Last updated: 2020-09-08

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Rmd	d3ddb89	jens-daniel-mueller	2020-09-08	considered qc flag
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Rmd	41d3ba3	jens-daniel-mueller	2020-09-04	explanation added
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Rmd	4dcad9a	jens-daniel-mueller	2020-09-01	plots subsetting maps with facets
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Rmd	be824b0	jens-daniel-mueller	2020-09-01	talk subsetting stats flagging resolved
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html	4049eb0	jens-daniel-mueller	2020-09-01	Build site.
Rmd	782c595	jens-daniel-mueller	2020-09-01	cleaning process maps
html	c575584	jens-daniel-mueller	2020-08-31	Build site.
Rmd	824d7b7	jens-daniel-mueller	2020-08-31	add tco2 sampling grid to map
html	13a76d5	jens-daniel-mueller	2020-08-28	Build site.
Rmd	2e6a4ca	jens-daniel-mueller	2020-08-28	XXX
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html	0772e79	jens-daniel-mueller	2020-08-21	Build site.
Rmd	e04b03d	jens-daniel-mueller	2020-08-21	Individual cruise plots with all relevant parameters and histograms in eMLR
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Rmd	1a4d1a8	jens-daniel-mueller	2020-08-18	parameters included in mapping
html	66382bf	jens-daniel-mueller	2020-08-18	Build site.
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html	29a537a	jens-daniel-mueller	2020-08-18	Build site.
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html	4faaf29	jens-daniel-mueller	2020-08-18	Build site.
Rmd	a24a414	jens-daniel-mueller	2020-08-18	re-run with all parameters in one list
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html	dec27b2	jens-daniel-mueller	2020-08-13	Build site.
html	6fd35c8	jens-daniel-mueller	2020-08-13	Build site.
Rmd	634ae4e	jens-daniel-mueller	2020-08-13	cleaning and reference to parameters and masks
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html	1176c9a	jens-daniel-mueller	2020-08-12	Build site.
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Rmd	6344282	jens-daniel-mueller	2020-08-12	updated lon conversion, variable names and masks
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html	2d179e7	jens-daniel-mueller	2020-08-12	Build site.
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Rmd	b4fa5a8	jens-daniel-mueller	2020-07-31	link to all cruise plots included
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Rmd	304aa74	jens-daniel-mueller	2020-07-29	plot improvements
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Rmd	26db597	jens-daniel-mueller	2020-07-29	cleaned code and output
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html	c84424c	jens-daniel-mueller	2020-07-23	Build site.
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html	2df2065	jens-daniel-mueller	2020-07-23	Build site.
Rmd	fa350cf	jens-daniel-mueller	2020-07-23	predictor correlation plots, bin2d map plots
html	9d1d67d	jens-daniel-mueller	2020-07-23	Build site.
Rmd	3b6658b	jens-daniel-mueller	2020-07-23	predictor correlation plots, bin2d map plots
html	2e3691a	jens-daniel-mueller	2020-07-23	Build site.
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html	556e6cc	jens-daniel-mueller	2020-07-23	Build site.
html	c1289a2	jens-daniel-mueller	2020-07-23	Build site.
html	2890e73	jens-daniel-mueller	2020-07-23	Build site.
html	fdfa7b9	jens-daniel-mueller	2020-07-22	Build site.
html	bb9c002	jens-daniel-mueller	2020-07-21	Build site.
Rmd	d2ed0f8	jens-daniel-mueller	2020-07-21	harmonied lat lon labeling
html	b47adc2	jens-daniel-mueller	2020-07-20	Build site.
Rmd	366d7d5	jens-daniel-mueller	2020-07-20	update plots
html	25812b9	jens-daniel-mueller	2020-07-20	Build site.
Rmd	e45a882	jens-daniel-mueller	2020-07-20	basins included, higher grid resolution for maps
html	6a6431e	jens-daniel-mueller	2020-07-20	Build site.
Rmd	50be8de	jens-daniel-mueller	2020-07-20	formating
html	b9769e8	jens-daniel-mueller	2020-07-20	Build site.
Rmd	82120fe	jens-daniel-mueller	2020-07-20	nitrate, gamma included, 65N removed
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Rmd	66540a9	jens-daniel-mueller	2020-07-19	formating and color scale
html	61b3390	jens-daniel-mueller	2020-07-18	Build site.
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html	3964fd7	jens-daniel-mueller	2020-07-17	Build site.
Rmd	4685ad7	jens-daniel-mueller	2020-07-17	150m depth limit implemented
html	56c3ed9	jens-daniel-mueller	2020-07-14	Build site.
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html	74d4abd	jens-daniel-mueller	2020-07-14	Build site.
html	1c511ce	jens-daniel-mueller	2020-07-14	Build site.
Rmd	e03016e	jens-daniel-mueller	2020-07-14	split read in per data set

library(tidyverse)
library(lubridate)
library(patchwork)
library(cutr)

1 Read files

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

GLODAP <-
  read_csv(
    here::here(
      "data/GLODAPv2_2020/Merged_data_product",
      "GLODAPv2.2020_Merged_Master_File.csv"
    ),
    na = "-9999",
    col_types = cols(.default = col_double())
  )


# select relevant columns
GLODAP <- GLODAP %>%
  select(cruise:talkqc)

# harmonize column names
GLODAP <- GLODAP  %>%
  rename(sal = salinity,
         tem = temperature)

# harmonize coordinates
GLODAP <- GLODAP  %>%
  rename(lon = longitude,
         lat = latitude) %>%
  mutate(lon = if_else(lon < 20, lon + 360, lon))

# remove irrelevant columns
GLODAP <- GLODAP %>%
  select(-c(month:minute,
            maxsampdepth, bottle, sigma0:sigma4,
            nitrite:nitritef))

GLODAP <- GLODAP %>%
  mutate_at(vars(ends_with(c("f", "qc"))), as.factor)

2 Exploratory data analysis

source("code/eda.R")
eda(GLODAP, "GLODAPv2_2020")
rm(eda)

The output of an automated Exploratory Data Analysis (EDA) performed with the package DataExplorer can be accessed here:

Link to EDA report of GLODAPv2_2020 raw data

3 Data preparation

3.1 Horizontal gridding

For merging with other data sets, all observations were grouped into latitude intervals of:

1° x 1°

GLODAP <- GLODAP %>% 
  mutate(lat = cut(lat, seq(-90, 90, 1), seq(-89.5, 89.5, 1)),
         lat = as.numeric(as.character(lat)),
         lon = cut(lon, seq(20, 380, 1), seq(20.5, 379.5, 1)),
         lon = as.numeric(as.character(lon)))

3.2 Reference eras

Samples were assigned to following eras:

JGOFS_WOCE: 1981 - 1999
GO_SHIP: 2000 - 2012
new_era: > 2013

GLODAP <- GLODAP %>%
  filter(year >= parameters$year_JGOFS_start) %>% 
  mutate(era = "JGOFS_WOCE",
         era = if_else(year > parameters$year_JGOFS_end, "GO_SHIP", era),
         era = if_else(year > parameters$year_GOSHIP_end, "new_era", era))

GLODAP <- GLODAP %>% 
  mutate(era = factor(era, c("JGOFS_WOCE", "GO_SHIP", "new_era")))

3.3 Spatial boundaries

3.3.1 Depth

Only observations were taken into consideration with:

minimum sampling depth: 150m

# create observations grid and summary stats before remving data

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

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

##

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

3.3.2 Bottomdepth

Only observations were taken into consideration with:

minimum bottom depth: 500m

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

3.3.3 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 and will be removed for further analysis (eg North Sea, Sea of Japan).

GLODAP <- inner_join(GLODAP, basinmask)

##

GLODAP_stats_temp <- GLODAP %>% 
  summarise(spatial_boundaries = 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 = "spatial_boundaries")

GLODAP_obs_grid <-
  bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)

rm(GLODAP_obs_grid_temp)

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

ggplot() +
  geom_raster(data = landmask,
              aes(lon, lat), fill = "grey80") +
  geom_raster(data = basinmask, aes(lon, lat, fill = basin)) +
  geom_raster(data = GLODAP_obs, aes(lon, lat)) +
  scale_fill_brewer(palette = "Dark2") +
  coord_quickmap(expand = 0) +
  theme(legend.position = "top",
        legend.title = element_blank(),
        axis.title = element_blank())

rm(GLODAP_obs)

3.4 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 or calculated value (or Average of replicate, Manual chromatographic peak measurement)
- 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

Summary statistics were calculated during cleaning process.

3.4.1 tco2

3.4.1.1 NA

The vast majority of rows is removed due to missing tco2 observations.

GLODAP <- GLODAP %>% 
  filter(!is.na(tco2))

##

GLODAP_stats_temp <- GLODAP %>% 
  summarise(tco2_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 = "tco2_values")

GLODAP_obs_grid <-
  bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)

rm(GLODAP_obs_grid_temp)

3.4.1.2 f flag

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

ggplot() +
  geom_raster(data = landmask,
              aes(lon, lat),
              fill = "grey80") +
  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) +
  coord_quickmap(expand = 0) +
  theme(legend.position = "top")

###

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

# ##
# 
# GLODAP_stats_temp <- GLODAP %>%
#   summarise(tco2_f_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_f_flag")
# 
# GLODAP_obs_grid <-
#   bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)
# 
# rm(GLODAP_obs_grid_temp)

3.4.1.3 qc flag

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

ggplot() +
  geom_raster(data = landmask,
              aes(lon, lat),
              fill = "grey80") +
  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) +
  coord_quickmap(expand = 0) +
  theme(legend.position = "top")

###

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

##

GLODAP_stats_temp <- GLODAP %>%
  summarise(tco2_qc_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_qc_flag")

GLODAP_obs_grid <-
  bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)

rm(GLODAP_obs_grid_temp)

3.4.2 talk

3.4.2.1 NA

Quite a few tco2 observations are discarded, due to missing talk data, in particular in the JGOFS/WOCE era. However, there seems to be a high number of observations remaining from the affected cruises, so that the coverage does not seem to be reduced drastically.

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

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

ggplot() +
  geom_raster(data = landmask,
              aes(lon, lat),
              fill = "grey80") +
  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) +
  coord_quickmap(expand = 0) +
  theme(legend.position = "top")

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)

3.4.2.2 f flag

Restricting the f flag to 2 would results in data gaps in the south-east Pacific. Interpolated or calculated data (f flag 0) are therefore included.

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

ggplot() +
  geom_raster(data = landmask,
              aes(lon, lat), fill = "grey80") +
  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) +
  coord_quickmap(expand = 0) +
  theme(legend.position = "top",
        legend.title = element_blank())

# ###

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

##
# 
# GLODAP_stats_temp <- GLODAP %>%
#   summarise(talk_f_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_f_flag")
# 
# GLODAP_obs_grid <-
#   bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)
# 
# rm(GLODAP_obs_grid_temp)

3.4.2.3 qc flag

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

ggplot() +
  geom_raster(data = landmask,
              aes(lon, lat), fill = "grey80") +
  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) +
  coord_quickmap(expand = 0) +
  theme(legend.position = "top",
        legend.title = element_blank())

###

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

##

GLODAP_stats_temp <- GLODAP %>%
  summarise(talk_qc_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_qc_flag")

GLODAP_obs_grid <-
  bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)

rm(GLODAP_obs_grid_temp)

3.4.3 Phosphate

3.4.3.1 NA

Quite a few tco2/talk observations are discarded, due to missing phosphate data. However, there seems to be a high number of observations remaining from the affected cruises, so that the coverage does not seem to be reduced drastically.

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

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

ggplot() +
  geom_raster(data = landmask,
              aes(lon, lat),
              fill = "grey80") +
  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) +
  coord_quickmap(expand = 0) +
  theme(legend.position = "top")

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)

3.4.3.2 f flag

Restricting the f flag to 2 would not drastically reduce available data. Interpolated or calculated data (f flag 0) are therefore included.

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

ggplot() +
  geom_raster(data = landmask,
              aes(lon, lat), fill = "grey80") +
  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) +
  coord_quickmap(expand = 0) +
  theme(legend.position = "top",
        legend.title = element_blank())

###

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

# ##
# 
# GLODAP_stats_temp <- GLODAP %>%
#   summarise(phosphate_f_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_f_flag")
# 
# GLODAP_obs_grid <-
#   bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)
# 
# rm(GLODAP_obs_grid_temp)

3.4.3.3 qc flag

Phosphate data for which secondary quality was not applied (qc flag 0) were removed.

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

ggplot() +
  geom_raster(data = landmask,
              aes(lon, lat), fill = "grey80") +
  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) +
  coord_quickmap(expand = 0) +
  theme(legend.position = "top",
        legend.title = element_blank())

###

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

##

GLODAP_stats_temp <- GLODAP %>%
  summarise(phosphate_qc_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_qc_flag")

GLODAP_obs_grid <-
  bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)

rm(GLODAP_obs_grid_temp)

3.4.4 eMLR variables

Rows with missing eMLR variables were removed, only the qc flag was considered.

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

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

GLODAP <- GLODAP %>%
  filter(salinityf %in% parameters$flag_f)

GLODAP <- GLODAP %>%
  filter(salinityqc %in% parameters$flag_qc)

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

GLODAP <- GLODAP %>%
  filter(silicatef %in% parameters$flag_f)

GLODAP <- GLODAP %>%
  filter(silicateqc %in% parameters$flag_qc)
##
  
GLODAP <- GLODAP %>% 
  filter(!is.na(oxygen))

GLODAP <- GLODAP %>%
  filter(oxygenf %in% parameters$flag_f)

GLODAP <- GLODAP %>%
  filter(oxygenqc %in% parameters$flag_qc)
##

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

GLODAP <- GLODAP %>%
  filter(aouf %in% parameters$flag_f)
##
  
GLODAP <- GLODAP %>% 
  filter(!is.na(nitrate))

GLODAP <- GLODAP %>%
  filter(nitratef %in% parameters$flag_f)

GLODAP <- GLODAP %>%
  filter(nitrateqc %in% parameters$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)

3.4.5 Manual adjustment A16 cruise

For harmonization with Gruber et al. (2019), cruises 1041 (A16N) and 1042 (A16S) were grouped into the GO_SHIP area despite taking place in 2013/14.

GLODAP_cruises <- GLODAP %>% 
  filter(basin == "Atlantic",
         year %in% c(2013, 2014)) %>% 
  count(lat, lon, cruise)

ggplot() +
  geom_raster(data = landmask,
              aes(lon, lat), fill = "grey80") +
  geom_raster(data = GLODAP_cruises, aes(lon, lat, fill = as.factor(cruise))) +
  scale_fill_brewer(palette = "Dark2") +
  coord_quickmap(expand = 0) +
  theme(legend.position = "top",
        legend.title = element_blank())

rm(GLODAP_cruises)

GLODAP <- GLODAP %>%
  mutate(era = as.character(era)) %>% 
  mutate(era = if_else(cruise %in% c(1041, 1042),
                      "GO_SHIP", era))

GLODAP <- GLODAP %>% 
  mutate(era = factor(era, c("JGOFS_WOCE", "GO_SHIP", "new_era")))

3.5 Write summary file

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

GLODAP_stats_long  %>%  write_csv(
  here::here(
    "data/GLODAPv2_2020/_summarized_data_files",
    "GLODAPv2.2020_stats.csv"
  )
)

##

GLODAP_obs_grid  %>%
  filter(cleaning_level == "eMLR_variables") %>%
  write_csv(
    here::here(
      "data/GLODAPv2_2020/_summarized_data_files",
      "GLODAPv2.2020_clean_obs_grid.csv"
    )
  )

##

GLODAP <- GLODAP %>% 
  select(year, era, basin, lat, lon, cruise,
         bottomdepth, depth, tem, sal, gamma,
         tco2, talk, phosphate,
         oxygen, aou, nitrate, silicate)

GLODAP  %>%  write_csv(
  here::here(
    "data/GLODAPv2_2020/_summarized_data_files",
    "GLODAPv2.2020_clean.csv"
  )
)

4 Overview plots

4.1 Cleaning stats

Number of observations at various steps of data cleaning.

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

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

rm(GLODAP_stats_long)

4.2 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 <- GLODAP %>% 
  mutate(lat_grid = cut(lat, seq(-90, 90, 5), seq(-87.5, 87.5, 5)),
         lat_grid = as.numeric(as.character(lat_grid)),
         lon_grid = cut(lon, seq(20, 380, 5), seq(22.5, 377.5, 5)),
         lon_grid = as.numeric(as.character(lon_grid)))

4.3 Histogram Zonal coverage

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

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

rm(GLODAP_histogram_lat)

4.4 Histogram temporal coverage

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

era_median_year <- GLODAP %>%
  group_by(era) %>%
  summarise(t_ref = median(year)) %>%
  ungroup()

GLODAP_histogram_year %>%
  ggplot() +
  geom_vline(xintercept = c(
    parameters$year_JGOFS_end + 0.5,
    parameters$year_GOSHIP_end + 0.5
  )) +
  geom_col(aes(year, n, fill = basin)) +
  geom_point(
    data = era_median_year,
    aes(t_ref, 0, shape = "Median year"),
    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()
  )

rm(GLODAP_histogram_year,
   era_median_year)

4.5 Zonal temporal coverage (Hovmoeller)

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

GLODAP_hovmoeller_year %>%
  ggplot(aes(year, lat_grid, fill = log10(n))) +
  geom_tile() +
  geom_vline(xintercept = c(1999.5, 2012.5)) +
  scale_fill_viridis_c(option = "magma", direction = -1) +
  facet_wrap( ~ basin, ncol = 1) +
  theme(legend.position = "top",
        axis.title.x = element_blank())

rm(GLODAP_hovmoeller_year)

4.6 Coverage maps by era

4.6.1 Subsetting process

GLODAP_obs_grid <- GLODAP_obs_grid %>%
  mutate(era = factor(era, c("JGOFS_WOCE", "GO_SHIP", "new_era")),
         cleaning_level = factor(
           cleaning_level,
           unique(GLODAP_obs_grid$cleaning_level)
         ))



ggplot() +
  geom_raster(data = landmask,
              aes(lon, lat),
              fill = "grey80") +
  geom_raster(data = GLODAP_obs_grid %>%
                filter(cleaning_level == "all") %>% 
                select(-cleaning_level),
              aes(lon, lat, fill = "all")) +
  geom_raster(data = GLODAP_obs_grid %>%
                filter(cleaning_level != "all"),
              aes(lon, lat, fill = "subset")) +
  coord_quickmap(expand = FALSE) +
  scale_fill_manual(values = c("grey", "red"), name = "") +
  facet_grid(cleaning_level ~ era) +
  theme(legend.position = "top",
        axis.title = element_blank())

4.6.2 eMLR variables

Grey pixels refer to sampling locations filtered for availability of tco2 data only.

GLODAP <- GLODAP %>% 
  mutate(era = factor(era, c("JGOFS_WOCE", "GO_SHIP", "new_era")))

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

GLODAP %>%
  ggplot(aes(lon, lat)) +
  geom_raster(data = landmask,
              aes(lon, lat), fill = "grey80") +
  geom_raster(data = GLODAP_tco2_grid, aes(lon, lat), fill = "grey80") +
  geom_bin2d(binwidth = c(1,1)) +
  scale_fill_viridis_c(option = "magma", direction = -1, trans = "log10",
                       name = "log10(eMLR_variables)") +
  coord_quickmap(expand = FALSE) +
  facet_wrap(~era, ncol = 1) +
  theme(legend.position = "top",
        axis.title = element_blank())

sessionInfo()

R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

locale:
[1] LC_COLLATE=English_Germany.1252  LC_CTYPE=English_Germany.1252   
[3] LC_MONETARY=English_Germany.1252 LC_NUMERIC=C                    
[5] LC_TIME=English_Germany.1252    

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

other attached packages:
 [1] cutr_0.0.0.9000 patchwork_1.0.1 lubridate_1.7.9 forcats_0.5.0  
 [5] stringr_1.4.0   dplyr_1.0.0     purrr_0.3.4     readr_1.3.1    
 [9] tidyr_1.1.0     tibble_3.0.3    ggplot2_3.3.2   tidyverse_1.3.0
[13] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5         here_0.1           assertthat_0.2.1   rprojroot_1.3-2   
 [5] digest_0.6.25      R6_2.4.1           cellranger_1.1.0   backports_1.1.8   
 [9] reprex_0.3.0       evaluate_0.14      httr_1.4.2         pillar_1.4.6      
[13] rlang_0.4.7        readxl_1.3.1       rstudioapi_0.11    whisker_0.4       
[17] blob_1.2.1         rmarkdown_2.3      labeling_0.3       munsell_0.5.0     
[21] broom_0.7.0        compiler_4.0.2     httpuv_1.5.4       modelr_0.1.8      
[25] xfun_0.16          pkgconfig_2.0.3    htmltools_0.5.0    tidyselect_1.1.0  
[29] viridisLite_0.3.0  fansi_0.4.1        crayon_1.3.4       dbplyr_1.4.4      
[33] withr_2.2.0        later_1.1.0.1      grid_4.0.2         jsonlite_1.7.0    
[37] gtable_0.3.0       lifecycle_0.2.0    DBI_1.1.0          git2r_0.27.1      
[41] magrittr_1.5       scales_1.1.1       cli_2.0.2          stringi_1.4.6     
[45] farver_2.0.3       fs_1.4.2           promises_1.1.1     xml2_1.3.2        
[49] ellipsis_0.3.1     generics_0.0.2     vctrs_0.3.2        RColorBrewer_1.1-2
[53] tools_4.0.2        glue_1.4.1         hms_0.5.3          yaml_2.2.1        
[57] colorspace_1.4-1   rvest_0.3.6        knitr_1.29         haven_2.3.1

GLODAPv2_2020

Jens Daniel Müller

08 September, 2020