You have probably heard of the uncertainty principles that arise in physics, with the most famous of all being the Heisenberg's uncertainty principle. But the uncertainty principle applies to signals as well. Just as Heisenberg's uncertainty is described by the following inequality:
$$
\Delta_x\, \Delta_p \ge \dfrac{h}{4\pi}
$$
The above imposes a limit in the level of resolution in the measurement of a particle's position and momentum. In fact their relationship is inverse meaning that as the particle's position measurements precision increases, then its momentum measurement precision decreases or in other words the uncertainty in its momentum measurement increases.
Now in signal processing the goal is to extract information about the signal's structure (if any) usually in the frequency or time domain (it could also be in the spatial domain).
But as you correctly described and analyzed in your graphs the more precise we get in the time domain the more uncertain our estimation of its frequency spectrum becomes. You can see it intuitively in the ripple. The smallest temporal window $N=16$ results in the biggest ripple at higher frequencies, taking also a sizeable proportion in the signal's power spectrum, but as $N$ increases so does the ripple increasing our certainty about the signal's frequency spectrum structure.
$$
\sigma_t\, \sigma_f \ge \dfrac{1}{4\pi}
$$
where $\sigma_t$ and $\sigma_f$ are the standard deviations of the time and frequency domain's estimations, respectively.
This minimum threshold is called the Gabor limit, and the principle is called Gabor's uncertainty.
Note:
What is meant by "a larger window provides less temporal resolution" is that some of the properties of the signal in the time domain may be less prominent whereas in the frequency domain we would be more certain about its properties and structure.
From a practical perspective this limit is also important because in addition to other factors it could help us choose an appropriate window for our particular application where it could be desirable to extract features from both domains, sometimes we care only for the time domain properties, sometimes for the frequency domain properties and other times for both (also sometimes implications can be made about properties in one domain from properties measured in the other domain).
In conclusion, the more a signal spreads in the time domain, the less it spreads in the frequency domain and vice versa. In analog signals, a classic example of this phenomenon is the a rectangular pulse in the time domain, which Fourier Transform is the sinc function (exhibits spectral leakage).
