World Ocean Atlas 2018
Jens Daniel Müller
23 July, 2020
Last updated: 2020-07-23
Checks: 7 0
Knit directory: Cant_eMLR/
This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20200707) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version ffd46da. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .Rproj.user/
Ignored: analysis/figure/
Ignored: data/GLODAPv2_2016b_MappedClimatologies/
Ignored: data/GLODAPv2_2020/
Ignored: data/World_Ocean_Atlas_2018/
Ignored: data/pCO2_atmosphere/
Ignored: dump/
Ignored: output/figure/
Unstaged changes:
Modified: analysis/eMLR.Rmd
Modified: analysis/read_GLODAPv2_2020.Rmd
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/read_World_Ocean_Atlas_2018.Rmd) and HTML (docs/read_World_Ocean_Atlas_2018.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 | ffd46da | jens-daniel-mueller | 2020-07-23 | WOA18 read in |
| 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 | 97dbf5b | jens-daniel-mueller | 2020-07-21 | Build site. |
| Rmd | 5def7e8 | jens-daniel-mueller | 2020-07-21 | create csvs for each parameter |
| html | b47adc2 | jens-daniel-mueller | 2020-07-20 | Build site. |
| Rmd | 366d7d5 | jens-daniel-mueller | 2020-07-20 | update plots |
| html | 1de1bc0 | jens-daniel-mueller | 2020-07-20 | Build site. |
| Rmd | 68e4615 | jens-daniel-mueller | 2020-07-20 | assigned ocean basins and plotted world map |
| html | 3149ffd | jens-daniel-mueller | 2020-07-20 | Build site. |
| Rmd | 918f8c8 | jens-daniel-mueller | 2020-07-20 | assigned ocean basins and plotted world map |
| html | 2fa2896 | jens-daniel-mueller | 2020-07-20 | Build site. |
| Rmd | 23220a8 | jens-daniel-mueller | 2020-07-20 | assigned ocean basins and plotted world map |
| html | 27d380d | jens-daniel-mueller | 2020-07-20 | Build site. |
| Rmd | 09f3f73 | jens-daniel-mueller | 2020-07-20 | basin masks read in |
| html | 22b588c | jens-daniel-mueller | 2020-07-18 | Build site. |
| Rmd | 87a4680 | jens-daniel-mueller | 2020-07-18 | added WOA blank script |
library(tidyverse)
library(lubridate)
library(tidync)
library(stars)1 Data source
- Data source: World Ocean Atlas 2018
2 Basin mask
2.1 Read data
The surface mask (0m) with 1x1° resolution from the file basinmask_01.msk was used.
basinmask_01 <- read_csv(here::here("data/World_Ocean_Atlas_2018",
"basinmask_01.msk"),
skip = 1)
basinmask_01 <- basinmask_01 %>%
select(Latitude:Basin_0m) %>%
mutate(Basin_0m = as.factor(Basin_0m)) %>%
rename(lat = Latitude, lon = Longitude)2.2 Basin labels
According to WOA FAQ website, number codes in the mask file refer to Ocean basins as follows:
- 1: Atlantic Ocean (with 10 - Southern Ocean, between 63°W and 20°E)
- 2: Pacific Ocean (with 10 - Southern Ocean, between 147°E and 63°W)
- 3: Indian Ocean (with 10 - Southern Ocean, between 20°E and 147°E)
From this, the Atlantik and the Indo-Pacific were labeled.
basinmask_01 <- basinmask_01 %>%
filter(Basin_0m %in% c("1", "2", "3", "10")) %>%
mutate(basin = if_else(Basin_0m == "10" & lon >= -63 & lon < 20,
"Atlantic", "Indo-Pacific"),
basin = if_else(Basin_0m == "1",
"Atlantic", basin)) %>%
select(-Basin_0m)2.3 Plot world map
mapWorld <- borders("world", colour="gray60", fill="gray60")
basinmask_01 %>%
ggplot(aes(lon, lat, fill=basin))+
mapWorld+
geom_raster()+
scale_fill_brewer(palette = "Dark2")+
coord_quickmap(expand = 0)
rm(mapWorld)2.4 Write file
basinmask_01 %>%
write_csv(here::here("data/World_Ocean_Atlas_2018/_summarized_files",
"basin_mask_WOA18.csv"))
rm(basinmask_01)3 Read/plot ncdf files
File naming conventions: [PREF][DDDD][V][TT][FF][GG].[EXT], where:
[PREF] - prefix
[DDDD] - decade
[V] - variable
[TT] - time period
[FF] - field type
[GG] - grid (5deg- 5°, 01- 1°, 04 - 1/4°)
[EXT] - file extentionNote: ‘.dat’ - ASCII; ‘.csv’ - comma separated value; ‘.dbf’, ‘.shp’, ‘.shx’ - ArcGIS shape files; ‘.nc’ - netCDF files
Short description of the statistical fields in WOA
- Objectively analyzed climatologies are the objectively interpolated mean fields for oceanographic variables at standard - depth levels for the World Ocean.
- The statistical mean is the average of all unflagged interpolated values at each standard depth level for each variable - in each 1° square which contains at least one measurement for the given oceanographic variable.
- The number of observations of each variable in each 1° square of the World Ocean at each standard depth level.
- The standard deviation about the statistical mean of each variable in each 1° square at each standard depth level.
- The standard error of the mean of each variable in each 1° square at each standard depth level.
- The seasonal or monthly climatology minus the annual climatology at each 1° square at each standard depth.
- The statistical mean minus the climatological mean at each 1° square at each standard depth. This value is used as an estimate of interpolation and smoothing error.
- The number of 1° squares within the smallest radius of influence around each 1° square which contain a statistical mean.
WOA_tem <- tidync(here::here("data/World_Ocean_Atlas_2018",
"woa18_decav_t00_01.nc"))
print(WOA_tem)
Data Source (1): woa18_decav_t00_01.nc ...
Grids (10) <dimension family> : <associated variables>
[1] D2,D1,D3,D4 : t_an, t_mn, t_dd, t_sd, t_se, t_oa, t_gp **ACTIVE GRID** ( 6609600 values per variable)
[2] D0,D1 : lat_bnds
[3] D0,D2 : lon_bnds
[4] D0,D3 : depth_bnds
[5] D0,D4 : climatology_bounds
[6] D1 : lat
[7] D2 : lon
[8] D3 : depth
[9] D4 : time
[10] S : crs
Dimensions 5 (4 active):
dim name length min max start count dmin dmax unlim coord_dim
<chr> <chr> <dbl> <dbl> <dbl> <int> <int> <dbl> <dbl> <lgl> <lgl>
1 D1 lat 180 -89.5 89.5 1 180 -89.5 89.5 FALSE TRUE
2 D2 lon 360 -180. 180. 1 360 -180. 180. FALSE TRUE
3 D3 depth 102 0 5500 1 102 0 5500 FALSE TRUE
4 D4 time 1 4326 4326 1 1 4326 4326 FALSE TRUE
Inactive dimensions:
dim name length min max unlim coord_dim
<chr> <chr> <dbl> <dbl> <dbl> <lgl> <lgl>
1 D0 nbounds 2 1 2 FALSE FALSE WOA_tem_tibble <- WOA_tem %>% hyper_tibble()
WOA_tem_tibble <- WOA_tem_tibble %>%
select(t_an, lon, lat, depth) %>%
drop_na()
WOA_tem_tibble %>%
filter(depth == 0) %>%
ggplot(aes(lon, lat, fill=t_an))+
geom_raster()+
coord_quickmap()+
scale_fill_viridis_c()
WOA_tem_tibble %>%
filter(lon == -20.5) %>%
ggplot(aes(lat, depth, z=t_an))+
geom_contour_filled()+
scale_y_reverse()+
coord_cartesian(expand = 0)
WOA_sal <- tidync(here::here("data/World_Ocean_Atlas_2018",
"woa18_decav_s00_01.nc"))
print(WOA_sal)
Data Source (1): woa18_decav_s00_01.nc ...
Grids (10) <dimension family> : <associated variables>
[1] D2,D1,D3,D4 : s_an, s_mn, s_dd, s_sd, s_se, s_oa, s_gp **ACTIVE GRID** ( 6609600 values per variable)
[2] D0,D1 : lat_bnds
[3] D0,D2 : lon_bnds
[4] D0,D3 : depth_bnds
[5] D0,D4 : climatology_bounds
[6] D1 : lat
[7] D2 : lon
[8] D3 : depth
[9] D4 : time
[10] S : crs
Dimensions 5 (4 active):
dim name length min max start count dmin dmax unlim coord_dim
<chr> <chr> <dbl> <dbl> <dbl> <int> <int> <dbl> <dbl> <lgl> <lgl>
1 D1 lat 180 -89.5 89.5 1 180 -89.5 89.5 FALSE TRUE
2 D2 lon 360 -180. 180. 1 360 -180. 180. FALSE TRUE
3 D3 depth 102 0 5500 1 102 0 5500 FALSE TRUE
4 D4 time 1 4326 4326 1 1 4326 4326 FALSE TRUE
Inactive dimensions:
dim name length min max unlim coord_dim
<chr> <chr> <dbl> <dbl> <dbl> <lgl> <lgl>
1 D0 nbounds 2 1 2 FALSE FALSE WOA_sal_tibble <- WOA_sal %>% hyper_tibble()
WOA_sal_tibble <- WOA_sal_tibble %>%
select(s_an, lon, lat, depth) %>%
drop_na()
WOA_sal_tibble %>%
filter(depth == 0) %>%
ggplot(aes(lon, lat, fill=s_an))+
geom_raster()+
coord_quickmap()+
scale_fill_viridis_c()
WOA_sal_tibble %>%
filter(lon == -20.5) %>%
ggplot(aes(lat, depth, z=s_an))+
geom_contour_filled()+
scale_y_reverse()+
coord_cartesian(expand = 0)
4 Open tasks
- none
5 Questions
- none
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: i386-w64-mingw32/i386 (32-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] stars_0.4-3 sf_0.9-4 abind_1.4-5 tidync_0.2.4
[5] lubridate_1.7.9 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.0
[9] purrr_0.3.4 readr_1.3.1 tidyr_1.1.0 tibble_3.0.3
[13] ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 whisker_0.4 knitr_1.29 xml2_1.3.2
[5] magrittr_1.5 maps_3.3.0 units_0.6-7 hms_0.5.3
[9] rvest_0.3.5 tidyselect_1.1.0 viridisLite_0.3.0 here_0.1
[13] colorspace_1.4-1 R6_2.4.1 rlang_0.4.7 fansi_0.4.1
[17] parallel_3.6.3 broom_0.7.0 xfun_0.15 e1071_1.7-3
[21] ncmeta_0.2.5 dbplyr_1.4.4 modelr_0.1.8 withr_2.2.0
[25] git2r_0.27.1 ellipsis_0.3.1 htmltools_0.5.0 class_7.3-17
[29] assertthat_0.2.1 rprojroot_1.3-2 digest_0.6.25 lifecycle_0.2.0
[33] RNetCDF_2.3-1 haven_2.3.1 rmarkdown_2.3 compiler_3.6.3
[37] cellranger_1.1.0 pillar_1.4.6 scales_1.1.1 backports_1.1.5
[41] generics_0.0.2 classInt_0.4-3 jsonlite_1.7.0 httpuv_1.5.4
[45] pkgconfig_2.0.3 rstudioapi_0.11 munsell_0.5.0 blob_1.2.1
[49] httr_1.4.1 tools_3.6.3 grid_3.6.3 gtable_0.3.0
[53] utf8_1.1.4 KernSmooth_2.23-16 DBI_1.1.0 cli_2.0.2
[57] readxl_1.3.1 yaml_2.2.1 lwgeom_0.2-5 crayon_1.3.4
[61] farver_2.0.3 RColorBrewer_1.1-2 later_1.1.0.1 promises_1.1.1
[65] fs_1.4.2 vctrs_0.3.1 isoband_0.2.2 ncdf4_1.17
[69] glue_1.4.1 evaluate_0.14 labeling_0.3 reprex_0.3.0
[73] stringi_1.4.6