We've all heard it from a meteorologist before, "Temperatures in July are forecast to be above normal for the month," or something to that extent. But have you wondered what that actually means? And why do meteorologists use above or below normal to describe certain weather parameters? Wouldn't it just be easier to give us a number? The use of "normal" to describe a weather parameter can be a bit misleading sometimes, and even counter-intuitive other times. Sometimes it doesn't offer us much information or can give us the wrong impression about what a forecast actually is.

WHAT DETERMINES "NORMAL"?

Meteorologists have often used the word "normal" to describe parameters like temperature, precipitation, humidity and others. But temperatures and precipitation are the most common. So, what is "normal"? Actually, it's probably the wrong word. Normal describes the average conditions over a climatological period. That period is usually the last 30 years, but it's not a rolling 30-year average. For simplicity, averages are calculated in 30-year periods that start at the beginning of a decade. In other words, the climatological period we are currently using is the period from January 1991 through December 2020.

How are these averages calculated? Well, it's fairly simple. If you wanted to get the average high temperature for July 1, you would go through all the high temperatures that occurred on July 1 from 1991 through 2020 and average them. Some days were hot, some were cool, but by using 30 years of data, we can get an average that is close to more of the numbers during that time period.

So, to make a long story short, "normal" is just the average of the weather parameter you are looking at for roughly the last 30 years. It could be temperature for a date, say the high on July 1 example from above, precipitation during the month, or even the variability in the precipitation during the summer. These averages allow meteorologists, climatologists and really anyone, to characterize how the past, current or forecast weather is fairing against the long-term average.

WHEN IS IT ABOVE OR BELOW NORMAL?

The difference in what parameter you are looking at to the normal temperature is what determines whether or not it is above or below normal. If your average high temperature for July 1 is 85 degrees Fahrenheit, then a forecast of 82 degrees would be 3 degrees below normal. Likewise, if you received 5 inches of rainfall in June, when the average over the last 30 Junes was 4 inches, the precipitation you saw was an inch above normal. That difference in normal is what we call an anomaly, a deviation from normal.

WHAT ARE THE BENEFITS OF USING ANOMALIES RATHER THAN JUST PLAIN NUMBERS?

Meteorologists often use anomalies to offer perspective. An anomaly can help identify how different something is from what it "should" be. Should is another bad word to use because the weather data is just data. It shouldn't be anything. But anomalies can help us to define when something deviates from the average. The summer is always hot, but if a high temperature of 95 F is forecast, is that usual? Or is 95 F high for even a hot season? Large anomalies can help us to identify instances when the weather is more extreme, like high temperatures being more than 20 degrees above normal, or rainfall of 1 inch being 4 inches below normal.

Anomalies can also help us to compare months that are not the same. Take January and July, for example. An average of 50 F in January might be extremely warm in Fargo, North Dakota, but it would be extremely cold for Fargo in July. But if we use anomalies, we can say that January was an extremely warm month, and July was an extremely cold month. It can offer more perspective than a raw number might.

We often hear something like "April was very wet." That is a qualitative remark about how the precipitation during the month of April was well above the 30-year average. It doesn't tell us by how much, but it gives us the perspective that wetter conditions during the month might have made an impact on soil moisture, flooding, or drought reduction that a simple number just cannot provide.

Meteorologists also use anomalies for small, but important, deviations. One example is with the El Nino Southern Oscillation (ENSO). An El Nino occurs when sea-surface temperatures in the equatorial Pacific Ocean average more than 0.5 degrees Celsius during a three-month period. If you were to look at a map of just ocean temperatures, it would be nearly impossible to pick that out, as ocean temperatures may only vary by 2 degrees Celsius throughout the entire year. But by using anomalies, it becomes a much easier task and can even show you structures in the sea-surface you wouldn't otherwise be able to see.

Another way of using anomalies is to offer confidence in a forecast. If a forecast shows you a slightly positive temperature anomaly of just 1 degree above normal for August, that would tell you something different than a 10-degree above-normal anomaly. Sometimes, looking at forecasts from complicated climate models can be more easily understood, and confidence expressed, when anomalies are large and consistent rather than when they are weak or inconsistent.

WHAT ARE THE DOWNSIDES OF USING ANOMALIES?

While useful for a vast variety of reasons, anomalies can only offer certain perspectives and they usually need data or comments around them to give you that perspective. They cannot tell you the true impact on something like crop development or drought reduction. They cannot tell you if the mountain snowpack will be enough to last deep into the spring, or how much a river may rise due to above-normal rainfall.

When used improperly, they can even give you a misleading idea about the current conditions. Sometimes, anomaly maps of the next five days of temperatures are used in a forecast. If a dark red anomaly is situated over the Midwest, that might lead you to believe that it will be extremely hot during that entire period. But it may be only one day of extreme heat surrounded by normal temperatures. In that same example, let's say that the high temperature in Kansas City is forecast to be 100 F on day one, but 70 F for days two to five. Let's also say that the average temperature for each of those days is 70 F. That 30-degree difference on day one would average out to an anomaly of 6 degrees for the five-day period. It may cause you to think the entire five days would be very warm, but only marginally so with just a 6-degree anomaly. That certainly was not the case though, with a very hot day one and very mild and average day two-to-five period.

Anomalies are only a single number, and they cannot tell you the variability that was used to get there. For example, for a precipitation anomaly map for the month of July, a month in which your area sees 4 inches of rainfall on average, your area is forecast to see near-normal precipitation for the month. You could have a scenario where it rains 4 inches on the first day of the month but doesn't rain again for the rest of the month. Or it could rain every day, but only at 0.13 inches each day. These two months would showcase the same anomaly, but not the same outcome. Heavy rain on the first day but none for the rest of the month could lead to drought in the first case, but the constant rainfall in the second case could mean soggy soil conditions.

And when specifically using temperatures, it may be a little confusing. Almost always, temperature anomaly maps are defined as a departure from the average daily temperature, not the high or the low. Average daily temperatures are calculated by taking the high and low temperature for that day and averaging it. This comes with its own set of problems that we will not discuss here, but it is important to know that temperature anomaly maps are not comparing the difference from the high or low, but rather the average of the day. Sometimes a raw number would be better than an anomaly to gain better perspective.

Anomalies also can be a poor way to contrast different regions. As another example, say that Kansas received 20 inches of snow during winter. The average may be closer to 10 inches, so 20 inches would be a noteworthy anomaly. In contrast, we could do the same exercise for Minnesota, which may see more than 50 inches on average. In that case, a 20-inch snowfall season would be an extreme anomaly in the other direction and mean something completely different. Soil moisture anomaly maps can work the same way, as very dry conditions in North Carolina would actually equate to very wet conditions in Arizona.

Thus, anomalies are only useful to convey information in certain instances and for specific reasons and often need some context in order to showcase a certain perspective. Meteorologists do their best to use complimentary resources and commentary to perform that task, but it is up to them to get the most use out of anomalies.

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John Baranick can be reached at john.baranick@dtn.com

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