Last updated: 2021-07-07

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html 58bc706 jens-daniel-mueller 2021-07-06 Build site.
html f600971 jens-daniel-mueller 2021-07-02 Build site.
html ad81465 jens-daniel-mueller 2021-06-17 Build site.
Rmd 3bc04be jens-daniel-mueller 2021-06-17 derive air sea disequilibrium
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Rmd 2730067 jens-daniel-mueller 2021-06-07 write preprocessed file
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Rmd f16f718 jens-daniel-mueller 2021-06-07 added multi parameter analysis global trends
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Rmd 663eb4d jens-daniel-mueller 2021-06-07 added multi parameter analysis
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Rmd 0f42222 jens-daniel-mueller 2021-06-07 improved revelle factor analysis
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Rmd 00065c8 jens-daniel-mueller 2021-06-04 included OceanSODA

1 Read source files

# use only three basin to assign general basin mask
# ie this is not specific to the MLR fitting
basinmask <- basinmask %>% 
  filter(MLR_basins == "2") %>% 
  select(lat, lon, basin_AIP)
OceanSODA <-
  tidync(paste(
    path_updata,
    "pco2_OceanSODA-ETHZ/OceanSODA-ETHZ_1985-2019_v2020b.nc",
    sep = ""
  ))

OceanSODA <- OceanSODA %>%
  hyper_tibble()

OceanSODA <- OceanSODA %>%
  mutate(date = as.Date(time, origin = '1985-01-01'),
         year = year(date))

OceanSODA <- OceanSODA %>%
  select(year, date, lat, lon,
         sal = salinity, temp = temperature,
         tco2 = DIC, talk = TA, 
         rev_fac = revelle_factor,
         pCO2)

OceanSODA <- OceanSODA %>%
  mutate(lon = if_else(lon < 20, lon + 360, lon))

OceanSODA <- inner_join(OceanSODA, basinmask)
path_SeaFlux <- 
paste0(path_updata,"pco2_SeaFlux/")

icefrac <-
  tidync(paste0(path_SeaFlux,
                "SeaFluxV2021.01_icefrac_1988-2018.nc")) %>% 
  hyper_tibble()

icefrac <- icefrac %>% 
  mutate(date = as.Date(time, origin = '1988-01-01'),
         year = year(date))

kw <-
  tidync(paste0(path_SeaFlux,
                "SeaFluxV2021.01_kwScaled16.5cmhr_1988-2018.nc"))

kw <- kw %>% 
  hyper_filter(wind = wind == "ERA5") %>% 
  hyper_tibble() %>% 
  select(-wind)


pCO2atm <-
  tidync(paste0(path_SeaFlux,
                "SeaFluxV2021.01_pCO2atm_NOAAmbl_ERA5mslp_1988-2018.nc")) %>% 
  hyper_tibble()

sol <-
  tidync(paste0(path_SeaFlux,
                "SeaFluxV2021.01_solWeis74.nc")) %>% 
  hyper_tibble()


all_variables <- full_join(icefrac, kw)
all_variables <- full_join(all_variables, pCO2atm)
all_variables <- full_join(all_variables, sol)

all_variables <- all_variables %>%
  mutate(lon = if_else(lon < 20, lon + 360, lon))

OceanSODA <- inner_join(all_variables %>% select(-c(time)),
                        OceanSODA)
all_variables <- OceanSODA %>%
  select(
    time_mon = date,
    lon,
    lat,
    spco2 = pCO2,
    pco2atm = pCO2atm,
    fice = ice,
    alpha = sol_Weiss74,
    Kw = kw_scaled
  ) %>%
  drop_na()

all_variables <- all_variables %>%
  mutate(area = earth_surf(lat = lat))

mol_to_g <- 12
P <- 1e-15
cm_to_m <- 100
hr_to_yr <- 24 * 365

unit_conversion_to_PgCyr <- mol_to_g * P * hr_to_yr / cm_to_m

all_variables <- all_variables %>%
  mutate(
    delta_pco2 = spco2 - pco2atm,
    scale = area * Kw * alpha * (1 - fice),
    fgco2 = delta_pco2 * scale
  )

delta_pco2_monthly <- all_variables %>%
  group_by(time_mon) %>%
  summarise(
    scaling_glob = sum(scale),
    fgco2_glob = sum(fgco2),
    delta_pco2_glob = fgco2_glob / scaling_glob
  ) %>%
  ungroup() %>%
  mutate(fgco2_glob = fgco2_glob * unit_conversion_to_PgCyr)

delta_pco2_annual <- delta_pco2_monthly %>%
  mutate(year = year(time_mon)) %>%
  group_by(year) %>%
  summarise(
    scaling_glob = mean(scaling_glob),
    fgco2_glob = mean(fgco2_glob),
    delta_pco2_glob = mean(delta_pco2_glob)
  ) %>%
  ungroup()

print(
  ggplot() +
    geom_path(data = delta_pco2_monthly,
              aes(
                decimal_date(time_mon), delta_pco2_glob, col = "monthly"
              )) +
    geom_path(data = delta_pco2_annual,
              aes(year, delta_pco2_glob, col = "annual")) +
    scale_color_brewer(palette = "Set1", name = "Average") +
    labs(x = "year")
)

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print(
  ggplot() +
    geom_path(data = delta_pco2_monthly,
              aes(decimal_date(time_mon), fgco2_glob, col = "monthly")) +
    geom_path(data = delta_pco2_annual,
              aes(year, fgco2_glob, col = "annual")) +
    scale_color_brewer(palette = "Set1", name = "Average") +
    labs(x = "year")
)

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print(ggplot() +
        geom_path(data = delta_pco2_annual,
                  aes(
                    year,
                    scaling_glob * unit_conversion_to_PgCyr
                  )))

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print(
  ggplot() +
    geom_path(
      data = delta_pco2_annual,
      aes(
        year,
        scaling_glob * delta_pco2_glob * unit_conversion_to_PgCyr,
        col = "scaled"
      )
    ) +
    geom_path(data = delta_pco2_annual,
              aes(year, fgco2_glob, col = "integrated")) +
    scale_color_brewer(palette = "Set1", name = "Estimate") +
    scale_y_continuous(name = "Air-sea flux [PgC yr-1]") +
    labs(x = "year")
)

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# write raw data file for GLODAP-based subsetting model variables
delta_pco2_annual %>%
  select(-c(scaling_glob, fgco2_glob)) %>%
  write_csv(file = paste(
    path_preprocessing,
    "OceanSODA_disequilibrium_annual.csv",
    sep = ""
  ))

rm(delta_pco2_annual,
   delta_pco2_monthly)
# calculate annual averaged fields
OceanSODA_annual_all <- OceanSODA %>%
  mutate(tco2_over_pCO2 = tco2 / pCO2) %>%
  group_by(year, lat, lon) %>%
  summarise_if(is.numeric, mean, na.rm = TRUE) %>%
  ungroup() %>%
  mutate(grid_area = earth_surf(lat = lat))

# grid data in space and time, remove data outside grid
OceanSODA_annual <- OceanSODA_annual_all %>%
  mutate(
    grid_area = earth_surf(lat = lat),
    lat_bands = cut(lat, seq(-80, 80, 20)),
    decade = cut(year,
                 seq(1990, 2020, 10),
                 right = FALSE,
                 labels = c("1990-1999", "2000-2009", "2010-2019"))
  ) %>%
  drop_na()


# calculate climatological fields
OceanSODA_clim <- OceanSODA_annual %>%
  select(-c(year, grid_area)) %>% 
  group_by(lat, lon) %>%
  summarise_if(is.numeric, mean, na.rm = TRUE) %>% 
  ungroup()


# calculate area-weighted annual mean within latitude band 
OceanSODA_annual_lat <- OceanSODA_annual %>%
  pivot_longer(sal:tco2_over_pCO2,
               names_to = "parameter",
               values_to = "value") %>%
  mutate(value_area = value * grid_area) %>% 
  group_by(year, lat_bands, decade, parameter) %>%
  summarise(
    area_total = sum(grid_area),
    value_area_total = sum(value_area),
    value_area_ave = value_area_total / area_total
  ) %>%
  ungroup() %>% 
  select(-c(area_total,value_area_total))


# fit decadel linear trends per latitude band
OceanSODA_annual_lat_trend <- OceanSODA_annual_lat %>% 
  nest(data = -c(decade, lat_bands, parameter)) %>% 
  mutate(tidy = map(data,
                    ~tidy(lm(value_area_ave ~ year, data = .x)))) %>% 
  select(-data) %>% 
  unnest(tidy)


# calculate area-weighted annual mean globally
OceanSODA_annual_glob <- OceanSODA_annual %>%
  pivot_longer(sal:tco2_over_pCO2,
               names_to = "parameter",
               values_to = "value") %>%
  mutate(value_area = value * grid_area) %>% 
  group_by(year, decade, parameter) %>%
  summarise(
    area_total = sum(grid_area),
    value_area_total = sum(value_area),
    value_area_ave = value_area_total / area_total
  ) %>%
  ungroup() %>% 
  select(-c(area_total,value_area_total))


# fit decadel linear trends globally
OceanSODA_annual_glob_trend <- OceanSODA_annual_glob %>% 
  nest(data = -c(decade, parameter)) %>% 
  mutate(tidy = map(data,
                    ~tidy(lm(value_area_ave ~ year, data = .x)))) %>% 
  select(-data) %>% 
  unnest(tidy)
OceanSODA_annual_all_test <- OceanSODA_annual_all

1.1 Write file

OceanSODA_annual_all %>%
  write_csv(paste0(path_preprocessing,
                   "OceanSODA.csv"))

1.2 Control plots

map +
  geom_raster(data = OceanSODA_annual %>%
                filter(year == 2010), aes(lon, lat, fill = lat_bands)) +
  scale_fill_brewer(palette = "Spectral") +
  labs(title = "Year: 2010")

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map +
  geom_raster(data = OceanSODA_annual %>%
                filter(year == 2010), aes(lon, lat, fill = grid_area)) +
  scale_fill_viridis_c() +
  labs(title = "Year: 2010")

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2 Climatology map

OceanSODA_clim %>%
  pivot_longer(sal:tco2_over_pCO2,
               names_to = "parameter",
               values_to = "value") %>%
  group_split(parameter) %>%
  # head(1) %>% 
  map( ~ map +
         geom_raster(data = .x,
                     aes(lon, lat, fill = value)) +
         scale_fill_viridis_c(name = .x$parameter))
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3 Time series

OceanSODA_annual_lat %>%
  group_split(parameter) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(year, value_area_ave, col = lat_bands)) +
      scale_color_brewer(palette = "Spectral") +
      geom_path() +
      geom_point() +
      labs(y = .x$parameter)
  )
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