NOAA’s Global Historical Climatology Network (GHCN) publishes 3 different monthly temperature sets for each of the ~7000 weather stations. 1) Average Temperatures 2) Minimum Temperatures 3) Maximum Temperatures. Seasonal variations are averaged out over a 30 year normalisation period to derive so-called ‘temperature anomalies’. This is done by subtracting the 30-year monthly averages from each temperature value which causes temperature trends to zero at the normalisation period. Normally only the average temperature (Tav) anomalies are presented, but I decided to analyse all three. These are the results for global temperatures (including oceans).
Fig 1. Comparison of temperature anomalies normalised to 1961-1990 for a)Tmax b)Tmin c)Tav. All curves pass through zero around 1975 due to the chosen normalisation period. The difference Tmax-Tmin is shown by the blue curve which is plotted versus the right hand Y-scale.
A reduction in Tmax-Tmin of about 0.1C is observed since 1950. Minimum temperatures always occur at night over land areas. This means that nights have been warming faster than days since 1950. The effect is actually much larger than 0.1C because nearly 70% of the earth’s surface is ocean with just single monthly average ‘anomalies’. So nights over land areas have on average warmed ~ 0.3C more than daytime temperatures.
If we assume that average land temperatures have risen by ~1C since 1900, then maximum temperatures have really risen only by 0.85C while minimum temperatures have risen by 1.15C.
This effect may also be apparent in equatorial regions where the night/day and winter/summer temperature differences are much smaller than at high latitudes.
Figure 2. All 117 meridional temperature anomaly profiles from 1990 to 2016. They are coloured blue if the annual global anomalies < -0.2C, Blue,-0.2<grey<0.2, 0.2<yellow<0.4, red > 0.4. Traces are 80% transparent to view them all.
Radiative cooling of the land surface mostly occurs at night. It is much greater when the air is dry such as over desert regions and at the poles. During the day convection and evaporation dominate heat loss. Enhanced CO2 reduces slightly night time cooling efficiency. UHI is also larger at night.
I have been experimenting with different ways to visualise triangulated global temperature data in 3D. Here are the monthly anomaly values for the last 20 years. Of course you can only see one side of the earth at a time, so I also slowly rotate it. The monthly variability is much more striking than a slow overall warming trend.
The temperature colour scale used in all animations is this one.
This is the latest data for March 2017 showing explicitly the spherical triangulation. The underlying data I use is GHCN V3 station data normalised to 1961-1990 combined with HSST3 ocean data.
There is also a youtube video of 137 years starting in 1880. The distorted earth shape in early years is caused by poor sampling.
The coldest winter months seen in Europe over the last 150 years have been 1963 and 2010. They have remarkably similar temperature distributions due to strong negative Arctic Oscillations pulling Arctic air down over Europe and America. As a result of this flow, polar temperature anomalies appear warmer than the 30 year average. Warmer air over North Africa and Asia is also blocked from moving northwards.
Temperature anomaly distribution for January 1963
December 2010 saw a similar pattern with AO values reaching very low values approaching -5
Arctic Oscillation for winter 2010/11 produced by NOAA
In December 2010 the lowest ever temperatures were recorded at several UK stations. This is how the global temperature anomaly distribution looked, not too different than 1963.
Temperature anomalies for December 2010
A negative phase of AO corresponds to high pressure at the pole. The Jet Stream moves further south with large meanders, causing cold polar air to be dragged with it. As a result polar temperatures actually appear to be slightly warmer than normal.