NOAA/OAR Technical Memorandum

Prepared for the South Florida Water Management District 

Under contract C-10806 

Investigation of Intra-seasonal to Multi-decadal 

Variability in South Florida Rainfall 

Alberto M. Mestas-Nuñez 

David B. Enfield 

Atlantic Oceanographic and Meteorological Laboratory 

Miami, Florida 

March 2003 

noaa Oceanic and Atmospheric Research

Laboratories

NATIONAL OCEANIC AND 

ATMOSPHERIC ADMINISTRATION
II

Investigation of Intra-seasonal to Multi-decadal Variability in 

South Florida Rainfall 

Final Report 

Alberto M. Mestas-Nuñez (1) and David B. Enfield (2)

(1) Cooperative Institute for Marine and Atmospheric Studies (CIMAS), U. of Miami 

(2) NOAA, Atlantic Oceanographic and Meteorological Laboratory (AOML) 

March 2003 

Abstract 

We investigated different climatic factors affecting South Florida (climate divisions 4, 5 and 

6) precipitation. The climatic indexes used here are based on over a century of global sea 

surface temperature anomalies. The climatic associations are studied for different seasons 

and in three frequency bands (intra-seasonal, inter-annual and decadal). We found that the 

known ENSO relationship for winter/spring (wetter than normal South Florida rainfall 

anomalies during El Niño) does not hold during the summer. We attribute this to an opposite 

relationship on intra-seasonal South Florida precipitation (dry during El Niño) that exists 

during that season. The ENSO associations with South Florida unfiltered precipitation 

depend on the different phases of the North Atlantic and Eastern North Pacific decadal 

variability. In general, decadal warming of the Eastern North Pacific and North Atlantic are 

associated with wetter decades of south Florida precipitation. Seasonal outlooks of south 

Florida precipitation ought to consider SST variability associated with ENSO, and from 

decadal modes of variability in the Pacific and the Atlantic. 
III

Table of Contents 

Cover Page............................................................................................. I 

Abstract................................................................................................. II 

Table of Contents..................................................................................... III 

Acknowledgements................................................................................... IV 

1. Introduction......................................................................................... 1 

2. Data and Methods................................................................................. 2 

3. Results................................................................................................ 3

(Task 1) 

a. Annual space patterns associated with south Florida precipitation variability....... 3 

b. Seasonal space patterns associated with south Florida precipitation variability...... 4 

c. Comparison of division 4-6 precipitation and SST indexes............................... 5

(Task2) 

d. Changes in D4 precipitation probability associated with climatic shifts............... 6 

e. Changes in D5 precipitation probability associated with climatic shifts............... 8 

f. Changes in D6 precipitation probability associated with climatic shifts............... 8 

g. Changes in LP precipitation probabilities associated with climatic shifts............. 9 

4. Conclusions........................................................................................ 10 

5. Diagnostic of the statistical relationships.................................................... 11 

(Task 3) 

a. ENSO winters..................................................................................... 11 

b. ENSO summers................................................................................... 11 

c. Decadal variability.............................................................................. 12 

6. Final remarks..................................................................................... 14
IV

Acknoweledgements 

We thank Paul Trimble and Chris Landsea for helpful discussions. Jay Harris helped with 

the preparation of the datasets and Gail Derr with the production of this report. Funding 

came from the South Florida Water Management District (contract C-10806) and the 

National Oceanic and Atmospheric Administration.
1

1. Introduction 

This report summarizes the results of an investigation into the climate factors affecting 

South Florida rainfall supported by a research contract between the NOAA Atlantic 

Oceanographic and Meteorological Laboratory (AOML) and the South Florida Water 

Management District (SFWMD). The work was started on May of 2000 and involved 

collaborations with Chris Landsea (AOML) and Paul Trimble (SFWMD). 

In our preliminary work we used a variety of climatic indices from our previous and 

ongoing work on global SST modes of variability (Enfield and Mestas-Nuñez 1999; 

Mestas-Nuñez and Enfield 1999; Mestas-Nuñez and Enfield 2001) to explore correlations 

with precipitation over South Florida and the Caribbean Ocean. Precipitation came from 

a 40-yr monthly gridded precipitation dataset. We were able to reproduce the know El 

Niño/Southern Oscillation (ENSO) relationship that affects South Florida precipitation 

during winter (e.g. Hanson and Maul 1991) and explored new associations with the 

Atlantic Multidecadal Oscillation (AMO). Our results for the AMO were published in 

Geophysical Research Letters (Enfield et al. 2001). 

In this final report we focus on south Florida precipitation at three climatic divisions 4-6 

(Fig.1). Of these divisions, division 4 includes the entire catchment for Lake Okeechobee 

inflow in south-central Florida. The association of precipitation with SST variability is 

investigated using correlations with global SST. Compared to our preliminary work, we 

know use a longer temporal record (over 100 years) and explore the associations for each 

season and in different frequency bands. This report is organized as follows: In section 2 

we present the data and methods, in section 3 we present the results, in section 4 we give 

our conclusions and in section 5 we make some final remarks. Additional useful 

information is given in the appendices, which include the summary of the AMO paper 

(Appendix A), and a list of scientific presentations of this work (Appendix B). 
2

2. Data and Methods 

This study is based on two datasets: an updated (1856-2001) version of the Kaplan et al. 

(1998) monthly analyses of global SST anomalies and monthly rainfall over South 

Florida climate divisions 4-6 (National Climatic Data Center). The Kaplan SST 

anomalies were calculated as deviations from the 1951-80 monthly climatology. 

Divisions 4-6 (hereafter, D4-D6) precipitation anomalies were calculated as deviations 

from a long-term monthly climatology (Fig. 2) estimated for the same (1951-80) period 

used for SST. Because in this study we focus on multi-decadal and shorter time scales 

we removed a linear trend from the precipitation anomaly time series to eliminate any 

secular trends. The spatial patterns of association between South Florida precipitation and 

SST were estimated from linear correlations analysis between the D4-D6 time series and 

the unfiltered Kaplan SST anomalies for the period 1895-1998. 

 

The temporal-scale dependence of the associations was studied by dividing the D4-D6 

precipitation anomaly time series in three frequency bands: intra-seasonal (T< 1.5 yr), 

interannual (1.5 < T < 8 yr) and decadal (T > 8 yr). The separation of the signal into 

these three respective high pass, band pass and low pass frequency bands was performed 

using a one-dimensional locally weighted regression (loess) algorithm (Cleveland and 

Devlin 1988). With this filter, each smoothed estimate is obtained by a weighted least 

square fit of a quadratic curve to the raw data near the estimation point. The degree of 

smoothing of the loess algorithm is determined by half the temporal width or span of the 

regression region around the estimation point. The smoothing of a loess filter with half 

span equal to1.5 (8) yr is roughly equivalent to what would be obtained from a 0.9 (4.8) 

yr running mean (Chelton et al. 1990). Seasonal dependences of the associations in each 

frequency band were studied from 3-month averages of the data for NovemberDecember-January (NDJ), February-March-April (FMA), May-June-July (MJJ) and 

August-September-October (ASO). Thus the Florida wet (dry) season is described by the 

periods MJJ-ASO (NDJ-FMA). 

The changes in precipitation probability associated with the relevant SST modes were 

studied using three SST-based indexes: an ENSO index (Nino3), an Eastern North Pacific 
3

(ENP) index and a North Atlantic (NA) index. These indexes were computed by 

averaging the Kaplan monthly SST anomalies in three rectangular areas bounded by 90-

150oW, 5oS-5oN for Nino3; 115-135oW, 15-35oN for ENP and 20-60oW, 45-65oN for NA. 

The same processing used with the precipitation time series was applied to the SST 

indexes. Briefly, a linear trend was removed, the three components of the variability 

(intra-seasonal, inter-annual and decadal) were separated using the loess filter, and the 

three-month seasonal averages (NDJ, FMA, MJJ and ASO) were computed. 

The probability analysis is based on building seasonal contingency tables using the threemonth averaged time series of unfiltered and filtered South Florida precipitation and the 

three-month averaged SST indexes. For South Florida precipitation variability, wet, 

neutral and dry conditions are defined by precipitation values falling in the upper, middle 

and lower tercile, respectively. In the case of ENSO variability, El Niño, neutral and La 

Niña conditions are defined by three-month averaged unfiltered Nino3 values falling in 

the upper, middle and lower tercile, respectively. The respective lower and upper Niño3 

threshold values are –0.57 and 0.24 oC for DJF, –0.37 and 0.16 oC for FMA, -0.30 and 

0.26 oC for MJJ, and –0.45 and 0.20 oC for ASO. For Pacific decadal variability 

associations, warm (cold) conditions are defined by positive (negative) values of the lowpassed ENP SST index. For Atlantic decadal variability, warm (cold) conditions are 

defined by positive (negative) values of the low-passed NA SST index. 

3. Results 

a. Annual space patterns associated with south Florida precipitation variability 

The time series for Raw, high pass (HP), band pass (BP) and low pass (LP) D4 

precipitation anomalies are shown in Fig. 3. For graphical purposes, the time series were 

divided by their standard deviation (SD) because of the large discrepancy in the 

amplitude of the variability in the different frequency bands. The SD of Raw D4 

precipitation is 1.98 in. The SD’s of the filtered time series in decreasing order are 1.84 

in for HP (intraseasonal), 0.6 in for BP (interannual), and 0.25 for LP (decadal). The 

ranges of variation of the time series are about ± 2 times the SD’s. 
4

The global SST patterns associated with these time series are shown in Fig. 4. The black 

box in the BP panel is the Niño-3 region and the boxes in the Pacific and Atlantic sectors 

of the in LP panel are the regions used for Pacific and Atlantic decadal variability 

indexes, respectively. The association of south Florida precipitation with ENSO is 

evident in the Raw and BP patterns because they look like the typical ENSO patterns. In 

the North Atlantic, the LP pattern shows high correlations over most of the basin, 

consistent with the results for the AMO (Enfield et al. 2001). In the Pacific, the LP 

pattern shows some indication of the known Pacific decadal pattern (which is similar to 

the ENSO pattern but has off equatorial maximum amplitudes in the eastern tropical 

Pacific). The HP pattern shows low correlations everywhere. 

The time series for Raw, HP, BP and LP D5 precipitation anomalies are shown in Fig. 5. 

The SD of these time series are 1.95 in for Raw, 1.83 in for HP (intraseasonal), 0.56 in 

for BP (interannual), and 0.25 for LP (decadal). The global SST patterns associated with 

these time series are shown in Fig. 6. These patterns are similar to the ones for D4. 

 

The time series for Raw, HP, BP and LP D6 precipitation anomalies are shown in Fig. 7. 

The SD of these time series are 2.52 in for Raw, 2.36 in for HP (intraseasonal), 0.71 in 

for BP (interannual), and 0.38 for LP (decadal). The global SST patterns associated with 

these time series are shown in Fig. 8. These patterns are similar to the ones for D4 and 

D5, except the LP pattern has smaller values of the correlation in the Atlantic sector 

suggesting a weaker association of D6 precipitation with Atlantic decadal variability. 

 

b. Seasonal space patterns associated with south Florida precipitation variability 

When the time series of D4 precipitation are averaged over 3-month seasons (Figs. 9, 11, 

13 and 15) and the spatial patterns are calculated (Figs. 10, 12, 14 and 16) the major 

differences with the annual patterns are for HP and therefore also for Raw (because the 

raw variability is dominated by HP intraseasonal variability these patterns always look 

alike). For NDJ and FMA the Raw and HP patterns are similar to the ENSO pattern. For 

MJJ the HP pattern is strongly anti-correlated with Atlantic sector variability with larger 
5

correlations over the tropical North Atlantic. For ASO the HP pattern is strongly anticorrelated with tropical Pacific variability with a pattern similar to ENSO. The 

implication is that during MJJ and ASO the interannual and intraseasonal patterns 

associated with Florida rainfall interfere with each other resulting in Raw patterns that 

look like weak HP patterns. 

The 3-month averaged time series of D5 precipitation are shown in Figs. 17, 19, 21 and 

23 and their associated spatial patterns of SST correlations are shown in Figs. 18, 20, 22 

and 24. These seasonal patterns associated with D5 are very similar to the ones for D4. 

 The 3-month averaged time series of D6 precipitation are shown in Figs. 25, 27, 29 and 

31 and their associated spatial patterns of SST correlations are shown in Figs. 26, 28, 30 

and 32. These seasonal patterns associated with D6 are very similar to the ones for D4 

and D5 but, as in the case of the annual patterns, the LP pattern has smaller correlations 

in the Atlantic sector. 

c. Comparison of division 4-6 precipitation and SST indexes 

The comparisons of D4 precipitation with SST indexes over the regions shown in Fig. 4 

are shown in Fig. 33. The top panel shows the comparison with Niño-3 when both time 

series are band-passed in the 1.5-8 yr interannual band. The correlation between the two 

time series is 0.44 (maximum at zero lag) and is statistically significant with greater than 

95% confidence accounting for serial correlations. This significant association illustrates 

the ENSO-related variability in South-Central Florida precipitation. 

The middle panel shows the comparison of D4 precipitation with the North Atlantic 

index when both time series are low-passed to remove scales shorter than 8 yr. This 

correlation (0.31) is not statistically significant. Enfield et al. (2001) found a significant 

relationship with Atlantic SSTs when focusing on the multidecadal time scales. They 

applied a 10 yr running mean compared to the equivalent 4.8 yr running mean used here. 

Thus our representation of Atlantic decadal variability combines decadal and 

multidecadal variations. 
6

The bottom panel shows the comparison with the Eastern North Pacific index when both 

time series are LP. The correlation is 0.51 and is statistically significant with greater 

than 95% confidence. This suggests an association between south Florida precipitation 

and Pacific decadal variability. The period of the oscillations is about 10 yr but the 

presence of longer period variations is evident from the 1920s to the 1950s. The 

correlation is weaker for the first half of the record, particularly from the 1920s to the 

1950s when the Atlantic multidecadal signal was in its warm phase. The record is too 

short, however, to study the interaction between the Atlantic and Pacific decadal signals 

with statistical reliability. The discrepancies at both ends of the records may be 

associated with edge effects of the filtering. 

The comparisons of Florida D5 precipitation with the SST indexes are shown in Fig. 34. 

The correlations of the BP Niño 3 and LP eastern North Pacific time series are 

respectively 0.42 and 0.41 and are significant with greater than 95% confidence. The BP 

correlation has a maximum at zero lag. The correlation with LP North Atlantic time 

series is 0.19 and it is not statistically significant. 

The comparisons of Florida D6 precipitation with the SST indexes are shown in Fig. 35. 

The BP D6 precipitation is significantly correlated with BP Niño 3 SST (0.38, maximum 

at zero lag) with greater than 95% confidence. There is no statistically significant 

correlation with LP North Atlantic SST (0.16). The correlation with ENP SST is 0.31 

and is statistically significant with greater than 90% confidence. 

d. Changes in D4 precipitation probability associated with climatic shifts 

To study the changes in D4 precipitation probability associated with ENSO we 

constructed contingency plots using terciles of precipitation and Niño-3. The lower, 

middle, and upper terciles of precipitation represent dry (brown), normal (white) and wet 

(green) conditions, respectively. The lower, middle and upper terciles of Niño-3 

represent La Niña, neutral and El Niño conditions, respectively. We also investigated 
7

changes of the contingency plots for the warm and cold phases of the Atlantic and Pacific 

decadal indexes. 

The seasonal contingency plots illustrating the ENSO effects on raw D4 precipitation are 

shown in Fig. 36 and the corresponding contingency tables in Table 1. Only concurrent 

seasons are considered because as shown in section 3.3 the maximum correlation 

between ENSO and south Florida precipitation occurs at zero lag. The changes in the 

ENSO relationship in the interannual (BP) and intraseasonal (HP) bands are shown in in 

Figs. 37 (Table 2) and 38 (Table 3), respectively. The know relation, wet (dry) 

winter/spring during El Niño (La Niña) events, is clearly seen in the upper NDJ-FMA 

panels of Fig. 36. This is consistent with the patterns in the top panels of Figs. 10 and 12. 

This relation does not hold for the MJJ and ASO seasons. In fact an opposite relation is 

seen during ASO, consistent with the pattern in the top panel of Fig. 16. The contingency 

plot for the BP component of D4 precipitation (Fig. 37) shows that the ENSO relation 

holds for all seasons in the interannual band. This relation breaks down during ASO 

because of the opposite ENSO effects on intraseasonal precipitation shown in Fig. 38 

(lower right panel). 

The changes in the ENSO-D4 contingency plots during the warm and cold phases of the 

decadal North Atlantic index is shown in Figs. 39 (Table 4) and 40 (Table 5), 

respectively. Some changes in the associations during the warm and cold phases of the 

Atlantic decadal signal are apparent in these figures. For example, during the warm 

phase of the Atlantic decadal index the ENSO effect is reinforced during FMA giving 

wetter conditions than normally expected for ENSO years – the probability of wet FMA 

shifts from 35% to about 80%. Also for neutral ENSO years, the probability of wet MJJ 

and ASO seasons nearly doubles from cold to warm phases of the Atlantic decadal signal. 

The changes in the ENSO-D4 contingency plots during the warm and cold phases of the 

decadal Pacific index are shown in Figs. 41 (Table 6) and 42 (Table 7), respectively. 

There some changes in unfiltered D4 precipitation probabilities during both phases of the 

Pacific decadal variability. For example, during warm ENSO years the FMA and MJJ 

seasons are somewhat wetter during warm phases of the Pacific decadal signal. 
8

e. Changes in D5 precipitation probability associated with climatic shifts 

The seasonal contingency plots illustrating the ENSO effects on raw D5 precipitation are 

shown in Fig. 43 and the corresponding contingency tables in Table 8. The changes in 

the ENSO relationship in the interannual (BP) and intraseasonal (HP) bands are shown in 

in Figs. 44 (Table 9) and 45 (Table 10), respectively. Similarly to the case of D4, the 

know relation, wet (dry) winter/spring during El Niño (La Niña) events, is clearly seen in 

the upper NDJ-FMA panels of Fig. 43. This is consistent with the patterns in the top 

panels of Figs. 18 and 20. This relation does not hold for the MJJ and ASO seasons. The 

contingency plot for the BP component of D5 precipitation (Fig. 44) shows that the 

ENSO relation holds for all seasons in the interannual band. 

The changes in the ENSO-D5 contingency plots during the warm and cold phases of the 

decadal North Atlantic index is shown in Figs. 46 (Table 11) and 47 (Table 12), 

respectively. Except during NDJ, D5 is wetter during the warm phase of the Atlantic 

decadal signal. 

The changes in the ENSO-D5 contingency plots during the warm and cold phases of the 

decadal Pacific index are shown in Figs. 48 (Table 13) and 49 (Table 14), respectively. 

In general there are little change in unfiltered D5 precipitation probabilities during both 

phases of the Pacific decadal variability except during the FMA season with wetter 

neutral and El Niño events during the warm phase of the Pacific decadal signal. 

f. Changes in D6 precipitation probability associated with climatic shifts 

The seasonal contingency plots illustrating the ENSO effects on raw D6 precipitation are 

shown in Fig. 50 and the corresponding contingency tables in Table 15. The changes in 

the ENSO relationship in the interannual (BP) and intraseasonal (HP) bands are shown in 

in Figs. 51 (Table 16) and 52 (Table 17), respectively. Similarly to D4 and D5, the know 

relation, wet (dry) winter/spring during El Niño (La Niña) events, is clearly seen in the 

upper NDJ-FMA panels of Fig. 50. This is consistent with the patterns in the top panels 

of Figs. 26 and 28. Similarly to D4 and D5, the ENSO relation does not hold for the MJJ 
9

and ASO seasons. The contingency plot for the BP component of D6 precipitation (Fig. 

51) shows that the ENSO relation holds for all seasons in the interannual band. 

The changes in the ENSO-D6 contingency plots during the warm and cold phases of the 

decadal North Atlantic index is shown in Figs. 53 (Table 18) and 54 (Table 19), 

respectively. Except during NDJ, D6 is wetter during the warm phase of the Atlantic 

decadal signal. 

The changes in the ENSO-D6 contingency plots during the warm and cold phases of the 

decadal Pacific index are shown in Figs. 55 (Table 20) and 56 (Table 21), respectively. 

In general there are little change in unfiltered D6 precipitation probabilities during both 

phases of the Pacific decadal variability except during the FMA season with wetter 

neutral ENSO events during the warm phase of the Pacific decadal signal. 

g. Changes in LP precipitation probabilities associated with climatic shifts 

The changes in the low-passed D4 precipitation probabilities (in terciles) during the warm 

and cold phases of the Atlantic decadal and Eastern Pacific decadal indexes are shown in 

Fig. 57 (Table 22). As in the figures for ENSO, the lower tercile of precipitation is 

indicated with brown, the middle with white and the upper with green. The relation seen 

in Fig. 57 is similar for both decadal indexes and is independent of the seasons. When 

the decadal indexes are warm (cold) the decadally smoothed D4 precipitation 

probabilities are higher (lower). In particular, the probability of a wet D4 decade 

changes from about 20% to 40% when the decadal indexes change from their cold to 

their warm phase. 

The changes in the low-passed D5 precipitation probabilities during the warm and cold 

phases of the Atlantic multidecadal and Eastern North Pacific decadal indexes are shown 

in Fig. 58 (Table 23). The changes in the low-passed D6 precipitation probabilities 

during the warm and cold phases of the Atlantic multidecadal and Eastern North Pacific 

decadal indexes are shown in Fig. 59 (Table 24). The decadal changes of D5 and D6 

precipitation probability are similar to D4 in general weaker. 
10

4. Conclusions 

We have investigated the effects of ENSO and decadal climate variability in south 

Florida (D4-D6) precipitation. Our approach here has been to determine the climatic 

patterns of SST variability that are associated with D4-D6 precipitation variability. We 

also looked at how these patterns vary when the precipitation variability is decomposed 

into intraseasonal, interannual and decadal frequency bands. 

For ENSO, we showed that the known winter/spring relation (50% probability of wet 

NDJ and FMA during El Niño) does not hold during the summer. We demonstrated that 

this relation breaks down because of the opposite effect (smaller during warm ENSO 

phase) that ENSO has on the dominant intraseasonal precipitation variability during that 

season. Regarding the effect of the Atlantic decadal variability we showed that the most 

sensitive season is FMA when the probability of a wet D4 season during ENSO increases 

from about 35% to 80%. Also for neutral ENSO years, the probability of wet MJJ and 

ASO seasons nearly doubles from cold to warm phases of the Atlantic decadal signal. 

During El Niño years the FMA and MJJ seasons of D4 are about 20% wetter during 

warm phases of the Pacific decadal signal. 

Regarding the effect of the decadal SST variability on the low-pass D4 precipitation, our 

results show that in general decadally warm Atlantic is associated with decadally wet 

South Florida – as was already discussed by Enfield et al. (2001) for the multidecadal 

scales. On decadal scale, we found a similar association with Pacific decadal variability. 

This association modulates the ENSO response and affects the precipitation probability in 

south Florida. Precipitation outlooks in south Florida should consider the effects of 

ENSO and the decadal Pacific and Atlantic variabilities.
11

5. Diagnostic of the statistical relationships 

a. ENSO winters 

On interannual time scales the link between South Florida rainfall and ENSO is related to 

latitudinal shifts in the subtropical jet stream and associated storm track activity over the 

eastern Pacific Ocean (e.g. Trenberth et al. 1998). This is especially clear during El Niño 

where a deepened Aleutian Low increases the baroclinicity of the storm track entering the 

US and shunts the track farther south. Because of the greater tropical humidity and the 

farther penetration of the storm systems into the more humid tropical regions, more 

moisture is entrained into these more energetic storms. The storms track preferentially 

across the southern tier states and pick up additional moisture from the Gulf of Mexico 

and Caribbean as they cross the Gulf States. The southern tier states (including Florida) 

are dry by contrast when La Niña conditions prevail and these mechanisms are absent. 

This mechanism for increased precipitation over the southern U.S. has been postulated 

primarily for winter conditions and they are important in determining the cumulative 

hydrological setup (during the dry season) as the wet season onset approaches. 

Exceptions to the typical ENSO behavior are not uncommon. December and January of 

the 2002-2003 El Niño winter were a case in point. Instead of seeing a series of wet 

storms propagating eastward across the southern tier, there were a long series of strong 

polar fronts that expanded south across the great plains bringing cold Canadian air 

masses to Florida, but very little rain. This may have been related to the confounding 

effect of the Atlantic state on the ridge-trough structure across the US. Once better 

understood, this winter may be a good example of how a contrary Atlantic configuration 

can trump a weak El Niño. 

b. ENSO summers 

Our tercile probabilities hint at different mechanisms for summer rainfall in El Niño 

years. When Florida rainfall is compared against the terciles of NINO3 SST we find that 

warm SST anomalies in the equatorial Pacific are associated with lower rainfall in the 
12

intraseasonal band. This suggests that tropical storm activity is less prevalent, in 

agreement with relevant hurricane research. The effect of ENSO variability on Atlantic 

tropical storm activity is opposite to that of the winter storm track activity shifts over the 

Pacific. During El Niño (La Niña) there is an increase (decrease) in the vertical shear of 

the horizontal wind over the Atlantic caused primarily by an increase (decrease) in the 

climatological westerly winds in the upper troposphere (Landsea 2000). This increase 

(decrease) in the shear leads directly to reduced (increased) Atlantic tropical storm 

activity. 

The shear effect is probably related to the disruption of the Walker & Hadley circulations 

in the Atlantic sector (Wang and Enfield, 2003) and careful examination of hurricane 

frequencies reveals that the effect is mainly prevalent in the summer of the El Niño onset 

year [EN-0] and not of the following year [EN+1]. During the EN+1 years, the El Niño 

peak is most often followed by the development of a larger-than-normal Atlantic warm 

pool, which is now partially understood (Wang and Enfield, 2001, 2003). During these 

summers the Atlantic SST favors tropical storm development and the Caribbean 

experiences greater rainfall, which may extend to Florida or it may remove moisture from 

the air masses affecting Florida and thus lead to drier conditions. Unfortunately, our 

tercile analysis does not separate warm summers according the El Niño phase because 

this would greatly reduce the degrees of freedom and reliability of the analysis. A very 

important topic for future research is to understand better the difference between both the 

rainfall probabilities and the mechanisms that affect rainfall and tropical storms during 

onset summers vis-à-vis following-year summers. For the present, SFWMD must be 

aware of this distinction in its approach to developing El Niño scenarios. 

c. Decadal variability 

On decadal and longer time scales, South Florida precipitation is modulated by Pacific 

and Atlantic variability. Both Pacific and Atlantic decadal modulations are similar in the 

sense that warm (cold) eastern/tropical Pacific and North Atlantic lead to increased 

(decreased) South Florida precipitation independent of the season. The way in which 

these teleconnections occur, however, appear to be quite different. 
13

As seen in Fig. 60, the winter 500 mb pattern associated with the Pacific decadal 

variability shows good correspondence with the Pacific-North American (PNA) pattern 

(Enfield and Mestas-Nuñez 1999). This is not surprising since although there are 

regional differences the large-scale structure of the decadal Pacific SST mode is similar 

to ENSO, which commonly excites the PNA pattern. Based on this analogy with the PNA 

pattern one can infer that the warm phase of the Pacific decadal variability is also 

associated with southward displacements of the subtropical storm track activity resulting 

in decadal wetter conditions over the southern U.S. including South Florida. Analysis of 

the ENSO and non-ENSO 500 mb vertical velocity patterns associated with Pacific SST 

variability confirms this suggestion showing anomalous ENSO and Pacific decadal 

vertical velocity fluctuations over South Florida that are in phase with each other 

(Mestas-Nuñez and Enfield 2001). 

Figure 61 shows that the winter 500 mb pattern associated with the Atlantic decadal SST 

variability is similar to the North Atlantic Oscillation pattern (Enfield and Mestas-Nuñez 

1999, their Fig. 8). This pattern shows that during the warm phase of the Atlantic decadal 

variability (especially the Atlantic Multidecadal Oscillation, hereafter AMO) the usual 

winter trough over the southeastern U.S. (which includes South Florida) also strengthens, 

while eastern Canada lies under higher pressures that favor cold air mass development 

there. This results in decreased winter cyclonic activity in the southwest and increased 

winter cyclonic activity in the southeast. But because the fronts bring in cold Canadian 

air masses from the north, these effects oppose the ENSO-related wet subtropical storm 

tendency when they occur in conjunction with El Niño. We note that this ENSO-Atlantic 

interaction scenario is consistent with the results of Enfield et al. (2001) who showed that 

when the North Atlantic is multi-decadally warm the southeastern states are less rainy 

during El Niño, and vice-versa for a cool North Atlantic (see Fig. 62). By the same 

reasoning, one would anticipate that a cold North Atlantic would reinforce the 

prototypical El Niño pattern, which is what Fig. 4 of Enfield et al. (2001) also shows. 

This interpretation of ENSO-AMO interactions is consistent with our tercile conditionalprobability analysis for ENSO-AMO during El Niño DJF conditions, more noticeable for 

South Florida Divisions 5 and 6. The opposite scenario during La Niña winters, however, 

is not supported buy our analysis. We caution, however, that these are linear notions of 
14

how ENSO and Atlantic variability may interact and that further research will be needed 

to understand these scenarios better. 

6. Final remarks 

The goal of this contract was to “identify links between various modes of ocean 

variability to that of the climatic and hydrologic variability in South Florida”. The focus 

of the study was to estimate in a probabilistic sense the effects of the global climate on 

the hydrology of South Florida. 

We performed exploratory analyses (Task 1) to determine what modes of global SST 

variability described in our previous work using Empirical Orthogonal Function (EOF) 

analysis are associated with south Florida hydrology. The result of this preliminary work 

allowed confirming that the main signal in south Florida hydrological variables comes 

from the ENSO mode. We were also able to determine that on multidecadal time scales 

the Atlantic multidecadal mode played a role and this led to a peer reviewed article 

(Enfield et al. 2001) where we described the associations of this index with U.S. 

precipitation (an abstract of this publication is included in Appendix A). In this final 

report we performed a correlation analysis of south Florida D4-D6 on global SST to 

determine the patterns associated with precipitation variability in three frequency bands 

(intraseasonal, interannual and decadal). The space patterns allowed determining the 

signature of the corresponding global modes that are involved. These patterns were used 

to define three indexes that capture the main global modes (ENSO, Atlantic decadal and 

Pacific decadal modes). The significance level of the correlation of these indexes with 

south Florida precipitation was also estimated taking into account serial correlations. 

 

The three indexes of global SST variability were used to estimate the changes of 

probability distribution associated with the climatic signals (Task 2). The approach we 

undertook was a practical one based on terciles of the distribution of the variables in a 

manner similar to what is used by the Climate Prediction Center. The changes of 

probability distributions of South Florida D4-D6 precipitation associated with changes in 

the climatic indexes were studied taking into account conditional probabilities (e.g. how 
15

does the ENSO associations change as a function of the phases of the decadal Pacific and 

Atlantic variability). 

A diagnostic of the atmospheric mechanisms behind the statistical associations between 

South Florida precipitation and the interannual and decadal modes of SST variability 

(Task 3) is included in section 5. There we summarize of our present understanding of 

the mechanisms behind these relationships. These ideas are the focus of active research in 

the community and although some of the proposed mechanisms during winter are 

partially accepted, there is little understanding for the summer and on the non-linear 

interaction among the relevant modes of variability. 
16

Fig. 1. Map of Florida showing the locations of Lake Okeechobee and climate divisions 

4, 5 and 6. 

Fig. 2. Long-term average seasonal cycle of monthly precipitation in Florida climate 

divisions 4 (thick solid), 5 (dashed), and 6 (thin solid) estimated for the 1951-80 period. 
17

Fig. 3. Unfiltered time series of D4 monthly precipitation anomalies (Raw) and filtered 

to capture variability in three bands: scales greater than 8 yrs (LP), scales between 1.5 

and 8 yrs (BP), and scales shorter than 1.5 yrs (HP). The time series have been 

standardized dividing them by their standard deviations: 1.98, 0.24, 0.60 and 1.84 inches, 

respectively. 
18

Fig. 4. Map of correlations between global monthly SST anomalies and the D4 monthly 

precipitation anomaly time series shown in Fig. 3. The boxes indicate the regions used to 

calculate the SST indices in the North Pacific, North Atlantic and tropical Pacific. 
19

Fig. 5. Unfiltered time series of division 5 monthly precipitation anomalies (Raw) and 

filtered to capture variability in three bands: scales greater than 8 yrs (LP), scales 

between 1.5 and 8 yrs (BP), and scales shorter than 1.5 yrs (HP). The time series have 

been standardized dividing them by their standard deviations: 1.95, 0.25, 0.56 and 1.83 

inches, respectively. 
20

 . 

Fig. 6. Map of correlations between global monthly SST anomalies and the division 5 

monthly precipitation anomaly time series shown in Fig. 5. 
21

Fig. 7. Time series of division 6 monthly precipitation anomalies (Raw) and filtered to 

capture variability in three bands: scales greater than 8 yrs (LP), scales between 1.5 and 8 

yrs (BP), and scales shorter than 1.5 yrs (HP). The time series have been standardized 

dividing them by their standard deviations: 2.52, 0.38, 0.71 and 2.36 inches, respectively. 
22

Fig. 8. . Map of correlations between global monthly SST anomalies and the division 6 

monthly precipitation anomaly time series shown in Fig. 7. 
23

Fig. 9. As in Fig. 3 but for the November-December-January (NDJ) average. 
24

Fig. 10. Map of correlations between global November-December-January (NDJ) 

average SST anomalies and the NDJ precipitation anomaly time series shown in Fig. 9. 
25

Fig. 11. Same as Fig. 3 but for the February-March-April (FMA) average. 
26

Fig. 12. Map of correlations between global February-March-April (FMA) average SST 

anomalies and the FMA precipitation anomaly time series shown in Fig. 11. 
27

Fig. 13. Same as Fig. 3 but for the May-June-July (MJJ) average. 
28

Fig. 14. Map of correlations between global May-June-July (MJJ) average SST 

anomalies and the MJJ precipitation anomaly time series shown in Fig. 13. 
29

Fig. 15. Same as Fig. 3 but for the August-September-October (ASO) average. 
30

Fig. 16. Map of correlations between global August-September-October (ASO) average 

SST anomalies and the ASO precipitation anomaly time series shown in Fig. 15.
31

Fig. 17. Same as Fig. 5 but for the November-December-January (NDJ) average. 
32

Fig. 18. Map of correlations between global November-December-January (NDJ) 

average SST anomalies and the NDJ precipitation anomaly time series shown in Fig. 17. 
33

Fig. 19. Same as Fig. 5 but for the February-March-April (FMA) average. 
34

Fig.20. Map of correlations between global February-March-April (FMA) average SST 

anomalies and the FMA precipitation anomaly time series shown in Fig. 19. 
35

Fig. 21. Same as Fig. 5 but for the May-June-July (MJJ) average. 
36

Fig. 22. Map of correlations between global May-June-July (MJJ) average SST 

anomalies and the MJJ precipitation anomaly time series shown in Fig. 21. 
37

Fig. 23. Same as Fig. 5 but for the August-September-October (ASO) average. 
38

Fig. 24. Map of correlations between global August-September-October (ASO) average 

SST anomalies and the ASO precipitation anomaly time series shown in Fig. 23. 
39

Fig. 25. Same as Fig. 7 but for the November-December-January (NDJ) average. 
40

Fig. 26. Map of correlations between global November-December-January (NDJ) 

average SST anomalies and the NDJ precipitation anomaly time series shown in Fig. 25. 
41

Fig. 27. Same as Fig. 7 but for the February-March-April (FMA) average. 
42

Fig. 28. Map of correlations between global February-March-April (FMA) average SST 

anomalies and the FMA precipitation anomaly time series shown in Fig. 27. 
43

Fig. 29. Same as Fig. 7 but for the May-June-July (MJJ) average. 
44

Fig. 30. Map of correlations between global May-June-July (MJJ) average SST 

anomalies and the MJJ precipitation anomaly time series shown in Fig. 29. 
45

Fig. 31. Same as Fig. 7 but for the August-September-October (ASO) average. 
46

Fig. 32. Map of correlations between global August-September-October (ASO) average 

SST anomalies and the ASO precipitation anomaly time series shown in Fig. 31. 
47

Fig. 33. Standardized comparison of: Band pass (BP) D4 rainfall anomalies and BP 

Niño3 SST anomalies (top), low pass (LP) D4 rainfall anomalies and LP North Atlantic 

(NA) SST anomalies (middle), and LP D4 rainfall anomalies and LP Eastern North 

Pacific (ENP) SST anomalies (bottom). The standard deviations of BP and LP D4 

rainfall are 0.6 and 0.24 inches, respectively. The standard deviations of BP Niño3, LP 

NA, and LP ENP are 0.69, 0.26 and 0.29 o

C, respectively. 
48

Fig. 34. Same as Fig. 33 but for Florida Division 5 (D5). The standard deviations of BP 

and LP D5 rainfall are 0.56 and 0.25 inches, respectively. 
49

 

Fig. 35. Same as Fig. 33 but for Florida Division 6 (D6). The standard deviations of BP 

and LP D6 rainfall are 0.71 and 0.38 inches, respectively. 
50

Fig. 36. Contingency plots (in percent) from terciles of Division 4 precipitation and 

Nino3 index for DJF, FMA, MJJ and ASO. 

 NDJ FMA 

Wet 20 22 58

Neutral 23 39 39

Dry 57 39 3

La Nina Neutral El Nino

Wet 6 39 56

Neutral 23 42 36

Dry 71 19 8

La Nina Neutral El Nino

 MJJ ASO 

Table 1. Contingency tables corresponding to the contingency plots shown in Fig. 36. 

Numbers are in percent. 

Wet 26 42 33

Neutral 43 19 39

Dry 31 39 28

La Nina Neutral El Nino

Wet 43 33 25

Neutral 29 39 33

Dry 29 28 42

La Nina Neutral El Nino
51

Fig. 37. Contingency plots (in percent) from terciles of band-passed (BP) Division 4 

precipitation and Nino3 index for DJF, FMA, MJJ and ASO. 

 NDJ FMA 

Wet 17 22 61

Neutral 37 36 28

Dry 46 42 11

La Nina Neutral El Nino

Wet 11 33 56

Neutral 31 47 22

Dry 57 19 22

La Nina Neutral El Nino

 MJJ ASO 

Table 2. Contingency tables corresponding to the contingency plots shown in Fig. 37. 

Numbers are in percent. 

Wet 14 33 53

Neutral 49 31 22

Dry 37 36 25

La Nina Neutral El Nino

Wet 14 33 53

Neutral 40 31 31

Dry 46 36 17

La Nina Neutral El Nino
52

Fig. 38. Contingency plots (in percent) from terciles of high-passed (HP) Division 4 

precipitation and Nino3 index for DJF, FMA, MJJ and ASO. 

 NDJ FMA 

Wet 20 28 53

Neutral 40 33 28

Dry 40 39 19

La Nina Neutral El Nino

Wet 3 42 56

Neutral 37 36 28

Dry 60 22 17

La Nina Neutral El Nino

 MJJ ASO 

Table 3. Contingency tables corresponding to the contingency plots shown in Fig. 38. 

Numbers are in percent. 

Wet 31 39 31

Neutral 34 39 28

Dry 34 22 42

La Nina Neutral El Nino

Wet 51 33 17

Neutral 29 44 28

Dry 20 22 56

La Nina Neutral El Nino
53

Fig. 39. Contingency plots (in percent) from terciles of Division 4 precipitation and 

Nino3 index when the AMO is in its cold phase for DJF, FMA, MJJ and ASO. 

 NDJ FMA 

Wet 21 29 60

Neutral 29 50 35

Dry 50 21 5

La Nina Neutral El Nino

Wet 0 19 35

Neutral 20 50 60

Dry 80 31 5

La Nina Neutral El Nino

 MJJ ASO 

Table 4. Contingency tables corresponding to the contingency plots shown in Fig. 39. 

Numbers are in percent. 

Wet 29 25 37

Neutral 43 25 32

Dry 29 50 32

La Nina Neutral El Nino

Wet 47 17 24

Neutral 35 58 33

Dry 18 25 43

La Nina Neutral El Nino
54

Fig. 40. Contingency plots (in percent) from terciles of Division 4 precipitation and 

Nino3 index when the AMO is in its warm phase for DJF, FMA, MJJ and ASO. 

 NDJ FMA 

Wet 19 18 56

Neutral 19 32 44

Dry 62 50 0

La Nina Neutral El Nino

Wet 10 55 81

Neutral 25 35 6

Dry 65 10 13

La Nina Neutral El Nino

 MJJ ASO 

Table 5. Contingency tables corresponding to the contingency plots shown in Fig. 40. 

Numbers are in percent. 

Wet 24 55 29

Neutral 43 15 47

Dry 33 30 24

La Nina Neutral El Nino

Wet 39 42 27

Neutral 22 29 33

Dry 39 29 40

La Nina Neutral El Nino
55

Fig. 41. Contingency plots (in percent) from terciles of Division 4 precipitation and 

Nino3 index when the PDO is in its cold phase for DJF, FMA, MJJ and ASO. 

 NDJ FMA 

Wet 14 14 65

Neutral 24 43 35

Dry 62 43 0

La Nina Neutral El Nino

Wet 5 31 44

Neutral 23 38 44

Dry 73 31 13

La Nina Neutral El Nino

 MJJ ASO 

Table 6. Contingency tables corresponding to the contingency plots shown in Fig. 41. 

Numbers are in percent. 

Wet 27 42 20

Neutral 36 8 47

Dry 36 50 33

La Nina Neutral El Nino

Wet 55 29 15

Neutral 23 43 38

Dry 23 29 46

La Nina Neutral El Nino
56

Fig. 42. Contingency plots (in percent) from terciles of Division 4 precipitation and 

Nino3 index when the PDO is in its warm phase for DJF, FMA, MJJ and ASO. 

 NDJ FMA 

Wet 29 27 53

Neutral 21 36 42

Dry 50 36 5

La Nina Neutral El Nino

Wet 8 43 65

Neutral 23 43 30

Dry 69 13 5

La Nina Neutral El Nino

 MJJ ASO 

Table 7. Contingency tables corresponding to the contingency plots shown in Fig. 42. 

Numbers are in percent. 

Wet 23 42 43

Neutral 54 25 33

Dry 23 33 24

La Nina Neutral El Nino

Wet 23 36 30

Neutral 38 36 30

Dry 38 27 39

La Nina Neutral El Nino
57

Fig. 43. Contingency plots (in percent) from terciles of Division 5 precipitation and 

Nino3 index for DJF, FMA, MJJ and ASO. 

 NDJ FMA 

Wet 14 22 64

Neutral 29 44 28

Dry 57 33 8

La Nina Neutral El Nino

Wet 6 39 56

Neutral 29 33 39

Dry 66 28 6

La Nina Neutral El Nino

 MJJ ASO 

Table 8. Contingency tables corresponding to the contingency plots shown in Fig. 43. 

Numbers are in percent. 

Wet 29 42 31

Neutral 43 19 39

Dry 29 39 31

La Nina Neutral El Nino

Wet 31 36 33

Neutral 31 42 28

Dry 37 22 39

La Nina Neutral El Nino
58

Fig. 44. Contingency plots (in percent) from terciles of band-passed (BP) Division 5 

precipitation and Nino3 index for DJF, FMA, MJJ and ASO. 

 NDJ FMA 

Wet 14 25 61

Neutral 34 36 31

Dry 51 39 8

La Nina Neutral El Nino

Wet 11 31 58

Neutral 34 44 22

Dry 54 25 19

La Nina Neutral El Nino

 MJJ ASO 

Table 9. Contingency tables corresponding to the contingency plots shown in Fig. 44. 

Numbers are in percent. 

Wet 23 33 44

Neutral 40 25 36

Dry 37 42 19

La Nina Neutral El Nino

Wet 20 33 47

Neutral 26 36 39

Dry 54 31 14

La Nina Neutral El Nino
59

Fig. 45. Contingency plots (in percent) from terciles of high-passed (HP) Division 5 

precipitation and Nino3 index for DJF, FMA, MJJ and ASO. 

 NDJ FMA 

Wet 26 25 50

Neutral 40 36 25

Dry 34 39 25

La Nina Neutral El Nino

Wet 14 36 50

Neutral 31 36 33

Dry 54 27 17

La Nina Neutral El Nino

 MJJ ASO 

Table 10. Contingency tables corresponding to the contingency plots shown in Fig. 45. 

Numbers are in percent. 

Wet 34 39 28

Neutral 40 33 28

Dry 26 28 44

La Nina Neutral El Nino

Wet 37 39 25

Neutral 26 44 31

Dry 37 17 44

La Nina Neutral El Nino
60

Fig. 46. Contingency plots (in percent) from terciles of Division 5 precipitation and 

Nino3 index when the AMO is in its cold phase for DJF, FMA, MJJ and ASO. 

 NDJ FMA 

Wet 21 36 70

Neutral 21 29 25

Dry 57 36 5

La Nina Neutral El Nino

Wet 0 25 45

Neutral 27 44 50

Dry 73 31 5

La Nina Neutral El Nino

 MJJ ASO 

Table 11. Contingency tables corresponding to the contingency plots shown in Fig. 46. 

Numbers are in percent. 

Wet 21 25 37

Neutral 57 19 37

Dry 21 56 26

La Nina Neutral El Nino

Wet 39 42 27

Neutral 22 29 33

Dry 39 29 40

La Nina Neutral El Nino
61

Fig. 47. Contingency plots (in percent) from terciles of Division 5 precipitation and 

Nino3 index when the AMO is in its warm phase for DJF, FMA, MJJ and ASO. 

 NDJ FMA 

Wet 10 14 56

Neutral 33 55 31

Dry 57 32 13

La Nina Neutral El Nino

Wet 10 50 69

Neutral 30 25 25

Dry 60 25 6

La Nina Neutral El Nino

 MJJ ASO 

Table 12. Contingency tables corresponding to the contingency plots shown in Fig. 47. 

Numbers are in percent. 

Wet 33 55 24

Neutral 33 20 41

Dry 33 25 35

La Nina Neutral El Nino

Wet 22 46 53

Neutral 33 33 7

Dry 44 21 40

La Nina Neutral El Nino
62

Fig. 48. Contingency plots (in percent) from terciles of Division 5 precipitation and 

Nino3 index when the PDO is in its cold phase for DJF, FMA, MJJ and ASO. 

 NDJ FMA 

Wet 10 21 59

Neutral 29 43 29

Dry 62 36 12

La Nina Neutral El Nino

Wet 9 23 50

Neutral 18 38 44

Dry 73 38 6

La Nina Neutral El Nino

 MJJ ASO 

Table 13. Contingency tables corresponding to the contingency plots shown in Fig. 48. 

Numbers are in percent. 

Wet 27 42 20

Neutral 45 17 47

Dry 27 42 33

La Nina Neutral El Nino

Wet 41 36 31

Neutral 36 50 31

Dry 23 14 38

La Nina Neutral El Nino
63

Fig. 49. Contingency plots (in percent) from terciles of Division 5 precipitation and 

Nino3 index when the PDO is in its warm phase for DJF, FMA, MJJ and ASO. 

 NDJ FMA 

Wet 21 23 68

Neutral 29 45 26

Dry 50 32 5

La Nina Neutral El Nino

Wet 0 48 60

Neutral 46 30 35

Dry 54 22 5

La Nina Neutral El Nino

 MJJ ASO 

Table 14. Contingency tables corresponding to the contingency plots shown in Fig. 49. 

Numbers are in percent. 

Wet 31 42 38

Neutral 38 21 33

Dry 31 38 29

La Nina Neutral El Nino

Wet 15 36 35

Neutral 23 36 26

Dry 62 27 39

La Nina Neutral El Nino
64

Fig. 50. Contingency plots (in percent) from terciles of Division 6 precipitation and 

Nino3 index for DJF, FMA, MJJ and ASO. 

 NDJ FMA 

Wet 23 25 53

Neutral 26 39 36

Dry 51 36 11

La Nina Neutral El Nino

Wet 9 50 42

Neutral 29 25 47

Dry 63 25 11

La Nina Neutral El Nino

 MJJ ASO 

Table 15. Contingency tables corresponding to the contingency plots shown in Fig. 50. 

Numbers are in percent. 

Wet 31 37 31

Neutral 39 25 36

Dry 28 39 33

La Nina Neutral El Nino

Wet 34 31 36

Neutral 26 39 36

Dry 40 31 28

La Nina Neutral El Nino
65

Fig. 51. Contingency plots (in percent) from terciles of band-passed (BP) Division 6 

precipitation and Nino3 index for DJF, FMA, MJJ and ASO. 

 NDJ FMA 

Wet 17 28 56

Neutral 29 36 36

Dry 54 36 8

La Nina Neutral El Nino

Wet 11 36 53

Neutral 31 42 28

Dry 57 22 19

La Nina Neutral El Nino

 MJJ ASO 

Table 16. Contingency tables corresponding to the contingency plots shown in Fig. 51. 

Numbers are in percent. 

Wet 23 31 47

Neutral 23 31 47

Dry 54 39 6

La Nina Neutral El Nino

Wet 26 33 42

Neutral 17 36 47

Dry 57 31 11

La Nina Neutral El Nino
66

Fig. 52. Contingency plots (in percent) from terciles of high-passed (HP) Division 6 

precipitation and Nino3 index for DJF, FMA, MJJ and ASO. 

 NDJ FMA 

Wet 34 19 47

Neutral 29 44 28

Dry 37 36 25

La Nina Neutral El Nino

Wet 20 39 42

Neutral 46 31 25

Dry 34 31 33

La Nina Neutral El Nino

 MJJ ASO 

Table 17. Contingency tables corresponding to the contingency plots shown in Fig. 52. 

Numbers are in percent. 

Wet 29 39 33

Neutral 43 33 25

Dry 29 28 42

La Nina Neutral El Nino

Wet 43 33 25

Neutral 23 39 39

Dry 34 28 36

La Nina Neutral El Nino
67

Fig. 53. Contingency plots (in percent) from terciles of Division 6 precipitation and 

Nino3 index when the AMO is in its cold phase for DJF, FMA, MJJ and ASO. 

 NDJ FMA 

Wet 36 21 60

Neutral 21 50 30

Dry 43 29 10

La Nina Neutral El Nino

Wet 7 44 30

Neutral 33 25 60

Dry 60 31 10

La Nina Neutral El Nino

 MJJ ASO 

Table 18. Contingency tables corresponding to the contingency plots shown in Fig. 53 . 

Numbers are in percent. 

Wet 43 25 37

Neutral 36 31 42

Dry 21 44 21

La Nina Neutral El Nino

Wet 29 8 33

Neutral 35 50 38

Dry 35 42 29

La Nina Neutral El Nino
68

Fig. 54. Contingency plots (in percent) from terciles of Division 6 precipitation and 

Nino3 index when the AMO is in its warm phase for DJF, FMA, MJJ and ASO. 

 NDJ FMA 

Wet 14 27 44

Neutral 29 32 44

Dry 57 41 13

La Nina Neutral El Nino

Wet 10 55 56

Neutral 25 25 31

Dry 65 20 13

La Nina Neutral El Nino

 MJJ ASO 

Table 19. Contingency tables corresponding to the contingency plots shown in Fig. 54. 

Numbers are in percent. 

Wet 24 45 29

Neutral 38 20 35

Dry 38 35 35

La Nina Neutral El Nino

Wet 39 42 40

Neutral 17 33 33

Dry 44 25 27

La Nina Neutral El Nino
69

Fig. 55. Contingency plots (in percent) from terciles of Division 6 precipitation and 

Nino3 index when the PDO is in its cold phase for DJF, FMA, MJJ and ASO. 

 NDJ FMA 

Wet 24 29 41

Neutral 29 43 41

Dry 48 29 18

La Nina Neutral El Nino

Wet 14 31 44

Neutral 36 38 38

Dry 50 31 19

La Nina Neutral El Nino

 MJJ ASO 

Fig. 20. Contingency tables corresponding to the contingency plots shown in Fig. 55. 

Numbers are in percent. 

Wet 27 42 33

Neutral 36 25 27

Dry 36 33 40

La Nina Neutral El Nino

Wet 45 21 31

Neutral 23 50 46

Dry 32 29 23

La Nina Neutral El Nino
70

Fig. 56. Contingency plots (in percent) from terciles of Division 6 precipitation and 

Nino3 index when the PDO is in its warm phase for DJF, FMA, MJJ and ASO. 

 NDJ FMA 

Wet 21 23 63

Neutral 21 36 32

Dry 57 41 5

La Nina Neutral El Nino

Wet 0 61 40

Neutral 15 17 55

Dry 85 22 5

La Nina Neutral El Nino

 MJJ ASO 

Table 21. Contingency tables corresponding to the contingency plots shown in Fig. 56. 

Numbers are in percent. 

Wet 38 33 33

Neutral 38 25 48

Dry 23 42 19

La Nina Neutral El Nino

Wet 15 36 39

Neutral 31 32 30

Dry 54 32 30

La Nina Neutral El Nino
71

Fig. 57. Contingency plots (in percent) showing terciles of low-passed Division 4 

precipitation versus the two phases (warm and cold) of the AMO (left) and PDO (right). 

Wet 24 41

Neutral 27 39

Dry 49 20

AMO- AMO+ 

Wet 24 42

Neutral 31 36

Dry 46 23

PDO- PDO+ 

Table 22. Contingency tables corresponding to the contingency plots in Fig. 57. Numbers 

are in percent. 
72

Fig. 58. Contingency plots (in percent) showing terciles of low-passed Division 5 

precipitation versus the two phases (warm and cold) of the AMO (left) and PDO (right). 

Wet 31 35

Neutral 21 44

Dry 48 21

AMO- AMO+ 

Wet 25 40

Neutral 33 33

Dry 41 26

PDO- PDO+ 

Table 23. Contingency tables corresponding to the contingency plots in Fig. 58. Numbers 

are in percent. 
73

Fig. 59. Contingency plots (in percent) showing terciles of low-passed Division 6 

precipitation versus the two phases (warm and cold) of the AMO (left) and PDO (right). 

Wet 31 36

Neutral 30 36

Dry 39 28

AMO- AMO+ 

Wet 32 34

Neutral 23 43

Dry 45 23

PDO- PDO+ 

Table 24. Contingency tables corresponding to the contingency plots in Fig. 59. Numbers 

are in percent. 
74

Fig. 60. Half the difference between composite averages of 500-hPa pressure heights with 

respect to positive and negative values of the Pacific decadal mode of SST anomaly 

variability. Contour interval is 20 geopotential meters, positive (negative) contours are 

solid (dashed), and the bold contour is zero. Positive values are shaded (after Enfield and 

Mestas-Nuñez 1999). 
75

Fig. 61. As in Fig. 60 but with respect to the temporal reconstruction of the Atlantic 

decadal mode of SST anomaly variability (after Enfield and Mestas-Nuñez 1999). 
76

Fig. 62. (a) The Atlantic Multi-decadal Oscillation index showing two contrasting 30-

year time periods for the calculation of ENSO-climate connections. (b) The correlation 

between Nino-3.4 SSTA index for Dec.-Feb. and the unsmoothed divisional rainfall for 

Jan.-Mar. during the 30 year period 1930-1959. (c) As in b, but for the 30 year period 

1965-1994. The Mississippi basin is highlighted by light gray fill. The colorbar applies 

to both maps (after Enfield et al. 2001, see Appendix A for an abstract of this work). 
77

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78

Appendix A 

The Atlantic Multidecadal Oscillation and its Relation to Rainfall and 

River Flows in the Continental U.S. 

David B. Enfield1

, Alberto M. Mestas-Nuñez2 and Paul J. Trimble3

(Geophysical Research Letters, Volumen 28, Number 10, May 15, 2001) 

1

 NOAA Atlantic Oceanographic and Meteorological Laboratory, Miami, Florida. 

2

 Cooperative Institute for Marine and Atmospheric Studies, University of Miami, Miami, Florida 

3

 South Florida Water Management District, West Palm Beach, Florida 

Abstract. North Atlantic sea surface temperature for 1856-1999 contain a 65-80 year 

cycle with a 0.4  ̊C range, referred to as the Atlantic Multidecadal Oscillation (AMO) by 

Kerr [2000]. AMO warm phases occurred during 1860-1880 and 1940-1960, and cool 

phases during 1905-1925 and 1970-1990. The signal is global in scope, with a positively 

correlated co-oscillation in parts of the North Pacific, but it is most intense in the North 

Atlantic and covers the entire basin there. During AMO warmings, most of the United 

States sees less than normal rainfall, including Midwest droughts in the 1930s and 

1950s. Between AMO warm and cool phases, Mississippi River outflow varies by 10% 

while the inflow to Lake Okeechobee, Florida varies by 40%. The geographical pattern 

of variability is influenced mainly by changes in summer rainfall. The winter patterns of 

interannual rainfall variability associated with El Niño-Southern Oscillation are also 

significantly changed between AMO phases. 
79

Appendix B 

Scientific Presentations 

The Atlantic Multidecadal Oscillation and its Relationship to Rainfall and River Flows in 

the Continental U.S. (D. B. Enfield and A. M. Mestas-Nuñez). Greater Everglades 

Ecosystem Restoration Science Conference, December 11-15, 2000, Naples, Florida. 

The multidecadal signal in global temperatures and its associated land-climate impacts. 

(A. M. Mestas-Nuñez and D. B. Enfield). 16th Biennial Conference of the Estuarine 

`Research Federation, November 4-8, 2001, St. Pete Beach, Florida. 

Atlantic multidecadal variability and inter-american rainfall (D. B. Enfield and A. M. 

Mestas-Nuñez). American Geophysical Union 2001 Fall Meeting, 10-14 December, 

2001, San Francisco, California