Variable preparation

Introduction

This vignette will walk through the steps needed to create mean ‘whatever’ states during all of the MHWs detected in the previous SST preparation vignette. These ‘whatever’ states are any of the abiotic variables that have a presence in the ocean heat budget equation and that have been deemed relevant w.r.t. forcing of extreme ocean surface temperatures.

After first going through this entire process with the NAPA model output it become clear that this analysis should be run first with obs/reanalysis data, and the NAPA model results may be compared against this as desired. To this end we will now use the following variables as found in the following data products:

• NOAA OISST: SST
• GLORYS: Mixed layer depth, U & V current vectors (~0.5m)
• ERA 5: U & V wind vectors (10m), air temperature (2m)
  • Net heat flux term: Latent & sensible heat flux, shortwave and longwave radiation

All of these variables have a daily resolution from at least 1993 – 2018. The spatial resolution varies from a standard 1/4 degree data to everything except for the hi-res (1/12 degree) GLORYS data from 2016 – 2018. The OISST, ERA 5, and GLORYS (1993 – 2015) data are natively at 1/4 degree, though the coordinates are slightly off between OISST and ERA 5 + GLORYS. The ERA5 data are also hourly, so will need to be binned into mean daily values. In order to trade between efficiency and accuracy we will be converting everything to the 1/4 degree grid of the GLORYS/ERA 5 data.

In order to compare the OISST dataset to the other 1/4 degree products the lat values for OISST will have 0.125 added to them, and the longitude values will have 0.125 subtracted from them. This is because the OISST coordinates give the centre value of each pixel, whereas the other coordinate system gives the top left corner (north-west corner).

# Packages used in this vignette
library(jsonlite, lib.loc = "../R-packages/")
library(tidyverse) # Base suite of functions
library(tidync, lib.loc = "../R-packages/")
library(lubridate) # For convenient date manipulation

# Set number of cores
doMC::registerDoMC(cores = 50)

# Disable scientific notation for numeric values
  # I just find it annoying
options(scipen = 999)

# Corners of the study area
NWA_corners <- readRDS("data/NWA_corners.Rda")

# The region pixel lon/lat values
NWA_info <- readRDS("data/NWA_info.Rda")

# Load the OISST grid
OISST_grid <- readRDS("data/OISST_grid.Rda")

# Load the OISST grid trimmed to the region coords
OISST_grid_region <- readRDS("data/OISST_grid_region.Rda")

# Load MHW results
OISST_region_MHW <- readRDS("data/OISST_region_MHW.Rda")

# MHW Events
MHW_event <- OISST_region_MHW %>%
  select(-cats) %>%
  unnest(events) %>%
  filter(row_number() %% 2 == 0) %>%
  unnest(events)

For the upcoming variable prep we are also going to want the OISST coordinates that are within our chosen study area as seen with NWA_corners.

# The OISST coordinates for the study area only
OISST_grid_study <- OISST_grid %>% 
  filter(lon >= NWA_corners[1], lon <= NWA_corners[2],
           lat >= NWA_corners[3], lat <= NWA_corners[4])
# saveRDS(OISST_grid_study, "data/OISST_grid_study.Rda")

Base datasets

Rather than go about performing all of the calculations below on piecemeal bits of data, it should be possible to load the entire datasets for each variable into memory at once. So in this section we will create functions that load each of the variables for the different data products. We will then create and save these complete datasets for ease of use later on.

# Load the functions used below
  # I've chosen to house a lot of code here to keep this vignette tidier
source("code/functions.R")

## SST
# The files with data in the study area
OISST_sst_files <- data.frame(files = dir("../../data/OISST", full.names = T),
                              lon = c(seq(0.125, 179.875, by = 0.25), seq(-179.875, -0.125, by = 0.25))) %>% 
  filter(lon >= NWA_corners[1]+0.125, lon <= NWA_corners[2]+0.125) %>% 
  mutate(files = as.character(files))
OISST_sst_files <- as.vector(OISST_sst_files$files)
# Aggregate data
system.time(
OISST_sst <- load_all_OISST(OISST_sst_files)
) # xxx seconds
# save
saveRDS(OISST_sst, "data/OISST_sst.Rda")

# The code used to create the rest of the base files may be seen in code/workflow.R

# The code used to perform these steps may be seen in code/workflow.R

Climatologies

In the data packets we need to create for the SOMs (see below) we will need to include anomaly values. In order to do this we need to first create daily climatology values for each variable for each pixel. In order to create a climatology of values we will need to load all of the files and then pixel-wise go about getting the seasonal (daily) climatologies. This will be done with the same function (ts2clm()) that is used for the MHW climatologies. One-by-one we will load an entire dataset (created above) into memory so that we can perform the necessary calculations. Hold on to your hats, this is going to be RAM heavy…

# Load functions required below
source("code/functions.R")

## SST
# Load
OISST_sst <- readRDS("data/OISST_sst.Rda")
# Calculate clims
system.time(
OISST_sst_clim <- OISST_sst %>% 
  group_by(lon, lat) %>% 
  nest() %>% 
  mutate(clims = map(data, ts2clm,
                     climatologyPeriod = c("1993-01-01", "2018-12-31"),
                     clmOnly = T, roundClm = FALSE)) %>% 
  select(-data) %>% 
  unnest()
) # 1139 seconds
OISST_sst_clim$thresh <- NULL
# Save
saveRDS(OISST_sst_clim, "data/OISST_sst_clim.Rda")

# The code used to create the other climatologies may be found in code/workflow.R

Anomalies

The last step before we can begin creating our data packets is to now subtract our climatology data from our base data for each of the variables used in this study. We will then save this large anomaly cubes to allow for easy synoptic state creation.

## SST
# Load
OISST_sst <- readRDS("data/OISST_sst.Rda") %>% 
  mutate(doy = format(t, "%m-%d"))
OISST_sst_clim <- readRDS("data/OISST_sst_clim.Rda") %>% 
  left_join(doy_index, by = c("doy" = "doy_int")) %>% 
  select(-doy) %>% 
  dplyr::rename(doy = doy.y)
# Join and calculate anomalies
system.time(
OISST_sst_anom <- left_join(OISST_sst, OISST_sst_clim, by = c("lon", "lat", "doy")) %>% 
  mutate(sst_anom = round(temp-seas, 2)) %>% 
  select(lon, lat, t, sst_anom)
) # 43 seconds
# Save
saveRDS(OISST_sst_anom, "data/OISST_sst_anom.Rda")

# The code used to create the rest of the anomalies may be found in code/workflow.R

Synoptic states

The next step is to create mean synoptic states for each variable during each of the MHWs detected in each region. To do this we simply load the combined anomaly dataset of all of our chosen variables and slice off only the bits we need during each of the observed MHWs. These slices are then meaned, pixel-wise, to create the synoptic states during each event.

One thing to note here is that during this process, before the slices are created, each pixel for the mixed layer depth anomaly is divided by the max value observed at that pixel, scaling these values to 1. This is done as the range of MLD can vary dramatically over the Labrador sea and we want to avoid this from influencing the results.

# Load anomalies and scale MLD
system.time(
  if(!exists("ALL_anom")) ALL_anom <- readRDS("data/ALL_anom.Rda") %>%
    group_by(lon, lat) %>%
    mutate(mld_anom = mld_anom/max(abs(mld_anom), na.rm = T)) %>%
    ungroup()
) # 99 seconds

# Function for creating packets
data_packet <- function(event_sub){

  # Filter base anomally range
  packet_base <- ALL_anom %>%
    filter(t >= event_sub$date_start, t <= event_sub$date_end)

  # Create mean synoptic values
  packet_mean <- packet_base %>%
    # select(-doy) %>%
    group_by(lon, lat) %>%
    summarise_all(mean, na.rm = T) %>%
    arrange(lon, lat) %>%
    ungroup()
  packet_mean[is.na(packet_mean)] <- NA
  packet_mean <- nest(packet_mean, .key = "synoptic")

  # Combine results with MHW dataframe
  packet_res <- cbind(event_sub, packet_mean)

  # Exit
  return(packet_res)
}

# Set number of cores
doMC::registerDoMC(cores = 50)

# Create one big packet
system.time(
synoptic_states <- plyr::ddply(OISST_MHW_event, c("region", "event_no"), data_packet, .parallel = T)
) # 3 seconds for first event, 50 for all events

# Save
saveRDS(synoptic_states, "data/synoptic_states.Rda")

With all of our mean synoptic states created it is now time to feed them to the Self-organising map (SOM) analysis.

Session information

sessionInfo()

R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/openblas-base/libblas.so.3
LAPACK: /usr/lib/libopenblasp-r0.2.18.so

locale:
 [1] LC_CTYPE=en_CA.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_CA.UTF-8        LC_COLLATE=en_CA.UTF-8    
 [5] LC_MONETARY=en_CA.UTF-8    LC_MESSAGES=en_CA.UTF-8   
 [7] LC_PAPER=en_CA.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] bindrcpp_0.2.2  lubridate_1.7.4 tidync_0.2.1    forcats_0.3.0  
 [5] stringr_1.3.1   dplyr_0.7.6     purrr_0.2.5     readr_1.1.1    
 [9] tidyr_0.8.1     tibble_1.4.2    ggplot2_3.0.0   tidyverse_1.2.1
[13] jsonlite_1.6   

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4  haven_1.1.2       lattice_0.20-35  
 [4] colorspace_1.3-2  htmltools_0.3.6   yaml_2.2.0       
 [7] rlang_0.2.2       R.oo_1.22.0       pillar_1.3.0     
[10] glue_1.3.0        withr_2.1.2       R.utils_2.7.0    
[13] RNetCDF_1.9-1     doMC_1.3.5        modelr_0.1.2     
[16] readxl_1.1.0      foreach_1.4.4     bindr_0.1.1      
[19] plyr_1.8.4        munsell_0.5.0     gtable_0.2.0     
[22] workflowr_1.1.1   cellranger_1.1.0  rvest_0.3.2      
[25] R.methodsS3_1.7.1 codetools_0.2-15  evaluate_0.11    
[28] knitr_1.20        parallel_3.6.1    broom_0.5.0      
[31] Rcpp_0.12.18      backports_1.1.2   scales_1.0.0     
[34] hms_0.4.2         digest_0.6.16     stringi_1.2.4    
[37] ncdf4_1.16.1      grid_3.6.1        rprojroot_1.3-2  
[40] cli_1.0.0         tools_3.6.1       magrittr_1.5     
[43] lazyeval_0.2.1    crayon_1.3.4      whisker_0.3-2    
[46] pkgconfig_2.0.2   xml2_1.2.0        iterators_1.0.10 
[49] assertthat_0.2.0  rmarkdown_1.10    httr_1.3.1       
[52] rstudioapi_0.7    R6_2.2.2          ncmeta_0.0.4     
[55] nlme_3.1-137      git2r_0.23.0      compiler_3.6.1