Forecast of 2017 Atlantic Hurricane Activity
June 8, 2017
CFAN’s inaugural seasonal forecast for Atlantic tropical cyclone activity is based on a breakthrough inunderstanding of the impact of global climate dynamics on Atlantic hurricane activity. CFAN has identified skillful new predictors for seasonal Atlantic Accumulated Cyclone Energy (ACE) and the number of U.S. landfalling hurricanes. CFAN’s prediction for the 2017 Atlantic hurricane season:
• ACE: 134 (average value 103 since 1982)
• # of U.S. landfalling hurricanes: 3 (average value 1.7 since 1900)
ACCUMULATED CYCLONE ENERGY (ACE)
North Atlantic ACE is an integrative metric of tropical cyclone duration and intensity based on the square of maximum sustained surface winds during Named Storms.
CFAN’s ACE forecast model was developed from historically correlated patterns of March-April-May
(MAM) anomalies over the period 1982-2016 of sea surface temperature (SST), SST tendencies, atmospheric sea-level pressure (SLP) and lower-stratospheric temperatures. To remove redundancies inthe SST and SLP predictor indices, a principal component (PC) analysis was performed, yielding a single dominant mode with significant ACE correlation (r = 0.67). This index reflects higher ACE totals with low pressure and warm surface temperatures over the North Atlantic, high SST in the SW Pacific, and high SLP and cool and falling SSTs over the eastern Pacific. The unexplained residual variability in the forecasted ACE was found to have significant negative correlations with lower stratospheric temperatures above the North Atlantic (r = -0.5). This stratospheric indicator was combined with the surface-based PC Index in a linear regression model to obtain a final ACE forecast index with improved hindcast skill (r =0.75).
Figure 1: Comparison of modeled versus observed Atlantic ACE, for the period 1982-2016. The model forecast for 2017 Atlantic ACE is 135.
Based upon statistics since 1982, the average error for CFAN’s ACE forecast is 35, compared to an error of 52 using the climatological average.
It is instructive to compare CFAN’s ACE forecast with the CSU forecast. For 2017, the CSU team predicts a normal year with a seasonal ACE value of 100. The average error of 35 for CFAN’s forecast compares to an average error of 30 units for CSU’s June forecast. In comparing CFAN’s ACE forecast (Figure 1) with CSU’s ACE forecast shown in Figure 2 (Figure 1 of the CSU forecast report),1 it is seen that the CSU forecast has greater skill prior to 1995, whereas CFAN has greater skill since 2008.
Figure 2: Comparison of observed ACE versus the CSU ACE model.1
The largest outliers in both CFAN’s and CSU’s forecast are 2004/2005. Our analysis has identified stratospheric anomalies that relate to the intense hurricane activity during 2004/2005; better understanding of these mechanisms is the subject of ongoing research.
U.S. LANDFALLING HURRICANES
ACE is moderately correlated with most U.S. landfall indices, however the landfall indices display remarkably weak relationships to the same surface anomalies that heavily influence ACE. Inspection of historical hurricane tracks reveals that many tropical storms never approach the U.S. coastline, instead migrating northward and westward over the open Atlantic. Recent research highlights notable differences between basin-scale cyclone activity and the frequency of hurricanes that reach the U.S. coastline, due in part to dynamical mechanisms that tend to increase general cyclone activity, but also inhibit U.S. landfalls. It is notable that elevated ACE totals from 1995 to 2014 overlapped with a historic 2006-2014 drought of major hurricane and Florida landfalls.
Annual hurricane landfalls for 5 U.S. regions were compiled from NOAA reports of 153 total events over the 1920-2016 period. Annual landfalls were tabulated for the Gulf Coast (TX to AL), Florida, the Mid-Atlantic (GA to MD), and New England (DE and states northward), as well as the Southeastern US (excluding New England) and the Eastern US, including all states.
We identified several precursors in the ocean, troposphere and stratosphere that reflect circulation patterns that apparently influence hurricane tracks and U.S. landfalls. The clearest signal of this circulation regime is seen in the Arctic and the global stratospheric circulations. Using observations from the Arctic during spring, a new Arctic Index has been developed that provides the basis for CFAN’s prediction of the number of U.S. landfalling hurricanes.
The predictors for landfalls vary with the low-frequency Atlantic circulation regime. For the current regime (warm phase of the AMO), we considered the period 1995-2016. Figure 3 compares the modeled number of U.S. hurricane landfalls with observations.
Figure 3: Comparison of modeled and observed annual U.S. hurricane landfalls, for the period 1995-2016.
For 2017, CFAN’s forecast for the number of U.S. landfalls is 2.7-3.4 (average value 3), with a mean absolute error of 1. For reference, the average number of hurricane landfalls since 1900 is 1.7. During the first half of the period, the model landfalls are generally lower than observed, whereas the modeled landfalls are slightly higher than observed since 2009. Because of the short training period (owing to the Atlantic regime change in 1995), the confidence in landfall forecast is lower than for the ACE forecast.
Figure 4 provides scatter plots of the Arctic Index versus the number of U.S. landfalls. For values of the Arctic Index > 0, there are no years since 1995 with zero U.S. landfalls. Identifying the regional location of landfalls is statistically less robust, but with the strongest signal for Florida.
Figure 4. Scatter plot of the springtime Arctic Index versus the number of hurricane landfalls, for the period 1995-2016.
FUTURE RESEARCH AND FORECAST PRODUCTS
We are continuing to conduct research on the climate dynamics of Atlantic hurricanes:
Investigation the dynamics of stratospheric connections to Atlantic hurricane activity
Improve understanding of the causes for the anomalous 2004/2005 hurricane activity
Early season predictability and prediction (December and April)
Multi-year forecasts (2-5 years)
Predicting the next regime shift in the Atlantic
Investigation of landfall dynamics during 1926-1971
In future, we plan to integrate the ECMWF seasonal forecasts more thoroughly into the forecast, along the lines of the paper by Kim and Webster (2010).
SUBSCRIBING TO CFAN’S SEASONAL HURRICANE FORECAST REPORTS
We anticipate publishing seasonal hurricane forecasts for the Atlantic on approximately the following schedule:
The June forecast summary and report will be issued publicly, although the technical forecast reports will be available only to subscribers. Options for subscription to CFAN’s seasonal hurricane forecast reports:
Subscribers: $250/year. August, December, April forecast summaries
Sponsors: $4K/year. Forecasts summaries; technical reports; consultation.
Acknowledgements. We acknowledge financial support from Florida Power & Light in developing the seasonal hurricane forecast.
 Kossin, James P. "Hurricane intensification along United States coast suppressed during active hurricane periods." Nature (2017).
Tropical cyclone-related publications by CFAN scientists
Belanger, JI, MT Jelinek, JA Curry, 2016: A climatology of easterly waves in the tropical Western Hemisphere. Geoscience Data Journal, DOI: 10.1002/gdj3.40
Kim, HM, D Kim, F Vitart, VE Toma, J-S Kug and PJ Webster, 2016: MJO propagation across the Maritime Continent in the ECMWF ensemble prediction system, J. Climate, 10.1175/JCLI-D-15-0862.1
Kim, HM, PJ Webster, VE Toma, and D. Kim, 2014: Predictability and prediction skill of the MJO in two operational forecasting systems, J. Climate, DOI: 10.1175/JCLI-D-13-00480.1
Kim HM, Lee MI, Webster PJ, Kim D, Yoo JH, 2013: A physical basis for the probabilistic prediction of the accumulated tropical cyclone kinetic energy in the western North Pacific. J. Clim. 26/20, 7981-7991. October.
Kim, H. M., P. J. Webster, V. E. Toma, and D. Kim, 2014: Predictability and prediction skill of the MJO in two operational forecasting systems, J. Climate, 27 (14), 5364-5378
Kim, H. M., M. I. Lee, P. J. Webster, D. Kim and J. Yoo, 2013: A physical basis for the probabilistic prediction of the accumulated tropical cyclone kinetic energy in the Western North Pacific, J. Climate, 26 (20), 7981-7991.
Belanger, JI., PJ Webster, JA Curry, and MT Jelinek, 2012: Extended Prediction of North Indian Ocean Tropical Cyclones, Weather & Forecasting, 27, 757-769.
Liu, J., Curry JA, Clayson CA, Bourassa, MA 2011: High resolution satellite surface latent heat fluxes in North Atlantic hurricanes. Mon Weather Rev., 139, 2735-2747.
Kim HM, Webster PJ, Curry JA, 2011: Modulation of North Pacific Tropical Cyclone Activity by Three Phases of ENSO. J. Climate, 24, 1839-1849.
Agudelo PA, Hoyos CD, Curry JA, Webster, PJ, 2011: Probabilistic discrimination between large-scale environments of intensifying and decaying African Easterly Waves. Clim. Dyn, 36, 1379-1401.
Belanger JI, Curry JA, Webster PJ, 2010: Predictability of North Atlantic Tropical Cyclone Activity on Intraseasonal Time Scales. Mon. Weather Rev., 138, 4362-4374.
Kim, HM. and PJ Webster, 2010: Extended range seasonal hurricane forecasts using a hybrid dynamical-statistical model. Geophys. Res. Letters 37 Article Number: L21705
Done, J, GJ Holland, PJ Webster, 2010: The Role of Wave Accumulation in Tropical Cyclone Genesis over the Tropical North Atlantic. Climate Dynamics (pdf)
Holland, G., JI Belanger, AM Fritz, 2010: A Revised Model for Radial Profiles of Hurricane Winds, Monthly Weather Review (pdf)
Belanger, JI, JA Curry, CD Hoyos, 2009: Variabiity in tornado frequency associated with U.S. landfalling tropical cyclones. Geophys. Res. Lett., 36, L17805.
Kim, HM, PJ Webster, JA Curry, 2009: Impact of shifting patterns of Pacific Ocean Warming on North Atlantic tropical cyclones. Science, 325, 77-80.
Hoyos, CD, PA Agudelo, PJ Webster, JA Curry, 2006: Deconvolution of the factors contributing to the increase in global hurricane activity. Science 312, (5770).
Webster, PJ, GJ Holland, JA Curry, HR Chang, 2005: Changes in tropical cyclone number, duration and intensity in a warming environment. Science. 309 (5742): 1844-1846