31 Chapter 31: Climate diagrams

Anastasia Chouvalova

In this textbook chapter, we will start by discussing the usefulness of graphs in biology. Then, you will see a variety of climate diagrams (also called climatograms) and answer the corresponding questions. Lastly, we will learn about how weather data is collected over time to eventually produce these climatograms.

Types of Data

There are different types of data that can be collected in an experiment. Typically, we try to design experiments that collect objective, quantitative data.

Objective data is fact-based, measurable, and observable. This means that if two people made the same measurement with the same tool, they would get the same answer. The measurement is determined by the object that is being measured. The length of a worm measured with a ruler is an objective measurement. The observation that a chemical reaction in a test tube changed color is an objective measurement. Both of these are observable facts.

Subjective data is based on opinions, points of view, or emotional judgment. Subjective data might give two different answers when collected by two different people. The measurement is determined by the subject who is doing the measuring. Surveying people about which of two chemicals smells worse is a subjective measurement. Grading the quality of a presentation is a subjective measurement. Rating your relative happiness on a scale of 1-5 is a subjective measurement. All of these depend on the person who is making the observation – someone else might make these measurements differently.

Quantitative measurements gather numerical data. For example, measuring a worm as being 5cm in length is a quantitative measurement.

Qualitative measurements describe a quality, rather than a numerical value. Saying that one worm is longer than another worm is a qualitative measurement.

After you have collected data in an experiment, you need to figure out the best way to present that data in a meaningful way. Depending on the type of data, and the story that you are trying to tell using that data, you may present your data in different ways.

Reading Question #1

Classify the following two statements as examples of quantitative or qualitative data.

Statement 1: Sample A is more opaque than Sample B.

Statement 2: Sample A is three times more contaminated than Sample B.

A. Both statements are quantitative data.

B. Both statements are qualitative data.

C. Statement 1 is quantitative and Statement 2 is qualitative.

D. Statement 1 is qualitative and Statement 2 is quantitative.

Data Tables

The easiest way to organize data is by putting it into a data table (for an example, see Figure 30.1). In most data tables, the independent variable (the variable that you are testing or changing on purpose) will be in the column to the left and the dependent variable(s) will be across the top of the table.

Figure 30.1

Reading Question #2

Examine Figure 30.1. What is the independent and dependent variable?

A. The independent variable is the water type (i.e., de-ionized and smoked) and the dependent variable is the seedling height.

B. The independent variable is the seedling height and the dependent variable is the water type (i.e., de-ionized and smoked).

C. The independent variable is the trial and the dependent variable is the water type (i.e., de-ionized and smoked).

D. The independent variable is the water type (i.e., de-ionized and smoked) and the dependent variable is the standard deviation.

Be sure to:

  • Label each row and column so that the table can be interpreted
  • Include the units that are being used
  • Add a descriptive caption for the table


Graphs are used to display data because it is easier to see trends in the data when it is displayed visually compared to when it is displayed numerically in a table. Complicated data can often be displayed and interpreted more easily in a graph format than in a data table.

In a graph, the X-axis runs horizontally (side to side) and the Y-axis runs vertically (up and down). Typically, the independent variable will be shown on the X axis and the dependent variable will be shown on the Y axis (just like you learned in math class!).

1. Line Graph

Line graphs (Figure 30.2) are the best type of graph to use when you are displaying a change in something over a continuous range. For example, you could use a line graph to display a change in temperature over time. Time is a continuous variable because it can have any value between two given measurements. It is measured along a continuum. Between 1 minute and 2 minutes are an infinite number of values, such as 1.1 minute or 1.93456 minutes.

Changes in several different samples can be shown on the same graph by using lines that differ in color, symbol, etc.

Figure 30.2 A line graph showing the change in height of Plant X over a period of seven days. (Credit: Western Sydney University)

2. Bar Graph

Bar graphs (Figure 30.3) are used to compare measurements between different groups. Bar graphs should be used when your data is not continuous, but rather is divided into different categories. If you counted the number of birds of different species, each species of bird would be its own category. There is no value between “robin” and “eagle”, so this data is not continuous.

Figure 30.3 A bar graph showing the % change in mass of plants placed in various environmental conditions. (Credit: Chart-Studio, Plotly)

3. Scatter Plot

Scatter plots (Figure 30.4) are used to evaluate the relationship between two different continuous variables. These graphs compare changes in two different variables at once. For example, you could look at the relationship between height and weight. Both height and weight are continuous variables. You could not use a scatter plot to look at the relationship between number of children in a family and weight of each child because the number of children in a family is not a continuous variable: you can’t have 2.3 children in a family.

Figure 30.4 A scatter plot showing the relationship of ear length and age. (Credit: Heathcoate, 1995)

How to make a graph

  1. Identify your independent and dependent variables.
  2. Choose the correct type of graph by determining whether each variable is continuous or not.
  3. Determine the values that are going to go on the X and Y axis. If the values are continuous, they need to be evenly spaced based on the value.
  4. Label the X and Y axis, including units.
  5. Graph your data.
  6. Add a descriptive caption to your graph. Note that data tables are titled above the figure and graphs are captioned below the figure.

Descriptive captions

All figures that present data should stand alone – this means that you should be able to interpret the information contained in the figure without referring to anything else (such as the methods section of the paper). This means that all figures should have a descriptive caption that gives information about the independent and dependent variable. Another way to state this is that the caption should describe what you are testing and what you are measuring. A good starting point to developing a caption is “the effect of [the independent variable] on the [dependent variable].”

Here are some examples of good caption for figures:

  • The effect of exercise on heart rate
  • Growth rates of E. coli at different temperatures
  • The relationship between heat shock time and transformation efficiency

Here are a few less effective captions:

  • Heart rate and exercise
  • Graph of E. coli temperature growth
  • Table for experiment 1

Reading Question #3

For your undergraduate research project, you have gathered data about the beak length of five bird species. What graph is most suited to visualize this information? (Hint: What type of data are we dealing with? Continuous, categorical, etc.?)

A. A line graph

B. A bar graph

C. A scatter plot

D. Either a bar graph or scatter plot would be suitable.


We can use climate to describe the long-term conditions of biomes. Several abiotic factors (e.g., temperature, precipitation, wind, humidity) interact to produce a biome’s unique climate which in turn, determines which flora and fauna are able to survive within that biome and establish a niche. 

For simplicity, climate is primarily classified by both average annual precipitation and average annual temperature ranges. Climatograms (for an example, see Figure 30.5) are a type of graph that show annual temperature ranges and precipitation totals for an average year for a given location. They often utilize a double-Y axis which plots both the average annual temperature and precipitation. A bar graph is often used to plot precipitation and is labeled on the left y-axis, while a line graph is used to show temperature and is labeled on the right y-axis. The x-axis contains the months of the year.

Figure 30.5 Climatogram for Carson City, Nevada. (Credit: Precipitation data from NASA’s TRMM Satellite, Average Monthly Precipitation Climatology, 1998-2010 retrieved from https://mynasadata.larc.nasa.gov/live-access-server/, temperature data from http://www.weather.com)

Reading Question #4 (0.5 points)

Take a look at Figure 30.5. Which of the following statements is false about the climate in Carson City, Nevada?

A. Most precipitation falls from December to February.

B. July is the month of highest temperature and lowest precipitation.

C. The temperature range is 21°C.

D. The precipitation consistently exceeds 100.00 mm.

Let’s look at another climatogram of El Paso, Texas.

Figure 30.6 Climatogram for El Paso, Texas. (Credit: Precipitation data from NASA’s TRMM Satellite, Average Monthly Precipitation Climatology, 1998-2010 retrieved from https://mynasadata.larc.nasa.gov/live-access-server/, temperature data from http://www.weather.com)

Reading Question #5 (0.5 points)

Examine Figure 30.6. Which of the following statements accurately describes the climate trend? Select all that apply.

A. Summer is the driest and hottest season.

B. Summer is the wettest and hottest season.

C. Winter tends to be dry and above-freezing temperatures.

D. Winter tends to be wet and below-freezing temperatures.

Collecting weather data

Weather forecasts are better than they ever have been. According to the World Meteorological Organization (WMO), a 5-day weather forecast today is as reliable as a 2-day forecast was 20 years ago! This is because forecasters now use advanced technologies to gather weather data, along with the world’s most powerful computers. Together, the data and computers produce complex models that more accurately represent the conditions of the atmosphere. These models can be programmed to predict how the atmosphere and the weather will change. Despite these advances, weather forecasts are still often incorrect. Weather is extremely difficult to predict because it is a complex and chaotic system.

To make a weather forecast, the conditions of the atmosphere must be known for that location and for the surrounding area. Temperature, air pressure, and other characteristics of the atmosphere must be measured and the data collected.


Thermometers measure temperature. In an old-style mercury thermometer, mercury is placed in a long, very narrow tube with a bulb. Because mercury is temperature sensitive, it expands when temperatures are high and contracts when they are low. A scale on the outside of the thermometer matches up with the air temperature.Some modern thermometers use a coiled strip composed of two kinds of metal, each of which conducts heat differently. As the temperature rises and falls, the coil unfolds or curls up tighter. Other modern thermometers measure infrared radiation or electrical resistance. Modern thermometers usually produce digital data that can be fed directly into a computer.


Meteorologists use barometers to measure air pressure. A barometer may contain water, air, or mercury, but like thermometers, barometers are now mostly digital. A change in barometric pressure indicates that a change in weather is coming. If air pressure rises, a high pressure cell is on the way and clear skies can be expected. If pressure falls, a low pressure cell is coming and will likely bring storm clouds. Barometric pressure data over a larger area can be used to identify pressure systems, fronts, and other weather systems.

Weather Stations

Weather stations contain some type of thermometer and barometer. Other instruments measure different characteristics of the atmosphere such as wind speed, wind direction, humidity, and amount of precipitation. These instruments are placed in various locations so that they can check the atmospheric characteristics of that location. According to the WMO, weather information is collected from 15 satellites, 100 stationary buoys, 600 drifting buoys, 3,000 aircraft, 7,300 ships, and some 10,000 land-based stations. The official weather stations used by the National Weather Service is called the Automated Surface Observing System (ASOS).


Radiosondes is a balloon that measures atmospheric characteristics, such as temperature, pressure, and humidity as they move through the air. Radiosondes in flight can be tracked to obtain wind speed and direction. Radiosondes use a radio to communicate the data they collect to a computer. Radiosondes are launched from about 800 sites around the globe twice daily to provide a profile of the atmosphere. Radiosondes can be dropped from a balloon or airplane to make measurements as they fall. This is done to monitor storms, for example, since they are dangerous places for airplanes to fly.


Radar stands for Radio Detection and Ranging. A transmitter sends out radio waves that bounce off the nearest object and then return to a receiver. Weather radar can sense many characteristics of precipitation: its location, motion, intensity, and the likelihood of future precipitation. Doppler radar can also track how fast the precipitation falls. Radar can outline the structure of a storm and can be used to estimate its possible effects.


Weather satellites have been increasingly important sources of weather data since the first one was launched in 1952 and are the best way to monitor large scale systems, such as storms. Satellites are able to record long-term changes, such as the amount of ice cover over the Arctic Ocean in September each year.

They also observe all energy from all wavelengths in the electromagnetic spectrum.The flagship of the National Weather Service is the Geostationary Operational Environmental Satellites (GOES). These satellites are the ones you see on the nightly news where it looks like the clouds are moving, but not the planet. That is because these satellites are “geo-fixed” on a particular location over Earth rotating around the planet as fast as Earth’s rotation at a distance of over 23,000 miles above the planet. There are basically three different types of GOES: visible, infrared, and water vapor. Visible light images record storms, clouds, fires, and smog. Infrared images record clouds, water and land temperatures, and features of the ocean, such as ocean currents. The final type of GOES imagery is water vapor. This type of imagery looks at the moisture content in the upper-half of the atmosphere. This is important for determining if clouds can grow to great heights like cumulonimbus thunderstorms.

The other type of satellite commonly used in weather forecasting is called a Polar Orbiting Environmental Satellites (POES). These types of satellites fly much lower to the earth, only about 530 miles, and orbit the planet pole-to-pole. You’ve probably seen these satellites at night when you see one crossing the sky. Look for their direction and odds are they are moving northward or southward toward each pole.

Just like the weather satellites on the news, you’ve seen these images often when you are looking at natural disasters like hurricanes or volcanic eruptions, wars like have occurred in Afghanistan, Iraq, or recently in Syria. Even the Malaysian flight that “disappeared” in the Indian Ocean for weeks was ultimately discovered using polar orbiting satellites. Common types of these satellites include: Landsat, MODIS, and the Tropical Rainfall Measuring Mission (TRMM).

Numerical Weather Prediction

The most accurate weather forecasts are made by advanced computers, with analysis and interpretation added by experienced meteorologists. These computers have up-to-date mathematical models that can use much more data and make many more calculations than would ever be possible by scientists working with just maps and calculators. Meteorologists can use these results to give much more accurate weather forecasts and climate predictions.

In Numerical Weather Prediction (NWP), atmospheric data from many sources are plugged into supercomputers running complex mathematical models. The models then calculate what will happen over time at various altitudes for a grid of evenly spaced locations. The grid points are usually between 10 and 200 kilometers apart. Using the results calculated by the model, the program projects weather further into the future. It then uses these results to project the weather still further into the future, as far as the meteorologists want to go. Once a forecast is made, it is broadcast by satellites to more than 1,000 sites around the world.

NWP produces the most accurate weather forecasts, but as anyone knows, even the best forecasts are not always right. Weather prediction is extremely valuable for reducing property damage and even fatalities. If the proposed track of a hurricane can be predicted, people can try to secure their property and then evacuate.

Weather maps

Weather maps, also called synoptic maps, simply and graphically depict meteorological conditions in the atmosphere from a spatial perspective. Weather maps may display only one feature of the atmosphere or multiple features. They can depict information from computer models or from human observations.

On a weather map, important meteorological conditions are plotted for each weather station. Metorologists use many different symbols as a quick and easy way to display information on the map.

Reading Question #5

Match the following instruments to their function.

A) Thermometer       B) Barometer       C) Radiosondes        D) Radar         E)  Satellites

  1. Can be used to track storms and wind parameters using radio waves.
  2. Can be used to track storms using all wavelengths.
  3. Used to measure air pressure and predict various weather systems.
  4. Can be used to track various properties of precipitation using radio waves
  5. Has historically used toxic metals that change volume in response to temperature changes.

Adapted from Lenkeit-Meezan. (2022). Physical Geography. LibreTexts Geosciences. Retrieved from https://geo.libretexts.org/Bookshelves/Geography_(Physical)/Physical_Geography_(Lenkeit-Meezan)/06:_Weather_and_Climate/6.03:_Global_Climate_Patterns

Adapted from Global Precipitation Measurement. (2022). Geographical Influences. Precipitation Education, NASA. Retrieved from https://gpm.nasa.gov/education/sites/default/files/lesson_plan_files/geographical%20influences/Geographical%20Influences%20-%20City%20Climatograms.pdf

Adapted from Bartee, L., Shriner, W., and Creech, C. (2021). Principles of Biology. LibreTexts Biology. Retrieved from https://bio.libretexts.org/Bookshelves/Introductory_and_General_Biology/Book%3A_Principles_of_Biology/01%3A_Chapter_1/01%3A_The_Process_of_Science/1.03%3A_Presenting_Data_-_Graphs_and_Tables

Adapted from Lumen. (2022). Physical Geography. LibreTexts Geosciences. Retrieved from https://geo.libretexts.org/Courses/Lumen_Learning/Book%3A_Physical_Geography_(Lumen)/11%3A_Weather_Processes_and_Systems/11.08%3A_Collecting_Weather_Data



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Introductory Biology 2 Copyright © 2023 by Anastasia Chouvalova is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, except where otherwise noted.

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