Extreme Weather Events as Risk Factors for Oil and Gas Facilities

The article discusses issues related to the influence of changes in the frequency, intensity and duration of extreme meteorological events on the objects of the oil and gas industry. Climate indices recommended by World Meteorological Organization (WMO) are used as characteristics of the extreme events. The possibilities of using climatic indices for identifying physical risks applicable to the objects of the oil and gas industry are demonstrated. The features of the spatiotemporal variability of climatic indices are considered and the geographical regions of the Commonwealth of Independent States (CIS) are identified as the most and least vulnerable to changes in the extreme characteristics of the climate. The results obtained can be used both at the level of individual enterprises and facilities of the oil and gas industry, and in the development of regulations common for the industry.

Pelagic primary production in the coastal Mediterranean Sea: variability, trends, and contribution to basin-scale budgets

We estimated pelagic primary production (PP) in the coastal (<200 m depth) Mediterranean Sea from satellite-borne data, its contribution to basin-scale carbon fixation, its variability, and long-term trends during the period 2002–2016. Annual coastal PP was estimated at 0.041 Gt C, which approximately represents 12 % of total carbon fixation in the Mediterranean Sea. About 51 % of this production occurs in the eastern basin, whereas the western and Adriatic shelves contribute with ∼25 % each of total coastal production. Strong regional variability is revealed in coastal PP, from high-production areas (>300 g C m−2) associated with major river discharges to less productive provinces (<50 g C m−2) located in the southeastern Mediterranean. PP variability in the Mediterranean Sea is dominated by interannual variations, but a notable basin-scale decline (17 %) has been observed since 2012 concurring with a period of increasing sea surface temperatures in the Mediterranean Sea and positive North Atlantic Oscillation and Mediterranean Oscillation climate indices. Long-term trends in PP reveal slight declines in most coastal areas (−0.05 to −0.1 g C m−2 per decade) except in the Adriatic where PP increases at +0.1 g C m−2 per decade. Regionalization of coastal waters based on PP seasonal patterns reveals the importance of river effluents in determining PP in coastal waters that can regionally increase up to 5-fold. Our study provides insight into the contribution of coastal waters to basin-scale carbon balances in the Mediterranean Sea while highlighting the importance of the different temporal and spatial scales of variability.

Impact of Climate Change on Tourism

Climate change is an influencing phenomenon in present global perspective having a wide range of impacts at different levels within the society and industries. This chapter introduces the climate change basics and its major impacts on the global environment. Further, it describes the tourism industry and identifies its relationship with climate change. Scientists take different approaches to deal with climate indices and their application to identify the impact of climate change on the tourism industry. This chapter classifies the tourism industry into different industry type based on the regional characteristics links with the geographical locations. Climate effects have been discussed with different case studies and regions. Then the chapter has been concluded with the major overall impact of climate change in terms of temperature rise, sea level rise (SLR), change in precipitation and extreme events in some cases, on the tourism industry, and next steps to be taken towards sustainable tourism industry.

Long-Term Ice Conditions in Yingkou, a Coastal Region Northeast of the Bohai Sea, between 1951/1952 and 2017/2018: Modeling and Observations

Bohai Sea ice creates obstacles for maritime navigation and offshore activities. A better understanding of ice conditions is valuable for sea-ice management. The evolution of 67 years of seasonal ice thickness in a coastal region (Yingkou) in the Northeast Bohai Sea was simulated by using a snow/ice thermodynamic model, using local weather-station data. The model was first validated by using seasonal ice observations from field campaigns and a coastal radar (the season of 2017/2018). The model simulated seasonal ice evolution well, particularly ice growth. We found that the winter seasonal mean air temperature in Yingkou increased by 0.33 °C/decade slightly higher than air temperature increase (0.27 °C/decade) around Bohai Sea. The decreasing wind-speed trend (0.05 m/s perdecade) was a lot weaker than that averaged (0.3 m/s per decade) between the early 1970s and 2010s around the entire Bohai Sea. The multi-decadal ice-mass balance revealed decreasing trends of the maximum and average ice thickness of 2.6 and 0.8 cm/decade, respectively. The length of the ice season was shortened by 3.7 days/decade, and ice breakup dates were advanced by 2.3 days/decade. All trends were statistically significant. The modeled seasonal maximum ice thickness is highly correlated (0.83, p < 0.001) with the Bohai Sea Ice Index (BoSI) used to quantify the severity of the Bohai Sea ice condition. The freezing-up date, however, showed a large interannual variation without a clear trend. The simulations indicated that Bohai ice thickness has grown continuously thinner since 1951/1952. The time to reach 0.15 m level ice was delayed from 3 January to 21 January, and the ending time advanced from 6 March to 19 February. There was a significant weakening of ice conditions in the 1990s, followed by some recovery in 2000s. The relationship between large-scale climate indices and ice condition suggested that the AO and NAO are strongly correlated with interannual changes in sea-ice thickness in the Yingkou region.

Statistical prediction of seasonal cyclonic activity over North Indian Ocean

The Northeast monsoon season of October to December (OND) is the primary season of cyclonic activity over the North Indian Ocean (NIO). The mean number of days of cyclonic activity over NIO during this season is about 20 days. In the present study, statistical prediction for seasonal cyclonic activity over the North Indian Ocean during the cyclone season of October to December is attempted using well known climate indices and regional circulation features during the recent 30 years of 1971-2000.Potential predictors are identified using correlation analysis and optimum numbers of predictors are chosen using screening regression technique. A qualitative prediction for number of Cyclonic Disturbance (CD) days is attempted by analysing the conditional means of the number of CD days during OND over NIO for different intervals of each predictor based on the 30 year data of 1971-2000. Predictions and their validations for the subsequent test period of 2001 to 2009, based on this scheme, are discussed. An attempt for quantitative prediction is also made by developing a multiple regression model for prediction of number of CD days over the NIO during OND using the same predictors. The regression model accounts for 70% of the inter annual variance. The root mean square error of estimate is 5 days and the bias error is 0.36 days. The regression model is cross validated by Jackknife method for each individual year using the data of 29 years from the sample excluding the year under consideration. The model is also tested for independent dataset for the years 2001 to 2009. Salient features of the model performance are discussed.

Índice de Tendências Climáticas de Longo Prazo em Área Urbana na Amazônia Oriental

Esta pesquisa tem como objetivo analisar a variabilidade e a tendência de variáveis meteorológicas no longo prazo para caracterizar o clima urbano da cidade de Macapá-AP. Compreender a variabilidade dos índices climáticos em ambientes urbanos tende a mostrar possíveis interferências na qualidade de vida dos moradores locais, bem como torna possível comparar a realidade das cidades amazônicas em um contexto regional, nacional e mundial, contribuindo ao debate acadêmico. No presente caso as variáveis-chave são a temperatura do ar e a precipitação pluviométrica. A metodologia consiste nas seguintes etapas: a) coleta e consistência da série de dados por um período contínuo de 52 anos para o Estado do Amapá (1968 – 2020), b) a utilização do aplicativo RClimDex 1.1/IPCC para estimar as variações e as tendências climáticas locais utilizando-se 27 parâmetros climáticos extremos previstos pela equipe de peritos do CCI/CLIVAR e Climate Change Detection Monitoring and Indices (ETCCDMI). Os resultados obtidos acusaram treze indicadores estatísticos significativos (p<0,05), sugerindo tendência generalizada da elevação da temperatura média do ar na zona urbana da cidade. Como consequência, estes indicadores mostraram não somente uma significativa elevação das temperaturas máximas, médias e mínimas, mas também quais são os indicadores mais coerentemente associados com tendências de aquecimento temporal de cidades amazônicas, tanto para períodos diurnos quanto para períodos noturnos. Esse comportamento dos indicadores confirma a hipótese de predisposição a formação de ilha de calor em Macapá. Esta tendência mudou significativamente a partir de 2010. Index of Long Term Climate Trends in Urban Area in the Eastern AmazonA B S T R A C T This research aims to analyze the variability and trend of meteorological variables in the long term to characterize the urban climate of the city of Macapá-AP. Understanding the variability of climate indices in urban environments reveals possible interferences in the quality of life of the inhabitants, especially in urban locations. However, it has been relatively difficult to quantify trends in historical series that reliably represent climate indices relevant to the reality of Amazonian cities, both at a local and regional level. In the present case, the key variables analyzed were air temperature and rainfall. The methodology followed the following steps: a) collection and consistency of the data series over a continuous period of 52 years for the State of Amapá (1968–2020), b) using the data series by the RClimDex 1.1/IPCC application to estimate the local climate variations and trends using 27 extreme weather parameters predicted by the CCI/CLIVAR and Climate Change Detection Monitoring and Indices (ETCCDMI) team of experts. The results showed thirteen significant statistical indicators (p<0.05), suggesting a general trend towards an increase in the average air temperature in the urban area of the city. As a consequence, these indicators showed not only a significant increase in maximum, average and minimum temperatures, but also the indicators most coherently associated with temporal warming trends in Amazonian cities. So many that these effects seem to affect both the day and night periods, confirming the hypothesis of a predisposition to the formation of an urban heat island, with a significant change in this trend from 2010 onwards. Keywords: RClimDex 1.1, climate change indice, Macapá, Amapá

Variability and trends of climate extremes indices from the observed and downscaled GCMs data over 1950–2020 period in Chattogram City, Bangladesh

This study was intended to evaluate the variability and trends of climate extremes by incorporating daily data from Chattogram station and from the high-resolution Coordinated Regional Climate Downscaling Experiment (CORDEX) for two different time series. Here, we also focused on evaluating the performance of the selected RCMs (CanESM2, CSIRO, and GFDL from CORDEX) using Taylor diagrams and heat map analysis. Twenty-two extreme climate indices from ETCCDI were computed for 1950–1989 and 1990–2020 periods. Mann–Kendall and Sen's slope test were performed to estimate the trends from the indices from both station and RCMs data. Highly significant increasing trend for the warm days and warm nights’ frequencies were found, whereas, the frequency of cold days and cold nights indicated significantly decreasing trend. On the other hand, mild increasing trend in 1-day and 5-day maximum rainfall was detected. Also, the average annual precipitation has increased by 6% from the 1950–1989 to 1990–2020 period. During the last three decades, the region has experienced more heavier rainfall in the monsoon but increased water stress in the dry season. The two-fold effects of climate change on the local hydrology revealed by this study need to be addressed properly for the sustainable development of this region.

Véderdő telepítésének lehetséges jövőbeli hatásai Szeged hőterhelésében

n this study the possible future thermal consequences of a fictional protective forest around Szeged were examined. The aims of this installation are the adaptation to climate change and reducing air pollution. However, the complex effects of local urban climate should be taken into consideration as well. Therefore, the changing of heat load due to the forest was studied by presenting the change of climate indices during the 21st century. In order to simulate the local circumstances of the city, a MUKLIMO_3 local scale model was applied. EURO-CORDEX regional model simulations ensured the climate data for periods 2021–2050 and 2071–2100 using scenarios RCP4.5 and RCP8.5. Our results show that the effect of the protective forest is not favourable in certain parts of the city due to the reduction or block of the ventilation. The forest induces cooling effect mostly during daytime, but the extent of unfavourable effects exceeds the advantages especially at night time.