Relationship between Florida freeze incidence and sea surface temperature in the tropical Pacific

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Relationship between Florida freeze incidence and sea surface temperature in the tropical Pacific
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Frost -- Forecasting   ( lcsh )
Ocean temperature   ( lcsh )
Citrus -- Effect of cold on   ( lcsh )
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J.David Martsolf.
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Caption title.
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At head of title: Proc. Fla. State Hort. Soc. 2001 ; Paper No. 63, Draft 17 ; Program Section: Krome.

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Proc. Fla. State Hort. Soc. 2001 Paper No. 63 Draft 17

Business phone (352) 392-4711 Ext 305 Program Section: Krome


RELATIONSHIP BETWEEN FLORIDA FREEE INCIDENCE AND SEA SURFACE

TEMPERATURE IN THE TROPICAL PACIFIC

J. David Martsolf

Horticultural Sciences Dept.
University of Florida, Gainesville, F~lorid~a 32611

Key WVor~ds. freeze hazard, climate prediction, climate change, frost control, cold

protection, FAWN, DISC.

Abstract. A climate prediction section has been added to weather information provided by

the Florida Automated Weather Network (FAWN), which can be accessed via the Internet at

http://fawn. ifas.ufl.edul. A cold protection module is under development for the Decision

Information System for Citrus (DISC) which may utilize these new features. it is likely to

contain a tool that relates sea surface temperatures in the El NiflolSouthern Oscillation

(ENSO) region of the Tropical Pacific (120E to 70W9, 25N to 258) to the possibility of

damaging freezes in Florida. FAWN is expected to provide predictions of sea surface

temperatures as much as one year into the future. Since 1980 there have been six major

freezes in Florida. A rule suggested by this work is that if the winter is expected to be near


SFlorida Agricultural Experiment Station Journal Series No. R-08179. Gratefully acknowledged are the

Florida Citrus Production Research Advisory Council for funding DISC, the FAWN Consortium through

their implementation of the Climate Prediction component and in particular Dr. James W. Jones, Dr. Fedro

Zazueta, Dr. James J. O'Brien, and Mr. David Zierden, Dr. Howard Beck, Mr. John L. Jackson, members

of the Weather Advisory Committee and especially Mr. Peter D. Spyke, Mr. Norman Todd, Jr. Dr. Paul M.

Lyrene and Dr. Lar K. Jackrson, and the Japan Meteorological Agency, the original source of the monthly

mean sea surface temperature data.









neutral (ENSO sea surface temperatures between -0.7 and +0.5 OC of their mean), the

likelihood of a major freeze in Florida is 50%. In the past two decades 60%/ of the winters

have been near neutral ENSO winters. When the winter is predicted to be a strong El Niflo

or a strong La Niiia, the odds of a major freeze in Florida are low. Availability of, and

confidence in, predictions of the future phase of the ENSO is increasing. Utility of rules

relating freeze incidence to ENSO phases depends on effective delivery of ENSO phase

predictions to management decision makers. Both FAWN and DISC are expected to play

collaborative roles in this communication.









The University of Florida's Institute of Food and Agricultural Sciences (IFAS)

developed and maintains the Florida Automated Weather Network (FAWN, 2001).

During the fall of 2000, a test of a recently installed climate prediction component of

FAWN (Climate Prediction Section, 2001) led to the finding documented in this paper.

This test led to an examination of Florida freeze dates relative to phases of the El Nifio /

Southern Oscillation (ENSO) as one of the examples of how the information might be

used in decision making processes. One of these decision making aids is under

development for the Decision Information System for Citrus (DISC). See DeSousa

(2001), The DISC Team (2001) and Martsolf (1997, 1998). The Climate Prediction

Center (CPC, 2001) can be expected to provide forecasts of sea surface temperatures in

the ENSO region at least a year in the future. The purpose of the paper is to defend the

formation of rules that might be used conveniently in deciding how serious the threat of a

major freeze may be in the next winter given these predictions.



Materials and Methods

FA WN. The Florida Automated Weather NetwJork (FAWN, 2001) recently acquired a

climate prediction section (Climate Prediction section, 2001) a collaborative effort of

three Universities (The Florida Consortium, 1999). Questions about the possibility of a

relationship between freeze incidence and particular ENSO phases arose during a study

of this section of FAWN.

Florida Freezes. A list of the recent severe freezes in Florida has been published by

Attaway (1997). U~se of the published list, which is broadly accepted, avoids a rather

detailed task of defining a freeze in terms of temperature, wind speed, and impact on









vulnerable horticultural crops. The lists of freezes in Tables 1 and 2 are precisely as

Attaway published them with one exception. H;-e chose to define the 1983-85

combination of two freezes as one freeze. In this analysis the time scale has monthly

resolution so this combination in the list was split into the two parts. These two freezes

are as described separately by Dr. Attaway making the combination in the list but one of

two ways to handle the freezes. Attaway documents two categories of freezes which he

terms "impact" and "near impact." Attaway defines the impact freezes "as those which

made the greatest contribution to the geographical relocation of the Florida citrus industry

from north to south." The near impact freezes were "those which did severe damage, in

some cases reinforcing decisions made as a result of an impact freeze." In this work the

two categories are treated as one which is taken to be a list of the severe freezes.

Sea Surface Temperature Data Set. In order to compare the date of the freezes with

the sea surface temperature in the ENSO region of the Tropical Pacific during the period

that included the date, a data set was acquired from the COAPS (2001). The data are a

list of temperature departures for each month. The temperature departures are both time

and space averages. This data set was developed and maintained by the Japan

Meteorological Agency (JMA). The JMA data set was the source of the departures of the

sea surface temperatures from their time and space mean values shown in Table 1 and to

plot to the temperature trace in Figure 1. The JMA data set was used to determine the

phase of ENSO during each of twenty winters between 1980 and 2000 as indicated in

Table 2. TIhe same JMA data set was used in all the analyses documented in this paper.

Near neutral ENSO Phase Definition. Near neutral is defined for this study to be

from 0.7 oC to + 0.5 oC. This zone is shown graphically as the shaded area in F~igure 1.









Most (e.g., Hansen et al., 1997) declare the neutral phase to be that period when the

spatial average sea surface temperature is within a half degree C of the time and space

average temperature, e.g. the zero departure line in Figure 1. Early in the deliberations

that led to this paper, the rather common definition of the neutral phase was used. But

the '85 Freeze that falls less than 0.2 C in the La Nifia territory (see Figure 1) made the

rule that would summarize the relationship between the freeze and the neutral zone rather

complex. So a near neutral zone was defined that would include all the freezes. The

analysis was made using the near neutral phase definition. The counts are of occurrences

of the near neutral (NN) phase (see Table 2).

Climate Prediction Center. At CPC (2001) one can find the description: "In 1970,

various federal weather and climate functions were consolidated into the National

Weather Service and placed in a new agency called the National Oceanic and

Atmospheric Administration. In the 1980's the National Weather Service established the

Climate Prediction Center (CPC), known at the time as the Climate Analysis Center,

located in Camp Springs, Maryland. CPC is best known for its US climate forecasts

based on El Nifio and La Nifia conditions in the tropical Pacifc." The purpose of the

CPC (2001) is declared to be: "...serve the public by assessing and forecasting the

impacts of short-term climate variability, emphasizing enhanced risks ofweather-related

extreme events, for use in mitigating losses and maximizing economic gains."

Methods. Two data sets, one of freeze dates (Attaway, 1997) and one of sea surface

temperature (SST, 2001), were compared on the same time axis (see Figure 1) to

visualize the ENSO phase during the incidence of the six major freezes. The ENSO

phases for each of the 20 winters were examined by the use of an ]Excel spreadsheet in









which the JMA, Index for the midpoint of each of the 240 months in the data set was

coded indicating which of the three phases, El Nifio (EL), La Nifia (LA), and near neutral

(NN). The results are documented in Table 2.


Results

Tables 1 provides results of an analysis of the relationship between freeze dates and

the monthly values of the departure of the sea surface temperature from its mean value.

The average departure, -0.25C, seems to indicate a slight bias toward the La Nina, or the

cool side and the standard deviation, 0.243C, suggests that while there is some dispersion

to the data points within the near neutral zone, there is likely a relationship between the

freeze incidence and when the ENSO is in a near neutral state.

Table 2 summarizes observations made as to the phase of the ENSO for each of 20

winters in the two decades used in the study. There were 12 near neutral (NN) winters.

So 60% of the winters in the 20 year series were near neutral and those winters included

all six of the freezes. The percentage of the NN winters in which there were severe

freezes is 50%. There were 14 of 20 winters, or 70%, in which no major freeze occurred.

Conversely there were six major freezes in the 20 years; 30% of the winters contained

freezes. So instead of concluding that if the future is similar to the past, that the odds of

having a major freeze in any future year is 30%; one can look at the forecast of the sea

surface temperature for the winter and if it is a near neutral winter the odds of a major

freeze increase to 50%. But there is another observation that may be even more valuable.

If the forecast is for a strong El Nif o or La Nif la, the odds of a severe freeze are low.

Figure 1 illustrates the rule: major Florida freezes have occurred only during periods

when the sea surface temperatures in the Tropical Pacific are near neutral, during the









most recent two decades of the 20f Century. No major freezes occurred during a period

when the sea surface temperatures were outside the near neutral zone.



Discussion

The most recent two decades were used in this study for several reasons. One of

these is rapid climate change is taking place. Many suggest the effect has been showing

up in such relationships since the mid to late 1970s (Zwiers and Weaver). A shorter time

span stretches out the graph so that the relationship between the short term freezes and

the longer term ENSO fluctuations becomes apparent.

Previous attempts (e.g. H-ansen, et al., 1997) have revealed a rather weak relationship

between citrus yield and any of the three phases of ETNSO in spite of the fact that citrus

yield is clearly influenced by freezes. So it would seem that this finding of a rather

convincing relationship between a particular phase of ENSO and freeze incidence calls

these studies into question. But Hansen, et al. (1997) documents significance when a

year's delay was inserted into the analysis. The freeze date, as opposed to the impact of

the freeze on yield, seems more likely to be associated with a current neutral phase, since

the ENSO phases and freezes are both weather phenomena. One might imagine that

during these neutral periods there is a deficiency of forcing in either direction, leaving the

weather to fluctuate more widely, and within such broad variations are rare events that

permit a Florida freeze to occur. One of these is a deep trough in the jet stream

permitting large volumes of artic air to be pressed down through the mid-West and into

Florida. Another is the unusually dry condition upwind that permits the air mass to arrive

with a minimum of moderation from energy that would otherwise by transferred from









water and water vapor (Martsolf, 1999). Even though freezes are rare events and remain

difficult to forecast with precision, horticulturists have been innovative in development of

methods to decrease the vulnerability of their crops to such events (Martsolf, 2000).

Increases in the precision of estimates of the likelihood of freeze incidence seem likely to

be of value in such decisions.

Conclusions

There is a 60% chance of having a near neutral winter using the occurrences during

the period from 1980 to 2000 as a guide. When a near neutral ENSO winter is

anticipated the odds of having a major freeze in Florida is 50%. Strong El Nif io phases

have occurred in only 20% of the winters. Strong La Nifia phases have occurred in only

20% of the 20 winters. No freezes have occurred in either the strong EN or strong LN

phases so the odds of one occurring in a future strong EN or LN seem small. A new rule

replaces the old rule which suggested there is about a 30% chance of a freeze in Florida

in any given year because climate predictions indicate whether the winter will be near

neutral or not up to a year in advance. The new rule suggests that when a near neutral

ENSO phase winter is predicted, the odds of a major freeze in Florida increase to 50%.









Literature Cited

Attaway, J. A. 1997. A History of Florida Citrus Freezes. Fla. Science Source., Lake
Alfred, FL., 368 pp.

Beck H. and J. Jackson. 1998. Florida Automated Weather Network. Proc. 7"
International Conference on Computers in ~Agriculture, Orlando, FL, ASAE, St.
Joseph, MI. pp. 595-601.

Climate Prediction Center. 2001. CPC, http://www.cpc. ncep.noaal~gov/

Climate Prediction Section. 2001. http://fawn.ifas.uff.edulpreview/cpred/, FAWN, IFAS,
UF.

Center for Ocean-Atmospheric Prediction Studies. 2001. (http://www.coaps .fsu.edul)
COAPS, FSU.

DeSousa, Jackie. 2001. Weather patterns demand an attentive eye. FFVA Harvester
37(2):12-13.

Florida Automated Weather Network. 2001. http://fawn.ifas. ufl.edul, FAWN IF;AS, UF.

Hansen, J. W., A. Irmak, and J. W. Jones. 1997. El Niiio Southern Oscillation
Influences on Florida Crop 'Yields. Soil and Crop Sci. Soc. of Fla. Proc 57:12-16.

Martsolf, J. David. 1997. Acquiring weather information via a decision making system
(DISC). Proc. Fla. State Hort. Soc. 110:92-96.

Martsolf, J. David. 1999. What fr-eezes of the past century taught us. Proc. Fla. State
Hort. Soc. 112: 95-98.

Martsolf, J. David. 2000. Diversity of frost protection methodology. Proc. Fla. State H-ort.
Soc. 113:133-137

Martsolf, J. D>., R. M. Peart, H. W. Beck, P. D. Spyke, Norman TTodd, Chet Townsend,
and J. K. Schueller. 1998. DISC makes progress with information integration. Proc.
Fla. State Hort. Soc. 111:144-147.

Sea Surface Temperatures. 2000. S ST (ftp://www. coaps.fsu.edulpublJ MASST_1 ndex/)

The DISC Team. 2001. DISC Project progress report. Citrus Industry 812(2):20,21,24.

The Florida Consortium. 1999. El Nino, La Nina and Florida's Climate: effects on
agriculture and forestry. COAPS/FSU, IFAS/UF, RSMAS/UM.

Zwiers, F. W. and A. J. Weaver. 2000. The causes of 20t Century warming. Science 290
(5499):2081, 2083.











Table 1. Relationship between recent Impact and Near Impact Freezes (as categorized by

Attaway, 1997) and the departure of sea surface temperatures from their mean in the

tropical Pacific.


Freeze


SST Departure


Jan. 1981


Jan. 1982


D~ec. 1983


Jan. 1985


Dec. 1989


Jan. 1997


-0.2


+0.1


-0.1


Ave.


-0.25


0.243





Table 2. Indication of the phase of ENSO associated with the incidence of major freezes in the

last two decades of the 20th Century.


Winter
80-81
81-82
82-83
83-84
84-85
85-86
86-87
87-88
88-89
89-90
90-91
91-92
92-93
93-94
94-95
95-96
96-97
97-98
98-99
99-2000


EN LN NN
X
X
X
X
X
X


Freeze name
81
82

83
85


Date
13-Jan
12-Jan

25-Dec
22-Jan


Year
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Counts


89 24-Dec


19-Jan


X
X
4
20%


4
20%


12
60%


14
70%


6
30%


Where: NN is Near Neutral
EN is El Nino
LN is La Nina
ENSO is El Nino I Southern Oscillation











OC
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