This study assessed the physical vulnerability of the coastal area of Nigeria to climate change effects using indices generated from a group of factors including relief, rock types, landforms, and erosion/deposition rates. Results show the very-high vulnerability class covering the largest proportion, about 53% of the area, amounting to about 23,850 km², largely found in the Niger Delta region. The next, high-vulnerability class covers 17%, about 7650 km², found mostly in Lagos State and the northern fringes of the Niger Delta region. The other classes i.e., moderate, low, and very-low vulnerability extend over 10% (4500 km²), 13%, (5850 km²) and 7% (3150 km²) of the coastal area, respectively. While the moderate-vulnerability class is found only in the western part of the coastal area, the low and very-low vulnerability classes dominate the extreme eastern flank and some northern edges of the western part. The low-vulnerability class is found mainly in Ondo, Ogun, Akwa Ibom and the Cross River States. The very-low vulnerability class is found covering the Ewen community of Cross River State only. Given that 70% of Nigeria’s coastal environment falls within very-high and high vulnerability classes, the region is evidently very vulnerable to the impacts of climate change.

This study explores the spatial pattern and climate modes’ impact on the Indian Ocean decadal upwelling variability by using observational dataset, Static Linear Regression Model (SLM) and Bayesian Dynamic Linear Model (BDLM). Our analysis shows that the Indian Ocean decadal upwellings averaged in the Eastern and Western Indian Ocean (EIO and WIO) regions are positively correlated. Moreover, the BDLM that represents the temporal modulations of the El Niño and Southern Ocean (ENSO) and Indian Ocean Dipole (IOD) impacts, reproduces the time series of the EIO and WIO upwellings more realistically than a conventional SLM does. BDLM simulations further suggest that in both EIO and WIO, IOD is more important than ENSO impact. The time-varying regression coefficients in BDLM indicate that the observed shift of the IOD impact on the EIO upwelling around 1985 is mainly associated with the changes of alongshore wind stress forcing and the sensitivity of the upper ocean temperature in the EIO through the surface warming tendency and the enhanced ocean stratification. This suggests that climate models need to consider the time-varying impact of different climate modes in order to simulate the Indian Ocean dynamics correctly.

The study combined the second-order polynomial and some elements of algebra and trigonometry with the ogive to objectively (mathematically) locate the rainfall onset, peak, and retreat on the ogive, which was visually done. The data used for the investigation are the daily rainfalls of 16 synoptic stations (Ikeja, Calabar, Port-Harcourt, Benin, Ondo, Enugu, Ilorin, Lokoja, Jos, Kaduna, Yola, Kano, Sokoto, Maiduguri, Potiskum, and Nguru) across all the ecological zones of Nigeria. The datasets spanning 50 years (1971–2020) were collected from the Archives of the Nigerian Meteorological Services, Abuja, Nigeria. The ogives were derived from the frequency of rainy days. The peak periods were best detected from the pentad graphs of the rainy-day frequency. The results showed that rainy-day frequency is better than rainfall amount in determining the various rainfall parameters over Nigeria. The second-order polynomial modeled the two curvatures of the rainfall perfectly. The rainy-day frequency in the southern part of the country exhibited double rainfall maxima, while those in the northern part showed a single rainfall maximum. The double rainfall maxima are not peculiar to the southwestern region of Nigeria, as previously widely asserted; they cut across the whole southern region, although the number of days and the depth of the trough between the peaks dimmish from the largest values in the extreme southwest to the least in the extreme southeast. The first rainfall peaks were attained in southern Nigeria in July, except at Ikeja, which was in June. The second rainfall peak was reached in September in all the southern stations. The single peaks were attained in all the extreme northern stations in August and in the other stations south of them in early September. The rainfall onset begins at Ikeja in the extreme southwestern corner of Nigeria around March ending. It spreads eastwards and northwards to cover the entire country by mid-June, reaching Nguru in the northeastern corner. It is generally earlier on the western flank than the eastern flank. Rainfall begins to retreat from the northernmost stations by the third week in September to reach the extreme southern stations between October and early November.

 International maritime trade has long played a pivotal role in human development; however, its environmental impact cannot be disregarded. Air pollution (that includes Carbon and Sulphur emissions—all together Greenhouse Gases) emanating from ships has emerged as a significant contributor to climate change, prompting growing concern among the international community. The combustion of fossil fuels in ship engines releases pollutants such as sulfur dioxide, nitrogen oxides, and particulate matter into the atmosphere, adversely affecting both human health and the climate. In recognition of the need to tackle this issue, international laws have been established to regulate ship emissions. This research paper analysed the IMO’s regulations under international law for mitigating climate change, with a particular focus on various global initiatives controlling sulphur, carbon and GHG emissions. It is suggested that cooperation between public and private interests as well as at regional levels will play a crucial role in combating climate change and promoting global shipping sustainability.

Deep sea mining involves the extraction of minerals from the ocean floor. As deep sea mining is a relatively new practice, there is still much to be learned about the technology and the potential risks associated with it by the developing countries. Developing states must ensure that proper safety measures are in place to prevent accidents and minimize the risk of environmental damage. While the potential benefits of deep sea mining are significant, it is essential to ensure that the process is carried out responsibly and sustainably to protect both the environment and local communities. Developing countries face numerous challenges when it comes to actively participating in deep-sea exploration. Some of these challenges include a lack of adequate resources, limited technological capabilities, and limited access to funding. Additionally, developing countries often lack the necessary legal frameworks and regulatory systems to effectively regulate deep-sea exploration, licensing and extraction activities, especially within the continental shelves and the Exclusive Economic Zones (EEZ). These challenges can make it difficult for developing countries to fully participate in this important area of scientific research for a sustainable blue economy. This paper suggests an effective partnership between oceanographic research institutes from developing countries with research institutes from countries like France, Germany, UK, Belgium, Netherlands, China and International seabed Authority (ISA) in deep sea marine scientific research. The paper identifies the need for a joint collaboration for the purpose of acquiring reliable data on the seabed topography, location, shape, coverage, and abundance of deep-sea mineral resources in the continental shelves, exclusive economic zones of developing countries, and area beyond the national jurisdiction. There is also a need to develop legal framework on deep sea policy for sustainable actualization of developing nations’ blue economy.

In recent years, artificial intelligence, particularly deep learning, has garnered significant attention among practitioners and scholars in meteorology and atmospheric sciences, leading to a substantial body of literature. This study aims to delineate the present research status and trends in climate innovation through CiteSpace visual analysis. To comprehend the current landscape, prevalent terms, and research frontiers of deep learning for climate change research (DLCCR) within meteorology and atmospheric applications, we gathered 256 published papers spanning from 2018 to 2022 from the Web of Science (WOS) core database. Employing these articles, we conducted co-authorship, co-citation, and keyword co-occurrence analyses. The findings unveiled a steady rise in DLCCR publications over the last five years. However, the correlation between high yield and high-citation authorship appears inconsistent and weak. Notably, prolific authors in this domain included Zhang Z.L. and Bonnet P. Furthermore, leading institutions such as the Chinese Academy of Sciences (China), le Centre National de la Recherche Scientifique (France), and Nanjing University of Information Science and Technology (China) have played pivotal roles in advancing DLCCR. The primary contributors among high-yield countries primarily cluster in a select group comprising China, the USA, South Korea, and Germany. Identifying significant information gaps in numerical weather, atmospheric physics and processes, algorithm parametrizations, and extreme events, our study underscores the necessity for future researchers to focus on these and related subjects. This study provides valuable insights into research hotspots, developmental trajectories, and emerging frontiers, thereby delineating the knowledge structure in this field and highlighting directions for further climate innovation research.