How will the Great Lakes region's climate change in the coming years? Using the maps in this lesson, you will determine what the major trends are that will be seen in the future. 

We will then try and figure out why these trends are occurring throughout the rest of this unit. 

About this map:
Projected increases in annual average temperature by 2041-2070 as compared to the 1971-2000 period, assuming emissions of greenhouse gases continue to rise (A2 scenario). Northern areas will likely see greater warming by mid-century. The Eastern Upper Peninsula and Northern Lower Peninsula of Michigan may see particularly significant changes.
 

About this map:
Projected increase in total annual precipitation by 2041-2070 as compared to the 1971-2000 period, assuming emissions of greenhouse gases continue to rise (A2 scenario). Projections of precipitation are highly variable by location, individual model, the timeframe considered. Total annual precipitation is generally projected to increase across the region, but may remain nearly stable or decrease in parts of the Upper Peninsula of Michigan, Northern Wisconsin, and Northern Minnesota. 

Use your list of factors in the system to make an initial model of the Great Lakes System and to try and explain the changes in temperature and precipitation. How do you think the factors in the system are interacting to change temperature and precipitation averages in the future?

Use what you have learned from the Modeling Intro activity and discussion to help you develop your initial model. Initial models are intended to show your current thinking, and you will revise your model multiple times as you learn new information throughout the unit. 

Scientists are constantly revising their models based on new information they learn. So we work through the same process to try and explain what is happening in the phenomena we study.

Using prior knowledge and what you heard others say in our class discussion, use the following questions to help think about the Great Lakes system.

Develop an initial model of the system, based on the list generated in the class discussion.

As you developed an initial model, you likely had questions about how different components interact. Capture these thoughts in the question below. 

We are building off of the demonstration of the water cycle to investigate what is happening to water in the Great Lakes system. 

In this lesson you will have a chance to think about the steps of the water cycle and what is happening in the system. You will also create an initial model in Sage Modeler and use simulation features to think about our phenomena of climate change in the Great Lakes.

In class, you looked at a cup of water with food coloring sealed in a ziploc bag and placed in the window. 

The changes you saw in the bag on a macroscopic scale are due to changes at the microscopic scale. 

As the water moves from the cup and into the bottom of the sealed plastic bag, we are witnessing changes in matter and energy.  On the first page, we determined that the energy source present here is the sun. The water in this demonstration is moving through 2 phases of the water cycle. 

In order for matter to move, it must change state. The three major states of matter are solid, liquid and gas. The specific molecules here are present in liquid or gas form. 

Think about what molecules of matter are moving, where are they moving to, and if there is matter that does not move. 

Draw a model to trace matter in the demonstration. Use the key on the model to show where matter is. Use amounts of a symbol to show proportion, such as the same amount, more of one than the other, etc. 

Use the image of the water cycle below.

In this lesson you will model the interactions as water moves through the system. You will use a computational model to discover what is happening on the micro scale as the water molecules move through the water cycle.

Use the computational model below to answer the questions. These questions allow you to look at the features of the model, you should be using the graph and slider titles to explain what the model shows. You do not need to run the model yet. 

Use the model to explain what is happening with energy in the system.

From what you learned about the water cycle, we are going to start building our model of the Great Lakes system. This is the first of four stages of model development. You will come back to sage modeler and add to it throughout the unit.

Sage Modeler Guide: Slideshow

Sage Modeler Guide: Slideshow

Below is a modeling software called "Sage Modeler." Some variables of the system are present and some connections are made for you. Use the slideshow linked above to learn more about specific actions you can do in this software, like adding arrows and labeling arrows. 

1. Add other connections that are present in the system by adding arrows.

2. Define the arrows relationship. Once you do that, it should turn red (direct relationship) or blue (indirect relationship). 

3. Label the arrows. The label should be the process the arrow represents, such as "evaporation."

Take a screenshot and upload the file below. 

Use the “Simulate” function to change the amount of a variable (See Sage Modeler Guide for assistance). Do this one variable at a time. You can also change the relationships set between variables (from “about the same” to “a little” or “a lot.”)

In this lesson you will be conducting a lab and collecting data to learn about humidity.

For this lab, you will use the following materials:

• Glass or plastic container, with lid
• Spray bottle filled with water
• Digital temperature and humidity meters
• Grow light
• Stopwatch or timer

Set up your first investigation using the following procedure: 

1. Open plastic box and put in humidity and temperature sensor. Note what units both variables are being measured in.

2. Close plastic box.

3. Take grow light and clip it on edge of table.

4. Position it so the light is approximately 10 cm above the box and the light is centered on the box (do not turn on the light).

5. Prepare a stopwatch or timer for you to count up.

6. Set up your data table below, so you will be able to record temperature and humidity data, you will take the measurement before turning on the light, and then every minute for 10 minutes.

7. When you are ready, turn on the grow light and begin timing.

In this lesson we will be using a data platform called CODAP. This will allow us to manipulate and visualize the data from the experiment. In the CODAP window below, add your data to the table. 

From the table we can create a graph. In this case we want to graph the relationship between the two variables. Click "graph" in order to begin a new graph.

On a graph, the independent variable should be represented on the x-axis. The dependent variable should be represented on the y-axis. 

Drag the appropriate attribute name from the data table to the appropriate axis. 

Set up your second investigation using the following procedure: 

1. Open plastic box and put in humidity and temperature sensor. Note what units both variables are being measured in.
2. Close plastic box.
3. Do not use grow light for this investigation.
4. Take the initial humidity and temperature.
5. Add 5 sprays of water and quickly close the container.
6. After 1 minute, record the humidity and temperature.
7. Repeat steps #5-6, 5 times.

Note: If your box is still hot from the light in investigation 1, allow it to cool down until the temperature is stable for a few minutes. 

From the table we can create a graph. In this case we want to graph the relationship between the variable we changed and humidity.

On a graph, the independent variable should be represented on the x-axis. The dependent variable should be represented on the y-axis. 

Drag the appropriate attribute name to the appropriate axis. 

With your classmates, have a discussion to determine rules that apply to humidity. These should be based on the major trends you saw in the investigations. 

Next, you will develop an investigation to test these rules in spaces in the building. If not in the same school building, think about your own homes.

Humidity needs to be added as a variable on your model.

1. Add a new variable that represents humidity.

2. Show the variables that influence humidity by adding arrows to show the relationship.

3. Be sure to set the relationship and label the arrows.

Refer to Sage Modeler Guide for help drawing arrows, setting relationships, and labeling.

This lesson provides multiple texts to build on your understanding of humidity. You will use these texts to evaluate the rules about humidity you came up with during the lab, as well as expand your knowledge of humidity. 

Text 1: Have you ever visited a place that just made you feel hot and sticky the entire time, no matter what you did to cool off? You can thank humidity for that unpleasant feeling.

Humidity is the amount of water vapor in the air. If there is a lot of water vapor in the air, the humidity will be high. The higher the humidity, the wetter it feels outside. On the weather reports, humidity is usually explained as relative humidity. Relative humidity is the amount of water vapor actually in the air, expressed as a percentage of the maximum amount of water vapor the air can hold at the same temperature.

Think of the atmosphere as a sponge that can hold a fixed amount of water, let's say a gallon of water. If there is no water in the sponge, then the relative humidity would be zero. Saturate the sponge with half a gallon of water - half of what it is capable of holding - and that relative humidity climbs to 50 percent. When humidity is high, the air is so clogged with water vapor that there isn't room for much else.

If you sweat when its humid, it can be hard to cool off because your sweat can't evaporate into the air like it needs to.

Source

Text 2:
Now, a new global study projects that in coming decades the effects of high humidity in many areas will dramatically increase. At times, they may surpass humans’ ability to work or, in some cases, even survive. Health and economies would suffer, especially in regions where people work outside and have little access to air conditioning. Potentially affected regions include large swaths of the already muggy southeastern United States, the Amazon, western and central Africa, southern areas of the Mideast and Arabian peninsula, northern India and eastern China.
 

“The conditions we’re talking about basically never occur now—people in most places have never experienced them,” said lead author Ethan Coffel, a graduate student at Columbia University’s Lamont-Doherty Earth Observatory. “But they’re projected to occur close to the end of the century.” The study will appears this week in the journal Environmental Research Letters.

Source

Hot summertime temperatures in Chicago are often accompanied by high humidity, making it feel much warmer than it actually is. The saying – “it’s not the heat, it’s the humidity” – is often true during Chicago’s summer. The high humidity often results in oppressive heat index values and dangerous heat waves during the summer months. The heat index is an index that combines air temperature and relative humidity to measure the human-perceived equivalent temperature (i.e. how hot it actually feels).

Studies have projected that the region will experience higher dew points in the future, leading to hot days feeling even hotter due to this increased humidity. As a result of higher temperatures and humidity in Chicago, the frequency, duration, and intensity of heat waves are likely to increase substantially in the future. One study projects an increase of between 166 to 2,217 excess deaths per year from heat wave-related mortality in the City of Chicago by 2081- 2100, depending on the climate model.

Source

In 1783, Horace Bénédict de Saussure built the first hygrometer, a device to measure humidity, and he built it with… hair. To understand how it worked, you need to know a thing or two about hair.

A single strand of hair has many layers. The inner layer is filled with proteins called keratins that bind to each other, giving shape to your luscious locks. These proteins bind by forming tough disulfide bonds or weaker hydrogen bonds. You can thank hydrogen bonds for the funny way your hair dries naturally after getting out of the shower. Water molecules (two hydrogens and an oxygen) are soaked up by your hair and act as a bridge linking keratin molecules together in place. These hydrogen bonds keep your hair fixed in shape until you wet it again, allowing new hydrogen bonds to form.

In high humidity, water molecules in the air find their way into straight strands. As hydrogen bonds connect keratin proteins, hair starts to fold back on itself and curl. Frizzy fly always occur when hair folds back enough to break the cuticle – or the outer layer of hair that looks like dragon scales under a microscope.

Enter hygrometer. Saussure attached one end of a 10-inch piece of human hair to a screw. The rest of the strand he maneuvered through a pulley and attached to a weight. As the hair took on moisture, the strand curled and shortened moving the pulley and lifting the weight. Saussure could then calculate how much humidity was in the air based on how much the weight moved.

Source

Use the texts you've read to answer the following questions. Refer to multiple texts in your answers. 

We have discovered that the temperature in the great lakes is rising, and precipitation is likely to increase as well. We have modeled the energy and matter flow in the water cycle, and discovered the factors that govern humidity. 

In this lesson, a text set will allow you to explore information about storms, and how the components in the system may change storm frequency or strength. As you read, take time to annotate, ask questions, and re-read. You may need to revisit different pages to develop a full explanation of what is happening. 

Read this first text or watch the video to learn more about how thunderstorms form.

How does a thunderstorm form?
Source: National Oceanic and Atmospheric Administration (NOAA)

Video Link: https://scijinks.gov/thunderstorms-video/

Extreme weather gets a boost from climate change

Source: Environmental Defense Fund

Scientists are detecting a stronger link between the planet's warming and its changing weather patterns. Though it can be hard to pinpoint whether climate change intensified a particular weather event, the trajectory is clear — hotter heat waves, drier droughts, bigger storm surges and greater snowfall.

Storms and floods

As more evaporation leads to more moisture in the atmosphere, rainfall intensifies. For example, we now know that the rainfall from Hurricane Harvey was 15 percent more intense and three times as likely to occur due to human-induced climate change. We expect to see a higher frequency of Category 4 and 5 storms, also, as temperatures continue to rise.

While scientists aren't certain about whether climate change has led to more hurricanes, they are confident that rising sea levels are leading to higher storm surges and more floods. 

Around half of sea-level rise since 1900 comes from the expansion of warming oceans, triggered by human-caused global warming. (Like all liquids, water generally expands as it heats up.) The rest of the rise comes from melting glaciers and ice sheets.

Snow and frigid weather

There is more moisture in a warmer atmosphere, which can lead to record snowfall.

It may seem counterintuitive, but the increase in snowfall during winter storms may be linked to climate change. Remember — there is more moisture in the warmer atmosphere. So when the temperatures are below freezing, snowfall can break records.

And scientists are studying a possible connection between a warming Arctic and cold spells in the eastern United States. The idea is that a rapidly warming Arctic can weaken the jet stream, allowing frigid polar air to travel farther south.

For the Midwest, Epic Flooding Is the Face of Climate Change (abridged)
By: MEGAN MOLTENI, 05.24.2019

Source: Wired.com

Scientists say it’s too early to tell to what degree this particularly relentless spring storm season is the result of human-induced climate change. But they agree that rising temperatures allow the atmosphere to hold more moisture—about 7 percent more for every 1 degree rise in Celsius—which produces more precipitation and has been fueling a pattern of more extreme weather events across the US. And perhaps more than any other part of the country, the Midwest has had its capacity to store excess water crippled by human developments like paved or concrete surfaces.

In 2015, researchers at the University of Iowa studied historical records of peak discharges from more than 700 stream gauge stations across the Midwest. Their analysis, reported in Nature, found that between 1962 and 2011, the magnitude of flood events hadn’t changed much. At a third of the locations, however, the number of floods was trending upward significantly.

More recent work, published in February by scientists at the University of Notre Dame, shows that floods aren't just getting more frequent—they'll also get more powerful in the future. Using a statistical method to blend data from global climate models with local information, the researchers predicted that the severity of extreme hydrologic events, so-called 100-year floods, hitting 20 watersheds in the Midwest and Great Lakes region will increase by as much as 30 percent by the end of the century. The approach, called “Hybrid Delta downscaling,” has been used to look at hydrological dynamics in other parts of the country before, but it was never applied to the Midwest. “What we’re seeing is that the past really is not a good predictor of the future,” says the study’s lead author, Kyuhyun Byun. “Especially when it comes to extreme weather events.”

In Byun’s case, the evidence is as much in his computer simulations as it is in his own waterlogged backyard. In South Bend, Indiana, where Notre Dame is located, the city is still recovering from back-to-back biblical deluges—a 500-year flood last spring preceded by a 1,000-year flood in 2016 that broke all historical records. 

Besides all the damage to homes, businesses, and municipal infrastructure, increasingly frequent flooding events in the Midwest would have a huge impact on the nation’s ability to produce food. Wet fields make it difficult for farmers to operate their large, heavy planting machinery without getting stuck. And seedlings struggle to develop root systems when there’s too much moisture in the ground.

Lake effect snow is a localized storm phenomena that results from a combination of variables.

When the lakes are not completely frozen over and the temperature of the lake is warmer than the temperature of the air, conditions are present that allows the air to increase the water vapor content. Air holds a certain amount of moisture dependent on its temperature. The amount of moisture in the air compared to the amount the air can hold is called relative humidity.

As wind currents move west to east (across the lake) and cold air moves down from Canada over the lakes, the air picks up moisture from the warmer lake and deposits it on the eastern or southeastern side (downwind) of the lake. This results in lake effect snow.

An increased rate of warming in the northern Great Lakes allow for more moisture to be absorbed. This may result in larger lake effect snow events to the east of a lake (see image). 

Connect the four texts to explain what is happening in the Great Lakes region.

In the Sage Modeler file below, use what you have learned to connect variables to storm strength. 

Refer to Sage Modeler Guide for help drawing arrows, setting relationships, and labeling.

We will analyze what the past 20 years have looked like in the Great Lakes. To do this we will use a simulation that uses Climate.gov data. You will be looking at 4 different months (1 per season) over the past 18-19 years. 

We want to see if what has happened since 2000 tells us anything about future temperature and precipitation projections. We also want to see how these changes look seasonally. You will collect and record data, then we will have a class discussion to think about what this data means. 

Use the simulation below to collect data. Collect data for temperature or precipitation by changing the months or years and clicking "record data" before you move to the next month/year. Use the graphing feature to visualize your data, by dragging from the table the variable you want on the x or y axis. 

Notes on using the simulation:

• Make sure "auto update" is clicked (dark blue), so the map will update. 
• Wait a few seconds after changing month or year for the map to update (you will see the new map show up).
• Change the months or years to see look for patterns in the data. 
• Look at temperature or precipitation maps.

The model in the previous lesson allowed you to determine some trends, perhaps in a single city or a single time of year. But, maybe you were frustrated by not being able to see the whole picture. Climate scientists often have to go through a lot of data to determine trends. In this lesson, you will see graphs that have used the data from the model in the previous lesson. There will also be data from other scientific organizations in the Great Lakes, like University of Michigan's Great Lakes Integrated Sciences and Assessments (GLISA). 

A warmer atmosphere holds more moisture, increasing the frequency and intensity of heavy rain and snow events. Overall U.S. annual precipitation increased 4% between 1901 and 2015, but the Great Lakes region saw an almost 10% increase over this interval with more of this precipitation coming as unusually large events. In the future, precipitation will likely redistribute across the seasons. We expect wetter winters and springs, while summer precipitation should decrease by 5-15% for most of Great Lake states by 2100.

(source: http://elpc.org/wp-content/uploads/2019/03/Great-Lakes-Climate-Change-Report.pdf)

The text above is from the Climate Change Report from the Environmental Law and Policy Center. Use the text along with the graphs on page 1 and 2 to answer the following questions.

Graphs from page 1 and 2:

In the 2019 Climate Trends report from GLISA, there is additional focus on lake water temperature and lake water levels. The following graphs show changes from 1950-2019 in the 5 lakes (Michigan and Huron are grouped together). 

Retrieved from: (https://glisa.umich.edu/2019-annual-climate-trends-and-impacts-summary-for-the-great-lakes-basin/)

Ice cover is another indicator and major factor that affects the Great Lakes' seasonal weather patterns. Ice cover is impacted by a number of different factors, and is especially dependent on the location of the jet stream, a band of fast moving air in the atmosphere. 

You have learned a LOT about the factors influencing climate in the Great Lakes. You are going to put that together to develop your final model. 

In the Sage Modeler window below, you have the model we have added to throughout this unit. Use this model to develop a final explanation for the unit, including collecting evidence by using the simulate and graphing functions.