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,s

TITLE: Ceographic variation in the relationships of temperature,

salinity or a t versus plant nutrient concentrations in the

world ocean.

g'(iV7n ""6. -r

REPORT: Final Technical

PRINCIPAL INVESTIGATOR: Daniel Kamykowski

t PERIOD: September 1, 1982 - April 31, 1985

INSTITUTION: North Carolina State University

GRANT NO. : NAGW-367

4

NASA -CR-175621) GEOGRAPHIC VARIATION IN

HE RELATIONSHIPS OE TEMPERATURE, SALINITY

OE SIGMA SUE t VIRSUS PLANT NUTRIENT

CONCENTRATIONS IN THE WORLD OCEAN Final

Technical Report, 1 Sep. (North Carolina

N85-23287

Unclas

G3/48 14764

s
ABSTRACT

A NCDC data set comprised of 230,202 oceanographic stations representing

all regions of the world ocean was statistically analyzed for temperature and

sigma-t relationships with nitrate, phosphate or silicic acid. Six cubic

regressions were computed for each ten degree square of latitude and longitude

containing adequate data. World maps display the locations that allow the

prediction of plant nutrient concentrations from temperature or sigma-t within

the limits of selected subjective and objective criteria. Geographic coverage

improves along the sequence: nitrate, phosphate and silicic acid and is

better for sigma-t than for temperature. Contour maps of the approximate

temperature or sigma-t at which nitrate, phosphate or silicic acid are no

longer measurable in a parcel of water are generated based on a percentile

analysis of the temperature or sigma-t at which less than a selected amount of

plant nutrient occurs. These regression and percentile reoults are stored on

magnetic tape in tabular form. The analyses summarizes into the global

potential to predict plant nutrient concentrations from remotely sensed

temperature or sigma-t and emphasize the latitudinally and longitudinally

changing phytoplankton growth environmenC in present and past oceans.
INTRODUCTION

Temperature versus salinity (T-S) plots have l:• ng been used to

characterize the different water masses in the world ocean. Mamayev (1975)

has traced the history of the technique to Forth et al. (1902) who proposed an

equation of state of sea water and Helland-Hansen (1912) who constructed a T-S

diagram on which he plotted the real T-S relations of the waters of the

oceans. Plant nutrients (PN) including nitrate (N), phosphate (P) and silicic

acid (Si) have been applied to water mass studies relatively recently.

Aston (1980) has provided a review of plant nutrient relationships with

salinity (S-PN) in estuaries. The scatter plots are primarily used to

determine whether plant nutrients act conservatively as river water mixes with

seawater. The exact pattern observed depends on the environmental conditions

in each estuary. These studies suggest that salinity may be a useful

predictor of plant nutrients that enter coastal water from rivers. In ocean

regions beyond the influence of rivers, salinity does not usually exhibit

sufficient variability over adequate spatial and temporal scales to serve as a

good predictor of plant nutrient concentrations. Temperature and sigma-t are

the conservative physical factors of choice under normal oceanic conditions.

According to Stefansson & Atkinson (1971a), Cooper (1952) was probably

the first to suggest the use of silicic acid in identifying certain deep-water

masses which are difficult to distinguish by means of their T-S

characteristics. The temperature-silicic acid ( T-Si) or the sigma-t-silicic

,.cid (o-Si) relationships have been used to identify water masses in the

Antilles Arc region (Richards, 1958), the North Atlantic and Western

Equatorial Atlantic (Metcalf, 1969), the Western Mid-Atlantic (Stefanson and

Atkinson 1971a; 1971b) and several other regions including the Gulf of Mexico

and the Southern Ocean (W. Nowlin, personal communication). Several GEOSEGS

1
Program (Craig and Turekian ) 1980) investigators have used various plant

nutrient or derived plant nutrient parameters (i.e. "NO") as conservative

markers of different water masses (Broecker, 1974) and have inferred the

contribution of different water masses to a given parcel through water mass

end-member interpolations (Broecker and Takahashi, 1980; Chung, 1980; Broecker

et al. 1980; Broecker and Peng 1982).

Oceanographers interested in water mass identity and propagation

rountinely avoid the upper 200 m of the water column because of the 

complexities inherent in the ocean layer affected by the atmosphere and by the 

enhanced biological activity. Strickland et al. (1970) were probably the

first to point out the surprisingly conservative nature of the temperature

-plant nutrient (T-PN) relationship in near-surface water and to discuss the

dynamic dependencies of the correlation. Nearly all of the data collected 

during the early weeks (May to mid-July) of their program in La Jolla Bight

lay near a single curve from which one r,ould predict plant nutrient

concentration almost to within experimental accuracy, irrespective of how deep

in the water column a given temperature was encounted. Data from later weeks

deviated somewhat from the initial curve. Strickland at al. (1970) have

stated that the relationship between plant nutrient depletion and the heating

of the water column cannot be expected to be exact and must depend upon the

amount and activity of plankton in the water.

In recent years other oceanographers have used T-PN, sigma-t versus

plant nutrient (o-PN) or other plant nutrient correlations that include the

upper 200 m of the water column for various purposes in several locations off

the California coast. Kamykowski (1973) has provided a temperature versus

nitrate (T-N) scatter plot that is composed of 12 months of data collected

from a 40 m water column a^ a station in La Jolla Bight. The range of nitrate

2
at any temperature below the temperature at which nitrate depletes (-14° C) is

generally less than 8 uM. Lower nitrate concentrations occur at a given

temperature in January-June than in July-December. Eppley et al. (1979) have

used the range of nitrate concentrations at a given sigma-t in o-N plots to

estimate the possible role of horizontal eddy diffusion in contributing to

nitrate flux in the Southern California Bight. Station differences in the o-N

relationships increased as the distances between stations increased over the

greater than 1° square grid of stations. As earlier suggested by Zentara and

Kamykowski (1977), Traganza et al. (1983) have used T-PN relationships to

predict plant nutrient distribution patterns off Northern California based on

temperature determined from satellite infrared images and has applied these

plant nutrient patterns to the interpretations of ocean color images of

chlorophyll from CZCS imagery.

off the Atlantic coasts of Canada and the United States, Smith (1978),

Lee at a1. (1981) and Atkinson et al. (1982) have used various plots of more

easily measured physical or chemical factors versus plant nutrients to predict

plant nutrient concentration. Long term mooring records of temperature,

salinity, or oxygen combined with current meter velocities were used in

conjunction with plant nutrient regressions to compute a time-series of

probable plant nutrient flux on the Scotian, the Georgia and the North

Carolina shelves.

In upwelling areas off South America and Africa, Friederich and Codispoti

(1979) and Tregeur and LeCorre (1979) have discussed the enrichment effects of

local plant nutrient regeneration on the concentrations of nitrate, phosphate

and silicic acid at the inshore end of a cross-shelf transect. Codispoti

(1982) has stated that T-N and/or S-N plots show that the source waters of

upwelling have concentrations that are higher than offshore waters with

3
similar temperatures and salinities. ^Friederich and Codispoti (1981) have

shown a similar enrichment occurring along the longshore path of the poleward

undercurrent that is the source of the upwelling waters' off Peru. The o-PN

relationships can exhibit complex patterns in this area. During July to

October 1976, the 26.0 sigma-t surface below 40 m was characterized by a

nitrate range from 11 to 22 pM and by a silicic acid range from 7 to 18 pM.

During March to May 1977 the ranges were 16 to 32 pM for nitrate and 6 to 36

pM for silicic acid. A times-series station at midshelf occupied during May

1976 showed a fluctuation of 9 pM in nitrate and 8 pM in silicic acid on the

26 sigma-t surface during a 24 hr period. This Peruvian coastal area is

influenced by unusually strong denitrification and by a complex intermingling

of water masses (equatorial and subantaretic) with very different hydrographic

and plant nutrient characteristics. The observed plant nutrient variability

may be extreme but it does emphasize the caution that must be exercised in the 

interpretation of plant nutrient relationships with temperature, salinity or

sigma-t. In contrast, Voituriez and Herbland (1984) have suggested that the

seasonal stability in the nitrate/temperature linear relationship observed in

the nitracline of the Gulf of Guinea indicates an equilibrium between physical

and biological processes in the region.

Zentara and Kamykowski (1977) have compiled a data set along the west

coast of North and South America that allowed the generation of temperature

versus nitrate, phosphate and silicic acid plots. Several trends were

evident: 1) the plant nutrient concentration generally increased linearly

with decreasing temperature for each block within the depth limits of the data

set (ocean bottom or the depth below which nitrate remained constant); 2) the

predicted plant nutrient depletion temperature decreased with increasing

latitude from the equator; and, 3) actual nitrate depletion temperatures

4
occurred throughout the northern hemisphere, but were less likely to occur

south of 45° S; silicic acid depletion temperatures occurred south of 45° S.

This larger data set verified the coherent latitudinal applicability of T-PN

relationships. The area studied, however, was chosen for its relatively

simple physics and geology and did not consider the complexities of large

river outflow, broad continental shelves, and large scale fronts. The latter

areas may offer special problems due to altered plant nutrient ratios and the

physical and chemical effects caused by the proximate sediment interface and

by complex water mass structure.

The present paper provides T-PN and o-PN analyses of a global data set

obtained from NODC. This analysis provides a global view of the present

capability to predict plant nutrient concentration or plant nutrient depletion

from temperature or sigma-t using a slightly modified NODC data set.

METHODS

Twenty magnetic tapes in Station Data II format containing 230,202

oceanographic stations with at least one plant nutrient (nitrate, phosphate or

silicic acid) represented were obtained from the National Oceanographic Data

Center (NODC). Each ocean area was successively processed through the

sequence of programs listed in Table 1 in which JCL refers to Job Control

Language, SAS refers to the Statistical Analysis System and CAMIVA refers to

the Cartographic Automatic Mapping System (Version IV-A). The reduced data

sets created by OCEAN were limited by depth (0000 m), temperature (-5 to 35°

C), salinity (28 to 38 ppt), and appropriate plant nutrient concentration

(nitrate -0 to 40 pm; phosphate -0 to 3.5 pM, or silicic acid -0 to 200 I1M).

Scatter plots of temperature or sigma-t versus nitrate, phosphate or silicic

acid were used to assign subjective values of overall data quality.

The possible data quality symbols were: 'X' -data generally fell along a main

5
line trend that was consistent with that expected from surrounding TENSQs

(10°Lat x 10°Lon); ' M' -a relatively large number of data points compared to

the number in a TENSQ deviated from the expected main line trend apparently as

a result of decimal point errors; and I S' -no expected main line trend was

discernible. Only TENSQs designated 1 X I were considered for this paper. No

attempt was made to correct suspect data. The statistical analyses performed

using the SAS procedures ( SAS Institute Inc., 1982) included cubic regression

calculations with associated statistics including the variance - covariance

table, estimates of means and ranges for the variables used in the cubic

regressions and percentile determinations of the temperature or sigma-t values

associated with plant nutrient levels below a selected level ( NVj; F <1;

Sit10). The levels were chosen as a compromise based on a survey of the data

and they certainly work better in some regions ( i.e. lower latitudes) than in

others. These percentiles provide a mechanism for estimating the temperature

or sigma-t at which the different plant nutrients tend to unmeasurable

concentrations. The 50th percentile approximates this ideal within the range

of the main contour intervals of 5°C for temperature or 1 unit for sigma-t.

This amount of error is considered acceptable for the global comparisons in

this paper but must be refined for actual application to a given region.

RESULTS

Table 2 provides a listing of the factors recorded in the six final

tables for the temperature or sigma-t versus nitrate, phosphate or silicic

acid relationships that are considered in this paper. In addition, the

variance- covariance tables are also available to allow the calculation of

confidence limits for predicted plant nutrients concentrations at a selected

temperature or sigma-t in a given TENSQ. These data sete are stored on

magnetic tape at North Carolina State University. Figure 1 provides examples

1

6
of the range of scatter plots chosen as suitable for predicting plant nutrient

concentration based on four criteria: 1) data point number (i.e., TNNC) :0 10;

2) subjective data quality (i.e. TNQUAL) . 'X'; 3) adjusted r 2 (i.e. TNARC)

0.5; and, 4) root mean square error (i.e. TNEC) t 6 (N), 0.4 (P) or 20 (Si).

The last criteria was chosen as the mid-range of error in each plant nutrient

data set. The cubic regression fits are superimposed over the data. The

entries for these scatter plots in the final tables are respectively listed in

Table 2 (except for variance-covariance matrix).

Figures 2-4 provide map summaries of the TENSQs with adequate data to

allow prediction of nitrate, phosphate or silicic acid, respectively, from

temperature (upper) or sigma-t (lower) within the criteria mentioned above.

For temperature or sigma-t, the geographic coverage is least for nitrate,

intermediate for phosphate and most for silicic acid. The region bracketed

between 40 0 N and 60 0 S is most suitable for plant nutrient predictions based

on temperature. Plant nutrient predictions based on sigma-t are useful into

higher latitudes. Several poorly represented regions (i.e. the U.S. east

coast) may require reduced spatial groupings of data (i.e. 1° Lat x 1° Lon or

smaller) to survive the selected criteria because of juxtapositioned water

masses with different temperature or sigma-t versus plant nutrient

signatures.

Figures 5-7 provide contour maps of the approximate temperature (upper)

or sigma-t (lower) at which nitrate, phosphate or silicic acid respectively

decline to unmeasurable concentrations using classical nutrient analysis

techniques (Strickland and Parsons, 1968). These data represent only the

TENSQa that are characterized by the subjective criteria 'X' and that contain

more than 10 data points below the selected low plant nutrient criterion. The

contours represent the 50th percentile of temperature (upper) or sigma-t

7
1

(lower) below these plant nutrient criteria within the acceptable TBNSQs for

each plant nutrient.

Figure 8 compares the latitudinally changing temperatures along the west

coasts of North and South Americ-a for nitrate (Figure 5) and silicic acid

(Figure 7) with those given in Zentara and Kamykowski (1977). The overall

e

patterns are quite similar, although specific latitudes exhibit significant

temperature differences. This can be attributed to the interaction between

the TBNSQ boundaries relative to the longitude of the coast line; the large

;- TP,NSQ area can incorporate more or less offshore water compared to the data in

I Zentara and Kamykowski (1980).

Since the temperature and sigma-t contours in Figures 5-7 represent plant

nutrient depletion, they are generally symmetrical around the equator and

depict the late growth season condition in both hemispheres. The contours

tend to be compressed toward the equator in the eastern parts of each ocean

and toward the poles in the western part of each ocean. The warmest

temperatures of plant nutrient depletion occur in the eastern Indian and

western Pacific Oceans. This region is also characterized by the lowest

sigma-t values of plant nutrient depletion as a result of the abundant

.rainfall and river runoff. The North Atlantic generally has warmer but denser

contour values than the North Pacific. The Southern Ocean exhibits generally

symmetrical contours around Antarctica that are occasionally distorted by

blocking land masses. The silicic acid contours tend to be colder and denser

than those of nitrate or phosphate at the same high southern latitudes.

DISCUSSION

The NODC data set is an oceanographic resource that has the potential to

provide global views of different factors and their interactions.

Unfortunately, the data set is compromised by several sources of error that

j 8
limit this potential. The results presented in this paper use both subjec r tve 

and objective criteria to circumvent the obvious errors. Altarnate criteria 

can be applied to the refined data set derived from the statistical analysis

to yield maps either more or less stringently controlled. The present

criteria demonstrate that useful information can be extracted from the NODC

data set and the potential utility of the NODC data set for examining plant

nuts-lent relationships with temperature and sigma-t.

The cubic regressions of temperature or sigma-t versus nitrate, phosphate

or silicic acid for each TENSQ in Figures 2-4 can be used to predict plant

nutrient concentrations. The required eatimatci of temperature or sigma-t can

come from various sources including buoy, airplane or satellite mounted

sensors. Traganza et al. (1983) hive demonstrated this application for

satellites and have used the added insight to more completely interpret CZCS

ocean color determinations of chlorophyll. The cubic equations derived in the

present analysis can be similarly applied to data such as that provided by

Shannon et al. (1984). These investigators presented contours of remotely

sensed surface temperature and chlorophyll collected off southwestern South

Africa during February 1980. This location occurs in TENSQ 500 which is

characterized by the following temperature-dependent cubic regressions:

N - 37.15 - 0.21T - 0.24T2 + O.00ST3 r 2 = 0.92 RMSE = 3.42

P = 1.48 + 0.35T - 0.05T2 + 0.001T3 r2 - 0.58 RMSE = 0.42

Si = 41.46 - 4.56T - 0.17T 2 - 0.002T 3 r 2 - 0.39 RMSE = 9.13

Since all relationships are subjectively rated 'X', the error term (>0.40)

prevents the appearance of this phosphate relationship in Figure 3 and the

adjusted r 2 term (<0.5) eliminates this eilicic acid relationship in Figure 4.

The nitrate relationship is well within the criteria used in this paper.

9
Depending on the standards selected by the investigator, the nitrate or all of

these equations could be used to supplement the published temperature contours

with derived plant nutrient contours that would help specify the future

growth potential of the measured chlorophyll.

The present analysis of the NODC data set is not the best choice to

predict plant nutrients from temperature in a given local study except if

concurrent plant nutrient measurements are unavailable or impossible. Based

on the present analysis, however, plant nutrient concentrations can be

uniquely predicted from temperature over broad geographic regions. The extent

of these regions for the different plant nutrients is reasonably delineated in

Figures 2-4. Each matrix of regression equations provides a potential

capability to generate global or ocean basin maps of plant nutrient

concentrations based on routine surveillance of surface temperature from

satellites. This capability can eventually contribute to the routine

surveillance of chlorophyll as primary production in the world . ocean (Perry,

1982). Eppley et al. (1985) have stated that plant nutrients are one of the

factors that control photosynthesis per chlorophyll and that the variation in

the relationship between primary production and chlorophyll that is explained

by temperature may be partly based on plant nutrient relationships with

temperature.

The plant nutrient predictions based on sigma-t are generally more

reliable and apply over a broader geographic area than those based on

temperature. The sigma-t based relationships may eventually be compatible

with satellite-sensed determinations if salinity can be remotely determined.

Alternately, these sigma-t based relationships provide a mechanism for

interpreting past or future mooring data as demonstrated by Atkinson et al.

(1982). For the latter purpose, however, local concurrent calibration will

10
usually be better than the NODC relationships alone. The nODC data can be

used to place local calibration in a spatial context.

Figures 5-7 provide approximate contours of the temperatures and sigma-t

values at which nitrate, phosphate and silicic acid are no longer measurab.4.,v

in the world ocean. These contours supplement the cubic regressions

represented in Figures 2-4 and demonstrate the dependence of the plant

nutrient relationships with temperature or sigma-t on the large scale

oceanographic processes. These processes must include the global seasonal

cycle of incident solar radiation and its effect on evaporation-precipitation 

and on wind, the major currents associated with surface and deep circulation,

and the location of the continental land masses and their associated river

systems. More local effects include the average environmental conditions that

determine the seasonal upper mixed layer stratification sequence and the 

cond',"ions that affect phytoplankton growth. These contours attest to the

a_hlal .y of organisms to maintain a high level of biological activity over a

broad range of temperature and sigma-t conditions determined according to

physical laws. This biological activity is adequate to drive near-surface

plant nutrient concentrations to unmeasurable levels over broad geographic

expanses of the world ocean.

These contours also emphasize the spatial and temporal application of

these relationships as factors in phytoplankton competition and community 

succession (Tilman et al., 1981). This temperature, sigma-t and plant

nutrient interaction combined with latitudinal patterns of other biologically

significant factors like day length, light intensity, and mixed layer depth

advances the conceptual view of the coordinated environmental factors

affecting planktonic phytogeography (Smayda, 1980). Recall, however, th6t

several investigators (i.e. Strickland et al. 1970) have emphasized that

seasonal adjustments between physical and biological processes can alter the

11 
local expression of these relationships within confined but measurable

limits.

Finally, these contours initiate paleoceanographic ♦ speculation concerning

variability of the planktonic growth environment during various stages of the

earth's history. CLINAP (1981) has provided a seasonal representation of the

sea—surface temperatures during a glacial period based on a factor analysis of

the abundance of organism remains in the underlying sediment. Although the

average temperature difference from the present condition is small (-1.5° C),

the glacial temperature can range 10° C lower at certain geographic locations.

The plant nutrient relationships with temperature and sigma—t provide an added

dimension to the causal factors that allow organism abundances to yield

temperature information. This is especially true when interpreted it light of

the complex interactive effects that these factors have on phytoplankton

growth (Goldman, 1977), nutrient uptake (Goldman, 1979) and behavior (Walsby

and Reynolds, 1980; Bienfang and Harrison, 1984; Cullen and Horrigan, 1981;

Kamykowsk_., 1981).

CONCLUSIONS

The statistical analysis of temperature and sigma — t relationships with

nitrate, phosphate and silicic acid using the NODC data base demonstrates the

existence of a global prediction capability and of a diverse global texture of

temperature, sigms r t and plant nutrient patterns acting on phytoplankton

processes. Plant nutrient concentrations can be predicted based on temperature

or sigma—t with varying amounts of certainty depending on the complexity of 

water masses in the region and on the quality of the existing data. These

predictions can contribute to the interpretation of the satellite chlorophyll

i 

data as primary production. Latitudinal and longitudinal variations in these

i

relationships as exemplified by the approximate temperature or sigma—t values

12
at which plant nutrients deplete can be related to the matrix of global

environmental processes and to the ability of phytoplankton to adapt to the

resulting range of environmental conditions. These contour maps of the

temperature or sigma-t at which nitrate, phosphate or silicic acid deplete

provide insight into present and paleoceanographic phytogeography.

ACKNOWLEDGEMENTS

This material is based on research supported by the U.S. National

Aeronautics and Space Administration under Grant No. NAGW-367. We thank the

personnel of the U.S. National Oceanographic Data Center, the NCSU Computing

Center (especially D. Myrick and L. Robinson) and the NCSU Statistics

Department for programming and logistical support and Dr. M. J. Perry for

encouragement to pursue this effort.

c', f

t

13
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Analysis. Fisheries Research Board of Canada Bulletin 167: 1-311.

Strickland, J.D.H., L. Solarzano, and R.W. Eppley, 1970. The ecology of the

plankton off LaJolla, California, in the period April through September,

1967 Pt. 1 General introduction, hydrography, and chemistry. Scripps

Institution of Oceanography Bulletin 17: 1-22.

Tilman, D., M. Mattson and S. Langer, 1981. Competitive and nutrient

kinetics along a temperature gradient: An experimental test of a

mechanistic approach to niche theory. Limnology and Oceanography

26: 1070-1033

Traganza, E.D., V.M. Silva, D.M. Austin, W.L. Hanson and S.H. Bronsink, 1983.

Nutrient mapping and recurrence of coastal upwelling centers by satellite

remote sensing: its implication to primary production and the sediment

record. In: E. Suess and J. Thiede (eds) Coastal Upwelling, PTA.,

Plenum Publishing Corporation, p. 61-83.

Tregeur, P. and P. LeCorre, 1979. The ratios of nitrate, phosphate and

silicate during uptake and regeneration phases of the Moroccan

upwelling regime. Deep Sea Research 26: 163-184.

Voituriez, B. and A. Berland, 1984. Signification de 1a relation nitrate/

temperature dams 1' upwelling equatorial du Golfe de Guinea.

Oceanologica Acts 7: 169-174.

16
Walsby, A.F. and C.S. Reynolds, 1950. Sinking and floating. In I. Morris

(ad). !a Physiological Ecology of Phytoplankton. Univ. of California

Press (Berkeley) p. 923-968.

2entara, S.-J. and D. Kamykowski, 1977. Latitudinal relationships among

temperature and selected plant nutrients along the west coast of North and

South America. Journal of Marine Research. 35: 321-337.

17
TABLE 1 - List of the program sequence applied to each ocean area

PROGRAM SOURCE FUNCTION

COPYDCTP JCL Copies the NODC data set for a given ocean area to

a private disk (AREA).

OCEAN SAS Creates two modified SAS data sets; one contains

the general information about each station (LOG);

the other contains the depth profile information

from each station (WATER).

SCATTER SAS Sorts the WATER data set according to 10° Canadian

Square designation and provides plots of

temperature or sigma-t versus nitrate, phosphate

or silicic acid.

REGRESC SAS Computes cubic regressions for temperature or

sigma-t versus nitrate, phosphate or silicic acid;

Calculates means and percentiles for selected data

subsets. Six versions - one for each

relationship.

TABLE SAS Corrects errors in the preliminary Table from

COMBINE. Six versions-one for each relationship.

WMSYM SAS Creates a Fortran compatible data set for selected

variables. Several versions depending on

variable.

WORLDMAP CAMIVA Superimposes specific variables from WMSYM on a

world map.

18
TABLE 2 - Representative list of the output terms in the final table resulting fromt the

statistical analysis of temperature versus nitrate data.

Yy ^

VARIABLE SAMPLE S

AREA - NODC ocean area designation 4 

TENSQ - Canadian square number for 100 longitude by 10° latitude 426 71

LAT - mid- latitude of the TENSQ; south is negative -45 -1

LON - mid-longitude of the TENSQ, east is negative - 115 13

TNNC - number of nitrate data points in the TENSQ and the number

of points used to compute the cubic regression 105 35

TNTL - lowest nitrate temperature in the TENSQ 2.9 

TNTM - mean nitrate temperature in the TENXSQ 9.7 1

TNTH - highest nitrate temperature in the TENSQ 17.6 2

TNIC - nitrate intercept of the cubic regression 18.15 4

TN1SC - first order slope coefficient of cubic regression 3.69 -

TN2SC - second order slope coefficient of cubic regression -0.67 

TN3SC - third order slope coefficient of cubic regression 0.02 

TNEC - root mean square error of the cubic regression 5.44 

TNARC - adjusted r2 for cubic regression 0.53 

TNQUAL - a subjective value of overall data quality x 

TNNL - lowest nitrate in TENSQ 0.2 

TNNM - mean nitrate in TENSQ 12.2 1

TNNH - highest nitrate in TENSQ 37.4 3

TLNTC - low nitrate cirterion for temperature of nitrate depletion 5 

TLNTN - number of temperatures with nitrate levels below criterion 20 21

TLNT25 - 25th percentile temperature with nitrate levels below criterion 9.4 2

TLNTM - 50th percentile temperature with nitrate levels below criterion 11.6 2

TLNT75 - 75th percentile temperature with nitrate levels below criterion 17.0 2

19
FIGURE CAPTIONS

Figure 1: Sample scatter plots demonstrating the range,of acceptable data

(top: best, bottom: worst) and cubic regression fits included in

the present analysis.

Figure 2: World maps of the LO O squares of latitude and longitude that

contain acceptable data and cubic regression fits for temperature

versus nitrate (top) and sigma-t versus nitrate (bottom) based on

specified selection criteria. The 'X' marks over continental

areas represent coastal regions within the bounds of the 10°

square.

Figure 3: Same as Figure 2 for temperature versus phosphate (top) and

sigma-t versus phosphate (bottom).

Figure 4: Same as Figure 2 for temperature versus silicic acid (top) and

sigma-t versus silicic acid (bottom).

Figure 5: Contour maps of the approximate temperature (top) or sigma-t

(bottom) at which nitrate approaches unmeasurable concentrations

in the world ocean. The cross hatched area in the temperature

plot marks the 27°C contour.

Figure 6: Same as Figure 5 for phosphate.

Figure 7: Same as Figure 5 for silicic acid.

Figure 8: A comparison of the temperatures at which nitrate or silicic acid

approach unmeasureable concentrations along the west coasts of

North and South America from Figure 5 (top) and Figure 7 (top),

respectively, and from the data in Zentara and Kamykowski (1977).

20
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APPENDIX I

The attached set of tables provide a record of the:

1. Temperature vs. nitrate cubic regression coefficients

2. Temperature vs. nitrate variance-covariance matrix

3. Temperature vs. nitrate means, ranges and percentiles

e

for each 10° square of latitude and longitude for which data exists in the

analyzed NODC data set. The labels are identified in Table 2 of the

accompanying manuscript entitled 'Predicting plant nutrient concentrations

from temperature and sigma-t in the world ocean'. Five additional sets of

tables for temperature vs. phosphate, temperature vs. silicic acid, sigma-t

vs. nitrate, sigma-t vs. phosphate and sigma-t vs. silicic acid are also

l available on the Triangle Universities Computer System in North Carolina.

Some specific aspects of the three tables within the attached set are

discussed in the following sections.

1. Temperature vs. nitrate cubic regression coefficients.

- The regression coefficients in this table are substituted into the

equation

N = a + blT + b2T2 + b3T3

or

N = TNIC + TN1SC(T) + TN2SC(T) 2 + TN3SC(T)3.

- The lowest (TNTL), mean (TNTM) and highest (TNTH) temperatures mark the

range of best application of the cubic regression equation. TNNC gives

the number of data points considered.

2. Temperature vs. nitrate variance-covariance matrix

- The 4x4 matrix (TNIC, TN1SC, TN2SC, TN3SC) associated with each TENSQ

corresponds to the labels

as abl ab2 ab3

abl b1b1 blb2 blb3

ab2

ab3

b1b2 b2b2 b2b31

b1b3 b2b3 b3b3

21
The variance of a predicted nitrate (N) concentration at a chosen

temperature (T-T) given by

Var (N) ° aT I1T°T <<(T) aT I T°T

3. Temperature vs. nitrate means, ranges and percentiles

- The lowest (TNNL; TNTL), mean (TNNM; TNTM) and highest (TNNH; TNTH)

nitrate concentrations and temperatures are ,given.

- The 25th (TLNT25), 50th (TLNTM) and 75th (TLNT75) percentiles of the

temperature at which nitrate is less than 5 yM are given. TLNTC gives

the number of data points considered.

22

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