Is there more extreme weather?

Max Berkelhammer, University of Illinois at Chicago

Initial Publication Date: June 17, 2015

Summary

There is an anecdotal perspective that weather is becoming more "extreme". Scientists, however, really grapple with proving whether this is true or not. In this activity students will address the problem of how to define a weather event as "extreme". Students will access historical weather datasets and assess whether their definition of "extreme" is effective. Students will then access future climate projections to assess whether extreme events are likely to be more common in the future.

Climate Change | College Upper (15-16)

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Context

Audience

This activity is best for a mid to upper level undergraduate course. It is designed specifically for a class called "Earth Systems" (200 level), which is a required course for Earth Science majors. It could be tailored to higher level classes and would be particularly be well-suited for "Geostatistics/Geomath", "Global Change", "Natural Hazards" or "Atmosphere and Oceans" courses.

Skills and concepts that students must have mastered

Students should have basic competence with Excel or a similar spreadsheet application. They will need to cut and paste fairly large arrays into a spreadsheet and make basic calculations such as mean, median and percentiles and as well make a histogram and know how to sort and/or filter the data. The class could be tailored for higher level statistics such as analyzing different types of distributions.

How the activity is situated in the course

This could be utilized as a stand-alone one or two-part lab. The activity addresses one of the impacts of anthropogenic climate change. The activity is also well-suited to address the role of the media and personal experiences in shaping our understanding of climate change. The activity could also be used very early in the class to help familiarize and introduce students to datasets available to study climate change and help to understand the difference between weather and climate.

Goals

Content/concepts goals for this activity

What is the difference between weather and climate? How variable is the climate? How does rising CO2 impact weather patterns? Basic statistics such as calculating mean, median, standard deviation and percentiles. What shapes our perceptions of climate change? How and where can we access historical meteorological data and climate model projections for the future?

Higher order thinking skills goals for this activity

Climate describes the mean state whereas weather describes singular events. However, a shift in the frequency of weather events is a type of climate change. There is a subtle take-away about the gray area in understanding how climate change relates to changes in extreme events.

Other skills goals for this activity

Familiarity with KNMI Climate Explorer. Cut, paste and analyze data using Excel.

Description and Teaching Materials

1) A first-pass definition of Extreme:

Students will work in groups of 3-4 to decide on a definition for "extreme weather". Is this defined by economic losses? Population affected? Wind speeds? Precipitation amounts? Can the students think of a quantitative or statistical definition that could be universally applied? What are come examples of extreme weather events they can think of. As a group, decide on one single event to explore in detail.

2) Access Met. Data:

After deciding on an event, for example the 2014 Polar Vortex, students will go to KNMI Climate Explorer (http://climexp.knmi.nl/) and access the "Daily Station Data" link. This provides them access to a global meteorological database of Precipitation and Temperature (min, max and mean). They will search for stations that were influenced by the Extreme event they are analyzing. (When searching for records it is preferable if students filter data to only access weather records that are 100 years or longer, this will place their weather event into a historical context.) Within KNMI explorer they can plot up the weather from their meteorological station. Is the event they chose observable in the timeseries from this site.

3) Download and process Met. timeseries:

They will download the raw data which, will be a time series of from their station. This will then be pasted into an Excel spreadsheet.

Calculate the mean, median, 25th/75th percentiles and standard deviation of this data set. Make a histogram of this data. Describe the basic characteristics of this dataset in terms of its distribution.

4) Analyze the frequency and characteristics of an extreme event:

Find the Extreme event you are analyzing in the timeseries and record its exact magnitude. For example, the minimum temperature during the 2014 Polar Vortex in Chicago was -26.7C.

How frequent did events of the magnitude of the chosen extreme event occur?

Over time, are there trends in the frequency of events of this size? For example, how many times did events of this magnitude occur during each decade of the 20th and 21st centuries?

Does this event fall into the 99 percentile of data?

5) A second-pass definition of Extreme:

After looking at one extreme event in detail, ask students to reconsider their definition of extreme. Can they come up with a definition that is more universal or quantitative? Was their first definition sufficient? Ask groups to submit their definitions and discuss together all the definitions. Can a better definition be reached using all the group's analyses of extreme events?

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Extension: Use 21st Century climate projections to see if the type of extreme event is going to be more common

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6) Access climate mode outputs:

In KNMI Climate Explorer there is an option for "Daily Fields" which provides climate model outputs. For example, "GFDL SRES A1B 2081-2100" provides global outputs for the year 2081-2100 using the Geophysical Fluid Dynamics Lab (GFDL) climate model run in a scenario where carbon emissions continue to increase and then eventually start to decrease (SRES A1B). Students could look at different model outputs or different scenarios depending on the amount of time made available for this activity.

7) Extract climate model timeseries:

Students will make a timeseries from the climate model output for the same latitude and longitude where their meteorological data in "Step 2" came from. This will be their "future" weather data. This data will be cut and pasted into Excel.

8) Analyze Future weather:

Students will calculate mean, median, standard deviation, and distribution (i.e. repeat Step 3) for the future data. Is there an observable change in the climate or weather in their future scenario?

9) Future extremes:

Using their climate model timeseries students will assess whether events comparable to their "extreme" events occur in the future scenario. Are they more or less frequent? Is this enough data to assess the presence of change?

10) A third-pass definition of Extreme

Having looked at historical and future weather data, would students want to modify how they define "extreme"?