North Atlantic Winter Storm Activity in Modern Reanalyses and Pressure-Based Observations

Feser, Frauke; Krueger, Oliver; Woth, Katja; Garderen, Linda van

doi:10.1175/jcli-d-20-0529.1

Cited by 12 publications

(4 citation statements)

References 59 publications

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“…Feser et al (2015) reviewed a wide variety of studies of wind speeds in Europe over the past several decades and their results suggested reanalyses and models tend to produce more positive storm trends than observational-based studies. Recent studies (Kreuger et al, 2019;Feser et al, 2021) indicate surface pressure gradients in reanalyses are consistent with observed (stationary) values over the past few decades, at scales of several hundred km. Therefore, any non-stationarity in observation systems would have to occur in the range from a few hundred km down to the sub-km scale of wind gusts that cause damage, in order to explain the loss trend.…”

Section: Discussionsupporting

confidence: 64%

Decadal variations of European windstorms: linking research to insurance applications

Cusack¹

2022

Preprint

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Abstract. The insurance sector is affected by decadal-scale variations in annual European windstorm losses amounting to a few billion euros, yet has not applied recent advances in understanding and predicting this variability to their pricing of windstorm risk. This is mainly due to an unknown relation between insured wind losses and meteorological definitions of storminess used in research. This study aimed to reduce this uncertainty. A history of windstorm insurance losses over the past 72 years was developed from winds in weather reanalyses. Then, typical storm proxies used by researchers, such as the North Atlantic Oscillation (NAO) and the Arctic Oscillation, were compared to the new windstorm loss record. The relationship between the proxies and losses has two distinct regimes: highly consistent from 1950 up to the 2000s, then a divergence in the past 10 to 15 years. The recent separation is large and robust, with high confidence that modern values of researchers’ proxies approach levels last seen 30 years ago, whereas decadal-mean losses are far lower today than in the 1980s and ‘90s. The cause of this divergence was explored. Storm damages are most closely associated with peak gusts deriving their momentum from winds in the free troposphere, and pressure gradients at the surface used in typical climate indices can only partially describe higher level winds. Based on this reasoning, a new Hemispheric Geostrophy Index (HGI) was defined as the difference in 700 hPa heights between the tropics and the Arctic. It was found to vary coherently with decadal losses in the past, and crucially retains this consistency in the past 15 years too. Breaking down the HGI into component parts revealed that lower storminess in recent times is linked to ongoing reductions in poleward baroclinicity. Further development of loss history and climate indices would help bridge decadal research to insurance applications.

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Section: Discussionsupporting

confidence: 64%

Decadal variations of European windstorms: linking research to insurance applications

Cusack¹

2022

Preprint

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“…In Northern Hemisphere winter, recent studies claim an increase in storm track activity related to Arctic warming. Recent research (Feser et al, 2021) reveals no clear trend but reports an increasing similarity over time in reanalyses, observations, and dynamically downscaled model data.…”

Section: Wind Stormsmentioning

confidence: 98%

Natural hazards and extreme events in the Baltic Sea region

et al. 2022

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Abstract. A natural hazard is a naturally occurring extreme event that has a negative effect on people and society or the environment. Natural hazards may have severe implications for human life and can potentially generate economic losses and damage ecosystems. A better understanding of their major causes, probability of occurrence, and consequences enables society to be better prepared to save human lives as well as to invest in adaptation options. Natural hazards related to climate change are identified as one of the Grand Challenges in the Baltic Sea region. Here, we summarize existing knowledge about extreme events in the Baltic Sea region with a focus on the past 200 years as well as on future climate scenarios. The events considered here are the major hydro-meteorological events in the region and include wind storms, extreme waves, high and low sea levels, ice ridging, heavy precipitation, sea-effect snowfall, river floods, heat waves, ice seasons, and drought. We also address some ecological extremes and the implications of extreme events for society (phytoplankton blooms, forest fires, coastal flooding, offshore infrastructure, and shipping). Significant knowledge gaps are identified, including the response of large-scale atmospheric circulation to climate change and also concerning specific events, for example, the occurrence of marine heat waves and small-scale variability in precipitation. Suggestions for future research include the further development of high-resolution Earth system models and the potential use of methodologies for data analysis (statistical methods and machine learning). With respect to the expected impacts of climate change, changes are expected for sea level, extreme precipitation, heat waves and phytoplankton blooms (increase), and cold spells and severe ice winters (decrease). For some extremes (drying, river flooding, and extreme waves), the change depends on the area and time period studied.

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“…Past studies have conducted broad assessments of winter storm climatology and impacts [6][7][8][9][10][11][12][13][14][15] . These previous analyses often considered differences among regional winter storms ranging from the Western United States and High Plains to the Mid-Atlantic and East Coast.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, these storms tend to impact less populated, more rural regions in the Intermountain West. East Coast and Mid-Atlantic winter storms, by contrast, impact the dense urban population centers along the Interstate 95 Corridor and often have ample access to moisture resulting in larger impacts [6,9,[12][13][14][15] . Several studies have attempted to categorize the impacts of winter storms through metrics known collectively as winter severity indices [16][17][18][19][20][21][22][23] .…”

Section: Introductionmentioning

confidence: 99%

Correlating Machine Learning Classi cation of Traf c Camera Images with Snow-related Vehicular Crashes in New York State

Chang¹,

Walker

2022

prev. and treat. of nat. n.a.

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Millions of motor vehicle crashes and tens of thousands of resulting deaths occur each year in the United States. While it is well known that wintry conditions make driving more difficult and dangerous, it is difficult to quantify and communicate the threat to motorists, especially in real time. This proof-of-concept research uses machine learning (ML) to approach this problem in a new way by creating a ML model that can identify snow on the road froma traf c camera image. This information is coupled with the number of coincident vehicular crashes to provide detailedconsideration of the impact of snow on the road to motorists and transportation agency decision-makers. It was foundthat, during meteorological winter, when the ML model determined there to be snow on the road in a traf c camera image, the chance of a vehicular crash pairing with that traf c camera increased by 61%. The systems developed as part of this research have potential to assist roadway of cials in assessing risk in real time and making informed decisionsabout snow removal and road closures. Moreover, the implementation of in-vehicle weather hazard information could promote driver safety and allow motorists to adjust their driving behavior and travel decision making as well.

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North Atlantic Winter Storm Activity in Modern Reanalyses and Pressure-Based Observations

Cited by 12 publications

References 59 publications

Decadal variations of European windstorms: linking research to insurance applications

Decadal variations of European windstorms: linking research to insurance applications

Natural hazards and extreme events in the Baltic Sea region

Correlating Machine Learning Classi cation of Traf c Camera Images with Snow-related Vehicular Crashes in New York State

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