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i

NASA/TP—2014–218198

July 2014

National Aeronautics and

Space Administration

Marshall Space Flight Center • Huntsville, Alabama 35812

Statistical Aspects of North Atlantic Basin 

Tropical Cyclones During the Weather 

Satellite Era, 1960–2013: Part 2

Robert M. Wilson

Marshall Space Flight Center, Huntsville, Alabama
ii

Available from:

NASA STI Information Desk

Mail Stop 148

NASA Langley Research Center

Hampton, VA 23681–2199, USA

757–864–9658

This report is also available in electronic form at

<http://www.sti.nasa.gov>
iii

TABLE OF CONTENTS

1. INTRODUCTION ............................................................................................................. 1

2. RESULTS AND DISCUSSION ......................................................................................... 2

2.1 Oceanic Niño Index and Southern Oscillation Index .................................................... 4

2.2 Atlantic Multidecadal Oscillation Index ....................................................................... 7

2.3 Quasi-Biennial Oscillation Index .................................................................................. 8

2.4 North Atlantic Oscillation Index ................................................................................... 9

2.5 Armagh Surface Air Temperature ................................................................................. 12

2.6 Global Land-Ocean Temperature Index and Mauna Loa Carbon Dioxide Index ......... 12

2.7 Correlative Behavior of the Climate-Related Factors .................................................... 14

2.8 Inferred Statistical Relationships Between Tropical Cyclone Parametric 

Values and Selected Climate-Related Factors ................................................................ 24

3. SUMMARY ........................................................................................................................ 65

REFERENCES ....................................................................................................................... 70
iv

LIST OF FIGURES

 1. Variation of (a) <ONI> and (b) number of months when ENL 

and LNL conditions prevailed for the interval 1960–2013 ......................................... 4

 2. Variation of <SOI> for the interval 1960–2013 ......................................................... 5

 3. Scatterplot of <SOI> versus <ONI> ........................................................................ 7

 4. Variation of <AMO> for the interval 1960–2013 ...................................................... 8

 5. Variation of <QBO> for the interval 1960–2013 ....................................................... 9

 6. Variation of <NAO> CPC for the interval 1960–2013 .............................................. 10

 7. Variation of <NAO> CRU for the interval 1960–2013 .............................................. 10

 8. Scatterplot of <NAO> CRU versus <NAO> CPC ................................................... 11

 9. Variation of <ASAT> for the interval 1960–2013 ..................................................... 12

10. Variation of (a) <GLOTI> and (b) <MLCO2> for the interval 1960–2013 .............. 13

11. Scatterplot of <GLOTI> versus <MLCO2> ............................................................ 14

12. Scatterplot of <NAO> CPC versus <AMO> ............................................................ 17

13. Scatterplot of <NAO> CRU versus <AMO> ........................................................... 18

14. Scatterplot of <ASAT> versus <AMO> ................................................................... 19

15. Scatterplot of <GLOTI> versus <AMO> ................................................................ 20

16. Scatterplot of <ASAT> versus <GLOTI> ................................................................ 21

17. Scatterplot of <AMO> versus <MLCO2> ............................................................... 22

18. Scatterplot of <ASAT> versus <MLCO2> ............................................................... 23

19. Scatterplot of NTC versus <AMO> ......................................................................... 28
v

LIST OF FIGURES (Continued)

20. Scatterplot of NH versus <AMO> ........................................................................... 29

21. Scatterplot of NMH versus <AMO> ........................................................................ 30

22. Scatterplot of <LAT> versus <AMO> ..................................................................... 31

23. Scatterplot of <LONG> versus <AMO> ................................................................. 32

24. Scatterplot of LP versus <AMO> ............................................................................. 33

25. Scatterplot of total ACE versus <AMO> ................................................................. 34

26. Scatterplot of HISACE versus <AMO> ................................................................... 35

27. Scatterplot of total PDI versus <AMO> ................................................................... 36

28. Scatterplot of HISPDI versus <AMO> .................................................................... 37

29. Scatterplot of <NSD> versus <AMO> .................................................................... 38

30. Scatterplot of total NSD versus <AMO> ................................................................. 39

31. Scatterplot of total NHD versus <AMO> ................................................................ 40

32. Scatterplot of total NMHD versus <AMO> ............................................................ 41

33. Scatterplot of NTCA versus <AMO> ....................................................................... 42

34. Scatterplots of (a) NTC, (b) NMH, and (c) <LP> versus <SOI> ............................. 43

35. Scatterplots of (a) total ACE and (b) LISNSD versus <SOI> ................................... 45

36. Scatterplots of (a) total NSD, (b) total NHD, and (c) NTCA versus <SOI> ............ 47

37. Scatterplot of LOS versus <MLCO2> ...................................................................... 48

38. Scatterplot of NTC versus <MLCO2> ..................................................................... 49

39. Scatterplot of <LAT> versus <MLCO2> ................................................................. 50

40. Scatterplot of total NSD versus <MLCO2> ............................................................. 51
vi

LIST OF FIGURES (Continued)

41. Scatterplots of (a) total NSD and (b) NTCA versus <GLOTI> ................................ 53

42. Scatterplots of (a) LSD and (b) NTC versus <ONI> ................................................ 55

43. Scatterplots of (a) NH, (b) <NSD>, and (c) total NSD versus <NAO> CRU .......... 57

44. Scatterplots of (a) NH and (b) total NSD versus <NAO> CPC ................................ 59

45. Scatterplot of NTC versus <ASAT> ......................................................................... 60
vii

LIST OF TABLES

1. Summary of selected climate-related parametric values, 1960–2013 ............................ 2

2. Summary of climate interrelational statistics, 1960–2013 ............................................ 15

3. Summary of tropical cyclone statistics against climate factors 

(single-variate fits), 1960–2013 .................................................................................... 25

4. Comparison of observed and predicted NTC values based on selected fits ................. 62

5. Summary of estimates for the North Atlantic basin tropical cyclone 

parameters (rounded to nearest whole number) for the 2014 hurricane 

season based on the means for the interval 1995–2013 for <AMO> 

and the projected value of <MLCO2> for 2014 .......................................................... 67

6. Summary of estimates for the North Atlantic basin tropical cyclone 

parameters based on whether the year 2014 is classified as ENY 

or NENY (rounded to nearest whole number) ............................................................ 68
viii

LIST OF ABBREVIATIONS, ACRONYMS, DESIGNATORS, AND SYMBOLS

ACE accumulated cyclone energy

<ACE> mean seasonal ACE

AMO Atlantic Multidecadal Oscillation (index)

<AMO> mean seasonal AMO

ASAT Armagh surface air temperature

<ASAT> mean seasonal ASAT

CO2 carbon dioxide

CPC Climate Prediction Center

CRU Climate Research Unit

DOY day of year

ENL El Niño-like

ENY El Niño year

FSD first storm day

GLOTI Global Land-Ocean Temperature Index

<GLOTI> mean seasonal GLOTI

HISACE highest individual storm ACE

HISPDI highest individual storm PDI

<LAT> mean seasonal latitude

LISNSD longest individual storm NSD

LNL La Niña-like
ix

LIST OF ABBREVIATIONS, ACRONYMS, DESIGNATORS, AND SYMBOLS (Continued)

LNY La Niña year

<LONG> mean seasonal longitude

LOS length of season

LP lowest pressure

<LP> mean seasonal LP

LSD last storm day

MLCO2 Mauna Loa CO2 (index)

<MLCO2> mean seasonal MLCO2

NAO North Atlantic Oscillation (index)

<NAO> mean seasonal NAO

NENM number of ENL months

NENY non-El Niño year

NH number of hurricanes

NHD number of hurricane days

NLNM number of LNL months

NMH number of major hurricanes

NMHD number of major hurricane days

NNM number of neutral months

NSD number of storm days

<NSD> mean seasonal NSD

NTC number of tropical cyclones
x

LIST OF ABBREVIATIONS, ACRONYMS, DESIGNATORS, AND SYMBOLS (Continued)

NTCA Net Tropical Cyclone Activity

NUSLFH number of United States land-falling hurricanes

<NUSLFH> mean seasonal NUSLFH

ONI Oceanic Niño Index

<ONI> mean yearly ONI

PDI Power Dissipation Index

<PDI> mean seasonal PDI

PWS peak wind speed

<PWS> mean seasonal PWS

QBO Quasi-Biennial Oscillation (index)

<QBO> mean seasonal QBO

SOI Southern Oscillation Index 

<SOI> mean yearly SOI

SST sea surface temperature

THC thermohaline circulation 

TP Technical Publication
xi

NOMENCLATURE

a y-intercept

b slope

cl confidence level

na number above median

nb number below median

nra number of positive runs

P probability

Ry12 sample coefficient of multiple correlation

r coefficient of correlation

r2 coefficient of determination

Sy12 sample coefficient of multiple standard error of estimate

sd standard deviation 

se standard error of estimate

t t-statistic for independent samples

x independent variable

y dependent variable

z normal deviate for the sample results
xii
1

TECHNICAL PUBLICATION

STATISTICAL ASPECTS OF NORTH ATLANTIC BASIN TROPICAL CYCLONES 

DURING THE WEATHER SATELLITE ERA, 1960–2013: PART 2

1. INTRODUCTION

This Technical Publication (TP) is part 2 of a two-part study of the North Atlantic basin 

tropical cyclones that occurred during the weather satellite era, 1960–2013. In particular, this TP 

examines the inferred statistical relationships between 25 tropical cyclone parameters and 9 specific 

climate-related factors, including the (1) Oceanic Niño Index (ONI), (2) Southern Oscillation Index 

(SOI), (3) Atlantic Multidecadal Oscillation (AMO) index, (4) Quasi-Biennial Oscillation (QBO) 

index, (5) North Atlantic Oscillation (NAO) index of the Climate Prediction Center (CPC), (6) NAO 

index of the Climate Research Unit (CRU), (7) Armagh surface air temperature (ASAT), (8) Global 

Land-Ocean Temperature Index (GLOTI), and (9) Mauna Loa carbon dioxide (CO2) (MLCO2) 

index. Part 1 of this two-part study examined the statistical aspects of the 25 tropical cyclone parameters (e.g., frequencies, peak wind speed (PWS), accumulated cyclone energy (ACE), etc.) and provided the results of statistical testing (i.e., runs-testing, the t-statistic for independent samples, and 

Poisson distributions). Also, the study gave predictions for the frequencies of the number of tropical 

cyclones (NTC), number of hurricanes (NH), number of major hurricanes (NMH), and number of 

United States land-falling hurricanes (NUSLFH) expected for the 2014 season, based on the statistics of the overall interval 1960–2013, the subinterval 1995–2013, and whether the year 2014 would 

be either an El Niño year (ENY) or a non-El Niño year (NENY).1
2

2. RESULTS AND DISCUSSION

Table 1 provides a detailed listing of the yearly values for each of the climate-related factors 

that will be discussed in the following subsections. Also given in the table are the extreme monthly 

values per year (highs and lows) for ONI and SOI, as well as the statistics for the overall interval 

1960–2013, the two subintervals 1960–1994 and 1995–2013, and the results of statistical testing.

Table 1. Summary of selected climate-related parametric values, 1960–2013.

Year NENM NNM NLNM <ONI>

ONI

(High/Low) <SOI>

SOI

(High/Low) <AMO> <QBO>

<NAO>

CPC

<NAO>

CRU <ASAT> <GLOTI> <MLCO2>

1960 – 12 – 0.00 0.32/–0.36 3.8 7.8/–2.3 0.237 –7.76 –0.56 –0.30 9.44 –0.04 316.91

1961 1 11 – 0.01 0.54/–0.38 0.8 13.8/–20.9 0.101 4.42 0.01 1.05 9.58 0.06 317.64

1962 – 11 1 –0.24 0.01/–0.52 5.4 17.0/–1.4 0.074 –7.73 –0.38 –0.13 8.76 0.05 318.45

1963 6 5 1 0.64 1.43/–0.50 –2.0 9.4/–12.9 0.006 –8.75 –0.56 –0.39 8.57 0.07 318.99

1964 2 2 8 –0.38 1.05/–0.86 6.3 14.3/–4.0 –0.094 2.38 –0.18 0.24 9.49 –0.20 319.62

1965 8 3 1 0.91 2.04/–0.58 –16.8 2.9/–22.6 –0.158 –11.93 –0.22 –0.23 8.82 –0.11 320.04

1966 4 8 – 0.37 1.29/–0.25 –4.2 4.0/–13.9 0.006 1.29 –0.49 –0.22 9.38 –0.05 321.38

1967 – 11 1 –0.23 0.12/–0.58 3.2 14.6/–5.5 –0.096 0.93 0.34 0.56 9.40 –0.01 322.16

1968 3 6 3 0.07 0.97/–0.87 3.0 14.7/–3.4 –0.165 –14.41 –1.04 –0.62 9.32 –0.05 323.04

1969 11 1 – 0.83 1.29/0.26 –5.4 3.7/–13.5 0.011 4.81 –0.19 –0.44 8.93 0.07 324.62

1970 1 5 6 –0.23 0.61/–0.98 3.9 19.7/–10.7 –0.102 –13.76 –0.35 0.18 9.29 0.03 325.68

1971 – – 12 –0.90 –0.63/–1.42 11.0 22.6/2.1 –0.311 4.80 –0.04 –0.55 9.72 –0.06 326.32

1972 8 3 1 0.94 2.27/–0.67 –7.4 8.2/–18.6 –0.353 –8.10 0.49 –0.04 8.74 0.01 327.45

1973 3 1 8 –0.57 1.91/–2.08 7.3 31.6/–13.5 –0.215 4.40 –0.16 –0.09 9.33 0.15 329.68

1974 – 2 10 –0.84 –0.30/–1.99 9.9 20.8/–1.4 –0.420 –13.42 0.11 0.59 8.94 –0.07 330.18

1975 – 2 10 –1.04 –0.33/–1.76 13.6 22.5/–4.9 –0.298 1.25 –0.16 0.05 9.70 –0.01 331.08

1976 3 5 4 –0.08 0.88/–1.78 1.1 13.2/–13.0 –0.363 0.85 0.18 –0.07 9.34 –0.12 332.05

1977 6 6 – 0.52 0.83/0.08 –9.9 7.7/–17.7 –0.189 –11.27 –0.42 –0.21 8.92 0.15 333.78

1978 1 11 – –0.08 0.81/–0.47 –1.7 16.3/–24.4 –0.179 6.81 0.28 0.21 9.21 0.06 335.41

1979 3 9 – 0.23 0.56/–0.10 –1.9 6.7/–8.2 –0.110 –13.16 0.05 0.19 8.35 0.12 336.78

1980 2 10 – 0.21 0.59/–0.09 –3.1 3.2/–12.9 –0.018 5.27 –0.55 –0.37 9.11 0.22 338.68

1981 – 9 3 –0.35 –0.12/–0.69 1.8 11.5/–16.6 –0.075 –1.91 –0.27 –0.09 9.09 0.28 340.10

1982 8 4 – 0.91 2.31/–0.11 –13.1 9.4/–31.1 –0.211 –5.74 0.36 0.67 9.44 0.09 341.44

1983 6 2 4 0.42 2.25/–1.09 –8.3 9.9/–33.3 –0.069 1.15 0.30 0.34 9.77 0.27 343.03

1984 – 7 5 –0.48 –0.08/–1.23 –0.1 5.8/–8.7 –0.206 –17.02 0.23 0.26 9.29 0.12 344.58

1985 – 5 7 –0.61 –0.26/–1.09 0.9 14.4/–9.6 –0.265 9.63 –0.28 –0.47 8.70 0.08 346.04

1986 4 7 1 0.26 1.17/–0.52 –10.7 10.7/–13.9 –0.273 –0.98 0.43 0.56 8.57 0.15 347.39

1987 12 – – 1.29 1.72/0.93 –13.1 –1.4/–24.4 0.069 –6.25 –0.22 –0.51 9.07 0.28 349.16

1988 1 3 8 –0.82 0.94/–1.89 7.8 21.0/–5.0 –0.002 1.56 –0.08 –0.32 9.66 0.34 351.56

1989 – 7 5 –0.63 0.01/–1.85 6.8 21.0/–6.3 –0.080 –10.70 0.68 0.57 10.07 0.24 353.07

1990 – 12 – 0.27 0.41/0.07 –2.2 13.1/–17.3 –0.035 7.38 0.61 1.23 9.94 0.39 354.35

1991 7 5 – 0.65 1.55/0.02 –8.8 5.1/–19.3 –0.129 –3.24 0.25 0.34 9.42 0.38 355.57
3

Year NENM NNM NLNM <ONI>

ONI

(High/Low) <SOI>

SOI

(High/Low) <AMO> <QBO>

<NAO>

CPC

<NAO>

CRU <ASAT> <GLOTI> <MLCO2>

1992 6 6 – 0.58 1.65/–0.45 –10.4 1.4/–25.4 –0.216 –7.61 0.54 1.11 9.45 0.19 356.38

1993 2 10 – 0.31 0.81/0.04 –9.5 1.6/–21.1 –0.207 3.90 0.16 0.12 9.27 0.20 357.07

1994 3 9 – 0.48 1.26/0.05 –11.9 0.6/–22.8 –0.173 –13.67 0.54 0.51 9.38 0.28 358.82

1995 3 4 5 –0.08 1.04/–0.96 –1.8 4.2/–16.2 0.139 7.82 –0.09 –0.61 10.23 0.43 360.80

1996 – 8 4 –0.43 –0.18/–0.85 14.0 13.9/–0.1 –0.054 –16.06 –0.25 –1.01 9.23 0.33 362.59

1997 8 3 1 1.11 2.38/–0.58 –11.7 13.3/–24.1 0.056 7.86 –0.20 –0.18 10.32 0.45 363.71

1998 5 1 6 –0.03 2.30/–1.57 –1.1 14.6/–28.5 0.377 –12.83 –0.54 0.26 10.09 0.61 366.65

1999 – – 12 –1.19 –0.87/–1.72 16.3 18.5/–0.4 0.123 10.17 0.33 0.05 10.18 0.40 368.33

2000 – 1 11 –0.88 –0.46/–1.78 7.8 22.4/–5.5 0.034 –6.49 0.16 0.04 9.93 0.40 369.52

2001 – 10 2 –0.26 0.10/–0.74 0.5 11.9/–9.1 0.118 –15.24 –0.24 –0.45 9.57 0.52 371.13

2002 8 4 – 0.65 1.39/–0.18 –6.1 7.7/–14.6 0.071 8.72 0.00 –0.04 10.20 0.61 373.22

2003 4 8 – 0.34 1.01/–0.49 –3.1 9.8/–12.0 0.237 –13.81 0.03 –0.16 10.03 0.60 375.77

2004 6 6 – 0.44 0.76/0.08 –4.8 13.1/–15.4 0.213 6.60 0.16 0.01 10.21 0.52 377.49

2005 1 9 2 0.08 0.64/–0.88 –3.6 10.9/–14.5 0.298 –17.13 –0.31 –0.25 10.25 0.65 379.80

2006 4 5 3 0.13 1.18/–1.01 –1.9 15.2/–15.9 0.273 4.47 –0.31 –0.20 10.42 0.59 381.90

2007 1 6 5 –0.45 0.72/–1.30 –0.6 14.4/–7.3 0.148 –15.91 0.11 –0.38 10.60 0.62 383.76

2008 – 5 7 –0.69 –0.12/–1.58 10.2 21.3/–4.3 0.146 6.98 –0.45 –0.73 9.76 0.49 385.59

2009 6 3 3 0.37 1.78/–0.94 –0.2 14.8/–14.7 0.047 –3.49 –0.32 –0.42 9.84 0.59 387.37

2010 4 2 6 –0.33 1.57/–1.53 9.8 27.1/–14.5 0.358 –7.95 –1.29 –2.19 8.72 0.66 389.85

2011 – 4 8 –0.73 –0.05/–1.59 13.3 25.1/0.2 0.110 1.96 0.20 0.64 10.28 0.55 391.63

2012 2 8 2 –0.06 0.75/–0.84 –0.8 9.4/–10.4 0.222 –20.79 –0.53 –0.63 9.70 0.57 393.82

2013 – 10 2 –0.34 0.00/–0.78 4.0 13.9/–3.6 0.176 9.06 0.15 0.59 9.84 0.60 396.48

1960–2013 (n= 54)

sum 178

mean 3 6 3 – 0.81/–0.80 –0.3 12.6/–12.8 –0.026 –3.38 –0.07 –0.04 9.50 0.25 350.52

sd 3 3 4 0.59 0.84/0.68 7.9 7.2/8.5 0.193 8.88 0.40 0.57 0.54 0.25 23.90

high – – – 1.29 2.38/0.93 16.3 31.6/2.1 0.377 10.17 0.68 1.11 10.60 0.66 396.48

low – – – –1.19 –0.87/–2.08 –16.8 –1.4/–33.3 –0.420 –20.79 –1.29 –2.19 8.35 –0.20 316.91

med – – – –0.02 0.81/–0.74 –0.7 13.2/–13.0 –0.027 –1.91 –0.08 –0.07 9.44 0.24 349.16

na – – – 30 – 28 – 27 27 27 28 27 27

nb – – – 24 – 26 – 27 27 27 26 27 27

nra – – – 11 – 12 – 7 23 14 15 10 4 1

z – – – –1.57 – –1.08 – 4.86 – 0.54 2.16 –5.40 –7.02

1960–1994 (n= 35)

sum 111 99

mean 3 6 3 0.07 0.85/–0.68 –1.5 11.4/–13.7 –0.129 –3.33 –0.02 0.11 9.24 0.10 335.67

sd 3 4 4 0.60 0.81/0.73 7.8 7.6/8.8 0.144 7.60 0.40 0.48 0.40 0.15 13.42

1995–2013 (n= 19)

sum 52 97 79

mean 3 5 4 –0.12 0.73/–1.01 2.1 14.8/–11.1 0.163 –3.48 –0.18 –0.30 9.97 0.54 377.86

sd 3 3 4 0.56 0.91/0.53 7.7 5.9/7.8 0.113 11.09 0.38 0.62 0.44 0.09 11.11

t test (1960–1994) and (1995–2013)

t – 1.0 1.14 0.5/1.7 –1.6 –1.7/–1.1 –7.642 0.06 1.43 2.70 –6.18 –11.67 –11.69

Table 1. Summary of selected climate-related parametric values, 1960–2013 (Continued).
4

2.1 Oceanic Niño Index and Southern Oscillation Index

Figure 1 displays the variation of (a) the mean yearly ONI (<ONI>) and (b) the number of 

months during the year when El Niño-like (ENL) and La Niña-like (LNL) conditions prevailed, 

based on the ONI monthly values for the overall interval 1960–2013. The <ONI> is simply the 

average of the 12 monthly values (January–December) contained in the listing of ONI values.2,3

The monthly values of the ONI are the anomalous deviations from the climate-adjusted monthly 

averages of the sea surface temperature (SST) in the Niño 3.4 region of the Pacific Ocean (i.e., the 

region bounded by ±5° latitude about the equator and ±25° longitude about long. 145° W.) based 

on centered 30-year base periods every 5 years and calculated by the National Oceanic and Atmospheric Administration/National Weather Service/CPC. The number of ENL (thick line) and LNL 

(thin line) months, as used in this study, is simply the number of months during the year when 

ONI ≥0.5 °C and ≤ –0.5 °C, respectively. As an example, for the year 2013, <ONI>=–0.34 °C and 

the number of ENL months (NENM) was 0, the number of neutral months (NNM) was 10, and the 

number of LNL months (NLNM) was 2 (January and February).

6

12

Number of Months

When ENL and LNL

Conditions Prevailed

1960 1970 1980 1990 2000

ENL LNL

2010 2020

F1

Year

Number of Months

(b)

0

–1

–0.5

0

0.5

1

1.5

–1.5

Mean Oceanic Niño Index (1960–2013)

ENL

(Warm)

LNL

(Cool)

<ONI>

mean=0.00 °C

sd=0.59 °C

median=–0.02 °C

1960–1994: mean=0.07

sd=0.60

1995–2013: mean=–0.12

sd=0.56

(a)

1.19 (1999)

1.29 (1987)

⇒t = 1.14

“+” <ONI>

“–” <ONI>

mean sd n

0.48 0.34 27

–0.48 0.32 27

Figure 1. Variation of (a) <ONI> and (b) number of months when ENL and LNL 

conditions prevailed for the interval 1960–2013. 
5

The mean <ONI> for the overall interval 1960–2013 is 0.00 °C, having a standard deviation 

sd=0.59 °C, median=–0.02 °C, and extremes of 1.29 °C in 1987 (an ENY) and –1.19 °C in 1999 

(a NENY, best described as a La Niña year (LNY)). Positive-valued <ONI> occurred in 27 years 

and negative-valued <ONI> also occurred in 27 years. Runs-testing suggests that <ONI> varies 

randomly, having a normal deviate for the sample results z=–1.57. Likewise, a comparison of the 

means for the two subintervals 1960–1994 and 1995–2013 suggests that the t-statistic for independent 

samples measures 1.14, inferring that the difference in the means for the two subintervals is not statistically important (even though the mean for the more recent subinterval is about 0.19 °C cooler than 

the mean for the earlier subinterval). The large positive spikes reflect ENL warm conditions synonymous with the occurrence of traditional El Niño events, and the large negative dips reflect LNL cool 

conditions synonymous with the occurrence of traditional La Niña events (i.e., at least 5 consecutive 

months with monthly ONI values meeting the conventional El Niño or La Niña definitions).

Figure 2 shows the variation of the mean yearly SOI (<SOI>). Like the ONI, the SOI gives 

an indication as to the development and intensity of El Niño and La Niña events. It is calculated by 

the Australian Bureau of Meteorology as the pressure difference between Tahiti, French Polynesia 

and Darwin, Australia, based on means and standard deviations calculated over the period 1933 to 

1992 inclusive.4,5 Sustained negative monthly values of the SOI below about –8 often indicate the 

occurrence of an El Niño event, while sustained positive monthly values of the SOI above 8 often 

indicate the occurrence of a La Niña event; hence, the SOI and ONI vary inversely, with positive 

(negative) SOI being associated with negative (positive) ONI.

1960 1970 1980 1990 2000 2010 2020

F2

Year

–15

–10

–5

0

5

10

15

20

–20

Mean Southern Oscillation Index (1960–2013)

LNL

(Cool)

ENL

(Warm)

<SOI>

mean=–0.3

sd=7.9

median=–0.7

1960–1994: mean= –1.5

sd=7.8

1995–2013: mean=2.1

sd=7.7

16.8 (1965)

16.3 (1999) ⇒t = –1.6

“+” <SOI>

“–” <SOI>

mean sd n

6.77 4.68 24

–5.87 4.72 30

Figure 2. Variation of <SOI> for the interval 1960–2013. 
6

The mean <SOI> for the overall interval 1960–2013 is –0.3, having sd=7.9, a median=–0.7, 

and extremes of 16.3 in 1999 (an LNY) and –16.8 in 1965 (an ENY). For the year 2013, <SOI> 

measured 4.0. Positive-valued <SOI> occurred in 24 years, and negative-valued <SOI> occurred in 

30 years. Runs-testing suggests that <SOI> varies randomly having z=–1.08, and a comparison of 

the means for the two subintervals 1960–1994 and 1995–2013 yields t=–1.6, inferring that the difference in the means is not statistically important (though to the eye, the observed variation hints of 

being cyclic, with warm ENL conditions prevailing in the late 1970s to the late 2000s and cooler LNL 

conditions prevailing in the early 1960s and now after about 2007).

Figure 3 depicts the scatterplot of <SOI> versus <ONI>. Plainly, the two parameters vary 

inversely (the diagonal line) as y=–0.238 – 12.004x, where y is <SOI> and x is <ONI>, having a coefficient of correlation r=–0.896, a coefficient of determination r2=0.802 (meaning that about 80% of 

the variance in <SOI> can be explained by the variation in <ONI> alone, and vice versa), a standard 

error of estimate se=3.530, and a confidence level cl» 99.9%. Identified in the scatterplot are the years 

of parametric extremes for <SOI> (1965 and 1999) and <ONI> (1987 and 1999). Also shown in the 

scatterplot is the result of Fisher’s exact test for 2× 2 contingency tables (the vertical and horizontal 

lines are the parametric medians), which indicates that the probability of obtaining the observed 

result, or one more suggestive of a departure from independence (chance), is P=1.6× 10–5%. Hence, 

if the year 2014 happens to be an ENY, one clearly should expect the <ONI> and <SOI> values to 

lie in the lower-right quadrant of the scatterplot; on the other hand, if the year 2014 happens to be 

a NENY, one should expect the <ONI> and <SOI> values to lie in the upper-left quadrant of the 

scatterplot. 
7

–20

–15

–10

–5

0

5

10

15

20

–1.5 –1 –0.5 0

<ONI>

<SOI>

0.5 1 1.5

23 4

4 23

⇒ P=1.6 × 10–5%

1987

1999

1965

F3

y

y = –0.238 – 12.004x

r = –0.896, r 2=0.802

se = 3.530, cl » 99.9%

Figure 3. Scatterplot of <SOI> versus <ONI>.

2.2 Atlantic Multidecadal Oscillation Index

Figure 4 displays the yearly variation of the mean seasonal AMO (<AMO>) for the overall interval 1960–2013. The AMO is defined as a fluctuation in the SST in the North Atlantic 

Ocean between the equator and lat. 70° N.6–11 It appears to have a cycle length or period of about 

70 years, fluctuating between warm (positive) and cool (negative) phases, with this pattern possibly 

being associated with variations in the strength of the Atlantic thermohaline circulation (THC), 

a density-driven, global circulation pattern that involves the movement of warm salty equatorial surface waters to higher latitudes and the subsequent cooling and sinking of these waters into the deep 

ocean (also called the Atlantic Meridional Overturning Circulation). As such, the warm phase of the 

AMO seems to represent faster THC, while the cool phase seems to represent slower THC.
8

1960 1970 1980 1990 2000 2010 2020

F4

Year

0

0.5

–0.5

Mean Atlantic Multidecadal Oscillation Index (1960–2013)

Warm

Cool

<AMO>

mean=–0.026

 sd=0.193

median=–0.054

1960–1994: mean= –0.129

sd=0.144

1995–2013: mean=0.163

sd=0.113

0.420 (1974)

0.377 (1998) ⇒t = –7.64

“+” <AMO>

“–” <AMO>

mean sd n

0.146 0.107 25

–0.175 0.108 29

Figure 4. Variation of <AMO> for the interval 1960–2013.

The mean <AMO> measures –0.026 °C for the overall interval 1960–2013, having sd=0.193 °C 

and a median=–0.054 °C. Runs-testing suggests that the variation of <AMO> is nonrandom, having z=–3.78 and cl>99.9%. Comparison of the means for the two subintervals shows that the mean 

for the more recent subinterval is nearly 0.3 °C warmer than the mean for the earlier subinterval, 

with t=–7.64 and cl>99.9%. During the overall interval 1960–2013, there have been 25 warm years 

and 29 cool years. Inspection of figure 4 indicates that the <AMO> switched from the warm phase 

to the cool phase about the mid-1960s, remaining in the cool phase until about the mid-1990s, when 

it switched back again to the warm phase where it has remained for the about the last 20 years. 

The peak yearly <AMO> occurred in 1998 (0.377 °C), although another peak occurred in 2010 

(0.358 °C). One anticipates that the warm phase will continue for at least several years or more. 

(For the year 2013, <AMO> measured 0.176 °C.)

2.3 Quasi-Biennial Oscillation Index

The QBO refers to the quasi-periodic oscillation of the equatorial zonal winds (in ms–1) in 

the tropical stratosphere having a mean period of about 28 months (range of 20 to 36 months) that 

was first discovered in the 1950s.12–17 The winds alternate between the stronger easterlies (negative 

values) and the somewhat weaker westerlies (positive values), first developing at the top of the lower 

stratosphere and then slowly propagating downwards at the rate of about 1 km per month until dissipating in the tropical tropopause.

Figure 5 depicts the yearly variation of the mean seasonal QBO (<QBO>) for the overall 

interval 1960–2013. Its mean measures about –3.38, having sd=8.88 and a median=–1.91. Runstesting reveals, as expected, that the variation of <QBO> values is nonrandom (z=4.86, cl>99.9%), 

although the comparison of the means for the two subintervals suggests that the difference in the 

means is not statistically important (t=0.06). Clearly, the easterlies (mean=–10.59) are about twice 

as strong as the westerlies (mean=4.98). The strongest easterly occurred in 2012 (–20.79), while the 

strongest westerly occurred in 1999 (10.17). (For the year 2013, <QBO> measured 9.06. Because 

back-to-back positive-valued <QBO> has only occurred twice (1966–1967 and 1975–1976) during 

the overall interval 1960–2013, one strongly suspects that the year 2014 will be of negative value. For 

the 10 negative-valued years in the subinterval 1995–2013, it has averaged about –13, having sd=5.4 

and extremes of –3.49 and –20.79.
9

1960 1970 1980 1990 2000 2010 2020

F5

Year

0

5

10

15

–25

–20

–15

–10

–5

Mean Quasi-Biennial Oscillation Index (1960–2013)

Westerly Winds

Easterly Winds

<QBO>

mean=–3.38

sd=8.88

median=–1.91

1960–1994: mean= –3.33

 sd=7.60

1995–2013: mean=–3.48

sd= 11.09

–20.79 (2012)

10.17 (1999)

⇒t = 0.06

“+” <QBO>

“–” <QBO>

mean sd n

4.98 3.01 25

–10.59 5.03 29

Figure 5. Variation of <QBO> for the interval 1960–2013.

2.4 North Atlantic Oscillation Index

Like the SOI, the NAO is one based on changes in surface air pressure between two widely 

separated locations (typically, Iceland and a location in the subtropical Atlantic basin: the Azores, 

Portugal or Gibraltar). Also, like the SOI, the NAO fluctuates between positive and negative values, where the positive phase is associated with a stronger-than-usual subtropical high pressure center and a deeper-than-usual Icelandic low, and the negative phase is associated with the opposite 

behavior.18–21

Figure 6 shows the yearly variation of the mean seasonal NAO (<NAO>) as computed 

by the CPC for the overall interval 1960–2013. Its mean measures –0.07, having sd=0.04 and 

a median=–0.08. Runs-testing reveals that the variation of <NAO> CPC is random (z=0.00), and 

comparison of the means for the two subintervals suggests that the difference in the means is not 

statistically important (t=1.43). The strongest positive yearly value occurred in 1989 (0.68), and the 

strongest negative yearly value occurred in 2010 (–1.29), although the year 1968 also had a strong 

negative value as well (–1.04). (For the year 2013, <NAO> CPC measured 0.15.)
10

1960 1970 1980 1990 2000 2010 2020

F6

Year

1

–1.5

–1

–0.5

0

0.5

Mean North Atlantic Oscillation Index From the Climate Prediction Center 

(1960–2013)

<NAO> CPC

mean=–0.07

sd= 0.04

median=–0.08

1960–1994: mean= –0.02

sd= 0.40

1995–2013: mean=–0.18

sd= 0.38

–1.29 (2010)

0.68 (1989)

⇒t = 1.43

“+” <NAO>

“–” <NAO>

mean sd n

0.27 0.19 25

–0.37 0.27 29

Figure 6. Variation of <NAO> CPC for the interval 1960–2013.

Figure 7 depicts the yearly variation of the <NAO> as computed by the CRU for the overall 

interval 1960–2013. Its mean measures –0.04, having sd=0.57 and a median=–0.07. Like <NAO> 

CPC, runs-testing of the <NAO> CRU values suggests that its variation appears random (z=0.54), 

but unlike <NAO> CPC, comparison of the means for the two subintervals suggests that the difference in the means is statistically important (t=2.70, cl>99%). The strongest positive yearly value 

occurred in 1990 (1.23), although there have been at least two other strong positive peaks (1.05 in 

1961 and 1.11 in 1992), and the strongest negative value occurred in 2010 (–2.19), which is also the 

strongest negative value ever recorded in the nearly 190-year record. (For the year 2013, <NAO> 

CRU measured 0.59.)

1960 1970 1980 1990 2000 2010 2020

F7

Year

0

–2.5

–2

–1.5

–1

–0.5

0.5

1

1.5 Mean North Atlantic Oscillation Index From the Climate Research Unit 

(1960–2013)

<NAO> CRU

mean=–0.04

sd= 0.57

median=–0.07

1960–1994: mean= 0.11

sd= 0.48

1995–2013: mean=–0.30

sd= 0.62

–2.19 (2010)

1.23 (1990)

⇒t = 2.70

“+” <NAO>

“–” <NAO>

mean sd n

0.43 0.34 24

–0.43 0.41 30

Figure 7. Variation of <NAO> CRU for the interval 1960–2013.
11

Figure 8 displays the scatterplot of <NAO> CRU versus <NAO> CPC. The inferred linear regression is y=0.044+1.079x, having r=0.758, r2=0.574, se=0.372, and cl>99.9%, where y is 

<NAO> CRU and x is <NAO> CPC. Based on Fisher’s exact test for 2× 2 contingency tables, the 

probability of obtaining the observed result, or one more suggestive a of a departure from independence (chance), is P=1.6× 10–5%

–2.5

–2

–1.5

–1

–0.5

0

0.5

1

1.5

–1.5 –1 –0.5 0 0.5 1

<NAO> CPC

<NAO> CRU

4 23

23 4

⇒ P=1.6 × 10–5%

1990

2010

F8

y

y = 0.044 + 1.079x

r = 0.758, r 2= 0.574

se = 0.372, cl > 99.9%

Figure 8. Scatterplot of <NAO> CRU versus <NAO> CPC.
12

2.5 Armagh Surface Air Temperature

The Armagh Observatory in Northern Ireland (lat. 54°21.2' N., long. 6°38.9' W.) is situated 

about 64 m above mean sea level at the top of a small hill in an estate of natural woodland and parkland that measures about 7 ha. Mean surface air temperature has been continuously measured there 

since 1844 using maximum and minimum thermometers.22–30

Figure 9 depicts the yearly variation of mean seasonal ASAT (<ASAT>) for the overall 

interval 1960–2013. Its mean measures 9.50 °C, having sd=0.54 °C and a median=9.44 °C. Runstesting suggests that the variation of the <ASAT> values appears nonrandom (z=2.16, cl>95%), and 

a comparison of the means for the two subintervals suggests that the difference in the means is statistically important (t=–6.18, cl>99.9%). The highest (warmest) yearly value of <ASAT> occurred 

in 2007 (10.60 °C), and the lowest (coolest) yearly value of <ASAT> occurred in 1979 (8.35 °C). For 

the more recent subinterval 1995–2013, only the years 1996 and 2010 had <ASAT> lower than the 

median (9.23 and 8.72 °C, respectively). (For the year 2013, <ASAT> measured 9.84 °C.)

1960 1970 1980 1990 2000 2010 2020

F9

Year

10.5

8

8.5

9

9.5

10

11 Mean Armagh Surface Air Temperature (1960–2013)

<ASAT>

mean=9.50 °C

sd=0.54 °C

median=9.44 °C

1960–1994: mean= 9.24 

sd= 0.40 

1995–2013: mean= 9.97 

sd= 0.44 

8.35 (1979)

10.60 (2007)

⇒t = –6.18

Figure 9. Variation of <ASAT> for the interval 1960–2013.

2.6 Global Land-Ocean Temperature Index and Mauna Loa Carbon Dioxide Index

Figure 10 shows the yearly variation of (a) mean seasonal GLOTI (<GLOTI>) and (b) mean 

seasonal MLCO2 (<MLCO2>) for the overall interval 1960–2013. The GLOTI is a measure of the 

anomaly in global land-ocean temperatures relative to the interval 1951–1980, where the data are 

taken from the Global Historical Climate Network, version 3, using elimination of outliers and 

homogeneity adjustment.27,31–34 The MLCO2 index is a measure of the amount of atmospheric 

concentration of CO2 as measured from the Mauna Loa Observatory in Hawaii, located in a barren 

lava field of an active volcano at lat. 19°32' N. and long. 155°35' W. and at an altitude of 3,397 m 

above mean sea level.35–41
13

1960 1970 1980 1990 2000 2010 2020

F10

Year

0

300

350

400

0.5

–0.5

(a)

(b)

1 Mean Global Land-Ocean Temperature Index (1960–2013)

Mean Mauna Loa Carbon Dioxide Index (1960–2013)

<GLOTI> <MLCO2>

mean = 0.25 °C

sd = 0.25 °C

median = 0.24 °C

1960–1994: mean = 0.10 

sd = 0.15 

1995–2013: mean = 0.54 

sd = 0.09 

316.91 (1960)

396.48 (2013)

0.66 (2010)

–0.02 (1964)

⇒t = –11.67

1960–1994: mean = 335.67

sd = 13.42

1995–2013: mean = 377.86

sd = 11.11

⇒t = –11.69

Figure 10. Variation of (a) <GLOTI> and (b) <MLCO2> for the interval 1960–2013.

For <GLOTI>, its mean measures 0.25 °C, having sd=0.25 °C and a median=0.24 °C. Runstesting suggests that the variation of <GLOTI> is nonrandom (z=–5.40, cl>99.9%). In fact, every 

year post-1976 has been of positive value, and every year post-1994 has had a value larger than the 

mean or median. Furthermore, a comparison of the means for the two subintervals suggests that the 

difference in the means is statistically very important (t=–11.67, cl» 99.9%). The warmest anomaly 

to date occurred in 2010, measuring 0.66 °C, and the coolest anomaly occurred in 1964, measuring 

–0.02 °C. (For the year 2013, <GLOTI> measured 0.60 °C.)

The <MLCO2> has been continuously rising from one year to the next, with the lowest 

value occurring in 1960 (316.91 ppm) and the highest value occurring in 2013 (396.48 ppm), rising at 

an average rate of about 1.47 ppm yr–1. However, the rate of rise actually is rising faster than 

a simple linear increase, as a straight-edge along the figure plainly shows. Clearly, runs-testing reveals 

that the distribution of <MLCO2> is nonrandom (z=–7.02, cl>99.9%), as is also demonstrated 

from the comparison of the means for the two subintervals (t=–11.69, cl»99.9%). (In May 2014, 

the monthly value of MLCO2 reached 401.88 ppm.42 However, the yearly average probably will not 

exceed 400 ppm until the year 2015, due to seasonal effects.)
14

Figure 11 shows the scatterplot of <GLOTI> versus <MLCO2>, identifying the years of parametric extremes (1960, 1964, 2010, and 2013). The inferred linear regression is y=–3.1258+0.0096x, 

having r=0.932, r2=0.869, se=0.1120, and cl>99.9%. Based on Fisher’s exact test for 2× 2 contingency tables, the probability of obtaining the observed result, or one more suggestive of a departure 

from independence, is P=6.4× 10–9%. Hence, one expects <GLOTI> to continue to increase with the 

passage of time due to the continuing increase in the greenhouse gas of CO2 (with the 2014 yearly 

values lying in the upper-right quadrant of the scatterplot).

–0.5

0

0.5

1

300 350

<MLCO2>

<GLOTI>

400

2 25

25 2

⇒ P=6.4 × 10–9%

2013

2010

1964

1960

F11

y

y = –3.1258 + 0.0096x

r = 0.932, r 2= 0.869

se = 0.1120, cl > 99.9%

Figure 11. Scatterplot of <GLOTI> versus <MLCO2>.

2.7 Correlative Behavior of the Climate-Related Factors

Table 2 provides a listing of the inferred correlative statistics between the various climaterelated factors. As an example, one finds that the only inferred correlation found to be statistically 

important (cl>98%) is the one between <ONI> and <SOI>. All other inferred correlations between 

either <ONI> or <SOI> and the other climate-related factors are found not to be statistically important (cl<90%). Also, one finds that <QBO> is not strongly correlated with any of the other climaterelated factors, whereas <NAO> CPC and <NAO> CRU are inferred to strongly correlate not only 

with each other, but also with <AMO>. <ASAT> is inferred to correlate strongly with <AMO>, 

<GLOTI>, and <MLCO2>; <GLOTI> with <AMO>, <ASAT>, and <MLCO2>; and <MLCO2> 

with <AMO>, <ASAT>, and <GLOTI>.
15

Table 2. Summary of climate interrelational statistics, 1960–2013. .

Parameter a b r r2 se cl

<ONI> vs. <SOI> –0.016 –0.067 –0.896 0.802 0.262 >99.9%*

<ONI> vs. <AMO> 0.002 0.030 0.010 0.000 0.593 <90%

<ONI> vs. <QBO> –0.015 –0.005 –0.071 0.005 0.591 <90%

<ONI> vs. <NAO> CPC 0.005 0.045 0.031 0.001 0.592 <90%

<ONI> vs. <NAO> CRU 0.004 0.073 0.071 0.005 0.591 <90%

<ONI> vs. <ASAT> 2.051 –0.216 –0.199 0.040 0.581 <90%

<ONI> vs. <GLOTI> 0.010 –0.034 –0.014 0.000 0.593 <90%

<ONI> vs. <MLCO2> 1.007 –0.003 –0.117 0.014 0.588 <90%

<SOI> vs. <ONI> –0.238 –12.004 –0.896 0.802 3.530 >99.9%*

<SOI> vs. <AMO> –0.141 4.296 0.106 0.011 7.898 <90%

<SOI> vs. <QBO> 0.020 0.081 0.091 0.008 7.909 <90%

<SOI> vs. <NAO> CPC –0.536 –3.833 –0.193 0.037 7.792 <90%

<SOI> vs. <NAO> CRU –0.357 –2.895 –0.208 0.043 7.768 <90%

<SOI> vs. <ASAT> –22.991 2.394 0.165 0.027 7.833 <90%

<SOI> vs. <GLOTI> –0.413 0.624 0.020 0.000 7.940 <90%

<SOI> vs. <MLCO2> –10.149 0.028 0.086 0.007 7.912 <90%

<AMO> vs. <ONI> –0.026 0.003 0.010 0.000 0.195 <90%

<AMO> vs. <SOI> –0.026 0.003 0.106 0.011 0.194 <90%

<AMO> vs. <QBO> –0.029 –0.001 –0.033 0.001 0.195 <90%

<AMO> vs. <NAO> CPC –0.041 –0.199 –0.408 0.167 0.178 >99.5%*

<AMO> vs. <NAO> CRU –0.030 –0.111 –0.325 0.105 0.185 >98%*

<AMO> vs. <ASAT> –1.740 0.180 0.505 0.255 0.168 >99.9%*

<AMO> vs. <GLOTI> –0.172 0.574 0.733 0.537 0.133 >99.9%*

<AMO> vs. <MLCO2> –1.704 0.005 0.591 0.349 0.162 >99.9%*

<QBO> vs. <ONI> –3.381 –1.080 –0.071 0.005 8.941 <90%

<QBO> vs. <SOI> –3.356 0.103 0.091 0.008 8.927 <90%

<QBO> vs. <AMO> –3.421 –1.495 –0.033 0.001 8.959 <90%

<QBO> vs. <NAO> CPC –3.089 3.978 0.178 0.032 8.821 <90%

<QBO> vs. <NAO> CRU –3.304 2.178 0.139 0.019 8.877 <90%

<QBO> vs. <ASAT> –35.536 3.386 0.207 0.043 8.771 <90%

<QBO> vs. <GLOTI> –3.250 –0.521 –0.015 0.000 8.963 <90%

<QBO> vs. <MLCO2> –2.748 –0.002 –0.005 0.000 8.951 <90%

<NAO> CPC vs. <ONI> –0.074 0.021 0.031 0.001 0.401 <90%

<NAO> CPC vs. <SOI> –0.076 –0.010 –0.193 0.037 0.393 <90%

<NAO> CPC vs. <AMO> –0.096 –0.838 –0.408 0.167 0.366 >99.5%*

<NAO> CPC vs. <QBO> –0.047 0.008 0.178 0.032 0.394 <90%

<NAO> CPC vs. <NAO> CRU –0.055 0.532 0.758 0.574 0.262 >99.9%*

<NAO> CPC vs. <ASAT> –1.391 0.139 0.189 0.036 0.394 <90%

<NAO> CPC vs. <GLOTI> –0.045 –0.112 –0.070 0.005 0.400 <90%

<NAO> CPC vs. <MLCO2> –0.180 0.000 0.018 0.000 0.391 <90%

*Inferred correlations having cl > 98%.
16

Parameter a b r r2 se cl

<NAO> CRU vs. <ONI> –0.036 0.068 0.071 0.005 0.568 <90%

<NAO> CRU vs. <SOI> –0.040 –0.015 –0.208 0.043 0.558 <90%

<NAO> CRU vs. <AMO> –0.061 –0.949 –0.325 0.105 0.540 >98%*

<NAO> CRU vs. <QBO> –0.006 0.009 0.139 0.019 0.565 <90%

<NAO> CRU vs. <NAO> CPC 0.044 1.079 0.758 0.574 0.372 >99.9%*

<NAO> CRU vs. <ASAT> –1.418 0.146 0.140 0.019 0.565 <90%

<NAO> CRU vs. <GLOTI> 0.086 –0.477 –0.209 0.044 0.558 <90%

<NAO> CRU vs. <MLCO2> 1.501 –0.004 –0.185 0.034 0.566 <90%

<ASAT> vs. <ONI> 9.498 –0.184 –0.199 0.040 0.533 <90%

<ASAT> vs. <SOI> 9.500 0.011 0.165 0.027 0.542 <90%

<ASAT> vs. <AMO> 9.535 1.416 0.505 0.255 0.467 >99.9%*

<ASAT> vs. <QBO> 9.540 0.013 0.207 0.043 0.547 <90%

<ASAT> vs. <NAO> CPC 9.516 0.258 0.189 0.036 0.541 <90%

<ASAT> vs. <NAO> CRU 9.502 0.134 0.140 0.019 0.544 <90%

<ASAT> vs. <GLOTI> 9.157 1.338 0.611 0.373 0.430 >99.9%*

<ASAT> vs. <MLCO2> 4.7949 0.0134 0.592 0.350 0.4998 >99.9%*

<GLOTI> vs. <ONI> 0.255 –0.006 –0.014 0.000 0.249 <90%

<GLOTI> vs. <SOI> 0.255 0.001 0.020 0.000 0.250 <90%

<GLOTI> vs. <AMO> 0.279 0.937 0.733 0.537 0.170 >99.9%*

<GLOTI> vs. <QBO> 0.253 0.000 –0.015 0.000 0.251 <90%

<GLOTI> vs. <NAO> CPC 0.251 –0.044 –0.070 0.005 0.249 <90%

<GLOTI> vs. <NAO> CRU 0.251 –0.091 –0.209 0.044 0.244 <90%

<GLOTI> vs. <ASAT> –2.391 0.279 0.611 0.373 0.195 >99.9%*

<GLOTI> vs. <MLCO2> –3.1258 0.0096 0.932 0.869 0.1120 >99.9%*

<MLCO2> vs. <ONI> 350.523 –4.756 –0.117 0.014 23.963 <90%

<MLCO2> vs. <SOI> 350.583 0.261 0.086 0.007 24.039 <90%

<MLCO2> vs. <AMO> 352.431 73.015 0.591 0.349 19.465 >99.9%*

<MLCO2> vs. <QBO> 350.472 –0.013 –0.005 0.000 24.134 <90%

<MLCO2> vs. <NAO> CPC 350.597 1.093 0.018 0.000 24.127 <90%

<MLCO2> vs. <NAO> CRU 350.237 –7.836 –0.185 0.034 23.708 <90%

<MLCO2> vs. <ASAT> 102.7349 26.0894 0.592 0.350 19.455 >99.9%*

<MLCO2> vs. <GLOTI> 327.5853 90.0584 0.932 0.8685 8.7483 >99.9%*

*Inferred correlations having cl > 98%.

Table 2. Summary of climate interrelational statistics, 1960–2013 (Continued).
17

Figures 12 and 13 display the scatterplots of <NAO> CPC and <NAO> CRU versus 

<AMO>, respectively, and identifies the years of extreme values. Both measures of <NAO> are 

inferred to correlate inversely against <AMO>, such that negative (positive) values of <NAO> usually are associated with positive (negative) values of <AMO>, and vice versa. Because <AMO> is 

expected to continue to be of positive value for the year 2014, one expects <NAO>, whether the CPC 

or CRU values, to probably be of negative value (about twice as likely as being that of positive value).

–1.5

–1

–0.5

0

0.5

1

–0.5 0

<AMO>

<NAO> CPC

0.5

17 10

10 17

⇒ P=5.1%

1974

2010

F12

y

y = –0.096 – 0.838x

r = –0.408, r 2=0.167

se = 0.366, cl > 99.5%

1989

1998

Figure 12. Scatterplot of <NAO> CPC versus <AMO>.
18

–2.5

–2

–1.5

–1

–0.5

0

0.5

1

1.5

–0.5 0

<AMO>

<NAO> CRU

0.5

19 8

8 19

⇒ P=0.3%

1990

1974

2010

F13

y

y = –0.061 + 0.949x

r = –0.325, r 2=0.105

se = 0.540, cl > 98%

1998

Figure 13. Scatterplot of <NAO> CRU versus <AMO>.
19

Figure 14 shows the scatterplot of <ASAT> versus <AMO>, identifying the years of 

extreme values. Clearly, <ASAT> tends to vary directly with <AMO>, having r=0.505, r2=0.255, 

se=0.467 °C, and cl>99.9%. Based on Fisher’s exact test for 2× 2 contingency tables, the probability of obtaining the observed result, or one more suggestive of a departure from independence, is 

P=0.12%. Hence, a positive-valued <AMO> for the year 2014 suggests that <ASAT> probably will 

be ≥9.44 °C, possibly considerably greater.

8

8.5

9

9.5

10

10.5

11

–0.5 0

<AMO>

<ASAT>

0.5

8 20

19 7

⇒ P=0.12%

1998

2007

1979

1974

F14

y

y = 9.535 + 1.416x

r = 0.505, r 2= 0.255

se = 0.467, cl > 99.9%

Figure 14. Scatterplot of <ASAT> versus <AMO>.
20

Figure 15 depicts the scatterplot of <GLOTI> versus <AMO>, identifying the years of 

extreme values. The inferred regression has r=0.733 and r2=0.537, suggesting that about 54% of 

the variance in <GLOTI> can be explained by the variation in <AMO>, at least during the interval 

1960–2013. Based on Fisher’s exact test for 2× 2 contingency tables, the probability of obtaining the 

observed result, or one more suggestive of a departure from independence, is P=0.045%. Clearly, 

since <AMO> is expected to be of positive value in the year 2014, <GLOTI> likewise is expected to 

be of positive value (≥0.24 °C, in the upper-right quadrant).

–0.5

0

0.5

1

–0.5 0

<AMO>

<GLOTI>

0.5

7 20

20 7

⇒ P=0.045%

1998

2009

1964

1974

F15

y

y = 0.279 + 0.937x

r = 0.733, r 2=0.537

se = 0.170, cl > 99.9%

Figure 15. Scatterplot of <GLOTI> versus <AMO>.
21

Figure 16 displays the scatterplot of <ASAT> versus <GLOTI>, identifying the years of 

extreme values. The inferred regression has r=0.611 and r2=0.323, suggesting that about one-third 

of the variance in <ASAT> can be explained by the variation in <GLOTI>. Fisher’s exact test for 

2× 2 contingency tables suggests that the probability of obtaining the observed result, or one more 

suggestive of a departure from independence, is P=0.015%. Because <GLOTI> undoubtedly will be 

of positive value in the year 2014, probably ≥0.6 °C, <ASAT> is expected to be ≥9.44 °C, probably 

≥9.53 °C (in the upper-right quadrant).

8

8.5

9

9.5

10

10.5

11

–0.5 0

<GLOTI>

<ASAT>

0.5 1

7 21

20 6

⇒ P=0.015%

2007

1979

2010

1964

F16

y

y = 9.157 + 1.338x

r = 0.611, r 2= 0.323

se = 0.430, cl > 99.9%

Figure 16. Scatterplot of <ASAT> versus <GLOTI>.
22

Figure 17 shows the scatterplot of <AMO> versus <MLCO2>, identifying the years of 

extreme values. The inferred regression has r=0.591 and r2=0.349, suggesting that about one-third 

of the variance in <AMO> can be explained by the variation in <MLCO2>, at least during the interval 1960–2013. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of obtaining the observed result, or one more suggestive of a departure from independence, is P=0.12%. 

Because <MLCO2> continues to grow year-by-year, undoubtedly, its value for the year 2014 will be 

greater than its value for the year 2013 (=396.48 ppm) suggesting that <AMO> will be of positive 

value for the year 2014, having a yearly value in the upper-right quadrant.

–0.5

0

0.5

300 350

<MLCO2>

<AMO>

400

8 20

19 7

⇒ P=0.12%

1998

2013

1960

1974

F17

y

y = –1.704 + 0.005x

r = 0.591, r 2= 0.349

se = 0.162, cl > 99.9%

Figure 17. Scatterplot of <AMO> versus <MLCO2>.
23

Figure 18 depicts the scatterplot of <ASAT> versus <MLCO2>, identifying the years of 

extreme values. The inferred regression has r=0.592 and r2=0.350, suggesting that about one-third 

of the variance in <ASAT> can be explained by the variation in <MLCO2>, at least during the interval 1960–2013. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of obtaining the observed result, or one more suggestive of a departure from independence, is P=0.015%. 

Because <MLCO2> continues to grow year-by-year, undoubtedly, its value for the year 2014 will be 

greater than its value for the year 2013 (=396.48 ppm) suggesting that <ASAT> will have a yearly 

value in the upper-right quadrant (≥9.44 °C).

8

8.5

9

9.5

10

10.5

11

300 350

<MLCO2>

<ASAT>

400

7 21

20 6

⇒ P=0.015%

2013

1960

2007

1979

F18

y

y = 4.7949 + 0.0134x

r = 0.592, r 2= 0.350

se = 0.4998, cl > 99.9%

Figure 18. Scatterplot of <ASAT> versus <MLCO2>.
24

2.8 Inferred Statistical Relationships Between Tropical Cyclone Parametric Values

and Selected Climate-Related Factors

Table 3 provides a summary of the statistics of the 25 tropical cyclone parameters identified 

in reference 1 against the 9 specific climate-related factors investigated in this TP. The 25 tropical 

cyclone parameters include: 

• First storm day (FSD).

• Last storm day (LSD).

• Length of season (LOS).

• NTC.

• NH.

• NMH.

• NUSLFH.

• Mean seasonal latitude (<LAT>).

• Mean seasonal longitude (<LONG>).

• PWS.

• Mean seasonal PWS (<PWS>).

• Lowest pressure (LP).

• Mean seasonal LP (<LP>).

• Mean seasonal ACE (<ACE>).

• Total ACE.

• Highest individual storm ACE (HISACE).

• Mean seasonal Power Dissipation Index (PDI) (<PDI>).

• Total PDI.

• Highest individual storm PDI (HISPDI).

• Mean seasonal number of storm days (NSD) (<NSD>).

• Longest individual storm NSD (LISNSD).

• Total NSD.

• Total number of hurricane days (NHD).

• Total number of major hurricane days (NMHD).

• Net Tropical Cyclone Activity (NTCA).

From the table, one finds that none of the tropical cyclone parameters correlates strongly 

against <QBO>. The best climate-related factor that correlates with the most tropical cyclone 

parameters is <AMO>, having 15 correlations with cl>98% and an additional 5 correlations with 

cl>95%. The second best climate-related factor is <SOI>, having 8 correlations with cl>98% and 

an additional 5 correlations with cl>95%. In the following subsections, each of the scatterplots 

having cl>98% is displayed, first those against <AMO>, then those against <SOI>, <MLCO2>, 

<GLOTI>, <ONI>, <NAO> CRU, <NAO> CPC, and <ASAT>. Furthermore, estimates are made 

for the selected tropical cyclone parameters based on the mean values of the specific climate-related 

factors for the more recent subinterval 1995–2013. When cl>98% (from the linear regression analysis) and P<5% (from Fisher’s exact test), only then is the estimate considered reliable, presuming of 

course the accuracy for the projected value of the specific climate-related factor.
25

T3a. 

Parameter

<ONI> <SOI> <AMO>

a b r r2 se cl a b r r2 se cl a b r r2 se cl

FSD 179.666 0.747 0.013 0.000 33.593 <90% 179.682 0.062 0.015 0.000 33.595 <90% 179.573 –3.579 –0.021 0.000 33.589 <90%

LSD 311.204 –14.770 –0.365 0.133 22.343 >99%** 311.398 0.838 0.277 0.077 23.052 >95%* 311.739 21.137 0.172 0.030 23.640 <90%

LOS 132.539 –15.517 –0.215 0.046 41.690 <90% 132.715 0.775 0.144 0.021 42.246 <90% 133.167 24.717 0.113 0.013 42.418 <90%

NTC 11.392 –2.562 –0.333 0.111 4.304 >98%** 11.440 0.200 0.348 0.121 4.278 >99%** 11.728 12.929 0.553 0.306 3.800 >99.9%**NH 6.168 –1.177 –0.249 0.062 2.718 >90% 6.193 0.103 0.292 0.085 2.684 >95%* 6.352 7.082 0.493 0.243 2.442 >99.9%**

NMH 2.446 –0.936 –0.296 0.087 1.794 >95%* 2.468 0.094 0.397 0.157 1.724 >99.5%** 2.560 4.411 0.459 0.211 1.668 >99.9%**

NUSLFH 1.445 –0.321 –0.127 0.016 1.490 <90% 1.449 0.017 0.092 0.008 1.496 <90% 1.471 0.997 0.130 0.017 1.489 <90%

<LAT> 22.537 1.314 0.233 0.054 3.250 >90% 22.507 –0.127 –0.301 0.091 3.184 >95%* 22.298 –9.176 –0.536 0.288 2.821 >99.9%**

<LONG> 63.459 0.166 0.017 0.000 5.818 <90% 63.442 –0.067 –0.091 0.008 5.795 <90% 63.185 –10.456 –0.351 0.123 5.448 >99%**

PWS 124.264 –3.318 –0.089 0.008 21.912 <90% 124.320 0.241 0.087 0.008 21.910 <90% 125.080 31.305 0.278 0.077 21.134 >95%*

<PWS> 72.936 –3.470 –0.236 0.056 8.461 >90% 73.010 0.311 0.284 0.081 8.351 >95%* 73.125 7.389 0.166 0.027 7.863 <90%LP 937.158 6.774 0.181 0.033 21.825 <90% 937.047 –0.472 –0.169 0.029 21.873 <90% 935.992 –44.795 –0.394 0.155 20.397 >99.5%**

<LP> 980.753 3.433 0.262 0.069 7.478 >90% 980.677 –0.317 –0.325 0.106 7.331 >98%** 980.446 –11.863 –0.299 0.089 7.421 >95%*

<ACE> 8.605 –0.788 –0.125 0.016 3.703 <90% 8.629 0.098 0.208 0.043 3.650 <90% 8.741 5.243 0.274 0.075 3.590 >95%*

Total ACE 100.507 –25.304 –0.251 0.063 57.816 >90% 101.087 2.416 0.321 0.103 56.564 >98%** 104.820 165.740 0.542 0.294 50.193 >99.9%**HISACE 32.746 –2.978 –0.087 0.008 20.125 <90% 30.989 0.386 0.188 0.035 15.967 <90% 31.754 32.914 0.395 0.156 14.933 >99.5%**

<PDI> 6.949 –0.803 –0.122 0.015 3.884 <90% 6.973 0.099 0.201 0.040 3.833 <90% 7.110 6.181 0.309 0.095 3.722 >95%*

Total PDI 81.871 –20.496 –0.216 0.047 54.938 <90% 82.358 2.024 0.286 0.082 53.919 >95%* 85.900 154.648 0.537 0.288 47.466 >99.9%**HISPDI 30.799 –1.963 –0.057 0.003 20.338 <90% 30.890 0.371 0.145 0.021 20.243 <90% 31.922 42.935 0.412 0.170 18.565 >99.8%**

<NSD> 4.942 –0.310 –0.135 0.018 1.347 <90% 4.952 0.045 0.261 0.068 1.314 >90% 5.007 2.528 0.363 0.132 1.269 >99%**

LISNSD 11.751 –0.456 –0.072 0.005 3.768 <90% 11.785 0.103 0.217 0.047 3.635 >99.8%** 11.907 5.638 0.292 0.085 3.631 >95%*Total NSD 58.154 –15.196 –0.275 0.076 31.490 >95%* 58.495 1.495 0.363 0.131 30.519 >99%** 60.702 98.628 0.588 0.346 26.480 >99.9%**Total NHD 23.712 –6.135 –0.246 0.061 14.304 <90% 23.854 0.593 0.319 0.102 13.987 >98%** 24.553 32.390 0.429 0.184 13.334 >99.8%**Total NMHD

5.576 –1.355 –0.136 0.019 5.838 <90% 5.615 0.162 0.218 0.048 5.751 <90% 5.964 14.861 0.493 0.243 5.128 >99.9%**

NTCA 109.639 –28.679 –0.278 0.077 58.840 >95%* 110.310 2.791 0.362 0.131 57.094 >99%** 114.271 178.073 0.568 0.323 50.409 >99.9%**

 *Inferred correlation is statistically significant at cl > 95%.**Inferred correlation is statistically significant at cl > 98%.

Table 3. Summary of tropical cyclone statistics against climate factors (single-variate fits), 1960–2013.
26

Table 3b. (Continued). 

Parameter

<QBO> <NAO>CPC <NAO>CRU

a b r r2 se cl a b r r2 se cl a b r r2 se cl

FSD 181.838 0.642 0.171 0.029 33.101 <90% 179.546 –1.631 –0.019 0.000 33.592 <90% 179.751 2.362 0.040 0.002 33.570 <90%LSD 309.893 –0.382 –0.143 0.020 23.750 <90% 311.408 3.022 0.050 0.003 23.963 <90% 311.052 –3.716 –0.088 0.008 23.903 <90%

LOS 129.055 –1.024 –0.215 0.046 41.694 <90% 132.861 4.653 0.044 0.002 42.652 <90% 132.301 –6.077 –0.081 0.007 42.552 <90%

NTC 11.312 –0.023 –0.044 0.002 4.558 <90% 11.223 –2.246 –0.197 0.039 4.474 <90% 11.303 –2.396 –0.300 0.090 4.354 >95%*NH 6.088 –0.023 –0.074 0.005 2.800 <90% 5.992 –2.372 –0.339 0.115 2.640 >98%** 6.100 –1.872 –0.381 0.145 2.595 <99.5%**

NMH 2.542 0.029 0.138 0.019 1.860 <90% 2.364 –1.089 –0.232 0.054 1.826 >90% 2.414 –0.840 –0.255 0.065 1.816 >90%

NUSLFH 1.493 0.014 0.085 0.007 1.496 <90% 1.435 –0.133 –0.035 0.001 1.501 <90% 1.435 –0.251 –0.095 0.009 1.496 <90%

<LAT> 22.556 0.005 0.014 0.000 3.340 <90% 22.615 1.033 0.124 0.015 3.316 <90% 22.577 1.077 0.184 0.034 3.285 <90%<LONG> 63.271 –0.056 –0.086 0.007 5.790 <90% 63.440 –0.262 –0.018 0.000 5.818 <90% 63.447 –0.346 –0.034 0.001 5.814 <90%PWS 123.948 –0.092 –0.038 0.001 21.986 <90% 123.772 –6.609 –0.120 0.015 21.841 <90% 124.113 –4.091 –0.106 0.011 21.877 <90%<PWS> 73.068 0.040 0.042 0.002 8.696 <90% 72.632 –4.064 –0.187 0.035 8.555 <90% 72.909 –0.637 –0.042 0.002 8.699 <90%LP 937.733 0.167 0.068 0.005 22.110 <90% 938.038 11.827 0.214 0.046 21.689 <90% 937.460 7.946 0.204 0.042 21.722 <90%<LP> 980.611 –0.043 –0.050 0.003 7.807 <90% 981.055 4.040 0.209 0.044 7.605 <90% 980.799 1.174 0.086 0.007 7.748 <90%<ACE> 8.734 0.039 0.093 0.009 3.718 <90% 8.522 –1.109 –0.119 0.014 3.706 <90% 8.602 –0.061 –0.009 0.000 3.732 <90%

Total ACE 101.624 0.340 0.051 0.003 59.652 <90% 98.251 –30.157 –0.202 0.041 58.493 <90% 99.661 –22.762 –0.217 0.047 58.299 <90%HISACE 31.177 0.085 0.047 0.002 16.242 <90% 30.404 –6.600 –0.163 0.026 16.042 <90% 30.679 –5.938 –0.208 0.043 15.901 <90%

<PDI> 7.053 0.031 0.071 0.005 3.903 <90% 6.857 –1.232 –0.126 0.016 3.882 <90% 6.947 –0.038 –0.006 0.000 3.913 <90%

Total PDI 82.720 0.259 0.041 0.002 56.217 <90% 79.952 –25.681 –0.183 0.033 55.314 <90% 81.206 –17.850 –0.181 0.033 55.335 <90%HISPDI 30.868 0.021 0.009 0.000 20.370 <90% 30.206 –8.008 –0.158 0.025 20.117 <90% 30.562 –6.564 –0.184 0.034 20.024 <90%

<NSD> 4.980 0.011 0.076 0.006 1.354 <90% 4.871 –0.950 –0.280 0.078 1.306 >95%* 4.913 –0.754 –0.316 0.100 1.291 >98%**

LISNSD 11.959 0.059 0.140 0.020 3.740 <90% 11.680 –1.067 –0.113 0.013 3.753 <90% 11.713 –1.304 –0.197 0.039 3.703 <90%Total NSD 58.130 0.004 0.001 0.000 32.747 <90% 56.072 –27.726 –0.339 0.115 30.803 >98%** 57.203 –25.537 –0.445 0.198 29.326 >99.9%**Total NHD 24.199 0.147 0.089 0.008 14.702 <90% 23.101 –8.175 –0.222 0.049 14.391 <90% 23.502 –5.654 –0.219 0.048 14.402 <90%Total NMHD

5.677 0.030 0.046 0.002 5.886 <90% 5.437 –1.858 –0.126 0.016 5.846 <90% 5.536 –1.071 –0.104 0.011 5.861 <90%

NTCA 110.633 0.305 0.045 0.002 61.186 <90% 106.689 –39.526 –0.259 0.067 59.160 >90% 108.467 –31.741 –0.296 0.087 58.507 >95%*

 *Inferred correlation is statistically significant at cl > 95%.**Inferred correlation is statistically significant at cl > 98%.

Table 3. Summary of tropical cyclone statistics against climate factors (single-variate fits), 1960–2013 (Continued).
27

Table 3. Summary of tropical cyclone statistics against climate factors (single-variate fits), 1960–2013 (Continued).

T3c. (Continued).

Parameter

<ASAT> <GLOTI> <MLCO2>

a b r r2 se cl a b r r2 se cl a b r r2 se cl

FSD 281.414 –10.713 –0.174 0.030 33.077 <90% 183.324 –14.362 –0.107 0.011 33.404 <90 287.671 –0.308 –0.221 0.049 32.636 <90%

LSD 223.568 9.225 0.210 0.044 23.482 <90% 304.156 27.607 0.287 0.083 22.980 >95%* 209.712 0.289 0.291 0.085 24.149 >95%*

LOS –56.846 19.939 0.256 0.065 41.268 >90% 121.832 41.968 0.245 0.060 41.387 >90% –76.959 0.598 0.338 0.114 39.956 >98%**

NTC –22.591 3.578 0.429 0.184 4.119 >99.8%** 8.815 10.107 0.553 0.306 3.803 >99.9%** –25.399 0.105 0.555 0.308 3.770 >99.9%**

NH –2.676 0.931 0.182 0.033 2.760 <90% 5.215 3.738 0.333 0.111 2.646 >98% –4.478 0.030 0.261 0.068 2.859 >90%NMH –5.958 0.885 0.258 0.066 1.812 >90% 1.983 1.810 0.241 0.058 1.823 >90% –2.980 0.015 0.199 0.040 1.953 <90%

NUSLFH –1.614 0.322 0.117 0.014 1.492 <90% 1.447 –0.010 –0.002 0.000 1.502 <90% 0.616 0.002 0.038 0.001 1.564 <90%

<LAT> 32.251 –1.023 –0.167 0.028 3.307 <90% 23.702 –4.566 –0.341 0.116 3.139 >98%** 38.425 –0.045 –0.327 0.107 2.708 >99%**

<LONG> 77.213 –1.448 –0.136 0.019 5.756 <90% 65.133 –6.575 –0.282 0.080 5.584 >95%* 85.754 –0.064 –0.264 0.070 6.372 >90%

PWS 84.665 4.169 0.104 0.011 21.881 <90% 122.866 5.471 0.062 0.004 21.959 <90% 138.463 –0.041 –0.044 0.002 22.465 <90%

<PWS> 88.026 –1.589 –0.100 0.010 8.650 <90% 74.734 –7.078 –0.203 0.041 8.525 <90% 109.771 –0.105 –0.291 0.085 8.169 >95%*

LP 1,033.173 –10.109 –0.249 0.062 21.561 >90% 942.603 –21.350 –0.240 0.058 21.544 >90% 981.652 –0.127 –0.138 0.019 22.642 <90%

<LP> 989.712 –0.943 –0.067 0.004 7.843 <90% 980.053 2.766 0.089 0.008 7.728 <90% 962.171 0.053 0.165 0.027 8.225 <90%<ACE> 6.323 0.240 0.035 0.001 3.732 <90% 9.219 –2.417 –0.162 0.026 3.684 <90% 20.601 –0.034 –0.221 0.049 3.541 <90%

Total ACE –223.135 34.073 0.312 0.097 56.749 >95%* 84.033 64.567 0.270 0.073 57.513 >95%* –85.405 0.530 0.214 0.046 58.438 <90%HISACE –15.568 4.892 0.165 0.027 16.034 <90% 30.294 2.342 0.036 0.001 16.249 <90% 32.379 –0.004 –0.006 0.000 16.172 <90%

<PDI> 3.275 0.387 0.054 0.003 3.905 <90% 7.498 –2.159 –0.138 0.019 3.876 <90% 18.702 –0.034 –0.207 0.043 3.979 <90%

Total PDI –206.843 30.395 0.295 0.087 53.769 >95%* 68.335 53.054 0.235 0.055 54.683 >90% –53.696 0.387 0.166 0.027 55.401 <90%HISPDI –22.487 5.610 0.151 0.023 20.141 <90% 29.481 5.164 0.063 0.004 20.331 <90% 31.348 –0.002 –0.002 0.000 20.488 <90%

<NSD> 5.627 –0.072 –0.029 0.001 1.356 <90% 4.818 0.485 0.089 0.008 1.354 <90% 3.706 0.004 0.062 0.004 0.993 <90%

LISNSD –1.586 1.405 0.204 0.041 3.701 <90% 11.451 1.212 0.080 0.006 3.765 <90% 5.564 0.018 0.113 0.013 3.562 <90%Total NSD –80.718 14.618 0.244 0.060 31.756 >90% 42.629 60.822 0.464 0.215 29.013 >99.9%** –154.552 0.607 0.447 0.200 29.193 >99.9%**Total NHD –30.148 5.670 0.210 0.044 14.431 <90% 21.963 6.836 0.116 0.013 14.660 <90% 11.831 0.034 0.055 0.003 14.699 <90%Total NMHD

–21.322 2.832 0.263 0.069 5.686 >90% 4.532 4.092 0.173 0.030 5.804 <90% –2.574 0.023 0.095 0.009 5.909 <90%

NTCA –204.500 33.072 0.295 0.087 58.516 >95%* 89.455 79.122 0.323 0.104 57.973 >98%** –127.784 0.677 0.267 0.071 59.110 >95%*

 *Inferred correlation is statistically significant at cl > 95%.**Inferred correlation is statistically significant at cl > 98%.
28

2.8.1 Inferred Correlations Against <AMO>

Figure 19 shows the scatterplot for NTC versus <AMO>, identifying the years of extreme 

values. The inferred regression is given approximately as y=11.7+12.9x, having r=0.55, r2=0.31, 

se=3.8, and cl>99.9%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of 

obtaining the observed result, or one more suggestive of a departure from independence, is P=0.6%. 

Hence, there is reason to believe, based on the expected value for <AMO> in the year 2014 (i.e., being 

of positive value), that NTC will lie in the upper-right quadrant of the scatterplot (i.e., NTC≥ 11). 

The mean value for <AMO> during the more recent interval 1995–2013 measures 0.163. Using this 

value for <AMO> for the year 2014, one infers NTC=13.8±3.8 (the ±1 se prediction interval), 

or NTC≥ 10.

5

0

10

15

20

25

30

–0.5 0

<AMO>

NTC

0.5

10 20

17 7

⇒ P=0.6%

1998

1974

1983

2005

F19

y

y = 11.728 + 12.929x

r = 0.553, r 2= 0.306

se = 3.800, cl > 99.9%

Figure 19. Scatterplot of NTC versus <AMO>.
29

Figure 20 depicts the scatterplot for NH versus <AMO>, identifying the years of extreme 

values. The inferred regression is given approximately as y=6.4+7.1x, having r=0.49, r2=0.24, 

se=2.4, and cl>99.9%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of 

obtaining the observed result, or one more suggestive of a departure from independence, is P=5.0%. 

Hence, based on the expected value for <AMO> in the year 2014 (i.e., being of positive value), NH 

is expected to lie in the upper-right quadrant of the scatterplot (i.e., NH≥ 6). The mean value for 

<AMO> during the more recent interval 1995–2013 measures 0.163. Using this value for <AMO> 

for the year 2014, one infers NH=7.6±2.4, or NH≥5 (rounded to the nearest whole number).

5

0

10

15

20

–0.5 0

<AMO>

NH

0.5

11 18

16 9

⇒ P=5.0%

1998

1974

1982 2013

2005

F20

y

y = 6.352 + 7.082x

r = 0.493, r 2= 0.243

se = 2.442, cl > 99.9%

Figure 20. Scatterplot of NH versus <AMO>.
30

Figure 21 displays the scatterplot for NMH versus <AMO>, identifying the years of extreme 

values. The inferred regression is given approximately as y=2.6+4.4x, having r=0.46, r2=0.21, 

se=1.7, and cl>99.9%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of 

obtaining the observed result, or one more suggestive of a departure from independence, is P=0.5%. 

Hence, based on the expected value for <AMO> in the year 2014 (i.e., being of positive value), NMH 

is expected to lie in the upper-right quadrant of the scatterplot (i.e., NMH≥2). The mean value for 

<AMO> during the more recent interval 1995–2013 measures 0.163. Using this value for <AMO> 

for the year 2014, one infers NMH=3.3±1.7, or NMH≥2 (rounded to the nearest whole number).

5

0

10

–0.5 0

<AMO>

NMH

0.5

12 22

15 5

⇒ P=0.5%

1998

1974

1961 2005

F21

y

y = 2.560 + 4.411x

r = 0.459, r 2= 0.211

se = 1.668, cl > 99.9%

Figure 21. Scatterplot of NMH versus <AMO>.
31

Figure 22 shows the scatterplot for <LAT> versus <AMO>, identifying the years of extreme 

values. The inferred regression is given approximately as y=22.3 – 9.2x, having r=–0.54, r2=0.29, 

se=2.8, and cl>99.9%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of 

obtaining the observed result, or one more suggestive of a departure from independence, is P=0.7%. 

Hence, based on the expected value for <AMO> in the year 2014 (i.e., being of positive value), 

<LAT> is expected to lie in the lower-right quadrant of the scatterplot (i.e., <LAT><22° N.). The 

mean value for <AMO> during the more recent interval 1995–2013 measures 0.163. Using this value 

for <AMO> for the year 2014, one infers <LAT>=20.8±2.8° N., or <LAT><23.6° N.

20

25

15

30

35

–0.5 0

<AMO>

<LAT>

0.5

19 9

8 18

⇒ P=0.7%

1998

1974

1972

1996

F22

y

y = 22.298 – 9.176x

r = –0.536, r 2= 0.288

se = 2.821, cl > 99.9%

Figure 22. Scatterplot of <LAT> versus <AMO>.
32

Figure 23 depicts the scatterplot for <LONG> versus <AMO>, identifying the years of 

extreme values. The inferred regression is given approximately as y=63.2 –10.5x, having r=–0.35, 

r2=0.12, se=5.4, and cl>99%. Fisher’s exact test for 2× 2 contingency tables suggests that the 

probability of obtaining the observed result, or one more suggestive of a departure from independence, is P=29.3%. The mean value for <AMO> during the more recent interval 1995–2013 measures 0.163. Using this value for <AMO> for the year 2014, one infers <LONG>=61.5±5.4° W., 

or <LONG><67° W.

55

60

50

65

70

75

80

–0.5 0

<AMO>

<LONG>

0.5

15 12

12 15

⇒ P=29.3%

1998

2009

1974

1977

F23

y

y = 63.185 – 10.456x

r = –0.351, r 2= 0.123

se = 5.448, cl > 99%

Figure 23. Scatterplot of <LONG> versus <AMO>. 
33

Figure 24 displays the scatterplot for LP versus <AMO>, identifying the years of extreme 

values. The inferred regression is given approximately as y=936.0 – 44.8x, having r=–0.39, r2=0.16, 

se=20.4, and cl>99.5%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of obtaining the observed result, or one more suggestive of a departure from independence, 

is P=2.8%. The mean value for <AMO> during the more recent interval 1995–2013 measures 0.163. 

Using this value for <AMO> for the year 2014, one infers LP=928.7±20.4 mb, or LP<949.1 mb.

900

950

1,000

–0.5 0

<AMO>

LP

0.5

18 10

9 17

⇒ P=2.8%

1998

2005

2013 

1974

F24

y

y = 935.992 – 44.795x

r = –0.394, r 2= 0.155

se = 20.397, cl > 99.5%

Figure 24. Scatterplot of LP versus <AMO>.
34

Figure 25 shows the scatterplot for total ACE versus <AMO>, identifying the years of 

extreme values. The inferred regression is given approximately as y=104.8+165.7x, having r=0.54, 

r2=0.29, se=50.2, and cl>99.9%. Fisher’s exact test for 2× 2 contingency tables suggests that the 

probability of obtaining the observed result, or one more suggestive of a departure from independence, is P=0.7%. The mean value for <AMO> during the more recent interval 1995–2013 measures 

0.163. Using this value for <AMO> for the year 2014, one infers total ACE=131.8±50.2, or total 

ACE≥81.6 (units are 104 kt2).

50

0

150

100

200

250

300

–0.5 0

<AMO>

Total ACE

0.5

9 19

18 8

⇒ P=0.7%

1998

2005

1983

1974

F25

y

y = 104.820 + 165.740x

r = 0.542, r 2= 0.294

se = 50.193, cl > 99.9%

Figure 25. Scatterplot of total ACE versus <AMO>.
35

Figure 26 depicts the scatterplot for HISACE versus <AMO>, identifying the years of extreme 

values. The inferred regression is given approximately as y=31.8+32.9x, having r=0.40, r2=0.16, 

se=14.9, and cl>99.5%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of obtaining the observed result, or one more suggestive of a departure from independence, is 

P=1.4%. The mean value for <AMO> during the more recent interval 1995–2013 measures 0.163. 

Using this value for <AMO> for the year 2014, one infers HISACE=37.1±14.9, or HISACE≥22.2 

(units are 104 kt2).

40

0

80

–0.5 0

<AMO>

HISACE

0.5

9 18

18 9

⇒ P=1.4%

1998

2004

1983

1974

F26

y

y = 31.754 + 32.914x

r = 0.395, r 2= 0.156

se = 14.933, cl > 99.5%

Figure 26. Scatterplot of HISACE versus <AMO>.
36

Figure 27 displays the scatterplot for total PDI versus <AMO>, identifying the years of 

extreme values. The inferred regression is given approximately as y=85.9+154.6x, having r=0.54, 

r2=0.29, se=47.5, and cl>99.9%. Fisher’s exact test for 2× 2 contingency tables suggests that the 

probability of obtaining the observed result, or one more suggestive of a departure from independence, is P=0.045%. The mean value for <AMO> during the more recent interval 1995–2013 measures 0.163. Using this value for <AMO> for the year 2014, one infers total PDI=111.1±47.5, or 

total PDI ≥ 63.6 (units are 106 kt3).

50

0

100

150

200

250

–0.5 0

<AMO>

Total PDI

0.5

7 20

20 7

⇒ P=0.045%

1998

2005

1983

1974

F27

y

y = 85.900 + 154.648x

r = 0.537, r 2= 0.288

se = 47.466, cl > 99.9%

Figure 27. Scatterplot of total PDI versus <AMO>.
37

Figure 28 shows the scatterplot for HISPDI versus <AMO>, identifying the years of extreme 

values. The inferred regression is given approximately as y=31.9+42.9x, having r=0.41, r2=0.17, 

se=18.6, and cl>99.8%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of obtaining the observed result, or one more suggestive of a departure from independence, is 

P=1.4%. The mean value for <AMO> during the more recent interval 1995–2013 measures 0.163. 

Using this value for <AMO> for the year 2014, one infers HISPDI=38.9±18.6, or HISPDI ≥20.3 

(units are 106 kt3).

50

0

100

–0.5 0

<AMO>

HISPDI

0.5

9 18

18 9

⇒ P=1.4%

2004

1998

1983

1974

F28

y

y = 31.922 + 42.935x

r = 0.412, r 2= 0.170

se = 18.565, cl > 99.8%

Figure 28. Scatterplot of HISPDI versus <AMO>. 
38

Figure 29 depicts the scatterplot for <NSD> versus <AMO>, identifying the years of extreme 

values. The inferred regression is given approximately as y=5.0+2.5x, having r=0.36, r2=0.13, 

se=1.3, and cl>99%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of 

obtaining the observed result, or one more suggestive of a departure from independence, is P=29.3%. 

The mean value for <AMO> during the more recent interval 1995–2013 measures 0.163. Using this 

value for <AMO> for the year 2014, one infers <NSD>=5.4±1.3 days, or <NSD>≥4.1 days.

10

5

0

15

–0.5 0

<AMO>

<NSD>

0.5

12 15

15 12

⇒ P=29.3%

2010

1998

1977

1974

F29

y

y = 5.007 + 2.528x

r = 0.363, r 2= 0.132

se = 1.269, cl > 99%

Figure 29. Scatterplot of <NSD> versus <AMO>.
39

Figure 30 displays the scatterplot for total NSD versus <AMO>, identifying the years of 

extreme values. The inferred regression is given approximately as y=60.7+98.6x, having r=0.59, 

r2=0.35, se=26.5, and cl>99.9%. Fisher’s exact test for 2× 2 contingency tables suggests that the 

probability of obtaining the observed result, or one more suggestive of a departure from independence, is P=0.3%. The mean value for <AMO> during the more recent interval 1995–2013 measures 

0.163. Using this value for <AMO> for the year 2014, one infers total NSD=76.8±26.5 days, or 

total NSD≥50.3 days.

100

50

0

150

–0.5 0

<AMO>

Total NSD

0.5

8 19

19 8

⇒ P=0.3%

2010

1998

1983

1974

F30

y

y = 60.702 + 98.628x

r = 0.588, r 2= 0.346

se = 26.480, cl > 99.9%

200

Figure 30. Scatterplot of total NSD versus <AMO>.
40

Figure 31 shows the scatterplot for total NHD versus <AMO>, identifying the years of 

extreme values. The inferred regression is given approximately as y=24.6+32.4x, having r=0.43, 

r2=0.18, se=13.3, and cl>99.8%. Fisher’s exact test for 2× 2 contingency tables suggests that the 

probability of obtaining the observed result, or one more suggestive of a departure from independence, is P=5.1%. The mean value for <AMO> during the more recent interval 1995–2013 measures 

0.163. Using this value for <AMO> for the year 2014, one infers total NHD=29.8±13.3 days, or 

total NHD≥ 16.5 days.

50

25

0

75

–0.5 0

<AMO>

Total NHD

0.5

10 17

17 10

⇒ P=5.1%

1998

1995

2013

1974

F31

y

y = 24.553 + 32.390x

r = 0.429, r 2= 0.184

se = 13.334, cl > 99.8%

Figure 31. Scatterplot of total NHD versus <AMO>.
41

Figure 32 depicts the scatterplot for total NMHD versus <AMO>, identifying the years of 

extreme values. The inferred regression is given approximately as y=6.0+14.9x, having r=0.49, 

r2=0.24, se=5.1, and cl>99.9%. Fisher’s exact test for 2× 2 contingency tables suggests that the 

probability of obtaining the observed result, or one more suggestive of a departure from independence, is P=0.3%. The mean value for <AMO> during the more recent interval 1995–2013 measures 

0.163. Using this value for <AMO> for the year 2014, one infers total NMHD=8.4±5.1 days, or 

total NMHD≥3.3 days.

10

5

0

15

20

25

–0.5 0

<AMO>

Total NMHD

0.5

8 19

19 8

⇒ P=0.3%

1998

1961

1974

F32

y

y = 5.964 + 14.861x

r = 0.493, r 2= 0.243

se = 5.128, cl > 99.9%

Figure 32. Scatterplot of total NMHD versus <AMO>.
42

Figure 33 displays the scatterplot for NTCA versus <AMO>, identifying the years of extreme 

values. The inferred regression is given approximately as y=114.3+178.1x, having r=0.57, r2=0.32, 

se=50.4, and cl>99.9%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of 

obtaining the observed result, or one more suggestive of a departure from independence, is P=0.3%. 

The mean value for <AMO> during the more recent interval 1995–2013 measures 0.163. Using this 

value for <AMO> for the year 2014, one infers NTCA=143.3±50.4 days, or NTCA≥ 92.9%.

100

50

0

150

200

250

–0.5

2005

1983

1974

1998

0

<AMO>

NTCA

0.5

8 19

19 8

⇒ P=0.3%

F33

y

300

y = 114.271 + 178.073x

r = 0.568, r 2= 0.323

se = 50.409, cl > 99.9%

Figure 33. Scatterplot of NTCA versus <AMO>.
43

2.8.2 Inferred Correlations Against <SOI>

Figure 34 shows the scatterplots of (a) NTC, (b) NMH, and (c) <LP> versus <SOI>, identifying the years of extreme values in each panel. Concerning NTC versus <SOI>, the inferred regression is given approximately as y=11.4+0.2x, having r=0.35, r2=0.12, se=4.3, and cl>99%. Fisher’s 

exact test for 2× 2 contingency tables suggests that the probability of obtaining the observed result, 

or one more suggestive of a departure from independence, is P=8.5%. The mean value for <SOI> 

during the more recent interval 1995–2013 measures 2.1. Using this value for <SOI> for the year 

2014, one infers NTC=11.9±4.3, or NTC≥ 8 (rounded to the nearest whole number). 

1,000

(c)

950

5 10 15 20

1999

1965

1961

–20 –15 –10 –5 0

2013

<SOI>

<LP>

16 12

11 15

⇒ P=20.7%

F34

y

y = 980.677 – 0.317x

r = –0.325, r 2= 0.106

se = 7.331, cl > 98%

10

(b)

5

0

1999

1965

1961 2005

NMH

14 20

13 7

⇒ P=7.9%

y

y = 2.468 + 0.094x

r = 0.397, r 2= 0.157

se = 1.724, cl > 99.5%

(a)

0

25

20

15

10

5

30

1965

1983

2005

1999

NTC

12 18

15 9

⇒ P=8.5%

y

y = 11.440 + 0.200x

r = 0.348, r 2= 0.121

se = 4.278, cl > 99%

Figure 34. Scatterplots of (a) NTC, (b) NMH, and (c) <LP> versus <SOI>.
44

Concerning NMH versus <SOI>, the inferred regression is given approximately as 

y=2.5+0.1x, having r=0.40, r2=0.16, se=1.7, and cl>99.5%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of obtaining the observed result, or one more suggestive of a departure from independence, is P=7.9%. The mean value for <SOI> during the more 

recent interval 1995–2013 measures 2.1. Using this value for <SOI> for the year 2014, one infers 

NMH=2.7±1.7, or NMH≥1. 

Concerning <LP> versus <SOI>, the inferred regression is given approximately as 

y=980.7–0.3x, having r=–0.33, r2=0.11, se=7.3, and cl>98%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of obtaining the observed result, or one more suggestive of a departure from independence, is P=20.7%. The mean value for <SOI> during the more 

recent interval 1995–2013 measures 2.1. Using this value for <SOI> for the year 2014, one infers 

<LP>=980.0±7.3 mb, or <LP><987.3 mb.

Figure 35 depicts the scatterplots of (a) total ACE and (b) LISNSD versus <SOI>, identifying the years of extreme values in each panel. Concerning total ACE versus <SOI>, the inferred 

regression is given approximately as y=101.1+2.4x, having r=0.32, r2=0.10, se=56.6, and cl>98%. 

Fisher’s exact test for 2× 2 contingency tables suggests that the probability of obtaining the observed 

result, or one more suggestive of a departure from independence, is P=20.7%. The mean value for 

<SOI> during the more recent interval 1995–2013 measures 2.1. Using this value for <SOI> for the 

year 2014, one infers total ACE=106.2±56.6, or total ACE≥ 49.6 (units in 104 kt2).

Concerning LISNSD versus <SOI>, the inferred regression is given approximately as 

y=11.8+0.1x, having r=0.22, r2=0.05, se=3.6, and cl>99.8%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of obtaining the observed result, or one more suggestive of a departure from independence, is P=39.2%. The mean value for <SOI> during the more 

recent interval 1995–2013 measures 2.1. Using this value for <SOI> for the year 2014, one infers 

LISNSD=12.0±3.6 days, or LISNSD≥8.4 days.
45

25

20

15

10

5

0

(b) 5 10 15 20

1999 1965

1977 1983

2010

–20 –15 –10 –5 0

1971

<SOI>

LISNSD

16 14

11 13

⇒ P=39.2%

F35

y

y = 11.785 + 0.103x

r = 0.217, r 2= 0.047

se = 3.635, cl > 99.8%

250

200

150

100

50

0

(a)

1999

1965

1983

2005

Total ACE

12 16

15 11

⇒ P=20.7%

y

300

y = 101.087 + 2.416x

r = 0.321, r 2= 0.103

se = 56.564, cl > 98%

Figure 35. Scatterplots of (a) total ACE and (b) LISNSD versus <SOI>.
46

Figure 36 displays the scatterplots of (a) total NSD, (b) total NHD, and (c) NTCA versus 

<SOI>, identifying the years of extreme values in each panel. Concerning total NSD versus <SOI>, 

the inferred regression is given approximately as y=58.5+1.5x, having r=0.36, r2=0.13, se=30.5, 

and cl>99%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of obtaining the observed result, or one more suggestive of a departure from independence, is P=50.0%. The 

mean value for <SOI> during the more recent interval 1995–2013 measures 2.1. Using this value for 

<SOI> for the year 2014, one infers total NSD=61.6±30.5 days, or total NSD≥31.1 days.

Concerning total NHD versus <SOI>, the inferred regression is given approximately as 

y=23.9+0.6x, having r=0.32, r2=0.10, se=14.0, and cl>98%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of obtaining the observed result, or one more suggestive 

of a departure from independence, is P=50.0%. The mean value for <SOI> during the more recent 

interval 1995–2013 measures 2.1. Using this value for <SOI> for the year 2014, one infers total 

NHD=25.1±14.0 days, or total NHD≥ 11.1 days.

Concerning NTCA versus <SOI>, the inferred regression is given approximately as 

y=110.3+2.8x, having r=0.36, r2=0.13, se=57.1, and cl>99%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of obtaining the observed result, or one more suggestive of a departure from independence, is P=29.3%. The mean value for <SOI> during the more 

recent interval 1995–2013 measures 2.1. Using this value for <SOI> for the year 2014, one infers 

NTCA=116.2±57.1%, or NTCA≥59.1%.
47

250

200

150

100

50

0

(c) 5 10 15 20

1999

2005

1965

1983

–20 –15 –10 –5 0

<SOI>

NTCA

12 15

15 12

⇒ P=29.3%

F36

y

300

y = 110.310 + 2.791x

r = 0.362, r 2= 0.131

se = 57.094, cl > 99%

100

50

0

(b)

1999

2013

1983

1965

1995

Total NHD

13 14

14 13

⇒ P=50.0%

y

y = 23.854 + 0.593x

r = 0.319, r 2= 0.102

se = 13.987, cl > 98%

200

150

100

50

0 (a)

1999

2010

1965

Total NSD

13 14

14 13

⇒ P=50.0%

y

y = 58.495 + 1.495x

r = 0.363, r 2= 0.131

se = 30.519, cl > 99%

Figure 36. Scatterplots of (a) total NSD, (b) total NHD, and (c) NTCA versus <SOI>.
48

2.8.3 Inferred Correlations Against <MLCO2>

Figure 37 shows the scatterplot of LOS versus <MLCO2>, identifying the years of extreme 

values. The inferred regression is given approximately as y=–77.0+0.60x, having r=0.34, r2=0.11, 

se=40.0, and cl>98%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of obtaining the observed result, or one more suggestive of a departure from independence, is 

P=13.8%. Because the atmospheric concentration of CO2 increases each year, it makes no sense 

to apply the mean of <MLCO2> for the more recent interval (377.86 ppm). Instead, it is better to 

use the average rate of increase during the more recent interval for determining the likely level of 

<MLCO2> for the year 2014. Hence, for the year 2014, one expects <MLCO2> to measure about 

396.48+1.98±0.48 ppm, or about 398.46±0.48 ppm. Using the value <MLCO2>=398.46 ppm for 

the year 2014, one infers LOS=161.3±40.0 days, or LOS≥121 days (rounded to the nearest whole 

day).

0

150

100

50

200

250

300 350

2003

1983

1960

2013

<MLCO2>

LOS

400

11 16

16 11

⇒ P=13.8%

F37

y

y = –76.959 + 0.598x

r = 0.338, r 2= 0.114

se = 39.956, cl > 98%

Figure 37. Scatterplot of LOS versus <MLCO2>.
49

Figure 38 depicts the scatterplot of NTC versus <MLCO2>, identifying the years of extreme 

values. The inferred regression is given approximately as y=–25.4+0.11x, having r=0.56, r2=0.31, 

se=3.8, and cl>99.9%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of 

obtaining the observed result, or one more suggestive of a departure from independence, is P=5.0%. 

Using the value of <MLCO2>=398.46 ppm for the year 2014, one infers NTC=16.4±3.8, or 

NTC≥13 (rounded to the nearest whole number).

0

15

10

5

20

25

30

2005

1983

1960

2013

NTC

11 19

16 8

⇒ P=5.0%

F38

y

y = –25.399 + 0.105x

r = 0.555, r 2= 0.308

se = 3.770, cl > 99.9%

300 350

<MLCO2>

400

Figure 38. Scatterplot of NTC versus <MLCO2>.
50

Figure 39 displays the scatterplot of <LAT> versus <MLCO2>, identifying the years of 

extreme values. The inferred regression is given approximately as y=38.4 –0.05x, having r=–0.33, 

r2=0.11, se=2.7, and cl>99%. Fisher’s exact test for 2× 2 contingency tables suggests that the 

probability of obtaining the observed result, or one more suggestive of a departure from independence, is P=0.1%. Using the value of <MLCO2>=398.46 ppm for the year 2014, one infers 

<LAT>=20.5±2.7° N., or <LAT><23.2° N.

15

30

25

20

35

300 350

1996

1960 2013

1972

<MLCO2>

<LAT>

400

20 8

7 19

⇒ P=0.1%

F39

y

y = 38.425 – 0.045x

r = –0.327, r 2= 0.107

se = 2.708, cl > 99%

Figure 39. Scatterplot of <LAT> versus <MLCO2>.
51

Figure 40 shows the scatterplot of total NSD versus <MLCO2>, identifying the years of 

extreme values. The inferred regression is given approximately as y=–154.6+0.61x, having r=0.45, 

r2=0.20, se=29.2, and cl>99.9%. Fisher’s exact test for 2× 2 contingency tables suggests that the 

probability of obtaining the observed result, or one more suggestive of a departure from independence, is P=5.1%. Using the value of <MLCO2>=398.46 ppm for the year 2014, one infers total 

NSD=87.3±29.2 days, or total NSD≥ 58 days (rounded to the nearest whole day).

0

150

100

50

200

300 350

1983

2013

1960

2010

<MLCO2>

Total NSD

400

10 17

17 10

⇒ P=5.1%

F40

y

y = –154.552 + 0.607x

r = 0.447, r 2= 0.200

se = 29.193, cl > 99.9%

Figure 40. Scatterplot of total NSD versus <MLCO2>.
52

2.8.4 Inferred Correlations Against <GLOTI>

Figure 41 depicts the scatterplots of (a) total NSD and (b) NTCA versus <GLOTI>, identifying the years of extreme values. Concerning total NSD versus <GLOTI>, the inferred regression is given approximately as y=42.6+60.8x, having r=0.46, r2=0.22, se=29.0, and cl>99.9%. 

Fisher’s exact test for 2× 2 contingency tables suggests that the probability of obtaining the observed 

result, or one more suggestive of a departure from independence, is P=1.4%. Using the value of 

<GLOTI>=0.54 °C for the year 2014 (i.e., the mean value for the more recent interval 1995–2013), 

one infers total NSD=75.5±29.0 days, or total NSD≥46 days (rounded to the nearest whole day). 

Concerning NTCA versus <GLOTI>, the inferred regression is given approximately as 

y=89.5+79.1x, having r=0.32, r2=0.10, se=58.0, and cl>98%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of obtaining the observed result, or one more suggestive 

of a departure from independence, is P=1.4%. Using the value of <GLOTI>=0.54 °C for the year 

2014, one infers NTCA=132.2±58.0%, or NTCA≥74.2%.
53

100

50

0

250

150

200

300

50

0

–0.5

2005

2010

1983

1964

2010

1964

1983

0

<GLOTI>

NTCA Total NSD

(b)

(a)

0.5 1

9 18

18 9

⇒ P=1.4%

9 18

18 9

⇒ P=1.4%

F41

y

y

200

150

100

y = 42.629 + 60.822x

r = 0.464, r 2= 0.215

se = 29.013, cl > 99.9%

y = 89.455 + 79.122x

r = 0.323, r 2= 0.104

se = 57.973, cl > 98%

Figure 41. Scatterplots of (a) total NSD and (b) NTCA versus <GLOTI>.
54

2.8.5 Inferred Correlations Against <ONI>

Figure 42 displays the scatterplots of (a) LSD and (b) NTC versus <ONI>, identifying the 

years of extreme values. Concerning LSD versus <ONI>, the inferred regression is given approximately as y=311.2 – 14.8x, having r=–0.37, r2=0.13, se=22.3, and cl>99%. Fisher’s exact test for 

2× 2 contingency tables suggests that the probability of obtaining the observed result, or one more 

suggestive of a departure from independence, is P=5.1%. Using the value of <ONI>=–0.12 °C for the 

year 2014 (i.e., the mean value for the more recent interval 1995–2013), one infers LSD=312.0±22.3 

(day of year (DOY)), or LSD≥291 (DOY=on or after about October 18, 2014).

Concerning NTC versus <ONI>, the inferred regression is given approximately as 

y=11.4–2.6x, having r=–0.33, r2=0.11, se=4.3, and cl>98%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of obtaining the observed result, or one more suggestive of 

a departure from independence, is P=0.1%. Using the value of <ONI>=–0.12 °C for the year 2014, 

one infers NTC=11.7±4.3, or NTC≥ 7 (rounded to the nearest whole number). (For the overall 

interval 1960–2013, there were 14 years classified as ENY. The mean NTC during these years equals 

8.7, having sd=3.8 and extremes of 4 to 18.)
55

25

30

15

10

20

5

0

(b)

1987

1969

1983

2005

1999

–1.5 –1 –0.5 0 0.5 1 1.5

<ONI>

NTC

21 9

6 18

⇒ P=0.1%

y

y = 11.392 – 2.562x

r = –0.333, r 2= 0.111

se = 4.304, cl > 98%

400

300

350

250

(a)

1987

1960

2005

1999

LSD

17 10

10 17

⇒ P=5.1%

y

y = 311.204 – 14.770x

r = –0.365, r 2= 0.133

se = 22.343, cl > 99%

Figure 42. Scatterplots of (a) LSD and (b) NTC versus <ONI>.
56

2.8.6 Inferred Correlations Against <NAO> CRU

Figure 43 shows the scatterplots of (a) NH, (b) <NSD>, and (c) total NSD versus <NAO> 

CRU, identifying the years of extreme values in each subpanel. Concerning NH versus <NAO> CRU, 

the inferred regression is given approximately as y=6.1 – 1.9x, having r=–0.38, r2=0.15, se=2.6, and 

cl>99.5%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of obtaining 

the observed result, or one more suggestive of a departure from independence, is P=29.3%. Using 

the value of <NAO> CRU=–0.30 for the year 2014, one infers NH=6.7±2.6, or NH<9.

Concerning <NSD> versus <NAO> CRU, the inferred regression is given approximately 

as y=4.9 – 0.8x, having r=–0.32, r2=0.10, se=1.3, and cl>98%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of obtaining the observed result, or one more suggestive of 

a departure from independence, is P=50.0%. Using the value of <NAO> CRU=–0.30 for the year 

2014, one infers <NSD>=5.1±1.3, or <NSD><6 days (rounded to the nearest whole day).

Concerning total NSD versus <NAO> CRU, the inferred regression is given approximately as 

y=57.2–25.5x, having r=–0.45, r2=0.20, se=29.3, and cl>99.9%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of obtaining the observed result, or one more suggestive 

of a departure from independence, is P=29.3%. Using the value of <NAO> CRU=–0.30 for the 

year 2014, one infers total NSD=64.9±29.3, or total NSD<94 days (rounded to the nearest whole 

day).
57

50

0

150

100

200

–2.5 –2 –1.5 –1 –0.5 0 0.5 1 1.5

1990

1983

1977

2010

<NAO> CRU

Total NSD

15 12

12 15

⇒ P=29.3%

F43

y

y = 57.203 – 25.537x

r = –0.445, r 2= 0.198

se = 29.326, cl > 99.9%

5

0

15

10

1990

2010

<NSD>

14 13

13 14

y ⇒ P=50.0%

y = 4.913 – 0.754x

r = –0.316, r 2= 0.100

se = 1.291, cl > 98%

5

0 (a)

(b)

(c)

15

20

10

1990

2013 1982

2010

2005

NH

16 13

11 14

y ⇒ P=29.3%

y = 6.100 – 1.872x

r = –0.381, r 2= 0.145

se = 2.595, cl > 99.5%

Figure 43. Scatterplots of (a) NH, (b) <NSD>, and (c) total NSD versus <NAO> CRU.
58

2.8.7 Inferred Correlations Against <NAO> CPC

Figure 44 depicts the scatterplots of (a) NH and (b) total NSD versus <NAO> CPC. Concerning NH versus <NAO> CPC, the inferred regression is given approximately as y=6.0–2.4x, 

having r=–0.34, r2=0.12, se=2.6, and cl>98%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of obtaining the observed result, or one more suggestive of a departure 

from independence, is P=29.3%. Using the value of <NAO> CPC=–0.18 for the year 2014, one 

infers NH=6.4±2.6, or NH<9.

Concerning total NSD versus <NAO> CPC, the inferred regression is given approximately as 

y=56.1–27.7x, having r=–0.34, r2=0.12, se=30.8, and cl>98%. Fisher’s exact test for 2× 2 contingency tables suggests that the probability of obtaining the observed result, or one more suggestive of 

a departure from independence, is P=29.3%. Using the value of <NAO> CPC=–0.18 for the year 

2014, one infers total NSD=61.1±30.8, or total NSD<92 days (rounded to the nearest whole day).
59

50

(b)

0

150

100

200

2010

0.5 1

1983

–1 –0.5 0

1989

<NAO> CPC

Total NSD

15 12

12 15

⇒ P=29.3%

F44

y

y = 56.072 – 27.726x

r = –0.339, r 2= 0.115

se = 30.803, cl > 98%

5

(a)

0

15

10

20

2010

2005

2013 1982

1989

NH

16 13

11 14

⇒ P=29.3%

y

y = 5.992 – 2.372x

r = –0.339, r 2= 0.115

se = 2.640, cl > 98%

Figure 44. Scatterplots of (a) NH and (b) total NSD versus <NAO> CPC.
60

2.8.8 Inferred Correlation Against <ASAT>

Figure 45 displays the scatterplot of NTC versus <ASAT>. The inferred correlation is given 

approximately as y=–22.6+3.6x, having r=0.43, r2=0.18, se=4.1, and cl>99.8%. Fisher’s exact test 

for 2× 2 contingency tables suggests that the probability of obtaining the observed result, or one more 

suggestive of a departure from independence, is P=0.6%. Using the value of <ASAT>=9.97 °C for 

the year 2014, one infers NTC=13.1±4.1, or NTC≥9.

5

0

10

15

20

25

30

8 8.5 9 9.5 10 10.5 11

1983

2007

1979

2005

<ASAT>

NTC

10 20

17 7

⇒ P=0.6%

F45

y

y = –22.591 + 3.578x

r = 0.429, r 2= 0.184

se = 4.119, cl > 99.8%

Figure 45. Scatterplot of NTC versus <ASAT>.
61

2.8.9 Inferred Correlations for NTC Using Single-Variate, Bivariate, and Trivariate Fits

Based on Selected Climate-Related Factors

Table 4 provides a comparison of the observed yearly NTC with those based on selected 

inferred single-variate fits (<AMO>, <SOI>, and <MLCO2>), bivariate fits (<AMO> and <SOI>, 

and <AMO> and <MLCO2>), and on a specific trivariate fit (<AMO>, <SOI>, and <MLCO2>). 

From the table, one finds that estimates made using <AMO> or <MLCO2>, both having r=0.55 

and se=3.8, appear to be slightly more reliable than the estimates using <SOI> (r=0.35 and se=4.3). 

Some improvement is found when using the bivariate fits (Ry12=0.62 and Sy12=3.6), with the trivariate fit providing the best improvement (Ry12=0.65 and Sy12=3.5). For the single-variate fits, the 

fit having the best ±1 difference between estimated and observed NTC, surprisingly, appears to be 

the one using <MLCO2> (having an estimate of ±1 for 20 of the 54 years, or about 37% success). 

Accepting a broader error range (±3), one finds that <AMO> is the better estimator for NTC (39 of 

54 years, or 72%). For the bivariate fits, the one using <AMO> and <SOI> appears to be the slightly 

better estimator of NTC, having ±1 for 22 of 54 years (41%) and ±3 for 41 of 54 years (76%). The 

trivariate fit has a ±1 error range for 23 of 54 years (43%) and a ±3 error range for 43 of 54 years 

(80%). Based on <AMO>, the estimates for NTC were poor (≥±5) for 11 of the 54 years, including 

the years 1960, 1962, 1969, 1971, 1974, 1983, 1987, 1995, 2005, 2006, and 2011. Based on the bivariate fit using <AMO> and <SOI>, the estimates for NTC were poor for 9 of the 54 years, including 

the years 1960, 1962, 1969, 1973,1983, 1995, 2005, 2006, and 2012. Based on the trivariate fit using 

<AMO>, <SOI>, and <MLCO2>, the estimates for NTC were poor for 9 of the 54 years, including 

the years 1960, 1962, 1969, 1973, 1983, 1995, 2005, 2006, and 2009. (Only NTC was examined using 

bivariate and trivariate fits as a test case simply to determine the amount of improvement one might 

expect by incorporating more than one variable for estimating a tropical cyclone parameter.) 
62

Table 4. Comparison of observed and predicted NTC values based on selected fits.

Year Observed P(<AMO>) P(<SOI>) P(<MLCO2>) P(<AMO>,<SOI>)

P(<AMO>,

<MLCO2>)

P(<AMO>,<SOI>,

<MLCO2>)

1960 7 15 12 8 15 11 11

1961 11 13 12 8 13 10 10

1962 5 13 13 8 14 10 11

1963 9 12 11 8 11 10 9

1964 12 11 13 8 12 9 10

1965 6 10 8 8 7 8 7

1966 11 12 11 8 11 10 9

1967 8 10 12 8 11 9 9

1968 8 10 12 9 10 8 9

1969 18 12 10 9 11 10 9

1970 10 10 12 9 14 9 11

1971 13 8 14 9 10 7 9

1972 7 7 10 9 6 7 6

1973 8 9 13 9 16 8 13

1974 11 6 13 9 8 7 8

1975 9 8 14 9 8 8 8

1976 10 7 12 9 7 7 7

1977 6 9 9 10 8 9 8

1978 12 9 11 10 9 9 9

1979 9 10 11 10 10 10 10

1980 11 11 11 10 11 11 10

1981 12 11 12 10 11 10 10

1982 6 9 9 10 7 9 8

1983 4 11 10 11 10 11 10

1984 13 9 11 11 9 10 9

1985 11 8 12 11 9 10 10

1986 6 8 9 11 7 9 8

1987 7 13 9 11 10 12 10

1988 12 12 13 12 13 12 12

1989 11 11 13 12 12 11 11

1990 14 11 11 12 11 12 11

1991 8 10 10 12 9 11 10

1992 7 9 9 12 7 10 9

1993 8 9 10 12 8 10 10

1994 7 9 9 12 8 11 10

1995 19 14 11 12 13 13 13

1996 13 11 14 13 13 12 13

1997 8 12 9 13 10 13 11

1998 14 17 11 13 16 16 15

1999 12 13 15 13 16 14 15

2000 15 12 13 13 13 13 14
63

Year Observed P(<AMO>) P(<SOI>) P(<MLCO2>) P(<AMO>,<SOI>)

P(<AMO>,

<MLCO2>)

P(<AMO>,<SOI>,

<MLCO2>)

2001 15 13 12 14 13 14 14

2002 12 13 10 14 12 14 13

2003 16 15 11 14 14 15 15

2004 15 14 10 14 14 15 15

2005 28 16 11 14 15 16 16

2006 10 15 11 15 15 16 16

2007 15 14 11 15 13 15 15

2008 16 14 13 15 15 15 16

2009 9 12 11 15 12 14 14

2010 19 16 13 16 18 17 18

2011 19 13 14 16 15 15 16

2012 19 15 11 16 14 16 16

2013 14 14 12 16 15 16 17

±1 17 12 20 22 19 23

±2 28 28 31 33 30 32

±3 39 38 36 41 40 43

Range 4–28 6–17 8–15 8–16 6–18 7–17 6–17

P(<AMO>) = 11.728 + 12.929 <AMO>, r = 0.553, se = 3.8.

P(<SOI>) = 11.440 + 0.200 <SOI>, r = 0.348, se = 4.3.

P(<MLCO2>) = –25.399 + 0.105 <MLCO2>, r = 0.555, se = 3.8.

P(<AMO>, <SOI>) = 11.750 + 12.225 <AMO> + 0.168 <SOI>, Ry12 = 0.625, Sy12 = 3.6.

P(<AMO>, <MLCO2>) = –11.535 + 8.098 <AMO> + 0.066 <MLCO2>, Ry12 = 0.620, Sy12 = 3.6.

P(<AMO>, <SOI>, <MLCO2>) = –20.025 + 0.600P(<AMO>, <SOI>) + 0.070 <MLCO2>, Ry12 = 0.653, Sy12 = 3.5.

Table 4. Comparison of observed and predicted NTC values based on selected fits (Continued).
64
65

3. SUMMARY

This is the second part of a two-part study of the 615 tropical cyclones that occurred in the 

North Atlantic basin during the weather satellite era, 1960–2013. Part 1 investigated the statistics of 

some 25 parameters associated with the tropical cyclones (e.g., NTC, NSD, PWS, ACE, etc.). Part 2 

has examined the statistics of 9 specific climate-related parameters, both in relation to each other and 

in relation to the aforementioned 25 parameters discussed in part 1.

Regarding the intercorrelational behavior of the climate-related factors, perhaps surprisingly, one finds that <ONI> and <SOI>, factors associated with the determination of the phasing 

for the El Niño Southern Oscillation phenomenon, are found to correlate strongly (r=–0.896 and 

cl>99.9%) only against each other and not against any of the other climate-related factors. On the 

other hand, <NAO>, <ASAT>, and <GLOTI> are all found to correlate strongly against <AMO>, 

while <QBO> is found not to correlate strongly against any of the other climate-related factors 

(|r|<0.21 and cl<90%). Also, <AMO>, <ASAT>, and <GLOTI> are found to correlate strongly 

against <MLCO2>, especially <GLOTI> (r=0.932 and cl>99.9%).

Regarding the correlational behavior of the 25 tropical cyclone parameters against the 

9 climate-related factors, one finds that more of the tropical cyclone parameters correlate strongly 

against <AMO> than any other factor, with 15 of the 25 tropical cyclone parameters correlating 

very strongly against <AMO> having cl>98% and an additional 5 tropical cyclone parameters correlating strongly against <AMO> having cl>95%. Only FSD, LSD, LOS, NUSLFH, and <PWS> 

fail to correlate strongly against <AMO>. The second best climate-related factor is <SOI>, with 

8 tropical cyclone parameters correlating very strongly against <SOI> having cl>98% and an additional 5 parameters correlating strongly against <SOI> having cl>95%.

Interestingly, none of the climate-related factors appear to correlate with the FSD. Hence, 

there appears to be no way for accurately predicting ahead of time using any of the 9 climate-related 

factors as to when to expect the FSD of a hurricane season. On the other hand, the LSD does appear 

to be related to <ONI>, <SOI>, <GLOTI>, and <MLCO2>, but in contrasting ways. For example, 

a positive (negative) <ONI> associated with a negative (positive) <SOI> suggests that the LSD will 

occur sooner (later) rather than later (sooner) in the hurricane season, while increased global warming (positive <GLOTI>) and increased atmospheric concentration of CO2 (<MLCO2>) suggests 

that the LSD will occur later rather than earlier during the hurricane season, with the correlation 

against <ONI> being the slightly stronger correlation (cl>99%). Regarding the LOS, it has been 

established that once the FSD is known, an estimate can be made for the LOS. Of the 9 climaterelated factors, only <MLCO2> appears to correlate strongly with the LOS (cl>98%). Hence, given 

the increasing atmospheric concentration of CO2, one expects the LOS to slowly increase in length 

over time.
66

For estimating the NTC, the climate-related factors <AMO>, <ASAT>, <GLOTI>, and 

<MLCO2> all provide some degree of being able to reliably estimate it. Since all of these climaterelated factors are now of positive value, this would seem to indicate a strong preference for the NTC 

to be of average to higher-than-average number for the current hurricane season. (The long-term 

average is about 11 tropical cyclones per season during the overall interval 1960–2013, although since 

1995, the average has been higher, about 15 tropical cyclones per season with the fewest number over 

the past 19 years being 8 in 1997.)

Regarding the NH and NMH, they are found to correlate more strongly against <AMO> 

than the other climate-related factors. Hence, as with NTC, because the <AMO> is presently in its 

warm (positive value) phase and is expected to remain so for another decade or so, one expects the 

NH and NMH probably to be of average to higher-than-average number during the current hurricane season (the long-term average of NH and NMH is 6 and 2, respectively, while being 8 and 3, 

respectively, over the past 19 years.)

Regarding the mean seasonal NUSLFH (<NUSLFH>), none of the climate-related factors 

provide any indication as to how many will occur. The <NUSLFH> appears random, ranging in 

number from 0 to 6 per yearly season.

Regarding the total NSD, total NHD, and total NMHD, again, because the <AMO> is in 

its warm (positive values) phase, one expects values for these tropical cyclone parameters during the 

current hurricane season to probably be average to higher-than-average number (the long-term averages for these parameters are about 58, 24, and 6 days, respectively, and about 82, 30, and 8 days, 

respectively, for the past 19 years). For the NTCA (which is based on NTC, NH, NMH, total NSD, 

total NHD, and total NMHD), one expects activity to be average to higher than average. (The longterm average of NTCA for the overall interval 1960–2013 is about 110%, while the average over the 

past 19 years has been about 148%.) 

Recall that table 3 provides the inferred linear regressions for the 25 tropical cyclone parameters against the 9 specific climate-related factors. Below is table 5, which gives a summary of the 

estimates for the tropical cyclone parameters on the basis of averages for the interval 1995–2013 

using <AMO> and <SOI> as the climate forcing agents and on the basis of the projected value of 

<MLCO2> for the year 2014. Likewise, table 6 is included, which gives a summary of the estimates 

for the tropical cyclone parameters on the basis of whether the year 2014 will be classified as either 

an ENY or a NENY. 
67

Table 5. Summary of estimates for the North Atlantic basin tropical cyclone 

parameters (rounded to nearest whole number) for the 2014 hurricane 

season based on the means for the interval 1995–2013 for <AMO> 

and the projected value of <MLCO2> for 2014.

Parameter

1960–2013 

Average

1995–2013 

Average P(<AMO>) P(<SOI>) P(<MLCO2>)

FSD 180(33) 170(31) 179(34) 180(34) 165(33)

LSD 311(24) 320(24) 315(24) 313(24) 325(24)

LOS 133(42) 152(43) 137(42) 134(42) 161(40)

NTC 11(5) 15(5) 14(4) 12(4) 16(4)

NH 6(3) 8(3) 8(2) 6(3) 7(3)

NMH 2(2) 3(2) 3(2) 3(2) 3(2)

NUSLFH 1(1) 2(2) 2(1) 1(1) 1(2)

<LAT> 22.5(3.3) 20.9(2.8) 21(3) 22(3) 20(3)

<LONG> 63.5(5.8) 62.0(4.6) 61(5) 63(6) 60(6)

PWS 124(22) 127(20) 130(21) 125(22) 122(22)

<PWS> 72.9(8.6) 71.9(9.6) 74(8) 74(8) 68(8)

LP 937(22) 930(21) 929(20) 936(22) 931(23)

<LP> 980.8(7.7) 980.1(7.7) 979(7) 980(7) 983(8)

<ACE> 8.6(3.7) 8.8(3.5) 10(4) 9(4) 7(4)

Total ACE 100.5(59.2) 134.6(64.8) 132(50) 106(57) 126(58)

HISACE 30.9(16.1) 35.9(15.7) 37(15) 32(16) 31(16)

<PDI> 6.9(3.9) 7.2(3.5) 8(4) 7(4) 5(4)

Total PDI 81.8(55.7) 110.8(61.9) 111(48) 87(54) 101(55)

HISPDI 30.8(20.2) 36.6(21.0) 39(19) 32(20) 31(20)

<NSD> 4.94(1.35) 5.31(1.66) 5(1) 5(1) 5(1)

LISNSD 11.76(3.74) 13.09(3.45) 13(4) 12(4) 13(4)

Total NSD 58.12(32.44) 82.05(38.60) 77(26) 62(31) 87(29)

Total NHD 23.70(14.62) 29.87(16.13) 30(13) 25(14) 25(15)

Total NMHD 5.57(5.84) 8.08(6.37) 8(5) 6(6) 7(6)

NTCA 109.6(60.7) 148.3(65.6) 143(50) 116(57) 142(59)
68

Table 6. Summary of estimates for the North Atlantic basin 

tropical cyclone parameters based on whether the year 

2014 is classified as ENY or NENY (rounded to nearest 

whole number).

Parameter ENY(14) NENY(40) t

FSD 190(38) 176(31) 1.4

LSD 299(18) 315(24) –2.3

LOS 110(41) 140(40) –2.4

NTC 9(4) 12(4) –2.4

NH 5(3) 7(3) –2.1

NMH 2(2) 3(2) –1.6

NUSLFH 1(1) 2(2) –1.8

<LAT> 25(4) 22(3) 2.0

<LONG> 65(7) 63(5) 1.2

PWS 125(21) 124(22) 0.1

<PWS> 71(9) 73(9) –0.7

LP 940(19) 936(23) 0.6

<LP> 983(7) 980(8) 1.2

<ACE> 8(4) 9(4) –0.8

Total ACE 72(60) 110(56) –2.1

HISACE 27(18) 32(15) –1.0

<PDI> 6(4) 7(4) –0.8

Total PDI 59(57) 90(54) –1.8

HISPDI 26(22) 32(20) –0.9

<NSD> 4(1) 5(1) –3.2

LISNSD 11(4) 12(3) –1.0

Total NSD 41(25) 64(33) –2.4

Total NHD 16(15) 26(14) –2.3

Total NMHD 4(6) 6(6) –1.1

NTCA 81(59) 120(59) –2.1
69

Concerning table 6, one observes that an ENY, on average, tends to have a later-occurring 

FSD, a sooner-occurring LSD, and a shorter LOS than a NENY, with the differences in means 

being statistically important (cl>95%) for LSD and LOS. Likewise, an ENY, on average, tends to 

have fewer NTC and NH than a NENY. Other statistically important differences include a higher 

<LAT>, lower total ACE, fewer <NSD>, fewer total NSD, fewer total NHD, and smaller NTCA 

than a NENY.

According to the June 5, 2014, diagnostic discussion, the CPC and the International Research 

Institute for Climate and Society have reported that there is a 70% chance of an El Niño developing 

during the Northern Hemisphere summer during the year 2014, increasing to an 80% chance during 

the fall and winter. As to whether or not the year will be classified as ENY or not, it is dependent 

simply upon how soon ENL conditions manifest themselves and how strong the event becomes. 

Through the first four months of the year, the ONI has been of negative value, with its values 

indicative of El Niño-neutral conditions. Should neutral conditions continue through July, then the 

year 2014 would, by the definition of ENY employed in this TP, necessarily have to be classified as 

a NENY, even if an El Niño event should actually develop during the latter portion of the year. During the 54 years spanning 1960–2013, there have been 14 years that had ENL conditions persisting 

at least 6 months within the year, or about one ENY every 4 years. The longest span between ENYs 

during the weather satellite era has been 5 years. Since the last ENY occurred in 2009, clearly one 

anticipates the year 2014 to be an ENY.
70

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74

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Weather satellites have been viewing the Earth’s weather systems almost continuously since the launch of TIROS-1 

in April 1960. During the weather satellite era (1960–2013), some 615 tropical cyclones have formed in the North 

Atlantic basin, varying in yearly number between 4 in 1983 and 28 in 2005 and averaging about 11 tropical cyclones 

per year. A comparison of the subintervals 1960–1994 and 1995–2013 clearly shows, however, that the number of 

tropical cyclones that formed in the North Atlantic basin substantially increased, from about 9 per year in the earlier 

interval to about 15 per year during the more recent interval, with the fewest number (8) during the more recent 

interval occurring in 1997. Examined in this Technical Publication, part 2 of a two-part study, are the inferred statistical relationships between a number of tropical cyclone parameters (25) and specific climate-related factors (9).

STI Help Desk at email: help@sti.nasa.gov

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Prepared by the Science and Research Office, Science and Technology Office

Robert M. Wilson

Statistical Aspects of North Atlantic Basin Tropical Cyclones During 

the Weather Satellite Era, 1960–2013: Part 2

tropical cyclones, North Atlantic basin, the 2014 tropical cyclone season, climate

National Aeronautics and

Space Administration

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George C. Marshall Space Flight Center

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