Temperature Trend: Measure, Accelerate & Analyze!

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In summary, the conversation discusses an experiment where the evolution of temperature was measured over a 5-week period. The individual wants to show that the increase in temperature is accelerating as summer approaches and asks if comparing the slope of linear trends over different time periods is appropriate. There are several factors to consider, such as the duration, time phases, and number of data points used to calculate the trend. The proposal to compare the slopes of linear trends over different time periods may be affected by fluctuations in weather and the reliability of the trend depends on the number of data points used. A more robust approach would be to look at successive differences in temperature over a specific time interval.
  • #1
Helena123
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Hi,

We made an experiment in class where we measured the evolution of temperature over the course of 5 weeks.

I'd like to show that the increase in temperature is accelerating as summer is coming.

Is it enough to calculate the slope of the linear trend over the full 5 weeks and show that it is smaller than the slope of the linear trend over the past week (increase in trends) to deduce that it is accelerating ?

I'm asking because my teacher said that comparing linear trends over different time periods is not always appropriate and can be biased, but i don't understand why.

Thanks.
 
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  • #2
Anyone ?

Is it correct to compare the slope of linear trends over different time periods ?
 
  • #3
What data points did you measure? Temperature at different times of day over a 5 week period? Or temperature at one particular time of day? Or what?
 
  • #4
There are many possible issues here:
a) Trends over different durations
b) Trends over different time phases
c) Trends over different numbers of datapoints
d) Trends over sufficient numbers of datapoints
e) Trends over sufficiently long intervals (for the purposes of the inference)

Weather temperatures tend to vary over cycles (daily obviously, but in cities also weekly). You should be ok on that if the hours and days of week are equally represented. E.g. whole weeks, same times of day, in both datasets, but not necessarily the same number of weeks.
They're also prone to fluctuations lasting several days as weather systems move through. This makes trends of that timescale or shorter rather useless for tracking the change of season.
(c) should not be an issue provided you understand that the reliability of the trend will depend on the number of datapoints used to calculate it. So if you compare a trend over two weeks of daily noontime data with another such over three weeks, the second trend will be the more reliable, but does not invalidate a comparison.

Your specific proposal might be invalidated by (e). You could make it more robust by looking at successive differences, i.e the increase over each period of exactly 24x7. Then you can see if there's a trend in these deltas.
 
  • #5


Hello,

Thank you for sharing your experiment and question with me. It is great to see that you are actively thinking about the results of your experiment and seeking to understand them further.

In order to accurately determine if the increase in temperature is accelerating, it would be beneficial to analyze the data using more than just the linear trend. While the slope of the linear trend can give us some information about the change in temperature over time, it may not fully capture the complexity of the data.

One way to further analyze the data would be to use a statistical method called regression analysis. This method looks at the relationship between variables and can help determine if there is a significant change in temperature over time. By using regression analysis, you can also account for any potential biases in the data and better understand the patterns of the temperature trend.

Furthermore, it is important to consider other factors that may be influencing the temperature trend, such as location, weather patterns, and human activities. By taking these factors into account, we can better understand the trend and make more accurate predictions for the future.

In conclusion, while calculating the slope of the linear trend can give us some information about the temperature trend, it is important to use other methods of analysis and consider other factors to fully understand and accurately interpret the data. I encourage you to continue exploring and analyzing your data to deepen your understanding of the temperature trend.

Best,
 

What is the purpose of measuring temperature trends?

Measuring temperature trends allows us to track changes in temperature over time and understand how the Earth's climate is changing. This information is important for making informed decisions about how to address climate change and its impacts.

How is temperature trend measured?

Temperature trends are measured using instruments such as thermometers, satellites, and buoys that record temperature data at regular intervals. This data is then analyzed and compared over time to detect patterns and trends.

Why is it important to accelerate the measurement of temperature trends?

Accelerating the measurement of temperature trends allows us to have more timely and accurate data, which is crucial for understanding and responding to the effects of climate change. It also allows us to identify any sudden changes or anomalies in temperature trends that may require immediate action.

What factors can accelerate or decelerate temperature trends?

Temperature trends can be influenced by a variety of factors, including greenhouse gas emissions, changes in land use, natural climate cycles, and human activities such as deforestation and industrialization. These factors can either accelerate or decelerate the rate at which temperature trends change.

How are temperature trends analyzed?

Temperature trends are analyzed by using statistical methods to identify patterns and changes over time. This can include looking at long-term averages, identifying trends and cycles, and comparing data from different locations to understand regional variations.

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