If you have a temperature data cube T whose dimensions correspond to lat x lon x time, the simplest way to get an 86x1 array of mean temperature time series is
Tm = squeeze(mean(mean(T,'omitnan'),'omitnan'));
then you can make a line plot of mean temperature as a function of time, like this:
plot(t,Tm)
However, I should point out that all of the grid cells in a geographic grid have areas that depend on latitude, so it's not appropriate to give equal weight to a polar and equatorial grid cells (they're very different sizes!). In the Climate Data Toolbox for Matlab, see Example 3 of wmean for what I'm talking about.
It would be much better to get the area of each grid cell in your global grid using cdtarea like this:
A = cdtarea(Lat,Lon);
Then calculate the weighted mean like this:
Tm = NaN(86,1); % (preallocate for efficiency)
% Loop through each time step:
for k = 1:86
Tm(k) = wmean(T(:,:,k),A,'all');
end
% Get a mask of the grid cells that always have valid data:
mask = all(isfinite(T),3);
% Calculate weighted mean temperature time series in the finite grid cells:
Tm = local(T,mask,'weight',A);
