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AN ABSTRACT OF THE THESIS OF FRANCISCO A. MEDINA for the degree of MASTER OF SCIENCE (Name) (Degree) in OCEANOGRAPHY (PHYSICAL) presented on 13 October 1978 (Major) (Date) Title: A STUDY OF WIND. OCEANIC TEMPERATURE AND HYDROGRAPHY IN THE UPWELLING AREA OFF PERU Abstract approved: Adrin Huyer Observations of sea temperature, wind and hydrography obtained during the three months' period of MAM 77 - CUEA experiment (JOINT-il) along the C-line, near 15°S are described. Wind and temperature observations made during March - May 1977 are compared to demonstrate that a relationship exists. Linear correlation analysis was performed on the temperature data and on the alongshore component of wind and wind stress. Auto- correlations and cross-correlations were computed for the temperature and wind. These analyses show that the aiongshore fluctuations of the wind are very similar at the different stations, and the wind at each location was favorable for upwelling throughout the period of measurements. Temperature fluctuations in the near-surface, the near- shore and the near-bottom water over the shelf are coherent with temperature changes at a depth of 150 - 280 m over the upper slope. Temperature-wind analysis show that there is a good response of the near-surface temperature to the wind force. The time lag between Redacted for privacy

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AN ABSTRACT OF THE THESIS OF

FRANCISCO A. MEDINA for the degree of MASTER OF SCIENCE

(Name) (Degree)

in OCEANOGRAPHY (PHYSICAL) presented on 13 October 1978

(Major) (Date)

Title: A STUDY OF WIND. OCEANIC TEMPERATURE AND HYDROGRAPHY IN THE

UPWELLING AREA OFF PERU

Abstract approved:

Adrin Huyer

Observations of sea temperature, wind and hydrography obtained

during the three months' period of MAM 77 - CUEA experiment (JOINT-il)

along the C-line, near 15°S are described. Wind and temperature

observations made during March - May 1977 are compared to demonstrate

that a relationship exists.

Linear correlation analysis was performed on the temperature

data and on the alongshore component of wind and wind stress. Auto-

correlations and cross-correlations were computed for the temperature

and wind. These analyses show that the aiongshore fluctuations of

the wind are very similar at the different stations, and the wind at

each location was favorable for upwelling throughout the period of

measurements. Temperature fluctuations in the near-surface, the near-

shore and the near-bottom water over the shelf are coherent with

temperature changes at a depth of 150 - 280 m over the upper slope.

Temperature-wind analysis show that there is a good response of

the near-surface temperature to the wind force. The time lag between

Redacted for privacy

the wind and the near-surface temperature was about 24 hours nearshore;

the wind always led the temperature. Subsurface temperatures were

not as well correlated with the alongshore wind, both negative and

positive correlations were observed in this analysis, and the time

lag between the wind and temperature was variable; on a few occasions

the temperature led the wind.

Vertical sections of sigma-t and temperature indicate the

existence of conditions consistent with upwelling. Oscillation in

the wind with periods of several days caused significant changes in

the region inshore of 25 km.

A STUDY OF WIND, OCEANIC TEMPERATURE ANDHYDROGRAPHY IN THE UPWELLING AREA OFF PERU

Francisco A. Medina

A THESIS

submitted to

Oregon State University

in partial fulfillment of

the requirements for the

degree of

Master of Science

June 1979

APPROVED:

Research Assistant Professor af\Oceanographyin charge ofrriajor

Dean of School of Oceanography

Dean of Graduate School

Date thesis is presented 13 October 1978

Typed by Laina Reynolds Hardenburger for Francisco A. Medina

Redacted for privacy

Redacted for privacy

Redacted for privacy

This thesis is dedicated to my wife, Mercy. Herconcern, good humor, encouragement and supporthave played a very important part in this effort.

AC KNO WL EDGMENTS

I would like to express my appreciation to Dr. Adriana Huyer for

the constant encouragement and guidance which she extended to me

during the period of this research.

The author wishes to thank all members of the CUEA family at

Oregon State University who contributed in one way or another to the

outcome of this project. For his ideas and suggestions, I am grateful

of Dr. Dave Enfield,. Dr. Ken Brink made useful comments on the

coastal wind analysis. Mr. Bill Gilbert drafted the illustrations.

Most of the data used in this research were provided by the

Oregon State University. I especially want to thank Dr. Dave Halpern

of the University of Washington who provided the wind and temperature

data from the surface moorings. I also want to express my thanks to

LASPAU (Latin American Scholarship Program American University) and

the Escuela Superior Politecnica del Litoral (ESPOL) in Ecuador for

their support while working on my M.S. program.

Finally, I am indebted to Laina Hardenburger for her gracious

and efficient help in typing the drafts and final version of this

thesis.

Research for this thesis was supported under grants OCE 76-00594

and OCE 78-03381 from the Office for the International Decade of

Ocean Exploration, National Science Foundation. This is a contribu-

tion to the Coastal Upwelling Ecosystems Analysis Program0

TABLE OF CONTENTS

PageI. INTRODUCTION 1

The 1976-1977 Coastal Upwelling Experiment - JOINT II 2

II. DESCRIPTION OF DATA 6

Time Series from Moored Instruments 9

Temperature time series 9Wind observations 13Temperature-wind relationship 20

Hydrographic Observations 21

Variations of the hydrographic field 26

III. DATA ANALYSIS 28

Correlation Analysis 28Wind-Wind Correlations 29

Temperature-Temperature Correlations 30

Wind-Temperature Correlations 44

Correlations between temperature and 44wind stress at PSS

Correlation between near-surface temperature 52

and wind at the same locationCorrelation with near-shore and offshore wind 54

IV. SUMMARY AND CONCLUSIONS 57

V. REFERENCES 60

LIST OF FIGURESFigure Page

1 Positions of the moorej instruments, coastal wind stations 4and the hydrographic section (C-line)occupied several times.

2 Time distributions of MAM 77 current meter temperature, 8buoy wind data and coastal wind data from March through May1977

3 Low-passed time series of temperature from the different 11

current meters at each moored array.

4 Overall mean and standard deviations of temperature time 14series from moored current meters during MAM 77 period,March-May 1977.

5 Low-passed time series of the alongshore component of the 15wind stress (TY) at PSS and alongshore wind (v) obtainedat the different stations, 6 March-15 May 1977.

6 Low-passed time series of the onshore component of the 17wind stress (IX) at PSS and onshore wind (u) obtained atthe different stations, 6 March-15 May 1977.

7 Mean and standard deviations of the low-passed alongshore 19wind obtained at the different stations, 6 March-15 May1977.

8 Vertical distribution of temperature, salinity and sigma-t: 22

(a) 9-10 and 12 April, (b) 15-16 and 19 April, and Cc)20-22 April 1977.

9 Lateral (a and b) and vertical (c and d) correlation between 34pairs of instruments at the moorings along the C-line.

10 Plot of correlation coefficients of temperature between 37one instrument at (a) Agave 26 m, (b) Ironwood 155 m,(c) Mila 19 m and (d) Ironwood 24 m and all otherinstruments.

11 Plot of correlation of temperature between the top 38instrument and each other instrument of each mooring.

12 Plot of the maximum lagged significant correlation 41

coefficient of temperature between the instrument at (a)Mila 59 m with the rest of the instruments.

Figure Page

13 Plot of the maximum lagged significant correlation 42

coefficient of temperature between one instrumentat (a) Agave 26 m, (b) Ironwood 155 m, (c) Mila 19 m,and (d) Ironwood 24 m with all other instruments.

14 Correlation between wind stress at PSS and temperature 49

from instruments along the C-Line.

15 Plots of (a) maximum lagged correlation between wind 54

stress (TY) arid temperature from current meters on themooring array along the C-line.

LIST OF TABLES

Table Page

1 Summary of positions, water depths and instruments 3

depths for instrument arrays in MAM 77.

2 Correlation coefficient at zero lag for onshore and 31

alongshore components of the windat different locations.

3 Simple linear regression coefficients between the "low 31

passe alongshore wind at PSS with other stations.

4 Correlation coefficients at zero lag between temperature 33records from different instruments along the C-line.

5 Maximum lagged correlation coefficients between temperature 39records from different instruments along the C-line.

6 Correlation coefficient between near-surface temperature 46at PSS, PS, Euphorbia and Parodia and the alongshore windstress at PSS.

7 Correlation coefficients between the alongshore wind 48stress at PSS and the temperature records from thesubsurface moorings along the C-line.

8 Correlation coefficient between the near-surface tempera- 53

ture records at PSS, PS, Euphorbia and Parodia and thealongshore wind at the same station.

9 Correlation coefficients between temperature at each 55

location and the alongshore component of the wind at PSS,Parodia and PD.

A STUDY OF WIND, OCEANIC TEMPERATURE ANDHYDROGRAPHY IN THE UPWELLING AREA OFF PERU

I. INTRODUCTION

Coastal upwelling is a dominant feature of the oceanographic

regime off the coast of Peru. Smith (1964) stated that it is along

the western coasts of the continents that the eastern boundary cur-

rents flow equatorward from high latitudes, and that it is there also

that the relative orientation of the predominant wind to the coastline

is suitable for sustained upwelling. This reasoning is applicable to

the area off Peru where the Peru current and a southerly wind are

prominent observed features.

Smith (1968) defined upwelling as an ascending motion of some

minimum duration and extent by which water from subsurface layers is

brought into the surface layer and is removed from the area of upwel-

ling by horizontal flow. In general, upwelling is the result of

divergence in the surface layer. Along the coast of Peru, a divergence,

hence coastal upwelling, occurs when the wind from the south causes an

Ekman tranposrt of surface waters away from the coast

This study describes wind and temperature data obtainedfrom

instrument arrays moored on the continental shelf and farther offshore

off Peru for the period from March through May 1977. Coastal wind

and hydrographic data are also described. The purpose of the present

work is to determine as well as possible the relationship between

wind and temperature fields for the three-month period. Also this

study is an attempt to use changes in wind and hydrographic data as

indicators of the oceanic phenomenon of upwelling.

The 1976-1977 Coastal Upwelling Experiment - JOINT-IL

The 1976-1977 interdisciplinary CUEA (Coastal Upwelling Ecosystems

Analysis) JOINT-LI experiment off Peru, has been the most recent study

carried out by the CUEA program, which has intensively studied other

areas of upwelling, i.e., Oregon and Northwest Africa (Huyer, 1976;

Halpern, 1976; Barton et al., 1977; Mittlestaedt, Pillsbury and Smith,

1975) to understand the characteristics that cause these areas to be

among the most biologically productive areas of the ocean.

The JOINT-Il experiment was a three phase field program. The

three intensive periods which JOINT-Il covered were March-May 1976

(MAM 76), July - October 1976 (JASON), and March-May 1977 (MAM 77).

The region studied extends about 660 kms in the alongshore direction:

from l6°S to about 1O°S, and about 200 km in the offshore direction.

Hydrographic surveys were made aboard ships from different U.S.

oceanographic institutions. Moored instruments were maintained

primarily by Oregon State University (OSU). In addition meteorologi-

cal buoys and near-surface current meters were installed by the NOAA

Pacific Marine Environmental Laboratory (PMEL).

This study utilizes the temperature data obtained from moored

current meters deployed along and near the

the MAM 77 phase field of JOINT-Il. Table

water depths and instrument depths for the

The locations of the moorings are shown in

During MAM 77, the continuous tempera

were supplemented by hydrographic sections

C-line (near l5°S) during

1 summarizes the positions,

different moorings examined.

Figure 1.

ture and wind measurements

along the C-line

Table I Summary of positions, water depths and instrunentdepths for instrument arrays in MAM 77. Onlyinstruments frorirwhich data were obtained arelisted M Meteorological buoy

Distance From WaterStation Position Shore (Kin) Depth (in) tnstrur'ent Depths (m)

Subsurface Moorings

Agave1 15°04 0'S 75°27 8'W 4 0 86 26 46 67 77

Mila1 15°06.O'S 75°30.8'W 12.0 121 19,39,59,80,100,115

Ironwood' 15°09 9'S 75°32 9'W 19 5 205 24,44,63 105,155 180

Lobivia' 15l1 5'S 75G34 3'W 24 0 580 58,83 183 283

Lagarta' 15°lO 0'S 75°36 O'W 24 0 620 92 115 214,512

Surface Moorings

Parodia 14°55 7'S 75°39 8W 10 5 12 M 3

Euphorbia' 15'31 2'S 75OO 8'W 7 0 123 M 3

P552 15°03 4'S 75°27 O'W 5 0 75 M 4 5 8,12 16

PS2 15°06 8'S 75°30 2'W 10 0 121 M 2 4 6 3 1 12,16 20 24

PD' 1°52 6'S 76°24 lW 125 D 26O '1

1 Oregon State University (OSU)

2 Pacif c arine Environrie'ital Laooraorj (?'4EL)

PDS

LCdbo Nozca

925 km

_____________I

00o

N /\Iosçna

76°W 75030 7c°

Figure 1. Positions of the moored instruments, coastal wind stations and thehydrographic sections (C-line) occupied several times.

150 S

5030'

5

(Figure 1) which were made from two ships: the R/V MELVILLE of

Scripps Institution of Oceanography and the R/V C. O'D. ISELIN of

University of Miami. This study utilizes only the hydrographic data

collected along the C-line by R/V ISELIN between 9 to 22 April 1977.

II. DESCRIPTION OF DATA

Current meter moorings were deployed along the C-line (near 15°S)

between March-May 1977. The moorings were of two kinds: surface and

subsurface taut-wire installations. The surface moorings were

deployed and recovered by Dr. Halpern's (Pacific Marine Environmental

Laboratory) group, CUEA Component 3. The mooring system used by this

group to measure wind, temperature and near-surface currents are

similar to the system used in previous expeditions (Halpern, Holbrook

and Reynolds, 1974) except that all current meters were VACM's

(Halpern, 1978). Measurements of the near-surface wind speed and

direction were made from surface buoys at 3 sites, called PSS, PS and

PD. The anemometer used to measure the wind was a VAWR type and its

calibration has been described in Halpern et al. (1974). Also,

temperature observations were obtained on the moorings at PSS and PS

on the continental shelf at a number of depths above 24.0 m.

Subsurface moorings were deployed and recovered by Oregon State

University (OSU). All these current meters were Aanderaa RCM4's

moored as describedby Pillsbury et al., (1974). Measurements of

the near surface wind were obtained at Parodia and Euphorbia (Table 1)

from meteorological buoys equipped with Aanderaa anemometers and data

loggers. Temperature data that are analyzed in this work are:

Parodia (3 m), and Euphorbia (3 m), and subsurface at Agave, Mila,

Ironwood, Lobivia, Lagarta and near surface at PSS and PS (Table 1).

In addition to wind measurements from buoys, the coastal

meteorological station at Pta. San Juan (15°2l2'S, 75°10.8'W),

7

installed and maintained by Dr. Stuartts group (CUEA Component 27),

provided us with an almost complete record of wind for this period.

In order to obtain a continuous coastal wind record, a gap of several

days in Stuart's record (Stuart, 1978) has been treated and filled

using a continuous wind record obtained during the same period at the

same location by the meteorological station maintained by Coorporacion

Peruana de Aviacion Civil (Medina, 1978). The time distribution of

the Aanderaa and VACM temperature data, the buoy winds and the coastal

wind at San Juan are shown in Figure 2.

Three different kinds of anemometers were used: Aanderaa (OSU)

data loggers, which recorded average speed and instantaneous

direction; vector averaging wind recorders (PMEL) which recorded

integrated values of the east and north components; and an MRI type

anemometer (Dr. Stuart's) which records speed and direction on a

strip chart recorder.

Two different types of temperature sensors (Aanderaa and VACM)

were used during MAM 77. Most of the temperature sensors on the

Aanderaa current meters used in MAM 77 as well as the temperature

sensors on the meteorological buoys at Parodia and Euphorbia were

calibrated before and after their use in JOINT-Il (Enfield, Smith

and Huyer, 1978). The resolutions of the temperature sensors of the

Aanderaa current meters and temperature sensors on meteorological

buoys are 0.02 and 0.08°C, respectively. For most of the current

meters employed in this study the post-calibration agreed well with

the pre-calibration except for Agave 46 m, and Mila 19 m which

LJ

I-

wwI-

March April May15 15 15

I I

I IAgave

I I

Pss

1

PS

I

Mila

I

Ironwood

Lobivia

ILagarta

I

Parodia

I

Euphorbia

I

PsS

II ----------------------- PS

I FPD

Parodia

Euphorbia

-i San Juan

Figure 2. Time distributions of MAM 77 current meters, temperature,buoy wind data and coastal wind data at San Juan fromMarch through May 1977. Dashed line means missing data.

disagreed. For the current meters which did not agree, the post-

calibration curve was used to process the data. Euphorbia and Parodia

temperature sensors showed good agreement on the calibration. To our

knowledge, the temperature sensors of the VACM's were not calibrated.

All the current meter measurements were quite nearshore and

within 25 km from the coast. Each Aanderaa current meter recorded

temperature at 20 or 30 minute intervals, while VACM types recorded

temperature at 15 minute intervals. Hourly data sets were formed and

these were filtered to eliminate tidal and higher frequencies by means

of a symmetrical low pass filter (half power at 0.6 cpd) spanning

121 hours. The filter-passed signals have nil amplitude at 1 cpd,

half amplitude at O7 cpd, and 95% amplitude at 0.5 cpd. Wind data

was rotated by 45° counterclockwise, the approximate general direction

of the coastline.

Time Series From Moored Instruments

Temperature time series. Figure 3 shows the low-passed time

series of temperature obtained from the different moorings. At the

nearshore surface mooring (PSS) the temperature variations between

5 m and 20 m were all in phase, while the temperature at the neighbor-

ing subsurface mooring (Agave) sometimes fluctuated out of phase

with the temperature above 20 m at PSS. Two other features are

observed in these low-passed time series of temperature recorded at

PSS and Agave: first, a fairly constant weak gradient is observed at

Agave from 26 to 77 m with some periods where the temperature is

isothermal between 67 and 77 m,

of the upper 16 m at PSS varies

.03°C m1 on April 11, when the

of uniformity in the temperatur

on several occasions during the

The plot of the low passed

10

and; second, the temperature gradient

between .16°C m1 on March 30 to about

temperature was very uniform. Periods

within the upper 16 m were observed

two months of observations.

time series of temperature at the

surface and subsurface midshelf moorings (PS and Mila) shows that the

temperature for the upper 24 m at PS is also decreasing and increasing

in phase as was observed at the nearshore PSS station. Despite the

fact that warmer temperatures were observed at PS (Le., 18.3°C at

2.5 m), the fluctuations for both PS and PSS, Agave and down to 80 m

at Mila show similar patterns. The vertical gradients of temperature

observed at PS and Mila are quite comparable to those measured at the

nearshore stations Agave and PSS; thus, for the same day, March 30,

the vertical gradient at PS was .1°C m1, decreasing to about

.009°C m1 on April 11.

The temperature time series at Ironwood, Lobivia and Lagarta

show that there exist two different regimes, above and below 120 m.

At Ironwood, the temperature variations above 120 m occur approxi-

mately in phase, especially in the last part of the record, i.e.

April and May. The water below 120 m seems to behave quite differently

from the water above 120 m. This is especially observed in the deeper

temperature time series for Ironwood (155 and 180 m), Lobivia (183 and

283 m) and Lagarta (214 m), which behave very similarly to each other,

but not to the shallower ones. This feature may perhaps be related

to the proximity of these measurements to the slope and shelf-break.

11

19.0

18.0 1 4.5 m PSS

18.0

17.0

1.o. 26

3.4.0 i. .19.0

m

.. .:17.0

6 Mcrch 1 April

Figure3. Low-passed time series of temperature from thedifferent current meters at each moored array.

AGAVE

May

18.0

17.0

16.0

15.0

14.0

13.0

12.0

16.0

15.0

14.0

13.0

12.0

il.0

10.0

18.0

15.0

14.0

13.0

12.0

IRON WOOD

24n,

9.0

8.0

7.0

8.0

17 Mcrch

Figure 3. Continued.

5/2

t May

12

13

The general properties of the temperature field measured at the

moored instruments is seen on Figure 4a, The mean temperature distri-

bution depicts isotherms (15 - 16°C) above 100 m rising upward toward

the coast as an indicator of the upwelling process which is in agree-

ment with Wyrtki (1963) who estimated upwelling off Peru to be limited

to the upper 100 m. Below 100 m the mean isotherms slope downward

toward the continental slope. The standard deviation of temperature

(Figure 4b) is highest near the surface and least between about 30 m

and 90 m.

Wind observations. Figures 5 and 6 show the plots of the low

passed alongshore Cv, northwestward) and onshore (u, northeastward)

component of the wind measured at the different locations during

MAM 77. The alongshore component (Figure 5) of the wind is northwest-

ward for almost the entire period of measurements, except for brief

calms and very short periods when the wind became southeastward, on

12 April at Parodia (P) and 21 April at Euphorbia CE). There seems

to be no significant difference in the mean alongshore wind between

stations PS, PSS, and PD (Brink, Halpern and Smith, 1978) or between

these and the meteorological station at San Juan (Figure 5). Parodia

and Euphorbia have the lowest mean alongshore wind through the period

of measurements, Figure 7 shows the mean and standard deviation of

the alongshore component of the wind for the different locations of

measurements. The San Juan wind has the highest mean ( = 5.8 m sec'),

while PSS has the most variability (V1= 2.0 m sec'),

The main feature observed in Figure 5 is that there is a similar-

ity in the fluctuations in the low passed alongshore wind measured

15°12 15°!0 15°0414

LoLa M A/6rn- 0

:_____:--- tOO

/4

200 E-

I/2fI

0

300

400

ITemperature (°C)Overall Mean

I52 t5°tO 5°O4

0/ March-May 1977 500 LoLa______________________I M A

/

/

0 <a2

20 km 0 02

04, 200

I

/ 300

4.)

I

0-I

Temperature (°CStandard Deviation

0 March-May 1977

20 km 0

Figure 4. Overall mean and standard deviations of temperature timeseries from moored current meters during MAN 77 period,March - May 1977.

2.0

-S

.0

9.0

6.0

3.0

.0

9.0

-St.)

6.0

. 3.0

.0

3

6

3

0

ALONGSHORE COMPONENTS 15

PSS

Pss

PS

March I Aprf May

Figure 5. Low-passed time series of the alongshore (northwestward)component of the wind stress (Y) at PSS and alongshore wind(v) obtained at the different stations, 6 March - 15 May 1977.

16

(J 3

0

-3

E

Figure 5. Continued.

ONSHORE-OFFSHORE COMPONENTS2.0

1

1.0

/- I_' r\ A - A

.0

3.0

.0 I I

3.0

.0 .

3.

17

PSs

flJ.l I_Pc _'t.# PP

-3. -

Figure 6. Low-passed time series of the onshore (northeastward) compo-nent of the wind stress (TX) at PSS and onshore wind (u)obtained at the different stations, 6 1'1arch 15 May 1977.

6r

3

3

-3

3

su

E

March I April I May

_3L.

Figure 6. Continued.

1I3

-

C

\'-

\\ 0 rn/Sec 5

N

750

5° 5

'505

750

Figure 7. Mean (top) and standard deviations (bottom) of the low-passedalongshore component of the wind obtained at the differentstations, 6 March - 15 May 1977.

20

over an area 125 km in the offshore and 50 m in the alongshore direc-

tion. Halpern (1978) has calculated the mean onshore-offshore gradient

of the alongshore wind. He obtained a value of 9.4 x l0 sec'

between PSS and PS and -7.3 x l0 sec1 between PS and PD. The

negative gradient obtained between PS and PD stations is the conse-

quence of larger mean wind at the offshore station PD (Figure 7). The

low passed onshore (u) component of the winds (Figure 6) did not show

the variability observed for the alongshore component.

Temperature-wind relationship. Zuta et al. (1975) observed that

temperature fluctuations over the area off San Juan have large

variations between 0 m and 20 m, but little variation between 50 m to

100 m. This agrees with the results presented in Figure 3.

Periods of strong favorable wind lasting 3 to 5 days were

separated by episodes of weak wind (Figure 5). During the periods of

intense upwelling as on 7-10 March and 10-14 April, the temperature

gradient of the upper 16 m at PSS and of the upper 24 m at PS is about

.03°C m1. With the decreasing of the southerly wind after these

periods, the upper layer at stations PSS and PS warmed considerably

and the temperature gradient increased to about .15°C m1 at station

PS, returning to temperature values near those measured before the

events.

The fluctuations of the nearshore temperature at PSS, PS and

Agave (Figure 3) are closely related to the changes in the alongshore

wind. Thus the appearance of colder (<l60°C) surface temperature

accompanied periods of northward wind stronger than 5 m sec1 and

21

warming periods occurred at times of weak wind. However, the apparent

response of the temperature field to the wind was not instantaneous.

A lag of about one day occurred between the onset of strong equator-

ward wind and a significant drop in temperature nearshore. A similar

lag was evident between the weakening of the wind and a rise of

temperature nearshore0

Hydrographic Observations

As a complement to the current meter observations, several

hydrographic sections were made along the C-line by the R/V MELVILLE

and the R/V C. O'D. ISELIN from early March to mid-May 1977. Station

separations ranged from 5 to 30 km. Observations were made with a

Geodyne Conductivity-Temperature-Depth (CTD) system and Niskin bottles

equipped with protected and unprotected thermometers0

As an early step in analyzing the CTD data, vertical sections of

temperature, salinity and sigma-t were drawn for every offshore line.

Examples are shown for the period 9-22 April 1977, surveyed by the

R/V ISELIN (Figure 8). The temperature data revealed evidence for

upwelling throughout the study period. Isotherms above 75 m slope

upward toward the coast and isotherms below 75 m slope downward

toward the continental slope. This divergence of isotherms is observed

within 30 km of the coast. The tendency of the isotherms to sink

shoreward at approximately 100 km from the coast is in agreement with

Zuta et al. (1975).

;;

7--

-

-

T (C)7 9-97

II

e5n

35./__5

347

346

345

II

250L/ 255

'-.- 260 _______________________260 /262

1100

-I-

L26.4' / 200

-26.6(

300L

68 ___________ 268__ 1I -400

270 I

270 /

/ SIGMA-I/

SIGMA-I soo

272I-

I I! I

'50 00 50 0 0 20 0

0/STANCE FROM SHORE (Rn,)

Figure 8a. Vertical distribution of temperature, salinityand sigma-t: 9-10 and 12 April 1977.

100

I4;-'3-----

/2

/

I

I 400

/

100C- L ne

2 April 977I

I R/V

5-

22

f09-8

I (CI

5-6 Ap 977

fl/V sehiv,

35. /.----------------------------

348

>34.8

348

---------- 34.7 -

26.8

27.0

/SALJNITY

25.9

260...

26.4

SIGMA-I

/6

I3

- -

T(C)C- LIM

9 Apr. 977

fl/V Is&m

262

SIGMA-I 5O0

0 40 20 0

0/STANCE FROM SHORE 1km)

Figure 8b. Vertical distribution of temperature, salinityand sigma-t: 15-16 and 19 April, 1977.

23

24

-J 300

T ICI - 500C - Une

7 ______________________________ 20-22 Ape, 77

P/V sCm,4500

I ii. I I I I I --i I 1 1

______L_.._________52__ ______350

348

300

346/SALINITYt246 250

j266/I I

220SIGMAT -soo

J. J J.1 I

50 too 50 0

DISTANCE FROM SHORE (kin)

Figure 8c. Vertical distribution of temperature, salinity andsigma-t: 20-22 April, 1977.

25

Barton (1977) has stated that the temperature field off Peru can

be taken as an accurate indicator of the density field. This conclu-

sion is verified in this study where temperature and sigma-t sections

both show similar distributions. The depth of the upwelling, based

upon the shoreward rise of isograms (temperature and sigma-t), is

estimated to be about 75 m which is the depth of the permanent thermo-

dine according to Wyrtki (1964). This estimate of the depth of

upwelling off Peru, is in agreement with Wyrtki (1963) who estimated

that upwelling off Peru is limited to the upper 100 m.

The sigma-t sections show that the 25.8 - 26.1 density band is

a good indicator of the upwelling process over the area off Peru.

That result differs from the 25.5 - 26.0 band commonly used for Oregon

(Pillsbury, 1972) and Northwest Africa (Ralpern, 1974). It is also

observed in Figure 8 that the isopycnals below 26.1, which was

approximately at 100 m, tended to sink shoreward resulting in a weak

density gradient between 75 m and 100 m. Enfield (1970) attributed

the sinking of the isograms to the presence of a coastal undercurrent.

The vertical distribution of salinity is also included in

Figure 8. In general, salinity decreases with depth from 35.1 o/oo

at the surface to 34.5% at depth of 500 m. Two prominent features

are observed on these sections: first, a relative minimum at approxi-

mately 100 m, and second, a relative maximum at about 175 m, both far

offshore. The existence of the minimum is attributed to a northward

flow (Wooster and Gilinartin, 1961) and the maximum observed below the

minimum is considered to be an indication of a poleward undercurrent.

26

Wyrtki (1963) stipulated that this undercurrent could be associated

with the Peru countercurrent. Apparently this result is not in agree-

ment with current observations obtained for MAM 77 (Brink, Allen and

Smith, 1978), where maximum mean poleward flow was observed at about

100 m, and only a weak poleward flow was observed below 200 m.

However, these current meter observations are far inshore of the region

where the minimum and maximum were observed.

Variations of the hydrographic field, The main features of the

hydrographic regime changes little during this sequence of observa-

tions. The isopycnals and isotherms generally rose and converged

toward the coast, and each of the sections was consistent with the

description of the hydrographic regime during the upwelling season

given by Enfield (1970) and by Zuta et al. (1975).

The similarity between the hydrographic sections was limited to

the interior of each section. Changes occurred in what appear to be

boundary layers near the surface, the shore and the bottom.

The first hydrographic section on 9-10 April (Figure 8a) clearly

shows the effect of upwelling. isopycnals rise toward the coast

gradually offshore and steeply as they approach the coast. The 26.0

sigma-t surface is at a depth of about 75 m at 75 km offshore and rises

to a minimum depth of about 25 m near the coast. The temperature

distribution resembles the sigma-t distributions, and the 16° isotherm

rises to the surface about 12 km from the coast. Farther offshore

than 100 km, the isotheniis are almost horizontal.

The wind began to decrease a little in intensity on 11 April and

its effects on the hydrography are apparent from the sections on

27

12 April (Figure 8b). The isopycnals have become less steeply inclined

nearshore. The 26.0 sigma-t surface which was observed on 9-10 April

at 25 m, is now found at 50 m depth. Only the 25.8 isopycnal is

observed at the surface very close to the coast. Even though the

distribution of temperatur does not show the changes observed in the

sigma-t distribution, it appears that an intrusion of warmer water

has squeezed the colder water (e.g. 16°C) against the coast. The

35.1 o/oo patches of salinity observed on the 9 April sections have

disappeared and only a narrower 35.1 0/00 patch is still observed at

about 50 km offshore.

From 12 April through 18 April the wind is variable in intensity.

The sections of 15-16 April resemble those of 12 April. On 18 April

the alongshore velocity of the wind is observed to reach values of

7.0 m sec1, The section of 19 April shows that the surface layer

nearshore became cooler and denser. Patches of 35.0 0/00 salinity

were observed at the surface to about 50 km offshore.

Large volumes of dense water (25.8 - 26.0 sigma-t band) were

observed over the shelf during all the sections examined0 Increased

isopycnal slopes were observed on 9-10 April, 18 April and 20-22

April, and we conclude that upwelling occurred during these periods.

Changes in the nearshore area were limited to the region less

than 20 km from the coast, and seemed to be related to the changes

in the surface layer. The isograms of temperature and sigma-t

diverged toward the shore. This characteristic is more apparent over

the shelf and break slope; this may be explained by the increase of

mixing in this region.

III. DATA ANALYSIS

Correlation Analysis

In order to make quantitative comparisons among the wind and

temperature time series, we computed auto and cross correlation

functions following Bendat and Piersol (1966).

The cross correlation function of two sets of random data

describes the general dependence of the values of one set of data on

the other. As a measure of time delays between sets of data, lagged

correlations are examined. The correlation coefficient, r, which

is dimensionless relates the change in y to the change in x was

computed as: (Crow et al., 1969).

- _xy

xy

where>and S, are the sample standard deviations of x and y,

respectively. Sxy is the covariance of x and y, which is defined as:

N

E

i=l (xj-X) (Yi-Y)xy N-1

Correlation coefficients at lags from 0 to 5 days at increments of

6 hours were computed between the various time series. For each cor-

relation the number of degrees of freedom was found using the method

suggested by Davis (1976), and the significance level was obtained

from Pearson and Hartley (1970, p. 146). For zero lag correlation,

the 95% confidence level (i.e., the probability that a given correla-

tion coefficient would be obtained from uncorrelated variables is less

29

than 0,05) is used as the test of significance. For lagged correla-

tion, the 99% confidence level was used as the test of significance.

The magnitudes of the critical correlation coefficients, rc, at

the 95% and 99% confidence levels varied from one pair of records to

another because of differences in the length of each time series.

The value of rc also varies because the integral scale T, which

estimates the time period between independent measurements, was

different for each pair of records.

The linear trend was removed from each time series before any of

the correlation computations were performed. The detrending was by

the least squares methods, The computations were performed on the

CDC 3300 computer of the OS-3 operating system. The correlations

were computed by a program which used the DETREND and CCORR sub-

routines in the OS-3 ARAND system (Ochs et al., 1973).

Wind-Wind Correlations

Comparison of the wind measurements made along the C-line and at

San Juan during MAM 77 (Figures 5 through 7) indicated that the along-

shore fluctuations of the wind are similar over the area of study.

Table 2 presents the values of correlation coefficients at zero lag

of the onshore and alongshore components of the wind at the different

stations. All values of correlations for alongshore components are

significant at the 99% confidence level (r > .43). This table shows

that the alongshore wind fluctuations at one location are correlated

with each other. No significant correlation values at 0 lag are

30

observed for the onshore components of the wind, except for the cor-

relations between Parodia and PD (r = .33) and between PS and all the

other instruments. However, high correlations obtained between PS

and all the other instruments for the alongshore and onshore component

of the wind do not reflect the behavior of the wind over the entire

MAM 77 period, because the last part of the data at PS is missing.

The values of lagged correlation (not included in Table 2) for

the alongshore component of the wind for lags up to 18 hours are also

significant at the 95% confidence level for most of the pairs of

stations. The regression coefficients between the alongshore wind

at PSS and the other locations are given in Table 3. Values varying

from .9 for PD to 1.3 for Euphorbia indicate that the amplitude of

the fluctuations in the alongshore wind is about the same at all

locations.

Generally speaking, the alongshore component of the wind was

favorable for coastal upwelling and had very similar fluctuations

at all locations.

Temperature-Temperature Correlations

Temperature time series obtained from the subsurface moorings

are compared in order to examine the vertical/offshore structure of

the temperature fluctuations. Time series of temperatures obtained

from the surface moorings are not included in this analysis because

there were surface moorings at only two stations (PSS and PS), hence

a distorted picture would be obtained.

31

Table 2. Correlation coefficients at zero lag for onshore (abovediagonal) and alongshore (below diagonal) components ofthe wind at different locations along and near the C-line.

Parodia Euphorbia San Juan PSS PS PD

Parodia - .06 -.28 .17 34* 33*

Euphorbia .80** - .09 .02 .31* - .22

San Juan .81** 77** - .23 .32* .13

PSS .89** .70** .80** - 73** .15

PS .92** .84** .87** .98** - 53**

PD .65** 47** 74** 76** 74** -

* Denotes correlation is significant at the 95% level.** Denotes correlation is significant at the 99% level.Italics are used for comparisons using data from PS because of itsmuch shorter record length.

Data intervals used in the comparisons are: PARODIA: 19 March-10 May(210 pts); EUPHORBIA: 22 March-10 May (199 pts); SAN JUAN: 6 March-13 May (273 pts); PSS: 6 March-13 May (273 pts); PS: 7 March-14April (156 pts); and PD: 9 March-15 May (270 pts).

Table 3. Simple linear regression coefficients between the "low pass"alongshore wind at PSS with the other stations

PS SJ P E PD

PSS 1.11 .94 .95 1.31 .90

Data intervals used in computations are the same as Table 2.

32

Table 4 presents the correlation coefficients at zero lag for

temperature between the stations for the entire period0 All the

values of correlations between measurements at Agave and Mila are

significant at the 99% confidence level (r > 38), except for Mila 19

with Mila 100 and 115. The correlations between the temperature at

Agave and Mila with the temperature at the offshore stations (Ironwood,

Lobivia and Lagarta) are among the lowest of all the pairs. An

exception to this result is that the Ironwood 155 m (shelf break)

record and the Agave (26, 46 and 77 m) and Mila (39 and 115 m) records

show significant correlations (ranging from 0.4 to 0.6) which are not

seen between the Ironwood time series themselves0 This particular

result is in agreement with the onshore-offshore coupling which

Brink, Allen and Smith (1978) suggested when they examined the

temperature and alongshore current correlation between the slope and

shelf station for this area during l976 A few significant negative

correlations are also observed in Table 4; in most cases these

occurred between temperature measurements at different depths at

the offshore stations0

To clarify Table 4, Figure 9 shows the lateral correlations

between pairs of instruments at approximately the same depth, and the

vertical correlations between pairs of instruments at the same

mooring. The lateral correlations show that in general the closer the

instruments are and the smaller the depth difference between them,

the higher the correlation is, e0g. Lobivia 183 m - Ironwood 180 m

(.92), Agave 46 m - Mila 39 m (.79), etc. However, the correlations

Table 4. Correlation coefficients at zero lag between temperature records from different instruments along the C-line.Agave Mila Ironwood Lobivia Lagarta

26 46 67 77 19 39 59 80 100 115 I 24 44 63 105 155 180 I 58 183 283 I 92 115 214 512

26 *92 *80 *75 *77 *82 *64 *50 *52 *54 .33 .08 -.00 .07 *54 *50 -.18 *43 .22 -.04 .06 .38 .35A

46 *91 *.82 *66 *79 *.78 *69 *.67 *58 .21 -.08 -.09 .08 *40 .28 -.29 .22 .01 -.07 .02 .15 .1867 *92 *56 *53 *.76 *.78 *73 *59 .05 -.23 -.08 .18 .29 .11 *_37 .07 -.17 -.02 .06 -.04 -.0477 *44 *55 *66 *.72 *.76 *.67 .04 -.18 -.06 .21 *40 .25 -.32 .23 -.03 -.02 .05 .06 .0219 *6] *41 *34 .28 .22 .63 .24 .01 -.14 -.06 .06 -.05 -.07 .22 -.09 -.11 .32 .3539 *.76 *50 *49 *46 .48 *53 *33 -.03 *40 .27 .20 .14 .11 .11 .11 .17 .11

*.88 *.72 *49 .01 -.01 .11 .16 .20 -.05 -.14 03 *.40 .04 .10 -.33 -.23Mila80 *87 *52 .02 -.27 -.11 .17 .04 -.13 -.26 -.09 *37 -.06 -.01 -.29 -.24

100 *49 .02 -.18 -.04 .28 .30 .09 -.18 .l3 -.12 .07 .08 -.09 -.15115 .14 .05 .08 .31 *60 *.38 -.02 *40 .22 *.83 .22 .16 .03

*61 Q5 *3p .02 .12 .20 ...67 .26 .18 .16 .31 .31*35 *.29 *3] *3] *65 .16 .26 .04 .07 .17 .10

63 * 34 * 26 04 * 55 - 02 03 * 62 * 57 - 16 -25Ironwood105 .22 -.18 -.15 -.03 -.17 *79 *78 -.28 *_5Q155 *78 .26 *77 .22 .25 *33 .29 .02180 OATA INTERVAL .23 *.92 .40 -.09 -.05 *.65 *4]58

.16 .11 .29 .28 .01 -.12Lobivia 183 AGAVE: 6 March - 13 May (272 pts) .33 -.03 .03 *59 35

283 MILA: 7 March - 13 May (270 pts) *.77*.48_QL_-.Q.89 -.22 *..4992 IRONWOOD: 18 March - 13 May (227 pts)

115 LOBIVIA: 17 March - 10 May (217 pts) -.29 *_53Lagarta214 LAGARTA: 17 March - 13 May (229 pts) *52512

Coefficients in italics are not significant at the 95% level. An asterisk indicates the correlation is significant at the 99% level.

()

IY2 l510 I50 504LoLa M A

.-63-77------- /I

,//--.?6--_... I

(0)

.77z.42,..

_65

00

200

00

400

500

15?IZ I5l0' u50

LoLa I M A.-33/

(b)

7/

40 20 km 0 40 20 km 0

Cc)

iT:''1

t1j.62/

00

200

300

4CC

500

Itl2 l5'I0 504LoLo M A

3o28.22T:;iCd)

34

U

m

100

200

300

400

500

U

m

'Co

200

300

400

500

40 20 km 0 40 20 km 0

Figure 9. Lateral (a and b) and vertical (c and d) correlationbetween pairs of instruments at the moorings alongthe C-line.

35

between Mila arid Agave are higher than between the offshore stations,

despite the greater distance between them. Also, there is essentially

no correlation between the mid-depth Mila (59 and 100 m) and Ironwood

(63 and 105 m) instruments. Except for these two pairs, all lateral

correlations between neighboring moorings are significant, but there

is no significant lateral correlation between moorings farther apart,

Le. between Ironwood and Agave or between Lobivia or Lagarta and

Mila (Figure 9b).

Figure 9c shows the vertical correlations between adjacent

instruments on the same moorings treating Lobivia and Lagarta together

as a single mooring, as well as separately. Generally speaking, the

temperature records from adjacent instruments are significantly cor-

related, although this is not observed at Lobivia, or at the inter-

mediate depths at Ironwood0 Only at Agave is the top temperature

record positively correlated with the deepest one (Figure 9d).

Records with similar vertical separations (about 50 m) at the other

moorings are riot significantly correlated (Figure 9d). There is weak

negative correlation between the 92 m and 512 m instruments at

Lagarta, but Figure 3 indicates that this negative correlation is due

to very low frequency trends: warming at 92 m and 115 m and cooling

at 512 m from the beginning of the record until about 10 April 1977,

and then cooling at 92 and 115 m and warming at 512 m until the end

of the record.

To see to what extent the temperature fluctuations at the differ-

ent instruments act together, Figure 10 shows the value of correlation

36

coefficients obtained between the shal lowest instruments at Agave,

Mila and Ironwood and Ironwood 155 and all the other instruments.

The significant correlation coefficients (r > .30) between Agave 26

and other instruments (Figure lOa) show that the fluctuations over

the inner shelf are correlated with those in the near-bottom water in

the vicinity of the shelf-break. Also the changes in the near bottom

water over the shelf are coherent with temperature changes at a depth

of 150 - 250 m offshore. This coherence is more clearly shown in

Figure lOb, which shows the correlation coefficients between Ironwood

155 m and the rest of the instruments. This result again emphasizes

the strong onshore-offshore coupling already mentioned for the correla-

tion between Ironwood 155 m with the shallower instruments at Agave

and Mila (Table 4).

Figures lOc and lOd are the plots of the correlations between

Mila 19 and Ironwood 24 with all other instruments. Those figures

suggest that besides the onshore-offshore coupling mentioned before,

the near surface temperature fluctuation seems to be coherent over

the shelf. Figure 11 shows the vertical correlations between the top

instrument and each other instrument of each mooring. The vertical

correlation decreases with increasing water depth and distance from

shore.

Table 5 presents the maximum lagged values of correlation

coefficients for temperature between all instruments. Only values

significant at the 99% confidence level and their corresponding lag

are included. A comparison between the zero lag correlation and

(a)

15°12 1510 I506 I5O4

LoLa I M A

\JIIll -

Correlation with// qove26/ / March-May 1977

4

U

m

100

200

300

40 20 km 0

15'IZ 1510 I5O I504

LoLa I M A

(c) x

x

x )(

x

x

Correlation with

/ Mila 19

400

500

U

m

100

200

300

400

500

37

1512 510 I5O6 I5O4

LoLa M A

(b)0.3x-..

0Jx

<03x

/ Correlation withj Ironwood 155

K

40 20 km 0

I592 5I0 I506 I504

LoLa I M A

(d)<.4

x

K X

x

K

Correlation with

/ Ironwood 24'I

Figure 10. Plot of correlation coefficients of temperature betweenone instrument (designated by a star) at (a) Agave 26 m,

(b) Ironwood 155 m, (c) Mila 19 m, and (d) Ironwood 24 m

and all other instruments. Instruments marked with an

X do not have significant correlation at the 95% level.

0

rn

100

200

300

400

500

U

m

100

200

300

400

500

-1.0 0 1.0 0I

I -r

50 Lobivk

I00Iz

Lagaric

200

300

4..

500

Ironwood

1.0 0.5 1.0 0-,

I

ii li/a

1.0-0m

-'100

A gave

Figure 11. Plot of correlation of temperature between the topinstrument (designated by a star) and each otherinstrument of each mooring.

Table 5. Maximum lagged correlation coefficients (above diagonal) between temperature records from different instruments along theC-line, March - May 1977, and the lag (below diagonal) at which the maximum occurs.

Agave

Mi la

I ronwoo

Lobibia

Lagarta

Agave Mila Ironwood Lobivia Lagarta

26 46 67 77 19 39 59 80 100 115 24 44 63 105 155 180 58 183 283 92 115 214 512

26 .92 .80 .75 .77 .82 .64 .50 .52 .54 .34 - - - .56 .54 - .51 - - - - -

46 0 .91 .82 .66 .79 .78 .69 .67 .58 .36 - - - .44 .34 - - - - - - -

67 0 0 .92 .57 .64 .76 .78 .74 .60 - - - - .41 - - - - - - - -

77 0 0 0 .46 .58 .66 .72 .77 .70 - - - - .47 .39 - - - - - - -

19 0 0 -1 -2 .67 .41 .35 - - .63 - - - - - - - - - - - .53

39 0 0 -1 -2 0 .76 .50 .50 .47 .48 .53 - - .40 - - - - - - - -

59 0 0 0 0 0 0 .88 .72 .49 - - .41 - - - - -.51 -.54 - - -.66 -

80 0 0 0 0 0 0 0 .87 .52 - - - - - -.54, - -.60 -.52 - - -.66 -

100 0 0 -1 -1 - 0 0 0 .49 - - - - .42 - - - - - - -.62 -.49

115 0 0 -1 -1 - 0 0 0 0 - - - - .69 .53 - .49 - .83 - - -

24 -1 -9 - - 0 0 - - - - .61 - -.36 - - - - - - - - -

44 - - - - - 0 - - - - 0 .36 - .34 - .65 ;-

63 - - - - - - 16 - - - - -1 .43 - - .59 - - .64 .57 - -

105 - - - - - - - - - - -2 - 3 .44 - - - - .80 .78 - -.51

155 -1 2 3 3 - 0 - - -5 3 - 2 - -10 .81 - .77 - - - - -

18023 - 4 - - - 8 --4 - - - - 1 - .92 - - - .65 -

58 - - - - - - - - - - - 0 0 - - - - - - - - -

1833- - - - - 8 8 --3 - - - - 0 0 - - - - .59 -

283 - - - - - - 8 8 - - - - - - - - - - - - . 79 -

92 - - - - - - - - - 0 - - -1 1 - - - - - . - -

115-- - - - - - - - - 0 0 - -: - - - U - -

214 - - - - - - 8 8 10 - - - - - - 0 - -1 -1 - - .63

512 - - - - -18 - - - 11 - - - - 0 - - - - - - - -1

Only correlation coefficients significantly different from zero at the 99% level are included. The lags are multiples of six hours,and are positive when the instrument nearer shore (or nearer the surface, if at the same location) leads. Data intervals are thesame as in Table 4.

c.)

40

lagged values (Tables 4 and 5) shows that the difference between the

zero lag and the maximum lagged correlation between Agave and Mila is

less than .03, and hence the lag is not significantly different from

zero. The lag (if any) for these nearshore stations varies from 6

to 12 hours, Mila leading' Agave. A similar result is observed for

the correlations between Agave and Ironwood, where the difference

between the zero lag and the lagged correlations did not exceed .07.

The lag varies from 6 to 54 hours, and in most cases Agave leads

Ironwood. Also, as was observed for zero lag, no significant correla-

tion at the 99% confidence level is observed between Agave and Lobivia

or Lagarta. The exception to this, as was observed for zero lag, is

found between Agave 26 and Lobivia 183.

Zero and lagged correlation coefficients between Mila and Iron-

wood showed almost no difference. The exception to this, is observed

between Mila 80 m and Ironwood 180 m which are uncorrelated at zero

lag but have a significant negative lagged correlation with Mila

leading. Significant negative correlations are also found between

the instruments at mid-depth at Mila (59 m, 80 m, and 100 m) and the

instruments at Lobivia and Lagarta from 180 to 512 m. Figure 12 shows

the plot of the maximum lagged correlation coefficient, and the lags,

between the instrument at Mila 59 and all other instruments. From

this figure it looks as if the temperature measured at depths of

about 150 - 200 m over the shelf break and upper slope is fluctuating

in the opposite direction than at mid-depth at Mila.

Figure 13 shows the distribution of the maximum lagged correla-

tion coefficients and the lags between all instruments and the

l5i2 5iO' 1506 5'04'

LoLa I M A

11i

!

J'Lagged Correlation

I with Mlo 59'C

20 km 0

0

m

I00

200

300

400

00

40

5'12' I5'IO I5O6' 1504'

LoLa I M A

'C'0

'C 0'C

'16 * 0.

'C

'8 'C

8

8

Lags,. Mila 59

20 km 0

Figure 12. Plot of the maximum lagged significant correlationcoefficient of temperature between the instrumentdesignated by a star at Mila 59 m and the otherinstruments. The lags (multiples of six hours)positive when Mila 59 leads are also shown. Instruments

marked with an X do not have significant correlation atthe 95% level.

shallowest instruments at Agave, Mila and Ironwood and Ironwood 155.

Although larger values are observed for lagged correlation than for

zero lag correlation, the distributions are very similar to those for

zero lag (Figure 10). The random distributions of the lags, which

range -60 to +12 hours (Figure 13), suggest that the lagged

correlations may not be very reliable.

41

0

m

00

500

l5t2 15°IO 1506' 1504

LoLa M A

(a)

x

Lagged Correlationj with Agave 26

x

40 20 km 0

I5I2 1510 506 15'04

LoLo M A

j Lagged Correlation

Jwith Ironwood 155

xf

0

In

00

200

300

400

500

0

m

00

200

300

400

500

1512 15010 15006 i5O4

LoLa I M A

*1g ;'o 0

xX

X

.3 '2

x

x

Logs, Agave 26

40 20 km 0

I5l2' 1510 1506 I504

LoLa I M A

x 0-I

x

x

'I

x

/Logs, Ironwood 155

x

40 20 km 0 40 20 km 0

Figure 13. Plot of the maximum lagged significant correlationcoefficient of temperature between one instrument(designated by a star at (a) Agave 26 m, and (b)Ironwood 155 iii, and all other instruments0 The lags(multiples of six hours) are positive when the starredinstrment leads. Instruments marked with an X donot have significant correlation at the 95% level.

U

m

tOO

200

300

00

500

U

m

00

200

300

400

500

15I2' l5'IO :506 tso4

LOLC I M A

Cc) x<

100

x

x X

200x

I'

x300

4CC

j Logged CorretatonI with Mila 19

40 20 Im 0

I5t2 I5l0 I5'06 504LOLO I M A

40

Cd)

U

m

00

200

300

/ Logged Correlation

Jwith tronwood 24

20 km 0

400

500

1512 1510 I5"06 i5'04'

LoLa I U A

x 0 0

x

x X

x

x

Lags, Mila t

I8j

43

U

m

100

zoo

300

400

500

40 20 km 0

I5I2' 1510 I506 i5'04'

LoLa I U A

40

* 0 .j0

x

-2

x

x

x

Ix I

Logs, tronwood 24

V

m

100

200

300

400

500

20 km 0

Figure 13. (Continuation). Plot of maximum lagged significantcorrelation coefficient of temperature between oneinstrument(designated by a star) at (c) Mila 19 m and(d) Ironwood 24, and all other instruments. The lags(multiples of six hours) are positive when the starredinstrument leads. Instruments marked with an X do nothave significant correlation at the 95% level.

44

Wind-Temperature Correlations

Several combinations of wind and temperature data are used for the

description of the relationship between temperature and wind during

MAM 77.

i) Alongshore wind stress (r'') at PSS with near-surface and

subsurface temperature at the different stations on the C-line.

ii) Alongshore wind (v) at PSS, PS, E and P with near-surface

temperature at each location.

iii) Alongshore wind (v) at PSS, P and PD with subsurface

temperature at the different stations on the C-line.

Each of these combinations is used to examine a different aspect

of the relationship between the wind and the temperature distribution over

the shelf and upper slope. First, since all wind records are well

correlated, and have similar fluctuations, we choose one (PSS) to be

representative of all, and compare the force associated with this

wind (i.e. wind stress) with all temperature records. Next, we compare

the near-surface temperatures to the wind at the same location to see

if the small spatial differences in the wind field affected the near-

surface temperature. Finally, temperatures from the sub-surface arrays

along the C-line are compared to the wind at two locations over the

shelf (PSS and Parodia) and one location offshore (PD) to see whether

nearshore winds or offshore winds are more effective in changing the

temperature field.

Correlation between temperature and wind stress at PSS. Time series

measurements of wind stress (rX, 1Y) were computed for PSS using the

45

stress equation:

= paCdIUaUa

where:

= Density of air-3

Cd = Dimensionless drag coefficient (1.5 x 10 )

and Ua = Wind velocity.

The calculations of wind stress were made with hourly wind data, fromy

which the low-passed time series of both the north-south (T ) and thex

east-west ( ) components were obtained. The low-passed north-south

component of the wind stress (') (Figure 5) was usually much larger

than the east-west component (Figure 6). The values of the lagged

and unlagged correlation coefficients between the alongshore component

of the wind stress and temperature time series at PSS, PS, Euphorbia

(3 m) and Parodia (3 m) are given in Table 6. That table shows that

all the values of correlation at lag 0 between wind stress and

temperature at PSS are significant at the 95% and 99% confidence

levels, except for the measurements at 16 m. Also Table 6 shows that

all the values of maximum lagged correlation between wind stress and

temperature are significant at the 99% confidence level. From these

values of correlation coefficients, we can say that fluctuations in

temperatures at PSS are highly correlated with the fluctuations in the

alongshore component of the wind stress at that location. The wind

stress leads the temperature by 1 day at this station.

46

Table 6. Correlation coefficients between near surface temperaturesat PSS, PS, Euphorbia and Parodia and the alongshore windstress at PSS. Unlagged, CC0, and maximum correlationcoefficients, CCm, are shown with the lag (in hours) whichis positive when wind leads. Correlation coefficientssignificantly different from zero at the 95% and 99%levels are denoted by one and two asterisks, respectively.

PSS (wind stress) vs. PSS (temperature)

Depth (m) CC0 CCm Lag (hours)

45 _,44** _.67** 24

8.0 _.38** _.63** 24

12.0 _.32* _55** 24

16.0 -.26 _.48** 24

PSS (wind stress) vs. PS (temperature)

Depth Cm) CC CC Lag (hours)0 m

2.5 _.42** _.65** 18

4.6 _.32* _.60** 24

8.1 -.21 _.52** 24

12.0 -.09 _.41** 30

16.0 -.07 _.30* 30

20.0 -.09 -.24 --

24.0 -.12 -.21 --

PSS (wind stress) vs. Euphorbia

Depth (m) CC0 CCm Lag (hours)

3.0 -.16 _.50** 38

PSS (wind stress) vs. Parodia

Depth (m) CC0 CCm Lag (hours)

3.0 ...34* _57** 24

47

The correlation between the PSS wind stress and the temperature

at PS shows similar results. However, significant correlation

values at lag 0 for the 95% and 99% confidence levels are only

observed at the shallowest instruments (2.5 m and 4.6 m). At PS not

all values of maximum lagged correlation are significant. Thus only

the upper 12 m shows significant correlation at the 99% confidence

level and the temperature at 16 m is significantly correlated at the

95% confidence level; below 16 m the observed values are low and not

significant. Again, the fluctuation in wind stress at PSS leads the

temperature at PS by about one day.

Finally, the correlation with Parodia and Euphorbia temperatures

(Table 6) show that only Parodia presents significant correlations at

zero lag for the 95% confidence level. However significant lagged

correlations are observed for both records and the lag varied from 24

hours at Parodia to 30 hours at Euphoriba, with the wind always leadina

temperature. Generally speaking, the temperature fluctuations in the

upper 16 m at both locations PS and PSS and at 3 m at Parodia and

Euphorbia respond strongly to the wind stress which leads by about one

day. This characteristic response of the temperature to the wind

stress off Peru is similar to that observed off Northwest Africa

(Barton et al., 1977) and off Oregon (Halpern, 1975; Huyer, 1976).

The correlation coefficients between the alongshore component

of the wind stress (r') at PSS and temperature time series from the

subsurface arrays along the C-line are presented in Table 7 and

Figure 14. The lag is included in Table 7 for the instruments which

show significant lagged correlations. Except at Labivia 183 m and Ironwood

Table 7. Correlation coefficients between the alongshore wind stressat PSS and the temperature records from the subsurface mooringsalong the C-line. Unlagged, CC0, and maximum correlationcoefficients, CCm, are shown with the lag (in hours) which ispositive when the wind leads. Correlation coefficientssignificantly different from zero at the 95% and 99% levelare denoted by one and two asterisks, respectively. Forpairs where there is high positive correlation, we havealso shown the maximum negative correlation and thecorresponding lag in parentheses.

Depth (m) CC0 CCm Lag (hours)

26 -.23 _.36* 24Agave 46 -.18 ..,33* 30

67 -.17 _39* 42

77 -.20 _37* 30

19 -.11 _35* 3039 -.15 -.18 --

Mila 59 -.17 -.25 --

80 -.17 -.28 --

100 -.16 -.20 --

115 -.14 -.14 --

24 -.04 -.05 --

44 -.11 -.13 --

63 .22 35* (-.05) 48 (-90)Ironwood 105 .26 .38* (-.18) -30 (78)

155 -.22 -.23 --

180 _.36* _.38* -12

58 -.09 -.15 --

Lobivia 183 - .50** -. 50 0

283 -.08 .31 (-.24) --

92 .29 .34*(_.05) -18 (90)115 .27 .37*(.09) -24 (90)

Lagarta 214 -.19 -.20 --

512 -.29 ...33* -60

iJ

-1.0 0 1.0 0I -, I

Lob/v/c

50

(I'ISO

Lager/c

i

200

500

-1.0 0 -0.5 0

0

I0

0I

I I

0

/Mi/c

/

/

Ironwood

0

I

0

0

-J 00

A gave

Figure 14. Plot of zero lag (solid line) and lagged (dashed line)correlation between wind stress at PSS and each otherinstrument of each mooring.

50

180 m, the zero lag correlation coefficients are not significant. The

maximum lagged correlation (Table 7) shows different results for each

location. At Agave, temperatures over the whole column (77 m) are

significantly correlated at the 95% confidence level with the wind

stress; wind stress leads temperature by about 24 hours at 26 m, 30

hours at 46 m and 77 m and 48 hours at 67 m. At Mila the 19 m tempera-

ture is significantly correlated with the wind stress with a lag of about

30 hours but deeper temperatures are not well correlated with the wind

stress. This is consistent with the results for nearby PS (Table 6).

At Ironwood, the shallowest temperatures are not significantly

correlated with the wind stress; the mid-depth temperatures are

positively correlated, i.e., temperature increases when the wind increases,

contrary to what one might expect; the deepest temperatures are

significantly correlated with wind stress, and in this case, the

correlation is negative as expected. The lags of maximum correlation

at Ironwood are also puzzling: at two instruments the temperature

leads the PSS wind stress. At Lobivia neither the shallowest nor the

deepest temperature records are significantly correlated with the wind

stress, but the 183 m temperature is very well correlated with the wind

stress at zero lag. Lagarta shows results similar to those of Ironwood:

temperatures at about 100 m are positively correlated with the wind, but

the deepest instrument shows significant negative correlation; as at

Ironwood, these temperatures lead the PSS wind stress.

The positive significant correlation values obtained between wind

stress and temperature for the offshore station (Ironwood 63 m and 105 m

and Lagarta 92 m and 115 rn) seemed doubtful at first. Maximum negative

51

correlations calculated for the same depths showed extremely low values

with large lags (parenthetical values in Table 7). From this we

concluded that the positive values are indeed significant.

Comparing Figures 11 and 14 it looks as if the general features

of both figures are quite comparable. The most striking feature is

observed at Ironwood where the depth of maximum lagged correlation of

temperature with wind stress, coincide with the depth of maximum

negative correlation for the temperature at the same station. Lagarta

shows a trend from positive to negative values of correlation which is

comparable in both figures, and the nearshorestation Agave and Mila

show similar trends in both figures. However, Lobivia did not show the

same pattern.

To clarify Tables 6 and 7, Figure 15 shows the distribution of

the maximum lagged correlation, and the lags, between the wind stress

at PSS and the temperature at all instruments along the C-line. We see

negative correlation over the entire water column nearshore, at PSS and

Agave. Over the mid-shelf, at PS and Mila, there is negative correlation

near the surface down to 19 m and no significant correlation below

20 m. Over the shelf break and upper slope, Ironwood, Lagarta and

Lobivia, there is no significant correlation at the upper instruments

(note that there are no near surface instruments here); positive correlation

between about 60 m and 120 m and negative correlation at about 180 m. The

region of positive correlation seems to correspond approximately to the

position of downward-sloping isotherms in Figure 8.

52

Correlation between near-surface temperature and wind at the same

location. There were four locations for which both near-surface

temperature data and wind data are available: PSS, PS, Euphorbia and

Parodia (Table 1). The values of lagged and unlagged correlation

coefficients between the near-surface temperature and wind at each of

these locations are shown Th Table 8. No significant values at 0 lag

are observed between wind and temperature at PS, except for the shallowest

instrument at 2.5 m. Parodia and Euphorbia temperatures seem to have

no significant unlagged correlation with the wind at the same location.

At PSS temperature and wind are significantly correlated at zero

lag at 4.5, 8 and 12 m.

Maximum lagged correlations (Table 8) show that all the temperatures

within the upper 20 m are significantly correlated with the alongshore

component of the wind at that location. The wind leads temperature at

each location; however, a difference in the lag between the temperature

and the wind is observed. Thus, while the lag of the temperature to

the wind at PSS (upper 16 m) and at Parodia (3 m) is 24 hours, at

Euphorbia 3 m the lag is 30 hours and at PS location the lag varies

from 24 hours at the shallower measurements (2.5 m-4.6 m) to 66 hours

at 20 m.

As expected, the correlatior coefficients obtained between the

alongshore component of the wind and the temperature at each location

are similar to that obtained between PSS wind stress and temperature

(Table 6). Note, however, that the temperatures at PSS are slightly

better correlated with wind rather than with wind stress: this is

53

Table 8. Correlation coefficients between the near-surface temperatureat PSS, PS, Euphorbia and Parodia and the alongshore windat the same location. Unlagged, CC0, and maximum correlationcoefficients, CCm, are shown with the lag (in hours) whichis positive when the wind leads.

PSS (alongshore wind) vs. PSS (temperature)

Depth (m) CC0 - CCm Lag (in hours)

4.5 _43** _.7l** 24

8.0 _.36* _.65** 24

12.0 _.30* 57** 24

16.0 -.25 _.48** 24

Parodia (wind) vs. Parodia (temperature)

Depth (m) CC0 CCm Lag (in hours)

3.0 -.32 ...59** 24

Euphorbia (wind) vs. Euphorbia (temperature)

Depth Cm) CC0 CCm Lag (in hours)

3.0 -.13 _.50** 30

PS (wind) vs. PS (temperature)

Depth (m) CC0 CCm Lag (in hours)

2.5 _.42* _.71** 24

4.6 -.32 _.65** 24

8.1 -.21 57** 30

12.0 -.12 _47* 36

16.0 -.13 _44* 60

20.0 -.17 _.40* 66

24.0 -.22 -.34 60

* Correlation is significant at 95%** Correlation is significant at 99%

5I2' 50 5°06 5'04'

LoLa M A

Correlation withPSS Wind Stress

0

m

too

200

300

I.

1512 I5°t l50 t5O4'

LoL.a I M A

x '6,

Lags, PSS Wind Stress

-12.

40 20 tim 0 40 20

54

km 0

Figure 15. Plot of the maximum lagged correlation between wind stress(TY) and temperature from all other instruments. The lags(multiple of six hours) are positive when wind stress leads.Instruments marked with an X do not have significantcorrelation at the 95% level.

U

m

200

300

probably because the noise as well as the signal is squared in the

process of calculating wind stress. Also, for each location, the tempera-

ture is better correlated with the local wind than with the PSS wind stress.

Correlation with near-shore and offshore wind. Table 9 presents the

value of the correlation coefficients at 0 lag between the alongshore

component of the wind measured at PSS, Parodia and PD stations, and the

temperature at the different subsurface moorings for the time period

each covered. Maximum correlation coefficients are also included. The

lag is presented only for the instruments which show significant

correlations at the 95% and 99% confidence levels.

T.thle 9. Correlation coefficients between temperature at each location and the alongshore componentof the wind at PSS, Parodia and PD. Unlagged, CC0, and maximum correlation coeFficientsCciii, are shown with lag (in hours) positive when wind leads. When there is high positivecorrelation, we have also shown the maximum negative correlation with the corresponding lag.

CC0 _______CCrn___ _______LagDepth (m) PSS Par PD PSS Par PD PSS Par

26 -.21 -.11 -.03 35* 4Q* -.18 24 24 -Acjtve 46 - .19 .06 .02 _35* _39* -.22 30 30 -

67 -.19 .04 -.03 _.41** _43** -.19 42 48 -77 -.22 -.11 -.03 _.38* _39* ]8 30 36 -

19 -.07 .02 -.11 33* .36* -.26 30 24 -39 - 13 - 07 - 01 - 17 - 22 - 18 - - -

tliit 59 -.18 .03 -.03 -.25 .36* +.25 - -54 -81) -.20 -.04 -.04 -.28 -.26 -.16 - -100 -.16 -.15 .01 -.20 -.23 -.10 - -115 13 - 2 01 14 21 14 -

24 - 01 - 11 - 13 - 01 - 16 - 21 * - -44 -.08 -.18 _.32* 09 -.22 .46** 4863 .20 .22 .07 .32 44** 34**

- 54 48IYO1)Od 105 21 3D 26* 33* 44** 32 -30 -24 24

155 - 25 - 23 - 20 25 - 23 45**- 54

180 37* - 3g* - 23 - 38* - 41* 33**_(_ 25)-6 -18 54 (-6)

58 - 06 - 12 - 19 - 11 - 23 33** 66

LOIHVia 183 - 51** - 51 - 36* - 51 - 51** 42k*..(_ 36*)0 0 60 (0)283 14 - 09 08 37* - 21 - 32* 72 - 66

92 25 21 22 32* 34* 22 -24 -24 -Lagarta 115 22 32 16 34k 39* 23 -24 -18 -

214 -16 -27 05 -19 -27 -15 - - -512 - 25 - 31* 10 - 32A - 37* - 30 60 12 -

C.),

U,

56

Almost none of the temperatures are significantly correlated with

the wind at zero lag; this is consistent with results presented earlier

in Tables 6-8. Only temperatures near the shelf break (Lobivia 183 and

Ironwood 180) are significantly correlated at zero lag with the near-

shore (PSS and Parodia) winds, and one of these also has significant

zero lag correlation with the offshore (PD) wind.

The lagged correlation (Table 9) shows quite different results for

the nearshore winds, at PSS and Parodia, than for the offshore wind at

PD. All temperatures at Agave are significantly correlated with the

nearshore winds, but not with the offshore wind. Almost all tempera-

tures at Ironwood are correlated with the offshore wind with wind

leading temperature, but only a few are correlated with the nearshore

wind, and Ironwood temperatures seem to lead the nearshore wind. The

temperature near the shelf break (180 m Ironwood and 183 m Lobivia)

are negatively correlated at zero or small negative lag with the

nearshore wind, but positively correlated with the offshore wind with

a lag of 2 1/2 days. Positive correlations may be more consistent with

the generally downward sloping isotherms at this location, and

certainly a finite lag between wind and temperature is more plausible

than no lag, or temperature leading the wind. We conclude that

temperatures over the outer shelf and upper slope are influenced more by

offshore than by nearshore wind variations. However, nearshore

temperatures vary in accordance with the near-shore rather than the

offshore wind.

57

IV. SUMMARY AND CONCLUSIONS

In the present study the relationship between temperature data

from moored instruments, as well as the analysis of wind with the

hydrography and temperature time series observations have been examined

for a zone of known upwellmg.

The temperature fluctuations nearshore and nearsurface over the

shelf behave coherently throughout the period of measurements. The

temperature at the upper slope also behave coherently with temperature

nearshore (Figure 3). However the temperature fluctuation at Mila

(midshelf) and Iroriwood (outersheif) do not fluctuate in phase with

temperature at Lobivia and Lagarta (upper slope), and it looks as if

warming of the water at the offshore station occurs simultaneously with

cooling at Mila and Ironwood, and vice versa. Temperature correlation

(Figure 9-12) confirms the above results, and positive correlation

between temperature nearshore at Agave and PSS with Lobivia and

Lagarta at about 150-200 rn is observed. The negative correlation

between temperature at mid-depth on Mila with temperature at Lobivia

and Lagarta at about 150-200 m could be attributed to very low

frequency trends: warming at Mila and cooling at Lobivia and

Lagarta and vice versa.

The definition of upwelling is in terms of upward vertical velocity,

but vertical velocity has not been successfully measured, and the process

of upwelling has been studied indirectly from other observations, e.g.

sloped isopycnals, modified water properties and a modified flow

regime. It is due to the near surface offshore Ekmian drift induced

by the winds that off Peru cold water is upwelled from about 70m at

approximately 30 km offshore arriving near the surface in a band from

the coastline to about 10 km offshore (Figures 14 and 8). We emphasize

however, that 70 m as the maximum depth of upwelling is only a qualitative

estimate based on the rising of isotherms and isopycnals, and perhaps

it would be more reasonabi' to state that the maximum depth of upwelling

off Peru is found at about 70-100 m depth.

We expected that the temperature fluctuations even in relatively

deep water (within 100 rn of the surface) would be related to the wind,

which drives upwelling. However the subsurface offshore stations do

not show this, and only the shallower instruments at Mila and the whole

water column at Agave (77 m) show some response to the wind variability

(Table 7 and 9). The shallower water responds more quickly to variations

in the wind strength as one might expect (Tables 6 and 8). The

response of the near surface temperature was not instantaneous: a lag

of about one day occurred between the strengthening of equatorward

winds and a significant drop in temperature nearshore. A similar lag was

evident between the weakening of the wind and a rise in nearsurface

temperature.

Comparing Figures 4 and 15 it is clearly seen that the depth where

positive correlation between temperature and wind is observed coincides

with the divergence of isotherm (70 m) at about 30 km offshore. The

maximum onshore current velocity which is observed at this depth

(Brink, Smith and Halpern, 1978), and the small variation of the 15°C

isotherm (Figure 4and 8) which determines the small temperature

gradient between 75-100 m, suggest to us that a small vertical shear in

59

the alongshore current velocity would also be observed. Changes on the

direction of the current nearshore and at the upper slope should be related

with changes in the thermal structure (VT) suggesting the potential

validity of the thermal wind equation. In order to explore how the

thermal-wind equation holds, spectral analysis with current and

temperature time series wóuld be useful.

Observing the wind records at the different stations (Figure 5)

it is apparent that the fluctuations of the alongshore wind are

correlated significantly over the area of measurements (Tables 2 and

3). Despite this fact the significant departure of the results for

correlation between temperature and wind at PD with that which results

for nearshore winds at PSS and Parodia (Table 9) suggest to us that

perhaps the temperature nearshore responds more to very high

frequency while temperature offshore is better responding to very low

frequency; spectral analysis for wind time series might answer this

question.

Additional research is required before conclusive answers can be

found regarding the relationship between temperature time series,

hydrography and wind, mainly to determine the physical process

involved in maintaining the different regimes observed.

V. REFERENCES

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Barton, E. D., A. Huyer and R. L. Smith. 1977. Temporal variationobserved in the hydrographic regime near Cabo Corveiro in theNorthwest African upwelling region, February to April 1974,Deep-Sea Res., 24: 7-23.

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Pearson, E. S. and H. 0. Hartley, editors. 1970. Brometrika

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Pillsbury, R. D. 1972. descriDtion of hydrography, winds and currentduring the upwelling season near Newport, Oregon. Ph.D.

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Pillsbury, R. 0., J. S. Bottero, R. E. Still and W. E. Gilbert. 1974.

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Smith, R. L. 1964. An investigation of upweuling along the Oregoncoast. Ph.D. Dissertation. Oregon State University, Corvallis.83 numbered pages.

Smith, R. L. 1968. Upwelling. Oceanogr. Mar. Biol. Ann. Rev., 6:11-46.

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