- 2-Meter Temperatures
- Skin Temperature
- 925 mb Temperature
- 850 mb Temperature
- 500 mb Temperature
- 300 mb Temperature
- Sea Level Pressure
- 925 mb Heights
- 850 mb Heights
- 500 mb Heights
- 300 mb Heights
- 300 mb u-wind (zonal)
- 850 mb u-wind (zonal)
- 300 mb v-wind (meridonal)
- 850 mb v-wind (meridonal)
- 200 mb - 850 mb u-wind (zonal wind shear)
Instead of looking at raw values or departures from normal, I thought it would be interesting to see how 2015 ranks on an ordinal scale. The best data sets for doing this type of analysis are the ESRL 1948-2015 Reanalysis (R1) products. In this case, I decided to use monthly data to compute the rankings. The ESRL Reanalysis data begins in 1948. Other Reanalysis products go back quite a bit farther but their utility is greatly limited by the data assimilation and interpolation methods.
Importantly, this is a spatial analysis exercise. I'm not looking to see where 2015 ranked globally. I am interested in seeing how that ranking varies across geographical space. For example, if January-December 2015 was the warmest on record in western Canada but the second warmest on record in eastern Canada, I want to see where that dividing line occurs. Using this example, it is possible that the departures from normal are less in western Canada compared to eastern Canada but compared to itself, the western Canada departures are the highest on record.
The ESRL Reanalysis contains a global grid of points separated by 2.5° of latitude and longitude. That produces a grid of just over 10,000 points. At each point, there are data for many, many climate variables for every month going back to January 1948. What I did was evaluate each one of the 10,000 points and chose a recurring analysis period and then did a high to low ranking of each value. For example, let's look at the July sea level pressure at latitude 35°N, longitude 90°W. The figure below shows the values since 2010 at that location.
Figure 1. Sample sea level pressure (SLP) ranking for 35°N/90°W since 2010.
A simple high-to-low ranking shows that July 2014 was the top ranked year and July 2015 was the 5th ranked year (using this six year sample). For the map set that will follow shortly, the area at 35°N/90°W will be shown in the color designated for a ranking on 5. Again, it doesn't show which year was in 1st place, it shows what place 2015 finished in. (note: my script generates a map showing the year where 1st place occurs but it is difficult to read)
In addition to analyzing recurring months, I also looked at recurring groups on months. In this case, I looked at January-December (12 months) and also January-December (24 months). That is why the data begins in 1948 but the map rankings begin in 1949. When looking at a 24-month period, I had to reserve the first year to look back at. Any number of month groupings can be evaluated and any number of variables can be assessed. For example, I looked at the June-November period (not shown) since it corresponds with the North Atlantic hurricane season. As I write this, my map folder contains 436 separate maps. That is far too many to include in a blog post; therefore, I will only show the January-December time period. But I reserve the right to add more later.
Each map shows the exact same area and uses the exact same legend. The first three and last three categories are all one ranking large. the middle entries on the color bar are rankings by group of five. This is done intentionally to highlight the extremes – both high and low. On all maps, anything in bright red means that 2015 had the highest value in the data set compared to other years. Anything in seep orange means 2015 had the second highest value. On the other side of the legend, anything in white indicates 2015 had the lowest values on record.
2015 Annual Maps
The next 13 maps show the surface skin temperature rankings. The skin temperature is slightly different that 2-meter temperature but it better captures the ocean surface temperatures. The first map shows how all of 2015 ranked compared to every other year starting in 1949. The maps after that show each of the twelve months and how 2015 ranked.
Fig. 4. Ranking of 925 mb temperature for 2015 using all twelve months.
Fig. 5. Ranking of 850 mb temperature for 2015 using all twelve months.
Fig. 6. Ranking of 500 mb temperature for 2015 using all twelve months.
Fig. 9. Ranking of 925 mb heights for 2015 using all twelve months.
Fig. 11. Ranking of 500 mb heights for 2015 using all twelve months.
Fig. 15. Ranking of 850 mb v-wind (meridonal) for 2015 using all twelve months.
Fig. 16. Ranking of 300 mb v-wind (meridonal) for 2015 using all twelve months.
Fig. 17. Ranking of 200 mb (minus) 850 mb u-wind for 2015 using all twelve months. This is a proxy for wind shear.