HW_Temp Proxies go to the Movies

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Temple University *

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0836

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Electrical Engineering

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Dec 6, 2023

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EES 0836 Temperature Proxies go to the Movies Disasters: Geology vs. Hollywood Introduction "Everyone talks about the weather, but no one ever does anything about it." --Mark Twain Mark Twain's famous quip no longer rings true. Apparently, humans are doing a great deal to influence the weather. At least that is what the "hockey stick" plot shows, published by Mann et al. in 1999 in an issue of Nature (inset image). The plot shows time on the x-axis and global average temperature relative to 1995 in degrees Celsius on the y-axis. Notice the rise of approximately half of a degree coinciding with the industrial revolution. Why is the plot disputed? Doesn't it clearly show that the temperature was stable for hundreds of years, and then suddenly shot up? Well, the catch is that we cannot go back in time and record the actual temperatures hundreds of years ago, so the plot is based on "proxy" data. A temperature proxy is something we can measure that is not temperature, but is related to temperature "well enough" to be used as a substitute. Mann et al. used combinations of paleoclimate data – tree rings, coral growth, stable isotope data, etc. – to deduce historic temperatures. For example, the amount a tree grows each year can be seen in the rings, and trees tend to grow more in warmer years. The interpretation of the proxy data is at the heart of the dispute over this famous plot. Learning Objectives ● Understand how proxies can be used to help make predictions. (1, 2, a, b) ● Explain the limitations with proxies relative to their use in climate science. (1, 2, c) Part 1 : A Hollywood Analogy Let's get the flavor of proxies by investigating a different type of data set. Suppose we want to predict the first-year box office receipts of a new movie that is based on a best-selling book. Hollywood has made movies based on books many times before, so we have earnings data for previous movies that are spun off books. We can compare with three things that might be related (proxies). Three possible proxies: 1. the movie's production costs (big budget films tend to make more money) 2. the amount of money spent on promotion (more ads, more sales) 3. total book sales (movies are usually based on best-sellers)

EES 0836 Here is our data set (all numbers are in millions of dollars): Box Office Receipts Production Costs Promotion Costs Book Sales 85.1 8.5 5.1 4.7 106.3 12.9 5.8 8.8 50.2 5.2 2.1 15.1 130.6 10.7 8.4 12.2 54.8 3.1 2.9 10.6 30.3 3.5 1.2 3.5 79.4 9.2 3.7 9.7 91.0 9.0 7.6 5.9 135.4 15.1 7.7 20.8 89.3 10.2 4.5 7.9 Using the provided graph, plot the Production Costs (horizontal X values) vs. the Box Office Receipts (vertical Y values). If you plot the data correctly, your plotted points should be close to the best fit line (thin red line).

EES 0836 Questions 1. Do you think it is reasonable to use production costs to predict box office receipts even though the line does not fit the data perfectly? Why or why not? Yes. It trends in the same directions for majority and are close to the line 2. If you were a Hollywood producer, would you rather your new movie plotted above or below the line? Why? Above. It means you would be making more money in the box office than predicted 3. Plotted below are the two other proxies for box office receipts. Of all three proxies plotted, which do you think is the best predictor? Explain your reasoning. The first plotted proxy because the plots are closer to the line Clearly, all three proxies hold some predictive value (there is at least some correlation between all three and the box office receipts). Is it possible to combine the information from all three to come up with an even better predictor? The easy answer is: Yes . This can be accomplished using a method called "multiple linear regression," which tells us what weighted combination of our three predictors yields the best proxy. For this data set, it turns out the best predictor is the combination shown in the last column of the table below. (As before, all numbers are in millions of dollars.) Finish filling in the values for the combined proxy in the last column of the table. NOTE there is an order of operations issue here. You need to do the multiplication of the values in the parentheses first , then add the four terms together to get the final answer. Combined Proxy = 7.7 + (3.7 x Production Cost) + (7.6 x Promotion Costs) + (0.8 x Book Sales)

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EES 0836 Box Office Receipts Production Costs Promotion Costs Book Sales Combined Proxy 85.1 8.5 5.1 4.7 81.6 106.3 12.9 5.8 8.8 106.55 50.2 5.2 2.1 15.1 54.98 130.6 10.7 8.4 12.2 120.89 54.8 3.1 2.9 10.6 49.69 30.3 3.5 1.2 3.5 32.57 79.4 9.2 3.7 9.7 77.62 91.0 9.0 7.6 5.9 103.48 135.4 15.1 7.7 20.8 138.73 89.3 10.2 4.5 7.9 85.96 Finish filling in the values and plot this proxy in the graph below. Draw a “best fit” line by eye using a ruler or edge of a book. This is not connect-the-dots , but more like an average of all the dots. Just try your best with this line.

EES 0836 1. Why is there a significant benefit to using this combined proxy over any one of the individual proxies? A combined proxy allows you to get a more accurate and reliable result 2. Is the new combined proxy as good or better than individual measurements? Why or why not? It is better than individual. The dots lie very close to the line which shows it is a good predictor involving those factors 3. Our data is limited in that it only accounts for a couple of unidentified recent movies. If we were to expand our data set to reflect the past 50 years worth of information, would you expect the same trends (how the data fits with each other) to continue, or would you expect the data to become more uncertain? I think it would become more uncertain because the film industry is un predictable. Part 2 : Back to Climate In this unit’s reaction video, Atsuhiro Muto, PhD, watched a clip from the movie The Day After Tomorrow (2004) and noted the value of proxies in reconstructing past climates. In reference to the Mann et al. plot at the beginning of this assignment, the plot includes “uncertainty limits” (fine dashed lines that surround the main curve). The degree of uncertainty is not constant throughout the plot. I know this might be difficult to see, but trust me, the “uncertainty” gets wider as you go back in time (to the left on the graph). 1. Think about what we have seen in this activity about the predictive value of individual vs. combined proxies and the different types of climate/temperature proxy data Dr. Muto discussed in the video we watched. Why does the uncertainty about temperature increase the farther back in time you go? There were less records of temperature recordings and the technology was less advance than it is today to record 2. If there is “uncertainty” with reconstructing past climate, why is it still important to reconstruct past climates? It is important because it gives a general idea of the earth’s climate so we can look at it again in the future 3. What function does proxy data and understanding past climate serve when trying to predict future trends in climate? It creates trends and analyzes data to create connections so future climate directions can be predicted 4. What role do you think climate science should play in driving the U.S. government to structure policy associated with curbing climate change? The U.S. government should be taking climate science into consideration when implementing new laws that might affect the environment. It allows lawmakers to have evidence to what is causing the climate change and change it

EES 0836 5. What role do you think climate science should play in driving international governments to structure international agreements associated with curbing climate change? It should play a major role in structuring international agreements related to climate change. We all live on the same Earth so whatever pollution a country emits, everyone else is affected by it

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