Koppen Climate Classification Map. Photo Credit: Wikipedia

Climate Change Myths Part 2: Weather and climate

In our first post on climate change myths, we wrote about why we should expect colder temperatures during winter months, that just because it’s cold in February, does not mean that the Earth’s climate isn’t changing. But when does it mean for the climate when there are a few warm days in January? Is that proof that the world is heating up? To really figure out what is happening to our Earth, we need to understand the important differences between weather and climate.

Myth 2: Weather is the same thing as climate

When atmospheric scientists talk about weather, they are referring to the conditions of the air at any given moment. If it’s snowing, cloudy, hot, cold, sunny, windy, etc. When we watch the news, and see the weather report, it’s a forecast of what the atmospheric conditions will be for the next few day. A snapshot of what is going on in the air around you. Climate refers to the long-term patterns of precipitation, humidity, atmospheric pressure, and temperature, usually taken over a 30 to 40 year period. Decades worth of data is used to describe a region’s weather patterns. Differences in these patterns result in a multitude of different climates. When, for example, the Southeastern United States is said to have a humid subtropical climate, it means that it can be described as having warm, moist summers, with dry and cool, but not cold, winters.

Koppen Climate Classification Map. Photo Credit: Wikipedia

These differences are subtle but important. Weather is what is happening on any given day in the atmosphere, one singular data point. Climate is modeled around long term trends based on years’ worth of data collected. Just because a region is experiencing a few days of atypical weather, doesn’t means that its climate is changing. There are warm days in Alaska and it occasionally rains in the Sahara. However, those data points aren’t enough to be indicative of a larger change forthcoming. What is needed is a larger sample size to give us more confidence in our conclusions. This is exactly what scientists do when researching the climate trends.

Data point vs. Trends

The data collected by researchers can be hard to digest, most people do not have an advanced degree in atmospheric science. So to illustrate the differences between weather and climate, I was inspired by famed data geek Nate Silver and chose an analogy that is near and dear to both our hearts: baseball.

Modern baseball has been played every year since 1903. 30 teams play 162 games a year, and starting position players getting about 450 at bats a season. This gives us a lot of data to work with, making baseball a good model for looking at the difference between an instantaneous data point and a long-term trend. In fact, a team’s general manager will look at similar data to make projections on a player’s performance and determine rosters. Let’s do just that.

Say we are the general manager of a team, and we are looking to bring in a big name player on for the new season. We will simplify this scenario by ignoring the unique positions on a baseball team, MLB salary rules, and player age. And while there are a lot of advanced metrics that could be used to determine a player’s value (OBP, OPS, WAR), we are going to stick to batting averages.

We start our search by looking at the free agency list, and immediately we see two big names: Mike Trout and Alex Rodriguez. But which one should we try to sign? We want an player of all-star caliber, someone who can hit for around 0.300 average or higher. We go back through the game logs from last year and take look at a 5 game stretch in June and compare the two players.

Mike Trout and Alex Rodriguez (yes, I these are the derpiest faces I could find). Photo Credit: Flickr

5 Game Average

Mike Trout

Alex Rodriguez

Date: At Bats Hits At Bats Hits
6/4/2016 4 1 5 3
6/5/2016 4 0 4 2
6/6/2016 3 0 4 0
6/7/2016 3 0 4 2
6/8/2016 5 3 5 2
5 Game Batting Average 0.210 0.409

Certainly, a great week for Rodriquez. With an average above 0.400, he would not only be an all-star, but very likely the best hitter in the league (last player to hit over 0.400 for the season was Hall of Famer Ted Williams in 1941). Trout on the other hand, really struggled, hitting barely above 0.200, the dreaded Mendoza line, the point in which most teams send players back to the minor leagues. In fact, by only looking at these 5 days, he would be lucky to be in the Majors at all. If this was the only data that we looked it, Alex Rodriguez would be the player to sign to a big contract, no brainer.

However, we want to cover our bases (terrible, I know). We want to be confident that our new player is going to perform at a high level. The more data we extract, the more accurate our prediction of how both Trout and Rodriguez will perform in the upcoming season. Instead of a few days’ worth of baseball, let’s look at their batting averages over the past several years. We go back to the records and pull the batting averages of each player from 2012-2016 to see their respective long-term trends.C

5 Year Average

Mike Trout Alex Rodriguez
Date: Batting Average Batting Average
2012 0.326 0.276
2013 0.323 0.244
2014 0.287 Suspended
2015 0.299 0.250
2016 0.315 0.200
5 Year Batting Average 0.309 0.229

We can see a big difference in player performance. Mike Trout has consistently hit at or around 0.300 in each of the last 5 years, all-star numbers. Alex Rodriguez, however, did not live up to his June 2016 hot streak. While having a solid year in 2012, his average has dropped precipitously since, spending a year hurt and several more hitting closer to 0.200. The likelihood of Rodriquez having another good season is much lower than him performing poorly. The trend over the last 5 years for Alex Rodriguez is clearly down. We sign Mike Trout to a large, expensive contract, and our team goes on to have a great season.

Weather vs. Climate

Just as the performance of a baseball player in any given game, doesn’t show their worth, a few days of weather cannot give us a clear picture on the climate. Taking a larger sample size gives us more confidence in what we are looking for. A small set of data could “disprove” the idea that global climate is not warming. In 2014, the Polar Jet Stream weakened, spilling bitterly cold, Arctic air over the entire continent of North America. This Polar Vortex bought record breaking low temperatures over many American cities, including where I live, Cincinnati, Ohio. Below is a data set of 5 days from the Polar Vortex week of February 2014.

5 Day Weather Sample

Cincinnati Weather
Date: High Temp F° (C°)
2/16/2014 12° (-11°)
2/17/2014 23° (-5°)
2/18/2014 19° (-7°)
2/19/2014 7° (-14°)
2/20/2014 19° (-7°)
Average Temperature 16° F (-9° C)

Even for February in Cincinnati, where the average temperature for the month is 44° F (7° C), those are some very cold days. But even though the high temperatures that week was nearly 30° F (16ׄ°) below average, it’s not enough data to give us any information about the climate. We could just as easily go back through the record and find several days in a row well above the historical average. The first week of 1961, before China and India had large scale industrialization, Cincinnati had quite the heat wave, with most highs at or above 48° F (9° C), several degrees above the January average of 39° F (4° C). But again, the sample size is too small to justify a claim on long term trends. The weather on any given day is not necessarily indicative of the climate, same as a handful of games doesn’t show a baseball player’s true value. A few data point does not prove anything, you’ve got to take a larger sample size.

So what is happening with climate data?

Luckily, we don’t have to limit our study to the weather patterns in one Midwestern city. Instead, we can look at the aggregated data from the last 130 years of weather across the entire world. As each city records the daily temperatures, we can compare that with its historical average. We can then look at how we have deviated from that average. NASA’s Earth Observatory, as well as several other organizations, have done just that, and have compiled the data in the chart you see below.

Global yearly temperature averages vs. the historical average. Photo Credit: NASA

The trend shows that temperatures have increased, by about 1.8° F (0.75° C) over the last 60-70 years. While that may not seem like a lot, we need to put that in perspective. During the last glacial maximum approximately 24,000 years ago, global temperatures were about 10.4° F (5.8° C) below the 20th century average. At that time most of Canada and a good chunk of Europe was covered in a layer of ice several miles thick. 2016 was also the hottest year on record, the third year in a row that has happened. In fact, 11 of the hottest 12 years to be recorded have occurred in the 21st century. The reality is that the long-term trends shows Earth’s temperature is increasing, and quickly. It’s not in dispute, it’s what the data is telling us.

So the data shows that the Earth is warming. But what is causing the warmth? In Part 3 of our series we put some of this change into perspective on a geologic scale and try to come up with a understanding of why our climate seems to be changing.

Learn More:

  • Elizabeth Kolbert’s book, The 6th Extinction, is an extremely informative, if not outright depressing, read on how humans activity have been causing the next great culling of biodiversity in our planet’s history.
  • Ezra Klein interview Kolbert on his podcast, The Ezra Klein Show. A great conversation between intelligent people.
  • Randell Munroe of the XKCD has an illustrated comic to put into perspective on how the climate has changed over the last 20,000 years, that is at the same time easily understandable, aesthetically pleasing, and disconcerting.

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