Improving Oil Slick Trajectory Simulations with Bayesian Optimization

Accurate simulations of oil spill trajectories are essential for supporting practitioners' response and mitigating environmental and socioeconomic impacts. Numerical models, such as MEDSLIK-II, simulate advection, dispersion, and transformation processes of oil particles. However, simulations heavily rely on accurate parameter tuning, still based on expert knowledge and manual calibration. To overcome these limitations, we integrate the MEDSLIK-II numerical oil spill model with a Bayesian optimization framework to iteratively estimate the best physical parameter configuration that yields simulation closer to satellite observations of the slick. We focus on key parameters, such as horizontal diffusivity and drift factor, maximizing the Fraction Skill Score (FSS) as a measure of spatio-temporal overlap between simulated and observed oil distributions. We validate the framework for the Baniyas oil incident that occurred in Syria between August 23 and September 4, 2021, which released over 12,000 $m^3$ of oil. We show that, on average, the proposed approach systematically improves the FSS from 5.82% to 11.07% compared to control simulations initialized with default parameters. The optimization results in consistent improvement across multiple time steps, particularly during periods of increased drift variability, demonstrating the robustness of our method in dynamic environmental conditions.

Transferring climate change knowledge

Accurate climate projections are required for climate adaptation and mitigation. Earth system model simulations, used to project climate change, inherently make approximations in their representation of small-scale physical processes, such as clouds, that are at the root of the uncertainties in global mean temperature's response to increased greenhouse gas concentrations. Several approaches have been developed to use historical observations to constrain future projections and reduce uncertainties in climate projections and climate feedbacks. Yet those methods cannot capture the non-linear complexity inherent in the climate system. Using a Transfer Learning approach, we show that Machine Learning, in particular Deep Neural Networks, can be used to optimally leverage and merge the knowledge gained from Earth system model simulations and historical observations to more accurately project global surface temperature fields in the 21st century. For the Shared Socioeconomic Pathways (SSPs) 2-4.5, 3-7.0 and 5-8.5, we refine regional estimates and the global projection of the average global temperature in 2081-2098 (with respect to the period 1850-1900) to 2.73{\deg}C (2.44-3.11{\deg}C), 3.92{\deg}C (3.5-4.47{\deg}C) and 4.53{\deg}C (3.69-5.5{\deg}C), respectively, compared to the unconstrained 2.7{\deg}C (1.65-3.8{\deg}C), 3.71{\deg}C (2.56-4.97{\deg}C) and 4.47{\deg}C (2.95-6.02{\deg}C). Our findings show that the 1.5{\deg}C threshold of the Paris' agreement will be crossed in 2031 (2028-2034) for SSP2-4.5, in 2029 (2027-2031) for SSP3-7.0 and in 2028 (2025-2031) for SSP5-8.5. Similarly, the 2{\deg}C threshold will be exceeded in 2051 (2045-2059), 2044 (2040-2047) and 2042 (2038-2047) respectively. Our new method provides more accurate climate projections urgently required for climate adaptation.

An Ensemble Machine Learning Approach for Tropical Cyclone Detection Using ERA5 Reanalysis Data

Tropical Cyclones (TCs) are counted among the most destructive phenomena that can be found in nature. Every year, globally an average of 90 TCs occur over tropical waters, and global warming is making them stronger, larger and more destructive. The accurate detection and tracking of such phenomena have become a relevant and interesting area of research in weather and climate science. Traditionally, TCs have been identified in large climate datasets through the use of deterministic tracking schemes that rely on subjective thresholds. Machine Learning (ML) models can complement deterministic approaches due to their ability to capture the mapping between the input climatic drivers and the geographical position of the TC center from the available data. This study presents a ML ensemble approach for locating TC center coordinates, embedding both TC classification and localization in a single end-to-end learning task. The ensemble combines TC center estimates of different ML models that agree about the presence of a TC in input data. ERA5 reanalysis were used for model training and testing jointly with the International Best Track Archive for Climate Stewardship records. Results showed that the ML approach is well-suited for TC detection providing good generalization capabilities on out of sample data. In particular, it was able to accurately detect lower TC categories than those used for training the models. On top of this, the ensemble approach was able to further improve TC localization performance with respect to single model TC center estimates, demonstrating the good capabilities of the proposed approach.