A new statistical-dynamical scheme is presented for predicting integrated kinetic energy (IKE) in North Atlantic tropical cyclones from a series of environmental input parameters. Predicting IKE is desirable because the metric quantifies the energy across a storm's entire wind field, allowing it to respond to changes in storm structure and size. As such, IKE is especially useful for quantifying risks in large, low-intensity, high-impact storms such as Sandy in 2012. The prediction scheme, named the Statistical Prediction of Integrated Kinetic Energy, version 2 (SPIKE2), builds upon a previous statistical IKE scheme, by using a series of artificial neural networks instead of more basic linear regression models. By using a more complex statistical scheme, SPIKE2 is able to distinguish nonlinear signals in the environment that could cause fluctuations in IKE. In an effort to evaluate SPIKE2's performance in a future operational setting, the model is calibrated using archived input parameters from Global Ensemble Forecast System (GEFS) control analyses, and is run in a hindcast mode from 1990 to 2011 using archived GEFS reforecasts. The hindcast results indicate that SPIKE2 performs significantly better than both persistence and climatological benchmarks.

Integrated kinetic energy (IKE) is a useful quantity that measures the size and strength of a tropical cyclone wind field. As a result, it is inherently related to the destructive potential of these powerful storms. In most current operational settings, there are limited resources designed to assess the IKE of a tropical cyclone because storm track and maximum intensity are typically prioritized. Therefore, to complement existing forecasting tools, a statistical scheme is created to project fluctuations of IKE in North Atlantic tropical cyclones for several forecast intervals out to 72 h. The resulting scheme, named Statistical Prediction of Integrated Kinetic Energy (SPIKE), utilizes multivariate normal regression models trained on environmental and storm-related predictors from all North Atlantic tropical cyclones occurring from 1990 to 2011. During this training interval, SPIKE outperforms persistence and is capable of explaining more than 80% of observed variance in total IKE values at a forecast interval of 12 h, trailing down to just below 60% explained variance at an interval of 72 h. The skill of the SPIKE model is evaluated further using bootstrapping exercises in order to gauge the predictive abilities of the statistical scheme. In addition, the performance of the SPIKE model is also evaluated for the 2012 Atlantic hurricane season, which notably falls outside of the training interval. Ultimately, the validation exercises return shared variance scores similar to those found in the training exercises, serving as a proof of concept that the SPIKE model can be used to project IKE values when given accurate predictor data.

In this paper, we show from observations that the Intra-American Seas precursor as characterized by the onset of the Atlantic Warm Pool (AWP; defined by the area enclosed by 28.5°C isotherm in the tropical Atlantic Ocean) has discernible impact on the boreal summer and fall seasonal climate variations over North America, a season and a region well known for relatively poor seasonal predictability. The onset of the AWP season is objectively defined as the day when the daily anomaly of the AWP area, west of 50°W, and north of the equator exceeds its climatological annual mean value. We show that early (late) onset of AWP is associated with following August-September-October (ASO) deficit (excess) seasonal rainfall anomalies over southern Mississippi valley extending to the Midwest US east of Iowa. On the other hand, Central America and the Caribbean region exhibit enhanced (decreased) ASO seasonal mean rainfall during an early (late) onset of AWP. We also find that early (late) onset of the AWP is associated with early (late) onset and early (late) demise of the rainy season over Mesoamerica. This relationship also leads to association of early (late) onset of AWP with increased (shortened) length of the rainy season over Mesoamerica. These teleconnections are dictated by the modulation of the low-level flow and moisture flux convergence associated with the varying development of the AWP. Similarly, we find that early (late) onset years of the AWP are associated with a more active (inactive) seasonal Atlantic tropical cyclone activity. These teleconnections are sustained from the fact that the AWP onset date variations are found to be a precursor to the seasonal AWP size variations.

The observed historical record of North Atlantic tropical cyclones (TCs) is relatively short and is subject to potential biases owing to a lack of observation platforms such as aircraft reconnaissance and satellite imagery in earlier decades. Therefore, studies of long‐term variability in TC activity are hindered by the limitations and uncertainty within the historical data. An alternative approach is to study long‐term Atlantic TC variability within the framework of a coupled ocean‐atmosphere climate model simulation. We have taken such an approach using a simulation of the National Center for Atmospheric Research Climate System Model 1.4 forced with estimated natural and anthropogenic forcing over the past millennium. Atmospheric variables from the long‐term model simulation are used to drive a recently developed downscaling relationship that simulates TC genesis and tracking over the course of the 1150 year model simulation. This downscaling process generates a long‐term synthetic TC track data set, free of observational biases, though subject to limitations in the model climatology. The synthetic TC data are used to perform an analysis of long‐term variability in Atlantic TCs, specifically focusing on TC landfalls, within the context of the coupled model simulation. Ultimately, analysis of various TC time series reveals that counts of landfalling TCs and even landfalling hurricanes track relatively well with the total basin‐wide TC activity on multidecadal and longer timescales.

State-of-the-art coupled global climate models are evaluated for their simulation of the Atlantic Warm Pool (AWP). Historical runs from 17 coupled climate models included in the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) serve as the basis for this model evaluation study. The model simulations are directly compared to observations and reanalysis data to evaluate the climatological features and variability of the AWP within each individual model. Results reveal that a select number of models—namely the GISS-E2-R, CSIRO-Mk3.6, and MPI-ESM-LR—are successful at resolving an appropriately sized AWP with some reasonable climatological features. However, these three models exhibit an erroneously broad seasonal peak of the AWP, and its variability is significantly underestimated. Furthermore, all of the CMIP5 models exhibit a significant cold bias across the tropical Atlantic basin, which hinders their ability to accurately resolve the AWP.

Using the historical Atlantic tropical cyclone record, this study examines the empirical relationships between climate state variables and Atlantic tropical cyclone counts. The state variables considered as predictors include indices of the El Niño/Southern Oscillation and Northern Atlantic Oscillation, and both “local” and “relative” measures of Main Development Region sea surface temperature. Other predictors considered include indices measuring the Atlantic Meridional Mode and the West African monsoon. Using all of the potential predictors in a forward stepwise Poisson regression, we examine the relationships between tropical cyclone counts and climate state variables. As a further extension on past studies, both basin‐wide named storm counts and cluster analysis time series representing distinct flavors of tropical cyclones, are modeled. A wide variety of cross validation metrics reveal that basin‐wide counts or sums over appropriately chosen clusters may be more skillfully modeled than the individual cluster series. Ultimately, the most skillful models typically share three predictors: indices for the main development region sea surface temperatures, the El Niño/Southern Oscillation, and the North Atlantic Oscillation.

Using the historical Atlantic tropical cyclone record, this study examines the empirical relationships between climate state variables and Atlantic tropical cyclone counts. The state variables considered as predictors include indices of the El Niño/Southern Oscillation and Northern Atlantic Oscillation, and both " local " and " relative " measures of Main Development Region sea surface temperature. Other predictors considered include indices measuring the Atlantic Meridional Mode and the West African monsoon. Using all of the potential predictors in a forward stepwise Poisson regression, we examine the relationships between tropical cyclone counts and climate state variables. As a further extension on past studies, both basin-wide named storm counts and cluster analysis time series representing distinct flavors of tropical cyclones, are modeled. A wide variety of cross validation metrics reveal that basin-wide counts or sums over appropriately chosen clusters may be more skillfully modeled than the individual cluster series. Ultimately, the most skillful models typically share three predictors: indices for the main development region sea surface temperatures, the El Niño/Southern Oscillation, and the North Atlantic Oscillation.