## Lab 2 – The display of oceanographic data

This lab describes the ways in which some of the OOI data are displayed and has students work with some of the data products, graphs and maps.

OOI data are presented as collected, the data have not been massaged to remove outliers or gaps or to eliminate data that distract from a trend. In other words, these are real, messy data.

#### How do oceanographers display data to help them answer scientific questions?

Much of the data scientists collect consists of numbers, measurements of something. These data need to be organized in a way to illustrate patterns in the data. For this reason graphs are frequently used to display data. Graphs are a pictorial way to visualize patterns that would be hard to identify in columns of numbers. Graphs often have x and y axes, and the variable (measured quantity) that one chooses to plot on these axes is chosen to answer a question. For example, oceanographers are often interested in how some measured property changes over time. The natural choice then is to plot time on the x-axis (increasing to the right) and the variable of interest on the y-axis. Sometimes oceanographers are interested in how a variable changes as one moves away from the beach, and in that case it would make sense to plot distance from shore on the x-axis. In this exercise we examine some of the ways in which data are collected and some of the common graphs used by oceanographers.

### Learning outcomes

After completing this lab students will be able to:

• LO1. Recognize graph types commonly used in oceanography (time series, station profile, vertical section contour plot, bubble or vector map).
• LO2. Identify key components of a scientific graph (axes scales, legends, multiple y-axes).
• LO3. Discover that real-world data can be quite variable.
• LO4. Describe data patterns such as minimum and maximum values, trends over time or distance.
• LO5. Identify gaps in data and infer likely reasons for the gaps.
• LO6. Describe the oceanographic convention of plotting depth increasing down for station profiles and vertical sections.

### Key Terms

#### Data visualization

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.  Graphs typically have labels on the 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. In addition, graphs often have a title or caption that provides even more information. In this exercise you are introduced and asked to explore graphs and answer questions. Maps and charts help visualize geographical location and spacial dynamics of the data.

#### X-axis, Y-axis variables

Reading graphs is a very important data skill! Often data are presented on X-Y graphs, because there is some expected relationship between two variables. Common relationships in oceanography are the variation of some measured property over time, over distance from shore or over depth in the ocean. When you become experienced at examining these types of graphs you will see that there is often a “typical” shape to a particular type of graph. Scientists get very excited when they see something different from the typical shape! That can mean they have made a discovery.

#### Variability

Real data is 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.

The plot below shows the tidal current at a location in Maine. Positive values indicate the tide is flooding, coming in. Negative values indicate the tide is ebbing, going out.

Figure 2.0.1

The figure below shows what the current record would look like at Portland Harbor if a 0.5 knot wind driven current was acting, in addition to the tidal current. The graphs look the same, but examine the y-axes. The tidal current graph shows that the current reverses direction (alternates between positive and negative numbers) while the wind and tidal current graph shows that although the strength of the current alternates the direction is always positive, into the harbor. Clearly the wind is blowing water into the harbor, but the tidal forces act to decrease the current when the tide ebbs, or flows away from the shore.

Figure 2.0.2

#### Time series

A time series plot shows how some measured property changes with time. The tidal current graphs above are time series plots because they display the change in speed of the currents over two days. A time series plot of visibility over the course of one day, from midnight to midnight, would show low visibility until dawn, then increasing visibility until the sun was completely up, and then decreasing visibility as the sun set.

Time series plots are often used to show daily (called diurnal), seasonal and interannual changes in some property. The plot below shows the daily springtime discharge for the Siletz River, Oregon for three different years, and the long term daily average. This river drains into the Pacific Ocean near the OOI Endurance Array. Note that the legend indicates how to interpret each of the lines. This kind of data, river discharge data, is very useful when looking at the salinity of coastal waters. You can see that there is a trend of declining discharge as the season progresses. In Oregon spring rains increase river discharge. Thus we would expect to see a change in coastal water salinity as the winter wanes.

Figure 2.0.3

#### Station Profile

Often there are water properties that vary with depth in the ocean. 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 than in deeper water.

A standard oceanographic plot that shows how some property varies with depth is called a station profile.  Typically these 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.  Below are two  plots of temperature, from different locations, that show how temperature varies at different depths in the ocean.

• Examine each of the stations profiles below.  Are these two profiles similar or different? Describe the ways in which temperature changes with depth.

To answer this question it is very important to note the x and y axes scales!  Scroll through the gallery to see how the appearance changes. Can you see that paying attention to the scale of the axes is necessary to understand the difference in depth between these two stations?

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Figure 2.0.4

#### Vertical Section

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. Perhaps you have used contour maps of the surface of the Earth while hiking. If so, you know that they show, through the patterns in the contour lines, where there are hills and valleys, and also show the steepness of the Earth’s surface. So too can a vertical section show how a property like salinity changes across a bay, the continental shelf or an entire ocean.

The sequence of figures below shows how contouring of vertical sections can be used to reveal patterns in the data. The top figure shows a vertical section with the salinity data posted on the figure. Can you see how difficult it is to make sense of any patterns in the data by examining the numbers? For this reason oceanographers never display data in this way. The second figure shows the same data with contour lines added. These lines immediately show that saltier water is on the left side of the figure and less salty water on the right. This makes sense since this transect was from the ocean (on the left) up a river (on the right). Typically such data products show just the contour lines, not the data points. The third figure below shows how this plot would look in this standard presentation. Finally, color is sometimes added to the figures to make the patterns even more obvious, as in the fourth figure below. Notice the scale on the right, it shows that the hot colors indicate low salinity and the cool colors high salinity. Can you see that reflected in the color vertical section?

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Figure 2.0.5

#### Bubble charts

Bubble charts display data with three variables by using marker size and/or color. These are commonly used for data collected at different geographic locations. The map below (Figure 2.0.6) shows the size of several oil spills in the Gulf of Mexico. The “bubbles” (blue circles) are located at the latitude and longitude of the spill. The size of each bubble represents the number of gallons of oil spilled. The legend on the right side of the map shows the gallons of oil represented by different sized bubbles. The two largest bubbles are the 1979 Ixtoc I and 2010 Deepwater Horizon oil rig disasters.

Figure 2.0.6 Gulf of Mexico Oil Spills

Instead of bubble size, we could use a color scale to represent this same geographic data. The map below (Figure 2.0.7) displays the same oil spills, but with gallons of oil represented by color. Light red markers indicate spills that were smaller than 20,000 gallons. The darkest red markers indicate spills that were larger than 100 million gallons of oil. Compare the two maps. Which one do you think is a better way to show differences in oil spill size?

2.0.7 Gulf of Mexico Oil Spills color coded by size

#### Application questions

Why do oceanographers plot some data with the y-axis showing depth increasing as you go down?

If you wanted to create a graph that showed how a dolphin has grown since it was born, what would you plot on the x-axis and what would you plot on the y-axis?