Sea Level Change
and its causes from other global problems
Real-world Data Analysis and Mathematical Modeling Spreadsheet Projects
Below in GSheets is a graph of the average annual tidal height (mm) as a function of time in years for a tide gauge in Alaska. Click on the tabs for additional sites.
Need GSheets help, see Dealing with Scientific Data in Google Sheets: Data > Model > Simulation - YouTube tutorial videos and accompanying GSheets spreadsheets to gain hands-on skills!
Using the Permanent Service for Mean Sea Level, illustrate how to find a tide gauge in or near your location, download the data (see YouTube video), graph and analyze the data, to determine relative sea level change for your local site. A little background on tides is given here - The Ups and Downs of Tides (NEW).
To see how to use this in the classroom, check out Paradise Lost: Chesapeake Bay and Sea Level Change and the interactive Excel file (right click and save as), which describes relative sea level change, its causes, and the inundation of land, especially low-lying areas. More resources for the classroom are given on links in the GSlides above.
With students working in groups, a nice online collaborative activity can be done by having groups enter results into a shared GSheets spreadsheet as shown below. To spur a discussion have groups or individuals leave comments (provides a record) or use the chat feature (does not save). Instructors may need to pose a question to help students get started.
What is the result of sea level rise? Coastal flooding, see data for US Coastal Flooding Events (NEW with interactive map).
Determining Global Mean Sea Level using NASA Satellite Altimetry (Sea Level Height) is summarized in the GSlides below. This project and the next will involve some data translations, for a review click here.
The GSheets spreadsheet required for analysis is provided below.
Here we will examine seven different satellite data sets to see how they influence sea level. To edit or interact with the spreadsheets , go to File > Make a copy… (this saves a copy into your Google account). This project gets students examining large data sets and data with a large amount of scatter, which is being referred to as "messy data."
Links to Google Sheets Spreadsheets
Each spreadsheet contains a large data set to model and address questions. For a summary table of the data sets, click here.
Antarctic Ozone Hole (for class discussion) (updated)
Carbon Dioxide (updated)
Methane (updated)
Nitrous Oxide (updated)
Global Temperature (NEW - now with choosing your baseline tab)
Glacial Ice Mass (updated)
Ocean Heat → thermal expansion (updated)
Increasing Volume of Seawater (validation)
Mountain Glaciers (NEW data set)
This is designed to be an online collaborative class project. Student groups are assigned a data set from the list above. Groups will report back to the class as a whole by producing a Google Slides summary and having a whole class discussion either face-to-face or via online.
THE BONUS ROUND... some data, math modeling, and chemical concepts in GSheets
Diagram from : https://www.mbari.org/climate-change/
Ocean Acidification: Aquatic Chemistry
How is seawater responding to increased atmospheric carbon dioxide levels?
Ocean Acidification II: Calcium Carbonate
What the SHELL is happening?
As ocean heat energy rises from global warming, what happens to oxygen gas solubility?
Ocean Acidification III: Data Project from Station Aloha in Pacific Ocean NEW
Examine 30+ years of data to discern seawater trends near Hawaii
Here is a free and useful guide for integrating climate science concepts into high school and college chemistry: Climate Science Concepts Fit Your Classroom: A Workbook for Teachers by Jerry Bell.
Project 4 - Messy Data: What is it?
Here we explore messy data, which is data with a large amount of scatter in it. The scatter is typically produced by random error during the measurement process in many big data sets. We attempt to develop a definition of messy data. Examining errors, especially in multivariable systems, is a mind-set that needs to be developed. See the Earthshine spreadsheet below to examine scatter. We will modify the function machine to handle errors and be multivariable!
We can modify the classic function machine to develop a new mindset about measurement error in multivariable systems that will help handle large scatter in big data.
In mathematics, the input and output have a standard deviation of zero. In the sciences, both the input and output have standard deviations greater than zero.
Here are some links to big data sets with large scatter that would be effects from climate change. They are all Google Sheets spreadsheets in various stages of development. Illustrates many different data treatment methods including climates stripes. For details on climates stripes - click here
Cherry Blossoms - Kyoto, Japan - updated
Cherry Blossoms - Washington, DC - updated
NYC Central Park Weather - updated
US Wildfires - updated
US Tornadoes - NEW - updated
Hurricanes in the Atlantic - NEW - updated
Rainfall in the United Kingdom - NEW + introduces Precipitation Stripes - click here
Rainfall in the Contiguous United States - NEW + introduces Precipitation Stripes
Tide Gauge at The Battery, NYC - NEW
Barrow (now Utqiaġvik), Alaska Temperature - NEW
Permafrost in Alaska (neat interactive map)
Examining the Global Surface Temperatures: What is in the pipeline for the future?
(full references/links to original sources are included on each spreadsheet)
Project 5 - Sea Ice: It's Just Floating Frozen "Seawater"
Sea ice, a part of the Earth’s cryosphere, is measured by two different methods - extent and area. Here we examine the differences and their changes over recent times for both polar regions.
Distribution of water on Earth - click here (NEW)
Three GSheets spreadsheets are provided:
The Making of Sea Ice (the composition of sea water and its freezing process)
Sea Ice: Extent vs. Area (Define each and demo a probability model for extent)
Sea Ice or Floating Frozen “Sea Water” (Big data, monthly and yearly for 1979-2023 for both poles, compare extent vs. area, seasonal behavior)
Big Data with Scatter Projects