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Natural hazards are of significant concern for engineering development in the offshore environment. Climate change phenomena are making these concerns even greater. The frequency and extent of natural hazards are undesirably evolving over time; so risk estimation for such events require special consideration. In most cases the existing extreme models (based on the extreme value theory) are unable to capture the changing frequency and extremeness of natural hazards. To capture the evolving frequency and extremeness of natural hazards and their effects on offshore process operations, an advanced probabilistic approach is proposed in this paper. The approach considers a heavy right tail probability model. The model parameter is estimated through the Bayesian inference. Hill and the SmooHill estimators are used to evaluate the lowest and highest exponent of the probability model. The application of the approach is demonstrated through extreme iceberg risk analysis for the Jeanne d’Arc basin. This study shows climate change or global warming is causing to appear a significant number of icebergs every year in the study area. Offshore structures are often designed to withstand the impact of 1 MT icebergs weight; however, the study observes large icebergs (10 MT weight) are sighted in recent years (14% of the total number of cited icebergs for the period of 2002–2017). As a result, the design philosophy needs to be revised. The proposed risk-based approach provides a robust design criterion for offshore structures.

The primary aim of this research is to consider the correlation among environmental factors in calculating 100 and 1000 years of extreme load design criteria. This is done by considering load as energy transferred from external environment to the offshore system. Also, incorporating spatial and temporal dependence of environmental variables in the context of offshore design. A bivariate extreme value distribution and a conditional joint return level function are developed using the Gumbel- Hougaard copula. The offshore design risk criteria are developed for the finer grid locations (0.10 × 0.10 latitude/longitude grid) considering joint extreme wind and wave energy. The developed approach is tested using data for the Flemish Pass basin off the east coast of Canada. Along with the primary aim, the impact of climate change is investigated (time and space variability) by implementing the proposed methodology in two cases: the periods from 1959 to 1988 and 1989 to 2018. This study observed that climate change has caused 30% less correlation between wind speed and wave height in recent years [1989-2018] compared to the period of 1959 to 1988. The proposed extreme design wind speed is 39.7 m/s, significant wave height is 16.4 m; their joint exceeding probability is 5.80E-05 over an annual basis for a scenario of 100-year.

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Abstract. This paper presents a computationally efficient stochastic approach to simulate atmospheric fields (specifically monthly mean temperature and precipitation) on large spatial-temporal scales. In analogy with Weather Generators (WG), the modelling approach can be considered a "Climate Generator" (CG). The CG can also be understood as a field-specific General Circulation climate Model (GCM) emulator. It invokes aspects of spatio-temporal downscaling, in this case mapping the output of an Energy Balance climate Model (EBM) to that of a higher resolution GCM. The CG produces a synthetic 5 climatology conditioned on various inputs. These inputs include sea level temperature from a fast low-resolution EBM, surface elevation, ice mask, atmospheric concentrations of carbon dioxide, methane, orbital forcing, latitude and longitude. Bayesian Artificial Neural Networks (BANN) are used for nonlinear regression against GCM output over North America, Antarctica and Eurasia.Herein we detail and validate the methodology. To impose natural variability in the CG (to make the CG indistinguishable 10 from a GCM) stochastic noise is added to each prediction. This noise is generated from a normal distribution with standard deviation computed from the 10% and 90% quantiles of the predictive distribution values from the BANNs for each time step.This derives from a key working assumption/approximation that the self-inferred predictive uncertainty of the BANNs is in good part due to the internal variability of the GCM climate. Our CG is trained against GCM (FAMOUS and CCSM) output for the last deglacial interval (22 ka to present year). For predictive testing, we compare the CG predictions against GCM Keywords 20Climate Generator, stochastic climate modelling, and emulation. 1Geosci. Model Dev. Discuss., https://doi