Estimation of the maximum annual number of North Atlantic tropical cyclones using climate models

Lavender, Sally L.; Walsh, Kevin; Caron, Louis-Philippe; King, Malcolm J.; Monkiewicz, Sam; Guishard, Mark; Zhang, Qiong; Hunt, B. G.

doi:10.1126/sciadv.aat6509

Cited by 19 publications

(22 citation statements)

References 35 publications

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“…Kerry Emanuel from the Lorenz Center, Massachusetts Institute of Technology, presented a keynote on 'Tropical Cyclones, Medicanes, and Polar Lows in a Warming World'. He started by showing compelling observational evidence for significantly increasing trend in the historical records for major hurricanes in the North Atlantic during 1851-2016 (Lavender et al, 2018) and for global Tropical Cyclone (TC) intensity during 1980-2016 (Zhan and Wang, 2017). Based on a simple Carnot cycle model to estimate the maximum intensity of TCs under somewhat warmer conditions, he showed that a 40-50% increase in the destructive potential of hurricanes and 12-15% increase in wind speed can be expected in a warmer climate caused by a doubling of CO2 (Emanuel, 1987).…”

Section: Presentations By the Invited Speakersmentioning

confidence: 99%

“…Swedish scientists have also been playing a major role in the development of EC-Earth model which provides global, high resolution and many ensemble member simulations. A recent study shows that Millennia-long simulation with EC-Earth can provide statistical estimation of maximum annual number of tropical cyclones (Lavender et al, 2018). hourly precipitation from all model grid points in Sweden.…”

Section: Group One On Storms and Cyclonesmentioning

confidence: 99%

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Summary of a workshop on extreme weather events in a warming world organized by the Royal Swedish Academy of Sciences

Chen¹,

Rodhe²,

Emanuel³

et al. 2020

Tellus B: Chemical and Physical Meteorology

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Climate change is not only about changes in means of climatic variables such as temperature, precipitation and wind, but also their extreme values which are of critical importance to human society and ecosystems. To inspire the Swedish climate research community and to promote assessments of international research on past and future changes in extreme weather events against the global climate change background, the Earth Science Class of the Royal Swedish Academy of Sciences organized a workshop entitled 'Extreme weather events in a warming world' in 2019. This article summarizes and synthesizes the key points from the presentations and discussions of the workshop on changes in floods, droughts, heat waves, as well as on tropical cyclones and extratropical storms. In addition to reviewing past achievements in these research fields and identifying research gaps with a focus on Sweden, future challenges and opportunities for the Swedish climate research community are highlighted.

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Section: Presentations By the Invited Speakersmentioning

confidence: 99%

Section: Group One On Storms and Cyclonesmentioning

confidence: 99%

Summary of a workshop on extreme weather events in a warming world organized by the Royal Swedish Academy of Sciences

Chen¹,

Rodhe²,

Emanuel³

et al. 2020

Tellus B: Chemical and Physical Meteorology

Self Cite

View full text Add to dashboard Cite

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“…Time series refers to the persistent recording of a phenomenon along time, a continuous and intermittent unfolding of chronological events subdivided into past, present, and future. In the last decades, time series analysis has been vital to predict dynamic phenomena on a wide range of applications, such as climate change [1]- [4], financial market [5]- [7], land-use monitoring [8]- [10], anomaly detection [11]- [13], energy consumption, and price forecasting [14]- [16], apart from epidemiology and healthcarerelated studies [17]- [22]. On such applications, an effective data-driven decision requires precise forecasting based on time series [23].…”

Section: Introductionmentioning

confidence: 99%

Pay Attention to Evolution: Time Series Forecasting With Deep Graph-Evolution Learning

Spadon

Hong

Brandoli

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

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Time-series forecasting is one of the most active research topics in artificial intelligence. It has the power to bring light to problems in several areas of knowledge, such as epidemiological studies, healthcare inference, and climate change analysis. Applications in real-world time series should consider two factors for achieving reliable predictions: modeling dynamic dependencies among multiple variables and adjusting the model's intrinsic hyperparameters. An open gap in the literature is that statistical and ensemble learning approaches systematically present lower predictive performance than deep learning methods. The existing applications consistently disregard the data sequence aspect entangled with multivariate data represented in more than one time series. Conversely, this work presents a novel neural network architecture for time-series forecasting that combines the power of graph evolution with deep recurrent learning on distinct data distributions, named after Recurrent Graph Evolution Neural Network (REGENN). The idea is to infer multiple multivariate relationships between co-occurring time-series by assuming that the temporal data depends not only on inner variables and intra-temporal relationships (i.e., observations from itself) but also on outer variables and inter-temporal relationships (i.e., observations from other-selves). An extensive set of experiments was conducted comparing REGENN with tens of ensemble methods and classical statistical ones. The results outperformed both statistical and ensemble-learning approaches, showing an improvement of 64.87% over the competing algorithms on the SARS-CoV-2 dataset of the renowned John Hopkins University for 188 countries simultaneously. For further validation, we tested our architecture in two other public datasets of different domains, the PhysioNet Computing in Cardiology Challenge 2012 and Brazilian Weather datasets. We also analyzed the Evolution Weights arising from the hidden layers of REGENN to describe how the variables of the dataset interact with each other; and, as a result of looking at inter and intra-temporal relationships simultaneously, we concluded that time-series forecasting is majorly improved if paying attention to how multiple multivariate data synchronously evolve.

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“…Recent research has demonstrated the application of dynamical models in reconstructing historical North Atlantic TC events using genesis parameters (Lavender et al, 2018). Recent research has demonstrated the application of dynamical models in reconstructing historical North Atlantic TC events using genesis parameters (Lavender et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…There is a need to reconstruct TC counts in the SWP to not only place the observed recent decline in the context of longer‐term natural variability but importantly identify whether recent trends are unusual or if they are part of a longer‐term cycle. Recent research has demonstrated the application of dynamical models in reconstructing historical North Atlantic TC events using genesis parameters (Lavender et al, ). While this methodology is useful, it is complicated by modeling the exceptionally chaotic climate system, parameterizing conditions required for tropical cyclogenesis, and evaluating the efficacy of a wide range of competing climate models.…”

Section: Introductionmentioning

confidence: 99%

Historical Variability of Southwest Pacific Tropical Cyclone Counts Since 1855

Magee

Verdon‐Kidd

2019

Geophysical Research Letters

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Tropical cyclones (TCs) represent a significant threat to people and places of the southwest Pacific (0°–35°S, 135°E–120°W). TC modeling is hindered by exceptionally short TC records (typically from 1970/1982 to present) because the brevity of TC data does not allow us to appropriately quantify TC‐related risk and/or identify long‐term trends/variability. Therefore, in this paper, we derive a series of statistical hindcast models to reconstruct TC counts in the western (135°E–160°E) and eastern southwest Pacific (160°E–120°W) regions to 1855. Reanalysis data representing Indo‐Pacific sea surface temperature variability and genesis parameters required for tropical cyclogenesis were identified and trained on post‐1970 TC observations and applied to the pre‐1970 period. By hindcasting up to an additional 115 TC seasons, using four separate hindcasts, this study offers a unique perspective on historical TC variability and reiterates the need to place the recent downward trend in TC counts within the context of long‐term variability.

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Estimation of the maximum annual number of North Atlantic tropical cyclones using climate models

Abstract: Millennia-long climate model simulations suggest that the 2005 North Atlantic hurricane season produced close to the maximum number of tropical cyclones that this basin can sustain in a given year under current climate conditions.

Cited by 19 publications

References 35 publications

Summary of a workshop on extreme weather events in a warming world organized by the Royal Swedish Academy of Sciences

Summary of a workshop on extreme weather events in a warming world organized by the Royal Swedish Academy of Sciences

Pay Attention to Evolution: Time Series Forecasting With Deep Graph-Evolution Learning

Historical Variability of Southwest Pacific Tropical Cyclone Counts Since 1855

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