Mapping cant

workflowr

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Last updated: 2021-06-18

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/mapping_cant_mod_truth.Rmd) and HTML (docs/mapping_cant_mod_truth.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
html	a4075ac	jens-daniel-mueller	2021-06-16	Build site.
Rmd	3347668	jens-daniel-mueller	2021-06-16	write cant files
html	4be8cf5	jens-daniel-mueller	2021-06-16	Build site.
Rmd	b9839eb	jens-daniel-mueller	2021-06-16	cant constant climate model
html	9dca16b	jens-daniel-mueller	2021-06-15	Build site.
Rmd	d4461ca	jens-daniel-mueller	2021-06-15	revise plots
html	993d705	jens-daniel-mueller	2021-06-15	Build site.
Rmd	d603f12	jens-daniel-mueller	2021-06-15	revise plots
html	3052a6c	jens-daniel-mueller	2021-06-15	Build site.
html	3cb64bd	jens-daniel-mueller	2021-06-15	Build site.
html	0d159f9	jens-daniel-mueller	2021-06-15	Build site.
Rmd	6873303	jens-daniel-mueller	2021-06-15	alpha model truth analysed
html	c6b3da6	jens-daniel-mueller	2021-06-14	Build site.
html	48c73fc	jens-daniel-mueller	2021-06-14	Build site.
html	439ee80	jens-daniel-mueller	2021-06-11	Build site.
html	33ffcab	jens-daniel-mueller	2021-06-10	Build site.
html	7e1f407	jens-daniel-mueller	2021-06-10	Build site.
html	2cbe18c	jens-daniel-mueller	2021-06-10	added zonal mean section control plots
html	69c79d0	jens-daniel-mueller	2021-06-08	Build site.
html	1772903	jens-daniel-mueller	2021-06-07	Build site.
html	594ed9a	jens-daniel-mueller	2021-06-04	Build site.
html	db7df0e	jens-daniel-mueller	2021-06-04	rebuild without overlapping eras
html	2edc791	jens-daniel-mueller	2021-06-04	Build site.
html	207339d	jens-daniel-mueller	2021-06-03	Build site.
html	315710b	jens-daniel-mueller	2021-06-03	include anomalous changes
html	be90356	jens-daniel-mueller	2021-06-02	Build site.
html	d37a85d	jens-daniel-mueller	2021-05-31	Build site.
html	4b7a5ee	jens-daniel-mueller	2021-05-28	Build site.
html	12b455a	jens-daniel-mueller	2021-05-27	Build site.
Rmd	ee13efb	jens-daniel-mueller	2021-05-27	optional source of local params fully implemented
html	8c736a6	jens-daniel-mueller	2021-05-27	Build site.
Rmd	dc8e4e1	jens-daniel-mueller	2021-05-27	optional source of local params implemented
html	25bd183	jens-daniel-mueller	2021-05-26	Build site.
html	b79cb2d	jens-daniel-mueller	2021-05-20	Build site.
html	62bd574	jens-daniel-mueller	2021-05-20	Build site.
html	7c56c39	jens-daniel-mueller	2021-05-19	Build site.
html	0de759e	jens-daniel-mueller	2021-05-13	Build site.
html	52e7583	jens-daniel-mueller	2021-05-12	Build site.
html	969e631	jens-daniel-mueller	2021-05-12	Build site.
html	d2a83bc	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
html	a00ec94	jens-daniel-mueller	2021-04-16	Build site.
Rmd	82df560	jens-daniel-mueller	2021-04-16	corrected tref assignment
html	b6fe355	jens-daniel-mueller	2021-04-16	Build site.
html	81b7c6d	jens-daniel-mueller	2021-04-16	Build site.
html	0a6099c	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.
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	f6ed7de	jens-daniel-mueller	2021-04-13	Build site.
Rmd	3efd03e	jens-daniel-mueller	2021-04-13	included model data
html	f8ce165	jens-daniel-mueller	2021-04-13	Build site.
Rmd	b7d14dc	jens-daniel-mueller	2021-04-13	included model data

---

1 Version ID

The results displayed on this site correspond to the Version_ID: v_XXX

2 Required data

2.1 Predictor fields

tref  <-
  read_csv(paste(path_version_data,
                 "tref.csv",
                 sep = ""))

cant_tref_1 <-
  read_csv(
    paste(
      path_model_preprocessing,
      "cant_annual_field_AD",
      "/cant_",
      unique(tref$median_year[1]),
      ".csv",
      sep = ""
    )
  )

cant_tref_1 <- cant_tref_1 %>%
  rename(cant_tref_1 = cant_total) %>%
  select(-year)

cant_tref_2 <-
  read_csv(
    paste(
      path_model_preprocessing,
      "cant_annual_field_AD",
      "/cant_",
      unique(tref$median_year[2]),
      ".csv",
      sep = ""
    )
  )

cant_tref_2 <- cant_tref_2 %>%
  rename(cant_tref_2 = cant_total) %>%
  select(-year)

cant_cc_tref_1 <-
  read_csv(
    paste(
      path_model_preprocessing,
      "cant_annual_field_CB",
      "/cant_",
      unique(tref$median_year[1]),
      ".csv",
      sep = ""
    )
  )

cant_cc_tref_1 <- cant_cc_tref_1 %>%
  rename(cant_tref_1 = cant_total) %>%
  select(-year)

cant_cc_tref_2 <-
  read_csv(
    paste(
      path_model_preprocessing,
      "cant_annual_field_CB",
      "/cant_",
      unique(tref$median_year[2]),
      ".csv",
      sep = ""
    )
  )

cant_cc_tref_2 <- cant_cc_tref_2 %>%
  rename(cant_tref_2 = cant_total) %>%
  select(-year)

climatology <-
  read_csv(paste(path_model_preprocessing, "climatology_runA_2007.csv", sep = ""))

climatology <- climatology %>%
  select(lon,lat,depth, gamma)

cant_average <- left_join(cant_tref_1, cant_tref_2) %>%
  mutate(cant = cant_tref_2 - cant_tref_1)

rm(cant_tref_1, cant_tref_2)

cant_average <- cant_average %>%
  mutate(cant_pos = if_else(cant <= 0, 0, cant))

cant_average <- inner_join(basinmask, cant_average)

cant_average <- full_join(cant_average, climatology)

cant_average <-
  m_cant_model_average(cant_average %>% mutate(eras = "blank"))

cant_average <- m_cut_gamma(cant_average, "gamma")

cant_average_vc <- cant_average %>% 
  mutate(data_source = "mod_truth") %>% 
  select(lon, lat, depth, eras, basin, basin_AIP, data_source,
            cant_tref_1, cant, cant_pos, cant_sd, cant_pos_sd,
            gamma, gamma_sd, gamma_slab)

cant_average <- left_join(cant_cc_tref_1, cant_cc_tref_2) %>%
  mutate(cant = cant_tref_2 - cant_tref_1)

rm(cant_cc_tref_1, cant_cc_tref_2)

cant_average <- cant_average %>%
  mutate(cant_pos = if_else(cant <= 0, 0, cant))

cant_average <- inner_join(basinmask, cant_average)

cant_average <- full_join(cant_average, climatology)

cant_average <-
  m_cant_model_average(cant_average %>% mutate(eras = "blank"))

cant_average <- m_cut_gamma(cant_average, "gamma")

cant_average_cc <- cant_average %>% 
  mutate(data_source = "mod_truth_cc") %>% 
  select(lon, lat, depth, eras, basin, basin_AIP, data_source,
            cant_tref_1, cant, cant_pos, cant_sd, cant_pos_sd,
            gamma, gamma_sd, gamma_slab)

rm(climatology, cant_average)

cant_average <- bind_rows(
  cant_average_cc,
  cant_average_vc
)

rm(
  cant_average_cc,
  cant_average_vc
)

2.2 Average model Cant

cant_average %>%
  group_split(data_source) %>%
  # head(1) %>%
  map(~ p_map_climatology(
    df = .x,
    var = "cant_pos",
    subtitle_text = paste("Climate: ", .x$data_source)
  ))

[[1]]

Past versions of cant_deep_climatology_map-1.png

Version	Author	Date
4be8cf5	jens-daniel-mueller	2021-06-16
7e1f407	jens-daniel-mueller	2021-06-10
2cbe18c	jens-daniel-mueller	2021-06-10
1772903	jens-daniel-mueller	2021-06-07
594ed9a	jens-daniel-mueller	2021-06-04
db7df0e	jens-daniel-mueller	2021-06-04
207339d	jens-daniel-mueller	2021-06-03
315710b	jens-daniel-mueller	2021-06-03
be90356	jens-daniel-mueller	2021-06-02
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
a00ec94	jens-daniel-mueller	2021-04-16
b6fe355	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
f8ce165	jens-daniel-mueller	2021-04-13

[[2]]

Past versions of cant_deep_climatology_map-2.png

Version	Author	Date
4be8cf5	jens-daniel-mueller	2021-06-16

cant_average %>%
  group_split(data_source) %>%
  # head(1) %>%
  map(~ p_section_climatology_regular(
    df = .x,
    var = "cant_pos",
    subtitle_text = paste("Climate: ", .x$data_source)
  ))

[[1]]

Past versions of cant_deep_climatology_sections-1.png

Version	Author	Date
4be8cf5	jens-daniel-mueller	2021-06-16
7e1f407	jens-daniel-mueller	2021-06-10
2cbe18c	jens-daniel-mueller	2021-06-10
594ed9a	jens-daniel-mueller	2021-06-04
db7df0e	jens-daniel-mueller	2021-06-04
207339d	jens-daniel-mueller	2021-06-03
315710b	jens-daniel-mueller	2021-06-03
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
a00ec94	jens-daniel-mueller	2021-04-16
b6fe355	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
f8ce165	jens-daniel-mueller	2021-04-13

[[2]]

Past versions of cant_deep_climatology_sections-2.png

Version	Author	Date
4be8cf5	jens-daniel-mueller	2021-06-16

2.3 Zonal mean sections

cant_average_zonal <- m_cant_zonal_mean_data_source(cant_average)
cant_average_zonal <- m_cut_gamma(cant_average_zonal, "gamma_mean")

2.4 Inventory calculation

To calculate Cant column inventories, we:

1. Convert Cant concentrations to volumetric units
2. Multiply layer thickness with volumetric Cant concentration to get a layer inventory
3. For each horizontal grid cell and era, sum cant layer inventories for different inventory depths (100, 500, 1000, 3000, 10^{4} m)

Step 2 is performed separately for all Cant and positive Cant values only.

cant_inv <- m_cant_inv_data_source(cant_average)

p_map_cant_inv(df = cant_inv,
               var = "cant_pos_inv",
               subtitle_text = "for predefined integration depths") +
  facet_grid(inv_depth ~ data_source)

Past versions of cant_inventories-1.png

Version	Author	Date
4be8cf5	jens-daniel-mueller	2021-06-16
7e1f407	jens-daniel-mueller	2021-06-10
2cbe18c	jens-daniel-mueller	2021-06-10
1772903	jens-daniel-mueller	2021-06-07
594ed9a	jens-daniel-mueller	2021-06-04
db7df0e	jens-daniel-mueller	2021-06-04
207339d	jens-daniel-mueller	2021-06-03
315710b	jens-daniel-mueller	2021-06-03
be90356	jens-daniel-mueller	2021-06-02
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
a00ec94	jens-daniel-mueller	2021-04-16
b6fe355	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
f8ce165	jens-daniel-mueller	2021-04-13

cant_total_inv <- m_cant_inv_data_source(
  cant_average %>%
    select(-c(cant, cant_pos)) %>%
    rename(cant = cant_tref_1) %>%
    mutate(cant_pos = if_else(cant <= 0, 0, cant))
)

p_map_cant_inv(df = cant_total_inv,
               var = "cant_pos_inv",
               subtitle_text = "for predefined integration depths",
               breaks = seq(0,50,5)) +
  facet_grid(inv_depth ~ data_source)

Past versions of cant_total_inventories-1.png

Version	Author	Date
4be8cf5	jens-daniel-mueller	2021-06-16
0d159f9	jens-daniel-mueller	2021-06-15

3 Alpha

3.1 3d fields

alpha_3d <- cant_average %>% 
  mutate(alpha = cant / cant_tref_1,
         alpha = if_else(cant <= 0 | cant_tref_1 <= 0,
                         NaN, alpha))

alpha_3d %>%
  filter(alpha < 1) %>%
  ggplot(aes(alpha)) +
  geom_histogram() +
  facet_grid(basin_AIP ~ data_source) +
  scale_x_continuous(breaks = seq(0, 1, 0.1))

Past versions of alpha_3d-1.png

Version	Author	Date
4be8cf5	jens-daniel-mueller	2021-06-16
0d159f9	jens-daniel-mueller	2021-06-15

median_alpha <- 
  alpha_3d %>% 
  group_by(depth, basin_AIP, data_source) %>% 
  summarise(alpha_median = median(alpha, na.rm = TRUE),
            alpha_mean = weighted.mean(alpha, cant_tref_1, na.rm = TRUE)) %>% 
  ungroup()

alpha_3d %>%
  filter(alpha < 1) %>%
  ggplot(aes(alpha, depth)) +
  geom_bin2d(binwidth = c(0.01, 200)) +
  geom_path(data = median_alpha,
            aes(alpha_median, depth, col="Median")) +
  geom_path(data = median_alpha,
            aes(alpha_mean, depth, col="Weighted mean")) +
  scale_color_manual(name = "Alpha",
                     values = c("red", "orange")) +
  scale_y_reverse() +
  scale_x_continuous(breaks = seq(0, 1, 0.1)) +
  scale_fill_viridis_c(trans = "log10") +
  coord_cartesian(expand = 0) +
  facet_grid(basin_AIP~data_source)

Past versions of alpha_3d-2.png

Version	Author	Date
4be8cf5	jens-daniel-mueller	2021-06-16
993d705	jens-daniel-mueller	2021-06-15
0d159f9	jens-daniel-mueller	2021-06-15

print(paste("mean:",
            round(mean(
              alpha_3d$alpha, na.rm = TRUE
            ), 3)))

[1] "mean: 0.36"

print(paste("mean, weighted with Cant:",
            round(
              weighted.mean(
                x = alpha_3d$alpha,
                w = alpha_3d$cant_tref_1,
                na.rm = TRUE
              ),
              3
            )))

[1] "mean, weighted with Cant: 0.166"

print(paste("median:",
            round(median(
              alpha_3d$alpha, na.rm = TRUE
            ), 3)))

[1] "median: 0.172"

3.2 Inventory maps

alpha_inv <- bind_rows(
  cant_inv %>% mutate(estimate = "delta_cant"),
  cant_total_inv %>% mutate(estimate = "total_cant")
)

alpha_inv <- alpha_inv %>%
  select(lon, lat, cant_inv, inv_depth, estimate, data_source) %>%
  pivot_wider(names_from = estimate,
              values_from = cant_inv)

alpha_inv <- alpha_inv %>% 
  mutate(alpha = delta_cant / total_cant)


map +
  geom_raster(data = alpha_inv #%>% filter(inv_depth == params_global$inventory_depth_standard)
              ,
              aes(lon, lat, fill = alpha)) +
  facet_grid(inv_depth ~ data_source) +
  scale_fill_divergent(
    midpoint = median(alpha_inv$alpha)
  )

Past versions of alpha_surface_distribution-1.png

Version	Author	Date
4be8cf5	jens-daniel-mueller	2021-06-16
9dca16b	jens-daniel-mueller	2021-06-15
993d705	jens-daniel-mueller	2021-06-15
0d159f9	jens-daniel-mueller	2021-06-15

4 Write csv

cant_average <- cant_average %>%
  select(-c(cant_tref_1))

cant_average %>%
  filter(data_source == "mod_truth") %>%
  write_csv(paste(path_version_data,
                  "cant_3d_mod_truth.csv", sep = ""))

cant_average %>%
  filter(data_source == "mod_truth_cc") %>%
  write_csv(paste(path_version_data,
                  "cant_3d_mod_truth_cc.csv", sep = ""))

cant_average_zonal %>%
  filter(data_source == "mod_truth_cc") %>%
  write_csv(paste(path_version_data,
                  "cant_zonal_mod_truth_cc.csv", sep = ""))

cant_average_zonal %>%
  filter(data_source == "mod_truth") %>%
  write_csv(paste(path_version_data,
                  "cant_zonal_mod_truth.csv", sep = ""))

cant_inv %>%
  filter(data_source == "mod_truth") %>%
  write_csv(paste(path_version_data,
                  "cant_inv_mod_truth.csv", sep = ""))

cant_inv %>%
  filter(data_source == "mod_truth_cc") %>%
  write_csv(paste(path_version_data,
                  "cant_inv_mod_truth_cc.csv", sep = ""))

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] metR_0.9.0      scico_1.2.0     patchwork_1.1.1 collapse_1.5.0 
 [5] forcats_0.5.0   stringr_1.4.0   dplyr_1.0.5     purrr_0.3.4    
 [9] readr_1.4.0     tidyr_1.1.2     tibble_3.0.4    ggplot2_3.3.3  
[13] tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] httr_1.4.2               jsonlite_1.7.1           viridisLite_0.3.0       
 [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] promises_1.1.1           checkmate_2.0.0          rvest_0.3.6             
[19] colorspace_1.4-1         htmltools_0.5.0          httpuv_1.5.4            
[22] Matrix_1.2-18            pkgconfig_2.0.3          broom_0.7.5             
[25] haven_2.3.1              scales_1.1.1             whisker_0.4             
[28] later_1.1.0.1            git2r_0.27.1             generics_0.0.2          
[31] farver_2.0.3             ellipsis_0.3.1           withr_2.3.0             
[34] cli_2.1.0                magrittr_1.5             crayon_1.3.4            
[37] readxl_1.3.1             evaluate_0.14            fs_1.5.0                
[40] fansi_0.4.1              xml2_1.3.2               RcppArmadillo_0.10.1.2.0
[43] tools_4.0.3              data.table_1.13.2        hms_0.5.3               
[46] lifecycle_1.0.0          munsell_0.5.0            reprex_0.3.0            
[49] isoband_0.2.2            compiler_4.0.3           rlang_0.4.10            
[52] grid_4.0.3               rstudioapi_0.11          labeling_0.4.2          
[55] rmarkdown_2.5            gtable_0.3.0             DBI_1.1.0               
[58] R6_2.5.0                 lubridate_1.7.9          knitr_1.30              
[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