A look at weather extremes and the big-picture climate connections.

Historical Perspective on the Russian Heat Wave of 2010

By Heidi Cullen and Claudia Tebaldi

The summer of 2010 brought intensely hot weather to large portions of the northeastern U.S., central Europe, and Russia. Russia was especially hard hit as a heat wave — with daily high temperatures hitting 100°F — contributing to the deaths of as many as 15,000 people in Moscow while wildfires tore across more than 2,900 square miles in the central and western part of the country. Drought accompanied the record high temperatures decimating more than a quarter of Russia’s grain harvest. Economists estimated the grain losses cost the Russian economy upwards of $15 billion dollars.

As climate scientists continue to study the underlying dynamics of this extreme heat event in order to better understand the extent to which human-caused climate change may have played a role, we wanted to put the Russian heat wave of 2010 into historical context. With that in mind, we collected temperature data from Moscow for July 2010 as well as summer (June through August 2010) and compared it to every year since 1950. (Our analysis is similar to that employed by Schär et. al in their 2004 Nature paper). 

We sought an answer to the question: how significant was the departure of the 2010 values from the typical summer temperature in Moscow?

Probability of June, July and August average temperature anomalies in Moscow, Russia since 1950.
This image shows that the average temperature in Moscow for summer 2010 was significantly hotter than in any year since 1950.
Credit: Claudia Tebaldi and Remik Ziemlinksi
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We have summarized the behavior of a typical summer in Moscow by a normal (bell) curve with mean and standard deviation derived from the summer temperature anomalies during the period 1970-2000 (the typical choice for current climatology). Compared to that distribution, the values experienced this summer are so unexpected as to be beyond three standard deviations for June, July and August means and four standard deviations for July means from the center of that distribution.

Another way to say the same thing is to look at the probability of such extreme values or larger with respect to the bell curve. This probability turns out to be on the order of a one and a half chance in 100,000 for the July anomaly and one in a thousand for the June, July and August anomalies.

Note that we are not saying this was a one in a 100,000 year event. For that kind of claim we would have to perform an analysis specifically focused on extreme events, while here we are only characterizing the normal behavior of the distribution. We are saying though, that the event was extraordinarily intense compared to historical records, and it is reasonable to explore alternative hypothesis to simple natural variability as the cause of such an event.

Probability of July average temperature anomalies in Moscow, Russia since 1950.
This image shows that the average temperature in Moscow for July 2010 was significantly hotter than in any year since 1950.
Credit: Claudia Tebaldi and Remik Ziemlinski.

Consider that statisticians start to question the sources of variations when they are as little as two standard deviations from the expected mean value, which corresponds to about a one in 100 chance! Therefore, it is no surprise that meteorologists and climatologists have been so interested in exploring the causes of such an extreme event.

The data used for this analysis was downloaded through the IRI/LDEO climate data library.

It is obtained from the NOAA/NCEP CPC Climate Anomaly Monitoring System in the form of monthly temperature anomalies during the period 1950-2010, where anomalies are calculated with respect to the baseline of 1970-2000. The data is on a two degree by two degree grid, and the temperature values for the grid point closest to the coordinates of Moscow, Russia (55.8N, 37.6E) were extracted and analyzed.

Note: A more rigorous analysis of the extreme nature of these temperatures would proceed by considering only previous extremes, and characterizing their statistics. But here we took a simpler look at the extreme nature of last summer’s temperatures.

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