Forecasted attribution of the human influence on Hurricane Florence

Reed, Kevin A.; Stansfield, Alyssa M.; Wehner, Michael; Zarzycki, Colin M.

doi:10.1126/sciadv.aaw9253

Cited by 83 publications

(67 citation statements)

References 43 publications

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“…Events ending in 2015 in the UK 71 and the western north Pacific; 72 in 2017 in the USA; 73 in 2018 in the USA; 74 Events have been listed according to the year in which they ended. In some countries and regions, multiple events in the same year were studied.…”

Section: Indicator 123: Lethality Of Extreme Weather Events-headlinmentioning

confidence: 99%

“….com Vol 396 trans portation, waste, and water) that have a direct impact on one or more of the basic determinants of health (ie, food, water, air, or shelter) and have been linked to health outcomes in the published literature. A full description of the methodology can be found in the appendix (pp[73][74][75].Spending on climate change adaptation within the health-care sector increased by 12•7% to $18•4 billion in 2018-19 compared with data from 2017-18 (figure 10). Spending on health adaptation made up 5•3% of all adaptation spending globally in 2018-19, a share higher than 5% for the first time.…”

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confidence: 99%

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The 2020 report of The Lancet Countdown on health and climate change: responding to converging crises

et al. 2021

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Section: Indicator 123: Lethality Of Extreme Weather Events-headlinmentioning

confidence: 99%

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confidence: 99%

The 2020 report of The Lancet Countdown on health and climate change: responding to converging crises

et al. 2021

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“…This technique is similar to the 'pseudo-global warming' approach and has been extensively used for storyline EA of tropical cyclones (TCs). Previous studies conducted a storyline EA aimed at TC-induced extreme events based on RCM and variable-resolution GCM simulations and showed significant impacts of global warming on rainfall amounts, wind speed, storm size and storm surge height [20][21][22][23][24] . However, storyline attribution cannot make a statement about the changes in cyclogenesis rates or the probability because it is conditional on the event's underlying existence 20,24 .…”

Section: Introductionmentioning

confidence: 99%

Advanced risk-based event attribution for heavy regional rainfall events

et al. 2020

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Risk-based event attribution (EA) science involves probabilistically estimating alterations of the likelihoods of particular weather events, such as heat waves and heavy rainfall, owing to global warming, and has been considered as an effective approach with regard to climate change adaptation. However, risk-based EA for heavy rain events remains challenging because, unlike extreme temperature events, which often have a scale of thousands of kilometres, heavy rainfall occurrences depend on mesoscale rainfall systems and regional geographies that cannot be resolved using general circulation models (GCMs) that are currently employed for risk-based EA. Herein, we use GCM large-ensemble simulations and high-resolution downscaled products with a 20-km non-hydrostatic regional climate model (RCM), whose boundary conditions are obtained from all available GCM ensemble simulations, to show that anthropogenic warming increased the risk of two record-breaking regional heavy rainfall events in 2017 and 2018 over western Japan. The events are examined from the perspective of rainfall statistics simulated by the RCM and from the perspective of background large-scale circulation fields simulated by the GCM. In the 2017 case, precipitous terrain and a static pressure pattern in the synoptic field helped reduce uncertainty in the dynamical components, whereas in the 2018 case, a static pressure pattern in the synoptic field provided favourable conditions for event occurrence through a moisture increase under warmer climate. These findings show that successful risk-based EA for regional extreme rainfall relies on the degree to which uncertainty induced by the dynamic components is reduced by background conditioning.

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“…Another notable wet zone is located in the upper Missouri, which may be related to multiple heavy rainfall events that occurred toward the end of August 2017 (https://weather.gov). September 2018 is most well known for Hurricane Florence (North and South Carolina, landfall September 14, 2018) (Reed et al., 2020). The Upper Missouri, Ohio, and Upper Mississippi regions were also flooded as a result of Tropical Storm Gordon (https://weather.gov).…”

Section: Resultsmentioning

confidence: 99%

Reconstruction of GRACE Total Water Storage Through Automated Machine Learning

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et al. 2021

Water Resources Research

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The Gravity Recovery and Climate Experiment (GRACE) satellite mission and its follow‐on, GRACE‐FO, have provided unprecedented opportunities to quantify the impact of climate extremes and human activities on total water storage at large scales. The ∼1‐year data gap between the two GRACE missions needs to be filled to maintain data continuity and maximize mission benefits. In this study, we applied an automated machine learning (AutoML) workflow to perform gridwise GRACE‐like data reconstruction. AutoML represents a new paradigm for optimal algorithm selection, model structure selection, and hyperparameter tuning, addressing some of the most challenging issues in machine learning applications. We demonstrated the workflow over the conterminous U.S. (CONUS) using six types of machine learning models and multiple groups of meteorological and climatic variables as predictors. Results indicate that the AutoML‐assisted gap filling achieved satisfactory performance over the CONUS. On the testing data, the mean gridwise Nash‐Sutcliffe efficiency is around 0.85, the mean correlation coefficient is around 0.95, and the mean normalized root‐mean‐square‐error is about 0.09. Trained models maintain good performance when extrapolating to the mission gap and to GRACE‐FO periods (after June 2017). Results further suggest that no single algorithm provides the best predictive performance over the entire CONUS, stressing the importance of using an end‐to‐end workflow to train, optimize, and combine multiple machine learning models to deliver robust performance, especially when building large‐scale hydrological prediction systems and when predictor importance exhibiting strong spatial variability.

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Forecasted attribution of the human influence on Hurricane Florence

Cited by 83 publications

References 43 publications

The 2020 report of The Lancet Countdown on health and climate change: responding to converging crises

The 2020 report of The Lancet Countdown on health and climate change: responding to converging crises

Advanced risk-based event attribution for heavy regional rainfall events

Reconstruction of GRACE Total Water Storage Through Automated Machine Learning

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