Lesson video

Hello, my name is Dr.

Rowlandson, and I hope you have your brains tuned in right now 'cause today's lesson is a thinker.

Let's get started with it.

Welcome to today's lesson from the unit on Maths and the Environment.

This lesson is called Studying Local Climate Change, and by the end of today's lesson, we will understand how the UK's weather has changed over a long time.

Here are some previous keywords that will be useful during today's lesson, so you may want to pause the video if you need to remind yourself what any of them mean, and press play when you're ready to continue.

The lesson is broken into two learning cycles.

I'm going to start by analysing local climate data.

Here we have a map of the world.

How the climate of a country changes over time may differ depending on its location, due to factors such as the following.

One is its latitude.

Another is its proximity to a sea or ocean.

Another is the carbon emissions in that particular country.

It is highly unlikely that any two countries will have the exact same influence from these factors combined.

Therefore, their climates will not always evolve over time in the same way.

Andeep says, "I wonder how the climate of the UK has changed over time." Let's investigate.

We can compare how one part of the climate is changing over time in two different locations within the same country.

For example, this graph shows data from the Met Office about the average number of days with air frost per month between the years 1941 and 2022 in two locations of the UK.

One is Nairn, which is in Scotland, and that data is represented by the grey crosses.

The other location is Oxford, which is in the south of England, and its data is represented by the black circles.

These two data points, here, for example, show the average number of days of air frost per month in 1947 in both Nairn and Oxford.

For Nairn, which is the grey cross we can see there, the average was just over six days per month, whilst in Oxford, it was just under six days per month.

One comparison that we can make is with the trend line for each set of data.

The trend line for Nairn is the grey one that goes through the middle of the crosses, and the trend line for Oxford is the dark black line that goes through the middle of the circles.

The trend line for Nairn, you might notice, is higher than the one for Oxford.

Therefore, the general pattern that we can conclude from this is that we expect to see more days per month of air frost in Nairn than in Oxford.

However, while this is usually true, there may be some years that break this trend.

For example, here we have two points, one for Nairn and one for Oxford, and these are for 1955, and what we can see here, in this particular year, Oxford had slightly higher average than Nairn did.

So the trends tell us generally what is going on with each data set, and generally Nairn tends to have more days of air frost in Oxford, but not every single year.

We could also look at the gradient of each trend line and interpret what that tells us.

The gradient of the trend line shows us how quickly a change is happening within the data.

So the trend line for Nairn has a very shallow, negative gradient.

So let's think what that means.

It means the long-term trend for the average number of days of air frost per month in Nairn is decreasing because it's a negative gradient, but it's not decreasing by very much because it's a very shallow negative gradient.

Now, the trend line for Oxford also has a negative gradient, which suggests that the number of days with air frost per month in Oxford is also decreasing.

However, this gradient is steeper than the one for Nairn, meaning that the average number of days with air frost per month in Oxford is decreasing at a faster rate than in Nairn.

So let's check what we've learned.

Here, you've got a graph that shows data from the Met Office about the average annual temperature from 1941 to 2022 in Wick and Oxford.

The grey crosses represent Wick, and the black circles represent Oxford.

You've got two statements about this data.

Which of those statements is true? Pause the video while you choose, and press play when you're ready for an answer.

The answer is B.

The average temperature in Oxford in 2022 was higher than the average temperature in any other year, and we can see that 'cause that particular data point is higher than the rest.

So here are two more statements.

Which of these is true? Pause the video while you choose, and press play for an answer.

The answer is B.

In Oxford, the general or long-term trend is that temperatures are increasing.

The reason why statement A is not true is 'cause the data points we can see in the graph represent averages.

So just because the average temperature in 2022 is hotter than the average temperature in 1990, it doesn't mean that every single data point within the year for 2022 is going to be higher than every single data point in the year for 1990.

So here, you have another graph, and this time it shows a number of days of air frost in Oakfield each month of January and February from 1970 to 2024.

You have three statements about this data.

Which of these statements are true? Pause the video while you choose, and press play when you're ready for answers.

The answers are B and C.

A long-term trend is that the number of days of air frost each month is decreasing, and we can see that because the trend line has a negative gradient.

Another long-term trend that you can detect from this graph is that there is now a great variety in the number of days with air frost each month compared to in the past.

The way we see that is not with the gradient of the trend line, but how far the points are around it.

If you look at the years before 1992 or maybe before 1995, you can see that most of those data points are pretty close to the line.

But from about 1995 onwards, maybe around about 2000 onwards, you can see that the data points are much further away from the line.

And here we have Aisha.

She says, "Gosh, the number of days of air frost "in each month in 2024 is close to zero." Hmm, that's not correct.

Can you please explain why Aisha's conclusion is incorrect? Pause while you write something down, and press play for an answer.

Well, the Y-axis of this graph starts at 10 days, not at zero days.

So I think Aisha has looked at the data points of 2024, and seen that they are close to the bottom of the graph, but the bottom of the graph here is at 10 days, not zero.

So whilst a trend is that the number of days of air frost each month is decreasing, the months in 2024 still have 13 and 14 days of air frost.

So which conclusion from the two presented here may be sensible from this data alone? Pause the video while you choose, and press play for an answer.

Well, let's take a look at these two conclusions in turn.

Statement A claims that the average temperature in Oakfield in January and February may be increasing over time.

Perhaps that's why they have fewer days with air frost per month.

That might be true.

However, temperature alone is not the only factor involved in determining whether there's air frost in the air.

We may need more data to determine why the trend is going down.

So it might be true, but we can't be conclusive.

Statement B says the number of days of air frost each January and February may be decreasing across the whole of the UK.

Well, that might be true as well, but we can't really see that from this data here.

There may be a factor in play in Oakfield that doesn't necessarily affect towns or cities that are far away from Oakfield elsewhere in the UK.

So we can't be absolutely conclusive about that claim either.

So neither conclusion can be made with confidence.

It is tricky to make broad conclusions from a limited dataset.

Okay, it's over to you then for Task A.

This task has one question, but it's in five parts.

Here are parts A and B.

Pause the video while you do it, and press play when you're ready for more parts.

And here are parts C and D of question one.

Pause while you do this, and press play when you're ready for part E.

And here is part E.

Pause while you do this, and press play to go through some answers.

Okay, let's go through some answers.

So the graph shows data from the Met Office about the average monthly rainfall each year from 1941 to 2000 in two locations, Stornoway and Southampton.

Andeep says we usually expect to see more rainfall in each month in Stornoway than Southampton, and in part A, you had to explain how Andeep might have come to that conclusion.

Well, the trend line for Stornoway is above the trend line for Southampton, so that would suggest it gets more rainfall.

And in part B, in 1968, more rain fell in Southampton than in Stornoway.

Explain whether this makes Andeep's claim incorrect or not.

Well, Andeep is still correct.

The trend is definitely showing that Stornoway has more rainfall than Southampton.

However, just because there's a few years that do not fit that trend doesn't mean that the whole trend is not true across a longer timeframe.

Then, Sofia said "Over time, the general trend in Stornoway "is that the average amount of rainfall is increasing." Part C says Sofia is correct, and you had to explain how Sofia might have come to that conclusion.

Well, Stornoway's trend line has a positive gradient.

In other words, the line is going up as you look at it from left to right.

Therefore, the average amount of rainfall in Stornoway seems to be increasing.

And then in part D, you had to write a sentence describing the long-term trend of Southampton's rainfall.

Well, one thing you could say is that, over time, the general trend in Southampton is that the average amount of rainfall is very slightly decreasing, or you might say that the average amount of rainfall is staying fairly consistent over time instead.

Either of those is pretty much fine for what you can see there in that data.

And then Izzy says, "Stornoway is in Scotland.

"That means the amount of rainfall "in other parts of Scotland is also increasing over time," and you had to explain whether Izzy's statement was correct or not.

Well, it's not sensible to generalise conclusions about one location to other locations, even within the same country.

For example, a town on the west coast of Scotland may have quite different weather to a town on the east coast of Scotland because they are both adjoined to different seas, and also there are mountains in between those towns as well, so they may experience quite different weather, and we can't assume that a trend that's occurring in one location is affecting the other.

Great work so far.

Let's now move on to the next part of this lesson, where we're going to be forming conclusions about local climate change.

It is possible to combine different sets of climate data about a location to identify correlations involving time.

For example, here we have a graph that shows data from the Met Office about the total monthly sunshine hours in January each year from 1941 to 2022 in the city of Sheffield.

The horizontal axis shows us what year the data was taken in and the vertical axis shows us how many hours of sunshine there were in January of that year in total.

Now, we can see a trend line here, and the long-term trend of this data suggests that the total number of hours of sunshine in Sheffield each January has increased over time.

So keep that in the back of your mind for a moment while we look at another graph for Sheffield.

This second graph shows data from the Met Office on the mean daily maximum temperatures and the total monthly sunshine hours for each month in Sheffield.

The horizontal axis shows us the mean daily maximum temperature that month, and the vertical axis shows us the total monthly sunshine hours.

Now, what we can see here, there is a positive correlation between the mean daily maximum temperature and the total monthly sunshine hours.

We can see that because as we look from left to right on the graph, the points generally seem to be going in an upwards direction.

This trend suggests that the greater the maximum temperature, the greater the total monthly sunshine hours.

Both of these graphs show a positive correlation, which you can see by the positive gradient of the trend line in each.

Furthermore, both graphs share the common variable of total monthly sunshine hours, so it may be possible to identify a correlation between the non-common variable in each graph.

Graph one shows that the total monthly sunshine hours increases over time, and graph two shows that the total monthly sunshine hours increases as the maximum temperature increases.

So what conclusion might you draw from that? Well, we may conclude that the maximum temperature may also increase over time.

So from these two graphs, we've identified a possible positive correlation between time and the mean daily maximum temperature in Sheffield, and conclusions made from comparing multiple sets of data may be more accurate than just one set of data.

So let's check what we've learned.

Here, we have two graphs.

These graphs show data about Oakfield for the months of January and February from 1970 to 2024.

The top graph shows you the number of days of air frost each month, and the bottom graph shows you the mean daily minimum temperature versus the number of days of air frost each month.

The horizontal axis would be the mean daily minimum temperature, and the vertical axis would be the number of days with air frost.

What is the common variable across both of these graphs? Pause while you write something down, and press play for an answer.

Answer is the number of days with air frost.

That is the data that appears in both of those graphs.

Could you please describe the correlation between the mean daily minimum temperature and the number of days with air frost? Pause the video while you write something down, and press play for an answer.

There is a negative correlation, which means as the temperature increases, the number of days of air frost decreases.

So could you please complete this possible conclusion using both the graphs? Because the long-term trend is that the number of days of air frost per month is decreasing over time, the mean daily minimum temperature is blank over time.

What goes in that blank? Pause the video while you write something down, and press play when you're ready for an answer.

The answer is increasing.

Because the long-term trend is that the number of days of air frost per month is decreasing over time, the mean daily minimum temperature is increasing over time.

Let's take a look at these two graphs again, which both show data from the Met Office for Sheffield.

The top graph shows data about the total monthly sunshine hours in January for each year from 1941 to 2022, and that is shown over time, and the bottom graph shows data about the mean daily maximum temperatures and the total monthly sunshine hours for every month from 1990 to 2022.

Now, earlier, we drew some conclusions by making comparisons between these two graphs.

However, the extent to which a conclusion is accurate may depend on the validity of the comparisons made between the two graphs.

Let's see what we mean by that.

If we look at the detail about what this data is about, we can see that one graph shows data from only January of each year, that's the top graph, whilst the other graph shows data from every single month of the year.

Also, the timeframes across both graphs are different, with the long-term trends from this first graph accounting for more years of data than the second graph.

The first graph is from 1941 to 2022 while the second graph is only from 1990 to 2022.

So the conclusion we made earlier that the maximum temperature may also increase over time may not be accurate, as only a small proportion of the data from each group has been taken from the same periods of time.

In other words, if you read off every single data point in the top graph for total monthly sunshine hours, and you read off the every single data point in the bottom graph for the total monthly sunshine hours, only some of those will be for the same months, and they are the ones which are January from 1990 to 2022, and that's because the bottom graph includes months which are not January, while the top graph includes Januarys that are before 1990.

So let's check what we've learned.

Here, we have two graphs which show data for Oakfield for the months of January and February from 1970 to 2024.

One graph shows the number of days of air frost each month, that's the top one, while the bottom graph shows the mean daily minimum temperatures versus the number of days with air frost with the horizontal axes showing you the mean daily minimum temperatures and the vertical axis showing you the number of days of air frost.

Could you please give one reason why conclusions from comparing these two graphs may be accurate? Pause the video while you write something down, and press play for an answer.

Well, both graphs show data from the same location and in the same time period.

That is January and February from 1970 to 2024.

Okay, it's over to you for Task B.

This task has three questions, and here is question one.

Pause while you do this, and press play for question two.

And here is question two.

Pause while you do this, and press play for question three.

And here is question three.

Pause while you do this, and press play when you're ready to go through some answers.

Okay, let's go through some answers.

In question one, you had a table showing data from the Office of National Statistics.

That's the ONS.

And the data shows the distance that people in the North-East and South-East of England travel to work each day in the year 2011.

In part A, you had to write how many people's information from the North-East were collected for this survey, and that was 976,945 people, which you get from adding together all of the frequencies for the North-East.

And in part B, you had to write down the fraction of people in the South-East of England who travel under 10 kilometres each day to work.

Well, there are 1,999,263 people in the South-East of England who fit that category out of a possible 3,378,119 people altogether there.

Now, one possible influence on the climate of an area is the amount of carbon emissions, which is linked with travelling longer distances.

In part C, you're asked in which location, either North-East or South-East, does a higher percentage of people travel 20 kilometres or further each week to work? Well, that would be the South-East.

Then, in question two, you had a graph that shows data from the Met Office on the mean yearly temperatures for two locations in the UK.

One data set is for Manston, which is from 1961 to 2022, while the other is for Durham, and that shows from 1941 to 2022.

The long-term trends of both Manston and Durham are similar.

Both locations show an increase in the mean yearly temperature over time.

However, in which location is the mean yearly temperature increasing at a faster rate? Well, that'll be Manston's yearly temperature, which is increasing at a faster rate than Durham's, because Manston's long-term trend has a steeper gradient.

Now, you may want to note here that temperature increasing at a faster rate is due to the steepness of Manston's trend line, not because the line itself is higher up than Durham's.

In question three, you had to use both your answer to question 1C and question two to write a possible link between the distance travelled to work and the mean yearly temperature.

Well, based on the answers we gave earlier, the link could be that locations where people travel longer distances to get to work, and therefore results in higher carbon emissions, those locations have temperatures that increase at a faster rate than locations where fewer people travel longer distances to get to work.

In part B, you had to explain the extent to which this link is accurate or not by assessing the validity of the comparisons across both sets of data.

Well, there's a few things you could say about this.

One is that whilst both the table in question one and the graph in question two show data from the South-East and North-East regions of England, the graph only shows data from one part of each region.

Therefore, it may not be valid to generalise the conclusions that we make about the rate of temperature increasing from Manston and Durham to the rest of each region.

But there are some positives that do give validity to our comparisons, and one of them is the timeframes.

Both regions in the graph show data from 1961 to 2022, with Durham having data going back to 1941.

This allows for light comparisons between the regions for those particular years from 1961 to 2022.

Furthermore, the table from question one shows data collected in 2011, which is within the same timeframe.

Fantastic work today.

Now, let's summarise what we've learned.

There are many elements to a climate, such as temperature, the amount of rainfall, the amount of air frost, et cetera.

A measurable element of the climate across multiple locations can be compared on a graph.

It is possible to establish and compare long-term trends between these locations.

However, these trends cannot be generalised to other locations or other elements of climate without further information.

It is, however, possible to begin forming conclusions about the climate of a location by considering data from multiple different elements of its climate, and analysing any links between each dataset.

However, the extent to which these conclusions may be helpful depends on whether each dataset shows trends from the same location over the same period of time.

Well done today.

Have a great day.