Main Development Region For Atlantic Hurricanes Research Articles

Multilevel vector autoregressive prediction of sea surface temperature in the North Tropical Atlantic Ocean and the Caribbean Sea

We use a multilevel vector autoregressive model (VAR-L), to forecast sea surface temperature anomalies (SSTAs) in the Atlantic hurricane Main Development Region (MDR). VAR-L is a linear regression model using global SSTA data from L prior months as predictors. In hindcasts for the recent 30 years, the multilevel VAR-L outperforms a state-of-the-art dynamic forecast model, as well as the commonly used linear inverse model (LIM). The multilevel VAR-L model shows skill in 6–12 month forecasts, with its greatest skill in the months of the active hurricane season. The optimized model for the best long-range skill score in the MDR, chosen by a cross-validation procedure, has 12 time levels and 12 empirical orthogonal function modes. We investigate the optimal initial conditions for MDR SSTA prediction using a generalized singular vector decomposition of the propagation matrix. We find that the added temporal degrees of freedom for the predictands in VAR12 as compared with a LIM model, which allow the model to capture both the local wind–evaporation–SST feedback in the Tropical Atlantic and the impact on the Atlantic of an improved medium-range ENSO forecast, elevate the long-range forecast skill in the MDR.

Seasonal tropical cyclone precipitation in Texas: A statistical modeling approach based on a 60 year climatology

Sixty years of tropical cyclone precipitation (TCP) in Texas has been analyzed because of its importance in extreme hydrologic events and the hydrologic budget. We developed multiple linear regression models to provide seasonal forecasts for annual TCP, TCP's contribution (percentage) to total precipitation, and the number of TCP days in Texas. The regression models are based on three or fewer predictors with model fits ranging from 0.18 to 0.43 (R2) and cross‐validation accuracy of 0.05–0.36 (R2). La Niña exhibits the most important control on TCP in Texas. It is the major driver in our models and acts to reduce the vertical shear in the Caribbean and the tropical Atlantic, thereby generating more precipitating storms in Texas. Lower maximum potential velocity, the theoretical maximum wind speed that storms can attain, in the Gulf of Mexico, and low‐level vorticity in the Atlantic hurricane main development region increased the modeled R2 by 20% or more. Both variables have negative coefficients in the TCP models. Lower maximum potential velocity and vorticity are associated with tropical cyclones with lower maximum wind speed and slower translation speed. Such weak TCs produce the majority of TCP and extreme TCP events in Texas. The quartiles of the TCs with strongest maximum wind speed and fastest translation speed are not associated with the largest mean daily precipitation based on observations in Texas. We have also shown that sea level pressure in the Gulf of Mexico, sea surface temperature in the Caribbean, and the North Atlantic Oscillation are potentially important predictors of seasonal TCP in Texas.

Response to CO2 Doubling of the Atlantic Hurricane Main Development Region in a High-Resolution Climate Model

Abstract Response of climate conditions in the Atlantic hurricane main development region (MDR) to doubling of atmospheric CO2 has been explored using the new high-resolution coupled climate model, version 2.5 (CM2.5), developed at the Geophysical Fluid Dynamics Laboratory (GFDL). In the annual mean, the SST in the MDR warms by about 2°C in the CO2 doubling run relative to the control run; the trade winds become weaker in the northern tropical Atlantic and the rainfall increases over the ITCZ and its northern region. The amplitude of the annual cycle of the SST over the MDR is not significantly changed by CO2 doubling. However, the authors find that the interannual variations show significant responses to CO2 doubling; the seasonal maximum peak of the interannual variations of the SST over the MDR is about 25% stronger than in the control run. The enhancement of the interannual variations of the SST in the MDR is caused by changes in effectiveness of the wind–evaporation–SST (WES) positive feedback; WES remains a positive feedback until boreal early summer in the CO2 doubling run. The enhancement of the interannual variability of the SST over the MDR in boreal early summer due to CO2 doubling could lead to serious damages associated with the Atlantic hurricane count and drought (or flood) in the Sahel and South America in a future climate.

Short-Term Climate Extremes: Prediction Skill and Predictability

Abstract Forecasts for extremes in short-term climate (monthly means) are examined to understand the current prediction capability and potential predictability. This study focuses on 2-m surface temperature and precipitation extremes over North and South America, and sea surface temperature extremes in the Niño-3.4 and Atlantic hurricane main development regions, using the Climate Forecast System (CFS) global climate model, for the period of 1982–2010. The primary skill measures employed are the anomaly correlation (AC) and root-mean-square error (RMSE). The success rate of forecasts is also assessed using contingency tables. The AC, a signal-to-noise skill measure, is routinely higher for extremes in short-term climate than those when all forecasts are considered. While the RMSE for extremes also rises, especially when skill is inherently low, it is found that the signal rises faster than the noise. Permutation tests confirm that this is not simply an effect of reduced sample size. Both 2-m temperature and precipitation forecasts have higher anomaly correlations in the area of South America than North America; credible skill in precipitation is very low over South America and absent over North America, even for extremes. Anomaly correlations for SST are very high in the Niño-3.4 region, especially for extremes, and moderate to high in the Atlantic hurricane main development region. Prediction skill for forecast extremes is similar to skill for observed extremes. Assessment of the potential predictability under perfect-model assumptions shows that predictability and prediction skill have very similar space–time dependence. While prediction skill is higher in CFS version 2 than in CFS version 1, the potential predictability is not.

A Statistical Forecast Model for Atlantic Seasonal Hurricane Activity Based on the NCEP Dynamical Seasonal Forecast

Abstract A hybrid dynamical–statistical model is developed for predicting Atlantic seasonal hurricane activity. The model is built upon the empirical relationship between the observed interannual variability of hurricanes and the variability of sea surface temperatures (SSTs) and vertical wind shear in 26-yr (1981–2006) hindcasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS). The number of Atlantic hurricanes exhibits large year-to-year fluctuations and an upward trend over the 26 yr. The latter is characterized by an inactive period prior to 1995 and an active period afterward. The interannual variability of the Atlantic hurricanes significantly correlates with the CFS hindcasts for August–October (ASO) SSTs and vertical wind shear in the tropical Pacific and tropical North Atlantic where CFS also displays skillful forecasts for the two variables. In contrast, the hurricane trend shows less of a correlation to the CFS-predicted SSTs and vertical wind shear in the two tropical regions. Instead, it strongly correlates with observed preseason SSTs in the far North Atlantic. Based on these results, three potential predictors for the interannual variation of seasonal hurricane activity are constructed by averaging SSTs over the tropical Pacific (TPCF; 5°S–5°N, 170°E–130°W) and the Atlantic hurricane main development region (MDR; 10°–20°N, 20°–80°W), respectively, and vertical wind shear over the MDR, all of which are from the CFS dynamical forecasts for the ASO season. In addition, two methodologies are proposed to better represent the long-term trend in the number of hurricanes. One is the use of observed preseason SSTs in the North Atlantic (NATL; 55°–65°N, 30°–60°W) as a predictor for the hurricane trend, and the other is the use of a step function that breaks up the hurricane climatology into a generally inactive period (1981–94) and a very active period (1995–2006). The combination of the three predictors for the interannual variation, along with the two methodologies for the trend, is explored in developing an empirical forecast system for Atlantic hurricanes. A cross validation of the hindcasts for the 1981–2006 hurricane seasons suggests that the seasonal hurricane forecast with the TPCF SST as the only CFS predictor is more skillful in inactive hurricane seasons, while the forecast with only the MDR SST is more skillful in active seasons. The forecast using both predictors gives better results. The most skillful forecast uses the MDR vertical wind shear as the only CFS predictor. A comparison with forecasts made by other statistical models over the 2002–07 seasons indicates that this hybrid dynamical–statistical forecast model is competitive with the current statistical forecast models.

Reply to comment by Joseph J. Barsugli on “Global warming and United States landfalling hurricanes”

[1] In our recent paper [Wang and Lee, 2008] (henceforth WL08), we perform an empirical orthogonal function (EOF) analysis on the global annual mean SST over the past 153 years (from 1854 to 2006). The first three EOF modes, which account for 28.3%, 15.3%, and 5.3% of the total variance in the SST data, may represent global warming, ENSO-like (including the Pacific decadal oscillation), and the Atlantic multidecadal oscillation (AMO), respectively. We then use the first EOF mode to study the relationship among global warming, vertical wind shear in the Atlantic hurricane main development region (MDR) and U.S. landfalling hurricanes. The second EOF mode (ENSO-like) and the third EOF mode (the AMO) are presented in another paper (see Figure 1 of Wang et al. [2008a] for these two modes). [2] In his comment, Barsugli [2009] (henceforth B09) points out that the SST spatial pattern of the first EOF mode is similar to that of an El Nino event and that its time series contains variations on interannual timescale. He thus concerns that the first EOF mode of global warming may include the interannual ENSO signal and therefore the observed relationship between global warming and U.S. landfalling hurricanes (also vertical wind shear in the hurricane MDR) of WL08 may be partly attributed to interannual ENSO’s effect. We appreciate and understand his concern since there is no ‘‘perfect’’ method for separating climate modes on various timescales from observational data such as global warming and ENSO modes. We agree that the first EOF mode contains some interannual variations possibly linked to ENSO and that it is difficult to distinguish anthropogenic climate change from natural lowfrequency variability in observational data. However, here we show that the conclusion of WL08 is still true even after removing interannual signals from the first EOF mode. We also present some additional evidences that support the conclusion of WL08. [3] B09 suggests that interannual variations are first removed by applying an 11-year running mean to the SST data prior to performing the EOF analysis. We agree that this approach may be a reasonable way to remove the interannual ENSO signal although it still keeps lowerfrequency variations of ENSO. We re-perform our EOF analysis following B09’s suggestion. The resulting spatial pattern and time series of the first EOF mode are shown in Figures 1a and 1b (Figure 1a is almost identical to Figure 1b of B09), which accounts for 57.4% of the total variance. In comparison with Figure 1 of WL08, the amplitude of SST in

Global warming and United States landfalling hurricanes

A secular warming of sea surface temperature occurs almost everywhere over the global ocean. Here we use observational data to show that global warming of the sea surface is associated with a secular increase of tropospheric vertical wind shear in the main development region (MDR) for Atlantic hurricanes. The increased wind shear coincides with a weak but robust downward trend in U.S. landfalling hurricanes, a reliable measure of hurricanes over the long term. Warmings over the tropical oceans compete with one another, with the tropical Pacific and Indian Oceans increasing wind shear and the tropical North Atlantic decreasing wind shear. Warmings in the tropical Pacific and Indian Oceans win the competition and produce increased wind shear which reduces U.S. landfalling hurricanes. Whether future global warming increases the vertical wind shear in the MDR for Atlantic hurricanes will depend on the relative role induced by secular warmings over the tropical oceans.