Climate change and extreme values analysis
Alexandre Martinez
University of California-Irvine
This activity was selected for the Teaching Computation in the Sciences Using MATLAB Exemplary Teaching Collection
Resources in this collection a) must have scored Exemplary or Very Good in all five review categories, and must also rate as “Exemplary†in at least three of the five categories. The five categories included in the peer review process are
- Computational, Quantitative, and Scientific Accuracy
- Alignment of Learning Goals, Activities, and Assessments
- Pedagogic Effectiveness
- Robustness (usability and dependability of all components)
- Completeness of the ActivitySheet web page
For more information about the peer review process itself, please see https://serc.carleton.edu/teaching_computation/materials/activity_review.html.
- First Publication: July 30, 2018
- Reviewed: November 19, 2018 -- Reviewed by the On the MATLAB Activity Review Process
Summary
In this activity, we learn where to get climate data (monthly temperature maxima and monthly precipitation maxima) from the National Oceanic and Atmospheric Administration (NOAA). We learn how to fit a GEV distribution for extreme events analysis and how to calculate the confidence intervals of the analysis. We learn how to be critical about the results calculated, how to ensure the quality of the calculations performed and to put the results calculated with model in perspective to the empirical results. We will also use full and half datasets to reveal the importance of long climate record.
Topics
Geoscience, Engineering
Grade Level
College Upper (15-16), Graduate/Professional
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Learning Goals
Students learn how to find and analyze climate data and how to be critical regarding the quality of the data they have and about the interpretation of their results. They will learn trend analysis and extreme values analysis using probabilistic distributions. MATLAB will be used for the full analysis, from data import to results visualization.
Context for Use
This activity is designed for the public who wants to learn how to be critical with climate data analysis and for the students who are working on data analysis, at any academic level (undergraduate or graduate). Students with limited coding experiences will be able to successfully complete this activity since it involves more thinking than coding. However basic experience in Matlab syntax is preferred. Basic knowledge of data analysis is also preferred but not required.
Description and Teaching Materials
Uploaded as "Teaching Material"
MartinezPorposal (Acrobat (PDF) 86kB Aug2 18)
MartinezData (Text File 27kB Aug2 18)
MartinezCode (Matlab File 6kB Aug2 18)
MartinezHandout (Acrobat (PDF) 449kB Aug2 18)
Teaching Notes and Tips
Be sure to check the format of the data download (columns) and if there are any missing values, typically replaced by -99 or sometines NaN.
Assessment
At the end of the activity, the student should have decided if the climate got warmer in its selected area within the past decades and what's his confidence interval. The student should also have calculated the return level of a 10-years event during the baseline and recent decade.
References and Resources
AghaKouchak A., Easterling D., Hsu K., Schubert S., Sorooshian S. (eds.), 2012, Extremes in a Changing Climate, Springer, ISBN 978-94-007-4478-3
AghaKouchak A., Sellars S., Sorooshian S., Methods of Tail Dependence Estimation, in Extremes in a Changing Climate (eds. AghaKouchak A., Easterling D., Hsu K., Schubert S. and Sorooshian S.), Springer, ISBN 978-94-007-4478-3.
NOAA - Climate at Glance: https://www.ncdc.noaa.gov/cag/global/time-series
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