The first and easiest pattern that one notices is in the precipitation-temperature connection, as exemplified by this bar-line graph for precip and temp in April. I chose to show only April as not to clutter this page, and because it gives the clearest correlation between the two. There are two major reasons for the connection between precipitation and warm temperatures. 1.) Inclement weather is associated with Low Pressure systems, also known for typically warmer parcels of air. On the other hand, cool temperatures are associated with the clear skies of a High Pressure parcel of air. This phenomenon is especially noticeable in a Wisconsin April when the competing Polar Continental and Tropic Continental air masses are competing for airspace, and account for sometimes sudden changes in moisture and heat. 2.) Warm air, as you may know, rises. However, as air rises, it cools and condenses which leads to rain (or snow. or hail...). Warmer temperatures make for greater adiabatic lifting, and by extension have more potential for rain.
Another observation which holds across the monthly observations (but is not visible here) is that there is always a change in prevailing wind direction associated with precipitation: for example, the winds before the mid-April spike in precip here were out of the SSE, but from the WNW after the storms rolled through. Speaking of wind direction, another pattern which emerged over time was that the dominant wind directions shift from NNW to ESE, which is a result of changing air masses. Those winds out of the ESE bring up warm, moist air which helps to fuel our May showers! The constant across wind patterns is the strong presence of the Westerly winds, which we know since our weather always seems to come out of the west. Coincidence? absolutely not, this is the result of both orographic factors (called the Rockies) and the Jet stream. These graphics were constructed to demonstrate more than just the connections between local weather phenomena however.
|
Year
|
Avg. Temperature
(deg F) |
Rank
Based on the Time Period Selected (1895-2012)* |
Rank
Based on the Period of Record (1895-2012)* |
|
2012
|
43.2
|
118
|
118
|
|
1946
|
42.5
|
117
|
117
|
|
1945
|
42.4
|
116
|
116
|
|
1921
|
41.2
|
115
|
115
|
|
1903
|
40.9
|
114
|
114
|
|
1973
|
39.9
|
113
|
113
|
|
2010
|
39.4
|
112
|
112
|
|
1977
|
39.0
|
111
|
111
|
|
1898
|
39.0
|
111
|
111
|
|
1910
|
38.5
|
109
|
109
|
|
…
|
…
|
…
|
…
|
|
1950
|
28.3
|
10
|
10
|
|
1940
|
27.9
|
9
|
9
|
|
1941
|
27.5
|
8
|
8
|
|
1926
|
27.1
|
6
|
6
|
|
1906
|
27.1
|
6
|
6
|
|
1984
|
26.9
|
5
|
5
|
|
1900
|
26.7
|
3
|
3
|
|
1896
|
26.7
|
3
|
3
|
|
1960
|
25.2
|
2
|
2
|
|
1916
|
25.1
|
1
|
1
|
The above chart was constructed from data of http://www.ncdc.noaa.gov/oa/climate/research/cag3/cag3.html. Notice how 2012 is already the warmest year on record? It has been an abnormal year indeed. According to the Weather Channel, January's average high is 23° F, and the average low is 5°. This year, the high temperature more than doubled that, and the lows were only on the "average" level or lower for five days out of the month, as you can tell in the graph below. In fact, nationally January shattered heat, snow, and tornado records. http://www.accuweather.com/en/weather-news/january-2012-breaks-heat-cold/60824. It should be noted that there is a temptation to use this chart as evidence against climate change - keep in mind that the U.S. accounts for roughly 2% of the planet's total surface area, and that while only a few of the recent years reach into our top ten (by this data), the case may not be true the world over. A similar concept shows up further down... do not jump to conclusions!
The results are very similar with February, where the average range is from 10°-29°, but this year saw only two days below average (at 3° and 7°), and 26 days where highs exceeded the average. March saw less extreme variation from the expected temperatures, and April has been the most 'normal' month yet, in which only 10 days were outside the typical 34°-58° range. May looks on track to be a roughly regular end to our spring.
It can be dangerous to merely focus in on one geographic area to draw broad conclusions: multiple factors can affect one city's readings that wont affect the neighboring city's findings. The urban heat island, lake effect, elevation, and even person conducting the weather observations can all make an impact on a single location's results. This is why we made climatographs from multiple area locations. In the interest of saving space, I have excluded a couple of locations from the blog. Please excuse the titles, I must've had issues converting the images to be blogger-friendly.
My favorite thing to do with this work is compare the Milwaukee and Minneapolis graphs. Both are very urbanized areas, both are in teh upper-midwest along very similar latitudes and both start with the letter "M." However, the lake effect dampens the summer and winter extremes in temperature. The connection between precipitation and heat is made very explicit in this data set, just look at the curves and the bars! What is unfortunate is that this data, pulled from http://www.worldclimate.com, is hardly complete. In some cases, only 10 years' worth of complete data, which is hardly adequate in dealing with climate as weather can, as we know, vary greatly between years. Also, none of the data is more recent than the '90's; a little surprising when you consider how the role of technology has changed in data collection since then, and how much easier it could be to compile that data. At any rate, the value of current information cannot be overstated, and it is unfortunate that this source didn't have any.
At last we have the trend chart derived from data from ndnc again http://www.ncdc.noaa.gov/oa/climate/research/cag3/cag3.html. This graph describes the region of the American Northeast since 1895, and it describes a 2°F increase in average temperature over the past century. Again, this is only one relatively small region of the world. However, the nice thing is that we have over a century's worth of data to manipulate, and it shows a clear trend. A similar graph was made for precipitation in the region over the same time period, which shows a minute decrease in precipitation.
Isn't it interesting how at every step in this process, you would be led into thinking that higher temperatures bring more rain, but then in the long-run that doesn't seem to be the case? I guess that's just the weather for ya.
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