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Sarah Strazzo, James B. Elsner, Timothy LaRow, Daniel J. Halperin, and Ming Zhao

Abstract

Of broad scientific and public interest is the reliability of global climate models (GCMs) to simulate future regional and local tropical cyclone (TC) occurrences. Atmospheric GCMs are now able to generate vortices resembling actual TCs, but questions remain about their fidelity to observed TCs. Here the authors demonstrate a spatial lattice approach for comparing actual with simulated TC occurrences regionally using observed TCs from the International Best Track Archive for Climate Stewardship (IBTrACS) dataset and GCM-generated TCs from the Geophysical Fluid Dynamics Laboratory (GFDL) High Resolution Atmospheric Model (HiRAM) and Florida State University (FSU) Center for Ocean–Atmospheric Prediction Studies (COAPS) model over the common period 1982–2008. Results show that the spatial distribution of TCs generated by the GFDL model compares well with observations globally, although there are areas of over- and underprediction, particularly in parts of the Pacific Ocean. Difference maps using the spatial lattice highlight these discrepancies. Additionally, comparisons focusing on the North Atlantic Ocean basin are made. Results confirm a large area of overprediction by the FSU COAPS model in the south-central portion of the basin. Relevant to projections of future U.S. hurricane activity is the fact that both models underpredict TC activity in the Gulf of Mexico.

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Sarah Strazzo, Dan C. Collins, Andrew Schepen, Q. J. Wang, Emily Becker, and Liwei Jia

Abstract

Recent research demonstrates that dynamical models sometimes fail to represent observed teleconnection patterns associated with predictable modes of climate variability. As a result, model forecast skill may be reduced. We address this gap in skill through the application of a Bayesian postprocessing technique—the calibration, bridging, and merging (CBaM) method—which previously has been shown to improve probabilistic seasonal forecast skill over Australia. Calibration models developed from dynamical model reforecasts and observations are employed to statistically correct dynamical model forecasts. Bridging models use dynamical model forecasts of relevant climate modes (e.g., ENSO) as predictors of remote temperature and precipitation. Bridging and calibration models are first developed separately using Bayesian joint probability modeling and then merged using Bayesian model averaging to yield an optimal forecast. We apply CBaM to seasonal forecasts of North American 2-m temperature and precipitation from the North American Multimodel Ensemble (NMME) hindcast. Bridging is done using the model-predicted Niño-3.4 index. Overall, the fully merged CBaM forecasts achieve higher Brier skill scores and better reliability compared to raw NMME forecasts. Bridging enhances forecast skill for individual NMME member model forecasts of temperature, but does not result in significant improvements in precipitation forecast skill, possibly because the models of the NMME better represent the ENSO–precipitation teleconnection pattern compared to the ENSO–temperature pattern. These results demonstrate the potential utility of the CBaM method to improve seasonal forecast skill over North America.

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James B. Elsner, Sarah E. Strazzo, Thomas H. Jagger, Timothy LaRow, and Ming Zhao

Abstract

A statistical model for the intensity of the strongest hurricanes has been developed and a new methodology introduced for estimating the sensitivity of the strongest hurricanes to changes in sea surface temperature. Here, the authors use this methodology on observed hurricanes and hurricanes generated from two global climate models (GCMs). Hurricanes over the North Atlantic Ocean during the period 1981–2010 show a sensitivity of 7.9 ± 1.19 m s−1 K−1 (standard error; SE) when over seas warmer than 25°C. In contrast, hurricanes over the same region and period generated from the GFDL High Resolution Atmospheric Model (HiRAM) show a significantly lower sensitivity with the highest at 1.8 ± 0.42 m s−1 K−1 (SE). Similar weaker sensitivity is found using hurricanes generated from the Florida State University Center for Ocean–Atmospheric Prediction Studies (FSU-COAPS) model with the highest at 2.9 ± 2.64 m s−1 K−1 (SE). A statistical refinement of HiRAM-generated hurricane intensities heightens the sensitivity to a maximum of 6.9 ± 3.33 m s−1 K−1 (SE), but the increase is offset by additional uncertainty associated with the refinement. Results suggest that the caution that should be exercised when interpreting GCM scenarios of future hurricane intensity stems from the low sensitivity of limiting GCM-generated hurricane intensity to ocean temperature.

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Kevin J. E. Walsh, Suzana J. Camargo, Gabriel A. Vecchi, Anne Sophie Daloz, James Elsner, Kerry Emanuel, Michael Horn, Young-Kwon Lim, Malcolm Roberts, Christina Patricola, Enrico Scoccimarro, Adam H. Sobel, Sarah Strazzo, Gabriele Villarini, Michael Wehner, Ming Zhao, James P. Kossin, Tim LaRow, Kazuyoshi Oouchi, Siegfried Schubert, Hui Wang, Julio Bacmeister, Ping Chang, Fabrice Chauvin, Christiane Jablonowski, Arun Kumar, Hiroyuki Murakami, Tomoaki Ose, Kevin A. Reed, Ramalingam Saravanan, Yohei Yamada, Colin M. Zarzycki, Pier Luigi Vidale, Jeffrey A. Jonas, and Naomi Henderson
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Kevin J. E. Walsh, Suzana J. Camargo, Gabriel A. Vecchi, Anne Sophie Daloz, James Elsner, Kerry Emanuel, Michael Horn, Young-Kwon Lim, Malcolm Roberts, Christina Patricola, Enrico Scoccimarro, Adam H. Sobel, Sarah Strazzo, Gabriele Villarini, Michael Wehner, Ming Zhao, James P. Kossin, Tim LaRow, Kazuyoshi Oouchi, Siegfried Schubert, Hui Wang, Julio Bacmeister, Ping Chang, Fabrice Chauvin, Christiane Jablonowski, Arun Kumar, Hiroyuki Murakami, Tomoaki Ose, Kevin A. Reed, Ramalingam Saravanan, Yohei Yamada, Colin M. Zarzycki, Pier Luigi Vidale, Jeffrey A. Jonas, and Naomi Henderson

Abstract

While a quantitative climate theory of tropical cyclone formation remains elusive, considerable progress has been made recently in our ability to simulate tropical cyclone climatologies and to understand the relationship between climate and tropical cyclone formation. Climate models are now able to simulate a realistic rate of global tropical cyclone formation, although simulation of the Atlantic tropical cyclone climatology remains challenging unless horizontal resolutions finer than 50 km are employed. This article summarizes published research from the idealized experiments of the Hurricane Working Group of U.S. Climate and Ocean: Variability, Predictability and Change (CLIVAR). This work, combined with results from other model simulations, has strengthened relationships between tropical cyclone formation rates and climate variables such as midtropospheric vertical velocity, with decreased climatological vertical velocities leading to decreased tropical cyclone formation. Systematic differences are shown between experiments in which only sea surface temperature is increased compared with experiments where only atmospheric carbon dioxide is increased. Experiments where only carbon dioxide is increased are more likely to demonstrate a decrease in tropical cyclone numbers, similar to the decreases simulated by many climate models for a future, warmer climate. Experiments where the two effects are combined also show decreases in numbers, but these tend to be less for models that demonstrate a strong tropical cyclone response to increased sea surface temperatures. Further experiments are proposed that may improve our understanding of the relationship between climate and tropical cyclone formation, including experiments with two-way interaction between the ocean and the atmosphere and variations in atmospheric aerosols.

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Daniela I.V. Domeisen, Christopher J. White, Hilla Afargan-Gerstman, Ángel G. Muñoz, Matthew A. Janiga, Frédéric Vitart, C. Ole Wulff, Salomé Antoine, Constantin Ardilouze, Lauriane Batté, Hannah C. Bloomfield, David J. Brayshaw, Suzana J. Camargo, Andrew Charlton-Pérez, Dan Collins, Tim Cowan, Maria del Mar Chaves, Laura Ferranti, Rosario Gómez, Paula L.M. González, Carmen González Romero, Johnna M. Infanti, Stelios Karozis, Hera Kim, Erik W. Kolstad, Emerson LaJoie, Llorenç Lledó, Linus Magnusson, Piero Malguzzi, Andrea Manrique-Suñén, Daniele Mastrangelo, Stefano Materia, Hanoi Medina, Lluís Palma, Luis E. Pineda, Athanasios Sfetsos, Seok-Woo Son, Albert Soret, Sarah Strazzo, and Di Tian

Abstract

Extreme weather events have devastating impacts on human health, economic activities, ecosys tems, and infrastructure. It is therefore crucial to anticipate extremes and their impacts to allow for preparedness and emergency measures. There is indeed potential for probabilistic subseasonal prediction on timescales of several weeks for many extreme events. Here we provide an overview of subseasonal predictability for case studies of some of the most prominent extreme events across the globe using the ECMWF S2S prediction system: heatwaves, cold spells, heavy precipitation events, and tropical and extratropical cyclones. The considered heatwaves exhibit predictability on timescales of 3-4 weeks, while this timescale is 2-3 weeks for cold spells. Precipitation extremes are the least predictable among the considered case studies. Tropical cyclones, on the other hand, can exhibit probabilistic predictability on timescales of up to 3 weeks, which in the presented cases was aided by remote precursors such as the Madden-Julian Oscillation. For extratropical cyclones, lead times are found to be shorter. These case studies clearly illustrate the potential for event - dependent advance warnings for a wide range of extreme events. The subseasonal predictability of extreme events demonstrated here allows for an extension of warning horizons, provides advance information to impact modelers, and informs communities and stakeholders affected by the impacts of extreme weather events.

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