fileIn="global-analysis-forecast-bio-001-028-monthly_1693423231336.nc";
The time data is stored as integers representing hours since 01-Jan-1950. Therefore we convert hours to seconds and convert seconds to datetimes, as shown below:
dt=datetime(t*3600,'ConvertFrom','epochtime','Epoch','1950-01-01');
To confirm that the code above worked, we display the first three date-time values:
16-Nov-2020 00:00:00 16-Dec-2020 12:00:00 16-Jan-2021 12:00:00
Get info about the .nc file:
Display fInfo.Dimensions (below). We see the data file includes data for 32 times, 8 latitudes, 9 longitudes, 3 depths.
Load some carbonate data and display the array size:
spco2=ncread(fileIn,"spco2");
9 8 32
This suggests that spco2 is given on a 9x8 long,lat grid, at 32 times.
You said you want descriptive statistics, and information across time. You can do that as follows:
spco2meanAll=mean(spco2,'All')
spco2meanAll = 36.2425
Next, we find the mean spCO2 at each time, by averaging across all locations. We do this by averaging across dimensions 1 and 2. We squeeze the result to eliminate uneeded dimensions from the result.
spco2meant=squeeze(mean(spco2,[1 2]));
Plot mean spco2 for each month versus time:
plot(dt,spco2meant,'-rx');
xlabel('Time'), ylabel('pCO_2 (Pa)')
grid on; title('Mean pCO_2 versus time')
which produces the figure below.
The plot shows that pCO2 reaches a local minimum in December of each year.
Best wishes with your research!
