Homework 07 — Is Your City Warming? A Climate Regression Comparison

Downloading real weather data and comparing warming rates to Duluth, MN

tidyverse
regression
climate-data

Download a long GSOD weather record for a city of your choice, fit a warming trend with lm(), and compare it to Duluth (week 4 weather lecture/worksheet 07).

Author

Name: _________________________________ | Date: _____________

Overview

In Lecture 07 and Worksheet 07 you downloaded Duluth’s long-term weather record directly into R, summarized it at the yearly level, and used lm() to estimate a warming rate in °C per decade. You also split the data into summer and winter to see whether one season is warming faster than the other.

This homework asks you to repeat that same workflow for a city of your own choosing anywhere in the world with a long GSOD weather station record, and compare what you find to Duluth’s numbers.

Submit to Canvas: (1) your completed .R script and (2) three saved PNG figures. Fill in all answer boxes in the script as # comments below the relevant code. The question numbers here match the # Q__ markers in the skeleton script.

The Data

You will download daily station data using the same GSODR package from Lecture 07. You need two things:

  1. A station ID for your city of choice
  2. A range of years — pick the longest record available (many stations go back to the 1940s–1970s)

Finding a station ID

▶ Run this in your console (not graded, just for exploration):

library(GSODR)
load(system.file("extdata", "isd_history.rda", package = "GSODR"))

# Search for stations by country code (2-letter ISO) or name
# Example: find stations in the United Kingdom
subset(isd_history, CTRY == "UK")

# Example: search by partial city name
isd_history[grepl("CHICAGO", isd_history$NAME), ]

Pick a station with a long period of record (check the BEGIN and END columns — both are in YYYYMMDD format). Avoid stations with only a few years of data; you want at least 30+ years for a meaningful trend.

Column What it holds
STNID the station ID to plug into get_GSOD()
NAME station name
CTRY 2-letter country code
BEGIN first date of record (YYYYMMDD)
END last date of record (YYYYMMDD)

Part 1 · Download and inspect (→ see Lecture 07, Worksheet 07)

After downloading your city’s data:

Q1 — City and station ID you chose: ________________

Q2 — Years of record you downloaded: ________ to ________

Q3 — Total rows in the raw daily download: ________

Part 2 · Summarize by year (→ see Lecture 07, Worksheet 07)

Summarize your city’s daily data to one mean temperature per year, the same way you did for Duluth.

Q4 — Rows in your yearly-summarized data frame: ________

Q5 — Mean temperature in your city’s first year of record: ________ °C

Q6 — Mean temperature in your city’s most recent year of record: ________ °C

Part 3 · Regression — rate of change (→ see Lecture 07, Worksheet 07)

Fit lm(TEMP ~ YEAR, data = your_yearly_df), the same model structure from Lecture 07.

Q7 — Slope (b), in °C per year: ________

Q8 — Warming rate, in °C per decade (slope × 10): ________

Q9 — p-value for the slope: ________

Q10 — Is the trend statistically significant at α = 0.05? Y / N: ________

Q11 — R²: ________

Compare to Duluth

Duluth’s annual warming rate from Worksheet 07 (fill in from your own worksheet results):

Q12 — Duluth’s warming rate (°C/decade), from Worksheet 07: ________

Q13 — Is your city warming faster or slower than Duluth? Faster / Slower / About the same

Q14 — Difference between your city’s rate and Duluth’s rate (°C/decade): ________

Part 4 · Summer vs. winter (→ see Lecture 07, Worksheet 07)

Use case_when() to label summer (Jun/Jul/Aug) and winter (Dec/Jan/Feb) months, then fit a separate lm() for each season — exactly as you did for Duluth.

Note

If your city is in the Southern Hemisphere, summer and winter months are reversed (summer = Dec/Jan/Feb, winter = Jun/Jul/Aug). Adjust your case_when() accordingly and note this in Q15.

Q15 — Hemisphere of your city (Northern / Southern): ________

Q16 — Summer warming rate (°C/decade): ________ p-value: ________

Q17 — Winter warming rate (°C/decade): ________ p-value: ________

Q18 — Which season is warming faster in your city? Summer / Winter / About the same

Q19 — Does this match the pattern you found for Duluth (winter warming faster)? Y / N

Part 5 · Results paragraph

✏️ Write a short paragraph (4–6 sentences) summarizing your findings. Include:

  • Your city, the years of record, and the overall annual warming rate
  • How your city’s overall warming rate compares to Duluth’s
  • The summer vs. winter comparison, and whether it matches Duluth’s pattern
  • One sentence on a possible reason for any difference (e.g., latitude, proximity to water, urban heat island)

Q20 — Results paragraph:

Submission checklist

Before uploading to Canvas, confirm:

Data: NOAA Global Surface Summary of the Day (GSOD), accessed via the GSODR R package.