CMEMS
Jens Daniel Müller
07 May, 2024
Last updated: 2024-05-07
Checks: 7 0
Knit directory:
heatwave_co2_flux_2023/analysis/
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| html | f9d2b99 | jens-daniel-mueller | 2024-03-25 | total cummulative intensity added |
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| html | 5c1676b | jens-daniel-mueller | 2024-03-24 | Build site. |
| Rmd | 31ffcb9 | jens-daniel-mueller | 2024-03-24 | CMEMS unit error fixed |
| html | a1c2e14 | jens-daniel-mueller | 2024-03-24 | Build site. |
| Rmd | c32eb8f | jens-daniel-mueller | 2024-03-24 | CMEMS analysis |
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| Rmd | cf4f62f | jens-daniel-mueller | 2024-03-23 | MHW stats and CMEMS added |
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
path_pCO2_products <-
"/nfs/kryo/work/datasets/gridded/ocean/2d/observation/pco2/"
path_CMEMS <- paste0(path_pCO2_products, "cmems_ffnn/v2023/r100_regridded/")library(ncdf4)
nc <-
nc_open(paste0(
path_pCO2_products,
"VLIZ-SOM_FFN/VLIZ-SOM_FFN_vBAMS2024.nc"
))
nc <-
nc_open(paste0(
path_CMEMS,
"kw_OceanSODA_ETHZ_HR_LR-v2023.01-1982_2023.nc"
))
nc <-
nc_open(paste0(
path_CMEMS,
"CO2_fluxes/fluxCO2_model_v2022_r100_202402.nc"
))
nc <-
nc_open(paste0(
path_CMEMS,
"SSH_r100_199205.nc"
))
nc <-
nc_open("/nfs/kryo/work/datasets/gridded/ocean/2d/observation/pco2/cmems_ffnn/v2020/v2020.nc")
print(nc)
ncatt_get(nc, varid = "time")
ncvar_get(nc, varid = "time")CMEMS_files <- list.files(path = path_CMEMS)
CMEMS_files <- CMEMS_files[!(CMEMS_files %>% str_detect("fuCO2_clim"))]
CMEMS_files <- CMEMS_files[!(CMEMS_files %>% str_detect("UV"))]
CMEMS_files <- CMEMS_files[!(CMEMS_files %>% str_detect("Ps"))]
print(CMEMS_files, max = 5000) [1] "CHL_r100_198501.nc" "CHL_r100_198502.nc"
[3] "CHL_r100_198503.nc" "CHL_r100_198504.nc"
[5] "CHL_r100_198505.nc" "CHL_r100_198506.nc"
[7] "CHL_r100_198507.nc" "CHL_r100_198508.nc"
[9] "CHL_r100_198509.nc" "CHL_r100_198510.nc"
[11] "CHL_r100_198511.nc" "CHL_r100_198512.nc"
[13] "CHL_r100_198601.nc" "CHL_r100_198602.nc"
[15] "CHL_r100_198603.nc" "CHL_r100_198604.nc"
[17] "CHL_r100_198605.nc" "CHL_r100_198606.nc"
[19] "CHL_r100_198607.nc" "CHL_r100_198608.nc"
[21] "CHL_r100_198609.nc" "CHL_r100_198610.nc"
[23] "CHL_r100_198611.nc" "CHL_r100_198612.nc"
[25] "CHL_r100_198701.nc" "CHL_r100_198702.nc"
[27] "CHL_r100_198703.nc" "CHL_r100_198704.nc"
[29] "CHL_r100_198705.nc" "CHL_r100_198706.nc"
[31] "CHL_r100_198707.nc" "CHL_r100_198708.nc"
[33] "CHL_r100_198709.nc" "CHL_r100_198710.nc"
[35] "CHL_r100_198711.nc" "CHL_r100_198712.nc"
[37] "CHL_r100_198801.nc" "CHL_r100_198802.nc"
[39] "CHL_r100_198803.nc" "CHL_r100_198804.nc"
[41] "CHL_r100_198805.nc" "CHL_r100_198806.nc"
[43] "CHL_r100_198807.nc" "CHL_r100_198808.nc"
[45] "CHL_r100_198809.nc" "CHL_r100_198810.nc"
[47] "CHL_r100_198811.nc" "CHL_r100_198812.nc"
[49] "CHL_r100_198901.nc" "CHL_r100_198902.nc"
[51] "CHL_r100_198903.nc" "CHL_r100_198904.nc"
[53] "CHL_r100_198905.nc" "CHL_r100_198906.nc"
[55] "CHL_r100_198907.nc" "CHL_r100_198908.nc"
[57] "CHL_r100_198909.nc" "CHL_r100_198910.nc"
[59] "CHL_r100_198911.nc" "CHL_r100_198912.nc"
[61] "CHL_r100_199001.nc" "CHL_r100_199002.nc"
[63] "CHL_r100_199003.nc" "CHL_r100_199004.nc"
[65] "CHL_r100_199005.nc" "CHL_r100_199006.nc"
[67] "CHL_r100_199007.nc" "CHL_r100_199008.nc"
[69] "CHL_r100_199009.nc" "CHL_r100_199010.nc"
[71] "CHL_r100_199011.nc" "CHL_r100_199012.nc"
[73] "CHL_r100_199101.nc" "CHL_r100_199102.nc"
[75] "CHL_r100_199103.nc" "CHL_r100_199104.nc"
[77] "CHL_r100_199105.nc" "CHL_r100_199106.nc"
[79] "CHL_r100_199107.nc" "CHL_r100_199108.nc"
[81] "CHL_r100_199109.nc" "CHL_r100_199110.nc"
[83] "CHL_r100_199111.nc" "CHL_r100_199112.nc"
[85] "CHL_r100_199201.nc" "CHL_r100_199202.nc"
[87] "CHL_r100_199203.nc" "CHL_r100_199204.nc"
[89] "CHL_r100_199205.nc" "CHL_r100_199206.nc"
[91] "CHL_r100_199207.nc" "CHL_r100_199208.nc"
[93] "CHL_r100_199209.nc" "CHL_r100_199210.nc"
[95] "CHL_r100_199211.nc" "CHL_r100_199212.nc"
[97] "CHL_r100_199301.nc" "CHL_r100_199302.nc"
[99] "CHL_r100_199303.nc" "CHL_r100_199304.nc"
[101] "CHL_r100_199305.nc" "CHL_r100_199306.nc"
[103] "CHL_r100_199307.nc" "CHL_r100_199308.nc"
[105] "CHL_r100_199309.nc" "CHL_r100_199310.nc"
[107] "CHL_r100_199311.nc" "CHL_r100_199312.nc"
[109] "CHL_r100_199401.nc" "CHL_r100_199402.nc"
[111] "CHL_r100_199403.nc" "CHL_r100_199404.nc"
[113] "CHL_r100_199405.nc" "CHL_r100_199406.nc"
[115] "CHL_r100_199407.nc" "CHL_r100_199408.nc"
[117] "CHL_r100_199409.nc" "CHL_r100_199410.nc"
[119] "CHL_r100_199411.nc" "CHL_r100_199412.nc"
[121] "CHL_r100_199501.nc" "CHL_r100_199502.nc"
[123] "CHL_r100_199503.nc" "CHL_r100_199504.nc"
[125] "CHL_r100_199505.nc" "CHL_r100_199506.nc"
[127] "CHL_r100_199507.nc" "CHL_r100_199508.nc"
[129] "CHL_r100_199509.nc" "CHL_r100_199510.nc"
[131] "CHL_r100_199511.nc" "CHL_r100_199512.nc"
[133] "CHL_r100_199601.nc" "CHL_r100_199602.nc"
[135] "CHL_r100_199603.nc" "CHL_r100_199604.nc"
[137] "CHL_r100_199605.nc" "CHL_r100_199606.nc"
[139] "CHL_r100_199607.nc" "CHL_r100_199608.nc"
[141] "CHL_r100_199609.nc" "CHL_r100_199610.nc"
[143] "CHL_r100_199611.nc" "CHL_r100_199612.nc"
[145] "CHL_r100_199701.nc" "CHL_r100_199702.nc"
[147] "CHL_r100_199703.nc" "CHL_r100_199704.nc"
[149] "CHL_r100_199705.nc" "CHL_r100_199706.nc"
[151] "CHL_r100_199707.nc" "CHL_r100_199708.nc"
[153] "CHL_r100_199709.nc" "CHL_r100_199710.nc"
[155] "CHL_r100_199711.nc" "CHL_r100_199712.nc"
[157] "CHL_r100_199801.nc" "CHL_r100_199802.nc"
[159] "CHL_r100_199803.nc" "CHL_r100_199804.nc"
[161] "CHL_r100_199805.nc" "CHL_r100_199806.nc"
[163] "CHL_r100_199807.nc" "CHL_r100_199808.nc"
[165] "CHL_r100_199809.nc" "CHL_r100_199810.nc"
[167] "CHL_r100_199811.nc" "CHL_r100_199812.nc"
[169] "CHL_r100_199901.nc" "CHL_r100_199902.nc"
[171] "CHL_r100_199903.nc" "CHL_r100_199904.nc"
[173] "CHL_r100_199905.nc" "CHL_r100_199906.nc"
[175] "CHL_r100_199907.nc" "CHL_r100_199908.nc"
[177] "CHL_r100_199909.nc" "CHL_r100_199910.nc"
[179] "CHL_r100_199911.nc" "CHL_r100_199912.nc"
[181] "CHL_r100_200001.nc" "CHL_r100_200002.nc"
[183] "CHL_r100_200003.nc" "CHL_r100_200004.nc"
[185] "CHL_r100_200005.nc" "CHL_r100_200006.nc"
[187] "CHL_r100_200007.nc" "CHL_r100_200008.nc"
[189] "CHL_r100_200009.nc" "CHL_r100_200010.nc"
[191] "CHL_r100_200011.nc" "CHL_r100_200012.nc"
[193] "CHL_r100_200101.nc" "CHL_r100_200102.nc"
[195] "CHL_r100_200103.nc" "CHL_r100_200104.nc"
[197] "CHL_r100_200105.nc" "CHL_r100_200106.nc"
[199] "CHL_r100_200107.nc" "CHL_r100_200108.nc"
[201] "CHL_r100_200109.nc" "CHL_r100_200110.nc"
[203] "CHL_r100_200111.nc" "CHL_r100_200112.nc"
[205] "CHL_r100_200201.nc" "CHL_r100_200202.nc"
[207] "CHL_r100_200203.nc" "CHL_r100_200204.nc"
[209] "CHL_r100_200205.nc" "CHL_r100_200206.nc"
[211] "CHL_r100_200207.nc" "CHL_r100_200208.nc"
[213] "CHL_r100_200209.nc" "CHL_r100_200210.nc"
[215] "CHL_r100_200211.nc" "CHL_r100_200212.nc"
[217] "CHL_r100_200301.nc" "CHL_r100_200302.nc"
[219] "CHL_r100_200303.nc" "CHL_r100_200304.nc"
[221] "CHL_r100_200305.nc" "CHL_r100_200306.nc"
[223] "CHL_r100_200307.nc" "CHL_r100_200308.nc"
[225] "CHL_r100_200309.nc" "CHL_r100_200310.nc"
[227] "CHL_r100_200311.nc" "CHL_r100_200312.nc"
[229] "CHL_r100_200401.nc" "CHL_r100_200402.nc"
[231] "CHL_r100_200403.nc" "CHL_r100_200404.nc"
[233] "CHL_r100_200405.nc" "CHL_r100_200406.nc"
[235] "CHL_r100_200407.nc" "CHL_r100_200408.nc"
[237] "CHL_r100_200409.nc" "CHL_r100_200410.nc"
[239] "CHL_r100_200411.nc" "CHL_r100_200412.nc"
[241] "CHL_r100_200501.nc" "CHL_r100_200502.nc"
[243] "CHL_r100_200503.nc" "CHL_r100_200504.nc"
[245] "CHL_r100_200505.nc" "CHL_r100_200506.nc"
[247] "CHL_r100_200507.nc" "CHL_r100_200508.nc"
[249] "CHL_r100_200509.nc" "CHL_r100_200510.nc"
[251] "CHL_r100_200511.nc" "CHL_r100_200512.nc"
[253] "CHL_r100_200601.nc" "CHL_r100_200602.nc"
[255] "CHL_r100_200603.nc" "CHL_r100_200604.nc"
[257] "CHL_r100_200605.nc" "CHL_r100_200606.nc"
[259] "CHL_r100_200607.nc" "CHL_r100_200608.nc"
[261] "CHL_r100_200609.nc" "CHL_r100_200610.nc"
[263] "CHL_r100_200611.nc" "CHL_r100_200612.nc"
[265] "CHL_r100_200701.nc" "CHL_r100_200702.nc"
[267] "CHL_r100_200703.nc" "CHL_r100_200704.nc"
[269] "CHL_r100_200705.nc" "CHL_r100_200706.nc"
[271] "CHL_r100_200707.nc" "CHL_r100_200708.nc"
[273] "CHL_r100_200709.nc" "CHL_r100_200710.nc"
[275] "CHL_r100_200711.nc" "CHL_r100_200712.nc"
[277] "CHL_r100_200801.nc" "CHL_r100_200802.nc"
[279] "CHL_r100_200803.nc" "CHL_r100_200804.nc"
[281] "CHL_r100_200805.nc" "CHL_r100_200806.nc"
[283] "CHL_r100_200807.nc" "CHL_r100_200808.nc"
[285] "CHL_r100_200809.nc" "CHL_r100_200810.nc"
[287] "CHL_r100_200811.nc" "CHL_r100_200812.nc"
[289] "CHL_r100_200901.nc" "CHL_r100_200902.nc"
[291] "CHL_r100_200903.nc" "CHL_r100_200904.nc"
[293] "CHL_r100_200905.nc" "CHL_r100_200906.nc"
[295] "CHL_r100_200907.nc" "CHL_r100_200908.nc"
[297] "CHL_r100_200909.nc" "CHL_r100_200910.nc"
[299] "CHL_r100_200911.nc" "CHL_r100_200912.nc"
[301] "CHL_r100_201001.nc" "CHL_r100_201002.nc"
[303] "CHL_r100_201003.nc" "CHL_r100_201004.nc"
[305] "CHL_r100_201005.nc" "CHL_r100_201006.nc"
[307] "CHL_r100_201007.nc" "CHL_r100_201008.nc"
[309] "CHL_r100_201009.nc" "CHL_r100_201010.nc"
[311] "CHL_r100_201011.nc" "CHL_r100_201012.nc"
[313] "CHL_r100_201101.nc" "CHL_r100_201102.nc"
[315] "CHL_r100_201103.nc" "CHL_r100_201104.nc"
[317] "CHL_r100_201105.nc" "CHL_r100_201106.nc"
[319] "CHL_r100_201107.nc" "CHL_r100_201108.nc"
[321] "CHL_r100_201109.nc" "CHL_r100_201110.nc"
[323] "CHL_r100_201111.nc" "CHL_r100_201112.nc"
[325] "CHL_r100_201201.nc" "CHL_r100_201202.nc"
[327] "CHL_r100_201203.nc" "CHL_r100_201204.nc"
[329] "CHL_r100_201205.nc" "CHL_r100_201206.nc"
[331] "CHL_r100_201207.nc" "CHL_r100_201208.nc"
[333] "CHL_r100_201209.nc" "CHL_r100_201210.nc"
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[1207] "MLD_r100_200703.nc" "MLD_r100_200704.nc"
[1209] "MLD_r100_200705.nc" "MLD_r100_200706.nc"
[1211] "MLD_r100_200707.nc" "MLD_r100_200708.nc"
[1213] "MLD_r100_200709.nc" "MLD_r100_200710.nc"
[1215] "MLD_r100_200711.nc" "MLD_r100_200712.nc"
[1217] "MLD_r100_200801.nc" "MLD_r100_200802.nc"
[1219] "MLD_r100_200803.nc" "MLD_r100_200804.nc"
[1221] "MLD_r100_200805.nc" "MLD_r100_200806.nc"
[1223] "MLD_r100_200807.nc" "MLD_r100_200808.nc"
[1225] "MLD_r100_200809.nc" "MLD_r100_200810.nc"
[1227] "MLD_r100_200811.nc" "MLD_r100_200812.nc"
[1229] "MLD_r100_200901.nc" "MLD_r100_200902.nc"
[1231] "MLD_r100_200903.nc" "MLD_r100_200904.nc"
[1233] "MLD_r100_200905.nc" "MLD_r100_200906.nc"
[1235] "MLD_r100_200907.nc" "MLD_r100_200908.nc"
[1237] "MLD_r100_200909.nc" "MLD_r100_200910.nc"
[1239] "MLD_r100_200911.nc" "MLD_r100_200912.nc"
[1241] "MLD_r100_201001.nc" "MLD_r100_201002.nc"
[1243] "MLD_r100_201003.nc" "MLD_r100_201004.nc"
[1245] "MLD_r100_201005.nc" "MLD_r100_201006.nc"
[1247] "MLD_r100_201007.nc" "MLD_r100_201008.nc"
[1249] "MLD_r100_201009.nc" "MLD_r100_201010.nc"
[1251] "MLD_r100_201011.nc" "MLD_r100_201012.nc"
[1253] "MLD_r100_201101.nc" "MLD_r100_201102.nc"
[1255] "MLD_r100_201103.nc" "MLD_r100_201104.nc"
[1257] "MLD_r100_201105.nc" "MLD_r100_201106.nc"
[1259] "MLD_r100_201107.nc" "MLD_r100_201108.nc"
[1261] "MLD_r100_201109.nc" "MLD_r100_201110.nc"
[1263] "MLD_r100_201111.nc" "MLD_r100_201112.nc"
[1265] "MLD_r100_201201.nc" "MLD_r100_201202.nc"
[1267] "MLD_r100_201203.nc" "MLD_r100_201204.nc"
[1269] "MLD_r100_201205.nc" "MLD_r100_201206.nc"
[1271] "MLD_r100_201207.nc" "MLD_r100_201208.nc"
[1273] "MLD_r100_201209.nc" "MLD_r100_201210.nc"
[1275] "MLD_r100_201211.nc" "MLD_r100_201212.nc"
[1277] "MLD_r100_201301.nc" "MLD_r100_201302.nc"
[1279] "MLD_r100_201303.nc" "MLD_r100_201304.nc"
[1281] "MLD_r100_201305.nc" "MLD_r100_201306.nc"
[1283] "MLD_r100_201307.nc" "MLD_r100_201308.nc"
[1285] "MLD_r100_201309.nc" "MLD_r100_201310.nc"
[1287] "MLD_r100_201311.nc" "MLD_r100_201312.nc"
[1289] "MLD_r100_201401.nc" "MLD_r100_201402.nc"
[1291] "MLD_r100_201403.nc" "MLD_r100_201404.nc"
[1293] "MLD_r100_201405.nc" "MLD_r100_201406.nc"
[1295] "MLD_r100_201407.nc" "MLD_r100_201408.nc"
[1297] "MLD_r100_201409.nc" "MLD_r100_201410.nc"
[1299] "MLD_r100_201411.nc" "MLD_r100_201412.nc"
[1301] "MLD_r100_201501.nc" "MLD_r100_201502.nc"
[1303] "MLD_r100_201503.nc" "MLD_r100_201504.nc"
[1305] "MLD_r100_201505.nc" "MLD_r100_201506.nc"
[1307] "MLD_r100_201507.nc" "MLD_r100_201508.nc"
[1309] "MLD_r100_201509.nc" "MLD_r100_201510.nc"
[1311] "MLD_r100_201511.nc" "MLD_r100_201512.nc"
[1313] "MLD_r100_201601.nc" "MLD_r100_201602.nc"
[1315] "MLD_r100_201603.nc" "MLD_r100_201604.nc"
[1317] "MLD_r100_201605.nc" "MLD_r100_201606.nc"
[1319] "MLD_r100_201607.nc" "MLD_r100_201608.nc"
[1321] "MLD_r100_201609.nc" "MLD_r100_201610.nc"
[1323] "MLD_r100_201611.nc" "MLD_r100_201612.nc"
[1325] "MLD_r100_201701.nc" "MLD_r100_201702.nc"
[1327] "MLD_r100_201703.nc" "MLD_r100_201704.nc"
[1329] "MLD_r100_201705.nc" "MLD_r100_201706.nc"
[1331] "MLD_r100_201707.nc" "MLD_r100_201708.nc"
[1333] "MLD_r100_201709.nc" "MLD_r100_201710.nc"
[1335] "MLD_r100_201711.nc" "MLD_r100_201712.nc"
[1337] "MLD_r100_201801.nc" "MLD_r100_201802.nc"
[1339] "MLD_r100_201803.nc" "MLD_r100_201804.nc"
[1341] "MLD_r100_201805.nc" "MLD_r100_201806.nc"
[1343] "MLD_r100_201807.nc" "MLD_r100_201808.nc"
[1345] "MLD_r100_201809.nc" "MLD_r100_201810.nc"
[1347] "MLD_r100_201811.nc" "MLD_r100_201812.nc"
[1349] "MLD_r100_201901.nc" "MLD_r100_201902.nc"
[1351] "MLD_r100_201903.nc" "MLD_r100_201904.nc"
[1353] "MLD_r100_201905.nc" "MLD_r100_201906.nc"
[1355] "MLD_r100_201907.nc" "MLD_r100_201908.nc"
[1357] "MLD_r100_201909.nc" "MLD_r100_201910.nc"
[1359] "MLD_r100_201911.nc" "MLD_r100_201912.nc"
[1361] "MLD_r100_202001.nc" "MLD_r100_202002.nc"
[1363] "MLD_r100_202003.nc" "MLD_r100_202004.nc"
[1365] "MLD_r100_202005.nc" "MLD_r100_202006.nc"
[1367] "MLD_r100_202007.nc" "MLD_r100_202008.nc"
[1369] "MLD_r100_202009.nc" "MLD_r100_202010.nc"
[1371] "MLD_r100_202011.nc" "MLD_r100_202012.nc"
[1373] "MLD_r100_202101.nc" "MLD_r100_202102.nc"
[1375] "MLD_r100_202103.nc" "MLD_r100_202104.nc"
[1377] "MLD_r100_202105.nc" "MLD_r100_202106.nc"
[1379] "MLD_r100_202107.nc" "MLD_r100_202108.nc"
[1381] "MLD_r100_202109.nc" "MLD_r100_202110.nc"
[1383] "MLD_r100_202111.nc" "MLD_r100_202112.nc"
[1385] "MLD_r100_202201.nc" "MLD_r100_202202.nc"
[1387] "MLD_r100_202203.nc" "MLD_r100_202204.nc"
[1389] "MLD_r100_202205.nc" "MLD_r100_202206.nc"
[1391] "MLD_r100_202207.nc" "MLD_r100_202208.nc"
[1393] "MLD_r100_202209.nc" "MLD_r100_202210.nc"
[1395] "MLD_r100_202211.nc" "MLD_r100_202212.nc"
[1397] "MLD_r100_202301.nc" "MLD_r100_202302.nc"
[1399] "MLD_r100_202303.nc" "MLD_r100_202304.nc"
[1401] "MLD_r100_202305.nc" "MLD_r100_202306.nc"
[1403] "MLD_r100_202307.nc" "MLD_r100_202308.nc"
[1405] "MLD_r100_202309.nc" "Sea_Ice_r100_198501.nc"
[1407] "Sea_Ice_r100_198502.nc" "Sea_Ice_r100_198503.nc"
[1409] "Sea_Ice_r100_198504.nc" "Sea_Ice_r100_198505.nc"
[1411] "Sea_Ice_r100_198506.nc" "Sea_Ice_r100_198507.nc"
[1413] "Sea_Ice_r100_198508.nc" "Sea_Ice_r100_198509.nc"
[1415] "Sea_Ice_r100_198510.nc" "Sea_Ice_r100_198511.nc"
[1417] "Sea_Ice_r100_198512.nc" "Sea_Ice_r100_198601.nc"
[1419] "Sea_Ice_r100_198602.nc" "Sea_Ice_r100_198603.nc"
[1421] "Sea_Ice_r100_198604.nc" "Sea_Ice_r100_198605.nc"
[1423] "Sea_Ice_r100_198606.nc" "Sea_Ice_r100_198607.nc"
[1425] "Sea_Ice_r100_198608.nc" "Sea_Ice_r100_198609.nc"
[1427] "Sea_Ice_r100_198610.nc" "Sea_Ice_r100_198611.nc"
[1429] "Sea_Ice_r100_198612.nc" "Sea_Ice_r100_198701.nc"
[1431] "Sea_Ice_r100_198702.nc" "Sea_Ice_r100_198703.nc"
[1433] "Sea_Ice_r100_198704.nc" "Sea_Ice_r100_198705.nc"
[1435] "Sea_Ice_r100_198706.nc" "Sea_Ice_r100_198707.nc"
[1437] "Sea_Ice_r100_198708.nc" "Sea_Ice_r100_198709.nc"
[1439] "Sea_Ice_r100_198710.nc" "Sea_Ice_r100_198711.nc"
[1441] "Sea_Ice_r100_198712.nc" "Sea_Ice_r100_198801.nc"
[1443] "Sea_Ice_r100_198802.nc" "Sea_Ice_r100_198803.nc"
[1445] "Sea_Ice_r100_198804.nc" "Sea_Ice_r100_198805.nc"
[1447] "Sea_Ice_r100_198806.nc" "Sea_Ice_r100_198807.nc"
[1449] "Sea_Ice_r100_198808.nc" "Sea_Ice_r100_198809.nc"
[1451] "Sea_Ice_r100_198810.nc" "Sea_Ice_r100_198811.nc"
[1453] "Sea_Ice_r100_198812.nc" "Sea_Ice_r100_198901.nc"
[1455] "Sea_Ice_r100_198902.nc" "Sea_Ice_r100_198903.nc"
[1457] "Sea_Ice_r100_198904.nc" "Sea_Ice_r100_198905.nc"
[1459] "Sea_Ice_r100_198906.nc" "Sea_Ice_r100_198907.nc"
[1461] "Sea_Ice_r100_198908.nc" "Sea_Ice_r100_198909.nc"
[1463] "Sea_Ice_r100_198910.nc" "Sea_Ice_r100_198911.nc"
[1465] "Sea_Ice_r100_198912.nc" "Sea_Ice_r100_199001.nc"
[1467] "Sea_Ice_r100_199002.nc" "Sea_Ice_r100_199003.nc"
[1469] "Sea_Ice_r100_199004.nc" "Sea_Ice_r100_199005.nc"
[1471] "Sea_Ice_r100_199006.nc" "Sea_Ice_r100_199007.nc"
[1473] "Sea_Ice_r100_199008.nc" "Sea_Ice_r100_199009.nc"
[1475] "Sea_Ice_r100_199010.nc" "Sea_Ice_r100_199011.nc"
[1477] "Sea_Ice_r100_199012.nc" "Sea_Ice_r100_199101.nc"
[1479] "Sea_Ice_r100_199102.nc" "Sea_Ice_r100_199103.nc"
[1481] "Sea_Ice_r100_199104.nc" "Sea_Ice_r100_199105.nc"
[1483] "Sea_Ice_r100_199106.nc" "Sea_Ice_r100_199107.nc"
[1485] "Sea_Ice_r100_199108.nc" "Sea_Ice_r100_199109.nc"
[1487] "Sea_Ice_r100_199110.nc" "Sea_Ice_r100_199111.nc"
[1489] "Sea_Ice_r100_199112.nc" "Sea_Ice_r100_199201.nc"
[1491] "Sea_Ice_r100_199202.nc" "Sea_Ice_r100_199203.nc"
[1493] "Sea_Ice_r100_199204.nc" "Sea_Ice_r100_199205.nc"
[1495] "Sea_Ice_r100_199206.nc" "Sea_Ice_r100_199207.nc"
[1497] "Sea_Ice_r100_199208.nc" "Sea_Ice_r100_199209.nc"
[1499] "Sea_Ice_r100_199210.nc" "Sea_Ice_r100_199211.nc"
[1501] "Sea_Ice_r100_199212.nc" "Sea_Ice_r100_199301.nc"
[1503] "Sea_Ice_r100_199302.nc" "Sea_Ice_r100_199303.nc"
[1505] "Sea_Ice_r100_199304.nc" "Sea_Ice_r100_199305.nc"
[1507] "Sea_Ice_r100_199306.nc" "Sea_Ice_r100_199307.nc"
[1509] "Sea_Ice_r100_199308.nc" "Sea_Ice_r100_199309.nc"
[1511] "Sea_Ice_r100_199310.nc" "Sea_Ice_r100_199311.nc"
[1513] "Sea_Ice_r100_199312.nc" "Sea_Ice_r100_199401.nc"
[1515] "Sea_Ice_r100_199402.nc" "Sea_Ice_r100_199403.nc"
[1517] "Sea_Ice_r100_199404.nc" "Sea_Ice_r100_199405.nc"
[1519] "Sea_Ice_r100_199406.nc" "Sea_Ice_r100_199407.nc"
[1521] "Sea_Ice_r100_199408.nc" "Sea_Ice_r100_199409.nc"
[1523] "Sea_Ice_r100_199410.nc" "Sea_Ice_r100_199411.nc"
[1525] "Sea_Ice_r100_199412.nc" "Sea_Ice_r100_199501.nc"
[1527] "Sea_Ice_r100_199502.nc" "Sea_Ice_r100_199503.nc"
[1529] "Sea_Ice_r100_199504.nc" "Sea_Ice_r100_199505.nc"
[1531] "Sea_Ice_r100_199506.nc" "Sea_Ice_r100_199507.nc"
[1533] "Sea_Ice_r100_199508.nc" "Sea_Ice_r100_199509.nc"
[1535] "Sea_Ice_r100_199510.nc" "Sea_Ice_r100_199511.nc"
[1537] "Sea_Ice_r100_199512.nc" "Sea_Ice_r100_199601.nc"
[1539] "Sea_Ice_r100_199602.nc" "Sea_Ice_r100_199603.nc"
[1541] "Sea_Ice_r100_199604.nc" "Sea_Ice_r100_199605.nc"
[1543] "Sea_Ice_r100_199606.nc" "Sea_Ice_r100_199607.nc"
[1545] "Sea_Ice_r100_199608.nc" "Sea_Ice_r100_199609.nc"
[1547] "Sea_Ice_r100_199610.nc" "Sea_Ice_r100_199611.nc"
[1549] "Sea_Ice_r100_199612.nc" "Sea_Ice_r100_199701.nc"
[1551] "Sea_Ice_r100_199702.nc" "Sea_Ice_r100_199703.nc"
[1553] "Sea_Ice_r100_199704.nc" "Sea_Ice_r100_199705.nc"
[1555] "Sea_Ice_r100_199706.nc" "Sea_Ice_r100_199707.nc"
[1557] "Sea_Ice_r100_199708.nc" "Sea_Ice_r100_199709.nc"
[1559] "Sea_Ice_r100_199710.nc" "Sea_Ice_r100_199711.nc"
[1561] "Sea_Ice_r100_199712.nc" "Sea_Ice_r100_199801.nc"
[1563] "Sea_Ice_r100_199802.nc" "Sea_Ice_r100_199803.nc"
[1565] "Sea_Ice_r100_199804.nc" "Sea_Ice_r100_199805.nc"
[1567] "Sea_Ice_r100_199806.nc" "Sea_Ice_r100_199807.nc"
[1569] "Sea_Ice_r100_199808.nc" "Sea_Ice_r100_199809.nc"
[1571] "Sea_Ice_r100_199810.nc" "Sea_Ice_r100_199811.nc"
[1573] "Sea_Ice_r100_199812.nc" "Sea_Ice_r100_199901.nc"
[1575] "Sea_Ice_r100_199902.nc" "Sea_Ice_r100_199903.nc"
[1577] "Sea_Ice_r100_199904.nc" "Sea_Ice_r100_199905.nc"
[1579] "Sea_Ice_r100_199906.nc" "Sea_Ice_r100_199907.nc"
[1581] "Sea_Ice_r100_199908.nc" "Sea_Ice_r100_199909.nc"
[1583] "Sea_Ice_r100_199910.nc" "Sea_Ice_r100_199911.nc"
[1585] "Sea_Ice_r100_199912.nc" "Sea_Ice_r100_200001.nc"
[1587] "Sea_Ice_r100_200002.nc" "Sea_Ice_r100_200003.nc"
[1589] "Sea_Ice_r100_200004.nc" "Sea_Ice_r100_200005.nc"
[1591] "Sea_Ice_r100_200006.nc" "Sea_Ice_r100_200007.nc"
[1593] "Sea_Ice_r100_200008.nc" "Sea_Ice_r100_200009.nc"
[1595] "Sea_Ice_r100_200010.nc" "Sea_Ice_r100_200011.nc"
[1597] "Sea_Ice_r100_200012.nc" "Sea_Ice_r100_200101.nc"
[1599] "Sea_Ice_r100_200102.nc" "Sea_Ice_r100_200103.nc"
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[1603] "Sea_Ice_r100_200106.nc" "Sea_Ice_r100_200107.nc"
[1605] "Sea_Ice_r100_200108.nc" "Sea_Ice_r100_200109.nc"
[1607] "Sea_Ice_r100_200110.nc" "Sea_Ice_r100_200111.nc"
[1609] "Sea_Ice_r100_200112.nc" "Sea_Ice_r100_200201.nc"
[1611] "Sea_Ice_r100_200202.nc" "Sea_Ice_r100_200203.nc"
[1613] "Sea_Ice_r100_200204.nc" "Sea_Ice_r100_200205.nc"
[1615] "Sea_Ice_r100_200206.nc" "Sea_Ice_r100_200207.nc"
[1617] "Sea_Ice_r100_200208.nc" "Sea_Ice_r100_200209.nc"
[1619] "Sea_Ice_r100_200210.nc" "Sea_Ice_r100_200211.nc"
[1621] "Sea_Ice_r100_200212.nc" "Sea_Ice_r100_200301.nc"
[1623] "Sea_Ice_r100_200302.nc" "Sea_Ice_r100_200303.nc"
[1625] "Sea_Ice_r100_200304.nc" "Sea_Ice_r100_200305.nc"
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[1631] "Sea_Ice_r100_200310.nc" "Sea_Ice_r100_200311.nc"
[1633] "Sea_Ice_r100_200312.nc" "Sea_Ice_r100_200401.nc"
[1635] "Sea_Ice_r100_200402.nc" "Sea_Ice_r100_200403.nc"
[1637] "Sea_Ice_r100_200404.nc" "Sea_Ice_r100_200405.nc"
[1639] "Sea_Ice_r100_200406.nc" "Sea_Ice_r100_200407.nc"
[1641] "Sea_Ice_r100_200408.nc" "Sea_Ice_r100_200409.nc"
[1643] "Sea_Ice_r100_200410.nc" "Sea_Ice_r100_200411.nc"
[1645] "Sea_Ice_r100_200412.nc" "Sea_Ice_r100_200501.nc"
[1647] "Sea_Ice_r100_200502.nc" "Sea_Ice_r100_200503.nc"
[1649] "Sea_Ice_r100_200504.nc" "Sea_Ice_r100_200505.nc"
[1651] "Sea_Ice_r100_200506.nc" "Sea_Ice_r100_200507.nc"
[1653] "Sea_Ice_r100_200508.nc" "Sea_Ice_r100_200509.nc"
[1655] "Sea_Ice_r100_200510.nc" "Sea_Ice_r100_200511.nc"
[1657] "Sea_Ice_r100_200512.nc" "Sea_Ice_r100_200601.nc"
[1659] "Sea_Ice_r100_200602.nc" "Sea_Ice_r100_200603.nc"
[1661] "Sea_Ice_r100_200604.nc" "Sea_Ice_r100_200605.nc"
[1663] "Sea_Ice_r100_200606.nc" "Sea_Ice_r100_200607.nc"
[1665] "Sea_Ice_r100_200608.nc" "Sea_Ice_r100_200609.nc"
[1667] "Sea_Ice_r100_200610.nc" "Sea_Ice_r100_200611.nc"
[1669] "Sea_Ice_r100_200612.nc" "Sea_Ice_r100_200701.nc"
[1671] "Sea_Ice_r100_200702.nc" "Sea_Ice_r100_200703.nc"
[1673] "Sea_Ice_r100_200704.nc" "Sea_Ice_r100_200705.nc"
[1675] "Sea_Ice_r100_200706.nc" "Sea_Ice_r100_200707.nc"
[1677] "Sea_Ice_r100_200708.nc" "Sea_Ice_r100_200709.nc"
[1679] "Sea_Ice_r100_200710.nc" "Sea_Ice_r100_200711.nc"
[1681] "Sea_Ice_r100_200712.nc" "Sea_Ice_r100_200801.nc"
[1683] "Sea_Ice_r100_200802.nc" "Sea_Ice_r100_200803.nc"
[1685] "Sea_Ice_r100_200804.nc" "Sea_Ice_r100_200805.nc"
[1687] "Sea_Ice_r100_200806.nc" "Sea_Ice_r100_200807.nc"
[1689] "Sea_Ice_r100_200808.nc" "Sea_Ice_r100_200809.nc"
[1691] "Sea_Ice_r100_200810.nc" "Sea_Ice_r100_200811.nc"
[1693] "Sea_Ice_r100_200812.nc" "Sea_Ice_r100_200901.nc"
[1695] "Sea_Ice_r100_200902.nc" "Sea_Ice_r100_200903.nc"
[1697] "Sea_Ice_r100_200904.nc" "Sea_Ice_r100_200905.nc"
[1699] "Sea_Ice_r100_200906.nc" "Sea_Ice_r100_200907.nc"
[1701] "Sea_Ice_r100_200908.nc" "Sea_Ice_r100_200909.nc"
[1703] "Sea_Ice_r100_200910.nc" "Sea_Ice_r100_200911.nc"
[1705] "Sea_Ice_r100_200912.nc" "Sea_Ice_r100_201001.nc"
[1707] "Sea_Ice_r100_201002.nc" "Sea_Ice_r100_201003.nc"
[1709] "Sea_Ice_r100_201004.nc" "Sea_Ice_r100_201005.nc"
[1711] "Sea_Ice_r100_201006.nc" "Sea_Ice_r100_201007.nc"
[1713] "Sea_Ice_r100_201008.nc" "Sea_Ice_r100_201009.nc"
[1715] "Sea_Ice_r100_201010.nc" "Sea_Ice_r100_201011.nc"
[1717] "Sea_Ice_r100_201012.nc" "Sea_Ice_r100_201101.nc"
[1719] "Sea_Ice_r100_201102.nc" "Sea_Ice_r100_201103.nc"
[1721] "Sea_Ice_r100_201104.nc" "Sea_Ice_r100_201105.nc"
[1723] "Sea_Ice_r100_201106.nc" "Sea_Ice_r100_201107.nc"
[1725] "Sea_Ice_r100_201108.nc" "Sea_Ice_r100_201109.nc"
[1727] "Sea_Ice_r100_201110.nc" "Sea_Ice_r100_201111.nc"
[1729] "Sea_Ice_r100_201112.nc" "Sea_Ice_r100_201201.nc"
[1731] "Sea_Ice_r100_201202.nc" "Sea_Ice_r100_201203.nc"
[1733] "Sea_Ice_r100_201204.nc" "Sea_Ice_r100_201205.nc"
[1735] "Sea_Ice_r100_201206.nc" "Sea_Ice_r100_201207.nc"
[1737] "Sea_Ice_r100_201208.nc" "Sea_Ice_r100_201209.nc"
[1739] "Sea_Ice_r100_201210.nc" "Sea_Ice_r100_201211.nc"
[1741] "Sea_Ice_r100_201212.nc" "Sea_Ice_r100_201301.nc"
[1743] "Sea_Ice_r100_201302.nc" "Sea_Ice_r100_201303.nc"
[1745] "Sea_Ice_r100_201304.nc" "Sea_Ice_r100_201305.nc"
[1747] "Sea_Ice_r100_201306.nc" "Sea_Ice_r100_201307.nc"
[1749] "Sea_Ice_r100_201308.nc" "Sea_Ice_r100_201309.nc"
[1751] "Sea_Ice_r100_201310.nc" "Sea_Ice_r100_201311.nc"
[1753] "Sea_Ice_r100_201312.nc" "Sea_Ice_r100_201401.nc"
[1755] "Sea_Ice_r100_201402.nc" "Sea_Ice_r100_201403.nc"
[1757] "Sea_Ice_r100_201404.nc" "Sea_Ice_r100_201405.nc"
[1759] "Sea_Ice_r100_201406.nc" "Sea_Ice_r100_201407.nc"
[1761] "Sea_Ice_r100_201408.nc" "Sea_Ice_r100_201409.nc"
[1763] "Sea_Ice_r100_201410.nc" "Sea_Ice_r100_201411.nc"
[1765] "Sea_Ice_r100_201412.nc" "Sea_Ice_r100_201501.nc"
[1767] "Sea_Ice_r100_201502.nc" "Sea_Ice_r100_201503.nc"
[1769] "Sea_Ice_r100_201504.nc" "Sea_Ice_r100_201505.nc"
[1771] "Sea_Ice_r100_201506.nc" "Sea_Ice_r100_201507.nc"
[1773] "Sea_Ice_r100_201508.nc" "Sea_Ice_r100_201509.nc"
[1775] "Sea_Ice_r100_201510.nc" "Sea_Ice_r100_201511.nc"
[1777] "Sea_Ice_r100_201512.nc" "Sea_Ice_r100_201601.nc"
[1779] "Sea_Ice_r100_201602.nc" "Sea_Ice_r100_201603.nc"
[1781] "Sea_Ice_r100_201604.nc" "Sea_Ice_r100_201605.nc"
[1783] "Sea_Ice_r100_201606.nc" "Sea_Ice_r100_201607.nc"
[1785] "Sea_Ice_r100_201608.nc" "Sea_Ice_r100_201609.nc"
[1787] "Sea_Ice_r100_201610.nc" "Sea_Ice_r100_201611.nc"
[1789] "Sea_Ice_r100_201612.nc" "Sea_Ice_r100_201701.nc"
[1791] "Sea_Ice_r100_201702.nc" "Sea_Ice_r100_201703.nc"
[1793] "Sea_Ice_r100_201704.nc" "Sea_Ice_r100_201705.nc"
[1795] "Sea_Ice_r100_201706.nc" "Sea_Ice_r100_201707.nc"
[1797] "Sea_Ice_r100_201708.nc" "Sea_Ice_r100_201709.nc"
[1799] "Sea_Ice_r100_201710.nc" "Sea_Ice_r100_201711.nc"
[1801] "Sea_Ice_r100_201712.nc" "Sea_Ice_r100_201801.nc"
[1803] "Sea_Ice_r100_201802.nc" "Sea_Ice_r100_201803.nc"
[1805] "Sea_Ice_r100_201804.nc" "Sea_Ice_r100_201805.nc"
[1807] "Sea_Ice_r100_201806.nc" "Sea_Ice_r100_201807.nc"
[1809] "Sea_Ice_r100_201808.nc" "Sea_Ice_r100_201809.nc"
[1811] "Sea_Ice_r100_201810.nc" "Sea_Ice_r100_201811.nc"
[1813] "Sea_Ice_r100_201812.nc" "Sea_Ice_r100_201901.nc"
[1815] "Sea_Ice_r100_201902.nc" "Sea_Ice_r100_201903.nc"
[1817] "Sea_Ice_r100_201904.nc" "Sea_Ice_r100_201905.nc"
[1819] "Sea_Ice_r100_201906.nc" "Sea_Ice_r100_201907.nc"
[1821] "Sea_Ice_r100_201908.nc" "Sea_Ice_r100_201909.nc"
[1823] "Sea_Ice_r100_201910.nc" "Sea_Ice_r100_201911.nc"
[1825] "Sea_Ice_r100_201912.nc" "Sea_Ice_r100_202001.nc"
[1827] "Sea_Ice_r100_202002.nc" "Sea_Ice_r100_202003.nc"
[1829] "Sea_Ice_r100_202004.nc" "Sea_Ice_r100_202005.nc"
[1831] "Sea_Ice_r100_202006.nc" "Sea_Ice_r100_202007.nc"
[1833] "Sea_Ice_r100_202008.nc" "Sea_Ice_r100_202009.nc"
[1835] "Sea_Ice_r100_202010.nc" "Sea_Ice_r100_202011.nc"
[1837] "Sea_Ice_r100_202012.nc" "Sea_Ice_r100_202101.nc"
[1839] "Sea_Ice_r100_202102.nc" "Sea_Ice_r100_202103.nc"
[1841] "Sea_Ice_r100_202104.nc" "Sea_Ice_r100_202105.nc"
[1843] "Sea_Ice_r100_202106.nc" "Sea_Ice_r100_202107.nc"
[1845] "Sea_Ice_r100_202108.nc" "Sea_Ice_r100_202109.nc"
[1847] "Sea_Ice_r100_202110.nc" "Sea_Ice_r100_202111.nc"
[1849] "Sea_Ice_r100_202112.nc" "Sea_Ice_r100_202201.nc"
[1851] "Sea_Ice_r100_202202.nc" "Sea_Ice_r100_202203.nc"
[1853] "Sea_Ice_r100_202204.nc" "Sea_Ice_r100_202205.nc"
[1855] "Sea_Ice_r100_202206.nc" "Sea_Ice_r100_202207.nc"
[1857] "Sea_Ice_r100_202208.nc" "Sea_Ice_r100_202209.nc"
[1859] "Sea_Ice_r100_202210.nc" "Sea_Ice_r100_202211.nc"
[1861] "Sea_Ice_r100_202212.nc" "Sea_Ice_r100_202301.nc"
[1863] "Sea_Ice_r100_202302.nc" "Sea_Ice_r100_202303.nc"
[1865] "Sea_Ice_r100_202304.nc" "Sea_Ice_r100_202305.nc"
[1867] "Sea_Ice_r100_202306.nc" "Sea_Ice_r100_202307.nc"
[1869] "Sea_Ice_r100_202308.nc" "Sea_Ice_r100_202309.nc"
[1871] "SSH_r100_198501.nc" "SSH_r100_198502.nc"
[1873] "SSH_r100_198503.nc" "SSH_r100_198504.nc"
[1875] "SSH_r100_198505.nc" "SSH_r100_198506.nc"
[1877] "SSH_r100_198507.nc" "SSH_r100_198508.nc"
[1879] "SSH_r100_198509.nc" "SSH_r100_198510.nc"
[1881] "SSH_r100_198511.nc" "SSH_r100_198512.nc"
[1883] "SSH_r100_198601.nc" "SSH_r100_198602.nc"
[1885] "SSH_r100_198603.nc" "SSH_r100_198604.nc"
[1887] "SSH_r100_198605.nc" "SSH_r100_198606.nc"
[1889] "SSH_r100_198607.nc" "SSH_r100_198608.nc"
[1891] "SSH_r100_198609.nc" "SSH_r100_198610.nc"
[1893] "SSH_r100_198611.nc" "SSH_r100_198612.nc"
[1895] "SSH_r100_198701.nc" "SSH_r100_198702.nc"
[1897] "SSH_r100_198703.nc" "SSH_r100_198704.nc"
[1899] "SSH_r100_198705.nc" "SSH_r100_198706.nc"
[1901] "SSH_r100_198707.nc" "SSH_r100_198708.nc"
[1903] "SSH_r100_198709.nc" "SSH_r100_198710.nc"
[1905] "SSH_r100_198711.nc" "SSH_r100_198712.nc"
[1907] "SSH_r100_198801.nc" "SSH_r100_198802.nc"
[1909] "SSH_r100_198803.nc" "SSH_r100_198804.nc"
[1911] "SSH_r100_198805.nc" "SSH_r100_198806.nc"
[1913] "SSH_r100_198807.nc" "SSH_r100_198808.nc"
[1915] "SSH_r100_198809.nc" "SSH_r100_198810.nc"
[1917] "SSH_r100_198811.nc" "SSH_r100_198812.nc"
[1919] "SSH_r100_198901.nc" "SSH_r100_198902.nc"
[1921] "SSH_r100_198903.nc" "SSH_r100_198904.nc"
[1923] "SSH_r100_198905.nc" "SSH_r100_198906.nc"
[1925] "SSH_r100_198907.nc" "SSH_r100_198908.nc"
[1927] "SSH_r100_198909.nc" "SSH_r100_198910.nc"
[1929] "SSH_r100_198911.nc" "SSH_r100_198912.nc"
[1931] "SSH_r100_199001.nc" "SSH_r100_199002.nc"
[1933] "SSH_r100_199003.nc" "SSH_r100_199004.nc"
[1935] "SSH_r100_199005.nc" "SSH_r100_199006.nc"
[1937] "SSH_r100_199007.nc" "SSH_r100_199008.nc"
[1939] "SSH_r100_199009.nc" "SSH_r100_199010.nc"
[1941] "SSH_r100_199011.nc" "SSH_r100_199012.nc"
[1943] "SSH_r100_199101.nc" "SSH_r100_199102.nc"
[1945] "SSH_r100_199103.nc" "SSH_r100_199104.nc"
[1947] "SSH_r100_199105.nc" "SSH_r100_199106.nc"
[1949] "SSH_r100_199107.nc" "SSH_r100_199108.nc"
[1951] "SSH_r100_199109.nc" "SSH_r100_199110.nc"
[1953] "SSH_r100_199111.nc" "SSH_r100_199112.nc"
[1955] "SSH_r100_199201.nc" "SSH_r100_199202.nc"
[1957] "SSH_r100_199203.nc" "SSH_r100_199204.nc"
[1959] "SSH_r100_199205.nc" "SSH_r100_199206.nc"
[1961] "SSH_r100_199207.nc" "SSH_r100_199208.nc"
[1963] "SSH_r100_199209.nc" "SSH_r100_199210.nc"
[1965] "SSH_r100_199211.nc" "SSH_r100_199212.nc"
[1967] "SSH_r100_199301.nc" "SSH_r100_199302.nc"
[1969] "SSH_r100_199303.nc" "SSH_r100_199304.nc"
[1971] "SSH_r100_199305.nc" "SSH_r100_199306.nc"
[1973] "SSH_r100_199307.nc" "SSH_r100_199308.nc"
[1975] "SSH_r100_199309.nc" "SSH_r100_199310.nc"
[1977] "SSH_r100_199311.nc" "SSH_r100_199312.nc"
[1979] "SSH_r100_199401.nc" "SSH_r100_199402.nc"
[1981] "SSH_r100_199403.nc" "SSH_r100_199404.nc"
[1983] "SSH_r100_199405.nc" "SSH_r100_199406.nc"
[1985] "SSH_r100_199407.nc" "SSH_r100_199408.nc"
[1987] "SSH_r100_199409.nc" "SSH_r100_199410.nc"
[1989] "SSH_r100_199411.nc" "SSH_r100_199412.nc"
[1991] "SSH_r100_199501.nc" "SSH_r100_199502.nc"
[1993] "SSH_r100_199503.nc" "SSH_r100_199504.nc"
[1995] "SSH_r100_199505.nc" "SSH_r100_199506.nc"
[1997] "SSH_r100_199507.nc" "SSH_r100_199508.nc"
[1999] "SSH_r100_199509.nc" "SSH_r100_199510.nc"
[2001] "SSH_r100_199511.nc" "SSH_r100_199512.nc"
[2003] "SSH_r100_199601.nc" "SSH_r100_199602.nc"
[2005] "SSH_r100_199603.nc" "SSH_r100_199604.nc"
[2007] "SSH_r100_199605.nc" "SSH_r100_199606.nc"
[2009] "SSH_r100_199607.nc" "SSH_r100_199608.nc"
[2011] "SSH_r100_199609.nc" "SSH_r100_199610.nc"
[2013] "SSH_r100_199611.nc" "SSH_r100_199612.nc"
[2015] "SSH_r100_199701.nc" "SSH_r100_199702.nc"
[2017] "SSH_r100_199703.nc" "SSH_r100_199704.nc"
[2019] "SSH_r100_199705.nc" "SSH_r100_199706.nc"
[2021] "SSH_r100_199707.nc" "SSH_r100_199708.nc"
[2023] "SSH_r100_199709.nc" "SSH_r100_199710.nc"
[2025] "SSH_r100_199711.nc" "SSH_r100_199712.nc"
[2027] "SSH_r100_199801.nc" "SSH_r100_199802.nc"
[2029] "SSH_r100_199803.nc" "SSH_r100_199804.nc"
[2031] "SSH_r100_199805.nc" "SSH_r100_199806.nc"
[2033] "SSH_r100_199807.nc" "SSH_r100_199808.nc"
[2035] "SSH_r100_199809.nc" "SSH_r100_199810.nc"
[2037] "SSH_r100_199811.nc" "SSH_r100_199812.nc"
[2039] "SSH_r100_199901.nc" "SSH_r100_199902.nc"
[2041] "SSH_r100_199903.nc" "SSH_r100_199904.nc"
[2043] "SSH_r100_199905.nc" "SSH_r100_199906.nc"
[2045] "SSH_r100_199907.nc" "SSH_r100_199908.nc"
[2047] "SSH_r100_199909.nc" "SSH_r100_199910.nc"
[2049] "SSH_r100_199911.nc" "SSH_r100_199912.nc"
[2051] "SSH_r100_200001.nc" "SSH_r100_200002.nc"
[2053] "SSH_r100_200003.nc" "SSH_r100_200004.nc"
[2055] "SSH_r100_200005.nc" "SSH_r100_200006.nc"
[2057] "SSH_r100_200007.nc" "SSH_r100_200008.nc"
[2059] "SSH_r100_200009.nc" "SSH_r100_200010.nc"
[2061] "SSH_r100_200011.nc" "SSH_r100_200012.nc"
[2063] "SSH_r100_200101.nc" "SSH_r100_200102.nc"
[2065] "SSH_r100_200103.nc" "SSH_r100_200104.nc"
[2067] "SSH_r100_200105.nc" "SSH_r100_200106.nc"
[2069] "SSH_r100_200107.nc" "SSH_r100_200108.nc"
[2071] "SSH_r100_200109.nc" "SSH_r100_200110.nc"
[2073] "SSH_r100_200111.nc" "SSH_r100_200112.nc"
[2075] "SSH_r100_200201.nc" "SSH_r100_200202.nc"
[2077] "SSH_r100_200203.nc" "SSH_r100_200204.nc"
[2079] "SSH_r100_200205.nc" "SSH_r100_200206.nc"
[2081] "SSH_r100_200207.nc" "SSH_r100_200208.nc"
[2083] "SSH_r100_200209.nc" "SSH_r100_200210.nc"
[2085] "SSH_r100_200211.nc" "SSH_r100_200212.nc"
[2087] "SSH_r100_200301.nc" "SSH_r100_200302.nc"
[2089] "SSH_r100_200303.nc" "SSH_r100_200304.nc"
[2091] "SSH_r100_200305.nc" "SSH_r100_200306.nc"
[2093] "SSH_r100_200307.nc" "SSH_r100_200308.nc"
[2095] "SSH_r100_200309.nc" "SSH_r100_200310.nc"
[2097] "SSH_r100_200311.nc" "SSH_r100_200312.nc"
[2099] "SSH_r100_200401.nc" "SSH_r100_200402.nc"
[2101] "SSH_r100_200403.nc" "SSH_r100_200404.nc"
[2103] "SSH_r100_200405.nc" "SSH_r100_200406.nc"
[2105] "SSH_r100_200407.nc" "SSH_r100_200408.nc"
[2107] "SSH_r100_200409.nc" "SSH_r100_200410.nc"
[2109] "SSH_r100_200411.nc" "SSH_r100_200412.nc"
[2111] "SSH_r100_200501.nc" "SSH_r100_200502.nc"
[2113] "SSH_r100_200503.nc" "SSH_r100_200504.nc"
[2115] "SSH_r100_200505.nc" "SSH_r100_200506.nc"
[2117] "SSH_r100_200507.nc" "SSH_r100_200508.nc"
[2119] "SSH_r100_200509.nc" "SSH_r100_200510.nc"
[2121] "SSH_r100_200511.nc" "SSH_r100_200512.nc"
[2123] "SSH_r100_200601.nc" "SSH_r100_200602.nc"
[2125] "SSH_r100_200603.nc" "SSH_r100_200604.nc"
[2127] "SSH_r100_200605.nc" "SSH_r100_200606.nc"
[2129] "SSH_r100_200607.nc" "SSH_r100_200608.nc"
[2131] "SSH_r100_200609.nc" "SSH_r100_200610.nc"
[2133] "SSH_r100_200611.nc" "SSH_r100_200612.nc"
[2135] "SSH_r100_200701.nc" "SSH_r100_200702.nc"
[2137] "SSH_r100_200703.nc" "SSH_r100_200704.nc"
[2139] "SSH_r100_200705.nc" "SSH_r100_200706.nc"
[2141] "SSH_r100_200707.nc" "SSH_r100_200708.nc"
[2143] "SSH_r100_200709.nc" "SSH_r100_200710.nc"
[2145] "SSH_r100_200711.nc" "SSH_r100_200712.nc"
[2147] "SSH_r100_200801.nc" "SSH_r100_200802.nc"
[2149] "SSH_r100_200803.nc" "SSH_r100_200804.nc"
[2151] "SSH_r100_200805.nc" "SSH_r100_200806.nc"
[2153] "SSH_r100_200807.nc" "SSH_r100_200808.nc"
[2155] "SSH_r100_200809.nc" "SSH_r100_200810.nc"
[2157] "SSH_r100_200811.nc" "SSH_r100_200812.nc"
[2159] "SSH_r100_200901.nc" "SSH_r100_200902.nc"
[2161] "SSH_r100_200903.nc" "SSH_r100_200904.nc"
[2163] "SSH_r100_200905.nc" "SSH_r100_200906.nc"
[2165] "SSH_r100_200907.nc" "SSH_r100_200908.nc"
[2167] "SSH_r100_200909.nc" "SSH_r100_200910.nc"
[2169] "SSH_r100_200911.nc" "SSH_r100_200912.nc"
[2171] "SSH_r100_201001.nc" "SSH_r100_201002.nc"
[2173] "SSH_r100_201003.nc" "SSH_r100_201004.nc"
[2175] "SSH_r100_201005.nc" "SSH_r100_201006.nc"
[2177] "SSH_r100_201007.nc" "SSH_r100_201008.nc"
[2179] "SSH_r100_201009.nc" "SSH_r100_201010.nc"
[2181] "SSH_r100_201011.nc" "SSH_r100_201012.nc"
[2183] "SSH_r100_201101.nc" "SSH_r100_201102.nc"
[2185] "SSH_r100_201103.nc" "SSH_r100_201104.nc"
[2187] "SSH_r100_201105.nc" "SSH_r100_201106.nc"
[2189] "SSH_r100_201107.nc" "SSH_r100_201108.nc"
[2191] "SSH_r100_201109.nc" "SSH_r100_201110.nc"
[2193] "SSH_r100_201111.nc" "SSH_r100_201112.nc"
[2195] "SSH_r100_201201.nc" "SSH_r100_201202.nc"
[2197] "SSH_r100_201203.nc" "SSH_r100_201204.nc"
[2199] "SSH_r100_201205.nc" "SSH_r100_201206.nc"
[2201] "SSH_r100_201207.nc" "SSH_r100_201208.nc"
[2203] "SSH_r100_201209.nc" "SSH_r100_201210.nc"
[2205] "SSH_r100_201211.nc" "SSH_r100_201212.nc"
[2207] "SSH_r100_201301.nc" "SSH_r100_201302.nc"
[2209] "SSH_r100_201303.nc" "SSH_r100_201304.nc"
[2211] "SSH_r100_201305.nc" "SSH_r100_201306.nc"
[2213] "SSH_r100_201307.nc" "SSH_r100_201308.nc"
[2215] "SSH_r100_201309.nc" "SSH_r100_201310.nc"
[2217] "SSH_r100_201311.nc" "SSH_r100_201312.nc"
[2219] "SSH_r100_201401.nc" "SSH_r100_201402.nc"
[2221] "SSH_r100_201403.nc" "SSH_r100_201404.nc"
[2223] "SSH_r100_201405.nc" "SSH_r100_201406.nc"
[2225] "SSH_r100_201407.nc" "SSH_r100_201408.nc"
[2227] "SSH_r100_201409.nc" "SSH_r100_201410.nc"
[2229] "SSH_r100_201411.nc" "SSH_r100_201412.nc"
[2231] "SSH_r100_201501.nc" "SSH_r100_201502.nc"
[2233] "SSH_r100_201503.nc" "SSH_r100_201504.nc"
[2235] "SSH_r100_201505.nc" "SSH_r100_201506.nc"
[2237] "SSH_r100_201507.nc" "SSH_r100_201508.nc"
[2239] "SSH_r100_201509.nc" "SSH_r100_201510.nc"
[2241] "SSH_r100_201511.nc" "SSH_r100_201512.nc"
[2243] "SSH_r100_201601.nc" "SSH_r100_201602.nc"
[2245] "SSH_r100_201603.nc" "SSH_r100_201604.nc"
[2247] "SSH_r100_201605.nc" "SSH_r100_201606.nc"
[2249] "SSH_r100_201607.nc" "SSH_r100_201608.nc"
[2251] "SSH_r100_201609.nc" "SSH_r100_201610.nc"
[2253] "SSH_r100_201611.nc" "SSH_r100_201612.nc"
[2255] "SSH_r100_201701.nc" "SSH_r100_201702.nc"
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[3325] "xCO2_r100_198809.nc" "xCO2_r100_198810.nc"
[3327] "xCO2_r100_198811.nc" "xCO2_r100_198812.nc"
[3329] "xCO2_r100_198901.nc" "xCO2_r100_198902.nc"
[3331] "xCO2_r100_198903.nc" "xCO2_r100_198904.nc"
[3333] "xCO2_r100_198905.nc" "xCO2_r100_198906.nc"
[3335] "xCO2_r100_198907.nc" "xCO2_r100_198908.nc"
[3337] "xCO2_r100_198909.nc" "xCO2_r100_198910.nc"
[3339] "xCO2_r100_198911.nc" "xCO2_r100_198912.nc"
[3341] "xCO2_r100_199001.nc" "xCO2_r100_199002.nc"
[3343] "xCO2_r100_199003.nc" "xCO2_r100_199004.nc"
[3345] "xCO2_r100_199005.nc" "xCO2_r100_199006.nc"
[3347] "xCO2_r100_199007.nc" "xCO2_r100_199008.nc"
[3349] "xCO2_r100_199009.nc" "xCO2_r100_199010.nc"
[3351] "xCO2_r100_199011.nc" "xCO2_r100_199012.nc"
[3353] "xCO2_r100_199101.nc" "xCO2_r100_199102.nc"
[3355] "xCO2_r100_199103.nc" "xCO2_r100_199104.nc"
[3357] "xCO2_r100_199105.nc" "xCO2_r100_199106.nc"
[3359] "xCO2_r100_199107.nc" "xCO2_r100_199108.nc"
[3361] "xCO2_r100_199109.nc" "xCO2_r100_199110.nc"
[3363] "xCO2_r100_199111.nc" "xCO2_r100_199112.nc"
[3365] "xCO2_r100_199201.nc" "xCO2_r100_199202.nc"
[3367] "xCO2_r100_199203.nc" "xCO2_r100_199204.nc"
[3369] "xCO2_r100_199205.nc" "xCO2_r100_199206.nc"
[3371] "xCO2_r100_199207.nc" "xCO2_r100_199208.nc"
[3373] "xCO2_r100_199209.nc" "xCO2_r100_199210.nc"
[3375] "xCO2_r100_199211.nc" "xCO2_r100_199212.nc"
[3377] "xCO2_r100_199301.nc" "xCO2_r100_199302.nc"
[3379] "xCO2_r100_199303.nc" "xCO2_r100_199304.nc"
[3381] "xCO2_r100_199305.nc" "xCO2_r100_199306.nc"
[3383] "xCO2_r100_199307.nc" "xCO2_r100_199308.nc"
[3385] "xCO2_r100_199309.nc" "xCO2_r100_199310.nc"
[3387] "xCO2_r100_199311.nc" "xCO2_r100_199312.nc"
[3389] "xCO2_r100_199401.nc" "xCO2_r100_199402.nc"
[3391] "xCO2_r100_199403.nc" "xCO2_r100_199404.nc"
[3393] "xCO2_r100_199405.nc" "xCO2_r100_199406.nc"
[3395] "xCO2_r100_199407.nc" "xCO2_r100_199408.nc"
[3397] "xCO2_r100_199409.nc" "xCO2_r100_199410.nc"
[3399] "xCO2_r100_199411.nc" "xCO2_r100_199412.nc"
[3401] "xCO2_r100_199501.nc" "xCO2_r100_199502.nc"
[3403] "xCO2_r100_199503.nc" "xCO2_r100_199504.nc"
[3405] "xCO2_r100_199505.nc" "xCO2_r100_199506.nc"
[3407] "xCO2_r100_199507.nc" "xCO2_r100_199508.nc"
[3409] "xCO2_r100_199509.nc" "xCO2_r100_199510.nc"
[3411] "xCO2_r100_199511.nc" "xCO2_r100_199512.nc"
[3413] "xCO2_r100_199601.nc" "xCO2_r100_199602.nc"
[3415] "xCO2_r100_199603.nc" "xCO2_r100_199604.nc"
[3417] "xCO2_r100_199605.nc" "xCO2_r100_199606.nc"
[3419] "xCO2_r100_199607.nc" "xCO2_r100_199608.nc"
[3421] "xCO2_r100_199609.nc" "xCO2_r100_199610.nc"
[3423] "xCO2_r100_199611.nc" "xCO2_r100_199612.nc"
[3425] "xCO2_r100_199701.nc" "xCO2_r100_199702.nc"
[3427] "xCO2_r100_199703.nc" "xCO2_r100_199704.nc"
[3429] "xCO2_r100_199705.nc" "xCO2_r100_199706.nc"
[3431] "xCO2_r100_199707.nc" "xCO2_r100_199708.nc"
[3433] "xCO2_r100_199709.nc" "xCO2_r100_199710.nc"
[3435] "xCO2_r100_199711.nc" "xCO2_r100_199712.nc"
[3437] "xCO2_r100_199801.nc" "xCO2_r100_199802.nc"
[3439] "xCO2_r100_199803.nc" "xCO2_r100_199804.nc"
[3441] "xCO2_r100_199805.nc" "xCO2_r100_199806.nc"
[3443] "xCO2_r100_199807.nc" "xCO2_r100_199808.nc"
[3445] "xCO2_r100_199809.nc" "xCO2_r100_199810.nc"
[3447] "xCO2_r100_199811.nc" "xCO2_r100_199812.nc"
[3449] "xCO2_r100_199901.nc" "xCO2_r100_199902.nc"
[3451] "xCO2_r100_199903.nc" "xCO2_r100_199904.nc"
[3453] "xCO2_r100_199905.nc" "xCO2_r100_199906.nc"
[3455] "xCO2_r100_199907.nc" "xCO2_r100_199908.nc"
[3457] "xCO2_r100_199909.nc" "xCO2_r100_199910.nc"
[3459] "xCO2_r100_199911.nc" "xCO2_r100_199912.nc"
[3461] "xCO2_r100_200001.nc" "xCO2_r100_200002.nc"
[3463] "xCO2_r100_200003.nc" "xCO2_r100_200004.nc"
[3465] "xCO2_r100_200005.nc" "xCO2_r100_200006.nc"
[3467] "xCO2_r100_200007.nc" "xCO2_r100_200008.nc"
[3469] "xCO2_r100_200009.nc" "xCO2_r100_200010.nc"
[3471] "xCO2_r100_200011.nc" "xCO2_r100_200012.nc"
[3473] "xCO2_r100_200101.nc" "xCO2_r100_200102.nc"
[3475] "xCO2_r100_200103.nc" "xCO2_r100_200104.nc"
[3477] "xCO2_r100_200105.nc" "xCO2_r100_200106.nc"
[3479] "xCO2_r100_200107.nc" "xCO2_r100_200108.nc"
[3481] "xCO2_r100_200109.nc" "xCO2_r100_200110.nc"
[3483] "xCO2_r100_200111.nc" "xCO2_r100_200112.nc"
[3485] "xCO2_r100_200201.nc" "xCO2_r100_200202.nc"
[3487] "xCO2_r100_200203.nc" "xCO2_r100_200204.nc"
[3489] "xCO2_r100_200205.nc" "xCO2_r100_200206.nc"
[3491] "xCO2_r100_200207.nc" "xCO2_r100_200208.nc"
[3493] "xCO2_r100_200209.nc" "xCO2_r100_200210.nc"
[3495] "xCO2_r100_200211.nc" "xCO2_r100_200212.nc"
[3497] "xCO2_r100_200301.nc" "xCO2_r100_200302.nc"
[3499] "xCO2_r100_200303.nc" "xCO2_r100_200304.nc"
[3501] "xCO2_r100_200305.nc" "xCO2_r100_200306.nc"
[3503] "xCO2_r100_200307.nc" "xCO2_r100_200308.nc"
[3505] "xCO2_r100_200309.nc" "xCO2_r100_200310.nc"
[3507] "xCO2_r100_200311.nc" "xCO2_r100_200312.nc"
[3509] "xCO2_r100_200401.nc" "xCO2_r100_200402.nc"
[3511] "xCO2_r100_200403.nc" "xCO2_r100_200404.nc"
[3513] "xCO2_r100_200405.nc" "xCO2_r100_200406.nc"
[3515] "xCO2_r100_200407.nc" "xCO2_r100_200408.nc"
[3517] "xCO2_r100_200409.nc" "xCO2_r100_200410.nc"
[3519] "xCO2_r100_200411.nc" "xCO2_r100_200412.nc"
[3521] "xCO2_r100_200501.nc" "xCO2_r100_200502.nc"
[3523] "xCO2_r100_200503.nc" "xCO2_r100_200504.nc"
[3525] "xCO2_r100_200505.nc" "xCO2_r100_200506.nc"
[3527] "xCO2_r100_200507.nc" "xCO2_r100_200508.nc"
[3529] "xCO2_r100_200509.nc" "xCO2_r100_200510.nc"
[3531] "xCO2_r100_200511.nc" "xCO2_r100_200512.nc"
[3533] "xCO2_r100_200601.nc" "xCO2_r100_200602.nc"
[3535] "xCO2_r100_200603.nc" "xCO2_r100_200604.nc"
[3537] "xCO2_r100_200605.nc" "xCO2_r100_200606.nc"
[3539] "xCO2_r100_200607.nc" "xCO2_r100_200608.nc"
[3541] "xCO2_r100_200609.nc" "xCO2_r100_200610.nc"
[3543] "xCO2_r100_200611.nc" "xCO2_r100_200612.nc"
[3545] "xCO2_r100_200701.nc" "xCO2_r100_200702.nc"
[3547] "xCO2_r100_200703.nc" "xCO2_r100_200704.nc"
[3549] "xCO2_r100_200705.nc" "xCO2_r100_200706.nc"
[3551] "xCO2_r100_200707.nc" "xCO2_r100_200708.nc"
[3553] "xCO2_r100_200709.nc" "xCO2_r100_200710.nc"
[3555] "xCO2_r100_200711.nc" "xCO2_r100_200712.nc"
[3557] "xCO2_r100_200801.nc" "xCO2_r100_200802.nc"
[3559] "xCO2_r100_200803.nc" "xCO2_r100_200804.nc"
[3561] "xCO2_r100_200805.nc" "xCO2_r100_200806.nc"
[3563] "xCO2_r100_200807.nc" "xCO2_r100_200808.nc"
[3565] "xCO2_r100_200809.nc" "xCO2_r100_200810.nc"
[3567] "xCO2_r100_200811.nc" "xCO2_r100_200812.nc"
[3569] "xCO2_r100_200901.nc" "xCO2_r100_200902.nc"
[3571] "xCO2_r100_200903.nc" "xCO2_r100_200904.nc"
[3573] "xCO2_r100_200905.nc" "xCO2_r100_200906.nc"
[3575] "xCO2_r100_200907.nc" "xCO2_r100_200908.nc"
[3577] "xCO2_r100_200909.nc" "xCO2_r100_200910.nc"
[3579] "xCO2_r100_200911.nc" "xCO2_r100_200912.nc"
[3581] "xCO2_r100_201001.nc" "xCO2_r100_201002.nc"
[3583] "xCO2_r100_201003.nc" "xCO2_r100_201004.nc"
[3585] "xCO2_r100_201005.nc" "xCO2_r100_201006.nc"
[3587] "xCO2_r100_201007.nc" "xCO2_r100_201008.nc"
[3589] "xCO2_r100_201009.nc" "xCO2_r100_201010.nc"
[3591] "xCO2_r100_201011.nc" "xCO2_r100_201012.nc"
[3593] "xCO2_r100_201101.nc" "xCO2_r100_201102.nc"
[3595] "xCO2_r100_201103.nc" "xCO2_r100_201104.nc"
[3597] "xCO2_r100_201105.nc" "xCO2_r100_201106.nc"
[3599] "xCO2_r100_201107.nc" "xCO2_r100_201108.nc"
[3601] "xCO2_r100_201109.nc" "xCO2_r100_201110.nc"
[3603] "xCO2_r100_201111.nc" "xCO2_r100_201112.nc"
[3605] "xCO2_r100_201201.nc" "xCO2_r100_201202.nc"
[3607] "xCO2_r100_201203.nc" "xCO2_r100_201204.nc"
[3609] "xCO2_r100_201205.nc" "xCO2_r100_201206.nc"
[3611] "xCO2_r100_201207.nc" "xCO2_r100_201208.nc"
[3613] "xCO2_r100_201209.nc" "xCO2_r100_201210.nc"
[3615] "xCO2_r100_201211.nc" "xCO2_r100_201212.nc"
[3617] "xCO2_r100_201301.nc" "xCO2_r100_201302.nc"
[3619] "xCO2_r100_201303.nc" "xCO2_r100_201304.nc"
[3621] "xCO2_r100_201305.nc" "xCO2_r100_201306.nc"
[3623] "xCO2_r100_201307.nc" "xCO2_r100_201308.nc"
[3625] "xCO2_r100_201309.nc" "xCO2_r100_201310.nc"
[3627] "xCO2_r100_201311.nc" "xCO2_r100_201312.nc"
[3629] "xCO2_r100_201401.nc" "xCO2_r100_201402.nc"
[3631] "xCO2_r100_201403.nc" "xCO2_r100_201404.nc"
[3633] "xCO2_r100_201405.nc" "xCO2_r100_201406.nc"
[3635] "xCO2_r100_201407.nc" "xCO2_r100_201408.nc"
[3637] "xCO2_r100_201409.nc" "xCO2_r100_201410.nc"
[3639] "xCO2_r100_201411.nc" "xCO2_r100_201412.nc"
[3641] "xCO2_r100_201501.nc" "xCO2_r100_201502.nc"
[3643] "xCO2_r100_201503.nc" "xCO2_r100_201504.nc"
[3645] "xCO2_r100_201505.nc" "xCO2_r100_201506.nc"
[3647] "xCO2_r100_201507.nc" "xCO2_r100_201508.nc"
[3649] "xCO2_r100_201509.nc" "xCO2_r100_201510.nc"
[3651] "xCO2_r100_201511.nc" "xCO2_r100_201512.nc"
[3653] "xCO2_r100_201601.nc" "xCO2_r100_201602.nc"
[3655] "xCO2_r100_201603.nc" "xCO2_r100_201604.nc"
[3657] "xCO2_r100_201605.nc" "xCO2_r100_201606.nc"
[3659] "xCO2_r100_201607.nc" "xCO2_r100_201608.nc"
[3661] "xCO2_r100_201609.nc" "xCO2_r100_201610.nc"
[3663] "xCO2_r100_201611.nc" "xCO2_r100_201612.nc"
[3665] "xCO2_r100_201701.nc" "xCO2_r100_201702.nc"
[3667] "xCO2_r100_201703.nc" "xCO2_r100_201704.nc"
[3669] "xCO2_r100_201705.nc" "xCO2_r100_201706.nc"
[3671] "xCO2_r100_201707.nc" "xCO2_r100_201708.nc"
[3673] "xCO2_r100_201709.nc" "xCO2_r100_201710.nc"
[3675] "xCO2_r100_201711.nc" "xCO2_r100_201712.nc"
[3677] "xCO2_r100_201801.nc" "xCO2_r100_201802.nc"
[3679] "xCO2_r100_201803.nc" "xCO2_r100_201804.nc"
[3681] "xCO2_r100_201805.nc" "xCO2_r100_201806.nc"
[3683] "xCO2_r100_201807.nc" "xCO2_r100_201808.nc"
[3685] "xCO2_r100_201809.nc" "xCO2_r100_201810.nc"
[3687] "xCO2_r100_201811.nc" "xCO2_r100_201812.nc"
[3689] "xCO2_r100_201901.nc" "xCO2_r100_201902.nc"
[3691] "xCO2_r100_201903.nc" "xCO2_r100_201904.nc"
[3693] "xCO2_r100_201905.nc" "xCO2_r100_201906.nc"
[3695] "xCO2_r100_201907.nc" "xCO2_r100_201908.nc"
[3697] "xCO2_r100_201909.nc" "xCO2_r100_201910.nc"
[3699] "xCO2_r100_201911.nc" "xCO2_r100_201912.nc"
[3701] "xCO2_r100_202001.nc" "xCO2_r100_202002.nc"
[3703] "xCO2_r100_202003.nc" "xCO2_r100_202004.nc"
[3705] "xCO2_r100_202005.nc" "xCO2_r100_202006.nc"
[3707] "xCO2_r100_202007.nc" "xCO2_r100_202008.nc"
[3709] "xCO2_r100_202009.nc" "xCO2_r100_202010.nc"
[3711] "xCO2_r100_202011.nc" "xCO2_r100_202012.nc"
[3713] "xCO2_r100_202101.nc" "xCO2_r100_202102.nc"
[3715] "xCO2_r100_202103.nc" "xCO2_r100_202104.nc"
[3717] "xCO2_r100_202105.nc" "xCO2_r100_202106.nc"
[3719] "xCO2_r100_202107.nc" "xCO2_r100_202108.nc"
[3721] "xCO2_r100_202109.nc" "xCO2_r100_202110.nc"
[3723] "xCO2_r100_202111.nc" "xCO2_r100_202112.nc"
[3725] "xCO2_r100_202201.nc" "xCO2_r100_202202.nc"
[3727] "xCO2_r100_202203.nc" "xCO2_r100_202204.nc"
[3729] "xCO2_r100_202205.nc" "xCO2_r100_202206.nc"
[3731] "xCO2_r100_202207.nc" "xCO2_r100_202208.nc"
[3733] "xCO2_r100_202209.nc" "xCO2_r100_202210.nc"
[3735] "xCO2_r100_202211.nc" "xCO2_r100_202212.nc"
[3737] "xCO2_r100_202301.nc" "xCO2_r100_202302.nc"
[3739] "xCO2_r100_202303.nc" "xCO2_r100_202304.nc"
[3741] "xCO2_r100_202305.nc" "xCO2_r100_202306.nc"
[3743] "xCO2_r100_202307.nc" "xCO2_r100_202308.nc"
[3745] "xCO2_r100_202309.nc" "xCO2_r100_202310.nc"
[3747] "xCO2_r100_202311.nc" "xCO2_r100_202312.nc"
[3749] "xCO2_r100_202401.nc" "xCO2_r100_202402.nc" file_names <- str_split(CMEMS_files, "_", simplify = TRUE)[,1] %>% unique()
# n_var <- length(file_names) - 1
# CMEMS_files <- CMEMS_files[c(1,470*1:n_var,470*1:n_var+1, 3215)]
# CMEMS_files <- sort(CMEMS_files)
for (i_file_name in file_names) {
# i_file_name <- file_names[1]
CMEMS_files_var <-
CMEMS_files[CMEMS_files %>% str_detect(i_file_name)]
if(i_file_name == "xCO2"){
CMEMS_files_var <-
CMEMS_files_var[!CMEMS_files_var %>% str_detect("fluxCO2")]
}
for (i_name in CMEMS_files_var) {
# i_name <- CMEMS_files_var[1]
print(i_name)
library(ncdf4)
nc <- nc_open(paste0(path_CMEMS, i_name))
var_name <- names(nc$var)[1]
i_pco2_product_var <-
read_ncdf(paste0(path_CMEMS, i_name),
make_units = FALSE,
var = var_name)
if (exists("pco2_product_var")) {
pco2_product_var <-
c(pco2_product_var,
i_pco2_product_var)
}
if (!exists("pco2_product_var")) {
pco2_product_var <- i_pco2_product_var
}
}
pco2_product_var <- pco2_product_var %>%
as_tibble()
if (exists("pco2_product")) {
pco2_product <-
full_join(pco2_product,
pco2_product_var)
}
if (!exists("pco2_product")) {
pco2_product <- pco2_product_var
}
rm(pco2_product_var)
}[1] "CHL_r100_198501.nc"
[1] "CHL_r100_198502.nc"
[1] "CHL_r100_198503.nc"
[1] "CHL_r100_198504.nc"
[1] "CHL_r100_198505.nc"
[1] "CHL_r100_198506.nc"
[1] "CHL_r100_198507.nc"
[1] "CHL_r100_198508.nc"
[1] "CHL_r100_198509.nc"
[1] "CHL_r100_198510.nc"
[1] "CHL_r100_198511.nc"
[1] "CHL_r100_198512.nc"
[1] "CHL_r100_198601.nc"
[1] "CHL_r100_198602.nc"
[1] "CHL_r100_198603.nc"
[1] "CHL_r100_198604.nc"
[1] "CHL_r100_198605.nc"
[1] "CHL_r100_198606.nc"
[1] "CHL_r100_198607.nc"
[1] "CHL_r100_198608.nc"
[1] "CHL_r100_198609.nc"
[1] "CHL_r100_198610.nc"
[1] "CHL_r100_198611.nc"
[1] "CHL_r100_198612.nc"
[1] "CHL_r100_198701.nc"
[1] "CHL_r100_198702.nc"
[1] "CHL_r100_198703.nc"
[1] "CHL_r100_198704.nc"
[1] "CHL_r100_198705.nc"
[1] "CHL_r100_198706.nc"
[1] "CHL_r100_198707.nc"
[1] "CHL_r100_198708.nc"
[1] "CHL_r100_198709.nc"
[1] "CHL_r100_198710.nc"
[1] "CHL_r100_198711.nc"
[1] "CHL_r100_198712.nc"
[1] "CHL_r100_198801.nc"
[1] "CHL_r100_198802.nc"
[1] "CHL_r100_198803.nc"
[1] "CHL_r100_198804.nc"
[1] "CHL_r100_198805.nc"
[1] "CHL_r100_198806.nc"
[1] "CHL_r100_198807.nc"
[1] "CHL_r100_198808.nc"
[1] "CHL_r100_198809.nc"
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[1] "xCO2_r100_201304.nc"
[1] "xCO2_r100_201305.nc"
[1] "xCO2_r100_201306.nc"
[1] "xCO2_r100_201307.nc"
[1] "xCO2_r100_201308.nc"
[1] "xCO2_r100_201309.nc"
[1] "xCO2_r100_201310.nc"
[1] "xCO2_r100_201311.nc"
[1] "xCO2_r100_201312.nc"
[1] "xCO2_r100_201401.nc"
[1] "xCO2_r100_201402.nc"
[1] "xCO2_r100_201403.nc"
[1] "xCO2_r100_201404.nc"
[1] "xCO2_r100_201405.nc"
[1] "xCO2_r100_201406.nc"
[1] "xCO2_r100_201407.nc"
[1] "xCO2_r100_201408.nc"
[1] "xCO2_r100_201409.nc"
[1] "xCO2_r100_201410.nc"
[1] "xCO2_r100_201411.nc"
[1] "xCO2_r100_201412.nc"
[1] "xCO2_r100_201501.nc"
[1] "xCO2_r100_201502.nc"
[1] "xCO2_r100_201503.nc"
[1] "xCO2_r100_201504.nc"
[1] "xCO2_r100_201505.nc"
[1] "xCO2_r100_201506.nc"
[1] "xCO2_r100_201507.nc"
[1] "xCO2_r100_201508.nc"
[1] "xCO2_r100_201509.nc"
[1] "xCO2_r100_201510.nc"
[1] "xCO2_r100_201511.nc"
[1] "xCO2_r100_201512.nc"
[1] "xCO2_r100_201601.nc"
[1] "xCO2_r100_201602.nc"
[1] "xCO2_r100_201603.nc"
[1] "xCO2_r100_201604.nc"
[1] "xCO2_r100_201605.nc"
[1] "xCO2_r100_201606.nc"
[1] "xCO2_r100_201607.nc"
[1] "xCO2_r100_201608.nc"
[1] "xCO2_r100_201609.nc"
[1] "xCO2_r100_201610.nc"
[1] "xCO2_r100_201611.nc"
[1] "xCO2_r100_201612.nc"
[1] "xCO2_r100_201701.nc"
[1] "xCO2_r100_201702.nc"
[1] "xCO2_r100_201703.nc"
[1] "xCO2_r100_201704.nc"
[1] "xCO2_r100_201705.nc"
[1] "xCO2_r100_201706.nc"
[1] "xCO2_r100_201707.nc"
[1] "xCO2_r100_201708.nc"
[1] "xCO2_r100_201709.nc"
[1] "xCO2_r100_201710.nc"
[1] "xCO2_r100_201711.nc"
[1] "xCO2_r100_201712.nc"
[1] "xCO2_r100_201801.nc"
[1] "xCO2_r100_201802.nc"
[1] "xCO2_r100_201803.nc"
[1] "xCO2_r100_201804.nc"
[1] "xCO2_r100_201805.nc"
[1] "xCO2_r100_201806.nc"
[1] "xCO2_r100_201807.nc"
[1] "xCO2_r100_201808.nc"
[1] "xCO2_r100_201809.nc"
[1] "xCO2_r100_201810.nc"
[1] "xCO2_r100_201811.nc"
[1] "xCO2_r100_201812.nc"
[1] "xCO2_r100_201901.nc"
[1] "xCO2_r100_201902.nc"
[1] "xCO2_r100_201903.nc"
[1] "xCO2_r100_201904.nc"
[1] "xCO2_r100_201905.nc"
[1] "xCO2_r100_201906.nc"
[1] "xCO2_r100_201907.nc"
[1] "xCO2_r100_201908.nc"
[1] "xCO2_r100_201909.nc"
[1] "xCO2_r100_201910.nc"
[1] "xCO2_r100_201911.nc"
[1] "xCO2_r100_201912.nc"
[1] "xCO2_r100_202001.nc"
[1] "xCO2_r100_202002.nc"
[1] "xCO2_r100_202003.nc"
[1] "xCO2_r100_202004.nc"
[1] "xCO2_r100_202005.nc"
[1] "xCO2_r100_202006.nc"
[1] "xCO2_r100_202007.nc"
[1] "xCO2_r100_202008.nc"
[1] "xCO2_r100_202009.nc"
[1] "xCO2_r100_202010.nc"
[1] "xCO2_r100_202011.nc"
[1] "xCO2_r100_202012.nc"
[1] "xCO2_r100_202101.nc"
[1] "xCO2_r100_202102.nc"
[1] "xCO2_r100_202103.nc"
[1] "xCO2_r100_202104.nc"
[1] "xCO2_r100_202105.nc"
[1] "xCO2_r100_202106.nc"
[1] "xCO2_r100_202107.nc"
[1] "xCO2_r100_202108.nc"
[1] "xCO2_r100_202109.nc"
[1] "xCO2_r100_202110.nc"
[1] "xCO2_r100_202111.nc"
[1] "xCO2_r100_202112.nc"
[1] "xCO2_r100_202201.nc"
[1] "xCO2_r100_202202.nc"
[1] "xCO2_r100_202203.nc"
[1] "xCO2_r100_202204.nc"
[1] "xCO2_r100_202205.nc"
[1] "xCO2_r100_202206.nc"
[1] "xCO2_r100_202207.nc"
[1] "xCO2_r100_202208.nc"
[1] "xCO2_r100_202209.nc"
[1] "xCO2_r100_202210.nc"
[1] "xCO2_r100_202211.nc"
[1] "xCO2_r100_202212.nc"
[1] "xCO2_r100_202301.nc"
[1] "xCO2_r100_202302.nc"
[1] "xCO2_r100_202303.nc"
[1] "xCO2_r100_202304.nc"
[1] "xCO2_r100_202305.nc"
[1] "xCO2_r100_202306.nc"
[1] "xCO2_r100_202307.nc"
[1] "xCO2_r100_202308.nc"
[1] "xCO2_r100_202309.nc"
[1] "xCO2_r100_202310.nc"
[1] "xCO2_r100_202311.nc"
[1] "xCO2_r100_202312.nc"
[1] "xCO2_r100_202401.nc"
[1] "xCO2_r100_202402.nc"rm(i_pco2_product_var,
nc, var_name,
i_file_name, file_names,
i_name, CMEMS_files_var,
CMEMS_files)
# rm(pco2_product)
pco2_product <-
pco2_product %>%
rename(chl = CHL,
mld = MLD,
sfco2 = CO2,
fgco2 = fCO2_mean,
salinity = SSS,
temperature = SST,
fice = SI_fraction)
pco2_product <-
pco2_product %>%
mutate(area = earth_surf(lat, lon),
year = year(time),
month = month(time))
pco2_product <-
pco2_product %>%
mutate(lon = if_else(lon < 20, lon + 360, lon),
fgco2 = -fgco2,
chl = log10(chl),
mld = log10(mld))
pco2_product <-
pco2_product %>%
filter(year <= 2023)names <- c("CO2_fluxes", "CO2_fugacity")
for (i_name in names) {
# i_name <- names[2]
CMEMS_files <- list.files(path = paste0(path_CMEMS, i_name, "/"),
full.names = TRUE)
# i_CMEMS_files <- CMEMS_files[2]
i_pco2_product <-
read_stars(CMEMS_files,
make_units = FALSE,
ignore_bounds = TRUE,
quiet = TRUE)
if (exists("pco2_product")) {
pco2_product <-
c(pco2_product,
i_pco2_product)
}
if (!exists("pco2_product")) {
pco2_product <- i_pco2_product
}
}
rm(CMEMS_files, i_pco2_product, i_name, names)
# rm(pco2_product)
pco2_product <- pco2_product %>%
as_tibble()
pco2_product <-
pco2_product %>%
rename(lon = x,
lat = y,
sfco2 = fuCO2_mean,
fgco2 = fCO2_mean) %>%
select(-contains("_std")) %>%
units::drop_units()
pco2_product <-
pco2_product %>%
mutate(area = earth_surf(lat, lon),
year = year(time),
month = month(time))
pco2_product <-
pco2_product %>%
mutate(lon = if_else(lon < 20, lon + 360, lon),
fgco2 = -fgco2)
pco2_product <-
pco2_product %>%
filter(year <= 2023)pCO2_product_preprocessing <-
knitr::knit_expand(file = here::here("analysis/child/pCO2_product_preprocessing.Rmd"))Preprocessing
Load masks
biome_mask <-
read_rds(here::here("data/biome_mask.rds"))
map <-
read_rds(here::here("data/map.rds"))
key_biomes <-
read_rds(here::here("data/key_biomes.rds"))
super_biomes <-
read_rds(here::here("data/super_biomes.rds"))
super_biome_mask <-
read_rds(here::here("data/super_biome_mask.rds"))Define labels and breaks
labels_breaks <- function(i_name) {
if (i_name == "dco2") {
i_legend_title <- "ΔpCO<sub>2</sub><br>(µatm)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "dfco2") {
i_legend_title <- "ΔfCO<sub>2</sub><br>(µatm)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "atm_co2") {
i_legend_title <- "pCO<sub>2,atm</sub><br>(µatm)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "atm_fco2") {
i_legend_title <- "fCO<sub>2,atm</sub><br>(µatm)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "sol") {
i_legend_title <- "CO<sub>2</sub> solubility<br>(mol m<sup>-3</sup> µatm<sup>-1</sup>)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "kw") {
i_legend_title <- "K<sub>w</sub><br>(m yr<sup>-1</sup>)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "spco2") {
i_legend_title <- "pCO<sub>2,ocean</sub><br>(µatm)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "sfco2") {
i_legend_title <- "fCO<sub>2,ocean</sub><br>(µatm)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "fgco2") {
i_legend_title <- "FCO<sub>2</sub><br>(mol m<sup>-2</sup> yr<sup>-1</sup>)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "fgco2_hov") {
i_legend_title <- "FCO<sub>2</sub><br>(PgC deg<sup>-1</sup> yr<sup>-1</sup>)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "fgco2_int") {
i_legend_title <- "FCO<sub>2</sub><br>(PgC yr<sup>-1</sup>)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "temperature") {
i_legend_title <- "SST<br>(°C)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "salinity") {
i_legend_title <- "SSS"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "chl") {
i_legend_title <- "lg(Chl-a)<br>(lg(mg m<sup>-3</sup>))"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "mld") {
i_legend_title <- "lg(MLD)<br>(lg(m))"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "press") {
i_legend_title <- "pressure<sub>atm</sub><br>(unit?)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "wind") {
i_legend_title <- "Wind <br>(m sec<sup>-1</sup>)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "SSH") {
i_legend_title <- "SSH <br>(m)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
if (i_name == "fice") {
i_legend_title <- "Sea ice <br>(%)"
# i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
# i_contour_level <- 50
# i_contour_level_abs <- 2200
}
all_labels_breaks <- lst(i_legend_title,
# i_breaks,
# i_contour_level,
# i_contour_level_abs
)
return(all_labels_breaks)
}
# labels_breaks("fgco2")
x_axis_labels <-
c(
"dco2" = labels_breaks("dco2")$i_legend_title,
"dfco2" = labels_breaks("dfco2")$i_legend_title,
"atm_co2" = labels_breaks("atm_co2")$i_legend_title,
"atm_fco2" = labels_breaks("atm_fco2")$i_legend_title,
"sol" = labels_breaks("sol")$i_legend_title,
"kw" = labels_breaks("kw")$i_legend_title,
"spco2" = labels_breaks("spco2")$i_legend_title,
"sfco2" = labels_breaks("sfco2")$i_legend_title,
"fgco2_hov" = labels_breaks("fgco2_hov")$i_legend_title,
"fgco2_int" = labels_breaks("fgco2_int")$i_legend_title,
"temperature" = labels_breaks("temperature")$i_legend_title,
"salinity" = labels_breaks("salinity")$i_legend_title,
"chl" = labels_breaks("chl")$i_legend_title,
"mld" = labels_breaks("mld")$i_legend_title,
"press" = labels_breaks("press")$i_legend_title,
"wind" = labels_breaks("wind")$i_legend_title,
"SSH" = labels_breaks("SSH")$i_legend_title,
"fice" = labels_breaks("fice")$i_legend_title
)Analysis settings
name_quadratic_fit <- c("atm_co2", "atm_fco2", "spco2", "sfco2")
start_year <- 1990
name_divergent <- c("dco2", "fgco2", "fgco2_hov", "fgco2_int")Data preprocessing
pco2_product <-
pco2_product %>%
filter(year >= start_year)pco2_product <-
full_join(pco2_product,
biome_mask)
# set all values outside biome mask to NA
pco2_product <-
pco2_product %>%
mutate(across(-c(lat, lon, time, area, year, month, biome),
~ if_else(is.na(biome), NA, .)))Compuations
# apply coarse grid
pco2_product_coarse <-
m_grid_horizontal_coarse(pco2_product)
pco2_product_coarse <-
pco2_product_coarse %>%
select(-c(lon, lat, time, biome)) %>%
group_by(year, month, lon_grid, lat_grid) %>%
summarise(across(-area,
~ weighted.mean(., area))) %>%
ungroup() %>%
rename(lon = lon_grid, lat = lat_grid)
pco2_product_coarse <-
pco2_product_coarse %>%
pivot_longer(-c(year, month, lon, lat)) %>%
drop_na() %>%
pivot_wider()
# compute annual means
pco2_product_coarse_annual <-
pco2_product_coarse %>%
select(-month) %>%
group_by(year, lon, lat) %>%
summarise(across(where(is.numeric),
~ mean(.))) %>%
ungroup()
pco2_product_coarse_annual <-
pco2_product_coarse_annual %>%
pivot_longer(-c(year, lon, lat))
## compute monthly means
pco2_product_coarse_monthly <-
pco2_product_coarse %>%
group_by(year, month, lon, lat) %>%
summarise(across(where(is.numeric),
~ mean(.))) %>%
ungroup()
pco2_product_coarse_monthly <-
pco2_product_coarse_monthly %>%
pivot_longer(-c(year, month, lon, lat))pco2_product_monthly_global <-
pco2_product %>%
filter(!is.na(fgco2)) %>%
select(-c(lon, lat, year, month, biome)) %>%
group_by(time) %>%
summarise(across(-c(fgco2, area),
~ weighted.mean(., area, na.rm = TRUE)),
across(fgco2,
~ sum(. * area, na.rm = TRUE) * 12.01 * 1e-15)) %>%
ungroup()
pco2_product_monthly_biome <-
pco2_product %>%
filter(!is.na(fgco2)) %>%
select(-c(lon, lat, year, month)) %>%
group_by(time, biome) %>%
summarise(across(-c(fgco2, area),
~ weighted.mean(., area, na.rm = TRUE)),
across(fgco2,
~ sum(. * area, na.rm = TRUE) * 12.01 * 1e-15)) %>%
ungroup()
pco2_product_monthly_biome_super <-
pco2_product %>%
filter(!is.na(fgco2)) %>%
mutate(
biome = case_when(
str_detect(biome, "NA-") ~ "North Atlantic",
str_detect(biome, "NP-") ~ "North Pacific",
str_detect(biome, "SO-") ~ "Southern Ocean",
TRUE ~ "other"
)
) %>%
filter(biome != "other") %>%
select(-c(lon, lat, year, month)) %>%
group_by(time, biome) %>%
summarise(across(-c(fgco2, area),
~ weighted.mean(., area, na.rm = TRUE)),
across(fgco2,
~ sum(. * area, na.rm = TRUE) * 12.01 * 1e-15)) %>%
ungroup()
pco2_product_monthly <-
bind_rows(pco2_product_monthly_global %>%
mutate(biome = "Global"),
pco2_product_monthly_biome,
pco2_product_monthly_biome_super)
rm(
pco2_product_monthly_global,
pco2_product_monthly_biome,
pco2_product_monthly_biome_super
)
pco2_product_monthly <-
pco2_product_monthly %>%
filter(!is.na(biome))
pco2_product_monthly <-
pco2_product_monthly %>%
rename(fgco2_int = fgco2)
pco2_product_monthly <-
pco2_product_monthly %>%
mutate(year = year(time),
month = month(time),
.after = time)
pco2_product_monthly <-
pco2_product_monthly %>%
pivot_longer(-c(time, year, month, biome))Absolute values
Hovmoeller plots
The following Hovmoeller plots show the value of each variable as provided through the pCO2 product. Hovmoeller plots are first presented as annual means, and than as monthly means.
Annual means
pco2_product_hovmoeller_monthly_annual <-
pco2_product %>%
select(-c(lon, time, month, biome)) %>%
group_by(year, lat) %>%
summarise(across(-c(fgco2, area),
~ weighted.mean(., area, na.rm = TRUE)),
across(fgco2,
~ sum(. * area, na.rm = TRUE) * 12.01 * 1e-15)) %>%
ungroup() %>%
rename(fgco2_hov = fgco2) %>%
filter(fgco2_hov != 0)
pco2_product_hovmoeller_monthly_annual <-
pco2_product_hovmoeller_monthly_annual %>%
pivot_longer(-c(year, lat)) %>%
drop_na()
pco2_product_hovmoeller_monthly_annual %>%
filter(!(name %in% name_divergent)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(year, lat, fill = value)) +
geom_raster() +
scale_fill_viridis_c(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
coord_cartesian(expand = 0) +
labs(title = "Annual means",
y = "Latitude") +
theme(axis.title.x = element_blank())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
pco2_product_hovmoeller_monthly_annual %>%
filter(name %in% name_divergent) %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(year, lat, fill = value)) +
geom_raster() +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
coord_cartesian(expand = 0) +
labs(title = "Annual means",
y = "Latitude") +
theme(axis.title.x = element_blank())
)[[1]]
Monthly means
pco2_product_hovmoeller_monthly <-
pco2_product %>%
select(-c(lon, time, biome)) %>%
group_by(year, month, lat) %>%
summarise(across(-c(fgco2, area),
~ weighted.mean(., area, na.rm = TRUE)),
across(fgco2,
~ sum(. * area, na.rm = TRUE) * 12.01 * 1e-15)) %>%
ungroup() %>%
rename(fgco2_hov = fgco2) %>%
filter(fgco2_hov != 0)
pco2_product_hovmoeller_monthly <-
pco2_product_hovmoeller_monthly %>%
pivot_longer(-c(year, month, lat)) %>%
drop_na()
pco2_product_hovmoeller_monthly <-
pco2_product_hovmoeller_monthly %>%
mutate(decimal = year + (month-1) / 12)
pco2_product_hovmoeller_monthly %>%
filter(!(name %in% name_divergent)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(decimal, lat, fill = value)) +
geom_raster() +
scale_fill_viridis_c(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
labs(title = "Monthly means",
y = "Latitude") +
coord_cartesian(expand = 0) +
theme(axis.title.x = element_blank())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
pco2_product_hovmoeller_monthly %>%
filter(name %in% name_divergent) %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(decimal, lat, fill = value)) +
geom_raster() +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
labs(title = "Monthly means",
y = "Latitude") +
coord_cartesian(expand = 0) +
theme(axis.title.x = element_blank())
)[[1]]
pCO2productanalysis_2023 <-
knitr::knit_expand(
file = here::here("analysis/child/pCO2_product_analysis.Rmd"),
product_name = "CMEMS",
year_anom = 2023
)2023 anomalies
Detection
For the detection of anomalies at any point in time and space, we fit regression models and compare the fitted to the actual value.
We use linear regression models for all parameters, except for , which are approximated with quadratic fits.
The regression models are fitted to all data since , except 2023.
anomaly_determination <- function(df,...) {
group_by <- quos(...)
# group_by <- quos(lon, lat)
# df <- pco2_product_coarse_annual
# Linear regression models
df_lm <-
df %>%
filter(year != 2023,
!(name %in% name_quadratic_fit)) %>%
nest(data = -c(name, !!!group_by)) %>%
mutate(
fit = map(data, ~ lm(value ~ year, data = .x)),
tidied = map(fit, tidy),
augmented = map(fit, augment)
)
df_lm_year_anom <-
full_join(
df_lm %>%
unnest(tidied) %>%
select(name, !!!group_by, term, estimate) %>%
pivot_wider(names_from = term,
values_from = estimate) %>%
mutate(fit = `(Intercept)` + year * 2023) %>%
select(name, !!!group_by, fit) %>%
mutate(year = 2023),
df %>%
filter(year == 2023,
!(name %in% name_quadratic_fit))
) %>%
mutate(resid = value - fit)
df_lm <-
bind_rows(
df_lm %>%
unnest(augmented) %>%
select(name, !!!group_by, year, value, fit = .fitted, resid = .resid),
df_lm_year_anom
)
rm(df_lm_year_anom)
# Quadratic regression models
df_quadratic <-
df %>%
filter(year != 2023,
name %in% name_quadratic_fit) %>%
nest(data = -c(name, !!!group_by)) %>%
mutate(
fit = map(data, ~ lm(value ~ year + I(year ^ 2), data = .x)),
tidied = map(fit, tidy),
augmented = map(fit, augment)
)
df_quadratic_year_anom <-
full_join(
df_quadratic %>%
unnest(tidied) %>%
select(name, !!!group_by, term, estimate) %>%
pivot_wider(names_from = term,
values_from = estimate) %>%
mutate(fit = `(Intercept)` + year * 2023 + `I(year^2)` * 2023 ^ 2) %>%
select(name, !!!group_by, fit) %>%
mutate(year = 2023),
df %>%
filter(year == 2023,
name %in% name_quadratic_fit)
) %>%
mutate(resid = value - fit)
df_quadratic <-
bind_rows(
df_quadratic %>%
unnest(augmented) %>%
select(name, !!!group_by, year, value, fit = .fitted, resid = .resid),
df_quadratic_year_anom
)
rm(df_quadratic_year_anom)
# Join linear and quadratic regression results
df_regression <-
bind_rows(df_lm,
df_quadratic)
df_regression <-
df_regression %>%
arrange(year)
rm(df_lm,
df_quadratic)
return(df_regression)
}Maps
The following maps show the absolute state of each variable in 2023 as provided through the pCO2 product, the change in that variable from 1990 to 2023, as well es the anomalies in 2023. Changes and anomalies are determined based on the predicted value of a linear regression model fit to the data from 1990 to 2022.
Maps are first presented as annual means, and than as monthly means. Note that the 2023 predictions for the monthly maps are done individually for each month, such the mean seasonal anomaly from the annual mean is removed.
Note: The increase the computational speed, I regridded all maps to 5X5° grid.
Annual means
2023 absolute
pco2_product_coarse_annual_regression <-
pco2_product_coarse_annual %>%
drop_na() %>%
anomaly_determination(lon, lat)
pco2_product_coarse_annual_regression <-
pco2_product_coarse_annual_regression %>%
drop_na()
pco2_product_coarse_annual_regression %>%
filter(year == 2023,
!(name %in% name_divergent)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ map +
geom_tile(data = .x,
aes(lon, lat, fill = value)) +
labs(title = paste("Annual mean", 2023)) +
scale_fill_viridis_c(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
pco2_product_coarse_annual_regression %>%
filter(year == 2023,
name %in% name_divergent) %>%
group_split(name) %>%
# head(1) %>%
map( ~ map +
geom_tile(data = .x,
aes(lon, lat, fill = value)) +
labs(title = paste("Annual mean", 2023)) +
scale_fill_divergent(
name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown())
)[[1]]
Trends
pco2_product_coarse_annual_regression <-
pco2_product_coarse_annual_regression %>%
group_by(name) %>%
filter(year %in% c(min(year), max(year), 2023)) %>%
ungroup()
pco2_product_coarse_annual_regression %>%
select(-c(value, resid)) %>%
filter(year %in% c(min(year), max(year))) %>%
arrange(year) %>%
group_by(lon, lat, name) %>%
mutate(change = fit - lag(fit),
period = paste(lag(year), year, sep = "-")) %>%
ungroup() %>%
filter(!is.na(change)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ map +
geom_tile(data = .x,
aes(lon, lat, fill = change)) +
labs(title = paste("Change: ",.x$period)) +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
2023 anomaly
pco2_product_coarse_annual_regression %>%
filter(year == 2023) %>%
group_split(name) %>%
# head(1) %>%
map( ~ map +
geom_tile(data = .x,
aes(lon, lat, fill = resid)) +
labs(title = paste(2023,"anomaly")) +
scale_fill_divergent(
name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
pco2_product_coarse_annual_regression %>%
write_csv(paste0("../data/","CMEMS","_","2023","_anomaly_map_annual.csv"))Monthly means
2023 absolute
pco2_product_coarse_monthly_regression <-
pco2_product_coarse_monthly %>%
drop_na() %>%
anomaly_determination(lon, lat, month)
pco2_product_coarse_monthly_regression <-
pco2_product_coarse_monthly_regression %>%
drop_na()
pco2_product_coarse_monthly_regression %>%
filter(year == 2023,
!(name %in% name_divergent)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ map +
geom_tile(data = .x,
aes(lon, lat, fill = value)) +
labs(title = paste("Monthly means", 2023)) +
scale_fill_viridis_c(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
facet_wrap( ~ month, ncol = 2)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
pco2_product_coarse_monthly_regression %>%
filter(year == 2023,
name %in% name_divergent) %>%
group_split(name) %>%
# head(1) %>%
map(
~ map +
geom_tile(data = .x,
aes(lon, lat, fill = value)) +
labs(title = paste("Monthly means", 2023)) +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
facet_wrap( ~ month, ncol = 2)
)[[1]]
Trends
pco2_product_coarse_monthly_regression %>%
group_by(name) %>%
filter(year %in% c(min(year), max(year))) %>%
ungroup() %>%
select(-c(value, resid)) %>%
arrange(year) %>%
group_by(lon, lat, name, month) %>%
mutate(change = fit - lag(fit),
period = paste(lag(year), year, sep = "-")) %>%
ungroup() %>%
filter(!is.na(change)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ map +
geom_tile(data = .x,
aes(lon, lat, fill = change)) +
labs(title = paste("Change: ", .x$period)) +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
facet_wrap(~ month, ncol = 2)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
2023 anomaly
pco2_product_coarse_monthly_regression %>%
filter(year == 2023) %>%
group_split(name) %>%
# head(1) %>%
map(
~ map +
geom_tile(data = .x,
aes(lon, lat, fill = resid)) +
labs(title = paste(2023, "anomaly")) +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
facet_wrap( ~ month, ncol = 2)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
pco2_product_coarse_monthly_regression %>%
filter(year == 2023) %>%
write_csv(paste0("../data/","CMEMS","_","2023","_anomaly_map_monthly.csv"))Hovmoeller plots
The following Hovmoeller plots show the anomalies from the prediction of the linear/quadratic fits.
Hovmoeller plots are first presented as annual means, and than as monthly means. Note that the predictions for the monthly Hovmoeller plots are done individually for each month, such the mean seasonal anomaly from the annual mean is removed.
2023 annual anomalies
pco2_product_hovmoeller_monthly_annual_regression <-
pco2_product_hovmoeller_monthly_annual %>%
anomaly_determination(lat) %>%
filter(!is.na(resid))
pco2_product_hovmoeller_monthly_annual_regression %>%
# filter(name == "mld") %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(year, lat, fill = resid)) +
geom_raster() +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
coord_cartesian(expand = 0) +
labs(title = "Annual mean anomalies",
y = "Latitude") +
theme(axis.title.x = element_blank())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
2023 monthly anomalies
pco2_product_hovmoeller_monthly_regression <-
pco2_product_hovmoeller_monthly %>%
select(-c(decimal)) %>%
anomaly_determination(lat, month) %>%
filter(!is.na(resid))
pco2_product_hovmoeller_monthly_regression <-
pco2_product_hovmoeller_monthly_regression %>%
mutate(decimal = year + (month - 1) / 12)
pco2_product_hovmoeller_monthly_regression %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(decimal, lat, fill = resid)) +
geom_raster() +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
coord_cartesian(expand = 0) +
labs(title = "Monthly mean anomalies",
y = "Latitude") +
theme(axis.title.x = element_blank())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
pco2_product_hovmoeller_monthly_regression %>%
write_csv(paste0("../data/","CMEMS","_","2023","_anomaly_hovmoeller_monthly.csv"))Three years prior 2023
pco2_product_hovmoeller_monthly_regression %>%
filter(between(year, 2023-2, 2023)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(decimal, lat, fill = resid)) +
geom_raster() +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
coord_cartesian(expand = 0) +
labs(title = "Monthly mean anomalies",
y = "Latitude") +
theme(axis.title.x = element_blank())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
Regional means and integrals
The following plots show regionally averaged (or integrated) values of each variable as provided through the pCO2 product, as well as the anomalies from the prediction of a linear/quadratic fit.
Anomalies are first presented relative to the predicted annual mean of each year, hence preserving the seasonality. Furthermore, anomalies are presented relative to the predicted monthly mean values, such that the mean seasonality is removed.
2023 absolute values
Global
fig.height <- pco2_product_monthly %>%
distinct(name) %>%
nrow()
fig.height <- (fig.height + 2) * 0.1pco2_product_monthly %>%
filter(biome %in% "Global") %>%
ggplot(aes(month, value, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 2023-1, 2023)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Absolute values | Global") +
facet_wrap(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
legend.title = element_blank(),
axis.title.y = element_blank()
)
Selected biomes
pco2_product_monthly %>%
filter(biome %in% key_biomes) %>%
ggplot(aes(month, value, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 2023-1, 2023)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Absolute values | Selected biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
legend.title = element_blank(),
axis.title.y = element_blank()
)
pco2_product_monthly %>%
filter(biome %in% key_biomes) %>%
group_split(biome) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(month, value, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(
data = . %>% filter(between(year, 2023-1, 2023)),
aes(col = as.factor(year)),
linewidth = 1
) +
scale_color_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = paste("Absolute values |", .x$biome)) +
facet_wrap(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
legend.title = element_blank(),
axis.title.y = element_blank()
)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
Super biomes
pco2_product_monthly %>%
filter(biome %in% super_biomes) %>%
ggplot(aes(month, value, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 2023-1, 2023)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Absolute values | Selected super biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
legend.title = element_blank(),
axis.title.y = element_blank()
)
pco2_product_monthly %>%
filter(biome %in% super_biomes) %>%
group_split(biome) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(month, value, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(
data = . %>% filter(between(year, 2023-1, 2023)),
aes(col = as.factor(year)),
linewidth = 1
) +
scale_color_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = paste("Absolute values |", .x$biome)) +
facet_wrap(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
legend.title = element_blank(),
axis.title.y = element_blank()
)
)[[1]]
[[2]]
[[3]]
2023 anomalies
Annual mean trends
pco2_product_annual <-
pco2_product_monthly %>%
group_by(year, biome, name) %>%
summarise(value = mean(value)) %>%
ungroup()
pco2_product_annual %>%
filter(biome %in% "Global") %>%
ggplot(aes(year, value)) +
geom_path() +
geom_point(data = . %>% filter(year != 2023)) +
geom_point(data = . %>% filter(year == 2023),
shape = 1) +
geom_smooth(data = . %>% filter(year != 2023,
!(name %in% name_quadratic_fit)),
method = "lm",
fullrange = TRUE,
aes(col = "linear"),
se = FALSE) +
geom_smooth(data = . %>% filter(year != 2023,
name %in% name_quadratic_fit),
method = "lm",
fullrange = TRUE,
formula = y ~ x + I(x^2),
aes(col = "quadratic"),
se = FALSE) +
scale_color_brewer(
palette = "Set1",
name = paste("Regression fit\nexcl.", 2023)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Annual mean trends | Global") +
facet_wrap(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank()
)
pco2_product_annual %>%
filter(biome %in% key_biomes) %>%
ggplot(aes(year, value)) +
geom_path() +
geom_point(data = . %>% filter(year != 2023),
size = 0.2) +
geom_point(data = . %>% filter(year == 2023),
shape = 1) +
geom_smooth(data = . %>% filter(year != 2023,
!(name %in% name_quadratic_fit)),
method = "lm",
fullrange = TRUE,
aes(col = "linear"),
se = FALSE) +
geom_smooth(data = . %>% filter(year != 2023,
name %in% name_quadratic_fit),
method = "lm",
fullrange = TRUE,
formula = y ~ x + I(x^2),
aes(col = "quadratic"),
se = FALSE) +
scale_color_brewer(
palette = "Set1",
name = paste("Regression fit\nexcl.", 2023)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Annual mean trends | Selected biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank()
)
pco2_product_annual %>%
filter(biome %in% super_biomes) %>%
ggplot(aes(year, value)) +
geom_path() +
geom_point(data = . %>% filter(year != 2023),
size = 0.2) +
geom_point(data = . %>% filter(year == 2023),
shape = 1) +
geom_smooth(data = . %>% filter(year != 2023,
!(name %in% name_quadratic_fit)),
method = "lm",
fullrange = TRUE,
aes(col = "linear"),
se = FALSE) +
geom_smooth(data = . %>% filter(year != 2023,
name %in% name_quadratic_fit),
method = "lm",
fullrange = TRUE,
formula = y ~ x + I(x^2),
aes(col = "quadratic"),
se = FALSE) +
scale_color_brewer(
palette = "Set1",
name = paste("Regression fit\nexcl.", 2023)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Annual mean trends | Selected super biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank()
)
pco2_product_annual_regression <-
pco2_product_annual %>%
anomaly_determination(biome)
pco2_product_annual_regression %>%
filter(biome %in% "Global") %>%
ggplot(aes(year, resid)) +
geom_hline(yintercept = 0) +
geom_path() +
geom_point(data = . %>% filter(!between(year, 2023-1, 2023)),
aes(fill = year),
shape = 21) +
scale_fill_grayC() +
new_scale_fill() +
geom_point(data = . %>% filter(between(year, 2023-1, 2023)),
aes(fill = as.factor(year)),
shape = 21, size = 2) +
scale_fill_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Residual from fit to annual means (actual - predicted) | Global") +
facet_wrap(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank()
)
pco2_product_annual_regression %>%
filter(biome %in% key_biomes) %>%
ggplot(aes(year, resid)) +
geom_hline(yintercept = 0) +
geom_path() +
geom_point(data = . %>% filter(!between(year, 2023-1, 2023)),
aes(fill = year),
shape = 21) +
scale_fill_grayC() +
new_scale_fill() +
geom_point(data = . %>% filter(between(year, 2023-1, 2023)),
aes(fill = as.factor(year)),
shape = 21, size = 2) +
scale_fill_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Residual from fit to annual means (actual - predicted) | Selected biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank()
)
pco2_product_annual_regression %>%
filter(biome %in% super_biomes) %>%
ggplot(aes(year, resid)) +
geom_hline(yintercept = 0) +
geom_path() +
geom_point(data = . %>% filter(!between(year, 2023-1, 2023)),
aes(fill = year),
shape = 21) +
scale_fill_grayC() +
new_scale_fill() +
geom_point(data = . %>% filter(between(year, 2023-1, 2023)),
aes(fill = as.factor(year)),
shape = 21, size = 2) +
scale_fill_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Residual from fit to annual means (actual - predicted) | Selected super biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank()
)
pco2_product_annual_regression %>%
filter(biome != "global") %>%
ggplot(aes(biome, resid)) +
geom_hline(yintercept = 0) +
geom_jitter(data = . %>% filter(!between(year, 2023-1, 2023)),
aes(fill = year),
shape = 21, width = 0.2) +
scale_fill_grayC() +
new_scale_fill() +
geom_point(data = . %>% filter(between(year, 2023-1, 2023)),
aes(fill = as.factor(year)),
shape = 21, size = 2) +
scale_fill_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
labs(title = "Residual from fit to annual means (actual - predicted) | All biomes") +
facet_grid(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)
)
pco2_product_annual_regression %>%
write_csv(paste0("../data/","CMEMS","_","2023","_biome_annual_regression.csv"))pco2_product_annual_detrended <-
full_join(pco2_product_monthly,
pco2_product_annual_regression %>% select(-c(value, resid))) %>%
mutate(resid = value - fit)
pco2_product_annual_detrended %>%
filter(biome %in% "Global") %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 2023-1, 2023)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted annual mean | Global") +
facet_wrap(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
pco2_product_annual_detrended %>%
filter(biome %in% key_biomes) %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 2023-1, 2023)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted annual mean | Selected biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)
)
pco2_product_annual_detrended %>%
filter(biome %in% super_biomes) %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 2023-1, 2023)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted annual mean | Selected super biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)
)
pco2_product_annual_detrended %>%
filter(biome %in% key_biomes) %>%
group_split(biome) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(
data = . %>% filter(between(year, 2023-1, 2023)),
aes(col = as.factor(year)),
linewidth = 1
) +
scale_color_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = paste("Anomalies from predicted annual mean |", .x$biome)) +
facet_wrap(
name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank()
)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
pco2_product_annual_detrended %>%
filter(biome %in% super_biomes) %>%
group_split(biome) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(
data = . %>% filter(between(year, 2023-1, 2023)),
aes(col = as.factor(year)),
linewidth = 1
) +
scale_color_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = paste("Anomalies from predicted annual mean |", .x$biome)) +
facet_wrap(
name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank()
)
)[[1]]
[[2]]
[[3]]
pco2_product_annual_detrended %>%
write_csv(paste0("../data/","CMEMS","_","2023","_biome_annual_detrended.csv"))Monthly mean trends
pco2_product_monthly %>%
filter(biome %in% "Global") %>%
mutate(month = as.factor(month)) %>%
ggplot(aes(year, value, col = month)) +
# geom_point() +
geom_smooth(data = . %>% filter(year != 2023,
!(name %in% name_quadratic_fit)),
method = "lm",
se = FALSE,
fullrange = TRUE) +
geom_smooth(
data = . %>% filter(year != 2023,
name %in% name_quadratic_fit),
method = "lm",
fullrange = TRUE,
formula = y ~ x + I(x ^ 2),
se = FALSE
) +
scale_color_scico_d(palette = "romaO",
name = paste("Regression fit\nexcl.", 2023)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Monthly mean trends | Global") +
facet_wrap(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank()
)
pco2_product_monthly %>%
filter(biome %in% key_biomes) %>%
mutate(month = as.factor(month)) %>%
ggplot(aes(year, value, col = month)) +
# geom_point() +
geom_smooth(data = . %>% filter(year != 2023,
!(name %in% name_quadratic_fit)),
method = "lm",
fullrange = TRUE,
se = FALSE) +
geom_smooth(
data = . %>% filter(year != 2023,
name %in% name_quadratic_fit),
method = "lm",
fullrange = TRUE,
formula = y ~ x + I(x ^ 2),
se = FALSE
) +
scale_color_scico_d(palette = "romaO",
name = paste("Regression fit\nexcl.", 2023)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Monthly mean trends | Selected biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank()
)
pco2_product_monthly_regression <-
pco2_product_monthly %>%
anomaly_determination(biome, month)
pco2_product_monthly_regression %>%
filter(biome %in% "Global") %>%
group_split(month) %>%
head(1) %>%
map(
~ ggplot(data = .x,
aes(year, resid)) +
geom_hline(yintercept = 0) +
geom_path() +
geom_point(
data = . %>% filter(!between(year, 2023-1, 2023)),
aes(fill = year),
shape = 21
) +
scale_fill_grayC() +
new_scale_fill() +
geom_point(
data = . %>% filter(between(year, 2023-1, 2023)),
aes(fill = as.factor(year)),
shape = 21,
size = 2
) +
scale_fill_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Residual from fit to monthly means (actual - predicted) | Global",
subtitle = paste("Month:", .x$month)) +
facet_wrap(
name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank()
)
)[[1]]
pco2_product_monthly_regression %>%
filter(biome %in% key_biomes) %>%
group_split(month) %>%
head(1) %>%
map(
~ ggplot(data = .x,
aes(year, resid)) +
geom_hline(yintercept = 0) +
geom_path() +
geom_point(
data = . %>% filter(!between(year, 2023-1, 2023)),
aes(fill = year),
shape = 21
) +
scale_fill_grayC() +
new_scale_fill() +
geom_point(
data = . %>% filter(between(year, 2023-1, 2023)),
aes(fill = as.factor(year)),
shape = 21,
size = 2
) +
scale_fill_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Residual from fit to monthly means (actual - predicted) | Selected biomes",
subtitle = paste("Month:", .x$month)) +
facet_grid(
name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y"
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank()
)
)[[1]]
Global
pco2_product_monthly_detrended <-
full_join(pco2_product_monthly,
pco2_product_monthly_regression %>% select(-c(value, resid, time))) %>%
mutate(resid = value - fit)
pco2_product_monthly_detrended %>%
filter(biome %in% "Global") %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 2023-1, 2023)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted monthly mean | Global") +
facet_wrap(
name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
pco2_product_monthly_detrended %>%
filter(biome %in% key_biomes) %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 2023-1, 2023)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted monthly mean | Selected biomes") +
facet_grid(
name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y"
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
pco2_product_monthly_detrended %>%
filter(biome %in% super_biomes) %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 2023-1, 2023)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted monthly mean | Selected super biomes") +
facet_grid(
name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y"
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
Selected biomes
pco2_product_monthly_detrended %>%
filter(biome %in% key_biomes) %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 2023-1, 2023)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted monthly mean | Selected biomes") +
facet_grid(
name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y"
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
pco2_product_monthly_detrended %>%
filter(biome %in% key_biomes) %>%
group_split(biome) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(
data = . %>% filter(between(year, 2023-1, 2023)),
aes(col = as.factor(year)),
linewidth = 1
) +
scale_color_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = paste("Anomalies from predicted monthly mean |", .x$biome)) +
facet_wrap(
name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
Super biomes
pco2_product_monthly_detrended %>%
filter(biome %in% super_biomes) %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 2023-1, 2023)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted monthly mean | Selected super biomes") +
facet_grid(
name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y"
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
pco2_product_monthly_detrended %>%
filter(biome %in% super_biomes) %>%
group_split(biome) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(
data = . %>% filter(between(year, 2023-1, 2023)),
aes(col = as.factor(year)),
linewidth = 1
) +
scale_color_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = paste("Anomalies from predicted monthly mean |", .x$biome)) +
facet_wrap(
name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
)[[1]]
[[2]]
[[3]]
2023 anomaly correlation
The following plots aim to unravel the correlation between regionally integrated monthly flux anomalies and the corresponding anomalies of the means/integrals of each other variable.
Anomalies are first presented are first presented in absolute units. Due to the different flux magnitudes, we need to plot integrated fluxes separately for each region. Secondly, we normalize the monthly anomalies to the spread (expressed as standard deviation) of the residuals from the fit.
Annual anomalies
Absolute
pco2_product_annual_regression %>%
filter(biome == "Global") %>%
select(-c(value, fit)) %>%
pivot_wider(values_from = resid) %>%
pivot_longer(-c(year, biome, fgco2_int)) %>%
ggplot(aes(value, fgco2_int)) +
geom_hline(yintercept = 0) +
geom_point(data = . %>% filter(!between(year, 2023-1, 2023)),
aes(fill = year),
shape = 21) +
geom_smooth(
data = . %>% filter(!between(year, 2023-1, 2023)),
method = "lm",
se = FALSE,
fullrange = TRUE,
aes(col = paste("Regression fit\nexcl.", 2023))
) +
scale_color_grey() +
scale_fill_grayC()+
new_scale_fill() +
geom_point(data = . %>% filter(between(year, 2023-1, 2023)),
aes(fill = as.factor(year)),
shape = 21, size = 2) +
scale_fill_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
labs(title = "Globally integrated fluxes",
y = labels_breaks("fgco2_int")$i_legend_title) +
facet_wrap(
~ name,
scales = "free_x",
labeller = labeller(name = x_axis_labels),
strip.position = "bottom",
ncol = 2
) +
theme(
strip.text.x.bottom = element_markdown(),
strip.placement = "outside",
strip.background.x = element_blank(),
axis.title.y = element_markdown(),
axis.title.x = element_blank(),
legend.title = element_blank()
)
Monthly anomalies
Absolute
pco2_product_monthly_detrended_anomaly <-
pco2_product_monthly_detrended %>%
select(year, month, biome, name, resid) %>%
pivot_wider(names_from = name,
values_from = resid)
pco2_product_monthly_detrended_anomaly %>%
filter(biome == "Global") %>%
pivot_longer(-c(year, month, biome, fgco2_int)) %>%
ggplot(aes(value, fgco2_int)) +
geom_hline(yintercept = 0) +
geom_point(data = . %>% filter(year != 2023),
aes(col = paste(min(year), max(year), sep = "-")),
alpha = 0.2) +
geom_smooth(
data = . %>% filter(year != 2023),
aes(col = paste(min(year), max(year), sep = "-")),
method = "lm",
se = FALSE,
fullrange = TRUE
) +
scale_color_grey(name = "") +
new_scale_color() +
geom_path(data = . %>% filter(year == 2023),
aes(col = as.factor(month), group = 1)) +
geom_point(data = . %>% filter(year == 2023),
aes(fill = as.factor(month)),
shape = 21,
size = 3) +
scale_color_scico_d(palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 2023)) +
scale_fill_scico_d(palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 2023)) +
labs(title = "Globally integrated fluxes",
y = labels_breaks("fgco2_int")$i_legend_title) +
facet_wrap(
~ name,
scales = "free_x",
labeller = labeller(name = x_axis_labels),
strip.position = "bottom",
ncol = 2
) +
theme(
strip.text.x.bottom = element_markdown(),
strip.placement = "outside",
strip.background.x = element_blank(),
axis.title.y = element_markdown(),
axis.title.x = element_blank()
)
pco2_product_monthly_detrended_anomaly %>%
filter(!(biome %in% c(super_biomes, "Global"))) %>%
pivot_longer(-c(year, month, biome, fgco2_int)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(value, fgco2_int)) +
geom_hline(yintercept = 0) +
geom_point(
data = . %>% filter(year != 2023),
aes(col = paste(min(year), max(year), sep = "-")),
alpha = 0.2
) +
geom_smooth(
data = . %>% filter(year != 2023),
aes(col = paste(min(year), max(year), sep = "-")),
method = "lm",
se = FALSE,
fullrange = TRUE
) +
scale_color_grey(name = "") +
new_scale_color() +
geom_path(data = . %>% filter(year == 2023),
aes(col = as.factor(month), group = 1)) +
geom_point(
data = . %>% filter(year == 2023),
aes(fill = as.factor(month)),
shape = 21,
size = 3
) +
scale_color_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 2023)
) +
scale_fill_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 2023)
) +
facet_wrap( ~ biome, ncol = 3, scales = "free") +
labs(
title = "Biome integrated fluxes",
y = labels_breaks("fgco2_int")$i_legend_title,
x = labels_breaks(.x %>% distinct(name))$i_legend_title
) +
theme(axis.title.x = element_markdown(),
axis.title.y = element_markdown())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
pco2_product_monthly_detrended_anomaly %>%
filter(biome %in% super_biomes) %>%
pivot_longer(-c(year, month, biome, fgco2_int)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(value, fgco2_int)) +
geom_hline(yintercept = 0) +
geom_point(
data = . %>% filter(year != 2023),
aes(col = paste(min(year), max(year), sep = "-")),
alpha = 0.2
) +
geom_smooth(
data = . %>% filter(year != 2023),
aes(col = paste(min(year), max(year), sep = "-")),
method = "lm",
se = FALSE,
fullrange = TRUE
) +
scale_color_grey(name = "") +
new_scale_color() +
geom_path(data = . %>% filter(year == 2023),
aes(col = as.factor(month), group = 1)) +
geom_point(
data = . %>% filter(year == 2023),
aes(fill = as.factor(month)),
shape = 21,
size = 3
) +
scale_color_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 2023)
) +
scale_fill_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 2023)
) +
facet_wrap( ~ biome, ncol = 3, scales = "free_x") +
labs(
title = "Super biome integrated fluxes",
y = labels_breaks("fgco2_int")$i_legend_title,
x = labels_breaks(.x %>% distinct(name))$i_legend_title
) +
theme(axis.title.x = element_markdown(),
axis.title.y = element_markdown())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
pco2_product_monthly_detrended_anomaly %>%
pivot_longer(-c(month, biome, year)) %>%
write_csv(paste0(
"../data/",
"CMEMS","_","2023",
"_biome_monthly_detrended_anomaly.csv"
))Relative to spread
pco2_product_monthly_detrended_anomaly_spread <-
pco2_product_monthly_detrended_anomaly %>%
pivot_longer(-c(month, biome, year)) %>%
filter(year != 2023) %>%
group_by(month, biome, name) %>%
summarise(spread = sd(value, na.rm = TRUE)) %>%
ungroup()
pco2_product_monthly_detrended_anomaly_relative <-
full_join(
pco2_product_monthly_detrended_anomaly_spread,
pco2_product_monthly_detrended_anomaly %>%
pivot_longer(-c(month, biome, year))
)
pco2_product_monthly_detrended_anomaly_relative <-
pco2_product_monthly_detrended_anomaly_relative %>%
mutate(value = value / spread) %>%
select(-spread) %>%
pivot_wider() %>%
pivot_longer(-c(month, biome, year, fgco2_int))
pco2_product_monthly_detrended_anomaly_relative %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(value, fgco2_int)) +
geom_vline(xintercept = 0) +
geom_hline(yintercept = 0) +
geom_point(
data = . %>% filter(year != 2023),
aes(col = paste(min(year), max(year), sep = "-")),
alpha = 0.2
) +
geom_smooth(
data = . %>% filter(year != 2023),
aes(col = paste(min(year), max(year), sep = "-")),
method = "lm",
se = FALSE,
fullrange = TRUE
) +
scale_color_grey(name = "") +
new_scale_color() +
geom_path(data = . %>% filter(year == 2023),
aes(col = as.factor(month), group = 1)) +
geom_point(
data = . %>% filter(year == 2023),
aes(fill = as.factor(month)),
shape = 21,
size = 3
) +
scale_color_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 2023)
) +
scale_fill_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 2023)
) +
facet_wrap( ~ biome, ncol = 3) +
coord_fixed() +
labs(
title = "Biome integrated fluxes normalized to spread",
y = str_split_i(labels_breaks("fgco2_int")$i_legend_title, "<br>", i = 1),
x = str_split_i(labels_breaks(.x %>% distinct(name))$i_legend_title, "<br>", i = 1)
) +
theme(axis.title.x = element_markdown(),
axis.title.y = element_markdown())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
pCO2productanalysis_2016 <-
knitr::knit_expand(
file = here::here("analysis/child/pCO2_product_analysis.Rmd"),
product_name = "CMEMS",
year_anom = 2016
)2016 anomalies
Detection
For the detection of anomalies at any point in time and space, we fit regression models and compare the fitted to the actual value.
We use linear regression models for all parameters, except for , which are approximated with quadratic fits.
The regression models are fitted to all data since , except 2016.
anomaly_determination <- function(df,...) {
group_by <- quos(...)
# group_by <- quos(lon, lat)
# df <- pco2_product_coarse_annual
# Linear regression models
df_lm <-
df %>%
filter(year != 2016,
!(name %in% name_quadratic_fit)) %>%
nest(data = -c(name, !!!group_by)) %>%
mutate(
fit = map(data, ~ lm(value ~ year, data = .x)),
tidied = map(fit, tidy),
augmented = map(fit, augment)
)
df_lm_year_anom <-
full_join(
df_lm %>%
unnest(tidied) %>%
select(name, !!!group_by, term, estimate) %>%
pivot_wider(names_from = term,
values_from = estimate) %>%
mutate(fit = `(Intercept)` + year * 2016) %>%
select(name, !!!group_by, fit) %>%
mutate(year = 2016),
df %>%
filter(year == 2016,
!(name %in% name_quadratic_fit))
) %>%
mutate(resid = value - fit)
df_lm <-
bind_rows(
df_lm %>%
unnest(augmented) %>%
select(name, !!!group_by, year, value, fit = .fitted, resid = .resid),
df_lm_year_anom
)
rm(df_lm_year_anom)
# Quadratic regression models
df_quadratic <-
df %>%
filter(year != 2016,
name %in% name_quadratic_fit) %>%
nest(data = -c(name, !!!group_by)) %>%
mutate(
fit = map(data, ~ lm(value ~ year + I(year ^ 2), data = .x)),
tidied = map(fit, tidy),
augmented = map(fit, augment)
)
df_quadratic_year_anom <-
full_join(
df_quadratic %>%
unnest(tidied) %>%
select(name, !!!group_by, term, estimate) %>%
pivot_wider(names_from = term,
values_from = estimate) %>%
mutate(fit = `(Intercept)` + year * 2016 + `I(year^2)` * 2016 ^ 2) %>%
select(name, !!!group_by, fit) %>%
mutate(year = 2016),
df %>%
filter(year == 2016,
name %in% name_quadratic_fit)
) %>%
mutate(resid = value - fit)
df_quadratic <-
bind_rows(
df_quadratic %>%
unnest(augmented) %>%
select(name, !!!group_by, year, value, fit = .fitted, resid = .resid),
df_quadratic_year_anom
)
rm(df_quadratic_year_anom)
# Join linear and quadratic regression results
df_regression <-
bind_rows(df_lm,
df_quadratic)
df_regression <-
df_regression %>%
arrange(year)
rm(df_lm,
df_quadratic)
return(df_regression)
}Maps
The following maps show the absolute state of each variable in 2016 as provided through the pCO2 product, the change in that variable from 1990 to 2016, as well es the anomalies in 2016. Changes and anomalies are determined based on the predicted value of a linear regression model fit to the data from 1990 to 2015.
Maps are first presented as annual means, and than as monthly means. Note that the 2016 predictions for the monthly maps are done individually for each month, such the mean seasonal anomaly from the annual mean is removed.
Note: The increase the computational speed, I regridded all maps to 5X5° grid.
Annual means
2016 absolute
pco2_product_coarse_annual_regression <-
pco2_product_coarse_annual %>%
drop_na() %>%
anomaly_determination(lon, lat)
pco2_product_coarse_annual_regression <-
pco2_product_coarse_annual_regression %>%
drop_na()
pco2_product_coarse_annual_regression %>%
filter(year == 2016,
!(name %in% name_divergent)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ map +
geom_tile(data = .x,
aes(lon, lat, fill = value)) +
labs(title = paste("Annual mean", 2016)) +
scale_fill_viridis_c(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
pco2_product_coarse_annual_regression %>%
filter(year == 2016,
name %in% name_divergent) %>%
group_split(name) %>%
# head(1) %>%
map( ~ map +
geom_tile(data = .x,
aes(lon, lat, fill = value)) +
labs(title = paste("Annual mean", 2016)) +
scale_fill_divergent(
name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown())
)[[1]]
Trends
pco2_product_coarse_annual_regression <-
pco2_product_coarse_annual_regression %>%
group_by(name) %>%
filter(year %in% c(min(year), max(year), 2016)) %>%
ungroup()
pco2_product_coarse_annual_regression %>%
select(-c(value, resid)) %>%
filter(year %in% c(min(year), max(year))) %>%
arrange(year) %>%
group_by(lon, lat, name) %>%
mutate(change = fit - lag(fit),
period = paste(lag(year), year, sep = "-")) %>%
ungroup() %>%
filter(!is.na(change)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ map +
geom_tile(data = .x,
aes(lon, lat, fill = change)) +
labs(title = paste("Change: ",.x$period)) +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
2016 anomaly
pco2_product_coarse_annual_regression %>%
filter(year == 2016) %>%
group_split(name) %>%
# head(1) %>%
map( ~ map +
geom_tile(data = .x,
aes(lon, lat, fill = resid)) +
labs(title = paste(2016,"anomaly")) +
scale_fill_divergent(
name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
pco2_product_coarse_annual_regression %>%
write_csv(paste0("../data/","CMEMS","_","2016","_anomaly_map_annual.csv"))Monthly means
2016 absolute
pco2_product_coarse_monthly_regression <-
pco2_product_coarse_monthly %>%
drop_na() %>%
anomaly_determination(lon, lat, month)
pco2_product_coarse_monthly_regression <-
pco2_product_coarse_monthly_regression %>%
drop_na()
pco2_product_coarse_monthly_regression %>%
filter(year == 2016,
!(name %in% name_divergent)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ map +
geom_tile(data = .x,
aes(lon, lat, fill = value)) +
labs(title = paste("Monthly means", 2016)) +
scale_fill_viridis_c(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
facet_wrap( ~ month, ncol = 2)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
pco2_product_coarse_monthly_regression %>%
filter(year == 2016,
name %in% name_divergent) %>%
group_split(name) %>%
# head(1) %>%
map(
~ map +
geom_tile(data = .x,
aes(lon, lat, fill = value)) +
labs(title = paste("Monthly means", 2016)) +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
facet_wrap( ~ month, ncol = 2)
)[[1]]
Trends
pco2_product_coarse_monthly_regression %>%
group_by(name) %>%
filter(year %in% c(min(year), max(year))) %>%
ungroup() %>%
select(-c(value, resid)) %>%
arrange(year) %>%
group_by(lon, lat, name, month) %>%
mutate(change = fit - lag(fit),
period = paste(lag(year), year, sep = "-")) %>%
ungroup() %>%
filter(!is.na(change)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ map +
geom_tile(data = .x,
aes(lon, lat, fill = change)) +
labs(title = paste("Change: ", .x$period)) +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
facet_wrap(~ month, ncol = 2)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
2016 anomaly
pco2_product_coarse_monthly_regression %>%
filter(year == 2016) %>%
group_split(name) %>%
# head(1) %>%
map(
~ map +
geom_tile(data = .x,
aes(lon, lat, fill = resid)) +
labs(title = paste(2016, "anomaly")) +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
facet_wrap( ~ month, ncol = 2)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
pco2_product_coarse_monthly_regression %>%
filter(year == 2016) %>%
write_csv(paste0("../data/","CMEMS","_","2016","_anomaly_map_monthly.csv"))Hovmoeller plots
The following Hovmoeller plots show the anomalies from the prediction of the linear/quadratic fits.
Hovmoeller plots are first presented as annual means, and than as monthly means. Note that the predictions for the monthly Hovmoeller plots are done individually for each month, such the mean seasonal anomaly from the annual mean is removed.
2016 annual anomalies
pco2_product_hovmoeller_monthly_annual_regression <-
pco2_product_hovmoeller_monthly_annual %>%
anomaly_determination(lat) %>%
filter(!is.na(resid))
pco2_product_hovmoeller_monthly_annual_regression %>%
# filter(name == "mld") %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(year, lat, fill = resid)) +
geom_raster() +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
coord_cartesian(expand = 0) +
labs(title = "Annual mean anomalies",
y = "Latitude") +
theme(axis.title.x = element_blank())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
2016 monthly anomalies
pco2_product_hovmoeller_monthly_regression <-
pco2_product_hovmoeller_monthly %>%
select(-c(decimal)) %>%
anomaly_determination(lat, month) %>%
filter(!is.na(resid))
pco2_product_hovmoeller_monthly_regression <-
pco2_product_hovmoeller_monthly_regression %>%
mutate(decimal = year + (month - 1) / 12)
pco2_product_hovmoeller_monthly_regression %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(decimal, lat, fill = resid)) +
geom_raster() +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
coord_cartesian(expand = 0) +
labs(title = "Monthly mean anomalies",
y = "Latitude") +
theme(axis.title.x = element_blank())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
pco2_product_hovmoeller_monthly_regression %>%
write_csv(paste0("../data/","CMEMS","_","2016","_anomaly_hovmoeller_monthly.csv"))Three years prior 2016
pco2_product_hovmoeller_monthly_regression %>%
filter(between(year, 2016-2, 2016)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(decimal, lat, fill = resid)) +
geom_raster() +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
coord_cartesian(expand = 0) +
labs(title = "Monthly mean anomalies",
y = "Latitude") +
theme(axis.title.x = element_blank())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
Regional means and integrals
The following plots show regionally averaged (or integrated) values of each variable as provided through the pCO2 product, as well as the anomalies from the prediction of a linear/quadratic fit.
Anomalies are first presented relative to the predicted annual mean of each year, hence preserving the seasonality. Furthermore, anomalies are presented relative to the predicted monthly mean values, such that the mean seasonality is removed.
2016 absolute values
Global
fig.height <- pco2_product_monthly %>%
distinct(name) %>%
nrow()
fig.height <- (fig.height + 2) * 0.1pco2_product_monthly %>%
filter(biome %in% "Global") %>%
ggplot(aes(month, value, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2016-1, 2016)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 2016-1, 2016)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Absolute values | Global") +
facet_wrap(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
legend.title = element_blank(),
axis.title.y = element_blank()
)
Selected biomes
pco2_product_monthly %>%
filter(biome %in% key_biomes) %>%
ggplot(aes(month, value, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2016-1, 2016)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 2016-1, 2016)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Absolute values | Selected biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
legend.title = element_blank(),
axis.title.y = element_blank()
)
pco2_product_monthly %>%
filter(biome %in% key_biomes) %>%
group_split(biome) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(month, value, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2016-1, 2016)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(
data = . %>% filter(between(year, 2016-1, 2016)),
aes(col = as.factor(year)),
linewidth = 1
) +
scale_color_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = paste("Absolute values |", .x$biome)) +
facet_wrap(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
legend.title = element_blank(),
axis.title.y = element_blank()
)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
Super biomes
pco2_product_monthly %>%
filter(biome %in% super_biomes) %>%
ggplot(aes(month, value, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2016-1, 2016)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 2016-1, 2016)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Absolute values | Selected super biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
legend.title = element_blank(),
axis.title.y = element_blank()
)
pco2_product_monthly %>%
filter(biome %in% super_biomes) %>%
group_split(biome) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(month, value, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2016-1, 2016)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(
data = . %>% filter(between(year, 2016-1, 2016)),
aes(col = as.factor(year)),
linewidth = 1
) +
scale_color_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = paste("Absolute values |", .x$biome)) +
facet_wrap(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
legend.title = element_blank(),
axis.title.y = element_blank()
)
)[[1]]
[[2]]
[[3]]
2016 anomalies
Annual mean trends
pco2_product_annual <-
pco2_product_monthly %>%
group_by(year, biome, name) %>%
summarise(value = mean(value)) %>%
ungroup()
pco2_product_annual %>%
filter(biome %in% "Global") %>%
ggplot(aes(year, value)) +
geom_path() +
geom_point(data = . %>% filter(year != 2016)) +
geom_point(data = . %>% filter(year == 2016),
shape = 1) +
geom_smooth(data = . %>% filter(year != 2016,
!(name %in% name_quadratic_fit)),
method = "lm",
fullrange = TRUE,
aes(col = "linear"),
se = FALSE) +
geom_smooth(data = . %>% filter(year != 2016,
name %in% name_quadratic_fit),
method = "lm",
fullrange = TRUE,
formula = y ~ x + I(x^2),
aes(col = "quadratic"),
se = FALSE) +
scale_color_brewer(
palette = "Set1",
name = paste("Regression fit\nexcl.", 2016)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Annual mean trends | Global") +
facet_wrap(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank()
)
pco2_product_annual %>%
filter(biome %in% key_biomes) %>%
ggplot(aes(year, value)) +
geom_path() +
geom_point(data = . %>% filter(year != 2016),
size = 0.2) +
geom_point(data = . %>% filter(year == 2016),
shape = 1) +
geom_smooth(data = . %>% filter(year != 2016,
!(name %in% name_quadratic_fit)),
method = "lm",
fullrange = TRUE,
aes(col = "linear"),
se = FALSE) +
geom_smooth(data = . %>% filter(year != 2016,
name %in% name_quadratic_fit),
method = "lm",
fullrange = TRUE,
formula = y ~ x + I(x^2),
aes(col = "quadratic"),
se = FALSE) +
scale_color_brewer(
palette = "Set1",
name = paste("Regression fit\nexcl.", 2016)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Annual mean trends | Selected biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank()
)
pco2_product_annual %>%
filter(biome %in% super_biomes) %>%
ggplot(aes(year, value)) +
geom_path() +
geom_point(data = . %>% filter(year != 2016),
size = 0.2) +
geom_point(data = . %>% filter(year == 2016),
shape = 1) +
geom_smooth(data = . %>% filter(year != 2016,
!(name %in% name_quadratic_fit)),
method = "lm",
fullrange = TRUE,
aes(col = "linear"),
se = FALSE) +
geom_smooth(data = . %>% filter(year != 2016,
name %in% name_quadratic_fit),
method = "lm",
fullrange = TRUE,
formula = y ~ x + I(x^2),
aes(col = "quadratic"),
se = FALSE) +
scale_color_brewer(
palette = "Set1",
name = paste("Regression fit\nexcl.", 2016)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Annual mean trends | Selected super biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank()
)
pco2_product_annual_regression <-
pco2_product_annual %>%
anomaly_determination(biome)
pco2_product_annual_regression %>%
filter(biome %in% "Global") %>%
ggplot(aes(year, resid)) +
geom_hline(yintercept = 0) +
geom_path() +
geom_point(data = . %>% filter(!between(year, 2016-1, 2016)),
aes(fill = year),
shape = 21) +
scale_fill_grayC() +
new_scale_fill() +
geom_point(data = . %>% filter(between(year, 2016-1, 2016)),
aes(fill = as.factor(year)),
shape = 21, size = 2) +
scale_fill_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Residual from fit to annual means (actual - predicted) | Global") +
facet_wrap(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank()
)
pco2_product_annual_regression %>%
filter(biome %in% key_biomes) %>%
ggplot(aes(year, resid)) +
geom_hline(yintercept = 0) +
geom_path() +
geom_point(data = . %>% filter(!between(year, 2016-1, 2016)),
aes(fill = year),
shape = 21) +
scale_fill_grayC() +
new_scale_fill() +
geom_point(data = . %>% filter(between(year, 2016-1, 2016)),
aes(fill = as.factor(year)),
shape = 21, size = 2) +
scale_fill_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Residual from fit to annual means (actual - predicted) | Selected biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank()
)
pco2_product_annual_regression %>%
filter(biome %in% super_biomes) %>%
ggplot(aes(year, resid)) +
geom_hline(yintercept = 0) +
geom_path() +
geom_point(data = . %>% filter(!between(year, 2016-1, 2016)),
aes(fill = year),
shape = 21) +
scale_fill_grayC() +
new_scale_fill() +
geom_point(data = . %>% filter(between(year, 2016-1, 2016)),
aes(fill = as.factor(year)),
shape = 21, size = 2) +
scale_fill_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Residual from fit to annual means (actual - predicted) | Selected super biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank()
)
pco2_product_annual_regression %>%
filter(biome != "global") %>%
ggplot(aes(biome, resid)) +
geom_hline(yintercept = 0) +
geom_jitter(data = . %>% filter(!between(year, 2016-1, 2016)),
aes(fill = year),
shape = 21, width = 0.2) +
scale_fill_grayC() +
new_scale_fill() +
geom_point(data = . %>% filter(between(year, 2016-1, 2016)),
aes(fill = as.factor(year)),
shape = 21, size = 2) +
scale_fill_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
labs(title = "Residual from fit to annual means (actual - predicted) | All biomes") +
facet_grid(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)
)
pco2_product_annual_regression %>%
write_csv(paste0("../data/","CMEMS","_","2016","_biome_annual_regression.csv"))pco2_product_annual_detrended <-
full_join(pco2_product_monthly,
pco2_product_annual_regression %>% select(-c(value, resid))) %>%
mutate(resid = value - fit)
pco2_product_annual_detrended %>%
filter(biome %in% "Global") %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2016-1, 2016)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 2016-1, 2016)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted annual mean | Global") +
facet_wrap(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
pco2_product_annual_detrended %>%
filter(biome %in% key_biomes) %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2016-1, 2016)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 2016-1, 2016)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted annual mean | Selected biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)
)
pco2_product_annual_detrended %>%
filter(biome %in% super_biomes) %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2016-1, 2016)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 2016-1, 2016)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted annual mean | Selected super biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)
)
pco2_product_annual_detrended %>%
filter(biome %in% key_biomes) %>%
group_split(biome) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2016-1, 2016)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(
data = . %>% filter(between(year, 2016-1, 2016)),
aes(col = as.factor(year)),
linewidth = 1
) +
scale_color_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = paste("Anomalies from predicted annual mean |", .x$biome)) +
facet_wrap(
name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank()
)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
pco2_product_annual_detrended %>%
filter(biome %in% super_biomes) %>%
group_split(biome) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2016-1, 2016)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(
data = . %>% filter(between(year, 2016-1, 2016)),
aes(col = as.factor(year)),
linewidth = 1
) +
scale_color_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = paste("Anomalies from predicted annual mean |", .x$biome)) +
facet_wrap(
name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank()
)
)[[1]]
[[2]]
[[3]]
pco2_product_annual_detrended %>%
write_csv(paste0("../data/","CMEMS","_","2016","_biome_annual_detrended.csv"))Monthly mean trends
pco2_product_monthly %>%
filter(biome %in% "Global") %>%
mutate(month = as.factor(month)) %>%
ggplot(aes(year, value, col = month)) +
# geom_point() +
geom_smooth(data = . %>% filter(year != 2016,
!(name %in% name_quadratic_fit)),
method = "lm",
se = FALSE,
fullrange = TRUE) +
geom_smooth(
data = . %>% filter(year != 2016,
name %in% name_quadratic_fit),
method = "lm",
fullrange = TRUE,
formula = y ~ x + I(x ^ 2),
se = FALSE
) +
scale_color_scico_d(palette = "romaO",
name = paste("Regression fit\nexcl.", 2016)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Monthly mean trends | Global") +
facet_wrap(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank()
)
pco2_product_monthly %>%
filter(biome %in% key_biomes) %>%
mutate(month = as.factor(month)) %>%
ggplot(aes(year, value, col = month)) +
# geom_point() +
geom_smooth(data = . %>% filter(year != 2016,
!(name %in% name_quadratic_fit)),
method = "lm",
fullrange = TRUE,
se = FALSE) +
geom_smooth(
data = . %>% filter(year != 2016,
name %in% name_quadratic_fit),
method = "lm",
fullrange = TRUE,
formula = y ~ x + I(x ^ 2),
se = FALSE
) +
scale_color_scico_d(palette = "romaO",
name = paste("Regression fit\nexcl.", 2016)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Monthly mean trends | Selected biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank()
)
pco2_product_monthly_regression <-
pco2_product_monthly %>%
anomaly_determination(biome, month)
pco2_product_monthly_regression %>%
filter(biome %in% "Global") %>%
group_split(month) %>%
head(1) %>%
map(
~ ggplot(data = .x,
aes(year, resid)) +
geom_hline(yintercept = 0) +
geom_path() +
geom_point(
data = . %>% filter(!between(year, 2016-1, 2016)),
aes(fill = year),
shape = 21
) +
scale_fill_grayC() +
new_scale_fill() +
geom_point(
data = . %>% filter(between(year, 2016-1, 2016)),
aes(fill = as.factor(year)),
shape = 21,
size = 2
) +
scale_fill_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Residual from fit to monthly means (actual - predicted) | Global",
subtitle = paste("Month:", .x$month)) +
facet_wrap(
name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank()
)
)[[1]]
pco2_product_monthly_regression %>%
filter(biome %in% key_biomes) %>%
group_split(month) %>%
head(1) %>%
map(
~ ggplot(data = .x,
aes(year, resid)) +
geom_hline(yintercept = 0) +
geom_path() +
geom_point(
data = . %>% filter(!between(year, 2016-1, 2016)),
aes(fill = year),
shape = 21
) +
scale_fill_grayC() +
new_scale_fill() +
geom_point(
data = . %>% filter(between(year, 2016-1, 2016)),
aes(fill = as.factor(year)),
shape = 21,
size = 2
) +
scale_fill_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Residual from fit to monthly means (actual - predicted) | Selected biomes",
subtitle = paste("Month:", .x$month)) +
facet_grid(
name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y"
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank()
)
)[[1]]
Global
pco2_product_monthly_detrended <-
full_join(pco2_product_monthly,
pco2_product_monthly_regression %>% select(-c(value, resid, time))) %>%
mutate(resid = value - fit)
pco2_product_monthly_detrended %>%
filter(biome %in% "Global") %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2016-1, 2016)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 2016-1, 2016)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted monthly mean | Global") +
facet_wrap(
name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
pco2_product_monthly_detrended %>%
filter(biome %in% key_biomes) %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2016-1, 2016)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 2016-1, 2016)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted monthly mean | Selected biomes") +
facet_grid(
name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y"
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
pco2_product_monthly_detrended %>%
filter(biome %in% super_biomes) %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2016-1, 2016)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 2016-1, 2016)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted monthly mean | Selected super biomes") +
facet_grid(
name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y"
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
Selected biomes
pco2_product_monthly_detrended %>%
filter(biome %in% key_biomes) %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2016-1, 2016)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 2016-1, 2016)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted monthly mean | Selected biomes") +
facet_grid(
name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y"
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
pco2_product_monthly_detrended %>%
filter(biome %in% key_biomes) %>%
group_split(biome) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2016-1, 2016)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(
data = . %>% filter(between(year, 2016-1, 2016)),
aes(col = as.factor(year)),
linewidth = 1
) +
scale_color_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = paste("Anomalies from predicted monthly mean |", .x$biome)) +
facet_wrap(
name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
Super biomes
pco2_product_monthly_detrended %>%
filter(biome %in% super_biomes) %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2016-1, 2016)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 2016-1, 2016)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted monthly mean | Selected super biomes") +
facet_grid(
name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y"
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
pco2_product_monthly_detrended %>%
filter(biome %in% super_biomes) %>%
group_split(biome) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 2016-1, 2016)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(
data = . %>% filter(between(year, 2016-1, 2016)),
aes(col = as.factor(year)),
linewidth = 1
) +
scale_color_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = paste("Anomalies from predicted monthly mean |", .x$biome)) +
facet_wrap(
name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
)[[1]]
[[2]]
[[3]]
2016 anomaly correlation
The following plots aim to unravel the correlation between regionally integrated monthly flux anomalies and the corresponding anomalies of the means/integrals of each other variable.
Anomalies are first presented are first presented in absolute units. Due to the different flux magnitudes, we need to plot integrated fluxes separately for each region. Secondly, we normalize the monthly anomalies to the spread (expressed as standard deviation) of the residuals from the fit.
Annual anomalies
Absolute
pco2_product_annual_regression %>%
filter(biome == "Global") %>%
select(-c(value, fit)) %>%
pivot_wider(values_from = resid) %>%
pivot_longer(-c(year, biome, fgco2_int)) %>%
ggplot(aes(value, fgco2_int)) +
geom_hline(yintercept = 0) +
geom_point(data = . %>% filter(!between(year, 2016-1, 2016)),
aes(fill = year),
shape = 21) +
geom_smooth(
data = . %>% filter(!between(year, 2016-1, 2016)),
method = "lm",
se = FALSE,
fullrange = TRUE,
aes(col = paste("Regression fit\nexcl.", 2016))
) +
scale_color_grey() +
scale_fill_grayC()+
new_scale_fill() +
geom_point(data = . %>% filter(between(year, 2016-1, 2016)),
aes(fill = as.factor(year)),
shape = 21, size = 2) +
scale_fill_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
labs(title = "Globally integrated fluxes",
y = labels_breaks("fgco2_int")$i_legend_title) +
facet_wrap(
~ name,
scales = "free_x",
labeller = labeller(name = x_axis_labels),
strip.position = "bottom",
ncol = 2
) +
theme(
strip.text.x.bottom = element_markdown(),
strip.placement = "outside",
strip.background.x = element_blank(),
axis.title.y = element_markdown(),
axis.title.x = element_blank(),
legend.title = element_blank()
)
Monthly anomalies
Absolute
pco2_product_monthly_detrended_anomaly <-
pco2_product_monthly_detrended %>%
select(year, month, biome, name, resid) %>%
pivot_wider(names_from = name,
values_from = resid)
pco2_product_monthly_detrended_anomaly %>%
filter(biome == "Global") %>%
pivot_longer(-c(year, month, biome, fgco2_int)) %>%
ggplot(aes(value, fgco2_int)) +
geom_hline(yintercept = 0) +
geom_point(data = . %>% filter(year != 2016),
aes(col = paste(min(year), max(year), sep = "-")),
alpha = 0.2) +
geom_smooth(
data = . %>% filter(year != 2016),
aes(col = paste(min(year), max(year), sep = "-")),
method = "lm",
se = FALSE,
fullrange = TRUE
) +
scale_color_grey(name = "") +
new_scale_color() +
geom_path(data = . %>% filter(year == 2016),
aes(col = as.factor(month), group = 1)) +
geom_point(data = . %>% filter(year == 2016),
aes(fill = as.factor(month)),
shape = 21,
size = 3) +
scale_color_scico_d(palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 2016)) +
scale_fill_scico_d(palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 2016)) +
labs(title = "Globally integrated fluxes",
y = labels_breaks("fgco2_int")$i_legend_title) +
facet_wrap(
~ name,
scales = "free_x",
labeller = labeller(name = x_axis_labels),
strip.position = "bottom",
ncol = 2
) +
theme(
strip.text.x.bottom = element_markdown(),
strip.placement = "outside",
strip.background.x = element_blank(),
axis.title.y = element_markdown(),
axis.title.x = element_blank()
)
pco2_product_monthly_detrended_anomaly %>%
filter(!(biome %in% c(super_biomes, "Global"))) %>%
pivot_longer(-c(year, month, biome, fgco2_int)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(value, fgco2_int)) +
geom_hline(yintercept = 0) +
geom_point(
data = . %>% filter(year != 2016),
aes(col = paste(min(year), max(year), sep = "-")),
alpha = 0.2
) +
geom_smooth(
data = . %>% filter(year != 2016),
aes(col = paste(min(year), max(year), sep = "-")),
method = "lm",
se = FALSE,
fullrange = TRUE
) +
scale_color_grey(name = "") +
new_scale_color() +
geom_path(data = . %>% filter(year == 2016),
aes(col = as.factor(month), group = 1)) +
geom_point(
data = . %>% filter(year == 2016),
aes(fill = as.factor(month)),
shape = 21,
size = 3
) +
scale_color_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 2016)
) +
scale_fill_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 2016)
) +
facet_wrap( ~ biome, ncol = 3, scales = "free") +
labs(
title = "Biome integrated fluxes",
y = labels_breaks("fgco2_int")$i_legend_title,
x = labels_breaks(.x %>% distinct(name))$i_legend_title
) +
theme(axis.title.x = element_markdown(),
axis.title.y = element_markdown())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
pco2_product_monthly_detrended_anomaly %>%
filter(biome %in% super_biomes) %>%
pivot_longer(-c(year, month, biome, fgco2_int)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(value, fgco2_int)) +
geom_hline(yintercept = 0) +
geom_point(
data = . %>% filter(year != 2016),
aes(col = paste(min(year), max(year), sep = "-")),
alpha = 0.2
) +
geom_smooth(
data = . %>% filter(year != 2016),
aes(col = paste(min(year), max(year), sep = "-")),
method = "lm",
se = FALSE,
fullrange = TRUE
) +
scale_color_grey(name = "") +
new_scale_color() +
geom_path(data = . %>% filter(year == 2016),
aes(col = as.factor(month), group = 1)) +
geom_point(
data = . %>% filter(year == 2016),
aes(fill = as.factor(month)),
shape = 21,
size = 3
) +
scale_color_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 2016)
) +
scale_fill_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 2016)
) +
facet_wrap( ~ biome, ncol = 3, scales = "free_x") +
labs(
title = "Super biome integrated fluxes",
y = labels_breaks("fgco2_int")$i_legend_title,
x = labels_breaks(.x %>% distinct(name))$i_legend_title
) +
theme(axis.title.x = element_markdown(),
axis.title.y = element_markdown())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
pco2_product_monthly_detrended_anomaly %>%
pivot_longer(-c(month, biome, year)) %>%
write_csv(paste0(
"../data/",
"CMEMS","_","2016",
"_biome_monthly_detrended_anomaly.csv"
))Relative to spread
pco2_product_monthly_detrended_anomaly_spread <-
pco2_product_monthly_detrended_anomaly %>%
pivot_longer(-c(month, biome, year)) %>%
filter(year != 2016) %>%
group_by(month, biome, name) %>%
summarise(spread = sd(value, na.rm = TRUE)) %>%
ungroup()
pco2_product_monthly_detrended_anomaly_relative <-
full_join(
pco2_product_monthly_detrended_anomaly_spread,
pco2_product_monthly_detrended_anomaly %>%
pivot_longer(-c(month, biome, year))
)
pco2_product_monthly_detrended_anomaly_relative <-
pco2_product_monthly_detrended_anomaly_relative %>%
mutate(value = value / spread) %>%
select(-spread) %>%
pivot_wider() %>%
pivot_longer(-c(month, biome, year, fgco2_int))
pco2_product_monthly_detrended_anomaly_relative %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(value, fgco2_int)) +
geom_vline(xintercept = 0) +
geom_hline(yintercept = 0) +
geom_point(
data = . %>% filter(year != 2016),
aes(col = paste(min(year), max(year), sep = "-")),
alpha = 0.2
) +
geom_smooth(
data = . %>% filter(year != 2016),
aes(col = paste(min(year), max(year), sep = "-")),
method = "lm",
se = FALSE,
fullrange = TRUE
) +
scale_color_grey(name = "") +
new_scale_color() +
geom_path(data = . %>% filter(year == 2016),
aes(col = as.factor(month), group = 1)) +
geom_point(
data = . %>% filter(year == 2016),
aes(fill = as.factor(month)),
shape = 21,
size = 3
) +
scale_color_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 2016)
) +
scale_fill_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 2016)
) +
facet_wrap( ~ biome, ncol = 3) +
coord_fixed() +
labs(
title = "Biome integrated fluxes normalized to spread",
y = str_split_i(labels_breaks("fgco2_int")$i_legend_title, "<br>", i = 1),
x = str_split_i(labels_breaks(.x %>% distinct(name))$i_legend_title, "<br>", i = 1)
) +
theme(axis.title.x = element_markdown(),
axis.title.y = element_markdown())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
pCO2productanalysis_1998 <-
knitr::knit_expand(
file = here::here("analysis/child/pCO2_product_analysis.Rmd"),
product_name = "CMEMS",
year_anom = 1998
)1998 anomalies
Detection
For the detection of anomalies at any point in time and space, we fit regression models and compare the fitted to the actual value.
We use linear regression models for all parameters, except for , which are approximated with quadratic fits.
The regression models are fitted to all data since , except 1998.
anomaly_determination <- function(df,...) {
group_by <- quos(...)
# group_by <- quos(lon, lat)
# df <- pco2_product_coarse_annual
# Linear regression models
df_lm <-
df %>%
filter(year != 1998,
!(name %in% name_quadratic_fit)) %>%
nest(data = -c(name, !!!group_by)) %>%
mutate(
fit = map(data, ~ lm(value ~ year, data = .x)),
tidied = map(fit, tidy),
augmented = map(fit, augment)
)
df_lm_year_anom <-
full_join(
df_lm %>%
unnest(tidied) %>%
select(name, !!!group_by, term, estimate) %>%
pivot_wider(names_from = term,
values_from = estimate) %>%
mutate(fit = `(Intercept)` + year * 1998) %>%
select(name, !!!group_by, fit) %>%
mutate(year = 1998),
df %>%
filter(year == 1998,
!(name %in% name_quadratic_fit))
) %>%
mutate(resid = value - fit)
df_lm <-
bind_rows(
df_lm %>%
unnest(augmented) %>%
select(name, !!!group_by, year, value, fit = .fitted, resid = .resid),
df_lm_year_anom
)
rm(df_lm_year_anom)
# Quadratic regression models
df_quadratic <-
df %>%
filter(year != 1998,
name %in% name_quadratic_fit) %>%
nest(data = -c(name, !!!group_by)) %>%
mutate(
fit = map(data, ~ lm(value ~ year + I(year ^ 2), data = .x)),
tidied = map(fit, tidy),
augmented = map(fit, augment)
)
df_quadratic_year_anom <-
full_join(
df_quadratic %>%
unnest(tidied) %>%
select(name, !!!group_by, term, estimate) %>%
pivot_wider(names_from = term,
values_from = estimate) %>%
mutate(fit = `(Intercept)` + year * 1998 + `I(year^2)` * 1998 ^ 2) %>%
select(name, !!!group_by, fit) %>%
mutate(year = 1998),
df %>%
filter(year == 1998,
name %in% name_quadratic_fit)
) %>%
mutate(resid = value - fit)
df_quadratic <-
bind_rows(
df_quadratic %>%
unnest(augmented) %>%
select(name, !!!group_by, year, value, fit = .fitted, resid = .resid),
df_quadratic_year_anom
)
rm(df_quadratic_year_anom)
# Join linear and quadratic regression results
df_regression <-
bind_rows(df_lm,
df_quadratic)
df_regression <-
df_regression %>%
arrange(year)
rm(df_lm,
df_quadratic)
return(df_regression)
}Maps
The following maps show the absolute state of each variable in 1998 as provided through the pCO2 product, the change in that variable from 1990 to 1998, as well es the anomalies in 1998. Changes and anomalies are determined based on the predicted value of a linear regression model fit to the data from 1990 to 1997.
Maps are first presented as annual means, and than as monthly means. Note that the 1998 predictions for the monthly maps are done individually for each month, such the mean seasonal anomaly from the annual mean is removed.
Note: The increase the computational speed, I regridded all maps to 5X5° grid.
Annual means
1998 absolute
pco2_product_coarse_annual_regression <-
pco2_product_coarse_annual %>%
drop_na() %>%
anomaly_determination(lon, lat)
pco2_product_coarse_annual_regression <-
pco2_product_coarse_annual_regression %>%
drop_na()
pco2_product_coarse_annual_regression %>%
filter(year == 1998,
!(name %in% name_divergent)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ map +
geom_tile(data = .x,
aes(lon, lat, fill = value)) +
labs(title = paste("Annual mean", 1998)) +
scale_fill_viridis_c(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
pco2_product_coarse_annual_regression %>%
filter(year == 1998,
name %in% name_divergent) %>%
group_split(name) %>%
# head(1) %>%
map( ~ map +
geom_tile(data = .x,
aes(lon, lat, fill = value)) +
labs(title = paste("Annual mean", 1998)) +
scale_fill_divergent(
name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown())
)[[1]]
Trends
pco2_product_coarse_annual_regression <-
pco2_product_coarse_annual_regression %>%
group_by(name) %>%
filter(year %in% c(min(year), max(year), 1998)) %>%
ungroup()
pco2_product_coarse_annual_regression %>%
select(-c(value, resid)) %>%
filter(year %in% c(min(year), max(year))) %>%
arrange(year) %>%
group_by(lon, lat, name) %>%
mutate(change = fit - lag(fit),
period = paste(lag(year), year, sep = "-")) %>%
ungroup() %>%
filter(!is.na(change)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ map +
geom_tile(data = .x,
aes(lon, lat, fill = change)) +
labs(title = paste("Change: ",.x$period)) +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
1998 anomaly
pco2_product_coarse_annual_regression %>%
filter(year == 1998) %>%
group_split(name) %>%
# head(1) %>%
map( ~ map +
geom_tile(data = .x,
aes(lon, lat, fill = resid)) +
labs(title = paste(1998,"anomaly")) +
scale_fill_divergent(
name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
pco2_product_coarse_annual_regression %>%
write_csv(paste0("../data/","CMEMS","_","1998","_anomaly_map_annual.csv"))Monthly means
1998 absolute
pco2_product_coarse_monthly_regression <-
pco2_product_coarse_monthly %>%
drop_na() %>%
anomaly_determination(lon, lat, month)
pco2_product_coarse_monthly_regression <-
pco2_product_coarse_monthly_regression %>%
drop_na()
pco2_product_coarse_monthly_regression %>%
filter(year == 1998,
!(name %in% name_divergent)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ map +
geom_tile(data = .x,
aes(lon, lat, fill = value)) +
labs(title = paste("Monthly means", 1998)) +
scale_fill_viridis_c(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
facet_wrap( ~ month, ncol = 2)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
pco2_product_coarse_monthly_regression %>%
filter(year == 1998,
name %in% name_divergent) %>%
group_split(name) %>%
# head(1) %>%
map(
~ map +
geom_tile(data = .x,
aes(lon, lat, fill = value)) +
labs(title = paste("Monthly means", 1998)) +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
facet_wrap( ~ month, ncol = 2)
)[[1]]
Trends
pco2_product_coarse_monthly_regression %>%
group_by(name) %>%
filter(year %in% c(min(year), max(year))) %>%
ungroup() %>%
select(-c(value, resid)) %>%
arrange(year) %>%
group_by(lon, lat, name, month) %>%
mutate(change = fit - lag(fit),
period = paste(lag(year), year, sep = "-")) %>%
ungroup() %>%
filter(!is.na(change)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ map +
geom_tile(data = .x,
aes(lon, lat, fill = change)) +
labs(title = paste("Change: ", .x$period)) +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
facet_wrap(~ month, ncol = 2)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
1998 anomaly
pco2_product_coarse_monthly_regression %>%
filter(year == 1998) %>%
group_split(name) %>%
# head(1) %>%
map(
~ map +
geom_tile(data = .x,
aes(lon, lat, fill = resid)) +
labs(title = paste(1998, "anomaly")) +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
facet_wrap( ~ month, ncol = 2)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
pco2_product_coarse_monthly_regression %>%
filter(year == 1998) %>%
write_csv(paste0("../data/","CMEMS","_","1998","_anomaly_map_monthly.csv"))Hovmoeller plots
The following Hovmoeller plots show the anomalies from the prediction of the linear/quadratic fits.
Hovmoeller plots are first presented as annual means, and than as monthly means. Note that the predictions for the monthly Hovmoeller plots are done individually for each month, such the mean seasonal anomaly from the annual mean is removed.
1998 annual anomalies
pco2_product_hovmoeller_monthly_annual_regression <-
pco2_product_hovmoeller_monthly_annual %>%
anomaly_determination(lat) %>%
filter(!is.na(resid))
pco2_product_hovmoeller_monthly_annual_regression %>%
# filter(name == "mld") %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(year, lat, fill = resid)) +
geom_raster() +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
coord_cartesian(expand = 0) +
labs(title = "Annual mean anomalies",
y = "Latitude") +
theme(axis.title.x = element_blank())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
1998 monthly anomalies
pco2_product_hovmoeller_monthly_regression <-
pco2_product_hovmoeller_monthly %>%
select(-c(decimal)) %>%
anomaly_determination(lat, month) %>%
filter(!is.na(resid))
pco2_product_hovmoeller_monthly_regression <-
pco2_product_hovmoeller_monthly_regression %>%
mutate(decimal = year + (month - 1) / 12)
pco2_product_hovmoeller_monthly_regression %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(decimal, lat, fill = resid)) +
geom_raster() +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
coord_cartesian(expand = 0) +
labs(title = "Monthly mean anomalies",
y = "Latitude") +
theme(axis.title.x = element_blank())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
pco2_product_hovmoeller_monthly_regression %>%
write_csv(paste0("../data/","CMEMS","_","1998","_anomaly_hovmoeller_monthly.csv"))Three years prior 1998
pco2_product_hovmoeller_monthly_regression %>%
filter(between(year, 1998-2, 1998)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(decimal, lat, fill = resid)) +
geom_raster() +
scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
theme(legend.title = element_markdown()) +
coord_cartesian(expand = 0) +
labs(title = "Monthly mean anomalies",
y = "Latitude") +
theme(axis.title.x = element_blank())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
Regional means and integrals
The following plots show regionally averaged (or integrated) values of each variable as provided through the pCO2 product, as well as the anomalies from the prediction of a linear/quadratic fit.
Anomalies are first presented relative to the predicted annual mean of each year, hence preserving the seasonality. Furthermore, anomalies are presented relative to the predicted monthly mean values, such that the mean seasonality is removed.
1998 absolute values
Global
fig.height <- pco2_product_monthly %>%
distinct(name) %>%
nrow()
fig.height <- (fig.height + 2) * 0.1pco2_product_monthly %>%
filter(biome %in% "Global") %>%
ggplot(aes(month, value, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 1998-1, 1998)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 1998-1, 1998)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Absolute values | Global") +
facet_wrap(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
legend.title = element_blank(),
axis.title.y = element_blank()
)
Selected biomes
pco2_product_monthly %>%
filter(biome %in% key_biomes) %>%
ggplot(aes(month, value, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 1998-1, 1998)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 1998-1, 1998)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Absolute values | Selected biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
legend.title = element_blank(),
axis.title.y = element_blank()
)
pco2_product_monthly %>%
filter(biome %in% key_biomes) %>%
group_split(biome) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(month, value, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 1998-1, 1998)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(
data = . %>% filter(between(year, 1998-1, 1998)),
aes(col = as.factor(year)),
linewidth = 1
) +
scale_color_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = paste("Absolute values |", .x$biome)) +
facet_wrap(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
legend.title = element_blank(),
axis.title.y = element_blank()
)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
Super biomes
pco2_product_monthly %>%
filter(biome %in% super_biomes) %>%
ggplot(aes(month, value, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 1998-1, 1998)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 1998-1, 1998)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Absolute values | Selected super biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
legend.title = element_blank(),
axis.title.y = element_blank()
)
pco2_product_monthly %>%
filter(biome %in% super_biomes) %>%
group_split(biome) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(month, value, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 1998-1, 1998)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(
data = . %>% filter(between(year, 1998-1, 1998)),
aes(col = as.factor(year)),
linewidth = 1
) +
scale_color_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = paste("Absolute values |", .x$biome)) +
facet_wrap(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
legend.title = element_blank(),
axis.title.y = element_blank()
)
)[[1]]
[[2]]
[[3]]
1998 anomalies
Annual mean trends
pco2_product_annual <-
pco2_product_monthly %>%
group_by(year, biome, name) %>%
summarise(value = mean(value)) %>%
ungroup()
pco2_product_annual %>%
filter(biome %in% "Global") %>%
ggplot(aes(year, value)) +
geom_path() +
geom_point(data = . %>% filter(year != 1998)) +
geom_point(data = . %>% filter(year == 1998),
shape = 1) +
geom_smooth(data = . %>% filter(year != 1998,
!(name %in% name_quadratic_fit)),
method = "lm",
fullrange = TRUE,
aes(col = "linear"),
se = FALSE) +
geom_smooth(data = . %>% filter(year != 1998,
name %in% name_quadratic_fit),
method = "lm",
fullrange = TRUE,
formula = y ~ x + I(x^2),
aes(col = "quadratic"),
se = FALSE) +
scale_color_brewer(
palette = "Set1",
name = paste("Regression fit\nexcl.", 1998)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Annual mean trends | Global") +
facet_wrap(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank()
)
pco2_product_annual %>%
filter(biome %in% key_biomes) %>%
ggplot(aes(year, value)) +
geom_path() +
geom_point(data = . %>% filter(year != 1998),
size = 0.2) +
geom_point(data = . %>% filter(year == 1998),
shape = 1) +
geom_smooth(data = . %>% filter(year != 1998,
!(name %in% name_quadratic_fit)),
method = "lm",
fullrange = TRUE,
aes(col = "linear"),
se = FALSE) +
geom_smooth(data = . %>% filter(year != 1998,
name %in% name_quadratic_fit),
method = "lm",
fullrange = TRUE,
formula = y ~ x + I(x^2),
aes(col = "quadratic"),
se = FALSE) +
scale_color_brewer(
palette = "Set1",
name = paste("Regression fit\nexcl.", 1998)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Annual mean trends | Selected biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank()
)
pco2_product_annual %>%
filter(biome %in% super_biomes) %>%
ggplot(aes(year, value)) +
geom_path() +
geom_point(data = . %>% filter(year != 1998),
size = 0.2) +
geom_point(data = . %>% filter(year == 1998),
shape = 1) +
geom_smooth(data = . %>% filter(year != 1998,
!(name %in% name_quadratic_fit)),
method = "lm",
fullrange = TRUE,
aes(col = "linear"),
se = FALSE) +
geom_smooth(data = . %>% filter(year != 1998,
name %in% name_quadratic_fit),
method = "lm",
fullrange = TRUE,
formula = y ~ x + I(x^2),
aes(col = "quadratic"),
se = FALSE) +
scale_color_brewer(
palette = "Set1",
name = paste("Regression fit\nexcl.", 1998)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Annual mean trends | Selected super biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank()
)
pco2_product_annual_regression <-
pco2_product_annual %>%
anomaly_determination(biome)
pco2_product_annual_regression %>%
filter(biome %in% "Global") %>%
ggplot(aes(year, resid)) +
geom_hline(yintercept = 0) +
geom_path() +
geom_point(data = . %>% filter(!between(year, 1998-1, 1998)),
aes(fill = year),
shape = 21) +
scale_fill_grayC() +
new_scale_fill() +
geom_point(data = . %>% filter(between(year, 1998-1, 1998)),
aes(fill = as.factor(year)),
shape = 21, size = 2) +
scale_fill_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Residual from fit to annual means (actual - predicted) | Global") +
facet_wrap(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank()
)
pco2_product_annual_regression %>%
filter(biome %in% key_biomes) %>%
ggplot(aes(year, resid)) +
geom_hline(yintercept = 0) +
geom_path() +
geom_point(data = . %>% filter(!between(year, 1998-1, 1998)),
aes(fill = year),
shape = 21) +
scale_fill_grayC() +
new_scale_fill() +
geom_point(data = . %>% filter(between(year, 1998-1, 1998)),
aes(fill = as.factor(year)),
shape = 21, size = 2) +
scale_fill_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Residual from fit to annual means (actual - predicted) | Selected biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank()
)
pco2_product_annual_regression %>%
filter(biome %in% super_biomes) %>%
ggplot(aes(year, resid)) +
geom_hline(yintercept = 0) +
geom_path() +
geom_point(data = . %>% filter(!between(year, 1998-1, 1998)),
aes(fill = year),
shape = 21) +
scale_fill_grayC() +
new_scale_fill() +
geom_point(data = . %>% filter(between(year, 1998-1, 1998)),
aes(fill = as.factor(year)),
shape = 21, size = 2) +
scale_fill_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Residual from fit to annual means (actual - predicted) | Selected super biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank()
)
pco2_product_annual_regression %>%
filter(biome != "global") %>%
ggplot(aes(biome, resid)) +
geom_hline(yintercept = 0) +
geom_jitter(data = . %>% filter(!between(year, 1998-1, 1998)),
aes(fill = year),
shape = 21, width = 0.2) +
scale_fill_grayC() +
new_scale_fill() +
geom_point(data = . %>% filter(between(year, 1998-1, 1998)),
aes(fill = as.factor(year)),
shape = 21, size = 2) +
scale_fill_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
labs(title = "Residual from fit to annual means (actual - predicted) | All biomes") +
facet_grid(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)
)
pco2_product_annual_regression %>%
write_csv(paste0("../data/","CMEMS","_","1998","_biome_annual_regression.csv"))pco2_product_annual_detrended <-
full_join(pco2_product_monthly,
pco2_product_annual_regression %>% select(-c(value, resid))) %>%
mutate(resid = value - fit)
pco2_product_annual_detrended %>%
filter(biome %in% "Global") %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 1998-1, 1998)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 1998-1, 1998)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted annual mean | Global") +
facet_wrap(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
pco2_product_annual_detrended %>%
filter(biome %in% key_biomes) %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 1998-1, 1998)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 1998-1, 1998)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted annual mean | Selected biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)
)
pco2_product_annual_detrended %>%
filter(biome %in% super_biomes) %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 1998-1, 1998)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 1998-1, 1998)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted annual mean | Selected super biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)
)
pco2_product_annual_detrended %>%
filter(biome %in% key_biomes) %>%
group_split(biome) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 1998-1, 1998)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(
data = . %>% filter(between(year, 1998-1, 1998)),
aes(col = as.factor(year)),
linewidth = 1
) +
scale_color_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = paste("Anomalies from predicted annual mean |", .x$biome)) +
facet_wrap(
name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank()
)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
pco2_product_annual_detrended %>%
filter(biome %in% super_biomes) %>%
group_split(biome) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 1998-1, 1998)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(
data = . %>% filter(between(year, 1998-1, 1998)),
aes(col = as.factor(year)),
linewidth = 1
) +
scale_color_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = paste("Anomalies from predicted annual mean |", .x$biome)) +
facet_wrap(
name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank()
)
)[[1]]
[[2]]
[[3]]
pco2_product_annual_detrended %>%
write_csv(paste0("../data/","CMEMS","_","1998","_biome_annual_detrended.csv"))Monthly mean trends
pco2_product_monthly %>%
filter(biome %in% "Global") %>%
mutate(month = as.factor(month)) %>%
ggplot(aes(year, value, col = month)) +
# geom_point() +
geom_smooth(data = . %>% filter(year != 1998,
!(name %in% name_quadratic_fit)),
method = "lm",
se = FALSE,
fullrange = TRUE) +
geom_smooth(
data = . %>% filter(year != 1998,
name %in% name_quadratic_fit),
method = "lm",
fullrange = TRUE,
formula = y ~ x + I(x ^ 2),
se = FALSE
) +
scale_color_scico_d(palette = "romaO",
name = paste("Regression fit\nexcl.", 1998)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Monthly mean trends | Global") +
facet_wrap(name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank()
)
pco2_product_monthly %>%
filter(biome %in% key_biomes) %>%
mutate(month = as.factor(month)) %>%
ggplot(aes(year, value, col = month)) +
# geom_point() +
geom_smooth(data = . %>% filter(year != 1998,
!(name %in% name_quadratic_fit)),
method = "lm",
fullrange = TRUE,
se = FALSE) +
geom_smooth(
data = . %>% filter(year != 1998,
name %in% name_quadratic_fit),
method = "lm",
fullrange = TRUE,
formula = y ~ x + I(x ^ 2),
se = FALSE
) +
scale_color_scico_d(palette = "romaO",
name = paste("Regression fit\nexcl.", 1998)) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Monthly mean trends | Selected biomes") +
facet_grid(name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y") +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank()
)
pco2_product_monthly_regression <-
pco2_product_monthly %>%
anomaly_determination(biome, month)
pco2_product_monthly_regression %>%
filter(biome %in% "Global") %>%
group_split(month) %>%
head(1) %>%
map(
~ ggplot(data = .x,
aes(year, resid)) +
geom_hline(yintercept = 0) +
geom_path() +
geom_point(
data = . %>% filter(!between(year, 1998-1, 1998)),
aes(fill = year),
shape = 21
) +
scale_fill_grayC() +
new_scale_fill() +
geom_point(
data = . %>% filter(between(year, 1998-1, 1998)),
aes(fill = as.factor(year)),
shape = 21,
size = 2
) +
scale_fill_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Residual from fit to monthly means (actual - predicted) | Global",
subtitle = paste("Month:", .x$month)) +
facet_wrap(
name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank()
)
)[[1]]
pco2_product_monthly_regression %>%
filter(biome %in% key_biomes) %>%
group_split(month) %>%
head(1) %>%
map(
~ ggplot(data = .x,
aes(year, resid)) +
geom_hline(yintercept = 0) +
geom_path() +
geom_point(
data = . %>% filter(!between(year, 1998-1, 1998)),
aes(fill = year),
shape = 21
) +
scale_fill_grayC() +
new_scale_fill() +
geom_point(
data = . %>% filter(between(year, 1998-1, 1998)),
aes(fill = as.factor(year)),
shape = 21,
size = 2
) +
scale_fill_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1980, 2020, 20)) +
labs(title = "Residual from fit to monthly means (actual - predicted) | Selected biomes",
subtitle = paste("Month:", .x$month)) +
facet_grid(
name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y"
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title = element_blank(),
legend.title = element_blank()
)
)[[1]]
Global
pco2_product_monthly_detrended <-
full_join(pco2_product_monthly,
pco2_product_monthly_regression %>% select(-c(value, resid, time))) %>%
mutate(resid = value - fit)
pco2_product_monthly_detrended %>%
filter(biome %in% "Global") %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 1998-1, 1998)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 1998-1, 1998)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted monthly mean | Global") +
facet_wrap(
name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
pco2_product_monthly_detrended %>%
filter(biome %in% key_biomes) %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 1998-1, 1998)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 1998-1, 1998)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted monthly mean | Selected biomes") +
facet_grid(
name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y"
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
pco2_product_monthly_detrended %>%
filter(biome %in% super_biomes) %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 1998-1, 1998)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 1998-1, 1998)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted monthly mean | Selected super biomes") +
facet_grid(
name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y"
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
Selected biomes
pco2_product_monthly_detrended %>%
filter(biome %in% key_biomes) %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 1998-1, 1998)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 1998-1, 1998)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted monthly mean | Selected biomes") +
facet_grid(
name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y"
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
pco2_product_monthly_detrended %>%
filter(biome %in% key_biomes) %>%
group_split(biome) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 1998-1, 1998)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(
data = . %>% filter(between(year, 1998-1, 1998)),
aes(col = as.factor(year)),
linewidth = 1
) +
scale_color_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = paste("Anomalies from predicted monthly mean |", .x$biome)) +
facet_wrap(
name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
Super biomes
pco2_product_monthly_detrended %>%
filter(biome %in% super_biomes) %>%
ggplot(aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 1998-1, 1998)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(data = . %>% filter(between(year, 1998-1, 1998)),
aes(col = as.factor(year)),
linewidth = 1) +
scale_color_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = "Anomalies from predicted monthly mean | Selected super biomes") +
facet_grid(
name ~ biome,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
switch = "y"
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
pco2_product_monthly_detrended %>%
filter(biome %in% super_biomes) %>%
group_split(biome) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(month, resid, group = as.factor(year))) +
geom_path(data = . %>% filter(!between(year, 1998-1, 1998)),
aes(col = year)) +
scale_color_grayC() +
new_scale_color() +
geom_path(
data = . %>% filter(between(year, 1998-1, 1998)),
aes(col = as.factor(year)),
linewidth = 1
) +
scale_color_manual(
values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)
) +
scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
labs(title = paste("Anomalies from predicted monthly mean |", .x$biome)) +
facet_wrap(
name ~ .,
scales = "free_y",
labeller = labeller(name = x_axis_labels),
strip.position = "left",
ncol = 2
) +
theme(
strip.text.y.left = element_markdown(),
strip.placement = "outside",
strip.background.y = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank()
)
)[[1]]
[[2]]
[[3]]
1998 anomaly correlation
The following plots aim to unravel the correlation between regionally integrated monthly flux anomalies and the corresponding anomalies of the means/integrals of each other variable.
Anomalies are first presented are first presented in absolute units. Due to the different flux magnitudes, we need to plot integrated fluxes separately for each region. Secondly, we normalize the monthly anomalies to the spread (expressed as standard deviation) of the residuals from the fit.
Annual anomalies
Absolute
pco2_product_annual_regression %>%
filter(biome == "Global") %>%
select(-c(value, fit)) %>%
pivot_wider(values_from = resid) %>%
pivot_longer(-c(year, biome, fgco2_int)) %>%
ggplot(aes(value, fgco2_int)) +
geom_hline(yintercept = 0) +
geom_point(data = . %>% filter(!between(year, 1998-1, 1998)),
aes(fill = year),
shape = 21) +
geom_smooth(
data = . %>% filter(!between(year, 1998-1, 1998)),
method = "lm",
se = FALSE,
fullrange = TRUE,
aes(col = paste("Regression fit\nexcl.", 1998))
) +
scale_color_grey() +
scale_fill_grayC()+
new_scale_fill() +
geom_point(data = . %>% filter(between(year, 1998-1, 1998)),
aes(fill = as.factor(year)),
shape = 21, size = 2) +
scale_fill_manual(values = c("orange", "red"),
guide = guide_legend(reverse = TRUE,
order = 1)) +
labs(title = "Globally integrated fluxes",
y = labels_breaks("fgco2_int")$i_legend_title) +
facet_wrap(
~ name,
scales = "free_x",
labeller = labeller(name = x_axis_labels),
strip.position = "bottom",
ncol = 2
) +
theme(
strip.text.x.bottom = element_markdown(),
strip.placement = "outside",
strip.background.x = element_blank(),
axis.title.y = element_markdown(),
axis.title.x = element_blank(),
legend.title = element_blank()
)
Monthly anomalies
Absolute
pco2_product_monthly_detrended_anomaly <-
pco2_product_monthly_detrended %>%
select(year, month, biome, name, resid) %>%
pivot_wider(names_from = name,
values_from = resid)
pco2_product_monthly_detrended_anomaly %>%
filter(biome == "Global") %>%
pivot_longer(-c(year, month, biome, fgco2_int)) %>%
ggplot(aes(value, fgco2_int)) +
geom_hline(yintercept = 0) +
geom_point(data = . %>% filter(year != 1998),
aes(col = paste(min(year), max(year), sep = "-")),
alpha = 0.2) +
geom_smooth(
data = . %>% filter(year != 1998),
aes(col = paste(min(year), max(year), sep = "-")),
method = "lm",
se = FALSE,
fullrange = TRUE
) +
scale_color_grey(name = "") +
new_scale_color() +
geom_path(data = . %>% filter(year == 1998),
aes(col = as.factor(month), group = 1)) +
geom_point(data = . %>% filter(year == 1998),
aes(fill = as.factor(month)),
shape = 21,
size = 3) +
scale_color_scico_d(palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 1998)) +
scale_fill_scico_d(palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 1998)) +
labs(title = "Globally integrated fluxes",
y = labels_breaks("fgco2_int")$i_legend_title) +
facet_wrap(
~ name,
scales = "free_x",
labeller = labeller(name = x_axis_labels),
strip.position = "bottom",
ncol = 2
) +
theme(
strip.text.x.bottom = element_markdown(),
strip.placement = "outside",
strip.background.x = element_blank(),
axis.title.y = element_markdown(),
axis.title.x = element_blank()
)
pco2_product_monthly_detrended_anomaly %>%
filter(!(biome %in% c(super_biomes, "Global"))) %>%
pivot_longer(-c(year, month, biome, fgco2_int)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(value, fgco2_int)) +
geom_hline(yintercept = 0) +
geom_point(
data = . %>% filter(year != 1998),
aes(col = paste(min(year), max(year), sep = "-")),
alpha = 0.2
) +
geom_smooth(
data = . %>% filter(year != 1998),
aes(col = paste(min(year), max(year), sep = "-")),
method = "lm",
se = FALSE,
fullrange = TRUE
) +
scale_color_grey(name = "") +
new_scale_color() +
geom_path(data = . %>% filter(year == 1998),
aes(col = as.factor(month), group = 1)) +
geom_point(
data = . %>% filter(year == 1998),
aes(fill = as.factor(month)),
shape = 21,
size = 3
) +
scale_color_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 1998)
) +
scale_fill_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 1998)
) +
facet_wrap( ~ biome, ncol = 3, scales = "free") +
labs(
title = "Biome integrated fluxes",
y = labels_breaks("fgco2_int")$i_legend_title,
x = labels_breaks(.x %>% distinct(name))$i_legend_title
) +
theme(axis.title.x = element_markdown(),
axis.title.y = element_markdown())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
pco2_product_monthly_detrended_anomaly %>%
filter(biome %in% super_biomes) %>%
pivot_longer(-c(year, month, biome, fgco2_int)) %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(value, fgco2_int)) +
geom_hline(yintercept = 0) +
geom_point(
data = . %>% filter(year != 1998),
aes(col = paste(min(year), max(year), sep = "-")),
alpha = 0.2
) +
geom_smooth(
data = . %>% filter(year != 1998),
aes(col = paste(min(year), max(year), sep = "-")),
method = "lm",
se = FALSE,
fullrange = TRUE
) +
scale_color_grey(name = "") +
new_scale_color() +
geom_path(data = . %>% filter(year == 1998),
aes(col = as.factor(month), group = 1)) +
geom_point(
data = . %>% filter(year == 1998),
aes(fill = as.factor(month)),
shape = 21,
size = 3
) +
scale_color_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 1998)
) +
scale_fill_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 1998)
) +
facet_wrap( ~ biome, ncol = 3, scales = "free_x") +
labs(
title = "Super biome integrated fluxes",
y = labels_breaks("fgco2_int")$i_legend_title,
x = labels_breaks(.x %>% distinct(name))$i_legend_title
) +
theme(axis.title.x = element_markdown(),
axis.title.y = element_markdown())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
pco2_product_monthly_detrended_anomaly %>%
pivot_longer(-c(month, biome, year)) %>%
write_csv(paste0(
"../data/",
"CMEMS","_","1998",
"_biome_monthly_detrended_anomaly.csv"
))Relative to spread
pco2_product_monthly_detrended_anomaly_spread <-
pco2_product_monthly_detrended_anomaly %>%
pivot_longer(-c(month, biome, year)) %>%
filter(year != 1998) %>%
group_by(month, biome, name) %>%
summarise(spread = sd(value, na.rm = TRUE)) %>%
ungroup()
pco2_product_monthly_detrended_anomaly_relative <-
full_join(
pco2_product_monthly_detrended_anomaly_spread,
pco2_product_monthly_detrended_anomaly %>%
pivot_longer(-c(month, biome, year))
)
pco2_product_monthly_detrended_anomaly_relative <-
pco2_product_monthly_detrended_anomaly_relative %>%
mutate(value = value / spread) %>%
select(-spread) %>%
pivot_wider() %>%
pivot_longer(-c(month, biome, year, fgco2_int))
pco2_product_monthly_detrended_anomaly_relative %>%
group_split(name) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(value, fgco2_int)) +
geom_vline(xintercept = 0) +
geom_hline(yintercept = 0) +
geom_point(
data = . %>% filter(year != 1998),
aes(col = paste(min(year), max(year), sep = "-")),
alpha = 0.2
) +
geom_smooth(
data = . %>% filter(year != 1998),
aes(col = paste(min(year), max(year), sep = "-")),
method = "lm",
se = FALSE,
fullrange = TRUE
) +
scale_color_grey(name = "") +
new_scale_color() +
geom_path(data = . %>% filter(year == 1998),
aes(col = as.factor(month), group = 1)) +
geom_point(
data = . %>% filter(year == 1998),
aes(fill = as.factor(month)),
shape = 21,
size = 3
) +
scale_color_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 1998)
) +
scale_fill_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 1998)
) +
facet_wrap( ~ biome, ncol = 3) +
coord_fixed() +
labs(
title = "Biome integrated fluxes normalized to spread",
y = str_split_i(labels_breaks("fgco2_int")$i_legend_title, "<br>", i = 1),
x = str_split_i(labels_breaks(.x %>% distinct(name))$i_legend_title, "<br>", i = 1)
) +
theme(axis.title.x = element_markdown(),
axis.title.y = element_markdown())
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
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] ggtext_0.1.2 broom_1.0.5 khroma_1.9.0
[4] ggnewscale_0.4.8 ncdf4_1.19 lubridate_1.9.0
[7] timechange_0.1.1 stars_0.6-0 abind_1.4-5
[10] terra_1.7-65 sf_1.0-9 rnaturalearth_0.1.0
[13] geomtextpath_0.1.1 colorspace_2.0-3 marelac_2.1.10
[16] shape_1.4.6 ggforce_0.4.1 metR_0.13.0
[19] scico_1.3.1 patchwork_1.1.2 collapse_1.8.9
[22] forcats_0.5.2 stringr_1.5.0 dplyr_1.1.3
[25] purrr_1.0.2 readr_2.1.3 tidyr_1.3.0
[28] tibble_3.2.1 ggplot2_3.4.4 tidyverse_1.3.2
[31] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] readxl_1.4.1 backports_1.4.1 systemfonts_1.0.4
[4] lwgeom_0.2-10 sp_1.5-1 splines_4.2.2
[7] digest_0.6.30 htmltools_0.5.3 ncmeta_0.3.5
[10] fansi_1.0.3 magrittr_2.0.3 checkmate_2.1.0
[13] memoise_2.0.1 googlesheets4_1.0.1 tzdb_0.3.0
[16] modelr_0.1.10 vroom_1.6.0 rvest_1.0.3
[19] textshaping_0.3.6 haven_2.5.1 xfun_0.35
[22] callr_3.7.3 crayon_1.5.2 jsonlite_1.8.3
[25] glue_1.6.2 polyclip_1.10-4 gtable_0.3.1
[28] gargle_1.2.1 scales_1.2.1 DBI_1.1.3
[31] Rcpp_1.0.11 viridisLite_0.4.1 gridtext_0.1.5
[34] units_0.8-0 bit_4.0.5 proxy_0.4-27
[37] httr_1.4.4 seacarb_3.3.1 RColorBrewer_1.1-3
[40] ellipsis_0.3.2 pkgconfig_2.0.3 farver_2.1.1
[43] sass_0.4.4 dbplyr_2.2.1 utf8_1.2.2
[46] here_1.0.1 tidyselect_1.2.0 labeling_0.4.2
[49] rlang_1.1.1 later_1.3.0 munsell_0.5.0
[52] cellranger_1.1.0 tools_4.2.2 cachem_1.0.6
[55] cli_3.6.1 generics_0.1.3 evaluate_0.18
[58] fastmap_1.1.0 yaml_2.3.6 oce_1.7-10
[61] processx_3.8.0 knitr_1.41 bit64_4.0.5
[64] fs_1.5.2 RNetCDF_2.6-1 nlme_3.1-160
[67] whisker_0.4 xml2_1.3.3 compiler_4.2.2
[70] rstudioapi_0.15.0 e1071_1.7-12 reprex_2.0.2
[73] tweenr_2.0.2 bslib_0.4.1 stringi_1.7.8
[76] highr_0.9 ps_1.7.2 lattice_0.20-45
[79] Matrix_1.5-3 classInt_0.4-8 commonmark_1.8.1
[82] markdown_1.4 vctrs_0.6.4 pillar_1.9.0
[85] lifecycle_1.0.3 jquerylib_0.1.4 gsw_1.1-1
[88] data.table_1.14.6 httpuv_1.6.6 R6_2.5.1
[91] promises_1.2.0.1 KernSmooth_2.23-20 codetools_0.2-18
[94] MASS_7.3-58.1 assertthat_0.2.1 rprojroot_2.0.3
[97] withr_2.5.0 SolveSAPHE_2.1.0 mgcv_1.8-41
[100] parallel_4.2.2 hms_1.1.2 grid_4.2.2
[103] rnaturalearthdata_0.1.0 class_7.3-20 rmarkdown_2.18
[106] googledrive_2.0.0 git2r_0.30.1 getPass_0.2-2