Ola M. Johannessen oceanographic observations from Roshydromet in 2006. In President of Nansen Scientific Society (NSS); Founding President of NIERSC; 2008 NIERSC received also a license from Roscosmos for Founding Director of NERSC; Professor Emeritus at the Geophysical Institute, University of Bergen conducting the space-related research activities. Bergen, Norway Members Prof. Sergey V. Aplonov Staff Vice-Rector, St. Petersburg State University (SPbSU), St. Petersburg, Russia Prof. Arthur N. Chilingarov At the end of 2017 NIERSC staff incorporated 29 employees Corresponding Member, RAS; Special presidential representative for international comprising core scientists, including one full Doctor of cooperation in the and Antarctic, Moscow, Russia Science and eight PhDs, part-time researchers, and Prof. Ivan Ye. Frolov Scientific Supervisor, Arctic and Antarctic Research Institute (AARI), St. Petersburg, administrative personnel. Six Nansen Fellowship PhD- Russia students were supervised and supported financially, all Prof. Matti Leppäranta holding also part-time positions of Junior Scientists at University of Helsinki (UH), Helsinki, Finland Prof. Valery L. Mikheev NIERSC. Rector, Russian State Hydrometeorological University (RSHU), St. Petersburg, Russia Prof. Igor Mokhov Academician, RAS; Director, Institute of Atmospheric Physics of RAS (IAP RAS) Production Moscow, Russia In 2017, totally 49 publications have been published, one Prof. Timo Vihma Head of Polar Meteorology and Climatology Group, Finnish Meteorological Institute (FMI), Helsinki, Finland Cover page: Zoning map of coccolithophores activity in the Barents over 1998-2016.

2 book, 13 papers in peer reviewed journals, 6 papers in other Another participant of the Nansen Fellowship Programme, journals and 29 conference proceedings (see the list at the end Valeria Selyuzhenok, has also successfully defended her of the report). Thesis “ and landfast ice: Mechanism of formation and variability of extent” on 13 National and International Cooperation October 2017 at the Jacobs University Bremen, Bremen, Germany. NIERSC has a long-lasting cooperation with Russian organisations such as St. Petersburg State University, Totally, 28 young Russian PhD-students have got their institutions of the Russian Academy of Sciences, Federal doctoral degrees with support of NFP since 1994. Space Agency, Federal Service for Hydrometeorology and Environmental Monitoring including the Northern Water Research Projects Problems Institute, Scientific Research Centre for Ecological Below is the list of the research projects implemented at Safety, Arctic and Antarctic Research Institute, Russian State NIERSC in 2017 in close cooperation with other national Hydrometeorological University, Voeikov Main Geophysi- and foreign scientific institutions: cal Observatory, Murmansk Marine Biological Institute, Research Centre of Operational Monitoring and other, Ships and Waves Reaching Polar (SWARP, EU-FP7, 2014-2017) totally about 40 institutions. Sea Ice CCI (ESA Climate Change Initiative), Phase 2 (ESA/subcontract to NERSC, 2015-2017) Fruitful relations are established also with number of foreign Great Lakes (Michigan Technical University, US, 2016-2017) and international organizations, universities and institutions including European Space Agency, Global Climate Forum, Meridional heat and moisture transport into Arctic and its role in the Arctic amplification (Russian Fund for Basic Research (RFBR), 2015-2017) Max-Planck Institute for Meteorology (Germany), Friedrich- Development of sea ice monitoring and forecasting system to support safe Schiller-University (Germany), Finnish Meteorological operations and navigation in Arctic (SONARC, Russian-Norwegian Institute (Finland), University of Helsinki (Finland), Project: RFBR-NORRUS, 2015-2017) University of Sheffield (UK), Stockholm University Terrestrial ecosystems and soil carbon accumulation (RFBR, 2015- 2017) (Sweden), Johanneum Research (Austria), and especially Consumer choice and herding behaviour in microeconomics (RFBR, 2015- with the NIERSC founders. Close cooperation is established 2017) with the Nansen Centre in Bergen. Most of scientific results MON-SWARP (Subsidy from Ministry of Education and Science of RF described below are achieved within the joint research No.14.618.21.0005 for the project “Ships and Waves Reaching Polar activities of both Nansen Centres, in St. Petersburg and Regions”, 2015-2017) Bergen, and cooperating partners. Role of soil temperature and moisture in linking the autumn Arctic sea ice and the summer climate over (Russian-Chinese Project, RFBR, 2016-2017) Nansen Fellowship Programme (Contract to FRECOM, 2017-2018) The main objective of the Nansen Fellowship Program (Contract to FRECOM, 2017-2018) (NFP) at NIERSC is to support PhD-students at Russian Ladoga-Onega Lakes (s/c to Northern Water Problems Institute, Petroza- educational and research institutions, including Russian vodsk, Russia, 2017) State Hydrometeorological University, St. Petersburg State Dynamics of deep oceanic convection in subpolar and polar oceanic regions University, Arctic and Antarctic Research Institute, and under the climate change, its relation on freshwater and heat contents and others. The research areas are climate and environmental effect on the Atlantic Meridional Overturning Circulation (Russian Science Foundation (RSF), 2017-2019) change and satellite remote sensing, including integrated use of satellite Earth observation techniques in combination with Assessment of calcifying phytoplankton role in CO2 dynamics in the atmosphere- system at subpolar and polar latitudes (RSF, 2017-2019) supporting in situ observations and numerical modelling for Complex assessment of polar low impact on maritime activities in the Arctic studies of the Earth System. NFP provides PhD-students Ocean under the ongoing climate changes (RSF, 2017-2019) with Russian and international scientific supervision, SAR-based sea ice monitoring in the Arctic (Contract to IANS, 2017-2018) financial fellowship, efficient working conditions at NIERSC, training and research visits to foreign research institutions within the Nansen Group and beyond, involvement into international research projects. NFP is St. Petersburg, 17 April 2018 sponsored by the Nansen Scientific Society and Nansen Centre in Bergen, Norway. Postgraduate student activity is Nikolay N. Filatov, NWPI RAS, President supervised by at least one Russian and one international Valentin Meleshko, VMGO, Co-President senior scientist. All NFP PhD-students obliged to publish their scientific results in the international refereed journals Hartmut Grassl, Max-Plank Society, Vice President and make presentations at the international scientific Stein Sandven, NERSC, Vice President symposia and conferences. Sergey V. Aplonov, SPbSU Natalia Gnatiuk, the participant of the Nansen Fellowship Andrey A. Tronin, SRCES RAS Programme, has successfully defended her Thesis Leonid P. Bobylev, Director “Projections of air temperature and precipitation in Ukraine in the 21st century” on 14 April 2017 at the Ukrainian Hydrometeorological Institute, Kiev, Ukraine.

3 25TH ANNIVERSARY OF THE NANSEN CENTRE On 19 October 2017, 25 years have passed since the Nansen International Environmental and Remote Sensing Centre has been established in St. Petersburg. To celebrate this remarkable event, a two-day jubilee scientific symposium has been held on 12-13 October 2017 at Petrovsky Hall of the St. Petersburg State University (first day) and the Nansen Centre premises (second day). About 100 guests participated in the Nansen Centre’s Jubilee Symposium. Among them invited speakers, representatives of the Norwegian Embassy in Moscow and General Consulate in St. Petersburg, NIERSC Founders and Associate Partners, Russian and foreign research institutes and centres including “sister” Nansen Centres in China and India, NIERSC Staff.

Official opening of the Nansen Centre’s 25- year Anniversary Symposium at the Petrovsky Hall of the St. Petersburg State University, 12 October 2017. Top: Symposium Presidium (from the right to the left: Prof. Sergey Aplonov, Vice-Rector, St. Petersburg State University; Prof. Nikolay Filatov, President, Nansen Centre; Mr. Dag Malmer Halvorsen, Consul General of Norway in St. Petersburg; Dr. Leonid Bobylev, Director, Nansen Centre). Right: Symposium participants at the Petrovsky Hall, St. Petersburg State University

The President of the Nansen Centre, Corresponding Member of the Russian Academy of Sciences, Prof. Nikolay Filatov opened the symposium. Welcome speeches have been delivered by Mr. Dag Malmer Halvorsen, Consul General of Norway in St. Petersburg, and Prof. Sergey Aplonov, Vice-Rector of St. Petersburg State University. Symposium programme (see Annex) included invited talks of leading experts in the Earth and Arctic climate systems, global climate change, meteorology, oceanography and satellite observations, and presentations by the NIERSC staff members illustrating past and current research activities at the Nansen Centre. The second day of symposium at the Nansen Centre premises. Acknowledgement: NIERSC is grateful to the Nansen Scientific Society, Bergen, Norway, for financial support of holding the symposium.

4 year-long regional simulations of MIT ocean T., Kuibin, P., Volkov, D., & Foux, V. Patterns of SCIENTIFIC general circulation model. The high- vertical velocity of the Lofoten vortex (the Norwegian resolution version of this model, used in this Sea), Ocean Dynamics, submitted. REPORT study, also provides a realistic horizontal and Pattern distribution of surface waves vertical structure of LV and adequately generated by polar lows in the Nordic Climate Variability and reproduces the main mechanisms responsible for the permanence of the vortex seas Change in High Northern in the basin. Julia Smirnova, Pavel Golubkin Latitudes Evident in in situ and MIT results, vertical The ongoing research is dedicated to profiles of temperature and salinity through assessment of threats that polar lows pose to Pattern of mean vertical velocity of the LV core show positive temperature-salinity maritime activities in the Arctic. The polar Lofoten vortex () anomalies in LV core at mid-depth and lows are extreme weather phenomena that negative anomalies over LV core. The colder characterized by storm winds and form at Igor Bashmachnikov, Tatyana Belonenko, Pavel sea-surface temperature is often observed Kuibin, Denis Volkov, Victor Foux high latitudes in both hemispheres over ice- over subsurface anticyclonic eddies, opposite free sea surface. In the first stage surface In this work, we focus on a relatively unex- to what is observed in the sea-surface waves generated by polar lows were assessed plored spatial pattern of the vertical velocity intensified anticyclones. in the Greenland, Norwegian, and Barents Fig. 1 shows time-mean r-z seas for 1995-2009. distributions of dy- namic characteristics of LV averaged over the period of MIT simula- tions. The distribution of the vertical velocity has a complex structure indicating a divergence in the upper and lower parts of the vortex. The rise of the water in the central part of the LV above 1300 m (the vortex core and above) is compensated by the Figure 1. Radial distribution of time-mean characteristics in water descend at 12-30 LV according to MIT results averaged over 2 years of the model km from the vortex axes. run: (a) vertical velocity (positive - upward); (b) divergence -1 This explains the negative with radial velocity (positive – directed outwards, s ); (c) radial upper ocean temperature velocity (positive - outwards); (d) horizontal shear stresses. anomalies over LV dis- Radial distributions of azimuthal velocity (black solid lines) cussed above. Water and radial velocity (gray dashed lines) for different vertical descend integrated over levels (out of scale) are offset vertically, referenced to the the vortex volume, is corresponding horizontal zero-velocity lines at the selected about 20% stronger than depth levels (dashed). Red and magenta vertical dashed lines the integrated water uplift mark distances from centre of vortex where depth-mean vertical in the vortex centre. It velocity crosses 0 (12 km) and depth-mean relative vorticity forms the source of mass anomaly of LV core crosses 0 (30 km), respectively. for the slower water Figure 2. Spatial distribution of polar low generated descend below the LV core, near the waves exceeding given height. Shades correspond to in a subsurface vortex using, as an example, ocean bottom (Fig. 1a). frequency per year in 100×100 km cells. Light blue the quasi-permanent anticyclonic Lofoten area represents median sea ice extent for study period. vortex (LV) in the Lofoten depression of the The drivers for the observed water Norwegian Sea. Formed by relatively weak divergence are suggested to be the horizontal non-geostrophic velocities in ocean vortices turbulent diffusion of momentum in the For this purpose, a previously developed and masked by more intense processes of upper and mid-ocean and the Ekman polar low climatology and a simplified vortex dynamic instability, mean structure of pumping in the bottom boundary layer. model of wind wave generation by a moving the circulation in r-z plain in ocean vortices Maximum radial divergence is observed in cyclone were used. Wave heights for every is typically difficult to derive using compara- the upper part of the vortex core (Fig. 1b). It cyclone were calculated using mean cyclone tively short observational time-series. The is formed by the radial velocities (Fig. 1c), directions, connecting first and last point on vertical velocity patterns were obtained for a which reach their maximum at 20-25 km trajectory of every polar low. Accounting for specific case of the seamounts-trapped quasi- from the vortex axis, at the inner boundary the width of polar low mean wind speed stationary vortices, but these are practically of the of maximum shear stress (25-20 profile, it was assumed that the wave unknown for other types of the open ocean km from the vortex axis), which is also the generation occurs in the right sector of a vortices. region of the maximum azimuthal velocities cyclone in a band from 0.1 D to 0.35 D, (Fig. 1d). It is expected that the processes of where D is polar low diameter. After the end The anticyclonic LV presents a rare case of the turbulent diffusion of momentum across of polar low lifetime, waves continued to an anticyclonic eddy permanently observed the vortex boundary is the most intensive in travel in the direction of polar low movement over the practically flat central areas of the this region, which drive the observed as swell until they reached coast line, ice or Lofoten Basin of 3000 m deep. Existing in a divergence. The intensity of the Ekman escaped from the analysed region. relatively calm oceanic background divergence in the bottom boundary layer in Fig. 2 shows the spatial distribution of waves conditions and not rigidly attached to a the presence of the observed anticyclonic exceeding a given height for the Greenland, distinct bottom topography feature, this rotation below the LV core is estimated to be Norwegian, and Barents seas as frequency vortex presents a "natural laboratory" for sufficient to maintain the resisted per year in 100×100 km cells. As follows study of the dynamics of freely propagating downwards motions below 1500 m depth from the figure, spatial distribution of the mesoscale ocean vortices. (Fig. 1a). medium height waves (5-6 m) approximately To study LV, we took advantage of the 12- Relevant publication: Bashmachnikov, I., Belonenko,

5 corresponds to the spatial distribution of during the studied period. A wavelet analysis These results were further used to quantify genesis areas of polar lows themselves. They was performed in the order to reveal periodic the spatial variations of ΔpCO2 in the North, are often observed in the whole analysed variation of sea ice volume in the regions Norwegian, Greenland, Barents, and Bering region; frequency is approximately from one (Fig. 3). The analysis revealed variation of seas over 18 years. The analysis was to almost two cases per year in most cells. sea ice volume with periods of 3 and 6-8 performed conjointly with the above time Apart from the Kola Peninsula, these waves years in the and 8-10 years in series of PIC and bloom area S. may be equally often observed in all coastal the Irminger and Labrador seas. The 6-8- In the third step, spaceborne data on areas. Higher waves are most frequent in the year cycle in the Greenland Sea lags between atmospheric columnar CO2 partial pressure northeast from the North Cape, a quarter and a half of the period of cor- were employed to assess the effect produced and in the western part of the Norwegian responding variations in the Irminger and by E. blooming on this parameter. This was Sea. Frequency of waves larger than 8 m in Labrador seas. These variations are in the done over the , which was taken as these regions is approximately one case agreement with the Arctic Oscillation Index. an example as this sea is known for its basin- every two years. Emergence of waves larger However, this linkage requires further wide blooms of this microalga. than 8-10 m is also possible in coastal investigation. regions, although more rarely – about one Analysis of obtained results have shown that case in five years. Aquatic Ecosystems in E. blooms in the North Atlantic and Arctic seas generally occurred annually across Changes in sea ice volume in the Response to Global 1998-2015, although the extent of blooms Greenlanad, Irminger and Labrador Change varied appreciably from year to year. seas between 1978 and 2016 Through the above years, maximum bloom E. huxleyi blooms influence on carbon area S in the target seas in some specific years Valeria Selyuzhenok, Igor Bashmachnikov, cycle was also very significant. Thus, for all target Aleksandra Mushta, Leonid Bobylev Dmitry Kondrik, Dmitry Pozdnyakov, Eduard seas, the bloom area ratio to the total sea area Deep convection in the Greenland, Irminger Kazakov, Lasse Pettersson varied from 4.8% (Greenland Sea) to 23.7% and Labrador seas is an inherent part of the (Barents Sea). The main aim of this study was to evaluate Atlantic Meridional Overturning Circulation Although no tendency in interannual the influence of vast E. huxleyi blooms on the (AMOC), which modulates its intensity. variations of the bloom area and blooming carbon cycle in the atmosphere-ocean Interannual variations in the intensity of duration was found, the pattern of temporal system. On a basis of our previous results deep convection, among other factors, are variations in S exhibits some salient features: from bloom delineation process in North, thought to be controlled by variations in the (a) were years of particularly vast blooms Norwegian, Greenland, Barents and Bering intensity of freshwater fluxes to these regions that interlaced with periods of smaller seas three significant steps blooms; (b) years of occurrence of spikes in S were performed. The first were different for the target seas; (c) the step was to evaluate the total intermissions between the spikes for the amount of Particulate North, Norwegian, Greenland, and Barents Inorganic Carbon (PIC) seas showed no periodicity so that no produced by each bloom apparent regularity is observed. during every year, the second step was to estimate In contrast to the target North Atlantic and CO2 partial pressure in- Arctic seas, the bloom activity in the Bering crease (ΔpCO2) due to bloom Sea exhibited a very specific sequence: there presence. Finally, the third was a period between 1998 and 2001 of very step consisted in assessing high blooming activity that was followed by the increase in CO2 partial a drastic drop in both the number of pressure in the atmospheric occurrences and the extent of E. blooms. The column over the blooming only exception was found for 2014 when a area. moderate spike in S took place. This specific feature inherent in the also shows In the first step, initially that in the years of anomalous high blooming identification and delinea- activity (1997/1998–2001) its annual tion of target blooms were duration reached 10 months, whereas in the performed using a triple North Atlantic seas it did not exceed ~1.5 Figure 3. Results of wavelet analysis of winter sea ice volume approach: the radiometric months. time series (trends excluded). properties of bloom areas and the spectral composition of A similar pattern was observed for the PIC and the overall sea-ice extent in winter. remote sensing reflection, Rrs, were maximum production within E. huxleyi Here we present the analysis of interannual conjointly used and paired with the data on blooms: 0.4 kt-0.14 Mt in the Greenland Sea and annual sea ice volume variations in the the location of the isoline of some threshold and ~0.35Mt in the Barents Sea. As for the . E. bloom areas in the Bering Sea during Greenland, Labrador and Irminger seas coccolith concentration, Ccc. Ccc values were between 1978 and 2016. The time series of retrieved also from Rrs with a multispectral 1997-2001, they are rather similar with those sea ice volume were taken from Pan-Arctic optimization algorithm based on the of the Barents Sea. However, the PIC Ice Ocean Modelling and Assimilation Levenberg-Marquardt multivariate opti- production in the Bering Sea appeared to be System (PIOMAS). Data present monthly mization technique. Thus, the use of the higher than in the Barents Sea by a factor of mean sea ice volume on a model grid, which above three advanced approaches assured a two, with maximum values reaching 0.4 Mt is re-projected to 25EASE-2 grid. We truly confident bloom area delineation. From and, in one case (in 2001), even ~0.7 Mt. extracted monthly sea ice volume data for Ccc data, PIC assessments were performed. pCO2 increments determined in surface two regions: Greenland Sea and Irminger Together with the bloom area data, the PIC waters within E. blooms in the target seas and Labrador seas. Analysis showed a results were jointly analysed to reveal the and normalized to respective background statistically significant trend towards reduc- dynamics of the E. phenomenon for the pCO2 values (pCO2)b are found to be sea- and tion of integral winter (January-April) sea ice whole period of study, i.e. 18 years. year-variable (Fig. 4): the mean maximum 3 volume with the speed of 14.3 km per year In the second step, an innovative algorithm and maximum values of normalization result in the Greenland Sea. The Irminger and was developed to calculate the so-called within the E. blooming area and over 18 Labrador seas winter sea ice volume appears background pCO2 values at 10°C within the years of observations varied in percent in the to have no statistically significant changes target area prior to the onset of E. blooming. target seas between 21.0-43.3, and 31.6-62.5,

6 respectively, with the highest and lowest about 1.5 times larger than in the Northern International Journal of Remote Sensing, 38(22), 6179- values belonging to the Greenland and Hemisphere. Thus, globally the effect of 6205. Barents seas. The time series of weakening of marine carbon sinks due to Kondrik, D., Pozdnyakov, D., & Johannessen, O.M. ΔpCO2/(pCO2)b maximum values through coccolithophore blooms might be apprecia- (2018). Satellite evidence that E. huxleyi phytoplankton 1998-2015 do not exhibit any discernible ble, and remote sensing offers the ability to blooms weaken marine carbon sinks. Geophysical trends but show a rather irregular pattern assess the E. blooming consequences for Research Letters, 45(2), 846-854. with intermittent periods of enhanced and worldwide marine envi- ronments. An explicit evidence of the above considerations was obtained over the Black Sea. The registered E. blooms in the Black Sea in 2015 and 2016 (Fig. 5a) occurred during the period extending roughly be- tween late April and late June with the maximum bloom area developing in mid-May. Our quantita- tive assessments of the E. bloom extent in the target sea indicate that we deal with a huge phenomenon. Indeed, as Fig. 5a illus- trates, both in 2015 and 2016 the bloom areas at the stage of maximum de- velopment reached 180- 200 thousand square kilometres, e.g. nearly half of the total surface of the Black Sea. For both periods of blooming, the concurrent OCO-2 data are reasonably ample. Varia- Figure 4 Spatial and temporal variations in Figure 5 Phenomenon of E. huxleyi blooms in the Black Sea in tions in XCO2 over the [ΔpCO2/(pCO2)b]·100% maximum values 2015 and 2016 as observed from space: (a) bloom area (green bars), within E. huxleyi bloom areas in the North, Black Sea (Fig. 5b) exhibit and number of XCO2 observations (black bars); (b) ΔpCO2 a characteristic seasonal Norwegian, Greenland, Barents, and Bering maximal values in water within bloom area (green bars); (c) pattern with high and low seas during 1998-2015. Note: data are absent intraannual variations of XCO ; (d) polynomial approximations values, respectively, in 2 for some years and target seas as the respective of XCO values illustrated in panel (c) (green curve), and XCO winter and summer, which 2 2 ΔpCO2 values proved to be lower the assessed. values registered beyond the period of blooming (red curve); (e) is in accordance with the XCO increments over bloom areas. Pale green columns in (a-e) relatively subdued peaks. global data. 2 reflect period of blooming. Other designations: boxes and thin These quantitative assessments have explic- vertical black bars show range of XCO values constituting, itly shown that vast E. bloom areas can Fig. 5c represents the 2 respectively, 50% and 49.3% of entire number of XCO retrievals; indeed play significant role in the exchange results of application of the 2 procedure of polynomial open black circles show the rest 0.7% of XCO2 values that are of carbon between the atmosphere and the approximation to all XCO2 beyond above ranges; red and green filled circles show medians for ocean. Indeed, the actual influence of E. values illustrated in Fig. 5b two cases illustrated in panel (d) (colour relates to colour of curves blooms on CO2 partial pressure in sea surface (green curve), and only in panel (d) . water should also be considered in terms of ) XCO2 values registered the phenomenon duration. The bloom beyond the period of blooming (red curve). Bio-optical retrieval algorithm for the duration (t) of this alga in the target seas in These two data sets reveal enhanced values the North Atlantic and Arctic is within 30-50 optically shallow waters of Lake of the column-averaged volume mixing ratio days, whereas in the Bering Sea during the Michigan of carbon dioxide XCO2 over the bloom years of 1998-2001 it was up to 270-300 days, areas. Calculated as the difference between Anton Korosov, Artem Moiseev, Dmitry that is, this impact is not short living but mid-box and respective red curve XCO2 Pozdnyakov contrarily rather long lasting. Besides, the values, the magnitude of XCO2 increment importance of the effect could be also We developed an original approach to the for both years proved to be appreciable: the optical-optical multi-sensor image fusion. assessed via the cumulative bloom area (ΣS) maximum increments of XCO2 are close to 2 Here we used multispectral images from throughout the vegetation period across all ppm (Fig. 5d), i.e. about 2 µatm, which Sentinel-2 and RGB images from MODIS. target seas. Thus, for the studied seas ΣS constitutes ~0.5% of the contemporary mean The images from both sources related to one proved to be within the range of about 220- pCO2 in the atmosphere. Thus, the and the same area and time of data acqui- 565 hundred thousand km2, that is, increcement established in this study should sition. It was purported to provide a visually constituting a very significant part of the generally be considered as reliable. easily perceivable result of fusion. A statis- water surface considered in our study. Given Acknowledgment: this study was conducted under the tical assessment of correspondence between that the E. blooms in the Northern remote sensing reflectance (Rrs) values Hemisphere regularly occur in a larger Russian Science Foundation financial support (project No. 1717-01117). inferred from the MODIS and fused data at number of seas, the overall value of ΣS is in three wavelengths used to generate the RGB the reality far higher. This phenomenon Relevant publications: Kondrik, D., Pozdnyakov, D., & images was intended to illustrate the fusion Pettersson, L. (2017). Particulate inorganic carbon proves to be omnipresent across the global procedure adequacy. . Moreover, the collective area of E. in production within E. huxleyi blooms in subpolar and the is reported by polar seas: a satellite time series study (1998-2013). The developed approach is based on

7 application of the neural network emulation MODIS-Aqua were used: they are available data fusion: application to optically shallow waters of Lake Michigan. Limnologia, doi: 10.17076/lim692. technique coined here as artificial neural at a much higher spectral resolution (6 bands network (ANN). In application to image in the visible and 1 band in the near IR), but Spatial data assimilation with a at a lower spatial resolution (with 1 km in the fusion, the ANN-based method employs a service-based GIS infrastructure for nonlinear response function that iterates visible). Analysis of MODIS-Aqua data to be many times in a special network structure in conjugated/fused with Sentinel-2a data has mapping and analysis of E. huxleyi the order to learn the complex functional shown that the NASA embedded atmos- blooms in the Arctic seas pheric correction was unsatisfactory, and to relationship between input and output Eduard Kazakov, Dmitry Kondrik, Dmitry training data. overcome this problem, we applied the MUMM code. Pozdnyakov As the above approach was applied to In the developed algorithm, the ANN A coccolithophore E. huxleyi is one of the optically shallow waters of Lake Michigan, most significant sources of inorganic carbon it was complemented by fuzzy k-mean consists of four layers of neurons. The RGB images developed from fused Sentinel-2a in the world oceans. Forming vast bloom classification algorithm for bottom type areas this species can affect the carbon identification. The important advantage of and MODIS-Aqua data exhibit a logical sequence of phases of phytoplankton balance in the atmosphere-ocean system, such algorithms is that they can be employed and thus interfere with climate and marine under conditions of a near-complete absence development in Lake Michigan. Indeed, the green areas (corresponding to enhanced ecology. A 19-year time series (1998-2016) of of information on the type of data distribu- spaceborne data on this phenomenon from 6 tion. concentrations of phytoplankton chloro- phyll) stand out twice in the year, viz., in seas located at high latitudes as well as data The Multispectral Imager (MSI) on Sentinel- spring and early autumn, which is in on the phenomenon-affecting oceanographic 2a provides data at high spatial resolution complete conformance with vernal and and atmospheric variables were collected. (10-60 m) in several spectral channels in the autumnal phytoplankton outbreaks in Lake These data were collected from the following visible. However, the number of spectral Michigan. To retrieve the concentration of spaceborne sources: Ocean Colour Climate phytoplankton chlorophyll from the two Change Initiative (OC) portal, AVHRR, sorts of spaceborne data, the BOLEALI- ASCAT QuikSCAT, OCO-2, and SSM/I. OSW algorithm was utilized. Fig. 6 Apart from the above listed spaceborne data, illustrates the spatial distributions of our study required the data on water salinity, phytoplankton chlorophyll concentrations availability of nutrients (such as nitrates, for the five dates as obtained from the fused phosphates, and silicates) and mixed layer MODIS-Aqua and Sentinel-2a data. The depth. These data were available from The paired plates in Fig. 6 explicitly show the GLobal Ocean Data Analysis Project advantage of the fusion procedure over the (GLODAP) database, The SOCAT database results from solely MODIS-Aqua. (The Surface Ocean CO2 Atlas), World Ocean Atlas 2013 (WOA13, NOAA). The Mapping of bottom type was performed for variety of data complicates their joint an area called Pyramid Point within the processing and collocated interpretation. Sandy Bear Dunes site. The k-means One of the most important steps in such a technique was applied to bottom substrate study is the space-time assimilation of all classification. Spectra of Rrs values from the data, bringing it to a single standardized fused data (at 60 m resolution) were form. The 8-day composites available from partitioned into three classes: sand, Chara the OC portal were used as basic data, stands, and some sandy substrate either accordingly all other complementary data sparsely covered by macrophytes Chara or were brought to the same time resolution. As Cladophora or slightly silted. The area was a spatial domain, a 2500×2500 grid was confined to depths not exceeding 15 m. chosen at a spatial resolution of 4 km within The clusterization thus performed permitted the limits between 5000000 and 5000000 on to produce a map of bottom type both coordinate axes in the Lambert heterogeneity at a 60-m spatial resolution. azimuthal equal area projection called The above-described bottom type classifica- NSIDC EASE-Grid North (EPSG: 3973). tion reveals that the area adjacent to the coast All data sets were recalculated into this is sandy. Further off-coast, the area with a domain with special software modules. To depth of 10-15 m is covered by Chara stands. efficiently concatenate and eventually The intermediate area belongs to the analyse such versatile data of huge size, a intermediate class, although it contains special GIS infrastructure (GISI) is Figure 6. Spatial distributions of sandy spots as well as spots covered by developed. It is built on the principles of a phytoplankton chlorophyll from (a) MODIS- submerged vegetation stands. These features service-based architecture with micro- Aqua data; and (b) fused MODIS-Aqua and explicitly indicate that the spatial services. The GISI includes both a server Sentinel-2a data for 09.05, 05.07, 26.07, heterogeneity of the area ascribed to class 2 application that controls information flows 15.08, 04.09 2017 (presented in the top to is not due to depth changes but is driven by and automated data processing. bottom sequence). changes in the bottom albedo. Given both the variety of complex and Thus, summing up, developed and realized heterogeneous data to be processed and channels in the visible (only four) and their in a computer code our own method of analysed in the study, and the need to placement are, respectively, rather limited ocean colour data fusion, and the possibility continuously update the baselines of data and not optimally suited. These deficiencies of employing the fused data for retrieving the and perform new calculations of the preclude the use of this sensor for efficient bottom type investigated. The attained thematic products, there arise the problem of retrieval of water quality parameters in results strongly suggest that the developed developing an integrated software situations of optically complex or shallow algorithm can be successfully used for fusion infrastructure that complies with the waters. Thus, in our studies MSI acted in the of data from Sentinel-2 and Sentinel-3 following tasks: (i) automation of new data capacity of a sensor with high spatial because Sentinel-3 is highly akin to MODIS- uploading; (ii) assimilation of data into a resolution (60 m) but low spectral resolution Aqua in terms of the spectral and spatial single space-time domain; (iii) unified (only 4 bands in the visible), i.e. its data were resolution. storage of data; (iv) automation of to be fused with a sensor providing higher Relevant publication: Korosov, A., Moiseev, A., & calculations of the basic thematic products; spectral resolution although at a rather Pozdnyakov, D. (2018). Modis-Aqua and Sentinel-2 (v) providing user interfaces for the retrieval coarse spatial resolution. As such, data from

8 of and access to the desired data; (vi) the open-source desktop geoinformation climatic and environmental blocks. providing mechanisms for making platform QGIS; and (3) a library for the Complex tasks, including both the provision calculations outside the infrastructure; (vii) Python, which provides access to the server of scientific analysis and the supply of providing opportunities for standardized kernel functions from the user programming operational data, determine the need to visualization of data in GIS, preparation of code. Such a three-sided approach to user create a single information space that would reports and maps. interfaces realization performs well and combine data flows, their processing and seems to be a full-fledged decision to provide presentation. Such system addresses the a variety of tasks. following main objectives: (i) provision of Some basic results continuous automatic collecting and of using such an archiving satellite data; (ii) storage and infrastructure, can presentation of these data in standard GIS- be illustrated with formats, provision of access to them by the zonation of means of different interfaces, also in the coccolithophores Internet network; (iii) thematic data activity in the processing; (iv) publication of thematic Barents Sea per- products in standard GIS-formats, provision formed on basis of of their operational delivery to users; (v) a set of blooms provision of functional and convenient set of data covering tools for data loading and processing. mentioned above Approach for developing GASSIM-NC and 19-year period its infrastructure are the same as those (see Figure on the described above in the section “Spatial data Figure 7. Spatio-temporal statistics of blooms in the Barents Sea. cover page of this assimilation with a service-based GIS report). The Bar- infrastructure for mapping and analysis of E. Unforeseen requirements for the ents Sea is a right representative of stably huxleyi blooms in the Arctic seas”. infrastructure scaling in the future, as well as blooming water areas. The spatio- Interaction with such an infrastructure for the overall complexity of the tasks to be temporal characteristics of the bloom in user (researcher) can be implemented from performed, make it necessary to look for different years are shown in Fig. 7. any convenient environment through modern approaches. To date, the popular Obviously, significant interannual varia- HTTP-requests. For convenience and the versions of the architecture of scientific tions are observed in both the intensity of solution of standard tasks, three basic user information systems are those built on the blooms and in their time ranges. Also, the interfaces for data access were developed: principle of micro-services within a server- localization of most frequently occurring NIERSC Data Centre - web portal; oriented architecture. With this approach, bloom areas are of great interest. extension for the open-source desktop each separate functional block of the geoinformation platform QGIS; a library for software infrastructure is an independent Acknowledgment: this study was conducted under the the Python (see above). service performing an atomized task, Russian Science Foundation financial support (project No. 1717-01117). accepting a standardized query, and generat- Another component of the system is ing the result in the same standardized experimental desktop with virtual representation, while the internal Applied Meteorological environment for client-side data processing. mechanisms of its operation are hidden from and Oceanographic Sharing such virtual machines based on the user. Vagrant and Oracle VirtualBox software can Research for Industrial be effective solution for the independent The information system developed in this processing with client's computing resources. study bases on a two-layer architecture: for Activities the user, the kernel of the system works as a Integrated satellite ice monitoring in During last year, system was evolved: al- server application that processes various gorithms for sea ice classification and drift requests received via the HTTP protocol in the Kara and Okhotsk seas for retrieval were assimilated, more than 15000 accordance with the REST concept. Inside supporting industrial activities SAR scenes were archived and pre- the server part, all interactions take place at Vladimir Volkov, Natalia Zakhvatkina, Eduard processed, scenes for sea ice monitoring the level of micro-services that perform tasks Kazakov, Denis Demchev, Stein Sandven; purposes were selected. in the following categories: (a) data storage; Eduard Khachatrian, Elena Shalina, In 2017, the system was used in the (b) storing of and searching for metadata; (c) Aleksandra Mushta, Kirill Smirnov framework of contracts with FRECOM Ltd data downloading from remote servers; (d) The Nansen Centre had developed a logistic for information support of some industrial preliminary preparation of data, their spatial- model for providing scientific and opera- companies operating in the Kara and temporal assimilation; (e) thematic pro- tional information, products and services to Okhotsk seas. All calculations were carried cessing of data, calculation of derivative potential users, which is necessary for out using advanced methods adapted to products. planning of prospecting and drilling works these water areas and based on a single Using micro-services allows to effectively and management of ice operations on the stream of satellite information. As a result, manage the infrastructure, complement new shelf of the Arctic seas, and ensuring ice and sea ice maps were created in GIS-formats in necessary blocks to it without affecting other environmental safety. accordance with the requirements of the parts of the system. This approach justifies customer. Analysis of ice conditions for the The key block of this system is the itself in the case of merging of new data number of recent years was carried out "on Geoinformation Automated System of sources, improvement of mechanics of pre- the background" of the results of analysis of Satellite Ice Monitoring of the Nansen liminary and thematic processing, potential long-term changes in the ice regime. Centre (GASSIM-NC), which includes emergence of new tasks related to other data original methods for processing satellite Using GASSIM-NC, the processing and and instruments. (mainly radar) data to obtain maps of sea ice analysis of the satellite information was Interaction with such an infrastructure for distribution by age, ice drift and carried out in several thematic areas. user can be implemented from any deformations (compression-rarefaction), Automatic classification of sea ice cover. convenient environment through HTTP- especially dangerous ice formations and Currently the most optimal way for the requests. For convenience and the solution phenomena. The ice conditions analysis sea-ice cover monitoring is the utilizing of a of standard tasks, three basic user interfaces methods developed at the Nansen Centre are high spatial resolution satellite radar images for data access were developed: (1) NIERSC constantly being improved. The system is acquired from Synthetic Aperture Radars Data Centre - web portal; (2) extension for "open" to connect similarly configured (SARs). Using these images, the automatic

9 (thermal noise effect). In the HH- performance. Therefore, suppression of polarization image, sea ice backscatter speckle is the main goal of SAR image coefficient of the same ice type is preprocessing stage. Along with averaging of varying with the incidence angle and the independent signal of the same object – corresponding difference can reach multilooking, spatial methods of SAR image significant values, especially for the filtering may be useful for the successful windy open water surface. The image interpretation. techniques for compensation of these Another challenging issue requiring special effects were developed. attention is the accuracy of resultant ice drift The ice type classification is carried out vectors. It is known that the error of just 100 by implementation of the Support m in ice displacement determination may Vector Machine method (SVM). lead to the error in ice deformation estima- Learning classification requires training tion of the same order. Hence, a special samples to obtain class statistics. The ice attention should be paid to this problem too experts perform a visual analysis of the for successful ice kinematic analysis. SAR data with selected ice types and In 2017, several case studies in the Western water samples for training and testing Arctic have been performed to evaluate the the classification. To improve the efficiency of different noise suppression and Figure 8. Performance of sea ice classification separation of the classes the training feature tracking techniques. The results algorithm for the Kara Sea, 17 April 2016. vectors of the classifier are formed from showed high potential of anisotropic data obtained with the texture analysis. diffusion based methods for SAR image Algorithm is trained for the Kara (Fig. processing. The state-of-the-art methods 8) and Okhotsk (Fig. 9) seas and is able have been compared in terms of spatial data to distinguish several classes: first-year density, accuracy and time efficiency. ice (FYI) medium, FYI thin, young ice Anisotropic diffusion technique exhibits the (with grey ice detection in the Okhotsk best compromise between tracking perfor- Sea), some very smooth (level) FYI, and mance and execution time (Fig. 10). open water – calm and windy. Also, a Method for identification and monitoring Marginal Ice Zone (MIZ) – rather of icebergs and other dangerous ice narrow area between open water and formations. To reveal the true regularities of ice, which, in general, consist of the the seasonal and interannual variability of same ice type as the main closed ice area the number of icebergs in the Arctic it is with more younger ice and some forms necessary to use remote sensing data and of broken ice – can be detected in the automated approaches to iceberg detection. Okhotsk Sea. The algorithm versions Thus, images of Sentinel-1 were employed to could not recognize the fast ice. Also, implement this task. the multiyear ice was not considered in these areas. The final step was to Based on the developed automated method provide a classified SAR image in the of iceberg detection, a test analysis of the GeoTIFF format in terms of WMO seasonal and interannual variability of the number of icebergs in the area of the Barents Figure 9. Performance of sea ice classification palette conditions. Sea was performed. For the test calculations algorithm for the Okhotsk Sea, 5 February 2015. Ice types and open water recognition and the regional analysis of regularities in the algorithm in the most cases gives stable iceberg distribution, the site in the Barents Sea was chosen to the west of the Novaya Zemlya Archi- pelago. As a result, a total of 765 dots on 51 images were detected, which could very likely correspond to Figure 10. A comparison study of various filtering techniques for ice drift retrieval from Sentinel-1 images in the : (left) icebergs (Fig. no filtering; (centre) SIFT (Gaussian kernel); (right) Anisotropic Diffusion Filter. 11). For a statistical sea-ice classification algorithms were applied satisfactory results. On the received charts, it description of the spatial distribution of as a part of an integrated system for the is possible not only to highlight a border icebergs the study region was divided into 16 satellite information processing. between ice and water and detect the ice cells with equivalent area. An obvious non- homogeneity of the spatial distribution of Within FRECOM projects, previously zones, but also to obtain information about irregularities such as large cracks and leads. icebergs is noted over the region (see Fig.11): elaborated algorithms for mapping ice and their maximum number (about half) is water surfaces and obtaining ice type classes Method of drift and deformation fields observed in the cell A2 and in adjacent cells using RADARSAT-2 images were adopted retrieval. There is ongoing development of – A1, B1, B2 where their number is also high and improved to detect ice types and water ice drift retrieval algorithms from radar – 85, 70 and 53, respectively; thus, 75% of all surface using Sentinel-1-Wide (EW) swath images at NIERSC. The physical features of detected iceberg-typical dots, is concentrated mode data. radar backscatter signal from ice surface here. manifest in specific brightness variability In the Sentinel-1 image the HV polarization Our study allowed to make for the first-time of EW mode is formed from narrower bands, from pixel to pixel and called speckle. This conclusion on the interannual and seasonal undesirable effect has to be considered that causes the visible modulation of the variability of the number of observed closely to improve ice drift retrieval image intensity in the range direction icebergs (Fig. 12). There is an obvious

10 summer sea was partly covered by ice, up to 50% of the whole sea area. In present century, we wit- ness the tendency of appearing larger ice-free areas or totally ice-free sea. The Figure 13. The presence of polynyas in the Kara Sea in the winters average sea ice of 2012-2017 (black cells - along the Novaya Zemlya, grey ones - at area observed by the cape of Zhelaniya). satellites in Sep- tember in the Kara Sea was 24% of the Figure 11. Spatial distribution of icebergs in the One of the features of the Kara Sea ice total sea area in the period 1979-1999 central region of the Barents Sea. Blue circle denotes extent is the polynyas along the Novaya and about 8% in 2000-2015; the overall location of the Shtokman field. At the corner of each Zemlya and north of cape of Zhelaniya, decline loss is 6400 km2 per year. The cell, the number of icebergs detected within it is given. which periodically appear and disappear. variability of sea ice area is very high: Letters and numbers denote lines and columns of The time of existence of these polynyas is the standard deviation amounts to 199 cells with equivalent area. shown in Fig. 13. Frequently the km2 being 88% of the average sea ice Novaya Zemlya polynya occurs in area in September. February, can last two months (2014) and general tendency for the increase of During 2014-2017 ice conditions in the Kara finally can disappear and appear again in their number in the winter hydrological sea in summer were not too complicated April (2015, 2016). In 2013, there was no season and the decrease in the summer compare with the climatic norms. In these Novaya Zemlya polynya during the season. years, the steady ice formation in winter; it appeared only in May and its the northern part of the Kara sea appearance caused a rapid ice melting. began in the first decade of In 2017, on the contrary, the polynya September, in the southwestern part appeared in December, disappeared - in October, on open water in the until February, then appeard again and absence of residual ice. In the last 5 existed until the sea was fully free of ice. years freezing began in October Polynya north of Novaya Zemlya appeared among the residual ice; the ice in winter 2012-2013 in January and lasted formation isochronous was directed until March, in 2013-2014 it appeared in from the northeast to the southwest. December-January, in 2014-2015 – in Ice accumulations appeared near October and May, in 2015-2016 – in the Northern Earth and blocked the January and lasted the whole year, in Vilkitsky Strait (2013, 2014). In Figure 12. Seasonal and interannual variability of the 2016-2017 – in December. 2014, these ice fields were a number of icebergs. Red color shows data for the period In the last two years there has been a ten- continuation of the Arctic drifting October 2014 to September 2015, blue color – from dency towards a decrease of fast ice area in ice field. In October 2012, 2015 and October 2015 to September 2016. the Kara sea. 2016 sea was free of ice; ice border was leaving behind 82º east. Freezing of the From 2011 to 2015 the main directions of ice took place on the open water, in the ice drift were the northeast (23%), north- Analysis of long-term changes of ice regime absence of residual ice of any cohesion and north-east (22%) and north-west (8%); see in the Kara and Okhotsk seas. Sea ice form. Fig. 14. In relation to Novaya Zemlya, variability and change in these seas over the several types of drift, occurring in the In general, it is possible to group winters with period 1979-2015 have been analysed using southwestern part of the Kara Sea during a similar development of ice cover: (i) 2012- satellite passive microwave measurements the winter season, can be distinguished: 2013 and 2016-2017; (ii) 2013-2014 and (see NSIDC web site). It is known that both along Novaya Zemlya to the north; along 2014-2015. The first group is characterized Kara and Okhotsk seas significantly Novaya Zemlya to the south into the Kara by a long preservation of open water and contribute to the sea ice decline in Gates Strait; from Novaya Zemlya to the rapid freezing in January with predominance the correspondingly Yamal Peninsula (northeast and of gray-white ice and a tongue of thin ice in summer and in winter. southeast); to the shores of Novaya penetrating from the north-eastern Zemlya from the Yamal peninsula (north- Kara Sea. The Kara Sea has been totally part of the sea. The second group is covered by ice in winter in most of the years characterized by freezing the rest of in the period 1979-2015. Meanwhile, in the the open water in the south of the sea recent years some record sea ice minimums in January and dominating of the thin have been observed in winter, with the ice. In 2015 along the coast of Novaya lowest in March 2012, when the south- Zemlya the thick ice was formed. western part of the sea was partly ice-free and According to the average long-term partly covered by the young ice. The near data, the steady melting of the snow- surface temperature in that region during ice surface of the Kara Sea begins in March 2012 was 4-6 degrees above the the region of the Amderma coast in average and in the following September the the 3rd decade of May. In the last 5 overall extent of the Arctic sea ice reached its years, the process of cleansing the sea all-time low. from ice began earlier - in April-May, The process of sea ice destruction in the Kara with melting points of rivers mouths Sea starts in June and enhances in July. Sea and the Kara Gates Strait. Cleansing ice minimum is usually observed in the Kara Sea from ice can pass both September. During the last century sea has from east to west, and from the west totally cleared from ice in summer only once, to the centre of the water area. in 1995. In other years in 1979-2000 in Figure 14. Ice drift rose in the Kara Sea (2011-2015).

11 west) or from the Northern Earth (south- Okhotsk Sea began in the third decade of Fast ice in the Okhotsk Sea was formed only west). Such a variety of directions is October - the second decade of November in in the shallow bays and inlets, as well as in determined by the synoptic conditions over shallow coastal areas. Further, the nilas and the Amur estuary. During the first three the Kara Sea and over the Arctic basin as a young ice began to form in the open water in weeks after beginning of ice formation, fast whole. the north-western part of the sea (island of ice covered the bays of Sakhalin Island Iona). In the more southerly regions (Baikal, Severny, Odoptu, Piltun, Chayvo, Frequently during the study period there (southeast coast of Sakhalin, Terpenia Bay), Nyisky, Nabilsky), where its destruction also was an ice drift directed along Novaya the first signs of ice freezing were observed in occurred in the second half of May. The Zemlya to the north (37%), to the pole or the first half of December. In the fall 2016, thickness of fast ice in the northern part of the Barents Sea. The eastern and ice formation was noted 1-2 decades earlier Sakhalin reached 160 cm or more. western directions arose in the same compare to previous years. number of cases (24% each), the southern The steady north-westerly wind and constant one - 17 %. The highest velocities were In recent years there have been negative currents determined the general direction of observed in the northeast and north-north- anomalies of the area occupied by sea ice. In ice drift to the south-southeast practically on east directions (from 6 to 12 m/sec). the Shelikhov Bay, mainly young winter ice the whole water area, with the exception of The variability of drift in the sea was very (up to 30 cm) predominated throughout the the Shelikhov Bay and the exit from it, where high: in very rare cases one type of drift winter with the inclusion of individual zones there was a remote south-west drift. The continued for more than two decades. of a one-year thin ice (30-70 cm). Due to the southern component in the direction of the remote drift, the ice here is constantly wind appeared more often from the Eastern drift, directed from the shores of updated and doesn't have time to reach a beginning of April. The average drift No-vaya Zemlya, was most frequent in greater thickness. The zone of accumulation velocities were 5-15 miles/day for the eastern March and in May. Its speed was small, 3-4 of ice was noted only in the Yamsk Bay, due part of the sea and 3-10 miles/day for the m/sec. The only case of high speed to the compression and layering of ice; it western part. connected with this type of drift was reached thickness of 70-120 cm. observed in the second decade of March In general, the ice cover variability in the 2011 (up to 12 m/s) when an extensive The maximum development of ice cover Okhotsk Sea has been highly mutable in the cyclone developed in the north of Novaya was, as a rule, in the end of March. From recent years. Zemlya. 2013 to 2016 the maximum ice cover was observed in the first half of The ice drift directed to the south-south- March, in 2017 – in early west toward the Kara Gates Strait was most February. Fig. 15 shows the likely in May-June, when the speed was not position of the edge of more than 5 m/sec. Its appearance in drifting ice at the time of winter was characterized by higher speeds, maximum ice cover over especially in the water area between Cape the past five years. Zhelaniya and Taimyr Peninsula (in the third decade of February 2013 velocities In the northern and north- reached 10 m/s). western parts of the sea, along the coast, the polynya Okhotsk Sea. Sea ice in this area has the covered by nilas, was re-gional origin. The ice cover starts to preserved throughout the develop in November in the north-western entire season. Along the part of the sea. The emerged ice drifts along eastern coast of Sakhalin the shore in the south-eastern direction, there is also a quasi- and the ice development process in the stationary polynya formed north-western part of the sea continues. The during the squeezing north- maximum sea ice cover is observed in westerly wind, behind February-March. It can extend over the which there was always a whole area of the sea, leaving only the strip of annual ice (thin near-shore region off the Kamchatka with medium inclusion) peninsula, the most remote part from the brought here from the , however in some years it can be north-western part of the substantially smaller, covering less than sea. East of this strip Figure 15. Position of the edge of the drifting sea ice in the 40% of the whole sea area. Changes from observed was a vast zone of Okhotsk Sea during the period of maximum ice area. year to year are quite high: the standard 2 young ice with the deviation is 182000 km , being 22% of the inclusion of annual thin ice of the local Relevant publication: Volkov, V., Demchev, D., & average February-March sea ice area Ivanov, N. (2017). Validation of the Model Obtained Ice 2 origin, brought here from more northern (834000 km ). The tendency of decrease of areas. Drift Fields Based on Satellite Derived Data Using a the winter sea ice area is described by 6.6% Vector Correlation Indexes in an Invariant Form. loss of the ice per decade. The first signs of the ice cover destruction Journal of Shipping and Ocean Engineering, 6, 250-261. were noted in the southern part of the sea The process of melting ice cover in the Demchev, D., Volkov, V., Kazakov, E., & Sandven, S. (near the island of Hokkaido, the Strait of La (2017). Feature tracking for sea ice drift retrieval from Okhotsk Sea starts in the second half of Perouse) in the second half of March – early March, becomes stronger in April and May; SAR images. IEEE International Geoscience and April, which corresponds to climatic data. Remote Sensing Symposium (IGARSS). sea becomes ice-free during June. During To the east of Sakhalin, ice began to melt at doi:10.1109/igarss.2017.8126963. summer the sea is also ice-free. The average 2 the end of March and beginning of April, and Demchev, D., Volkov, V., Kazakov, E., Alcantarilla, P. sea ice area in June amounts to 19800 km completely thawed in the first half of June. F., Sandven, S., & Khmeleva, V. (2017). Sea Ice Drift with the standard deviation of 18400 km2, Sakhalin Bay and the area to the north of it Tracking from Sequential SAR Images Using which is 92.8% from the average. That high Accelerated-KAZE Features. IEEE Transactions on variability prevents a statistically significant became ice-free in the second half of June, Geoscience and Remote Sensing, 55(9), 5174-5184. conclusion about the trend. although in 2012/13 ice stayed there until the end of July. The longest ice remained in Ice formation in the Okhotsk Sea begins in the Shantar Sea. November in the north-western part of the sea. The maximum area of the ice cover is observed in February-March. In the last 5 years ice formation in the

12 ANNEX: Programme of the Nansen Centre's 25th Anniversary Symposium 12 October, Thursday Petrovsky Hall, St. Petersburg State University 09:30-10:00 Opening. Prof. Nikolay Filatov, President, Nansen Centre, Petrozavodsk, Russia Welcome speeches: Mr. Dag Malmer Halvorsen, Consul General of Norway in St. Petersburg Prof. Sergey Aplonov, Vice-Rector, St. Petersburg State University, St. Petersburg, Russia 10:00-10:30 St. Petersburg Nansen Centre: 25 years of environmental and climate research Dr. Leonid Bobylev, Director, Nansen Centre, St. Petersburg, Russia 10:30-10:55 From Waves and Ocean Remote Sensing to Climate and Climate Policy - 25 years of fruitful collaboration with NIERSC Prof. Klaus Hasselmann and Prof. Hartmut Grassl, Max-Planck Institute for Meteorology, Hamburg, Germany 10:55-11:20 Studies of the atmosphere, hydrosphere and educational programs of St. Petersburg State University Prof. Kirill Chistyakov, Director, Earth Sciences Institute, St. Petersburg State University/ Vice-President, Russian Geographic Society, St. Petersburg, Russia 11:40-12:05 Arctic sea ice and processes in the Marginal Ice Zone: a brief review Prof. Ola M. Johannessen, President, Nansen Scientific Society, Bergen, Norway 12:05-12:30 Arctic in the context of Paris Agreement Prof. Vladimir Kattsov, Director, Voeikov Main Geophysical Observatory, St. Petersburg, Russia 14:00-14:25 Climate Change and Melting Glaciers and Ice Sheets - From Arctic to High Dr. Sebastian Mernild, Director, Nansen Centre, Bergen, Norway 14:25-14:50 Research activities of the Arctic and Antarctic Research Institute in the Arctic Prof. Alexander Makarov, Director, Arctic and Antarctic Research Institute, St. Petersburg, Russia 14:50-15:15 Monitoring and modelling atmospheric gases Prof. Yury Timofeev and Prof. Sergey Smyshlyaev, St. Petersburg State University, St. Petersburg, Russia

15:15-15:40 Calcifying phytoplankton role in CO2 dynamics in the atmosphere-ocean system at subpolar and polar latitudes Prof. Dmitry Pozdnyakov, Nansen Centre, St. Petersburg, Russia 15:40-16:05 Largest lakes of Eurasia: integrated studies of ecosystems Prof. Nikolay Filatov, Corresponding Member RAS, President, Nansen Centre/ Councillor RAS, Northern Water Problems Institute, Petrozavodsk, Russia 16:25-16:50 Ice climatology of polar lakes Prof. Matti Leppäranta, University of Helsinki, Helsinki, Finland 16:50-17:15 Integrated Arctic observation system (INTAROS) Prof. Stein Sandven, Nansen Centre, Bergen, Norway 17:15-17:40 The impact of climate change on wind energy in Dr. Richard Davy, Nansen Centre, Bergen, Norway

13 October, Friday Nansen Centre 10:00-10:25 Global warming in the Arctic: role of atmospheric transport and solar radiation Prof. Genrikh Alekseev, Arctic and Antarctic Research Institute, St. Petersburg, Russia 10:25-10:40 Seasonal and interannual variability of heat fluxes in the Barents Sea region Dr. Igor Bashmachnikov, St. Petersburg State University/Nansen Centre, St. Petersburg, Russia 10:40-10:55 Atmospheric heat advection in the Kara Sea region Dr. Alla Yurova, St. Petersburg State University/Nansen Centre, St. Petersburg, Russia 11:20-11:45 Arctic climate change: anomalies and trends Prof. Igor Mokhov, Academician RAS, Director, Obukhov Institute of Atmospheric Physics, Moscow, Russia 11:45-12:10 Effects of changing Arctic on mid-latitude weather and climate Prof. Timo Vihma, Finnish Meteorological Institute, Helsinki, Finland 12:10-12:35 Arctic warming and cold weather over - great scientific challenge Prof. Valentin Meleshko, Voeikov Main Geophysical Observatory, St. Petersburg, Russia 12:35-12:50 Effects of surface temperature anomalies in the Arctic on 2-m air temperature in Europe and Northern Dr. Natalia Gnatiuk, Nansen Centre, St. Petersburg, Russia

14:00-14:15 A new methodology of remote sensing determination of CO2 partial pressure dynamics in surface ocean due to E. huxleyi blooms Dmitry Kondrik, Nansen Centre/Arctic and Antarctic Research Institute, St. Petersburg, Russia 14:15-14:35 Applied meteorological and oceanographic research at NIERSC Dr. Vladimir Volkov, Nansen Centre, St. Petersburg, Russia 14:35-14:50 Operational algorithm for automated sea-ice retrieval in the Arctic using SAR data Dr. Natalia Zakhvatkina, Arctic and Antarctic Research Institute/Nansen Centre, St. Petersburg, Russia 14:50-15:05 Sea ice drift retrieval from satellites Denis Demchev, Arctic and Antarctic Research Institute/Nansen Centre, St. Petersburg, Russia 15:05-15:20 NIERSC data archiving and processing system for research and environmental monitoring in the Arctic Eduard Kazakov, Nansen Centre, St. Petersburg, Russia 15:45-16:00 Introducing Climate Service Centre Germany: think tank for climate services Dr. Lola Kotova, Climate Service Centre Germany (GERICS), Hamburg, Germany 16:00-16:15 Presentation of the Nansen-Zhu Centre (NZC) Prof. Jianqi Sun, Nansen-Zhu Centre, Beijing, China 16:15-16:30 Presentation of the Nansen Environmental Research Centre, India (NERCI) Dr. Ajith Joseph, Nansen Environmental Research Centre, Kochi, Kerala, India

13 subpolar and polar seas: A satellite time Volkov, V.А., Demchev, D.M. and Ivanov, PUBLICATIONS series study (1998-2013). International N.E. (2017). Validation of the model Journal of Remote Sensing, 38:22, 6179-6205, obtained ice drift fields based on satellite doi: 10.1080/01431161.2017.1350304. derived data using a vector correlation indexes in an invariant form. Journal of Book Korosov, A., Pozdnyakov, D., Shuchman, R., Shipping and Ocean Engineering (JSOE), 7(6), Sayers, Sawtell, Moiseev, A. (2017). Bio- Pozdnyakov, D., Pettersson, L., and Korosov, A. 250-261. optical retrieval algorithm for the optically (2017). Exploring the marine ecology from shallow waters of Lake Michigan. I. Model space: Experience from Russian-Norwegian description and sensitivity/robustness cooperation. Springer, 215 pp. assessment. Limnologia. 3, 79-93. Conference Proceedings Zakhvatkina, N.Yu., A. Korosov, S. Alekseev G., S. Kuzmina, A. Urazgildeeva, and Refereed Papers Muckenhuber, S. Sandven, and M. Babiker L. Bobylev. Influence of atmospheric energy (2017). Operational algorithm for ice–water transport on amplification of winter Davy, R., N. Gnatiuk, L. Pettersson, L. Bobylev classification on dual-polarized warming in the Arctic. Poster. International (2017). Climate change impacts on wind RADARSAT-2 images. The Cryosphere, 11, Conference on Arctic Science: Bringing energy potential in the European domain 33-46, doi:10.5194/tc-11-33-2017. Knowledge to Action, Reston, Virginia, USA, with a focus on the Black Sea. Renewable April 24-27, 2017 Demchev, D., V. Volkov, E. Kazakov, P.F. and Sustainable Energy Reviews, 81, 1652- Alcantarilla, S. Sandven, and V. Khmeleva Alekseev G., S. Kuzmina, and N. Glok. 1659 (2017). Sea Ice Drift Tracking from Influence of SST anomalies in low latitudes Bashmachnikov, I., M.A. Sokolovskiy, T.V. Sequential SAR Images Using Accelerated- on atmospheric heat transport to the Arctic. Belonenko, D.L. Volkov, P.E. Isachsen and X. KAZE Features. IEEE Transactions on Poster. EGU General Assembly 2017, Viena, Carton (2017). On the vertical structure and geoscience and Remote Sensing, 55(9), 5174- Austria, 23-28 April 2017 stability of the Lofoten vortex in the 5184, Bashmachnikov, I.L. Seasonal and Norwegian Sea. Deep-Sea Research I, 128, 1- (http://ieeexplore.ieee.org/document/7938 interannual variations in the dynamic and 27 663/). thermal fronts of the Barents, Norwegian Ciani, D., X. Carton, A.C. Barbosa Aguiar, Á. Vesman, A., B. Ivanov, V. Volkov (2017). and Greenland Seas. Proceedings of the Peliz, I. Bashmachnikov, F. Ienna, B. Chapron, Changes in thermohaline system on the conference “Russian seas: science, security and R. Santoleri. (2017). Surface signature of west Spitsbergen shelf since 1950 to present resources”, pp. 29-30, Sebastopol, Russia, 3-7 Mediterranean water eddies in a long-term time. Czech Polar Reports, 7 (1), 62-73. October 2017 high-resolution simulation. Deep-Sea Bashmachnikov, I.L., A.Yu. Yurova, L.P. Research I, 130, 12-29, Bobylev, A.V. Vesman. On links between https://doi.org/10.1016/j.dsr.2017.10.001. Other Papers variations of oceanic and atmospheric heat fluxes to the Barents Sea region. Belonenko, T.V., Bashmachnikov, I.L., Bashmachnikov, I., Belonenko, T.V., Kuibin, Proceedings of the conference “Russian seas: Koldunov, A.V., Kuibin, P.A, (2017). On the P.A. (2017). Application of the theory of science, security and resources”, pp. 29-30, Vertical Velocity Component in the columnar Q-vortices with helical structure Sevastopol, Russia, 3-7 October 2017 Mesoscale Lofoten Vortex of the to derivation of dynamic properties of the Norwegian Sea. Izvestiya RAS, Atmospheric Lofoten Vortex in the Norwegian Sea. Gnatiuk, N., Vihma, T., Bobylev, L. Effects of and Oceanic Physics, 53 (6), 641-648 Vestnik SPbSU (Bulletin of St. Petersburg surface temperature anomalies in the Kraev, G., Schulze, E.D., Yurova, A., Kholodov, University), Earth Sciences, 62 (3), 221-236, Northern Hemisphere on 2-m air A., Chuvilin, E., & Rivkina, E. (2017). doi: 0.21638/11701/spbu07.2017.301. temperature in Europe and Northern Asia. Arctic Science Summit Week (ASSW 2017), Cryogenic Displacement and Accumulation Krakovska, S.V., Palamarchuk, L.V., Gnatiuk, Prague, Czech Republic, 4-7 April 2017 of Biogenic Methane in Frozen Soils. N.V., Shpytal, Т.M., Shedemenko, I.P. (2017). Atmosphere, 8(6), 105. Changes in precipitation distribution in Vihma, T., Gnatiu,k N., Bobylev, L. Effects of Selyuzhenok, V., Mahoney, A.R., Krumpen, T., Ukraine for the 21st century based on data Earth surface temperature anomalies on 2- Castellani, G., Gerdes, R. (2017). Mechanisms of regional climate model ensemble. m air temperature in the Northern of fast ice development in the southeastern Geoinformatika, 4(64), 62-74. Hemisphere. US CLIVAR Workshop, Washington, DC, USA, 1-3 February 2017 Laptev Sea: A Case study for winter of Shalina, E., Bobylev, L. (2017). Sea ice 2007/08 and 2009/10. Polar Research, 36, transformations in the Arctic from satellite Krakovska, S., Chyhareva, A., Cheva,l S., doi: 10.1080/17518369.2017.1411140. observations. Current problems in remote Gnatiuk, N. Shifts of season duration in the Kovalevsky, D.V., Hewitt, R., de Boer, C., sensing of the Earth from space, 14(6), 28-41, Pannonia region in the period 1961-2010. Hasselmann, K. (2017). A dynamic systems doi: 10.21046/2070-7401-2017-14-6-28-41 3rd PannEx Workshop, Cluj-Napoca, approach to the representation of policy (in Russian). Romania, 20-22 March 2017 implementation processes in a multi-actor Bobylev, L.P., Romanyuk, L.P., Pozdnyakov, Krakovska, S., Palamarchuk, L., Chyhareva, A., world. Discontinuity, Nonlinearity, and D.V. (2017). Experience of International Gnatiuk, N., Shpytal, T. Some hydrothermal Complexity, 6(3), 219-245. DOI: Cooperation in the Research Area of characteristics of the Pannonian region 10.5890/DNC.2017.09.001 Climate Change in high Northern under the current climate change. 3rd Kondrik, D., Pozdnyakov, D., Pettersson L. Latitudes. Prikladnaya Ecologia (Applied PannEx Workshop, Cluj-Napoca, Romania, (2017). Multi-year tendencies in E. huxleyi Ecology), 3(49), 82-86 (in Russian). 20-22 March 2017 blooms over latitudinally and meridianally Zakhvatkina N., S. Mikhaltseva (2017). Gnatiuk, N., Vihma, T., Bobylev, L. Lagged distanced marine environments in the Identification of the multiyear floes. Section effects of Earth surface temperature northern hemisphere as revealed from 3.4 in: Smirnov V.G., ed. Detection of the anomalies on northeast European weather. satellite observations during 1998-2013. dangerous ice features near engineering objects of 3rd PEEX Science Conference, Moscow, Earth Observation from Space, 2, 1-12 (in economic activity in the shelf areas of the Arctic Russia, 19-22 September 2017. Russian). seas using remote sensing data. Methodical Shalina, E., Bobylev, L. Sea ice Kondrik, D., Pozdnyakov, D., Pettersson, L. manual. St. Petersburg, AARI, 63-71 (in Russian). transformations in the Arctic from satellite (2017). Particulate inorganic carbon observations. Abstracts of 15th Conference production within E. huxleyi blooms in “Current problems in remote sensing of the Earth

14 from space”, Moscow, 13-17 November 2017", 27-30 June 2017, St. Petersburg- regions of the Barents Sea from 1997 to 2017. Petrodvorets. 2016. Conference “Russian Seas: Science, http://smiswww.iki.rssi.ru/d33_conf/thesi Security, Resources”, 3-7 October 2017, Demchev, D., Volkov, V., Kazakov, E., sshow.aspx?page=144&thesis=6169 (in Sevastopol, Russia Russian). Sandven, S. Feature Tracking for sea ice drift retrieval from SAR images. Proceedings Vesman, A.V., Bashmachnikov, I.L., Ivanov, Shalina, E., Bobylev, L. Sea ice of the Geoscience and Remote Sensing B.V. The links between variations of oceanic transformations in the Arctic from the Symposium (IGARSS), 2017, 330-333. heat flux trough the Fram strait and the sea period of satellite observations. Proceedings of ice conditions to the north of Svalbard Khmeleva, V.S., Volkov, V.A, Demchev, the conference “Hydrometeorology and Ecology: Archipelago. Svalbard Science Conference, 6-8 D.M. Methods of automated calculation of Scientific and Educational Achievements and November 2017, Oslo, Norway Prospects for the Development”, St. Petersburg, sea-ice drift fields from sequential radar Demchev, D., Volkov, V., Kazakov, E., & 19–20 December 2017, 484-486 (in images of the Sentinel satellite system. XXX Russian). All-Russian Symposium "Radar Survey of Sandven, S. Feature tracking for sea ice drift Natural Environments", 18-19April 2017, retrieval from SAR images. 2017 IEEE Hewitt, R., Kovalevsky, D.V., de Boer, C., A.F. Mozhaisky Academy, St. Petersburg International Geoscience and Remote Hasselmann, K. (2017). Modelling actors’ Sensing Symposium (IGARSS), 23-28 July, Mushta, A.V., V.A. Volkov, D.M. Demchev. influence on land use change: a dynamic Texas, USA. systems approach. In: Bregt, A., Sarjakoski, On the climatic changes in the large-scale doi:10.1109/igarss.2017.8126963. T., Lammeren, R. van, Rip, F. (Eds.). field of ice and ice drift in the Societal Geo-Innovation: short papers, at the turn of the 20th and 21st centuries. II posters and poster abstracts of the 20th All-Russian Conference of Young Scientists AGILE Conference on Geographic "Integrated Studies of the World Ocean", 10- Information Science. Wageningen 15April 2017, Institute of Oceanography University, 9-12 May 2017, Wageningen, RAS, Moscow the Netherlands. ISBN 978-90-816960-7-4. Kazakov, E., Volkov, V., Demchev, D. Arctic Accessible through https://agile- sea ice monitoring system based on public online.org/index.php/conference/proceedi domain satellite radar data. In proceedings ngs/proceedings-2017. of 2nd International Conference on Applied Zaitsev L.V., Kovalevsky D.V. (2017). Science «Geodesy, Cartography, Geoinformatics Strategies of price formation and inventory and Cadastre. From idea to application», 8- management under stochastic demand 10November 2017, St. Petersburg, Russia, caused by ‘herding behaviour’ of 33-39 consumers. The Third Readings in Memory of Shirshova, V., Kazakov, E. Automated Prof. B.L. Ovsievich “Economic-Mathematical monitoring system of ice regime of major Studies: Mathematical Models and Information rivers in the northern latitudes based on Technologies” – Proceedings of the All-Russian open satellite data. In proceedings of 2nd Conference, 7-9 November 2017, St. International Conference on Applied Science Petersburg, Russia, 104-107 (in Russian). «Geodesy, Cartography, Geoinformatics and Pozdnyako,v D., Kondrik, D., Pettersson L. Cadastre. From idea to application», 8-10 Inorganic carbon production by November 2017, St. Petersburg, Russia, 86- coccolithophores: a space borne study. The 92 3rd Pan-Eurasian Experiment (PEEX) Science Shabalina, E., Kazakov, E. Combined Conference, 19-22 September 2017, Moscow, algorithm for automated classification of Russia radar images with C-SAR (Sentinel-1 A/B) Kondrik, D., Pozdnyakov, D., Pettersson, L. A sensor data. In proceedings of 2nd spaceborne study on E. huxleyi bloom extent International Conference on Applied Science dynamics at high northern latitudes. Poster. «Geodesy, Cartography, Geoinformatics and The 3rd Pan-Eurasian Experiment (PEEX) Cadastre. From idea to application», 8-10 Science Conference, 19-22 September 2017, November 2017, St. Petersburg, Russia, 80- Moscow, Russia 85 Bobylev, L., Volkov, V., Kazakov, E., Demchev, Andreeva, T., Sukhacheva, E., Aparin, B., D. Satellite data based automated arctic sea Lazareva, M., Kazakov, E. Medium-scale ice monitoring system. Proceedings of the digital soil mapping as a basis for regional Eight EuroGOOS International Conference: policy on environmental management on Operational Oceanography Serving Sustainable the example of Leningrad Region. In Marine Development, 3-5 October 2017, proceedings of 2nd International Conference on Bergen, Norway, 409-414. Applied Science «Geodesy, Cartography, Geoinformatics and Cadastre. From idea to Volkov, V.A., Kazakov, E.E., Demchev, D.M., application», 8-10 November 2017, St. Zakhvatkina, N.Y., & Khmeleva, V.S. A Petersburg, Russia, 262-266 Monitoring System of Ice Conditions to Support Geological Surveying in Arctic Shabalina, E., Kazakov, E. Combined method Seas. Abstracts of the International of automated urban mapping with Sentinel- Conference Geomodel 2017, available 1A/B data based on textural and noise at http://www.earthdoc.org/publication/p radar images characteristics. In proceedings ublicationdetails/?publication=43784 of International conference for young scientists «Theory and practice of modern geographical Mushta, A.V., Volkov, V.A., Demchev, research», 7-9 April 2017, St. Petersburg, D.M. Investigation of large-scale structures Russia, 583-587 of ice drift in the Arctic Basin based on satellite remote sensing data. Symposium on Vesman, A.V., Ivanov, B.V., Volkov, V.A. Atmospheric Radiation and Dynamics "ISARD- Changes in the ice cover in the northern

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