For data, I'm going to use the Climate Forecast System Reanalysis (v2). I'll also be using the high resolution, in time and space, versions of the data. This leads to some pretty big files (unpacked, it is about 2 Gb per month, and remember there'll be 360 months for a 30 year climatology). So you might want to go with the lower resolution for your own initial exploration.
To start with, let's look at the 2 meter air temperature, where I've converted temperatures to Celsius (from Kelvin). 30 C = 86 F, 0 C = 32 F. The total planetary range is a bit over 70 C from the very coldest areas (Antarctic Plateau -- below -40 C) to the warmest (pretty much the whole tropics).
We also see in the Himalayas and Andes good illustration that higher elevations are colder. A different matter is that this map display (as do all) distorts the areas of the earth. In truth, half the earth is between 0 and 30, a third is between 30 and 60, and only one sixth is 60 to 90. Handy figure, by the way: 10 million square kilometers is about the size of the US, Antarctica, Canada, Brazil, and China. All are about the same size, and a nice round number.
If we average the temperature properly, by area, the global mean in 1981 was 14.5 C. Given the map, that's a little surprising. So let's split out how much of the earth (area) is in different temperature bins, by degrees C:
This is a case where the mean (14.5) is far from the mode (27). The median (half the area is warmer, half colder) is about 18.5 -- far from both the mean and the mode. Shows why we want more than one way of describing statistics. And why a normal curve (bell curve, Gaussian distribution) is not always the right way to describe your data.
I'm also from an area where we claim the temperatures change enormously day to day and winter to summer. If the variability truly followed a normal (bell curve) distribution (I doubt it does), then the square root of the variance is the standard deviation. Here's the map for that:
In general, oceans are areas of low variability. Land is higher variability. Higher latitudes show higher variability. The farther you are to the east of a water to land transition, the more variable it is (Eastern Siberia, Central North America -- limited by Hudson Bay). High elevations might be high variability (Himalayas). Highest variability in the oceans is off the east coast of continents.
Again, let's split things out by bins. Global mean for 'standard deviation' is 4.5 C (woohoo, Chicago nearly triples that!):
I was recently seeing people commenting about climate change of a few degrees being trivial because day to day and seasonal variation is so much larger. Starts to look like the variability over the globe is actually pretty small, and are even smaller than plausible climate sensitivities.
Armed with this exploration, I've got a better handle on how to proceed in making a 30 year climatology. One major point is, I need some pretty careful numerical analysis to preserve the rather subtle variations that most of the globe shows.
A bit of mathematics:
The first point is, the data files are in Kelvin. That's good for universal understanding -- if a temperature is -35, you can't be sure if it is Fahrenheit or Celsius -- but poor for doing arithmetic on computers.
The thing is, computers do truly carry out real number arithmetic. This is no surprise to the scientists who work on data analysis, their job includes understanding numerical analysis. But I don't think I've ever seen it in a climate blog before, so here goes. I'm going to be adding up the hourly temperatures for all of 1981 (to start with). That's 8760 hours*. The temperatures have been saved to a precision of 0.001 K. So we have numbers (8760 of them) to add up that look like:
and makes a numerical analyst rather concerned.
When we add up the numbers (to compute the average we add up all the numbers and then divide by how many we have), we wind up with a number like
Or, rather, that's what we get if computers had infinite precision, or we do it by hand (any volunteers?).
Count this off -- there are 9 digits involved. Ordinary arithmetic on computers uses a number representation that only can show 6-7 digits exactly. So, although the original number, 273.151, can be represented exactly, after a while, we lose digits. Let's say this really is our average. After 1000 hours of the average, our number is (say)
273151.0 and we add the next observation of 273.252 (a little different from the average, but we don't expect numbers always to be average). Both have 6 digits, so you might think we're safe. We're not. When the computer goes to add them, it sees something like:
You and I know that this should add to 273424.252. But the computer only can deal with 6 digits here, and will give you and answer of 273424.0** -- it will truncate the smaller number before adding to the larger one. Losing 0.252 of a degree may not seem like much. But, remember that the computer will be truncating every single addition from here on, another 7760. After enough truncations, your accuracy is compromised. (Note: the computer is actually working in binary, not decimal, but the principle holds. Only so many bits are used to represent a number.)
So .. as a first step use Celsius instead. In this case, the numbers vary from -60 (Antarctic) to +40 (hot areas). Now we only have 4-5 digits in our temperature (areas close to freezing will have 4 -- 1.012 C for instance). So more protection against numerical issues. (In practice I'm using double precision arithmetic, but this only gets you so far. Far enough for the average to be reliable even in K. But finding the standard deviation requires computing and summing squares of numbers, which rapidly runs you out of digits again.)
* Numbers stick in my mind. This one is from when I was working on my master's degree doing tidal analysis. The length of a year is an important number for tides :-)
** It may or may not do so in practice. Depends on some behind the scenes things which I can't guarantee. But the principle holds. Try adding up 273.151 a zillion+ times and printing out the results. Sooner or later, you will see numbers different than what you should.
+ Not sure zillion is globally known. Think of a really big number. A zillion is bigger. It isn't rigorously defined, obviously, but it's handy.