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Mean Sea Surface Temperature 1958 - 2022

| Esteban Meneses

Sea Surface Mean Temperature

Data donwloaded from here:

https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-oras5?tab=form

First, create an account, then sign in and get it

upload r packages

library(ncdf4)
library(raster)

open your netcdf files from your directory and make it writable

nc <- nc_open("Inputs_and_Data/sst_1958_2022.nc", write = T)

Explore the dataset

nc$var$sosstsst
tail(nc$var$nav_lat)
nc$dim$time_counter
tail(nc$dim$lon$vals) # -179.98 to 179
tail(nc$dim$lat$vals) # -89.98 to -89.98
nc$dim$time$vals 
nc2$dim$time
nc2$var$time

Get some variables

lon <- ncvar_get(nc, 'lon')
lat <- ncvar_get(nc, 'lat')
temp <- ncvar_get(nc, 'sosstsst')
time <- ncvar_get(nc, 'time_counter')
head(time)

Transform date variable

library(chron)


month.day.year(time, c(month = 1, day = 1, year = 2011))
time2 <- month.day.year(time/86400, c(month = 1, day = 1, year = 1981))
month <- time2$month
day <- time2$day
year <- time2$year
dates_df <- cbind(year, month, day)
dates_df <- as.data.frame(dates_df)
vec_time <- paste(dates_df$year, dates_df$month, dates_df$day, sep = '-')
vec_time

rasters from netcdf files

library(oceanmap)


sst <- nc2raster(nc, varname = 'sosstsst')

Calculate the mean of all layers of sea surface temperature

mean_nc2 <- calc(sst, fun = mean)

Map mean sea surface temperature



extent(mean_nc2) <- extent(-180, 180, -77, 90)

v(mean_nc2,  replace.na= F, v_contour = T, fill = T, 
  cb.title = "(\u00B0C)", cbpos='r', )#cb= colorbar|RasterStack object

Read next

Inter-islands maritime traffic disruption in Las Palmas: Meteoceanic conditions and economic impacts

By Esteban Meneses

The Canary Islands being around two thousand kilometers from the Spain mainland, has certain corresponding peculiarities of an archipelago for its socio-economic development. Its remoteness and fragmented territory make them highly dependent of maritime transport, since their commerce and movement of people happens mainly by sea. In this context, the disruption of inter-island shipping lines due to different reasons, in particular severe meteoceanic conditions, leads to negative socio-economic implications. The present study focuses on the identification of adverse oceanographic and meteorological conditions inducing disruptions along inter-island maritime traffic routes for transporting passengers and cargo in Las Palmas province. The results indicate that every maritime route can be affected by disruptive stormy events, mainly depending on the directionality of these episodes, which have a marked seasonality, and the characteristics of the vessel covering a specific route. These bad weather conditions cause the disruption of maritime regular lines and with it the loss of hundreds of euros every hour of delay, detour, or disruption of a maritime route only by the tourism consumption side.