Climate zones of Europe

This learning scenario is designed in a way that allows and encourages students to explore data while learning about the climate zones of Europe. The teacher guides them through the process and encourages them to think about the climate zones and their characteristics.

The scenario can also be implemented as a simple frontal-style lesson.

Observing the Data

1. 

   Open the prepared Orange workflow. While it might appear complex, it is in fact quite simple, so no reason to panic. :)

   If we haven’t already installed the Geo add-on for displaying maps in Orange, the software will remind us to do so and will restart once we have confirmed the installation.

   In the first part of the lesson, we will work with the upper half of the workflow.

2. 

   By double-clicking on the Table widget (the one on the left, attached to the Datasets widget), we can view the data we will be using: average monthly temperatures and precipitation levels. In addition, the first columns contain location names, geographical coordinates, and altitudes.

   While looking at the table with the students, we can play a game where they answer questions, such as which place is the coldest in January and which has the highest temperatures in August.

3. 

   This stage of the activity is also the perfect opportunity to revise the European capitals. We can use either the Table (which only reveals the cities but not the countries) or the Map widget (the one attached to the Datasets widget).

   The map is set in such a way that clicking on dots representing the cities reveals their names. The names of some bigger cities are – depending on how zoomed in the map is, and on the size of our screen – already displayed on the map.

   Do the students know which country each city belongs to? We can turn this into a bit of a competition – for instance, who can find Chișinău (Kishinev) the fastest?

4. If the lesson is intended as a revision exercise of different climate characteristics, we then revise the climate zones. But if the students are meant to discover them on their own, we skip this part and move straight to the next step. :)

5. 

   The workflow is composed in such a way that the Table widget is connected to two widgets for column selection: the first one selects variables with temperatures, and the second one those with precipitation. Those two widgets are then linked to two other widgets that display temperature and precipitation trends over months. With the widgets combined in this way, we can explore monthly temperatures and precipitation patterns in different cities.

   By double-clicking on the widgets Table, Temperature, and Precipitation in the upper part of the workflow, we can simultaneously open and view all three of them. It is recommended that you organise the three windows in such a way that you can see all three at once. If necessary, you can minimise the windows and hide their left part.

   

   Clicking on a row in the table will show the temperature and precipitation for that city by month.

   We can also select multiple cities: in that case, the thinner lines will indicate the temperature and precipitation in the selected cities, while the thicker line will show the average temperature and precipitation.

   We can see that the temperature patterns are not that interesting or surprising in this case – in all these places, it is warmer in summer than in winter. So let’s then challenge the students to try and look for precipitation patterns. They should discover that some places have more precipitation in summer, while in others, it is the opposite (i.e. more precipitation in winter). They can also be a little more (but not too) precise than this.

The Idea of Clustering

1. We show the students this image (click to download).

   

   The scatter plot shows the average February and August precipitation levels for some European capitals. Let’s make sure the students understand how the graph works: the dots represent the capitals, the x-coordinate represents the precipitation level in February, and the y-coordinate the precipitation in August. Let’s read the data together with the students for some locations.

2. Let’s discuss the meaning of the term climate zone. It’s an area with similar climatic characteristics. Can the students find clusters of places with similar weather in this graph?

The scatter plot reveals three obvious groups. Eastern European capitals on the right side are dry in winter and wet in summer. Western European capitals on the top left are wet all year long (although less so in winter – note that the scales on the x-axis and on the y-axis are different!). At the bottom, we have three Mediterranean capitals (do the students know where Nicosia and Valletta are located?), with drought in summer, while in winter, the precipitation levels are somewhere between those of Eastern and Western European capitals.

3. Do these clusters make sense? Do they match what the students know about the European climate zones?

4. 

   We have then admit that the above graph was somewhat misleading. It only includes some typical cases, but there are, in fact, many other cities in between the ones represented that we have hidden. The distances between the actual groups are, in fact, smaller. Let’s show the students what the graph would look like with all the cities included (click to download).

   The second problem is that the data we have is for February and August only. What about other months? How would the clusters change if we took that data into account, too? And if we also included temperature data, not just precipitation?

   The issue is that we cannot draw that, since we can only plot two values at a time, not 24 (12 months × 2 variables). That is also why we cannot discover clusters manually.

5. We explain to the students that for that reason, we use computer algorithms, which have the capacity to find groups described by multiple variables.

   The technical details of the clustering algorithm that we will be using are described in additional materials for teachers. We can also demonstrate how it works by using the animation in Orange’s Educational add-on. How much detail to provide depends on the students’ age and interest. What is most important is that after this step, students understand that the computer can find groups that we would not be able to discover manually, but it essentially performs the same task: looking for and grouping together similar places. It just works with more data.

Observing Groups – Climate Zones

Now, let’s move to the lower part of the workflow.

1. 

   Let’s open the Map widget – not the one connected to the Workflow widget, but the one that receives data from the k-Means widget.

   Explain to the students what the map shows: the k-Means widget has divided the capitals into four clusters. (Why four? Because we set it that way. Some background: Podgorica is so different when it comes to precipitation that it gets its own group. The second cluster would then includes the Mediterranean, and the third one all non-Mediterranean Europe.) The map shows cities coloured by clusters.

2. Ask the students to name the different types of climate that the different colours represent. Can they describe what type of weather they would expect in each climate zone? If they struggle, we can also skip this part and uncover the characteristics of the different climate zones in the next step.

   One of the »climate zones« only contains one city (Podgorica). For now, the students should ignore that, we will get to it later.

3. 

   Like in the upper part of the workflow, the k-Means widget is connected to two other widgets, which select columns with temperatures and precipitation. These, in turn, are connected to two widgets that display that data as a line chart. Let’s open these last two widgets and place the windows one below the other. Let’s read what they show.

   Like before, let’s, for now, ignore the green line (because of the nature of the clustering process, the colours are somewhat random and may be different each time!). It is easy to recognise the Mediterranean climate: precipitation in winter and temperatures are higher than in other places all year round. The rest of Europe mainly differs in temperature. The north is colder and has more summer precipitation, and precipitation there also starts slightly later than elsewhere.

   That description does not fully match the typical textbook description, however. We can explain that by pointing out that we are only looking at capital cities, and those are typically not found in random locations, but where the conditions for living are more favourable. That also makes them less representative of their whole countries.

4. 

   The two widgets that display temperature and precipitation line charts are connected to two Table widgets. These tables display the data for the curve(s) that we select by dragging over them with the mouse. We show the students how to do that.

5. Give the students some questions. They should, for instance, find:

   • the city with the lowest …
   • … and the one with the highest level of precipitation in summer,
   • the coldest capital,
   • and, most importantly, the city with »its own climate zone.«

6. We will probably want to explain what the deal is with Podgorica: it is close to the sea but has mountains behind it, which is why there is so much more precipitation there in winter than is typical for countries with a Mediterranean climate. Podgorica is the European capital with the highest levels of precipitation.