Atmospheric CO2
Jens Daniel Müller
13 March, 2024
Last updated: 2024-03-13
Checks: 7 0
Knit directory: heatwave_co2_flux_2023/
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center <- -160
boundary <- center + 180
target_crs <- paste0("+proj=robin +over +lon_0=", center)
# target_crs <- paste0("+proj=eqearth +over +lon_0=", center)
# target_crs <- paste0("+proj=eqearth +lon_0=", center)
# target_crs <- paste0("+proj=igh_o +lon_0=", center)
worldmap <- ne_countries(scale = 'small',
type = 'map_units',
returnclass = 'sf')
worldmap <- worldmap %>% st_break_antimeridian(lon_0 = center)
worldmap_trans <- st_transform(worldmap, crs = target_crs)
# ggplot() +
# geom_sf(data = worldmap_trans)
coastline <- ne_coastline(scale = 'small', returnclass = "sf")
coastline <- st_break_antimeridian(coastline, lon_0 = 200)
coastline_trans <- st_transform(coastline, crs = target_crs)
# ggplot() +
# geom_sf(data = worldmap_trans, fill = "grey", col="grey") +
# geom_sf(data = coastline_trans)
bbox <- st_bbox(c(xmin = -180, xmax = 180, ymax = 65, ymin = -78), crs = st_crs(4326))
bbox <- st_as_sfc(bbox)
bbox_trans <- st_break_antimeridian(bbox, lon_0 = center)
bbox_graticules <- st_graticule(
x = bbox_trans,
crs = st_crs(bbox_trans),
datum = st_crs(bbox_trans),
lon = c(20, 20.001),
lat = c(-78,65),
ndiscr = 1e3,
margin = 0.001
)
bbox_graticules_trans <- st_transform(bbox_graticules, crs = target_crs)
rm(worldmap, coastline, bbox, bbox_trans)
# ggplot() +
# geom_sf(data = worldmap_trans, fill = "grey", col="grey") +
# geom_sf(data = coastline_trans) +
# geom_sf(data = bbox_graticules_trans)
lat_lim <- ext(bbox_graticules_trans)[c(3,4)]*1.002
lon_lim <- ext(bbox_graticules_trans)[c(1,2)]*1.005
# ggplot() +
# geom_sf(data = worldmap_trans, fill = "grey90", col = "grey90") +
# geom_sf(data = coastline_trans) +
# geom_sf(data = bbox_graticules_trans, linewidth = 1) +
# coord_sf(crs = target_crs,
# ylim = lat_lim,
# xlim = lon_lim,
# expand = FALSE) +
# theme(
# panel.border = element_blank(),
# axis.text = element_blank(),
# axis.ticks = element_blank()
# )
latitude_graticules <- st_graticule(
x = bbox_graticules,
crs = st_crs(bbox_graticules),
datum = st_crs(bbox_graticules),
lon = c(20, 20.001),
lat = c(-60,-30,0,30,60),
ndiscr = 1e3,
margin = 0.001
)
latitude_graticules_trans <- st_transform(latitude_graticules, crs = target_crs)
latitude_labels <- data.frame(lat_label = c("60°N","30°N","Eq.","30°S","60°S"),
lat = c(60,30,0,-30,-60)-4, lon = c(35)-c(0,2,4,2,0))
latitude_labels <- st_as_sf(x = latitude_labels,
coords = c("lon", "lat"),
crs = "+proj=longlat")
latitude_labels_trans <- st_transform(latitude_labels, crs = target_crs)
# ggplot() +
# geom_sf(data = worldmap_trans, fill = "grey", col = "grey") +
# geom_sf(data = coastline_trans) +
# geom_sf(data = bbox_graticules_trans) +
# geom_sf(data = latitude_graticules_trans,
# col = "grey60",
# linewidth = 0.2) +
# geom_sf_text(data = latitude_labels_trans,
# aes(label = lat_label),
# size = 3,
# col = "grey60")Read data
co2_mm_gl <- read_csv("data/co2_mm_gl.csv",
skip = 38)
global_co2_merged <- read_table("data/global_co2_merged.txt",
comment = "!",
col_names = c("decimal", "average"))
atm_co2 <-
bind_rows(
co2_mm_gl %>%
select(decimal, average) %>%
mutate(source = "NOAA - Global marine surface"),
global_co2_merged %>%
mutate(source = "GCB - MLO & SPO")
)
atm_co2 <-
atm_co2 %>%
mutate(date = date_decimal(decimal),
year = year(date),
month = month(date))atm_co2 %>%
filter(decimal > 2010) %>%
ggplot(aes(decimal, average, col = source)) +
geom_path() +
geom_point() +
labs(y = expression(Atm.~CO[2]~(ppm))) +
geom_smooth(method = "lm", se = FALSE) +
scale_color_okabeito() +
theme_bw() +
scale_x_continuous(breaks = seq(1900,2100,2)) +
theme(axis.title.x = element_blank(),
legend.title = element_blank(),
legend.position = c(0.8,0.2))
| Version | Author | Date |
|---|---|---|
| 5ac6300 | jens-daniel-mueller | 2024-03-07 |
atm_co2 <-
atm_co2 %>%
filter(year < 2023 | source != "GCB - MLO & SPO")
atm_co2 <-
atm_co2 %>%
group_by(year, source) %>%
mutate(annual_mean = mean(average)) %>%
ungroup() %>%
mutate(monthly_anomaly = average - annual_mean)
atm_co2 %>%
filter(decimal > 2013,
decimal < 2023) %>%
ggplot(aes(month, monthly_anomaly, col = source, group = interaction(source, year))) +
geom_path() +
geom_point() +
labs(y = expression(Monthly~anomaly~from~annual~mean~(ppm)),
title = expression(Atmospheric~CO[2]~seasonality),
subtitle = "2013 - 2022") +
scale_color_manual(values = c("#000000", "#E69F00", "#56B4E9")) +
theme_bw() +
scale_x_continuous(breaks = seq(1,12,1), expand = c(0.01,0)) +
theme(legend.title = element_blank(),
legend.position = c(0.3,0.2))
ggsave(
here::here(
paste0(
"output/atm_CO2_seasonality.png"
)
),
width = 6,
height = 4,
dpi = 600,
bg = "white"
)
atm_co2 %>%
filter(decimal > 1980,
decimal < 2023) %>%
ggplot(aes(month, monthly_anomaly, group = interaction(year), col = year)) +
geom_path() +
geom_point() +
labs(y = expression(Monthly~anomaly~atm.~CO[2]~(ppm))) +
scale_color_viridis_c() +
theme_bw() +
scale_x_continuous(breaks = seq(1,12,3)) +
facet_wrap(~ source) +
theme(legend.title = element_blank())
| Version | Author | Date |
|---|---|---|
| a2ce6d8 | jens-daniel-mueller | 2024-03-07 |
atm_co2_monthly_anomaly <-
atm_co2 %>%
filter(decimal > 2013,
decimal < 2023) %>%
group_by(source, month) %>%
summarise(monthly_anomaly = mean(monthly_anomaly),
decimal_mean = mean(decimal - year)) %>%
ungroup()
atm_co2_annual_means <-
atm_co2 %>%
group_by(source, year) %>%
summarise(annual_mean = mean(average)) %>%
ungroup()
atm_co2_annual_means %>%
filter(year >= 2013,
year <= 2022) %>%
group_by(source) %>%
summarise(long_term_mean = mean(annual_mean)) %>%
ungroup() %>%
pull(long_term_mean)[1] 405.7722 406.2577annual_mean_2023_predicted <-
atm_co2 %>%
filter(decimal > 2013,
decimal < 2023) %>%
group_by(source, year) %>%
summarise(annual_mean = mean(average)) %>%
ungroup() %>%
nest(data = -source) %>%
mutate(fit = map(data, ~ lm(annual_mean ~ year, data = .x)),
tidied = map(fit, tidy)) %>%
unnest(tidied) %>%
select(source, term, estimate) %>%
pivot_wider(names_from = term,
values_from = estimate) %>%
mutate(annual_mean = `(Intercept)` + year * 2023) %>%
filter(source == "GCB - MLO & SPO") %>%
pull(annual_mean)
atm_co2_monthly_anomaly <-
atm_co2_monthly_anomaly %>%
filter(source == "GCB - MLO & SPO") %>%
mutate(
year = 2023,
decimal = year + decimal_mean,
annual_mean = annual_mean_2023_predicted,
average = annual_mean + monthly_anomaly,
source = "GCB - MLO & SPO (linear prediction)",
date = date_decimal(decimal)
) %>%
select(-decimal_mean)
atm_co2_predicted <-
bind_rows(atm_co2 %>% filter(year < 2023 |
source != "GCB - MLO & SPO"),
atm_co2_monthly_anomaly)
atm_co2_annual_means %>%
filter(year > 2021)# A tibble: 3 × 3
source year annual_mean
<chr> <dbl> <dbl>
1 GCB - MLO & SPO 2022 416.
2 NOAA - Global marine surface 2022 417.
3 NOAA - Global marine surface 2023 419.atm_co2_predicted %>%
filter(decimal > 2013) %>%
ggplot() +
geom_path(aes(decimal, average, col = source)) +
geom_point(aes(decimal, average, col = source)) +
geom_path(aes(decimal, annual_mean, col = source, group = interaction(source, year),
linetype = "annual mean")) +
scale_linetype_manual(values = 2) +
labs(y = expression(CO[2]~(ppm)),
title = expression(Atmospheric~CO[2]~timeseries),
subtitle = "2013 - 2022") +
scale_color_manual(values = c("#000000", "#56B4E9", "#E69F00")) +
theme_bw() +
scale_x_continuous(breaks = seq(1900,2100,2), expand = c(0.01,0)) +
theme(axis.title.x = element_blank(),
legend.title = element_blank(),
legend.position = c(0.3,0.7))
ggsave(
here::here(
paste0(
"output/atm_CO2_timeseries.png"
)
),
width = 6,
height = 4,
dpi = 600,
bg = "white"
)
sessionInfo()R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.5
Matrix products: default
BLAS: /usr/local/R-4.2.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.2.2/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] broom_1.0.5 khroma_1.9.0 lubridate_1.9.0
[4] timechange_0.1.1 terra_1.7-65 sf_1.0-9
[7] rnaturalearth_0.1.0 geomtextpath_0.1.1 colorspace_2.0-3
[10] marelac_2.1.10 shape_1.4.6 ggforce_0.4.1
[13] metR_0.13.0 scico_1.3.1 patchwork_1.1.2
[16] collapse_1.8.9 forcats_0.5.2 stringr_1.5.0
[19] dplyr_1.1.3 purrr_1.0.2 readr_2.1.3
[22] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.4
[25] tidyverse_1.3.2 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] googledrive_2.0.0 ellipsis_0.3.2 class_7.3-20
[4] rprojroot_2.0.3 fs_1.5.2 rstudioapi_0.15.0
[7] proxy_0.4-27 farver_2.1.1 bit64_4.0.5
[10] fansi_1.0.3 xml2_1.3.3 splines_4.2.2
[13] codetools_0.2-18 cachem_1.0.6 knitr_1.41
[16] polyclip_1.10-4 jsonlite_1.8.3 gsw_1.1-1
[19] dbplyr_2.2.1 compiler_4.2.2 httr_1.4.4
[22] backports_1.4.1 Matrix_1.5-3 assertthat_0.2.1
[25] fastmap_1.1.0 gargle_1.2.1 cli_3.6.1
[28] later_1.3.0 tweenr_2.0.2 htmltools_0.5.3
[31] tools_4.2.2 rnaturalearthdata_0.1.0 gtable_0.3.1
[34] glue_1.6.2 Rcpp_1.0.11 cellranger_1.1.0
[37] jquerylib_0.1.4 vctrs_0.6.4 nlme_3.1-160
[40] xfun_0.35 ps_1.7.2 rvest_1.0.3
[43] lifecycle_1.0.3 googlesheets4_1.0.1 oce_1.7-10
[46] getPass_0.2-2 MASS_7.3-58.1 scales_1.2.1
[49] vroom_1.6.0 ragg_1.2.4 hms_1.1.2
[52] promises_1.2.0.1 parallel_4.2.2 yaml_2.3.6
[55] memoise_2.0.1 sass_0.4.4 stringi_1.7.8
[58] highr_0.9 e1071_1.7-12 checkmate_2.1.0
[61] rlang_1.1.1 pkgconfig_2.0.3 systemfonts_1.0.4
[64] evaluate_0.18 lattice_0.20-45 SolveSAPHE_2.1.0
[67] labeling_0.4.2 bit_4.0.5 processx_3.8.0
[70] tidyselect_1.2.0 here_1.0.1 seacarb_3.3.1
[73] magrittr_2.0.3 R6_2.5.1 generics_0.1.3
[76] DBI_1.1.3 mgcv_1.8-41 pillar_1.9.0
[79] haven_2.5.1 whisker_0.4 withr_2.5.0
[82] units_0.8-0 sp_1.5-1 modelr_0.1.10
[85] crayon_1.5.2 KernSmooth_2.23-20 utf8_1.2.2
[88] tzdb_0.3.0 rmarkdown_2.18 grid_4.2.2
[91] readxl_1.4.1 data.table_1.14.6 callr_3.7.3
[94] git2r_0.30.1 reprex_2.0.2 digest_0.6.30
[97] classInt_0.4-8 httpuv_1.6.6 textshaping_0.3.6
[100] munsell_0.5.0 viridisLite_0.4.1 bslib_0.4.1