GMU CDS-301 Final Project (Group 4)

Hyeyeon Kim, Jaehyoung Jang

December 19 2024

Temperature Trends in South Korea from 1984 to 2024

Research Question: “Are Summer and Winter Getting Hotter in South Korea During 40 years?”

Contents

  1. Reference Visualization
  2. About the Dataset
  3. Moving Average
  4. Line Plot
  5. Sparkline
  6. Conclusion

1. Reference Visualization

  • “Climate Change” by The National Atlas of Korea: http://nationalatlas.ngii.go.kr/pages/page_2361.php
library(knitr)

include_graphics("https://postfiles.pstatic.net/MjAyNDEyMThfMTAy/MDAxNzM0NTIxNjUwNTYx.tFFboe87k3ODvvvoKwjsREKTcIemE1lLyEcVGa3UKF0g.5sRioZVUqk-aFRhHCd_C0Jy3OXGAs2uaKKUi2G-z89Ag.JPEG/CDS301_22222.jpg?type=w773")

include_graphics("https://postfiles.pstatic.net/MjAyNDEyMThfMTAx/MDAxNzM0NTIxNjQ3MTgz.Efliyk3uolzPTtQMAoq1-hpWQBxE4oAe6FoxGxoDKRIg.VA4REXMNfVYaxuN1-5gVpaxJqvoi5LuM7sb8TrDAwe8g.JPEG/CDS301_11111.jpg?type=w773")

  • The first graph shows South Korea’s annual mean temperature from 1975 to 2019 by 7-year and 10-year moving averages. This graph shows the moving average of the annual mean temperature, making it difficult to analyze the moving average for individual months, specifically January, the representative month of winter in Korea, and August, the representative month of summer, in which months we are interested. Therefore, we will visualize the 7-year and 10-year moving average graph of the mean minimum temperature in January 1984 to 2024 in South Korea, and the 7-year and 10-year moving average graph of the mean maximum temperature in August

  • The second graph shows the monthly air temperature and precipitation in 13 regions of Korea into two periods (1971-2000 and 1981-2010) and shows the temperature as a line plot and precipitation as a bar plot. Since what we are interested about in this research is the daily temperature changes in January and August of 1984 and 2024, we will make a line plot with the x-axis as a date of month and the y-axis as the temperature to directly compare the temperatures in January and August of 1984 and 2024.

  • In the second graph, it is also difficult to compare temperature changes between regions, so we will select eight cities and create a sparkline to find the temperature changes over 40 years by city and see which cities had the largest temperature changes.

  • Prior to starting, the temperature, which is a numerical value, highlights the difficulty of visually inspecting temperature changes caused by fluctuations and anomalies.

2. About the Dataset

  • Data resource: Korean Meteorological Administration
  • Use 8 Datasets
  • Total 119312 rows
  • 7 Columns: Year, Month, Day, Location, Mean Temperature, Minimum Temperature, Maximum Temperature
  • The period of Year: 1984-2024
  • Location: Seoul, Incheon, Busan, Daegu, Daejeon, Gwangju, Ulsan, Wonju

Package Load

library(readr)
library(tidyr)
library(ggplot2)
library(ggthemes)
library(dplyr)
## 
## 다음의 패키지를 부착합니다: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(zoo)
## 
## 다음의 패키지를 부착합니다: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

  • The reader package was used to load CSV files.

  • Tidyr, ggplot2, ggthemes, and dplyr packages were used for data processing and to create moving averages, line plots, and sparklines.

  • The zoo package was used to make the moving average.

Read CSV file

Busan <- read_csv("Busan.csv")
## Rows: 14885 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (1): Location
## dbl  (3): Mean Temperature(°C), Minimum Temperature(°C), Maximum Temperature...
## date (1): Date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Daegu <- read_csv("Daegu.csv")
## Rows: 14885 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (1): Location
## dbl  (3): Mean Temperature(°C), Minimum Temperature(°C), Maximum Temperature...
## date (1): Date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Daejeon <- read_csv("Daejeon.csv")
## Rows: 14885 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (1): Location
## dbl  (3): Mean Temperature(°C), Minimum Temperature(°C), Maximum Temperature...
## date (1): Date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Gwangju <- read_csv("Gwangju.csv")
## Rows: 14885 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (1): Location
## dbl  (3): Mean Temperature(°C), Minimum Temperature(°C), Maximum Temperature...
## date (1): Date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Incheon <- read_csv("Incheon.csv")
## Rows: 14885 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (1): Location
## dbl  (3): Mean Temperature(°C), Minimum Temperature(°C), Maximum Temperature...
## date (1): Date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Seoul <- read_csv("Seoul.csv")
## Rows: 14885 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (1): Location
## dbl  (3): Mean Temperature(°C), Minimum Temperature(°C), Maximum Temperature...
## date (1): Date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Ulsan <- read_csv("Ulsan.csv")
## Rows: 14885 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (1): Location
## dbl  (3): Mean Temperature(°C), Minimum Temperature(°C), Maximum Temperature...
## date (1): Date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Wonju <- read_csv("Wonju.csv")
## Rows: 14885 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (1): Location
## dbl  (3): Mean Temperature(°C), Minimum Temperature(°C), Maximum Temperature...
## date (1): Date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Data Processing

Busan <- Busan %>%
  separate(col = Date, into = c("Year", "Month", "Day"), sep="-")

Busan <- drop_na(Busan)
Busan$Year <- as.numeric(as.character(Busan$Year))

Daegu <- Daegu %>%
  separate(col = Date, into = c("Year", "Month", "Day"), sep="-")

Daegu <- drop_na(Daegu)
Daegu$Year <- as.numeric(as.character(Daegu$Year))

Daejeon <- Daejeon %>%
  separate(col = Date, into = c("Year", "Month", "Day"), sep="-")

Daejeon <- drop_na(Daejeon)
Daejeon$Year <- as.numeric(as.character(Daejeon$Year))

Gwangju <- Gwangju %>%
  separate(col = Date, into = c("Year", "Month", "Day"), sep="-")

Gwangju <- drop_na(Gwangju)
Gwangju$Year <- as.numeric(as.character(Gwangju$Year))

Incheon <- Incheon %>%
  separate(col = Date, into = c("Year", "Month", "Day"), sep="-")

Incheon <- drop_na(Incheon)
Incheon$Year <- as.numeric(as.character(Incheon$Year))

Seoul <- Seoul %>%
  separate(col = Date, into = c("Year", "Month", "Day"), sep="-")

Seoul <- drop_na(Seoul)
Seoul$Year <- as.numeric(as.character(Seoul$Year))

Ulsan <- Ulsan %>%
  separate(col = Date, into = c("Year", "Month", "Day"), sep="-")

Ulsan <- drop_na(Ulsan)
Ulsan$Year <- as.numeric(as.character(Ulsan$Year))

Wonju <- Wonju %>%
  separate(col = Date, into = c("Year", "Month", "Day"), sep="-")

Wonju <- drop_na(Wonju)
Wonju$Year <- as.numeric(as.character(Wonju$Year))

Korea <- rbind(Busan, Daegu, Daejeon, Gwangju, Incheon, Seoul, Ulsan, Wonju)

Korea <- Korea %>%
  rename('maxT' = 'Maximum Temperature(°C)',
         'miniT' = 'Minimum Temperature(°C)',
         'meanT' = 'Mean Temperature(°C)')

  • For the eight CSV files loaded through the reader package the date column changed from combined into one column to divided into three columns: “Year”, “Month”, and “Day” using the ’separate() function.

  • The “na” value was erased from each city’s dataset and the Year column was converted to a numerical value.

  • Finally, the eight city datasets were combined into a single dataset named “Korea” and the name of three columns was changed to “maxT”, “Minimum Temperature (°C)” to “miniT”, and “Mean Temperature (°C) to”meanT”.

3. Moving Average

  • On the moving average, we filtered January and August for each from the dataset and calculated the total mean temperature by the mean temperature of all cities, and the averages of 7 and 10 years were calculated and visualized as moving averages.

3-1. January

Jan_Mean <- Korea %>%
  filter(Month == "01")

Jan_Mean <- Jan_Mean %>%
  group_by(Year) %>%
  summarise(mean(meanT))

Jan_Mean <- Jan_Mean %>%
  rename('mmeanT' = 'mean(meanT)')

Jan_Mean$'mmeanT' <- round(Jan_Mean$'mmeanT', 1)

Jan_Mean$Jan_Decade <- (Jan_Mean$Year %/% 10) * 10 
Jan_avg <- Jan_Mean %>%
  group_by(Jan_Decade) %>%
  summarize(Decade_Avg = mean(mmeanT, na.rm = TRUE))

Jan_Mean$Jan_7ma <- rollmean(Jan_Mean$mmeanT, 7, fill = NA, align = "center")

ggplot() +
  geom_bar(data = Jan_avg, aes(x = Jan_Decade, y = Decade_Avg, fill = "Decadal Average"), 
           stat = "identity", alpha = 0.7, width = 9) +
  geom_line(data = Jan_Mean, aes(x = Year, y = mmeanT, color = "Yearly Temperature"), size = 1) +
  geom_line(data = Jan_Mean, aes(x = Year, y = Jan_7ma, color = "7-Year Moving Average"), size = 1) +
  labs(
    title = "Change in January Mean Temperature (1984-2024)",
    x = "Year",
    y = "Mean Temperature (°C)",
    color = "",
    fill = ""
  ) +
  scale_color_manual(values = c("Yearly Temperature" = "yellowgreen", "7-Year Moving Average" = "purple")) +
  scale_fill_manual(values = c("Decadal Average" = "blue")) +
  theme_minimal() +
  theme_tufte() 
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_line()`).

  • First, the green line represents the yearly temperature. We can see extreme highs and lows, indicating strong variability in January temperatures from year to year.

  • Second, the red line, which is the 7-year moving average, shows a smoother pattern. There are periods of mean temperature decrease, such as in the late 2000s. However, the mean temperature usually increases for 40 years. Particularly in the late 1990s and the 2020s.

  • Lastly, the blue bars represent the 10-year moving average, showing the long-term trend. Here, we notice that while there are ups and downs, the overall tendency is a gradual increase in temperature over the 40-year period.

  • In summary, this graph highlights significant yearly variability in January temperatures, but the long-term trend suggests January is getting warmer in Korea.

3-2. August

Aug_Mean <- Korea %>%
  filter(Month == "08")

Aug_Mean <- Aug_Mean %>%
  group_by(Year) %>%
  summarise(mean(meanT))

Aug_Mean <- Aug_Mean %>%
  rename('mmeanT' = 'mean(meanT)')
  
Aug_Mean$'mmeanT' <- round(Aug_Mean$'mmeanT', 1)

Aug_Mean$Aug_Decade <- (Aug_Mean$Year %/% 10) * 10 
Aug_avg <- Aug_Mean %>%
  group_by(Aug_Decade) %>%
  summarize(Decade_Avg = mean(mmeanT, na.rm = TRUE))

Aug_Mean$Aug_7ma <- rollmean(Aug_Mean$mmeanT, 7, fill = NA, align = "center")

ggplot() +
  geom_bar(data = Aug_avg, aes(x = Aug_Decade, y = Decade_Avg, fill = "Decadal Average"), 
           stat = "identity", alpha = 0.6, width = 8) +
  geom_line(data = Aug_Mean, aes(x = Year, y = mmeanT, color = "Yearly Temperature"), size = 1) +
  geom_line(data = Aug_Mean, aes(x = Year, y = Aug_7ma, color = "7-Year Moving Average"), size = 1) +
  labs(
    title = "Change in August Mean Temperature (1984-2024)",
    x = "Year",
    y = "Mean Temperature (°C)",
    color = "",
    fill = ""
  ) +
  scale_color_manual(values = c("Yearly Temperature" = "yellowgreen", "7-Year Moving Average" = "purple")) +
  scale_fill_manual(values = c("Decadal Average" = "red")) +
  theme_minimal() +
  theme_tufte()
## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_line()`).

  • In this graph, the red bars represent the 10-year moving average, while the green line shows the yearly August temperatures. The purple line indicates the 7-year moving average.

  • The August temperatures seem relatively stable, with less drastic fluctuations in the yearly temperatures. However, there is a noticeable gradual increase in both the yearly values and the 7-year moving average over time, especially after the 2000s.

  • In conclusion, while August temperatures show smaller variability compared to January, the overall trend points to August getting hotter in Korea over the decades.

4. Line Plot

Total <- Korea %>%
  group_by(Year, Month, Day) %>%
  summarise(mean(maxT), mean(miniT))
## `summarise()` has grouped output by 'Year', 'Month'. You can override using the
## `.groups` argument.
Total <- Total %>%
  mutate(Location = "Total") %>%
  select("Year", "Month", "Day", "Location", "mean(maxT)", "mean(miniT)") %>%
  filter(Year == 1984 | Year == 2024)

Total <- Total %>%
  rename('mmaxT' = 'mean(maxT)',
         'mminiT' = 'mean(miniT)')

Total$'mmaxT' <- round(Total$'mmaxT', 1)
Total$'mminiT' <- round(Total$'mminiT', 1)

4-1. January

Jan_Total <- Total %>%
  filter(Month == "01") %>%
  select("Year", "Month", "Day", "mminiT")

Jan_Count <- Jan_Total %>%
  group_by(Year) %>%
  summarize(days_under_0 = sum(mminiT < 0))

ggplot(Jan_Total, aes(x = Day, y = mminiT, group = Year, color = as.factor(Year))) +
  geom_hline(yintercept = 0, color = "gray", linetype = "solid", size = 0.7) +
  geom_line(linewidth = 1, alpha = 0.7) +
  scale_color_manual(values = c("1984" = "blue", "2024" = "red"), 
                     name = "Year") +
  labs(x = "Day of January", 
       y = "Minimum Temperature (°C)", 
       title = "Daily Minimum Temperatures in January Between 1984 and 2024") +
  theme_minimal() +
  theme_tufte() 

Jan_Count <- Jan_Total %>%
  group_by(Year) %>%
  summarize(days_under_0 = sum(mminiT < 0))

Jan_Count

  • January minimum temperatures in 2024 were higher than in 1984 on nearly every day, with temperatures reaching above zero

  • In 1984, there were no days with temperatures above zero, and most days stayed below -5°C.

  • In 2024, about 9 out of 31 days recorded above-zero temperatures, and the rest of the days still showed higher temperatures compared to 1984

4-2. August

Aug_Total <- Total %>%
  filter(Month == "08") %>%
  select("Year", "Month", "Day", "mmaxT")

ggplot(Aug_Total, aes(x = Day, y = mmaxT, group = Year, color = as.factor(Year))) +
  geom_hline(yintercept = 33, color = "gray", linetype = "solid", size = 0.7) +
  geom_line(linewidth = 1, alpha = 0.7) +
  scale_color_manual(values = c("1984" = "blue", "2024" = "red"), 
                     name = "Year") +
  labs(x = "Day of August", 
       y = "Maximum Temperature (°C)", 
       title = "Daily Maximum Temperatures in August Between 1984 and 2024") +
  theme_minimal() +
  theme_tufte()

Aug_Count <- Aug_Total %>%
  group_by(Year) %>%
  summarize(days_over_33 = sum(mmaxT >= 33))

Aug_Count

  • August minimum temperatures in 2024 were higher than in 1984 on nearly every day, with more days exceeding the heatwave threshold of 33°C.

  • 2024 were higher than in 1984 on all days except from the 9th to the 12th.

  • Over the past 40 years, the strength and frequency of heat waves during summer have increased, which can be seen as a result of climate change

5. Sparkline

5-1. January

CW_df <- Korea %>%
  select("Location", "Year", "Month", "Day", "miniT") 

Jan_CW <- CW_df %>%
  filter(Month == "01")

Jan_CWL <- Jan_CW %>%
  group_by(Year, Location) %>%
  summarise(mean(miniT))
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
Jan_CWT <- Jan_CW %>%
  group_by(Year) %>%
  summarise(mean(miniT))

Jan_CWT <- Jan_CWT %>%
  mutate(Location = "Total") %>%
  select("Year", "Location", "mean(miniT)")

Jan_Cmean <- rbind(Jan_CWL, Jan_CWT)

Jan_Cmean <- Jan_Cmean %>%
  rename('mminiT' = 'mean(miniT)')

Jan_Cmean$'mminiT' <- round(Jan_Cmean$'mminiT', 1)

Jan_Cmean <- Jan_Cmean %>%
  mutate(Location = factor(Location, levels = c("Total", setdiff(unique(Location), "Total"))))

Jan_Mins <- group_by(Jan_Cmean, Location) %>% slice(which.min(mminiT))
Jan_Maxs <- group_by(Jan_Cmean, Location) %>% slice(which.max(mminiT))
Jan_Ends <- group_by(Jan_Cmean, Location) %>% filter(Year == max(Year))
Jan_quarts <- Jan_Cmean %>% group_by(Location) %>%
  summarize(Jan_quart1 = quantile(mminiT, 0.25),
            Jan_quart2 = quantile(mminiT, 0.75)) %>%
  right_join(Jan_Cmean)
## Joining with `by = join_by(Location)`
ggplot(Jan_Cmean, aes(x = Year, y = mminiT)) + 
  facet_grid(Location ~ ., scales = "free_y") + 
  geom_ribbon(data = Jan_quarts, aes(ymin = Jan_quart1, ymax = Jan_quart2), fill = 'grey90') +
  geom_line(linewidth = 0.3) +
  geom_point(data = Jan_Mins, col = 'blue') +
  geom_point(data = Jan_Maxs, col = 'red') +
  geom_text(data = Jan_Mins, aes(label = round(mminiT, 1)), size = 3, vjust = -1) +
  geom_text(data = Jan_Maxs, aes(label = round(mminiT, 1)), size = 3, vjust = 2.5) +
  geom_text(data = Jan_Ends, aes(label = round(mminiT, 1)), size = 3, hjust = 0, nudge_x = 1) +
  geom_text(data = Jan_Ends, aes(label = Location), size = 3, hjust = 0, nudge_x = 5) +
  expand_limits(x = max(Jan_Cmean$Year) + (0.25 * (max(Jan_Cmean$Year) - min(Jan_Cmean$Year)))) +
  scale_x_continuous(breaks = seq(min(Jan_Cmean$Year), max(Jan_Cmean$Year), 5)) +
  scale_y_continuous(expand = c(0.1, 0)) +
  theme_tufte(base_size = 12) +
  theme(
    axis.title = element_blank(), 
    axis.text.y = element_blank(), 
    axis.ticks = element_blank(), 
    strip.text = element_blank()
  )

Jan_filtered <- Jan_Cmean[Jan_Cmean$Year %in% c(1984, 2024), ]

Jan_1984 <- Jan_filtered[Jan_filtered$Year == 1984, ]
Jan_2024 <- Jan_filtered[Jan_filtered$Year == 2024, ]

Jan_diff <- merge(Jan_1984, Jan_2024, by = "Location", suffixes = c("_1984", "_2024"))

Jan_diff <- Jan_diff %>%
  mutate(Difference = mminiT_2024 - mminiT_1984)

Jan_diff

  • In January, the overall trend shows a steady increase in minimum temperatures from 1984 to 2024, with a sharp drop in 2011 and a significant rise in 2020.

  • Comparing 1984 and 2024, Wonju showed the largest temperature increase, rising by 15.6°C.

  • The coldest northern regions of South Korea have also experienced temperature increases, making their minimum temperatures in 2024 similar to those of other regions

5-2. August

HW_df <- Korea %>%
  select("Location", "Year", "Month", "Day", "maxT") 

Aug_HW <- HW_df %>%
  filter(Month == "08")

Aug_HWL <- Aug_HW %>%
  group_by(Year, Location) %>%
  summarise(mean(maxT))
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
Aug_HWT <- Aug_HW %>%
  group_by(Year) %>%
  summarise(mean(maxT))

Aug_HWT <- Aug_HWT %>%
  mutate(Location = "Total") %>%
  select("Year", "Location", "mean(maxT)")

Aug_Hmean <- rbind(Aug_HWL, Aug_HWT)

Aug_Hmean <- Aug_Hmean %>%
  rename('mmaxT' = 'mean(maxT)')

Aug_Hmean$'mmaxT' <- round(Aug_Hmean$'mmaxT', 1)

Aug_Hmean <- Aug_Hmean %>%
  mutate(Location = factor(Location, levels = c("Total", setdiff(unique(Location), "Total"))))

Aug_Mins <- group_by(Aug_Hmean, Location) %>% slice(which.min(mmaxT))
Aug_Maxs <- group_by(Aug_Hmean, Location) %>% slice(which.max(mmaxT))
Aug_Ends <- group_by(Aug_Hmean, Location) %>% filter(Year == max(Year))
Aug_quarts <- Aug_Hmean %>% group_by(Location) %>%
  summarize(Aug_quart1 = quantile(mmaxT, 0.25),
            Aug_quart2 = quantile(mmaxT, 0.75)) %>%
  right_join(Aug_Hmean)
## Joining with `by = join_by(Location)`
ggplot(Aug_Hmean, aes(x = Year, y = mmaxT)) + 
  facet_grid(Location ~ ., scales = "free_y") + 
  geom_ribbon(data = Aug_quarts, aes(ymin = Aug_quart1, ymax = Aug_quart2), fill = 'grey90') +
  geom_line(linewidth = 0.3) +
  geom_point(data = Aug_Mins, col = 'blue') +
  geom_point(data = Aug_Maxs, col = 'red') +
  geom_text(data = Aug_Mins, aes(label = round(mmaxT, 1)), size = 3, vjust = -1) +
  geom_text(data = Aug_Maxs, aes(label = round(mmaxT, 1)), size = 3, vjust = 2.5) +
  geom_text(data = Aug_Ends, aes(label = round(mmaxT, 1)), size = 3, hjust = 0, nudge_x = 1) +
  geom_text(data = Aug_Ends, aes(label = Location), size = 3, hjust = 0, nudge_x = 5) +
  expand_limits(x = max(Aug_Hmean$Year) + (0.25 * (max(Aug_Hmean$Year) - min(Aug_Hmean$Year)))) +
  scale_x_continuous(breaks = seq(min(Aug_Hmean$Year), max(Aug_Hmean$Year), 5)) +
  scale_y_continuous(expand = c(0.1, 0)) +
  theme_tufte(base_size = 12) +
  theme(
    axis.title = element_blank(), 
    axis.text.y = element_blank(), 
    axis.ticks = element_blank(), 
    strip.text = element_blank()
  )

Aug_filtered <- Aug_Hmean[Aug_Hmean$Year %in% c(1984, 2024), ]

Aug_1984 <- Aug_filtered[Aug_filtered$Year == 1984, ]
Aug_2024 <- Aug_filtered[Aug_filtered$Year == 2024, ]

Aug_diff <- merge(Aug_1984, Aug_2024, by = "Location", suffixes = c("_1984", "_2024"))

Aug_diff <- Aug_diff %>%
  mutate(Difference = mmaxT_2024 - mmaxT_1984)

Aug_diff

  • In August, the overall trend shows a steady rise in maximum temperatures from 1984 to 2024

  • Except for Daegu, all cities reached their highest temperatures after 2010

  • Comparing 1984 and 2024, Seoul and Incheon showed the largest increases, rising by 2.8°C and 2.7°C

6. Conclusion

For Winter

  • From 1984 to 2024, both minimum and average temperatures in January have been steadily rising, with extreme cold events becoming less frequent. This indicates that winters are gradually getting warmer over time.

For Summer

  • Both maximum and mean temperatures in August have consistently increased since 1984, and heat waves, which are days over 33 degrees, have become more frequent and prolonged. This shows that summers are getting progressively hotter.