GLODAPv2_2020 subsetting

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Last updated: 2021-04-19

Checks: 7 0

Knit directory: emlr_obs_v_XXX/

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/eMLR_GLODAPv2_2020_subsetting.Rmd) and HTML (docs/eMLR_GLODAPv2_2020_subsetting.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	e51e059	jens-daniel-mueller	2021-04-19	write grid with duplicated data
html	d2a83bc	jens-daniel-mueller	2021-04-16	Build site.
Rmd	f7f10e1	jens-daniel-mueller	2021-04-16	overlapping eras manually defined
html	84b1fe3	jens-daniel-mueller	2021-04-16	Build site.
html	c0a47df	jens-daniel-mueller	2021-04-16	Build site.
html	50290e8	jens-daniel-mueller	2021-04-16	overlapping eras
Rmd	934e795	jens-daniel-mueller	2021-04-16	overlapping eras
html	a00ec94	jens-daniel-mueller	2021-04-16	Build site.
Rmd	10ffab3	jens-daniel-mueller	2021-04-16	use consistent tref
html	7e4f671	jens-daniel-mueller	2021-04-16	Build site.
Rmd	bdd1c8a	jens-daniel-mueller	2021-04-16	use consistent tref
html	b6fe355	jens-daniel-mueller	2021-04-16	Build site.
Rmd	6e80fb5	jens-daniel-mueller	2021-04-16	manual cruise shifting, Indian only
html	858c4e6	jens-daniel-mueller	2021-04-16	Build site.
Rmd	f82b9f2	jens-daniel-mueller	2021-04-16	manual cruise shifting
html	81b7c6d	jens-daniel-mueller	2021-04-16	Build site.
html	ddec5b7	jens-daniel-mueller	2021-04-15	Build site.
html	29edae5	jens-daniel-mueller	2021-04-14	Build site.
html	099d566	jens-daniel-mueller	2021-04-14	Build site.
Rmd	e76af46	jens-daniel-mueller	2021-04-14	2 basins, high VIF
html	bb44686	jens-daniel-mueller	2021-04-14	Build site.
html	bf40480	jens-daniel-mueller	2021-04-13	Build site.
html	9f31fe3	jens-daniel-mueller	2021-04-13	Build site.
Rmd	80e1ee3	jens-daniel-mueller	2021-04-13	rerun post-2000 with model data
html	c98be84	jens-daniel-mueller	2021-04-13	Build site.
html	338dd3c	jens-daniel-mueller	2021-04-09	Build site.
html	a79ca2c	jens-daniel-mueller	2021-04-09	included model data
html	7831fd3	jens-daniel-mueller	2021-04-09	Build site.
html	20b26d4	jens-daniel-mueller	2021-04-08	Build site.
html	c0895f8	jens-daniel-mueller	2021-04-07	Build site.
html	156d5b7	jens-daniel-mueller	2021-04-07	Build site.
html	eb827c9	jens-daniel-mueller	2021-04-07	Build site.
html	857bad3	jens-daniel-mueller	2021-03-24	Build site.
Rmd	02e91d4	jens-daniel-mueller	2021-03-24	included model data
html	05cc66b	jens-daniel-mueller	2021-03-24	Build site.
Rmd	b897e71	jens-daniel-mueller	2021-03-24	included model data
html	03b6009	jens-daniel-mueller	2021-03-23	removed before copying template
html	555750f	jens-daniel-mueller	2021-03-23	Build site.
Rmd	2021931	jens-daniel-mueller	2021-03-23	restriction to two eras and new definition procedure
html	685338e	jens-daniel-mueller	2021-03-23	Build site.
Rmd	64b4756	jens-daniel-mueller	2021-03-23	restriction to two eras and new definition procedure
html	886f523	jens-daniel-mueller	2021-03-23	Build site.
Rmd	8a3a9dd	jens-daniel-mueller	2021-03-23	restriction to two eras and new definition procedure
html	d7831b0	jens-daniel-mueller	2021-03-23	Build site.
Rmd	d9dec77	jens-daniel-mueller	2021-03-23	restriction to two eras and new definition procedure
html	10c1346	jens-daniel-mueller	2021-03-23	Build site.
Rmd	b697e7a	jens-daniel-mueller	2021-03-23	restriction to two eras and new definition procedure
html	f155edd	jens-daniel-mueller	2021-03-23	Build site.
html	380d215	jens-daniel-mueller	2021-03-21	Build site.
html	33b385b	jens-daniel-mueller	2021-03-20	Build site.
html	330dcd0	jens-daniel-mueller	2021-03-20	Build site.
html	83a13de	jens-daniel-mueller	2021-03-20	Build site.
html	cf98c6d	jens-daniel-mueller	2021-03-16	Build site.
html	a1d52ff	jens-daniel-mueller	2021-03-15	Build site.
html	0bade3b	jens-daniel-mueller	2021-03-15	Build site.
html	27c1f4b	jens-daniel-mueller	2021-03-14	Build site.
html	af75ebf	jens-daniel-mueller	2021-03-14	Build site.
Rmd	e3dde84	jens-daniel-mueller	2021-03-14	test without filtering
html	5017709	jens-daniel-mueller	2021-03-11	Build site.
html	585b07f	jens-daniel-mueller	2021-03-11	Build site.
html	6482ed7	jens-daniel-mueller	2021-03-11	Build site.
Rmd	0ad56e3	jens-daniel-mueller	2021-03-11	cleaned filtering based on comparison to CANYON-B
html	85a5ed2	jens-daniel-mueller	2021-03-10	Build site.
Rmd	749b5db	jens-daniel-mueller	2021-03-10	filter based on comparison to CANYON-B
html	52a5ea4	jens-daniel-mueller	2021-03-10	Build site.
Rmd	0e57f29	jens-daniel-mueller	2021-03-10	rerun with GLODAP row numbers
html	6b260df	jens-daniel-mueller	2021-03-10	Build site.
html	00688a1	jens-daniel-mueller	2021-03-05	Build site.
html	6c0bec6	jens-daniel-mueller	2021-03-05	Build site.
html	3c2ec33	jens-daniel-mueller	2021-03-05	Build site.
html	af70b94	jens-daniel-mueller	2021-03-04	Build site.
html	86406d5	jens-daniel-mueller	2021-02-24	Build site.
html	3d3b4cc	jens-daniel-mueller	2021-02-23	Build site.
html	7b672f7	jens-daniel-mueller	2021-01-11	Build site.
Rmd	ba7cb46	jens-daniel-mueller	2021-01-11	run sensitivity test
html	33ba23c	jens-daniel-mueller	2021-01-07	Build site.
Rmd	0ad30ba	jens-daniel-mueller	2021-01-07	removed GLODAP gamma filter, target variable mapped by eras+era
Rmd	a8c4dce	jens-daniel-mueller	2020-12-23	manual activation of A16 switch
Rmd	cfef575	jens-daniel-mueller	2020-12-23	Option to select era shift of A16 cruise
html	318609d	jens-daniel-mueller	2020-12-23	adapted more variable predictor selection
html	6949b06	jens-daniel-mueller	2020-12-23	Build site.
Rmd	d3ad704	jens-daniel-mueller	2020-12-23	test 106
html	0aa2b50	jens-daniel-mueller	2020-12-23	remove html before duplication
html	39113c3	jens-daniel-mueller	2020-12-23	Build site.
html	2886da0	jens-daniel-mueller	2020-12-19	Build site.
html	02f0ee9	jens-daniel-mueller	2020-12-18	cleaned up for copying template
html	965dba3	jens-daniel-mueller	2020-12-18	Build site.
html	5d452fe	jens-daniel-mueller	2020-12-18	Build site.
Rmd	ca65bf5	jens-daniel-mueller	2020-12-18	rebuild after final cleaning
html	7bcb4eb	jens-daniel-mueller	2020-12-18	Build site.
html	d397028	jens-daniel-mueller	2020-12-18	Build site.
html	7131186	jens-daniel-mueller	2020-12-17	Build site.
html	22b07fb	jens-daniel-mueller	2020-12-17	Build site.
html	f3a708f	jens-daniel-mueller	2020-12-17	Build site.
html	e4ca289	jens-daniel-mueller	2020-12-16	Build site.
html	158fe26	jens-daniel-mueller	2020-12-15	Build site.
Rmd	449195a	jens-daniel-mueller	2020-12-15	rebuild without subsetting nitrate
html	7a9a4cb	jens-daniel-mueller	2020-12-15	Build site.
html	61b263c	jens-daniel-mueller	2020-12-15	Build site.
html	4d612dd	jens-daniel-mueller	2020-12-15	Build site.
html	953caf3	jens-daniel-mueller	2020-12-15	Build site.
html	42daf5c	jens-daniel-mueller	2020-12-14	Build site.
html	5cc68e2	jens-daniel-mueller	2020-12-14	Build site.
Rmd	00de567	jens-daniel-mueller	2020-12-14	rebuild with new path
html	984697e	jens-daniel-mueller	2020-12-12	Build site.
html	3ebff89	jens-daniel-mueller	2020-12-12	Build site.
Rmd	a1aaa71	jens-daniel-mueller	2020-12-12	selectable basinmask, rebuild
html	7d82772	jens-daniel-mueller	2020-12-11	Build site.
Rmd	6069c23	jens-daniel-mueller	2020-12-11	selectable basinmask, try 5
html	c8acfcb	jens-daniel-mueller	2020-12-11	Build site.
html	2fd0a2a	jens-daniel-mueller	2020-12-11	Build site.
Rmd	cd436c1	jens-daniel-mueller	2020-12-11	selectable basinmask
html	b01a367	jens-daniel-mueller	2020-12-09	Build site.
Rmd	71c63b0	jens-daniel-mueller	2020-12-09	rerun with variable predictor assignment
html	24a632f	jens-daniel-mueller	2020-12-07	Build site.
html	92dca91	jens-daniel-mueller	2020-12-07	Build site.
html	6a8004b	jens-daniel-mueller	2020-12-07	Build site.
Rmd	a4ec6a0	jens-daniel-mueller	2020-12-07	Keppler comparison run
html	70bf1a5	jens-daniel-mueller	2020-12-07	Build site.
html	7555355	jens-daniel-mueller	2020-12-07	Build site.
html	143d6fa	jens-daniel-mueller	2020-12-07	Build site.
html	abc6818	jens-daniel-mueller	2020-12-03	Build site.
html	090e4d5	jens-daniel-mueller	2020-12-02	Build site.
html	37e9dac	jens-daniel-mueller	2020-12-02	Build site.
Rmd	9ff071b	jens-daniel-mueller	2020-12-02	minor improvement of tref adejustment, formatting
html	7c25f7a	jens-daniel-mueller	2020-12-02	Build site.
Rmd	5ffd065	jens-daniel-mueller	2020-12-02	rebuild with nice era labels
html	ec8dc38	jens-daniel-mueller	2020-12-02	Build site.
html	c987de1	jens-daniel-mueller	2020-12-02	Build site.
html	d5c5378	jens-daniel-mueller	2020-12-02	Build site.
Rmd	3cc6d3c	jens-daniel-mueller	2020-12-02	code cleaning and commenting
html	083b3b0	jens-daniel-mueller	2020-12-02	Build site.
Rmd	f4520b2	jens-daniel-mueller	2020-12-02	code cleaning and commenting
html	632c84f	jens-daniel-mueller	2020-12-02	Build site.
Rmd	8f3d29d	jens-daniel-mueller	2020-12-02	code cleaning and commenting
html	22d0127	jens-daniel-mueller	2020-12-01	Build site.
html	0ff728b	jens-daniel-mueller	2020-12-01	Build site.
Rmd	6287a18	jens-daniel-mueller	2020-12-01	auto eras naming
html	b02b7a4	jens-daniel-mueller	2020-12-01	Build site.
Rmd	60bea48	jens-daniel-mueller	2020-12-01	auto eras naming
html	cf19652	jens-daniel-mueller	2020-11-30	Build site.
html	196be51	jens-daniel-mueller	2020-11-30	Build site.
Rmd	7a4b015	jens-daniel-mueller	2020-11-30	first rebuild on ETH server

---

1 Read files

Main data source for this project is the preprocessed version of the GLODAPv2.2020_Merged_Master_File.csv downloaded from glodap.info in June 2020.

CAVEAT: This file still contains neutral densities gamma calculated with a preliminary method. However, this is consistent with the way gamma is currently calculated in this script and should therefore be maintained until changed on all levels.

GLODAP <-
  read_csv(
    paste(
      path_preprocessing_model,
      "GLODAPv2.2020_preprocessed_model_runA_both.csv",
      sep = ""
    )
  )

GLODAP <- GLODAP %>%
  arrange(date) %>%
  relocate(row_number)

GLODAP_CB <-
  read_csv(paste(path_preprocessing,
                 "GLODAPv2.2020_Canyon-B.csv",
                 sep = ""))

2 Data preparation

2.1 Reference eras

Samples were assigned to following eras:

# create labels for era
era_labels <- bind_cols(
  start = params_local$era_start,
  end = params_local$era_end)

era_labels <- era_labels %>% 
  mutate(start = if_else(start == -Inf, max(GLODAP$year), start),
         end = if_else(end == Inf, max(GLODAP$year), end),
         era = as.factor(paste(start, end, sep = "-")))

# filter GLODAP data within eras
GLODAP <- expand_grid(
  GLODAP,
  era_labels
)

# extract A16 cruise data from 2013/14
GLODAP_A16_backup <- GLODAP %>% 
  filter(cruise %in% c(1041, 1042),
         era == sort(unique(GLODAP$era))[2])

# select data within each era
GLODAP <- GLODAP %>% 
  filter(year >= start & year <= end)


if (params_local$A16_GO_SHIP == "y") {
  GLODAP <- bind_rows(GLODAP,
                      GLODAP_A16_backup)
  
  map +
    geom_raster(data = GLODAP_A16_backup %>% distinct(lat, lon, cruise),
                aes(lon, lat, fill = as.factor(cruise))) +
    scale_fill_brewer(palette = "Dark2") +
    labs(
      title = "Manual adjustment of A16 cruise",
      subtitle = paste(
        "For harmonization with Gruber et al. (2019),
        cruises 1041 (A16N) and 1042 (A16S) were grouped into the",
        sort(unique(GLODAP$era))[2], "era
        despite taking place in 2013/14"
      )
    ) +
    theme(legend.title = element_blank())
  
}

GLODAP <- GLODAP %>% 
  select(-c(start, end))

rm(era_labels, GLODAP_A16_backup)

2.2 Spatial boundaries

2.2.1 Basin mask

The basin mask from the World Ocean Atlas was used. For details consult the data base subsection for WOA18 data.

Please note that some GLODAP observations were made outside the WOA18 basin mask (i.e. in marginal seas) and will be removed for further analysis.

# use only data inside basinmask
GLODAP <- inner_join(GLODAP, basinmask)

2.2.2 Depth

Observations collected shallower than:

• minimum sampling depth: 150m

were excluded from the analysis to avoid seasonal bias.

GLODAP <- GLODAP %>% 
  filter(depth >= params_local$depth_min)

2.2.3 Bottomdepth

Observations collected in an area with a:

• minimum bottom depth: 0m

were excluded from the analysis to avoid coastal impacts. Please note that minimum bottom depth criterion of 0m means that no filtering was applied here.

GLODAP <- GLODAP %>% 
  filter(bottomdepth >= params_local$bottomdepth_min)

2.3 Flags and missing data

Only rows (samples) for which all relevant parameters are available were selected, ie NA’s were removed.

According to Olsen et al (2020), flags within the merged master file identify:

• f:

  • 2: Acceptable
  • 0: Interpolated (nutrients/oxygen) or calculated (CO[2] variables)
  • 9: Data not used (so, only NA data should have this flag)

• qc:

  • 1: Adjusted or unadjusted data
  • 0: Data appear of good quality but have not been subjected to full secondary QC
  • data with poor or uncertain quality are excluded.

Following flagging criteria were taken into account:

• flag_f: 2, 0
• flag_qc: 1, 0

The cleaning process was performed successively and the maps below represent the data coverage at various cleaning levels.

Summary statistics were calculated during cleaning process.

2.3.1 tco2

2.3.1.1 NA

Rows with missing tco2 observations were already removed in the preprocessing. The map below shows the coverage of preprocessed GLODAP data.

GLODAP_stats <- GLODAP %>% 
  summarise(tco2_values = n())

GLODAP_obs_grid <- GLODAP %>% 
  count(lat, lon, era) %>% 
  mutate(cleaning_level = "tco2_values")

GLODAP_obs <- GLODAP %>% 
  group_by(lat, lon) %>% 
  summarise(n = n()) %>% 
  ungroup()

map +
  geom_raster(data = basinmask, aes(lon, lat, fill = basin)) +
  geom_raster(data = GLODAP_obs, aes(lon, lat)) +
  scale_fill_brewer(palette = "Dark2") +
  theme(legend.position = "top",
        legend.title = element_blank())

Past versions of WOA18_basin_mask_GLODAP_observations_map-1.png

Version	Author	Date
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
81b7c6d	jens-daniel-mueller	2021-04-16
099d566	jens-daniel-mueller	2021-04-14
bb44686	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
05cc66b	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
685338e	jens-daniel-mueller	2021-03-23
10c1346	jens-daniel-mueller	2021-03-23
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
6949b06	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
984697e	jens-daniel-mueller	2020-12-12
3ebff89	jens-daniel-mueller	2020-12-12
c8acfcb	jens-daniel-mueller	2020-12-11
2fd0a2a	jens-daniel-mueller	2020-12-11
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
7c25f7a	jens-daniel-mueller	2020-12-02
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

rm(GLODAP_obs)

2.3.1.2 f flag

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, tco2f)

map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era ~ tco2f) +
  theme(legend.position = "top")

Past versions of tco2_f_subset-1.png

Version	Author	Date
84b1fe3	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
05cc66b	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
685338e	jens-daniel-mueller	2021-03-23
886f523	jens-daniel-mueller	2021-03-23
d7831b0	jens-daniel-mueller	2021-03-23
10c1346	jens-daniel-mueller	2021-03-23
f155edd	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
6949b06	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
2fd0a2a	jens-daniel-mueller	2020-12-11
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
37e9dac	jens-daniel-mueller	2020-12-02
7c25f7a	jens-daniel-mueller	2020-12-02
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

rm(GLODAP_obs_grid_temp)

GLODAP <- GLODAP %>%
  filter(tco2f %in% params_local$flag_f)

2.3.1.3 qc flag

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, tco2qc)

map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era ~ tco2qc) +
  theme(legend.position = "top")

Past versions of tco2_qc_subset-1.png

Version	Author	Date
84b1fe3	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
05cc66b	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
685338e	jens-daniel-mueller	2021-03-23
886f523	jens-daniel-mueller	2021-03-23
d7831b0	jens-daniel-mueller	2021-03-23
10c1346	jens-daniel-mueller	2021-03-23
f155edd	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
6949b06	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
2fd0a2a	jens-daniel-mueller	2020-12-11
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
37e9dac	jens-daniel-mueller	2020-12-02
7c25f7a	jens-daniel-mueller	2020-12-02
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

##

GLODAP <- GLODAP %>%
  filter(tco2qc %in% params_local$flag_qc)

GLODAP_stats_temp <- GLODAP %>%
  summarise(tco2_flag = n())

GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)

##

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era) %>%
  mutate(cleaning_level = "tco2_flag")

GLODAP_obs_grid <-
  bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)

rm(GLODAP_obs_grid_temp)

2.3.2 talk

2.3.2.1 NA

GLODAP <- GLODAP %>% 
  mutate(talkna = if_else(is.na(talk), "NA", "Value"))

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, talkna)

map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era ~ talkna) +
  theme(legend.position = "top")

Past versions of talk_na_subset-1.png

Version	Author	Date
84b1fe3	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
05cc66b	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
685338e	jens-daniel-mueller	2021-03-23
886f523	jens-daniel-mueller	2021-03-23
d7831b0	jens-daniel-mueller	2021-03-23
10c1346	jens-daniel-mueller	2021-03-23
f155edd	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
6949b06	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
2fd0a2a	jens-daniel-mueller	2020-12-11
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
70bf1a5	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
37e9dac	jens-daniel-mueller	2020-12-02
7c25f7a	jens-daniel-mueller	2020-12-02
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

GLODAP <- GLODAP %>% 
  select(-talkna) %>% 
  filter(!is.na(talk))

##

GLODAP_stats_temp <- GLODAP %>% 
  summarise(talk_values = n())

GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)

##

GLODAP_obs_grid_temp <- GLODAP %>% 
  count(lat, lon, era) %>% 
  mutate(cleaning_level = "talk_values")

GLODAP_obs_grid <-
  bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)

rm(GLODAP_obs_grid_temp)

2.3.2.2 f flag

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, talkf)

map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era ~ talkf) +
  theme(legend.position = "top",
        legend.title = element_blank())

Past versions of talk_f_subset-1.png

Version	Author	Date
84b1fe3	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
05cc66b	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
685338e	jens-daniel-mueller	2021-03-23
886f523	jens-daniel-mueller	2021-03-23
d7831b0	jens-daniel-mueller	2021-03-23
10c1346	jens-daniel-mueller	2021-03-23
f155edd	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
6949b06	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
2fd0a2a	jens-daniel-mueller	2020-12-11
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
70bf1a5	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
37e9dac	jens-daniel-mueller	2020-12-02
7c25f7a	jens-daniel-mueller	2020-12-02
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

# ###

GLODAP <- GLODAP %>%
  filter(talkf %in% params_local$flag_f)

2.3.2.3 qc flag

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, talkqc)

map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era ~ talkqc) +
  theme(legend.position = "top",
        legend.title = element_blank())

Past versions of talk_qc_subset-1.png

Version	Author	Date
84b1fe3	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
05cc66b	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
685338e	jens-daniel-mueller	2021-03-23
886f523	jens-daniel-mueller	2021-03-23
d7831b0	jens-daniel-mueller	2021-03-23
10c1346	jens-daniel-mueller	2021-03-23
f155edd	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
6949b06	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
2fd0a2a	jens-daniel-mueller	2020-12-11
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
70bf1a5	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
37e9dac	jens-daniel-mueller	2020-12-02
7c25f7a	jens-daniel-mueller	2020-12-02
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

###

GLODAP <- GLODAP %>%
  filter(talkqc %in% params_local$flag_qc)

##

GLODAP_stats_temp <- GLODAP %>%
  summarise(talk_flag = n())

GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)

##

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era) %>%
  mutate(cleaning_level = "talk_flag")

GLODAP_obs_grid <-
  bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)

rm(GLODAP_obs_grid_temp)

2.3.3 Phosphate

2.3.3.1 NA

GLODAP <- GLODAP %>% 
  mutate(phosphatena = if_else(is.na(phosphate), "NA", "Value"))

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, phosphatena)

map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era ~ phosphatena) +
  theme(legend.position = "top")

Past versions of phosphate_na_subset-1.png

Version	Author	Date
84b1fe3	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
05cc66b	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
685338e	jens-daniel-mueller	2021-03-23
886f523	jens-daniel-mueller	2021-03-23
d7831b0	jens-daniel-mueller	2021-03-23
10c1346	jens-daniel-mueller	2021-03-23
f155edd	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
6949b06	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
2fd0a2a	jens-daniel-mueller	2020-12-11
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
70bf1a5	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
37e9dac	jens-daniel-mueller	2020-12-02
7c25f7a	jens-daniel-mueller	2020-12-02
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

GLODAP <- GLODAP %>% 
  select(-phosphatena) %>% 
  filter(!is.na(phosphate))

##

GLODAP_stats_temp <- GLODAP %>% 
  summarise(phosphate_values = n())

GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)

##

GLODAP_obs_grid_temp <- GLODAP %>% 
  count(lat, lon, era) %>% 
  mutate(cleaning_level = "phosphate_values")

GLODAP_obs_grid <-
  bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)

rm(GLODAP_obs_grid_temp)

2.3.3.2 f flag

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, phosphatef)

map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
    scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era~phosphatef) +
  theme(legend.position = "top",
        legend.title = element_blank())

Past versions of phosphate_f_subset-1.png

Version	Author	Date
84b1fe3	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
05cc66b	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
685338e	jens-daniel-mueller	2021-03-23
886f523	jens-daniel-mueller	2021-03-23
d7831b0	jens-daniel-mueller	2021-03-23
10c1346	jens-daniel-mueller	2021-03-23
f155edd	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
6949b06	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
2fd0a2a	jens-daniel-mueller	2020-12-11
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
70bf1a5	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
37e9dac	jens-daniel-mueller	2020-12-02
7c25f7a	jens-daniel-mueller	2020-12-02
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

###

GLODAP <- GLODAP %>%
  filter(phosphatef %in% params_local$flag_f)

2.3.3.3 qc flag

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, phosphateqc)

map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
    scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era~phosphateqc) +
  theme(legend.position = "top",
        legend.title = element_blank())

Past versions of phosphate_qc_subset-1.png

Version	Author	Date
84b1fe3	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
05cc66b	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
685338e	jens-daniel-mueller	2021-03-23
886f523	jens-daniel-mueller	2021-03-23
d7831b0	jens-daniel-mueller	2021-03-23
10c1346	jens-daniel-mueller	2021-03-23
f155edd	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
6949b06	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
2fd0a2a	jens-daniel-mueller	2020-12-11
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
70bf1a5	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
37e9dac	jens-daniel-mueller	2020-12-02
7c25f7a	jens-daniel-mueller	2020-12-02
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

###

GLODAP <- GLODAP %>%
  filter(phosphateqc %in% params_local$flag_qc)

##

GLODAP_stats_temp <- GLODAP %>%
  summarise(phosphate_flag = n())

GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)

##

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era) %>%
  mutate(cleaning_level = "phosphate_flag")

GLODAP_obs_grid <-
  bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)

rm(GLODAP_obs_grid_temp)

2.3.4 eMLR variables

Variables required as predictors for the MLR fits, are subsetted for NAs and flags.

if ("temp" %in% params_local$MLR_predictors) {
  GLODAP <- GLODAP %>%
    filter(!is.na(temp))
}

##

if ("sal" %in% params_local$MLR_predictors) {
  GLODAP <- GLODAP %>%
    filter(!is.na(sal))
  
  GLODAP <- GLODAP %>%
    filter(salinityf %in% params_local$flag_f)
  
  GLODAP <- GLODAP %>%
    filter(salinityqc %in% params_local$flag_qc)
}

##

if ("silicate" %in% params_local$MLR_predictors) {
  GLODAP <- GLODAP %>%
    filter(!is.na(silicate))
  
  GLODAP <- GLODAP %>%
    filter(silicatef %in% params_local$flag_f)
  
  GLODAP <- GLODAP %>%
    filter(silicateqc %in% params_local$flag_qc)
}

##

if ("oxygen" %in% params_local$MLR_predictors |
    "phosphate_star" %in% params_local$MLR_predictors) {
  GLODAP <- GLODAP %>%
    filter(!is.na(oxygen))
  
  GLODAP <- GLODAP %>%
    filter(oxygenf %in% params_local$flag_f)
  
  GLODAP <- GLODAP %>%
    filter(oxygenqc %in% params_local$flag_qc)
}

##

if ("aou" %in% params_local$MLR_predictors) {
  GLODAP <- GLODAP %>%
    filter(!is.na(aou))
  
  GLODAP <- GLODAP %>%
    filter(aouf %in% params_local$flag_f)
}

##

if ("nitrate" %in% params_local$MLR_predictors) {
  GLODAP <- GLODAP %>%
    filter(!is.na(nitrate))
  
  GLODAP <- GLODAP %>%
    filter(nitratef %in% params_local$flag_f)
  
  GLODAP <- GLODAP %>%
    filter(nitrateqc %in% params_local$flag_qc)
}

##

GLODAP <- GLODAP %>%
  filter(!is.na(depth))

GLODAP <- GLODAP %>%
  filter(!is.na(gamma))

##

GLODAP_stats_temp <- GLODAP %>%
  summarise(eMLR_variables = n())

GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)

rm(GLODAP_stats_temp)

##

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era) %>%
  mutate(cleaning_level = "eMLR_variables")

GLODAP_obs_grid <-
  bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)

rm(GLODAP_obs_grid_temp)

GLODAP <- GLODAP %>% 
  select(-ends_with(c("f", "qc")))

2.4 CANYON-B comparison

Cruises are removed, when the mean offset of the observation from the value predicted with CANYON-B is higher than 3 times the standard deviation of all cruise mean offsets. This critrion is evaluated individually for each variable involved in the eMLR approach.

# join data frames
GLODAP_combined <- left_join(GLODAP,
                             GLODAP_CB)

# calculate offset by parameter
GLODAP_combined <- GLODAP_combined %>%
  mutate(
    offset_talk = talk - talk_CANYONB,
    offset_tco2 = tco2 - tco2_CANYONB,
    offset_nitrate = nitrate - nitrate_CANYONB,
    offset_phosphate = phosphate - phosphate_CANYONB,
    offset_silicate = silicate - silicate_CANYONB
  ) %>%
  select(row_number,
         year,
         cruise,
         basin_AIP,
         lat,
         lon,
         starts_with("offset_"))

# pivot to long format
GLODAP_combined <- GLODAP_combined %>%
  pivot_longer(
    starts_with("offset"),
    names_to = "parameter",
    names_prefix = "offset_",
    values_to = "offset"
  )

2.4.1 Residual histograms

GLODAP_combined %>%
  ggplot(aes(offset)) +
  geom_histogram() +
  scale_y_continuous(trans = "log10") +
  facet_grid(basin_AIP ~ parameter, scales = "free_x")

Past versions of CANYON_B_residual_histograms-1.png

Version	Author	Date
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05cc66b	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
685338e	jens-daniel-mueller	2021-03-23
886f523	jens-daniel-mueller	2021-03-23
10c1346	jens-daniel-mueller	2021-03-23
6482ed7	jens-daniel-mueller	2021-03-11

2.4.2 Ranked residuals

# calculate mean cruise offset by parameter
cruise_all <- GLODAP_combined %>%
  group_by(cruise, parameter) %>%
  summarise(
    mean_offset = mean(offset, na.rm = TRUE),
    sd_offset = sd(offset, na.rm = TRUE)
  ) %>%
  ungroup()

# rank offsets and calculate offset threshols
cruise_all <- cruise_all %>%
  group_by(parameter) %>%
  mutate(
    rank_offset = rank(mean_offset),
    threshold_offset = sd(mean_offset) * params_local$CANYON_B_max,
    cruise = as.factor(cruise)
  ) %>%
  ungroup() %>%
  arrange(parameter, rank_offset)

cruise_out <- cruise_all %>%
  filter(parameter %in% c("tco2", "talk", params_local$MLR_predictors)) %>% 
  filter(abs(mean_offset) > threshold_offset)

# length(unique(cruise_out$cruise))

for (i_parameter in unique(cruise_all$parameter)) {
  # i_parameter <- unique(cruise_all$parameter)[1]
  
  i_cruise_all <- cruise_all %>%
    filter(parameter == i_parameter)
  
  i_cruise_out <- cruise_out %>%
    filter(parameter == i_parameter)
  
  print(
  ggplot() +
    geom_hline(data = i_cruise_all,
               aes(yintercept = c(-1, 1) * threshold_offset),
               lty = 2) +
    geom_ribbon(
      data = i_cruise_all,
      aes(
        x = rank_offset,
        ymax = mean_offset + sd_offset,
        ymin = mean_offset-+sd_offset
      ),
      alpha = 0.3
    ) +
    geom_path(data = i_cruise_all,
              aes(rank_offset, mean_offset)) +
    geom_point(data = i_cruise_out,
               aes(rank_offset, mean_offset, col = cruise)) +
    labs(title = i_parameter)
  )
  
}

Past versions of CANYON_B_ranked_residuals-1.png

Version	Author	Date
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05cc66b	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
555750f	jens-daniel-mueller	2021-03-23
685338e	jens-daniel-mueller	2021-03-23
886f523	jens-daniel-mueller	2021-03-23
10c1346	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
6482ed7	jens-daniel-mueller	2021-03-11

Past versions of CANYON_B_ranked_residuals-2.png

Version	Author	Date
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05cc66b	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
555750f	jens-daniel-mueller	2021-03-23
685338e	jens-daniel-mueller	2021-03-23
886f523	jens-daniel-mueller	2021-03-23
10c1346	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
6482ed7	jens-daniel-mueller	2021-03-11

Past versions of CANYON_B_ranked_residuals-3.png

Version	Author	Date
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05cc66b	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
555750f	jens-daniel-mueller	2021-03-23
685338e	jens-daniel-mueller	2021-03-23
886f523	jens-daniel-mueller	2021-03-23
10c1346	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
6482ed7	jens-daniel-mueller	2021-03-11

Past versions of CANYON_B_ranked_residuals-4.png

Version	Author	Date
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05cc66b	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
555750f	jens-daniel-mueller	2021-03-23
685338e	jens-daniel-mueller	2021-03-23
886f523	jens-daniel-mueller	2021-03-23
10c1346	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
6482ed7	jens-daniel-mueller	2021-03-11

Past versions of CANYON_B_ranked_residuals-5.png

Version	Author	Date
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05cc66b	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
555750f	jens-daniel-mueller	2021-03-23
685338e	jens-daniel-mueller	2021-03-23
886f523	jens-daniel-mueller	2021-03-23
10c1346	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
6482ed7	jens-daniel-mueller	2021-03-11

rm(i_parameter, i_cruise_all, i_cruise_out)

2.4.3 Remove cruises

GLODAP_out <- GLODAP %>%
  filter(cruise %in% cruise_out$cruise)

if (nrow(GLODAP_out) > 0) {
  map +
  geom_raster(data = GLODAP_out %>% distinct(lat, lon, era),
              aes(lon, lat)) +
  facet_wrap( ~ era, ncol = 1) +
  labs(title = "Maps of removed cruises")

} else {
  print("no cruises removed")
  
}

Past versions of CANYON_B_removed_cruises_map-1.png

Version	Author	Date
84b1fe3	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
05cc66b	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
555750f	jens-daniel-mueller	2021-03-23
685338e	jens-daniel-mueller	2021-03-23
886f523	jens-daniel-mueller	2021-03-23
10c1346	jens-daniel-mueller	2021-03-23
f155edd	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
6482ed7	jens-daniel-mueller	2021-03-11

The ratio (%) of removed cruises is:

nrow(GLODAP_out) / nrow(GLODAP) * 100

[1] 0.8900126

GLODAP <- GLODAP %>%
  filter(!(cruise %in% cruise_out$cruise))

2.5 Median years (tref)

Median years of each era (tref) were determined as:

# calculate reference year
tref <- GLODAP %>%
  group_by(era) %>%
  summarise(median_year = median(year)) %>%
  ungroup()

tref

# A tibble: 2 x 2
  era       median_year
  <fct>           <dbl>
1 2000-2009        2006
2 2010-2019        2014

2.6 Manual cruise duplication

map +
  geom_raster(data =
                GLODAP %>%
                distinct(year, lat, lon, basin_AIP),
              aes(lon, lat, fill=basin_AIP)) +
  scale_fill_brewer(palette = "Set1") +
  facet_wrap(~ year, ncol = 3)

Past versions of Manual_cruise_duplication-1.png

Version	Author	Date
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16

# Duplicate Indian ocean cruises manually

# GLODAP_dup <- GLODAP %>% 
#   filter(basin_AIP == "Indian",
#          year >=2007,
#          year <=2009) %>% 
#   mutate(era = sort(unique(GLODAP$era))[2])
# 
# GLODAP <- bind_rows(GLODAP, GLODAP_dup)
# rm(GLODAP_dup)

# Duplicate overlapping eras manually

GLODAP_dup_era1 <- GLODAP %>%
  filter(year >=2007,
         year <=2009) %>%
  mutate(era = sort(unique(GLODAP$era))[2])

GLODAP_dup_era2 <- GLODAP %>%
  filter(year >=2010,
         year <=2012) %>%
  mutate(era = sort(unique(GLODAP$era))[1])

GLODAP <- bind_rows(GLODAP %>% mutate(duplicate = "no"),
                    GLODAP_dup_era1 %>% mutate(duplicate = "yes"),
                    GLODAP_dup_era2 %>% mutate(duplicate = "yes"))

GLODAP_grid_dup <- GLODAP %>%
  distinct(era, duplicate, lon, lat)
  
GLODAP <- GLODAP %>% 
  select(-duplicate)

rm(GLODAP_dup_era1, GLODAP_dup_era2)

2.7 Create clean observations grid

Grid containing all grid cells where at least one observation remains available after cleaning.

GLODAP_obs_grid_clean <- GLODAP %>% 
  distinct(lat, lon)

2.8 Write summary file

GLODAP_obs_grid_clean  %>%
  write_csv(paste(path_version_data,
                  "GLODAPv2.2020_clean_obs_grid.csv",
                  sep = ""))

GLODAP_grid_dup  %>%
  write_csv(paste(path_version_data,
                  "GLODAPv2.2020_clean_obs_grid_duplicates.csv",
                  sep = ""))

# select relevant columns for further analysis
GLODAP <- GLODAP %>% 
  select(year, date, era, basin, basin_AIP, lat, lon, cruise,
         bottomdepth, depth,
         temp, sal, gamma,
         tco2, talk, phosphate,
         oxygen, aou, nitrate, silicate,
         temp_model, sal_model = so_model, gamma_model,
         tco2_model = dissic_model, talk_model, phosphate_model = po4_model,
         oxygen_model = o2_model, aou_model,
         nitrate_model = no3_model, silicate_model = si_model
         )


GLODAP  %>%  write_csv(paste(path_version_data,
                             "GLODAPv2.2020_clean.csv",
                             sep = ""))

tref  %>%  write_csv(paste(path_version_data,
                           "tref.csv",
                           sep = ""))

3 Overview plots

3.1 Number of overservations

GLODAP %>% 
  group_by(era, basin_AIP) %>% 
  count() %>% 
  ggplot(aes(basin_AIP, n, fill = era)) +
  geom_col() +
  scale_fill_brewer(palette = "Dark2")

Past versions of counts-1.png

Version	Author	Date
d2a83bc	jens-daniel-mueller	2021-04-16
84b1fe3	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13

3.2 Cleaning stats

Number of observations at various steps of data cleaning.

GLODAP_stats_long <- GLODAP_stats %>%
  pivot_longer(1:length(GLODAP_stats),
               names_to = "parameter",
               values_to = "n")

GLODAP_stats_long <- GLODAP_stats_long %>%
  mutate(parameter = fct_reorder(parameter, n))

GLODAP_stats_long %>% 
  ggplot(aes(parameter, n/1000)) +
  geom_col() +
  coord_flip() +
  theme(axis.title.y = element_blank())

Past versions of GLODAP_cleaning_stats-1.png

Version	Author	Date
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05cc66b	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
685338e	jens-daniel-mueller	2021-03-23
886f523	jens-daniel-mueller	2021-03-23
10c1346	jens-daniel-mueller	2021-03-23
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
6949b06	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
5d452fe	jens-daniel-mueller	2020-12-18
158fe26	jens-daniel-mueller	2020-12-15
3ebff89	jens-daniel-mueller	2020-12-12
2fd0a2a	jens-daniel-mueller	2020-12-11
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
70bf1a5	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

rm(GLODAP_stats_long)

3.3 Assign coarse spatial grid

For the following plots, the cleaned data set was re-opened and observations were gridded spatially to intervals of:

• 5° x 5°

GLODAP <- m_grid_horizontal_coarse(GLODAP)

3.4 Histogram Zonal coverage

GLODAP_histogram_lat <- GLODAP %>%
  group_by(era, lat_grid, basin_AIP) %>%
  tally() %>%
  ungroup()

GLODAP_histogram_lat %>%
  ggplot(aes(lat_grid, n, fill = era)) +
  geom_col() +
  scale_fill_brewer(palette = "Dark2") +
  facet_wrap( ~ basin_AIP) +
  coord_flip(expand = 0) +
  theme(legend.position = "top",
        legend.title = element_blank())

Past versions of coverage_histogram_zonal-1.png

Version	Author	Date
d2a83bc	jens-daniel-mueller	2021-04-16
84b1fe3	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
bb44686	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05cc66b	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
555750f	jens-daniel-mueller	2021-03-23
685338e	jens-daniel-mueller	2021-03-23
886f523	jens-daniel-mueller	2021-03-23
10c1346	jens-daniel-mueller	2021-03-23
f155edd	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
85a5ed2	jens-daniel-mueller	2021-03-10
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
6949b06	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
158fe26	jens-daniel-mueller	2020-12-15
984697e	jens-daniel-mueller	2020-12-12
3ebff89	jens-daniel-mueller	2020-12-12
c8acfcb	jens-daniel-mueller	2020-12-11
2fd0a2a	jens-daniel-mueller	2020-12-11
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
70bf1a5	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
7c25f7a	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

rm(GLODAP_histogram_lat)

3.5 Histogram temporal coverage

GLODAP_histogram_year <- GLODAP %>%
  group_by(year, basin_AIP) %>%
  tally() %>%
  ungroup()

GLODAP_histogram_year %>%
  ggplot() +
  geom_vline(xintercept = sort(params_local$era_end)[1] + 0.5) +
  geom_col(aes(year, n,
               fill = basin_AIP)) +
  geom_point(
    data = tref,
    aes(median_year, 100, shape = "tref"),
    size = 2,
    fill = "white"
  ) +
  scale_fill_brewer(palette = "Dark2") +
  scale_shape_manual(values = 24, name = "") +
  scale_y_continuous() +
  coord_cartesian(expand = 0) +
  theme(
    legend.position = "top",
    legend.direction = "vertical",
    legend.title = element_blank(),
    axis.title.x = element_blank()
  )

Past versions of coverage_histogram_temporal-1.png

Version	Author	Date
d2a83bc	jens-daniel-mueller	2021-04-16
84b1fe3	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
7e4f671	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
099d566	jens-daniel-mueller	2021-04-14
bb44686	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05cc66b	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
555750f	jens-daniel-mueller	2021-03-23
685338e	jens-daniel-mueller	2021-03-23
886f523	jens-daniel-mueller	2021-03-23
10c1346	jens-daniel-mueller	2021-03-23
f155edd	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
85a5ed2	jens-daniel-mueller	2021-03-10
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
6949b06	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
158fe26	jens-daniel-mueller	2020-12-15
984697e	jens-daniel-mueller	2020-12-12
3ebff89	jens-daniel-mueller	2020-12-12
c8acfcb	jens-daniel-mueller	2020-12-11
2fd0a2a	jens-daniel-mueller	2020-12-11
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
70bf1a5	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
0ff728b	jens-daniel-mueller	2020-12-01
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

rm(GLODAP_histogram_year,
   era_median_year)

3.6 Zonal temporal coverage (Hovmoeller)

GLODAP_hovmoeller_year <- GLODAP %>%
  group_by(year, lat_grid, basin_AIP) %>%
  tally() %>%
  ungroup()

GLODAP_hovmoeller_year %>%
  ggplot(aes(year, lat_grid, fill = n)) +
  geom_tile() +
  geom_vline(xintercept = sort(params_local$era_end)[1] + 0.5) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  coord_cartesian(expand = 0) +
  facet_wrap( ~ basin_AIP, ncol = 1) +
  theme(legend.position = "top",
        axis.title.x = element_blank())

Past versions of coverage_hovmoeller-1.png

Version	Author	Date
d2a83bc	jens-daniel-mueller	2021-04-16
84b1fe3	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
099d566	jens-daniel-mueller	2021-04-14
bb44686	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05cc66b	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
555750f	jens-daniel-mueller	2021-03-23
685338e	jens-daniel-mueller	2021-03-23
886f523	jens-daniel-mueller	2021-03-23
10c1346	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
85a5ed2	jens-daniel-mueller	2021-03-10
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
6949b06	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
158fe26	jens-daniel-mueller	2020-12-15
984697e	jens-daniel-mueller	2020-12-12
3ebff89	jens-daniel-mueller	2020-12-12
c8acfcb	jens-daniel-mueller	2020-12-11
2fd0a2a	jens-daniel-mueller	2020-12-11
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
70bf1a5	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
37e9dac	jens-daniel-mueller	2020-12-02
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

rm(GLODAP_hovmoeller_year)

3.7 Coverage maps by era

3.7.1 Subsetting process

The following plots show the remaining data after individual cleaning steps, separately for each era.

GLODAP_obs_grid <- GLODAP_obs_grid %>%
  mutate(cleaning_level = factor(
           cleaning_level,
           unique(GLODAP_obs_grid$cleaning_level)
         ))

map +
  geom_raster(data = GLODAP_obs_grid %>%
                filter(cleaning_level == "tco2_values") %>%
                select(-cleaning_level),
              aes(lon, lat, fill = "tco2_values")) +
  geom_raster(data = GLODAP_obs_grid %>%
                filter(cleaning_level != "tco2_values"),
              aes(lon, lat, fill = "subset")) +
  scale_fill_brewer(palette = "Set1", name = "") +
  facet_grid(cleaning_level ~ era) +
  theme(legend.position = "top",
        axis.title = element_blank())

Past versions of coverage_maps_era_subsetting_era_cleaning_level-1.png

Version	Author	Date
84b1fe3	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
05cc66b	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
685338e	jens-daniel-mueller	2021-03-23
886f523	jens-daniel-mueller	2021-03-23
10c1346	jens-daniel-mueller	2021-03-23
f155edd	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
6949b06	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
158fe26	jens-daniel-mueller	2020-12-15
3ebff89	jens-daniel-mueller	2020-12-12
2fd0a2a	jens-daniel-mueller	2020-12-11
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
70bf1a5	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
37e9dac	jens-daniel-mueller	2020-12-02
7c25f7a	jens-daniel-mueller	2020-12-02
d5c5378	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

3.7.2 Final input data

The following plots show the remaining data density in each grid cell after all cleaning steps, separately for each era.

GLODAP_tco2_grid <- GLODAP %>%
  count(lat, lon)

map +
  geom_bin2d(data = GLODAP,
             aes(lon, lat),
             binwidth = c(1,1)) +
  scale_fill_viridis_c(option = "magma", direction = -1, trans = "log10") +
  facet_wrap(~era, ncol = 1) +
  labs(title = "Cleaned GLODAP observations",
       subtitle = paste("Version:", params_local$Version_ID)) +
  theme(axis.title = element_blank())

Past versions of coverage_maps_era_clean-1.png

Version	Author	Date
d2a83bc	jens-daniel-mueller	2021-04-16
84b1fe3	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
05cc66b	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
555750f	jens-daniel-mueller	2021-03-23
685338e	jens-daniel-mueller	2021-03-23
886f523	jens-daniel-mueller	2021-03-23
10c1346	jens-daniel-mueller	2021-03-23
f155edd	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
85a5ed2	jens-daniel-mueller	2021-03-10
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
6949b06	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
158fe26	jens-daniel-mueller	2020-12-15
3ebff89	jens-daniel-mueller	2020-12-12
2fd0a2a	jens-daniel-mueller	2020-12-11
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
70bf1a5	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
37e9dac	jens-daniel-mueller	2020-12-02
7c25f7a	jens-daniel-mueller	2020-12-02
d5c5378	jens-daniel-mueller	2020-12-02
083b3b0	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

ggsave(path = path_version_figures,
       filename = "data_distribution_era.png",
       height = 8,
       width = 5)

3.8 Model vs observation

variables <-
  c(
    "temp",
    "sal",
    "gamma",
    "tco2",
    "talk",
    "phosphate",
    "oxygen",
    "aou",
    "nitrate",
    "silicate"
  )

for (i_var in variables) {
  #  i_var <- variables[1]
  
  # calculate equal axis limits and binwidth
  axis_lims <- GLODAP %>%
    summarise(max_value = max(c(max(!!sym(
      i_var
    )),
    max(!!sym(
      paste0(i_var, "_model")
    )))),
    min_value = min(c(min(!!sym(
      i_var
    )),
    min(!!sym(
      paste0(i_var, "_model")
    )))))
  
  binwidth_value <- (axis_lims$max_value - axis_lims$min_value) / 40
  axis_lims <- c(axis_lims$min_value, axis_lims$max_value)
  
  # obs-model plot (season)
  print(
    ggplot(GLODAP, aes(
      x = !!sym(i_var),
      y = !!sym(paste0(i_var, "_model"))
    )) +
      geom_bin2d(binwidth = binwidth_value) +
      scale_fill_viridis_c() +
      geom_abline(slope = 1,
                  col = 'red') +
      coord_equal(xlim = axis_lims,
                  ylim = axis_lims) +
      labs(x = "observation", y = "model", title = i_var)
  )
}

Past versions of model_obs_comparison-1.png

Version	Author	Date
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
857bad3	jens-daniel-mueller	2021-03-24
05cc66b	jens-daniel-mueller	2021-03-24

Past versions of model_obs_comparison-2.png

Version	Author	Date
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
857bad3	jens-daniel-mueller	2021-03-24
05cc66b	jens-daniel-mueller	2021-03-24

Past versions of model_obs_comparison-3.png

Version	Author	Date
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
857bad3	jens-daniel-mueller	2021-03-24
05cc66b	jens-daniel-mueller	2021-03-24

Past versions of model_obs_comparison-4.png

Version	Author	Date
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
857bad3	jens-daniel-mueller	2021-03-24
05cc66b	jens-daniel-mueller	2021-03-24

Past versions of model_obs_comparison-5.png

Version	Author	Date
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
857bad3	jens-daniel-mueller	2021-03-24
05cc66b	jens-daniel-mueller	2021-03-24

Past versions of model_obs_comparison-6.png

Version	Author	Date
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
857bad3	jens-daniel-mueller	2021-03-24
05cc66b	jens-daniel-mueller	2021-03-24

Past versions of model_obs_comparison-7.png

Version	Author	Date
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
857bad3	jens-daniel-mueller	2021-03-24
05cc66b	jens-daniel-mueller	2021-03-24

Past versions of model_obs_comparison-8.png

Version	Author	Date
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
857bad3	jens-daniel-mueller	2021-03-24
05cc66b	jens-daniel-mueller	2021-03-24

Past versions of model_obs_comparison-9.png

Version	Author	Date
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
857bad3	jens-daniel-mueller	2021-03-24
05cc66b	jens-daniel-mueller	2021-03-24

Past versions of model_obs_comparison-10.png

Version	Author	Date
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
858c4e6	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
857bad3	jens-daniel-mueller	2021-03-24
05cc66b	jens-daniel-mueller	2021-03-24

Session information

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.2

Matrix products: default
BLAS:   /usr/local/R-4.0.3/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.0.3/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] gt_0.2.2        lubridate_1.7.9 metR_0.9.0      scico_1.2.0    
 [5] patchwork_1.1.1 collapse_1.5.0  forcats_0.5.0   stringr_1.4.0  
 [9] dplyr_1.0.5     purrr_0.3.4     readr_1.4.0     tidyr_1.1.2    
[13] tibble_3.0.4    ggplot2_3.3.3   tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] httr_1.4.2               viridisLite_0.3.0        jsonlite_1.7.1          
 [4] here_0.1                 modelr_0.1.8             assertthat_0.2.1        
 [7] blob_1.2.1               cellranger_1.1.0         yaml_2.2.1              
[10] pillar_1.4.7             backports_1.1.10         lattice_0.20-41         
[13] glue_1.4.2               RcppEigen_0.3.3.7.0      digest_0.6.27           
[16] RColorBrewer_1.1-2       promises_1.1.1           checkmate_2.0.0         
[19] rvest_0.3.6              colorspace_1.4-1         htmltools_0.5.0         
[22] httpuv_1.5.4             Matrix_1.2-18            pkgconfig_2.0.3         
[25] broom_0.7.5              haven_2.3.1              scales_1.1.1            
[28] whisker_0.4              later_1.1.0.1            git2r_0.27.1            
[31] generics_0.0.2           farver_2.0.3             ellipsis_0.3.1          
[34] withr_2.3.0              cli_2.1.0                magrittr_1.5            
[37] crayon_1.3.4             readxl_1.3.1             evaluate_0.14           
[40] fs_1.5.0                 fansi_0.4.1              xml2_1.3.2              
[43] RcppArmadillo_0.10.1.2.0 tools_4.0.3              data.table_1.13.2       
[46] hms_0.5.3                lifecycle_1.0.0          munsell_0.5.0           
[49] reprex_0.3.0             compiler_4.0.3           rlang_0.4.10            
[52] grid_4.0.3               rstudioapi_0.13          labeling_0.4.2          
[55] rmarkdown_2.5            gtable_0.3.0             DBI_1.1.0               
[58] R6_2.5.0                 knitr_1.30               utf8_1.1.4              
[61] rprojroot_2.0.2          stringi_1.5.3            parallel_4.0.3          
[64] Rcpp_1.0.5               vctrs_0.3.5              dbplyr_1.4.4            
[67] tidyselect_1.1.0         xfun_0.18