High mortality of fish in the early stages of ontogenesis requires the detection of factors affecting it and is an extremely important problem. For this reason, we tried to consider the influence of some hydrometeorological factors on the intra-decadal dynamics and spatial distribution of ichthyoplankton abundance in Sevastopol Bay. To this end, we analyzed ichthyoplankton samples collected in 2012–2021 in Sevastopol Bay, and the four districts were identified. The ichthyoplankton distribution by areas was associated with the peculiarities of the pollution distribution. The maximum numbers in eggs (34.7 ± 7.5 ind. m⁻²) and larvae (10.0 ± 2.4 ind. m⁻²) were recorded in July–August. For the first time, a positive effect of temperature and repeatability of north and northeast winds on the ichthyoplankton number during the summer spawning in all identified areas of Sevastopol Bay using principal component analysis (PCA) was reported. In the southern region, positive influence of temperature and northerly winds on the ichthyoplankton abundance was confirmed by ANOVA (p = 0.02), and Pearson’s correlation analysis revealed significant correlations between these parameters on inter-annual scale (r > 0.5). In the eastern area, the discharge of rivers is an additional factor affecting the abundance of ichthyoplankton, while in the western and central areas, these factors were the wind characteristics caused by the orography peculiarities. The pollution spread in Sevastopol Bay is also largely due to the wind regime. The importance of comprehensive monitoring studies using intra-decadal data on ichthyoplankton is emphasized.

The distribution of ecosystem components (phytoplankton, crustacean epiplankton, bathyplankton, and jellyfish plankton) was analyzed taking into account the hydrochemical and hydrometeorological conditions based on the results of comprehensive surveys in the Black Sea. The environmental factors considered in the study include: hydrochemical factors (biogenic elements, i.e., nitrates, nitrites, phosphates, silicon) as limiting factors of phytoplankton biomass growth, and hydrometeorological factors (wind speed and direction, Ekman pumping, upwelling, and density fronts), which affect the distribution and concentrations of biogenic matter. It was found that the spatial distribution of biogenic elements and phytoplankton is in good agreement with seasonal variations of the vertical water rise―Ekman pumping, associated with the heterogeneity of wind field above the sea. Analysis of the year-to-year variations of Ekman pumping over period 1979–2016 showed that in winter, cyclonic vorticity dominates in wind field, accompanied by vertical rise of water, thus creating favorable conditions for enrichment of the top seawater layer by nutrients. In summer season, cyclonic vorticity of wind dominates mostly over the eastern part of the sea, thus contributing to the rise of deep water. The anticyclonic vorticity, which dominates on the average over the western part of the sea in summer, hampers deep water rise.

Based on the spectral analysis of a number of estimates of the ice extent of the Barents Sea, obtained from instrumental observational data for 1900–2014, and for the selected CMIP5 project models (MPI-ESM-LR, MPI-ESMMR and GFDL-CM3) for 1900–2005, a typical period of ~60‑year inter-annual variability associated with the Atlantic multidecadal oscillation (AMO) in conditions of a general significant decrease in the ice extent of the Barents Sea, which, according to observations and model calculations, was 20 and 15%, respectively, which confirms global warming. The maximum contribution to the total dispersion of temperature, ice cover of the Barents Sea, AMO, introduces variability with periods of more than 20 years and trends that are 47, 20, 51% and 33, 57, 30%, respectively. On the basis of the cross correlation analysis, significant links have been established between the ice extent of the Barents Sea, AMO, and North Atlantic Oscillation (NAO) for the period 1900–2014. A significant negative connection (R = −0.8) of ice cover and Atlantic multi-decadal oscillations was revealed at periods of more than 20 years with a shift of 1–2 years; NAO and ice cover (R = −0.6) with a shift of 1–2 years for periods of 10–20 years; AMO and NAO (R = −0.4 ÷ −0.5) with a 3‑year shift with AMO leading at 3–4, 6–8 and more than 20 years. The periods of the ice cover growth are specified: 1950–1980 and the reduction of the ice cover: the 1920–1950 and the 1980–2010 in the Barents Sea. Intensification of the transfer of warm waters from the North Atlantic to the Arctic basin, under the atmospheric influence caused by the NAO, accompanied by the growth of AMO leads to an increase in temperature, salinity and a decrease of ice cover in the Barents Sea. During periods of ice cover growth, opposite tendencies appear. The decrease in the ice cover area of the entire Northern Hemisphere by 1.5 × 106 km2 since the mid-1980s. to the beginning of the 2010, identified in the present work on NOAA satellite data, confirms the results obtained on the change in ice extent in the Barents Sea.

In this study, we estimated the transport and potential temperature of the Antarctic Bottom Water (AABW) and its variability on intra-annual, interannual, and decadal scales based on eight reanalyses and direct measurements at 26° N in the subtropical North Atlantic. The GLORYS2v4 and GLORYS12v1 reanalyses were selected, which give the closest results of the estimates for average AABW transport, calculated below 5000 m, and their variability with RAPID. It was revealed that the seasonal cycle of AABW transport for 2004–2015 is described by superposition of the annual and semiannual harmonics of ~80% with an amplitude of ~0.4 Sv, with maxima in April and October, and minima in February and July. The intra-annual temperature cycle is described by the annual harmonic, the contribution of which is more than 70% of the total variability, with a maximum in spring and minimum in autumn, and an amplitude of ~0.003°C. The study highlights the characteristic periods of variability in the transport and potential temperature of AABW of 12 and 6 years, with maxima in 2009 and 2014 and minima in 2006 and 2011 based on the ORAS5 and GLORYS2v4 reanalyses and RAPID estimates. The amplitude of interannual fluctuations in the transport and potential temperature of AABW according to the ORAS5, GLORYS2v4, and GLORYS12v1 reanalyses is ~0.6 Sv and ~0.01°C, respectively. Analysis of the long-term trends in average annual AABW transport showed an increase of 0.2 Sv based on the reanalyses and RAPID data with a slight decrease in temperature (–0.04°C) for 2004–2015.

The climatic variability of near-surface air temperature (NSAT) over the Caspian region (35–60° N; 40–65° E) was analyzed in this study. The analysis was based on a comparison of data from various sources: weather stations, NOAA OISSTv2 satellite-based data, atmospheric reanalyses ECMWF ERA5, NASA MERRA-2, and NCEP/NCAR, and the outputs from 33 Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). CMIP6 models results from the historical and Shared Socioeconomic Pathways (SSPs) experiments were utilized. Over the period 1940–2023, NSAT exhibited variable changes across the Caspian region. Weather stations in the northwestern part of the region indicated NSAT increases of 0.9 ± 0.2 °C for 1985–2023. In the central-western part of the Caspian region, the increase in average NSAT between 1940–1969 and 1994–2023 was 1.4 °C with a spatial standard deviation of 0.3 °C. In the southern part of the Caspian region, the increase in average NSAT between 1986–2004 and 2005–2023 was 0.8 ± 0.1 °C. Importantly, all 33 CMIP6 models, as well as the ERA5 reanalysis, captured an average NSAT increase of approximately 1.3 ± 0.5 °C for the whole Caspian region between 1940–1969 and 1994–2023. From the ERA5 data, the increase in NSAT was more pronounced in the north (~1.6 °C) than in the central Caspian region, with the most significant warming observed in the mountainous regions of Iran (up to 3.0 °C). Under various CMIP6 SSPs scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5), projections indicate an increase in average NSAT across the study region. Comparing the periods 1994–2023 and 2070–2099, the projected NSAT increases are 1.7 ± 0.7 °C, 2.8 ± 0.8 °C, 4.0 ± 0.9 °C, and 5.2 ± 1.2 °C, respectively. For the earlier period of 2024–2053 relative to 1994–2023, the projected NSAT increases are 1.2 ± 0.4 °C, 1.3 ± 0.4 °C, 1.4 ± 0.4 °C, and 1.7 ± 0.5 °C. Notably, the projected increase in NSAT is slower over the Caspian Sea compared to the surrounding land areas.

This study investigates changes in Near-Surface Air Temperature (NSAT) over the South African region using weather station data, reanalysis products, and Coupled Model Intercomparison Project Phase 6 (CMIP6) model outputs. It is shown that, based on ERA5 reanalysis, the average NSAT increase in the region (45–10° S, 0–50° E) for the period 1940–2023 was 0.11 ± 0.04 °C. Weak multi-decadal changes in NSAT were observed from 1940 to the mid-1970s, followed by a rapid warming trend starting in the mid-1970s. Weather station data generally confirm these results, although they exhibit considerable inter-station variability. An ensemble of 33 CMIP6 models also reproduces these multi-decadal NSAT change characteristics. Specifically, the average model-simulated NSAT values for the region increased by 0.63 ± 0.12 °C between the periods 1940–1969 and 1994–2023. Based on the results of the comparison between weather station observations, reanalysis, and models, we utilize projections of NSAT changes from the analyzed ensemble of 33 CMIP6 models until the end of the 21st century under various Shared Socioeconomic Pathway (SSP) scenarios. These projections indicate that the average NSAT of the South African region will increase between 1994–2023 and 2070–2099 by 0.92 ± 0.36 °C under the SSP1-2.6 scenario, by 1.73 ± 0.44 °C under SSP2-4.5, by 2.52 ± 0.50 °C under SSP3-7.0, and by 3.17 ± 0.68 °C under SSP5-8.5. Between 1994–2023 and 2025–2054, the increase in average NSAT for the studied region, considering inter-model spread, will be 0.49–1.15 °C, depending on the SSP scenario. Furthermore, climate warming in South Africa, both in the next 30 years and by the end of the 21st century, is projected to occur according to all 33 CMIP6 models under all considered SSP scenarios. The main spatial feature of this warming is a more significant increase in NSAT over the landmass of the studied region compared to its surrounding waters, due to the stabilizing role of the ocean.