Increased population exposure to Amphan‐scale cyclones under future climates

Mitchell, Dann; Hawker, Laurence; Savage, James; Bingham, Rory J.; Lord, Natalie S.; Khan, Jamal Uddin; Bates, Paul; Durand, Fabien; Hassan, Ahmadul; Huq, Saleemul; Islam, Akm Saiful; Krien, Yann; Neal, Jeffrey; Sampson, Chris; Smith, Andy; Testut, Laurent

doi:10.1002/cli2.36

Cited by 10 publications

(5 citation statements)

References 71 publications

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“…We view this forecast-based approach as synthesising the storyline and probabilistic approaches to event attribution, keeping the event specificity of the storyline approach while still providing meaningful estimates of the changing risk of the extreme in question. Given that it is increasingly clear that we need to go beyond the meteorology of event attribution, and into the societal impacts 6,58 , we suggest that our approach would be particularly well placed to advance this agenda, especially in the context of extremes in a future climate.…”

Section: Discussionmentioning

confidence: 99%

Reliable heatwave attribution based on successful operational weather forecasts

Leach

Roberts

Heathcote

et al. 2022

Preprint

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Extreme weather attribution, quantifying the role of human influence in specific weather events, is of interest to scientists, adaptation planners and the general public. However, the devastating 2021 Pacific Northwest heatwave challenged conventional statistical approaches to attribution due to the absence of similar events in the historical record, and model-based approaches due to poor representation of key causal processes in current climate models. Here we use state-of-the-art operational medium-range and seasonal weather prediction systems, applied for the first time to this kind of climate question and unequivocally able to simulate the detailed physics of the heatwave in question, to show that human influence on the climate made this event at least 8 [2--30] times more likely to occur. Quantifying the absolute probability of such an unprecedented event is more challenging, but the length of the observational record suggests at least a multi-decade return-time in the current climate, with the likelihood doubling every 17 [10--50] years at the current rate of global warming. Our forecast-based approach synthesises the storyline approach, which examines human influence on the physical drivers of an event in a deterministic manner, and the probabilistic approach, which assesses how the frequency of a class of events has been affected by human influence. If developed as a routine service in a number of forecasting centres, it could provide reliable estimates of the changing probabilities of all extreme events that can be represented in forecast models, which is critical to supporting effective adaptation planning.

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Section: Discussionmentioning

confidence: 99%

Reliable heatwave attribution based on successful operational weather forecasts

Leach

Roberts

Heathcote

et al. 2022

Preprint

Self Cite

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“…The WGAN struggles to reach these high accumulated rainfall values of above 350 mm, but has estimated the spatial patterns of rainfall up to 350 mm to a good degree. Cyclone Amphan made landfall as a category 2 TC near the border of India and Bangladesh, with winds of 155 km/hr (95 mph) (Mitchell et al, 2022). The WGAN misses the high rainfall patch at 90.3°E, 25.5°N which corresponds to Nokrek National Park (India) which has a mountain range (elevation ∼1,200 m) after the relatively flat land of Bangladesh (Figure 8, final 2 panels).…”

Section: Wgan Performance At Predicting Accumulated Rainfallmentioning

confidence: 99%

“…

Tropical cyclones (TCs) are among the most impactful natural disasters, with associated flood hazards frequently being the leading cause of mortality and damages (Mitchell et al, 2022;Rezapour & Baldock, 2014). Current research indicates that each degree of ocean warming will likely raise average TC rainfall by 7%, while the proportion of category 4-5 TCs might also increase with global warming (Knutson et al, 2019).

…”

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confidence: 99%

Deep Learning for Downscaling Tropical Cyclone Rainfall to Hazard‐Relevant Spatial Scales

et al. 2023

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Flooding, driven in part by intense rainfall, is the leading cause of mortality and damages from the most intense tropical cyclones (TCs). With rainfall from TCs set to increase under anthropogenic climate change, it is critical to accurately estimate extreme rainfall to better support short‐term and long‐term resilience efforts. While high‐resolution climate models capture TC statistics better than low‐resolution models, they are computationally expensive. This leads to a trade‐off between capturing TC features accurately, and generating large enough simulation data sets to sufficiently sample high‐impact, low‐probability events. Downscaling can assist by predicting high‐resolution features from relatively cheap, low‐resolution models. Here, we develop and evaluate a set of three deep learning models for downscaling TC rainfall to hazard‐relevant spatial scales. We use rainfall from the Multi‐Source Weighted‐Ensemble Precipitation observational product at a coarsened resolution of ∼100 km, and apply our downscaling model to reproduce the original resolution of ∼10 km. We find that the Wasserstein Generative Adversarial Network is able to capture realistic spatial structures and power spectra and performs the best overall, with mean biases within 5% of observations. We also show that the model can perform well at extrapolating to the most extreme storms, which were not used in training.

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“…Risk assessments of coastal flooding require high-quality and high-resolution flood hazard data, typically in the form of flood inundation maps. Inundation maps are usually derived from hydraulic models, which use high resolution extreme sea level (ESL) exceedance probabilities as a key input (e.g., Bates et al, 2021;Mitchell et al, 2022). The development of coastal inundation maps is reliant on coastal boundary conditions points that vary in resolution depending on application.…”

Section: Introductionmentioning

confidence: 99%

Global application of a regional frequency analysis on extreme sea levels

Collings,

Quinn,

Haigh

et al. 2023

Preprint

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Abstract. Coastal regions face increasing threats from rising sea levels and extreme weather events, highlighting the urgent need for accurate assessments of coastal flood risk. This study presents a novel approach to estimating global Extreme Sea Level (ESL) exceedance probabilities, using a Regional Frequency Analysis (RFA) approach. The research combines observed and modelled hindcast data to produce a high-resolution (~1 km) dataset of ESL exceedance probabilities, including wave setup, along the entire global coastline, excluding Antarctica.The RFA approach offers several advantages over traditional methods, particularly in regions with limited observational data. It overcomes the challenge of short and incomplete observational records by substituting long historical records with a collection of shorter but spatially distributed records. This spatially distributed data not only retains the volume of information but also addresses the issue of sparse tide gauge coverage in less populated areas and developing nations. The RFA process is illustrated using Cyclone Yasi (2011) as a case study, demonstrating how the approach can significantly improve the characterisation of ESLs in regions prone to tropical cyclone activity.In conclusion, this study provides a valuable resource for quantifying global coastal flood risk, offering an innovative methodology that can contribute to preparing for, and mitigating against, coastal flooding.

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Increased population exposure to Amphan‐scale cyclones under future climates

Cited by 10 publications

References 71 publications

Reliable heatwave attribution based on successful operational weather forecasts

Reliable heatwave attribution based on successful operational weather forecasts

Deep Learning for Downscaling Tropical Cyclone Rainfall to Hazard‐Relevant Spatial Scales

Global application of a regional frequency analysis on extreme sea levels

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