Climatology

AEMET predicts May June July to see amongst the highest temperatures recorded in 30 years in Spain

AEMET predicts May June July to see amongst the highest temperatures recorded in 30 years Credit: Twitter @AEMET_Esp

AEMET predicts May June July to see amongst the highest temperatures recorded in Spain over a 30 year period.

Taking to Twitter on Monday, 2, May, Spain’s State Meteorological Agency AEMET predicted that the next three months may see some of the hottest temperatures recorded in Spain over a 30 year period ranging from 1981-2010:

“#AEMET prediction
TEMPERATURE 🌡️
For MAY-JUNE-JULY 2022 there is a high probability that the temperature will be in the upper tercile for the whole of Spain (reference period 1981-2010). ”

Credit: Twitter @AEMET_Esp

On their website AEMET explained how their system works:

“On a seasonal scale the prediction is probabilistic in nature. The predictions reflect the probability that the average value of a variable (normally accumulated precipitation or surface temperature) over the three-month period will be above or below the values ​​considered normal or usual. These normal values ​​are defined from the historical records of each point over a reference period (in our case 1981-2010). In order to characterize the values ​​of a specific point and station, the total of records is divided into three groups: one of them will contain the lowest third of values ​​(lower tercile), another third of them will contain the highest values ​​(upper tercile) ), and the remaining values, intermediate between both, will be those considered normal values ​​(central tercile). For each point, there will be two values ​​that will mark the boundary between the lower and the normal, and between the normal and the upper tercile ”

For example, for surface air temperature (left), the probability that the seasonal average temperature is in the upper (above normal, or warm), middle (normal) or lower (below normal, or cold) third or tercile of the temperature distribution recorded in the reference climatology for each location is calculated. At each point, only the color scale of the category that is most likely to occur is represented. For example, for regions marked in yellow-orange-red, the mean surface temperature forecast indicates that the category most likely to occur is warm. Similarly, blue colors indicate regions where the average surface temperature is most likely to be in the cool tercile. Stronger colors mean higher probabilities that the average surface temperature falls within that category. Uncolored areas indicate that the average surface temperature is equally likely to be in any of the three categories. ”

“The fact that an area is marked with the color corresponding to a category does not mean that there is certainty that the observed temperature will be in that category, but that it is more likely to be in that category. As a result, the average temperature finally recorded has a non-negligible probability (indicated for each area by bars) of being in a different category than the one indicated on the map as “most likely”. This probabilistic nature of the seasonal forecast needs to be taken into account when interpreting the forecast and making decisions. It is also important to note that the absolute values ​​of surface air temperature that correspond to the definitions “above normal”, “normal” or “below normal” depend on the climatology (historical information) of each location. ”


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