3. Climates

This is my favorite part. I have chosen a simple segmentation for the climates where I need the average annual temperature and its seasonal oscillation (cold/warm/hot), and the distance to the coast line (oceanic/continental). There are many sources of information and the model can be as complex as hell, but I found this link in particular quite useful for simple answers. My model is based in two simple elements:

Average temperature depends on latitude (maximum at y=0 with around 30ºC, minimum at y=1/2 and -1/2 with -30ºC) and elevation (linear dependence with z with a lapse rate of 6.5ºC per kilometer).

(maximum at y=0 with around 30ºC, minimum at y=1/2 and -1/2 with -30ºC) and (linear dependence with z with a lapse rate of 6.5ºC per kilometer). Annual temperature range depends on latitude and also on the distance from the sea. However, this distance from the sea has to be measured as distance downwind of the ocean. Yeah, we need a model for the wind!

A realistic model for the wind could be a nightmare of (beautiful) fluid equations, thus I have reduced it in a very simple way: the average total wind in a point is composed by the contribution of the prevailing winds plus the contribution of the pressure gradient. I assume that (i) prevailing winds depend only in latitude; and (ii) pressure gradient depend only in that due to elevation: as higher, lower the pressures. Since what I need is the distance following the wind vector, I will not worry about the intensity but just the direction.

I'm showing below the wind map for n=6 (for seeing clearly the direction for every cell). Because of the prevailing component, winds converge at y=0, diverge at y=1/6 and -1/6 (the tropics) and converge again at y=2/6 and -2/6 (you may note a contradiction here: this pattern for prevailing winds is due to the rotation of Earth and because it is spherical :P ). On the other hand, the pressure gradient disturbs the regular pattern following the geography (I'm only using straight lines and diagonals for simplifying it and computing the distance in units of pixels). Now I can obtain the distance downwind of the ocean which may look quite different to the distance to the closest shore: