The next blog post will examine a solar cycle model which, according to the authors of two articles published eight years ago, claims that the temperatures in some northern regions are dependent on the length of the previous solar cycle. The model predicted cold temperatures in solar cycle 24, which ended in November 2019 after having lasted for 11 years. It is time to check if the prediction came true.
The blog post that you are reading now, examines the local and regional temperature series that will be applied in the next blog post. It shows graphically the temperature series read from these sources, and it compares them against each other.
The average length of a solar cycle is 11 years. The temperatures are therefore averaged over 11 years in this blog post before plotting and comparison. The average is shown as a moving average, in which each point on the temperature curve shows the average of the monthly temperatures five and a half years before and after that point.
1 The temperature series
eKlima is a web portal which gives free access to the climate database of the Norwegian Meteorological Institute. The climate database contains data from all present and past weather stations of the Norwegian Meteorological institute, as well as data from other institutions (owners) that are allowed to be distributed.
I downloaded the monthly temperatures and the monthly temperature anomalies. The anomalies are relative to the mean in the reference period from January 1961 till December 1990.
Rimfrost is a web portal managed by Sigmund Hov Moen which gives access to a unique collection of climate data. It is running on an Amazone server. As recommended, I downloaded a private Rimfrost version to my PC to access the temperatures. The monthly temperatures are as measured or as reconstructed, so I had to calculate the anomalies before plotting and comparison.
1.3 BEST - Berkeley Earth
Berkeley Earth offers temperature data from weather stations, from locations evenly distributed over the globe's land areas, and from many regions. The regions may be countries or greater regions such as the northern hemisphere land areas.
The temperature data from the weather stations consists of monthly temperatures. The other temperature data consists of monthly temperature anomalies with associated quality data, just as the global BEST temperature series do. The anomalies are relative to the reference period from January 1951 till December 1980.
The locations, which are evenly distributed over the globe's land areas, are selected by either clicking on a map or by specifying a city. The distance between these locations are approximately 200 kilometers. Berkeley Earth explains: 'The Berkeley Earth averaging method takes temperature observations from a large collection of weather monitoring stations and produces an estimate of the underlying global temperature field across all of the Earth's land areas. Once this temperature field has been generated, it is possible to estimate the temperature evolution of individual locations simply by sampling the field at the location in question.' When we specify a city, we get the temperatures series from the nearest of these evenly distributed locations. I name these series 'BEST near' followed by the city name.
The 'BEST near' temperature series all start before 1850. Now, at the end of 2020, they are updated up to an including June 2020. In order to get a correct comparison between the different temperature sources, I use the temperatures up to and including June 2020 for the other temperature series as well.
1.4 NASA GISS
NASA GISS offers temperature data from many weather stations. The user selects how 'raw' the temperatures shall be. The temperatures that I downloaded are both adjusted and homogenized. I name these series 'GISS' followed by the name of the weather station.
The temperatures are monthly temperatures. I therefore had to calculate the monthly anomalies before plotting and comparing them against the other local temperature series.
2 Plots and comparisons
All temperatures in this chapter are anomalies relative to the reference period from January 1881 till December 1910. The temperature in this reference period is often referred to as the pre-industrial temperature. The plots consequently show the warming since pre-industrial time.
The 11 years moving averages of the temperatures from the different series are usually rather close to each other. The monthly temperatures differ much more.
2.1 Global and regional temperature series
Figure 1 shows that the land temperatures in both Norway including Svalbard and on the northern hemisphere have increased more than the global temperatures have.
2.2 Stykkisholmur and Iceland
|Figure 2: Eleven years moving average of two temperature series for Stykkisholmur and one for Iceland.|
2.3 Longyearbyen (Svalbard lufthavn)
|Figure 3: Eleven years moving average of two temperature series for Longyearbyen, Svalbard.|
|Figure 4: Eleven years moving average of four temperature series for Vardø.|
|Figure 5: Eleven years moving average of two temperature series for Dombås.|
The 'BEST near' location is 40 kilometers southwest of Dombås. Both that location and Dombås are in valleys far from the sea. I therefore expected that the curves in Figure 5 should be closer to each other than they are. The RMS of the monthly differences between the temperatures is 0.57ºC, and the average is 0.10ºC.
The weather stations in Oslo have been moved many times since 1850. I therefore downloaded the homogenized eKlima temperature series which has monthly temperatures since January 1850.
|Figure 6: Eleven years moving average of two temperature series for Oslo|
The 'BEST near' location is far away from the Oslo fjord, 69 kilometer northwest of the city center. I therefore did not expect that the curves in Figure 6 should be that close to each other. The RMS of the differences between the monthly temperatures is 0.56ºC, and the average is 0.00ºC.
I checked the solar cycle model back in 2012 and 2014. Then I mostly used temperature series from eKlima and Rimfrost for the local series. This time, as you will see in the next blog post, I will use eKlima and 'Best near' for the local series. I will use eKlima homogenized temperature series for locations with weather stations that have been moved around since the measurements started.
eKlima and BEST apply different methods for adjustment and homogenization. For Longyearbyen I will apply both of them to see how robust my conclusion with respect to the solar cycle model is.