1.5: How are oceanographic data displayed?

How do oceanographers display data to help them answer questions about the ocean?

Much of the data scientists collect consists of numbers, measurements of something. Deriving the meaning from large data sets can be difficult especially if data is in a table format. You may not see the relationship between variables in a table, which is why scientific data is plotted in different data visualizations that help to organize and display the data. Data visualizations include maps, graphs, charts or diagrams that put data into a visual context that can make it easier to detect patterns, trends, and outliers in groups of data. In this section, you will be introduced to various graph types that are used to interpret oceanographic data. 

Tools of Science: Data as a Tool

Trends in data often are a change in some variable over space or time. Examples include the increase in water depth with increasing distance from the beach, the increasing in temperature with decreasing distance from the oven in your kitchen, or the increase in height as a child gets older. Patterns in data may be more complex than a simple increase or decrease in a variable (which is a trend). Air temperature measured over the course of a year shows a pattern of high temperatures in the summer and low temperature in the winter. River discharge typically also has an annual pattern of high discharge in the spring due to snow melt and lower discharge in the rest of the year or a similar pattern associated with wet (rainy) and dry seasons. Scientists may be looking at a temporal relationship such as spawning activity of a coral and the relationship to moon cycle or time of year.

 

X-axis, Y-axis variables

Trends or patterns in data presented on a graph should be easy to identify. The process includes first identifying the variables. Graphs typically have labels on the X and Y axes or scales that indicate both the variable that is plotted and the units of the variable. If more than one variable is plotted there is often a legend that helps you figure out what each of the plotted lines means. Sometimes a graph may have two Y-axes, with each axis representing a different variable and scale. In addition, graphs often have a title or caption that provides even more information. Maps and charts help visualize geographical location and spatial dynamics of the data. With maps you need to orient yourself to the location, look to see if there is land or bathymetric features and identify the variable that is plotted by seeking out the legend.

 

In the interactive figure below, click on each + to find out more information about the graph figure, and to think about what information is missing.

 

Identifying trends or patterns

Once you have identified key information from the graphic, you then need to look for patterns in the data, these may be similarities, differences, trends or other relationships. When identifying patterns in the data, you want to look for positive, negative and no correlation, as well as creating best fit lines (trend lines) for given data. The best fit line often helps you identify patterns when you have really messy, or variable data. A trend line is the line formed between a high and a low. If that line is going up, the trend is positive or increasing and if the trend line is sloping downward, the trend is negative or decreasing. You may have data or the trend(s) goes both up and down but on a different temporal (time) scale, such as seasons, daily, or based on tides. 

Once you have identified a pattern or trend, think about what the trend might mean by relating it to the concepts you learned in your course about oceanography. You may be comparing multiple different variables to each other, or you may need to compare different graphs. When comparing different graphs, be sure to pay attention to the scales, as they may be different. First, look to see if there is a correlation between the 2 or more variables on the graph, or spatial relationship on a map. The correlation could be direct where both variables increase and decrease together, or inverse where one variable increases and the other decreases. Sometimes there may be a correlation based on a temporal scale, seasons for instance. Remember, correlation does not always mean causation. A correlation indicates that there is an association between the variables, but doesn’t tell us why. Sometimes there is a reason and sometimes it is just a coincidence. 

Simple graph diagram examples of Inverse, Direct and Seasonal Inverse Correlations of two generic variables. Remember that real data is sometimes messy, so graphs are not always this simple to interpret, however, this provides you with general patterns.

 

Variability

Real data can sometimes be very messy. It often shows a trend that looks messy because of natural variability. What causes variability? It can be any number of things, for example, in the ocean water movement can have multiple sources. So there may be a wind driven current in one direction that when plotted shows variability, oscillations back and forth every 12 hours. These oscillations are due to the tide. If you were to plot the distance traveled by a floating object in the water you would see a gradual movement in the direction of the wind driven current with some sloshing back and forth. Analyzing such data requires identifying the trend and the variability, in this case the tidal oscillations.

 

Reading a Time Series Graph

Time series plots are often used to show daily, seasonal and interannual changes in some property. You have probably experienced daily and seasonal changes in temperature. There are daily and seasonal changes in temperature in the ocean, too, though they are not as extreme as those experienced on land. For example, the lowest recorded temperature on land is -88°C (-126°F) and the highest is 58°C (136°F). Compare that to the temperature range in the ocean, -1°C (30.2°F) to 30°C (86°F). As we will learn later, these differences have implications for how well land versus ocean animals can adapt to temperature changes.

We will use data from Oceanographic Observatories Initiative Coastal Pioneer Array, near the edge of the U.S. east coast continental shelf, as an example of time series data.

 

Air temperature in 2018 at the Coastal Pioneer array.

 

Sometimes it is easier to look at long term data by taking averages, for example it is often useful to average temperature data over a 24 hour period to remove the temperature change that occurs between day and night. For other studies an average of all the data collected during a month may be appropriate. The graph below shows the same data set examined in the graph above, but the data points are monthly averages of temperature. The temporal (time) scale at which you average your data - minutes, days, months, years - will depend on the scientific question of interest. Sometimes, the variability in your data is the interesting part!

 

Monthly averaged air temperature in 2018

 

When scientists want to compare two different types of measurements over time they can plot them on the same graph, but have to use different y-axes. Examine the graph below, which shows the surface water temperature data and surface salinity data at this location. Note that the temperature data are plotted relative to the y-axis on the LEFT, while the salinity data are plotted relative to the y-axis on the RIGHT. The units given for salinity are PSU. This stands for practical salinity units.

 

 

Bathymetric Charts

As we will learn in the next chapter, the ocean’s floor contains a vast array of shapes and textures formed over millions of years of geologic processes. These different seafloor formations provide habitat for marine species and sometimes contain valuable resources, such as oil and natural gas. Scientists and industry professionals use detailed bathymetric maps or charts to explore seafloor habitat and resources. Bathymetry is the word used to describe how ocean depth varies across an oceanscape, similar to the word topography used to describe changes in height across a landscape.

Let's explore some bathymetric charts for the Gulf of Mexico.

The Texas-Louisiana continental shelf has many active oil wells due to the interesting geology of this region. The oil in the rocks tends to accumulate around circular shaped features called salt domes. These features form when salt that is buried deeply in the shelf sedimentary rocks rises toward the surface, as a chimney shaped feature. In rising the salt deforms the surrounding rocks, and sometimes the sea floor, pushing it up into domes. One such salt dome is the East Flower Garden Bank, within the Flower Garden Banks National Marine Sanctuary (labeled EFGB on the figure below).

Now let's look at the bathymetry of this region. The three maps below are all showing the same data, but in a slightly different format - increasing in detail from left to right. They all show the bathymetry of the East Flower Garden Bank - we will talk more about longitude and latitude soon, but for now notice that the figure above has a much wider range of latitudes than the figures below, as the figures below are "zoomed in" to the area around 27° N, where the East Flower Garden Bank is located.

The figure on the left is a contour map. Each line on the contour map outlines an area of the seafloor that is a certain depth below the surface of the water. Locate the contour line labeled “60” and trace your mouse or eye around it. Everything on that path has a depth of 60 meters. The figure in the middle is the same contour map, now just filled in with colors to showcase the differences in depth. The figure on the right shows a representation of bathymetry called a shaded relief map. In this case, there are no contour lines and change in depth is represented only by the color scale. Additionally, some parts of the map contain shadows as if light shone onto the Bank from an angle. The shadows help highlight small seafloor features and abrupt changes in depth.

Exercise \(\PageIndex{1}\)

Estimate the depth of the seafloor at 27° 56’ N, 93° 37’ W. Remember that the depth changes continuously between contours.

Answer

The depth at this location is approximately 60 m.

Exercise \(\PageIndex{2}\)

What is the deepest depth in this region?

Answer

The deepest depth is approximately 120 m.

Exercise \(\PageIndex{3}\)

What information can you use to tell when the ocean floor is steepest?

Answer

When the color changes abruptly/contour lines are closest together.

 

Reading Station Profiles 

The Structure of Ocean Water

Although ocean water looks much the same wherever you go, once you start to measure properties such as salinity and temperature you would see that there are considerable differences. A standard type of graph used to examine some of these differences is called a station or depth profile. Typically these are graphs showing a data type, such as temperature, salinity, or something else, plotted against the depth of the water. Oceanographers make these graphs because many of the properties of seawater change with depth.

Temperature is one property that does just this. Temperature often decreases as depth increases since the source of the heat is the sun. Sunlight is quickly absorbed in the upper layers of water resulting in higher temperatures there than in deeper water. To make a station profile for temperature you take all the temperature data collected at one oceanographic station. This would be a place where the research vessel stopped and lowered a temperature measuring device (one of the sensors on a CTD, which stands for Conductivity, Temperature, Depth) into the water, and collected data as the instrument was lowered to the sea floor. A station profile plot shows the way that temperature varies with increasing depth. Typically station profiles are plotted with depth increasing down on the y-axis and the property of interest is plotted on the x-axis. This convention, plotting with depth increasing down, is used because it makes it easier to picture the distribution of the property in the ocean, where depth does increase in the downward direction.

What can oceanographers learn from temperature data? Just as there are heat waves on the surface of the Earth, there are heat waves in the ocean. One such oceanic heat wave occurred from 2013 to 2015. During this time there was a huge pool of warm water in the Pacific, nicknamed The Blob by the office of the Washington State Climatologist. The figure below shows the difference in sea surface temperature between “normal” times and the time when The Blob was present in the North Pacific. The huge area covered in red shows the location of the anomalously warm water. This feature was not just interesting to those who study ocean temperature. The high temperatures of this water impacted the weather along the west coast of the U.S. and adversely impacted marine life because it was missing some of the chemicals necessary for the marine algae called phytoplankton. In the activity below you will work with a type of graph called a station profile. This kind of data in The Blob helped oceanographers figure out the depth of the warm water and how much heat it contained.

Sea surface temperature anomaly (the difference between normal and observed temperatures) in September 2014 (Courtesy: NOAA Fisheries)

 

Station Profiles Exploration

The map above shows the surface area of The Blob. But how deep is this anomalously warm water? A plot showing the temperature of the water with depth, called a station profile, can be used to answer this question, especially if we compare a station profile from a “normal” time to one from The Blob.

The station profile below (Figure 2.4.2) shows the temperature in the North Pacific at a time before The Blob developed. It shows a few things that are often present in station profiles, a surface mixed layer, a depth range where the temperature undergoes significant change (called the thermocline) and a deeper layer with either uniformly cold water or very small changes in temperature with increasing depth. The surface mixed layer often is mixed due to the action of currents and waves, although in some places and seasons mixing may occur due to heat being removed from the surface water. The result would be that the water would become colder, and therefore more dense so it would sink. But regardless of the cause of the mixing, the surface mixed layer is easy to identify because of its uniform temperature.

 

We can look at a profile from 2014, when the warm surface water of The Blob was present, and answer the same questions.

 

To see the effect of The Blob on the waters in the North Pacific it is useful to examine both profiles on the same set of axes, as shown below (Figure 2.4.4).

 

We will make much more use of station/depth profiles later in the class, as we explore other ocean variables (salinity, oxygen) and how they vary across space and time. But before we finish up, let's talk about how we can combine station profiles to make one useful graph that shows variation at multiple locations.

 

Combining Station Profiles

Station profiles are very effective for looking at data from a single location. But when you are interested in looking at how a property changes with distance from shore or across the ocean they become less useful. They certainly contain a lot of information but it is difficult for a person to examine a lot of station profiles and make sense of patterns in the data. For this reason oceanographers have developed a different way of displaying data from lots of station profiles. This tool is called a vertical section. Vertical sections are a graphical way of showing how a property of the water changes in both the vertical and horizontal direction. This is done by contouring the data on a “vertical slice” of the ocean. 

The sequence of figures below shows how contouring of vertical sections can be used to reveal patterns in the data.

Vertical sections show changes in a property (such as temperature in the one below) by using either color coding or contour lines, or both. So they are similar to contoured or color coded bathymetry maps, but they use color to show the value of some property (in this case temperature) other than water depth. Look at the figure. Note how the sea surface is at the top, and water depth increases as you go down. Horizontal distance is on the x-axis. So the figure essentially represents a vertical wall of water, as if the ocean were sliced top to bottom and one side of the slice is shown. Vertical sections use contouring to map the changes in a property on this vertical surface. The figure below shows a slice of temperature at the edge of the continental shelf.

 

Let's try to solidify your understanding with some practice. Points A and B below represent a single location, a "slice" from our vertical section.

 

 

Let's watch a brief video of how to turn this into a single station profile. 

 

Exercise \(\PageIndex{4}\)

Imagine that we collected a station profile at location A. Draw a station profile graph with depth on the y-axis and temperature on the x-axis.

Answer