We know where we’ve been. Where we go is a choice.

Atmospheric CO₂ at Mauna Loa rose from 316.9 ppm (1960) to 424.6 ppm (2024) — an increase of 108 ppm in 65 years. Beyond 2024, three IPCC AR6 scenarios show where concentrations could go. The gap between them is not prediction error, it is the consequence of different emissions choices.

A fan chart tracing atmospheric CO₂ at Mauna Loa from 1960 to 2024, then projecting three IPCC AR6 scenarios forward to 2075. The observed trend is rendered as a single authoritative line; beyond the 2024 boundary, three diverging ribbons show the likely range under aggressive decarbonization (SSP1-2.6), current policies (SSP2-4.5), and no additional action (SSP5-8.5). Built in R with ggplot2.

Figure 1: Line chart showing atmospheric CO₂ concentration at Mauna Loa from 1960 to 2024, with three IPCC AR6 scenario projection bands extending to 2075. The observed line rises steeply from 316.9 ppm in 1960 to 424.6 ppm in 2024. Beyond the “Last observed (2024)” divider, three diverging fan bands represent SSP5-8.5 (no additional action, reaching ~736 ppm), SSP2-4.5 (policies as implemented, ~521 ppm), and SSP1-2.6 (aggressive decarbonization, ~415 ppm) by 2075. Title reads: “We know where we’ve been. Where we go is a choice.”

Steps to Create this Graphic

1. Load Packages & Setup

```{r}
#| label: load
#| warning: false
#| message: false      
#| results: "hide"     

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
pacman::p_load(
  tidyverse, ggtext, showtext, 
  janitor, scales, glue, here
  )
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 10,
  height = 7,
  units  = "in",
  dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false

### |- observed CO₂: read from local cache ----
co2_raw <- read_csv(
  here::here("data/30DayChartChallenge/2026/co2_mauna_loa_raw.csv"),
  show_col_types = FALSE
)
```

3. Examine the Data

```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(co2_raw)

co2_raw |>
  filter(year >= 2022) |>
  tail(12)
```

4. Tidy Data

```{r}
#| label: tidy
#| warning: false

### |- annual mean CO₂, 1960–2024 ----
co2_obs <- co2_raw |>
  slice(-1) |>                    # drop row 1: duplicated header written into CSV body
  mutate(
    year    = as.integer(year),
    co2_avg = as.numeric(co2_avg)
  ) |>
  filter(co2_avg > 0) |>          # drop fill values (-99.99)
  group_by(year) |>
  summarise(co2_ppm = mean(co2_avg, na.rm = TRUE), .groups = "drop") |>
  filter(year >= 1960, year <= 2024)

### |- anchor: actual 2024 observed mean (used to seam projections) ----
co2_2024_actual <- co2_obs |> filter(year == 2024) |> pull(co2_ppm)
co2_1960_actual <- co2_obs |> filter(year == 1960) |> pull(co2_ppm)

### |- IPCC AR6 scenario projections: hardcoded tribble ----
# Approximate medians and 66% likely ranges (ppm) from IPCC AR6 WG1 SPM Fig. 8
# Anchor year 2024 set to 424 ppm (approximate); offset-corrected below
# to match the actual Mauna Loa 2024 annual mean.
# Projections run to 2075 (not 2100) to keep SSP5-8.5 upper bound legible.

proj_raw <- tribble(
  ~year,  ~scenario,    ~median,  ~lo,   ~hi,
  # SSP1-2.6: peaks ~2040, then slowly declines
  2024,   "SSP1-2.6",   424,     424,   424,
  2030,   "SSP1-2.6",   437,     430,   444,
  2040,   "SSP1-2.6",   443,     430,   456,
  2050,   "SSP1-2.6",   438,     418,   456,
  2060,   "SSP1-2.6",   428,     403,   452,
  2075,   "SSP1-2.6",   415,     385,   446,
  # SSP2-4.5: continues rising, moderates post-2060
  2024,   "SSP2-4.5",   424,     424,   424,
  2030,   "SSP2-4.5",   444,     436,   452,
  2040,   "SSP2-4.5",   467,     454,   480,
  2050,   "SSP2-4.5",   487,     469,   506,
  2060,   "SSP2-4.5",   504,     481,   528,
  2075,   "SSP2-4.5",   521,     492,   551,
  # SSP5-8.5: rapid continued rise
  2024,   "SSP5-8.5",   424,     424,   424,
  2030,   "SSP5-8.5",   453,     443,   463,
  2040,   "SSP5-8.5",   497,     480,   515,
  2050,   "SSP5-8.5",   553,     527,   580,
  2060,   "SSP5-8.5",   624,     588,   662,
  2075,   "SSP5-8.5",   736,     688,   786
) |>
  mutate(
    scenario = factor(scenario, levels = c("SSP1-2.6", "SSP2-4.5", "SSP5-8.5"))
  )

### |- anchor correction ----
# Shift all projection values so the 2024 seam matches observed Mauna Loa mean
proj_offset <- co2_2024_actual - 424

proj <- proj_raw |>
  mutate(
    median = median + proj_offset,
    lo     = lo     + proj_offset,
    hi     = hi     + proj_offset
  )

### |- label positions at 2075 ----
proj_labels <- proj |>
  filter(year == 2075) |>
  select(scenario, median)

### |- key annotation values ----
co2_1960_label <- round(co2_1960_actual, 1)
co2_2024_label <- round(co2_2024_actual, 1)
ppm_rise       <- round(co2_2024_actual - co2_1960_actual)

### |- y positions for seam segment and seam label ----
seam_y_lo    <- 308
seam_y_hi    <- 790
seam_label_y <- 315

### |- label x position (right of 2075, inside clip = "off") ----
label_x <- 2077
```

5. Visualization Parameters

```{r}
#| label: params
#| include: true
#| warning: false

### |- plot aesthetics ----
colors <- get_theme_colors(
  palette = list(
      "bg"         = "#F5F3EE",
      "observed"   = "#8B3030",
      "ssp126"     = "#4A7C59",
      "ssp245"     = "#C28A2C",
      "ssp585"     = "#7A3020",
      "seam"       = "#6B6B6B",
      "annotation" = "#3D3D3D",
      "muted"      = "#8A8A8A",
      "grid"       = "#E4E1DB"
    )
  )
  
col_bg         <- colors$palette$bg
col_obs        <- colors$palette$observed
col_ssp126     <- colors$palette$ssp126
col_ssp245     <- colors$palette$ssp245
col_ssp585     <- colors$palette$ssp585
col_seam       <- colors$palette$seam
col_annotation <- colors$palette$annotation
col_muted      <- colors$palette$muted
col_grid       <- colors$palette$grid

### |- titles and caption ----
title_text <- "We know where we've been. Where we go is a choice."

subtitle_text <- paste0(
  "Atmospheric CO\u2082 at Mauna Loa rose from <b>", co2_1960_label, " ppm</b> (1960) ",
  "to <b>", co2_2024_label, " ppm</b> (2024) — an increase of <b>",
  ppm_rise, " ppm</b> in 65 years.<br>",
  "Beyond 2024, three IPCC AR6 scenarios show where concentrations could go. ",
  "The gap between them is not prediction error,<br>it is the consequence of different emissions choices."
)

caption_text <- create_dcc_caption(
  dcc_year    = 2026,
  dcc_day     = 26,
  source_text = "NOAA GML Mauna Loa (observed) \u00b7 IPCC AR6 WG1 SPM Fig. 8 (projections, approximate)"
)

### |- fonts ----
setup_fonts()
fonts <- get_font_families()

base_theme   <- create_base_theme(colors)

weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    plot.background = element_rect(fill = col_bg, color = NA),
    panel.background = element_rect(fill = col_bg, color = NA),
    panel.grid.major.x = element_blank(),
    panel.grid.minor.x = element_blank(),
    panel.grid.major.y = element_line(color = col_grid, linewidth = 0.3),
    panel.grid.minor.y = element_blank(),
    axis.line.x = element_line(color = "#C8C4BC", linewidth = 0.4),
    axis.ticks.x = element_line(color = "#C8C4BC", linewidth = 0.3),
    axis.ticks.y = element_blank(),
    axis.text = element_text(family = fonts$text, size = 10, color = col_muted),
    axis.title.y = element_text(
      family = fonts$text, size = 10, color = col_muted,
      margin = margin(r = 8)
    ),
    axis.title.x = element_blank(),
    plot.title = element_markdown(
      family     = fonts$title,
      size       = 24,
      face       = "bold",
      color      = col_annotation,
      lineheight = 1.1,
      margin     = margin(b = 8)
    ),
    plot.subtitle = element_markdown(
      family     = fonts$text,
      size       = 11,
      color      = col_annotation,
      lineheight = 1.5,
      margin     = margin(b = 20)
    ),
    plot.caption = element_markdown(
      family = fonts$text,
      size   = 8,
      color  = col_muted,
      hjust  = 1,
      margin = margin(t = 12)
    ),
    plot.margin = margin(t = 20, r = 75, b = 15, l = 20)
  )
)

theme_set(weekly_theme)
```

6. Plot

```{r}
#| label: plot
#| warning: false

### |- main plot ----
p <- ggplot() +

  # Geoms
  geom_ribbon(
    data = proj |> filter(scenario == "SSP5-8.5"),
    aes(x = year, ymin = lo, ymax = hi),
    fill = col_ssp585,
    alpha = 0.14
  ) +
  geom_ribbon(
    data = proj |> filter(scenario == "SSP5-8.5", year >= 2050),
    aes(x = year, ymin = lo, ymax = hi),
    fill = col_ssp585,
    alpha = 0.10
  ) +
  geom_line(
    data = proj |> filter(scenario == "SSP5-8.5"),
    aes(x = year, y = median),
    color = col_ssp585,
    linewidth = 0.65,
    linetype = "dashed"
  ) +
  geom_ribbon(
    data = proj |> filter(scenario == "SSP2-4.5"),
    aes(x = year, ymin = lo, ymax = hi),
    fill = col_ssp245,
    alpha = 0.18
  ) +
  geom_ribbon(
    data = proj |> filter(scenario == "SSP2-4.5", year >= 2050),
    aes(x = year, ymin = lo, ymax = hi),
    fill = col_ssp245,
    alpha = 0.12
  ) +
  geom_line(
    data = proj |> filter(scenario == "SSP2-4.5"),
    aes(x = year, y = median),
    color = col_ssp245,
    linewidth = 0.65,
    linetype = "dashed"
  ) +
  geom_ribbon(
    data = proj |> filter(scenario == "SSP1-2.6"),
    aes(x = year, ymin = lo, ymax = hi),
    fill = col_ssp126,
    alpha = 0.20
  ) +
  geom_ribbon(
    data = proj |> filter(scenario == "SSP1-2.6", year >= 2050),
    aes(x = year, ymin = lo, ymax = hi),
    fill = col_ssp126,
    alpha = 0.12
  ) +
  geom_line(
    data = proj |> filter(scenario == "SSP1-2.6"),
    aes(x = year, y = median),
    color = col_ssp126,
    linewidth = 0.65,
    linetype = "dashed"
  ) +

  # Annotate
  annotate(
    "segment",
    x = 2024, xend = 2024,
    y = seam_y_lo, yend = seam_y_hi,
    color = "#4A4A4A",
    linewidth = 0.55,
    linetype = "dashed"
  ) +
  annotate(
    "text",
    x = 2024,
    y = seam_label_y,
    label = "Last observed (2024)",
    hjust = 0.5,
    vjust = 1,
    size = 2.9,
    color = "#4A4A4A",
    family = fonts$text,
    fontface = "bold"
  ) +
  annotate(
    "text",
    x = 2024,
    y = seam_label_y - 12,
    label  = "Observed \u2192 Scenarios",
    hjust = 0.5,
    vjust = 1,
    size = 2.6,
    color = col_muted,
    family = "sans"
  ) +
  geom_line(
    data = co2_obs,
    aes(x = year, y = co2_ppm),
    color = col_obs,
    linewidth = 1.1,
    lineend = "round"
  ) +
  annotate(
    "text",
    x = 1963,
    y = co2_1960_actual + 10,
    label = paste0(co2_1960_label, " ppm\n(1960)"),
    hjust = 0,
    vjust = 0,
    size = 3.1,
    color = col_annotation,
    family = fonts$text,
    lineheight = 1.3
  ) +
  annotate(
    "text",
    x = 2019,
    y = co2_2024_actual + 12,
    label = paste0(co2_2024_label, " ppm\n(2024)"),
    hjust = 1,
    vjust = 0,
    size = 3.1,
    color = col_annotation,
    family = fonts$text,
    lineheight = 1.3
  ) +
  annotate(
    "text",
    x = label_x,
    y = proj_labels |> filter(scenario == "SSP5-8.5") |> pull(median),
    label = "SSP5-8.5\nNo additional\naction",
    hjust = 0, vjust = 0.5,
    size = 2.85,
    color = col_ssp585,
    family = fonts$text,
    lineheight = 1.3
  ) +
  annotate(
    "text",
    x = label_x,
    y = proj_labels |> filter(scenario == "SSP2-4.5") |> pull(median),
    label = "SSP2-4.5\nPolicies as\nimplemented",
    hjust = 0, vjust = 0.5,
    size = 2.85,
    color = col_ssp245,
    family = fonts$text,
    lineheight = 1.3
  ) +
  annotate(
    "text",
    x = label_x,
    y = proj_labels |> filter(scenario == "SSP1-2.6") |> pull(median),
    label = "SSP1-2.6\nAggressive\ndecarbonization",
    hjust = 0, vjust = 0.5,
    size = 2.85,
    color = col_ssp126,
    family = fonts$text,
    lineheight = 1.3
  ) +

  # Scales
  scale_x_continuous(
    breaks = c(1960, 1980, 2000, 2024, 2050, 2075),
    labels = c("1960", "1980", "2000", "2024", "2050", "2075"),
    expand = expansion(mult = c(0.02, 0.0))
  ) +
  scale_y_continuous(
    labels = label_number(suffix = " ppm"),
    breaks = seq(300, 800, by = 100),
    limits = c(300, 800),
    expand = expansion(mult = c(0.02, 0.06))
  ) +
  coord_cartesian(clip = "off") +

  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    y = "Atmospheric CO\u2082 (ppm)"
  )
```

7. Save

```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot(
  p,
  type = "30daychartchallenge",
  year = 2026,
  day = 26,
  width = 10,
  height = 7
  )
```

8. Session Info

TipExpand for Session Info

R version 4.5.3 (2026-03-11 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default
  LAPACK version 3.12.1

locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] here_1.0.2      glue_1.8.0      scales_1.4.0    janitor_2.2.1  
 [5] showtext_0.9-8  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
 [9] lubridate_1.9.5 forcats_1.0.1   stringr_1.6.0   dplyr_1.2.1    
[13] purrr_1.2.2     readr_2.2.0     tidyr_1.3.2     tibble_3.3.1   
[17] ggplot2_4.0.3   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.57          htmlwidgets_1.6.4  tzdb_0.5.0        
 [5] vctrs_0.7.3        tools_4.5.3        generics_0.1.4     curl_7.0.0        
 [9] parallel_4.5.3     gifski_1.32.0-2    pacman_0.5.1       pkgconfig_2.0.3   
[13] RColorBrewer_1.1-3 S7_0.2.1           lifecycle_1.0.5    compiler_4.5.3    
[17] farver_2.1.2       textshaping_1.0.5  codetools_0.2-20   snakecase_0.11.1  
[21] litedown_0.9       htmltools_0.5.9    yaml_2.3.12        pillar_1.11.1     
[25] crayon_1.5.3       camcorder_0.1.0    magick_2.9.1       commonmark_2.0.0  
[29] tidyselect_1.2.1   digest_0.6.39      stringi_1.8.7      rsvg_2.7.0        
[33] rprojroot_2.1.1    fastmap_1.2.0      grid_4.5.3         cli_3.6.6         
[37] magrittr_2.0.5     utf8_1.2.6         withr_3.0.2        bit64_4.6.0-1     
[41] timechange_0.4.0   rmarkdown_2.31     bit_4.6.0          otel_0.2.0        
[45] ragg_1.5.2         hms_1.1.4          evaluate_1.0.5     knitr_1.51        
[49] markdown_2.0       rlang_1.2.0        gridtext_0.1.6     Rcpp_1.1.1        
[53] xml2_1.5.2         svglite_2.2.2      rstudioapi_0.18.0  vroom_1.7.1       
[57] jsonlite_2.0.0     R6_2.6.1           systemfonts_1.3.2 

9. GitHub Repository

TipExpand for GitHub Repo

The complete code for this analysis is available in 30dcc_2026_26.qmd.

For the full repository, click here.

10. References

TipExpand for References

1. Data Sources:
   • NOAA Global Monitoring Laboratory — Mauna Loa CO₂ monthly mean concentrations. Scripps Institution of Oceanography / NOAA GML. Retrieved from: https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_mm_mlo.csv
   • IPCC AR6 Working Group I (2021). Climate Change 2021: The Physical Science Basis. Summary for Policymakers, Figure SPM.8 — Projected atmospheric CO₂ concentrations under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios (approximate digitisation). Retrieved from: https://www.ipcc.ch/report/ar6/wg1/
2. Chart Inspiration:
   • Intergovernmental Panel on Climate Change (2021). SPM Figure 8: Projected changes in annual greenhouse gas concentrations. In Climate Change 2021: The Physical Science Basis. Cambridge University Press. https://doi.org/10.1017/9781009157896

11. Custom Functions Documentation

Note📦 Custom Helper Functions

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

• fonts.R: setup_fonts(), get_font_families() - Font management with showtext
• social_icons.R: create_social_caption() - Generates formatted social media captions
• image_utils.R: save_plot() - Consistent plot saving with naming conventions
• base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils