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Timothy E. LaRow

Abstract

The impact of sea surface temperature (SST) bias correction on seasonal North Atlantic hurricane retrospective forecasts is examined with the Florida State University/Center for Ocean–Atmospheric Prediction Studies (FSU/COAPS) atmospheric global spectral model. The retrospective forecasts cover a 28-yr period (1982–2009). For each year of the retrospective forecast, two, four-member ensembles are developed using predicted SSTs from the National Oceanic and Atmospheric Administration’s Climate Forecast System, version 1 (CFSv1), model. The first ensemble uses the SSTs forecasted from the CFSv1 model and the second ensemble uses the same SSTs with a simple bias correction applied. Seasonal hurricane counts determined using both SSTs are shown to have skill, although the skill is much greater using the bias-corrected SSTs. In addition, a positive significant trend in the hurricane counts is noted using the bias-corrected SSTs, in good agreement with observations while no significant trend is noted using the non-bias-corrected SST. The reason for the enhanced skill is related in part to the magnitude of the mean SST allowing stronger convective activity in the equatorial Pacific to more effectively alter the vertical wind shear in the tropical North Atlantic through changes in the Walker cell.

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Timothy E. LaRow, Lydia Stefanova, and Chana Seitz

Abstract

The effects on early and late twenty-first-century North Atlantic tropical cyclone statistics resulting from imposing the patterns of maximum/minimum phases of the observed Atlantic multidecadal oscillation (AMO) onto projected sea surface temperatures (SSTs) from two climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are examined using a 100-km resolution global atmospheric model. By imposing the observed maximum positive and negative phases of the AMO onto two CMIP5 SST projections from the representative concentration pathway (RCP) 4.5 scenario, this study places bounds on future North Atlantic tropical cyclone activity during the early (2020–39) and late (2080–99) twenty-first century. Averaging over both time periods and both AMO phases, the mean named tropical cyclones (NTCs) count increases by 35% when compared to simulations using observed SSTs from 1982 to 2009. The positive AMO simulations produce approximately a 68% increase in mean NTC count, while the negative AMO simulations are statistically indistinguishable from the mean NTC count determined from the 1995–2009 simulations—a period of observed positive AMO phase. Examination of the tropical cyclone track densities shows a statistically significant increase in the tracks along the East Coast of the United States in the future simulations compared to the models’ 1982–2009 climate simulations. The increase occurs regardless of AMO phase, although the negative phase produces higher track densities. The maximum wind speeds increase by 6%, in agreement with other climate change studies. Finally, the NTC-related precipitation is found to increase (approximately by 13%) compared to the 1982–2009 simulations.

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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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Rongqing Han, Hui Wang, Zeng-Zhen Hu, Arun Kumar, Weijing Li, Lindsey N. Long, Jae-Kyung E. Schemm, Peitao Peng, Wanqiu Wang, Dong Si, Xiaolong Jia, Ming Zhao, Gabriel A. Vecchi, Timothy E. LaRow, Young-Kwon Lim, Siegfried D. Schubert, Suzana J. Camargo, Naomi Henderson, Jeffrey A. Jonas, and Kevin J. E. Walsh

Abstract

An assessment of simulations of the interannual variability of tropical cyclones (TCs) over the western North Pacific (WNP) and its association with El Niño–Southern Oscillation (ENSO), as well as a subsequent diagnosis for possible causes of model biases generated from simulated large-scale climate conditions, are documented in the paper. The model experiments are carried out by the Hurricane Work Group under the U.S. Climate Variability and Predictability Research Program (CLIVAR) using five global climate models (GCMs) with a total of 16 ensemble members forced by the observed sea surface temperature and spanning the 28-yr period from 1982 to 2009. The results show GISS and GFDL model ensemble means best simulate the interannual variability of TCs, and the multimodel ensemble mean (MME) follows. Also, the MME has the closest climate mean annual number of WNP TCs and the smallest root-mean-square error to the observation.

Most GCMs can simulate the interannual variability of WNP TCs well, with stronger TC activities during two types of El Niño—namely, eastern Pacific (EP) and central Pacific (CP) El Niño—and weaker activity during La Niña. However, none of the models capture the differences in TC activity between EP and CP El Niño as are shown in observations. The inability of models to distinguish the differences in TC activities between the two types of El Niño events may be due to the bias of the models in response to the shift of tropical heating associated with CP El Niño.

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Hui Wang, Lindsey Long, Arun Kumar, Wanqiu Wang, Jae-Kyung E. Schemm, Ming Zhao, Gabriel A. Vecchi, Timothy E. Larow, Young-Kwon Lim, Siegfried D. Schubert, Daniel A. Shaevitz, Suzana J. Camargo, Naomi Henderson, Daehyun Kim, Jeffrey A. Jonas, and Kevin J. E. Walsh

Abstract

The variability of Atlantic tropical cyclones (TCs) associated with El Niño–Southern Oscillation (ENSO) in model simulations is assessed and compared with observations. The model experiments are 28-yr simulations forced with the observed sea surface temperature from 1982 to 2009. The simulations were coordinated by the U.S. Climate Variability and Predictability Research Program (CLIVAR) Hurricane Working Group and conducted with five global climate models (GCMs) with a total of 16 ensemble members. The model performance is evaluated based on both individual model ensemble means and multimodel ensemble mean. The latter has the highest anomaly correlation (0.86) for the interannual variability of TCs. Previous observational studies show a strong association between ENSO and Atlantic TC activity, as well as distinctions during eastern Pacific (EP) and central Pacific (CP) El Niño events. The analysis of track density and TC origin indicates that each model has different mean biases. Overall, the GCMs simulate the variability of Atlantic TCs well with weaker activity during EP El Niño and stronger activity during La Niña. For CP El Niño, there is a slight increase in the number of TCs as compared with EP El Niño. However, the spatial distribution of track density and TC origin is less consistent among the models. Particularly, there is no indication of increasing TC activity over the U.S. southeast coastal region during CP El Niño as in observations. The difference between the models and observations is likely due to the bias of the models in response to the shift of tropical heating associated with CP El Niño, as well as the model bias in the mean circulation.

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