Replicating Annual North Atlantic HurricaneActivity 1878-2012 from Environmental Variables

Philip J. KlotzbachColorado State University, Fort Collins, USA

philk@atmos.colostate.edu

AbstractStatistical models can replicate annual North Atlantic hurricane activity from large-scale environmentalfield data for August and September, the months of peak hurricane activity. We assess how well the sixenvironmental fields used most often in contemporary statistical modeling of seasonal hurricaneactivity replicate North Atlantic hurricane numbers and Accumulated Cyclone Energy (ACE) over the135 year period from 1878 to 2012. We find that these fields replicate historical hurricane activitysurprisingly well, showing that contemporary statistical models and their seasonal physical links havelong-term robustness. We find that August–September zonal trade wind speed over the Caribbean Seaand the tropical North Atlantic is the environmental field which individually replicates long-termhurricane activity the best and that trade wind speed combined with the difference in sea surfacetemperature 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 thatthey 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 whetherreplicated real-time prediction skill 1983–2012 increases as the model training period lengthens andfind evidence that this happens slowly. We identify a dropout in hurricane replication centered on the1940s and show that this is likely due to a decrease in data quality which affects all data sets butAtlantic sea surface temperatures in particular. Finally, we show how well these six environmentalfields worked at diagnosing the extraordinarily active 2017 Atlantic hurricane season.

Summary

• Statistical models replicate North Atlantic hurricane activity well over a 135 year period.• August-September trade wind speed is the environmental field which best replicates long-term

hurricane activity.• The use of multiple environmental fields gives only a modest increase in skill over using trade wind

speed alone.• Trade wind speed and other August-September environmental fields are consistent with the observed

well above-normal 2017 North Atlantic hurricane season.

Data• Atlantic basin HURDAT2 data (1878-2017) – Landsea and Franklin (2013)• Adjusted Atlantic hurricanes (1878-1965) – Vecchi and Knutson (2011)• 20th Century Reanalysis version 2C (1878-2012) – Compo et al. (2011)• NCEP/NCAR Reanalysis (1948-2017) – Kistler et al. (2001)• HadSST3 (1878-2017) – Kennedy et al. (2011)• NOAA Optimum Interpolation SST (1982-2017) – Reynolds et al. (2007)• Southern Oscillation Index (1878-2017) – Australian Bureau of Meteorology – Available

online at http://www.bom.gov.au/climate/current/soihtm1.shtml

Aug-Sep Environmental Variables/Atlantic Hurricane Predictands

Application to 2017 Atlantic Hurricane Season

Values of each environmental variable in Aug-Sep 2017 and comparison with 1981-2010 climatology

Compo, G. P., 2011: The Twentieth Century Reanalysis Project. Q. J. Roy. Meteor. Soc., 137, doi: 10.1002/qj.776.Kennedy, J. J., N. A. Rayner, R. O. Smith, D. E. Parker, and M. Saunby, 2011: Reassessing biases and other uncertainties in sea surface temperature

observations measured in situ since 1850: 1. Measurement and sampling uncertainties, J. Geophys. Res., 116, D14103, doi: 10.1029/2010JD015218.Kistler, R., and Co-Authors, 2001: The NCEP-NCAR 50-Year Reanalysis: Monthly means CD-ROM and documentation. Bull. Amer. Meteor. Soc., 82, 247-267. Landsea, C. W., and J. L. Franklin, 2013: Atlantic hurricane database uncertainty and presentation of a new database format. Mon. Wea. Rev., 141, 3576-3592.Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolution-blended analyses for sea surface temperature. J.

Climate, 20, 5473-5496.Saunders, M. A., P. J. Klotzbach, and A. S. Lea, 2017: Replicating annual North Atlantic hurricane activity 1878-2012 from environmental variables. J. Geophys. Res.,

122, 6284-6297.Vecchi, G. A., and T. R. Knutson, 2011: Estimating annual numbers of Atlantic hurricanes missing from the HURDAT database (1878-1965) using ship track density.

J. Climate, 24, 1736-1746.

References

Mark A. SaundersMullard Space Science Laboratory

University College London, UK

Adam S. R. LeaMullard Space Science Laboratory

University College London, UK

The strength, significance, and stability of the links between the six environmental fields and North Atlantic hurricane activity between1878 and 2012. The environmental fields are grouped by type into three columns: (a, b) atmospheric fields (left column), (c, d) SST fields(center column), and (e, f) ENSO fields (right column). The two measures of hurricane activity examined are ACE (solid lines) and thenumber of adjusted hurricanes (dashed lines). The strength and significance of the associations are shown respectively by rankcorrelations (blue lines) and by p values (red lines). Values are computed for running 30 year periods. “AS” signifies August–September.

Locations of the environmental variables considered for explaining Atlantic hurricane variations

Nature of the data time series between 1878 and 2012. The panels display (a) the anomalies in the North Atlantic ACE index, (b) theanomalies in the adjusted number of North Atlantic hurricanes, and (c–h) anomalies in each of six environmental fields used to replicateannual North Atlantic hurricane activity. The data anomalies are relative to the following 1878–2012 climate norms: 106 × 104 kt2

(annual ACE), 6.6 (annual adjusted hurricane numbers), 4.19 ms-1 (Aug–Sep 950 hPa U), 1013.35 hPa (Aug–Sep SLP), 0.04°C (Aug–SepMDR SST), 0.12°C (Aug–Sep Relative SST), 0.19°C (Aug–Sep Niño 3.4 SST), and 0.19 (Aug–Sep SOI).

Comparing Performance of Best Single- and Best Multi-Predictor Models

Comparison of the performance of the best multi-predictor statisticalmodel (AS 950 hPa U and AS Relative SST) and the best single-predictorstatistical model (AS 950 hPa U) in replicating North Atlantic hurricaneactivity 1878–2012. The two measures of hurricane activity examined areACE (purple lines) and number of adjusted hurricanes (green lines). Thereplication skill is assessed using mean square skill score (panel a) andrank correlation (panel b). Values are computed for running 30-yearperiods.

Assessment of the influence of model training period length on predictive skill

Key Point: Multi-predictor model improves upon single-predictor hurricane forecast model but only modestly improves upon single-predictor ACE forecast model.

Key Point: Increasing the length of the model training dataset slightly improves the hindcastskill for the 1983-2012 30-year period.

Red colors indicate 10 highest values from 1950-2017 while blue colors indicate 10 lowest values from 1950-2017

The hindcast error time series between 1878 and 2012 obtained with each environmental field. (a–f) The six panels display byenvironmental field the hindcast errors in the North Atlantic ACE index (purple lines) and the hindcast errors in the adjusted number ofNorth Atlantic hurricanes (green lines). The mean square error (MSE) of the 135-year hindcast errors for each hurricane activitymeasure is included in the top corners of each panel.

Replicating Historical Hurricane Activity