Chapter 2 - The Historical Setting

Chapter 2 Contents

2.5 The Role of Observations in Oceanography

The brief tour of theoretical ideas suggests that observations are essential for understanding the oceans. The theory describing a convecting, wind-forced, turbulent fluid in a rotating coordinate system has never been sufficiently well known that important features of the oceanic circulation could be predicted before they were observed. In almost all cases, oceanographers resort to observations to understand oceanic processes.

At first glance, we might think that the numerous expeditions mounted since 1873 would give a good description of the oceans. The results are indeed impressive. Hundreds of expeditions have extended into all oceans. Yet, much of the ocean is poorly explored.

By the year 2000, most areas of the ocean will have been sampled crudely from top to bottom only once. Some areas, such as the Atlantic, will have been sparsely sampled four times: during the Meteor Expedition from 1925 to 1925, during the International Geophysical Year in 1959, during the Geochemical Sections cruises in the early 1970s, and during the World Ocean Circulation Experiment from 1991 to 1996. All areas will be vastly under sampled. This is the sampling problem (See box below). Our samples of the ocean are insufficient to describe the ocean well enough to predict its variability and its response to changing forcing. Lack of sufficient samples is the largest source of error in our understanding of the ocean.

The lack of observations has led to a very important and widespread conceptual error:

The absence of evidence was taken as evidence of absence The great difficulty of observing the ocean meant that when a phenomenon was not observed, it was assumed it was not present. The more one is able to observe the ocean, the more the complexity and subtlety that appears.
–Wunsch (2002a).

As a result, our understanding of the ocean is often too simple to be correct.

Sampling Error

Sampling error is caused by a set of samples (measurements) not representing the population of the variable being measured. A population is the set of all possible measurements, and a sample is the sampled subset of the population. We assume each measurement is perfectly accurate.

To determine if your measurement has a sampling error, you must first completely specify the problem you wish to study. This defines the population. Then, you must determine if the samples represent the population. All steps are necessary.

Global Warming Example

  1. The Problem: Suppose your problem is to measure the annual-mean sea-surface temperature of the ocean to determine if global warming is occurring.
  2. The Population: For this problem, the population is the set of all possible measurements of surface temperature, in all regions in all months. If the sample mean is to equal the true mean, the samples must be uniformly distributed throughout the year and over all the area of the ocean, and sufficiently dense to include all important variability in time and space. This is impossible.
  3. The Sample: Ships avoid stormy regions such as high latitudes in winter, so ship sample tend not to represent the population of surface temperatures. Satellites may not sample uniformly throughout the daily cycle, and they may not observe temperature at high latitudes in winter because of persistent clouds, although they tend to sample uniformly in space and throughout the year in most regions. If daily variability is small, the satellite samples will be more representative of the population than the ship samples.

From the above, it should be clear that oceanic samples rarely represent the population we wish to study. We always have sampling errors.

Sampling and Instrument Error
In defining sampling error, we must clearly distinguish between instrument errors and sampling errors.

  1. Instrument errors are due to the inaccuracy of the instrument.
  2. Sampling errors are due to a failure to make a measurement.
  3. An example: Consider the example above: the determination of mean sea-surface temperature.
    1. If the measurements are made by thermometers on ships, each measurement has a small error because thermometers are not perfect. This is an instrument error.
    2. If the ships avoids high latitudes in winter, the absence of measurements at high latitude in winter is a sampling error.

Meteorologists designing the Tropical Rainfall Mapping Mission have been investigating the sampling error in measurements of rain. Their results are general and may be applied to other variables. For a general description of the problem see North & Nakamoto (1989).

Selecting Oceanic Data Sets
Much of the existing oceanic data have been organized into large data sets. For example, satellite data are processed and distributed by groups working with NASA. Data from ships have been collected and organized by other groups. Oceanographers now rely more and more on such collections of data produced by others.

The use of data produced by others introduces problems:

  1. How accurate are the data in the set?
  2. What are the limitations of the data set? And,
  3. How does the set compare with other similar sets?

Anyone who uses public or private data sets is wise to obtain answers to such questions.

If you plan to use data from others, here are some guidelines.

  1. Use well documented data sets. Does the documentation completely de-scribe the sources of the original measurements, all steps used to process the data, and all criteria used to exclude data? Does the data set include version numbers to identify changes to the set?
  2. Use validated data. Has accuracy of data been well documented? Was accuracy determined by comparing with different measurements of the
    same variable? Was validation global or regional?
  3. Use sets that have been used by others and referenced in scientific papers. Some data sets are widely used for good reason. Those who produced the sets used them in their own published work and others trust the data.
  4. Conversely, don't use a data set just because it is handy. Can you document the source of the set? For example, many versions of the digital, 5-minute maps of the sea-floor are widely available. Some date back to the first sets produced by the U. S. Defense Mapping Agency, others are from the ETOPO-5 set. Don't rely on a colleague's statement about the source. Find the documentation. If it is missing, find another data set.

Designing Oceanic Experiments
Observations are exceedingly important for oceanography, yet observations are expensive because ship time and satellites are expensive. As a result, oceanographic experiments must be carefully planned. While the design of experiments may not t well within an historical chapter, perhaps the topic merits a few brief comments because it is seldom mentioned in oceanographic textbooks, although it is prominently described in texts for other scientific fields. The design of experiments is particularly important because poorly planned experiments lead to ambiguous results, they may measure the wrong variables, or they may produce completely useless data.

The first and most important aspect of the design of any experiment is to determine why you wish to make a measurement before deciding how you will make the measurement or what you will measure.

  1. What is the purpose of the observations? Do you wish to test hypotheses or describe processes?
  2. What accuracy is required of the observation?
  3. What temporal and spatial resolution is required? What is the duration of measurements?

Consider, for example, how the purpose of the measurement changes how you might measure salinity or temperature as a function of depth:

  1. If the purpose is to describe water masses in an ocean basin, then measurements with 20-50m vertical spacing and 50-300 km horizontal spacing, repeated once per 20-50 years in deep water are required.
  2. If the purpose is to describe vertical mixing in the open equatorial Pacific, then 0.5-1.0mm vertical spacing and 50-1000 km spacing between locations repeated once per hour for many days may be required.

Accuracy, Precision, and Linearity
While we are on the topic of experiments, now is a good time to introduce three concepts needed throughout the book when we discuss experiments: precision, accuracy, and linearity of a measurement.

Accuracy is the difference between the measured value and the true value.

Precision is the difference among repeated measurements.

The distinction between accuracy and precision is usually illustrated by the simple example of firing a rifle at a target. Accuracy is the average distance from the center of the target to the hits on the target. Precision is the average distance between the hits. Thus, ten rifle shots could be clustered within a circle 10 cm in diameter with the center of the cluster located 20cm from the center of the target. The accuracy is then 20 cm, and the precision is roughly 5cm.

Linearity requires that the output of an instrument be a linear function of the input. Nonlinear devices rectify variability to a constant value. So a nonlinear response leads to wrong mean values. Non-linearity can be as important as accuracy. For example, let

Output = Input +0.1 (Input)2

Input = a sin ωt

then

Output = a sin ωt + 0.1 (a sin ωt)2

Output = a sin ωt + 1/2 a2 - 1/2 a2 cos 2ωt

Note that the mean value of the input is zero, yet the output of this non-linear instrument has a mean value of 0.5 a2 plus an equally large term at twice the input frequency. In general, if input has frequencies ω1 and ω2, then output of a non-linear instrument has frequencies ω1 ± ω2.

Linearity of an instrument is especially important when the instrument must measure the mean value of a turbulent variable. For example, we require linear current meters when measuring currents near the sea surface where wind and waves produce a large variability in the current.

Sensitivity to other variables of interest.
Errors may be correlated with other variables of the problem. For example, measurements of conductivity are sensitive to temperature. So, errors in the measurement of temperature in salinometers leads to errors in the measured values of conductivity or salinity.

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