zzzEmilyRiley-IDLresults

=Basic parts:=

1. Download and read...
- I opted to download the data then transform it into readable IDL data using cdf2idl.pro.

My __language code file for making the below figures is linked__ here__.__ This code was developed by me from scratch. I tried to add a couple interesting plotting tricks, which I will point out below.

2. Calculate zonally averaged P and v over longitude, and plot the resulting latitude-time series.
Here is the latitude-time sections of P and v in the zonal mean, and at 90W: Note, I've opted to label my contours instead of using a color bar.



The meridional wind figures have both positive and negative values, so I contoured the positive solid and negative dashed, and then thickened the zero contour. Good practice for plotting. I also decided to only label every other contour. This is the code I used to modify the contour style, thickness, and labeling:

;;; Perhaps I only want every other contour labeled labels = intarr(17) ;;;first define an array of zeros for i=0, 16 do $ if i mod 2 eq 0 then labels[i] = 1 ;;; now fill everyother element with 1

;;; Since we have both neg and pos winds let's make the neg ;;; dashed contours and thicken the zero contour contour_style = (levels lt 0)*2 thick_array = intarr(17) + 3 thick_array[where(levels eq 0)] = 5

Here I label //all// the latitudes. It's a bit of overkill, but it's an example of altering the default axis labels. ;;; more axis labels values = indgen(19)*10-90 contour, rotate(vwnd_xbar, 4), time_axis, lat, /fill, levels = levels, $ title = 'Meridional Wind (m/s)', xtitle = 'Month' , ytitle = 'latitude' , $
 * yticks = 20, ytickv = values, ytickn = str(values) ;;; This line specifies the axis labels**



Notice that the mean precipitation follows the annual march of the ITCZ and monsoons; the ITCZ shifts north of the equator during the boreal summer and then southward in the boreal winter, likewise the monsoons are active during the respective hemisphere's summer months. The precipitation rate is also larger during the boreal summer months. At 90°W max precipitation rates stay north of the equator during the entire year and have magnitudes twice as great as the all longitude average.

For the meridional wind, there is an equatorial (lat < |30°|) shift in tropical southerlies during the boreal summer and fall months, with the southerlies reaching as far north as ~15°N during July and August, almost taking over the northerly winds in the tropics north of the equator. Peak magnitude of the tropical southerlies is June - August at > 3 m/s. During the boreal winter and spring months, the southerlies retreat south of the equator and weaken in magnitude, while tropical northerlies north of the equator (so 0-30°N) increase and have peak magnitude during December - February. These winds are consistent with the annual cycle of the ITCZ (seen in the precipitation figure). At 90°W the tropical southerlies don't show much of an annual equatorward progression, rather they remain in a band between 30°S-10°N throughout the year, with peak magnitude during July - November. The wind speed are about twice as high at 90°W compared to the longitudinally averaged winds.

Outside the tropics, the northern hemisphere meridional winds are weaker than the southern hemisphere. The northern hemisphere winds are weakly southerly through most of the hemisphere, except for a weak northerly band between ~65-75°N. The southern hemisphere, on the other hand, has a strong northerly belt in the mid-latitudes (35-65°S) that has a slight annual cycle, with winds peaking in the austreal fall and spring. Poleward of 65°S winds return to southerly. Again, the wind pattern is similar at 90°W compared to the longitudinally averaged figure, but with about twice the magnitude.

3. Average air temperature over both lat and lon, to make a 12-month time series. Which season has the warmest global mean surface temperature? Can you understand why?
Here I set the code to plot 2X2 plots per page: ;;; I want to make these plots smaller, so I plot 2X2 figs per page !p.multi = [0, 2, 2] ;;; Once !p.multi is set it doesn't change until you reset the value. When I'm done with these plots, I set !p.multi = 0 to get 1 plot per page

The plot on the left uses the default y-range, while on the right I set the yrange = [0, 16]. If you just looked at the plot on the left, you might be deceived that the globe has a large temperature time series, while the plot on the right shows a mild annual cycle.

Global mean surface temperature has a mild annual cycle: The global maximum temperatures in the boreal summer months is consistent with the notion that the Northern Hemisphere is a land hemisphere, while the Southern Hemisphere is an ocean hemisphere. Since the heat capacity of land is less than that of ocean, the global annual cycle will reflect the seasonal temperature cycle of the northern hemisphere.

Here is a map expressing the seasonality of precipitation:

map_world.pro was used to add the continents. Places with intensely seasonal rainfall tend to be over the Pacific and Atlantic cold tongues, the Sahel, and monsoon regions (i.e. Indian sub-continent, Arabian Sea, Gulf of California, Northern Australia, and 10-20°N over Africa). The Congo region also has large seasonal rainfall.

5. What is the space-time standard deviation of 'air' (temperature)?
The total space-time standard deviation of temperature is 15.2. The challenge of computing it is that we want area averages over the Earth, but we started with lat-lon grids. A simple call of stdev(air array) gives 21.8, which is too large because the poles are being weighted more than the tropics, and the poles have larger temperature variations than the tropics, since the amount of incoming solar radiation varies much more at the poles than at the tropics.

=Extra credit / extensions of the basic assignment:=

I wanted to address the question, ? So I ___. The result is (picture). Apparently (interpretation). This is confirmed by (rejiggering) which produces (picture2), illustrating (point).