Replicating Annual North Atlantic Hurricane Activity 1878-2012 from Environmental Variables

• Main Authors: Saunders, MA, Klotzbach, PJ, Lea, ASR
• Format: Article in Journal/Newspaper
• Language: English
• Published: American Geophysical Union 2017
• Subjects: North Atlantic
• Online Access: https://discovery.ucl.ac.uk/id/eprint/1558802/13/Saunders_et_al-2017-Journal_of_Geophysical_Research__Atmospheres.pdf; https://discovery.ucl.ac.uk/id/eprint/1558802/

Statistical models can replicate annual North Atlantic hurricane activity from large-scale environmental field data for August and September, the months of peak hurricane activity. We assess how well the six environmental fields used most often in contemporary statistical modeling of seasonal hurricane activity replicate North Atlantic hurricane numbers and Accumulated Cyclone Energy (ACE) over the 135-year period from 1878 to 2012. We find that these fields replicate historical hurricane activity surprisingly well, showing that contemporary statistical models and their seasonal physical links have long-term robustness. We find that August-September zonal trade wind speed over the Caribbean Sea and the tropical North Atlantic is the environmental field which individually replicates long-term hurricane activity the best, and that trade wind speed combined with the difference in sea surface temperature between the tropical Atlantic and the tropical mean is the best multi-predictor model. Comparing the performance of the best single-predictor and best multi-predictor models shows that they exhibit little difference in hindcast skill for predicting long-term ACE but that the best multi-predictor model offers improved skill for predicting long-term hurricane numbers. We examine whether replicated real-time prediction skill 1983-2012 increases as the model training period lengthens and find evidence that this happens slowly. We identify a dropout in hurricane replication centered on the 1940s and show that this is likely due to a decrease in data quality which affects all data sets but Atlantic sea surface temperatures in particular. Finally we offer insights on the implications of our findings for seasonal hurricane prediction.