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.

Evaluation of Regional Climate Models (RCMs) Using Precipitation and Temperature-Based Climatic Indices: A Case Study of Florida, USA

The overarching objective of this study was to evaluate the performance of nine precipitation-based and twelve temperature-based climatic indices derived from four regional climate models (CRCM5-UQUAM, CanRCM4, RCA4 and HIRHAM5) driven by three global circulation models (CanESM2, EC-EARTH and MPI-ESM-LR) and their ensemble mean for the reference period of 31 years (1975–2005). The absolute biases, pattern correlation, the reduction of variance (RV) and the Standardized Precipitation Evapotranspiration Index (SPEI at 3-, 6- and 12-month aggregate periods) techniques were used to evaluate the climate model simulations. The result, in general, shows each climate model has a skill in reproducing at least one of the climatic indices considered in this study. Based on the pattern correlation result, however, EC-EARTH.HIRHAM5 and MPI-ESM-LR.CRCM5-UQAM RCMs showed a relatively good skill in reproducing the observed climatic indices as compared to the other climate model simulations. EC-EARTH.RCA4, CanESM2.RCA4 and MPI-ESM-LR.CRCM5-UQAM RCMs showed a good skill when evaluated using the reduction of variance. The ensemble mean of the RCMs showed relatively better skill in reproducing the observed temperature-based climatic indices as compared to the precipitation-based climatic indices. There were no exceptional differences observed among the performance of the climate models compared to the SPEI, but CanESM2.CRCM5-UQAM, EC-EARTH.RCA4 and the ensemble mean of the RCMs performed relatively good in comparison to the other climate models. The good performance of some of the RCMs has good implications for their potential application for climate change impact studies and future trend analysis of extreme events. They could help in developing an early warning system to mitigate and prepare for possible future impacts of climate extremes (e.g., drought) and vulnerability to climate change across Florida.

Computer Simulations of Air Quality and Bio-Climatic Indices for the City of Sofia

Air pollution is responsible for many adverse effects on human beings. Thermal discomfort, on the other hand, is able to overload the human body and eventually provoke health implications due to the heat imbalance. Methods: The aim of the presented work is to study the behavior of two bio-climatic indices and statistical characteristics of the air quality index for Sofia city—the capital of Bulgaria for the period 2008–2014. The study is based on the WRF-CMAQ model system simulations with a spatial resolution of 1 km. The air quality is estimated by the air quality index, taking into account the influence of different pollutants and the thermal conditions by two indices, respectively, for hot and cold weather. It was found that the recurrence of both the heat and cold index categories and of the air quality categories have heterogeneous space distribution and well manifested diurnal and seasonal variability. For all of the situations, only O3 and PM10 are the dominant pollutants—these which determine the AQI category. It was found that AQI1, AQI2, and AQI3, which fall in the “Low” band, have the highest recurrence during the different seasons, up to more than 70% in some places and situations. The recurrence of AQI10 (very high) is rather small—no more than 5% and concentrated in small areas, mostly in the city center. The Heat index of category “Danger” never appears, and the Heat index of category “Extreme caution” appears only in the spring and summer with the highest recurrence of less than 5% in the city center. For the Wind-chill index category, “Very High Risk” never appears, and the category “High Risk” appears with a frequency of about 1–2%. The above leads to the conclusion that both from a point of view of bioclimatic and air quality indices, the human health risks in the city of Sofia are not as high.

Accuracy of Interpolated Versus In-Vineyard Sensor Climate Data for Heat Accumulation Modelling of Phenology

Background and AimsIn response to global heating, accurate climate data are required to calculate climatic indices for long-term decisions about vineyard management, vineyard site selection, varieties planted and to predict phenological development. The availability of spatially interpolated climate data has the potential to make viticultural climate analyses possible at specific sites without the expense and uncertainty of collecting climate data within vineyards. The aim of this study was to compare the accuracy and precision of climatic indices calculated using an on-site climate sensor and an interpolated climate dataset to assess whether the effect of spatial variability in climate at this fine spatial scale significantly affects phonological modelling outcomes.Methods and ResultsFour sites comprising two topographically homogenous vineyards and two topographically diverse vineyards in three wine regions in Victoria (Australia) were studied across four growing seasons. A freely available database of interpolated Australian climate data based on government climate station records (Scientific Information for Land Owners, SILO) provided temperature data for grid cells containing the sites (resolution 0.05° latitude by 0.05° longitude, approximately 5 km × 5 km). In-vineyard data loggers collected temperature data for the same time period. The results indicated that the only significant difference between the two climate data sources was the minimum temperatures in the topographically varied vineyards where night-time thermal layering is likely to occur.ConclusionThe interpolated climate data closely matched the in-vineyard recorded maximum temperatures in all cases and minimum temperatures for the topographically homogeneous vineyards. However, minimum temperatures were not as accurately predicted by the interpolated data for the topographically complex sites. Therefore, this specific interpolated dataset was a reasonable substitute for in-vineyard collected data only for vineyard sites that are unlikely to experience night-time thermal layering.Significance of the StudyAccess to accurate climate data from a free interpolation service, such as SILO provides a valuable tool tomanage blocks or sections within vineyards more precisely for vineyards that do not have a weather station on site. Care, nevertheless, is required to account for minimum temperature discrepancies in topographically varied vineyards, due to the potential for cool air pooling at night, that may not be reflected in interpolated climate data.

Temporal Variability, Trends of Climatic Variables and Drought Analysis of Rajshahi and Sylhet District, Bangladesh

The study was conducted to quantify the change of selected climatic variables (rainfall, relative humidity, maximum and minimum temperature) over 50 years at Rajshahi and Sylhet districts in Bangladesh. Annual, seasonal, and monthly climatic data comparisons have been executed between 1968-1992 and 1993-2017 through trend analysis. The Mann-Kendall statistic and Sen's Slope model were used to reveal the trends and estimate the magnitude of change respectively. Prediction of the climatic variable of 10 years (2018-2027) was made based on the ARAR algorithm using MaxStat Pro software. Rainfall data were used to analyze drought by using climatic indices (De Mortone Aridity Index, IdM; Seleaninov Hydrothermic Index, IhS; Donciu Climate Index, IcD). Average rainfall was decreasing dramatically in monsoon season at Rajshahi and in both premonsoon and monsoon seasons at Sylhet. The negative change of average rainfall in the monsoon at Rajshahi from 1968-1992 to 1993-2017 was found 29.17 mm. The maximum temperature was increasing in all seasons in both Rajshahi and Sylhet. Annual Mannkendall trend test and Sen’s slope revealed that relative humidity was decreasing and maximum temperature was increasing significantly at Sylhet for the period 1993-2017. At Rajshahi, during 1968-1992, relative humidity was increasing by 0.247 % per year, and minimum temperature was decreasing 0.049℃ per year. Rainfall was decreasing insignificantly in both time scales. ARAR algorithm predicted that average maximum temperature might become comparatively higher than the previous 50 years. 1992 and 2010 were identified as drought years from all climatic indices, and 1969, 1981, and 1997 as excessive wet years at Rajshahi. No drought events were identified during 1968-2017 at Sylhet and the year 2017 to be an excessively wet year. IhS predicted 2020, 2025, and 2027 as drought years and 2024 as an excessive wet year at Sylhet. Asiat. Soc. Bangladesh, Sci. 46(2): 133-141, December 2020