Weather Events and Climate Change: Separating Fact from Fiction – Part 1

by ROGER PIELKE JR.

It is now a ubiquitous cultural ritual to blame any and every weather event on climate change. Those hot days? Climate change. That hurricane? Climate change. The flood somewhere that I saw on social media? Climate change.

With today’s post, the first in a series, I go beyond the cartoonish media caricatures of climate change, which I expect are here to stay, and explore the actual science of extreme events — how they may or may not be changing, and how we think we know what we know, and what we simply cannot know.

Quite apart from the outsized and oversimplified role of climate-fueled extreme weather in culture and politics, climate is fascinating and important — and worth understanding as more than a meme. This post lays the groundwork for this new THB series, starting with some important definitions and a quantitative thought experiment.

Let’s start with the IPCC (Intergovernmental Panel on Climate Change) definition of climate (bold emphasis added):

In a narrow sense, climate is usually defined as the average weather, or more rigorously as the statistical description in terms of the mean and variability of relevant quantities over a period of time ranging from months to thousands or millions of years. The classical period for averaging these variables is 30 years, as defined by the World Meteorological Organization (WMO). The relevant quantities are most often surface variables such as temperature, precipitation and wind. Climate in a wider sense is the state, including a statistical description, of the climate system.

Climate refers to a “statistical description”1 of the climate system, defined as:

The global system consisting of five major components: the atmosphere, the hydrosphere, the cryosphere, the lithosphere and the biosphere and the interactions between them. The climate system changes in time under the influence of its own internal dynamics and because of external forcings such as volcanic eruptions, solar variations, orbital forcing, and anthropogenic forcings such as the changing composition of the atmosphere and land-use change.

 
The climate system is complicated, but at a high level, we can get our brain around it (above). There is a deeper discussion to be had about why the climate research community decided that people are not included as part of the “climate system,” but let’s leave that for another day.2

That brings us to climate change:

A change in the state of the climate that can be identified (e.g., by using statistical tests) by changes in the mean and/or the variability of its properties and that persists for an extended period, typically decades or longer. Climate change may be due to natural internal processes or external forcings such as modulations of the solar cycles, volcanic eruptions and persistent anthropogenic changes in the composition of the atmosphere or in land use.3

Let’s correct one pervasive and pathological misunderstanding endemic across the media and in policy, and sometimes spotted seeping into peer-reviewed scientific research:

Neither climate nor climate change cause, fuel, or influence weather.

Yes, you read that right.

Climate change is a change in the statistics of weather — It is an outcome, not a cause.

I often use hitting in baseball as an analogy. A hitter’s batting average does not cause hits. Instead, a batter’s hits result in their overall batting average. Lots of things can change a batter’s hitting performance, but batting average change is not one of them.

As the Google NGrams figure below indicates, the idea that climate change is a causal agent has become increasingly common in recent decades, departing dramatically from its use in the IPCC and much of the scientific community. I am sure you can point to examples that you encounter every day.

 
Using the IPCC definitions, how would we identify “climate change” in the statistics of weather?

The IPCC explains how we detect climate change:

Detection of change is defined as the process of demonstrating that climate or a system affected by climate has changed in some defined statistical sense, without providing a reason for that change. An identified change is detected in observations if its likelihood of occurrence by chance due to internal variability alone is determined to be small, for example, <10%.

Let’s illustrate through a practical analogy. Hold on to your wallet.

Imagine that you are dealt two cards from a standard combined blackjack deck of 6×52 cards (that is, six combined 52-card decks). From this combined deck, the chances of being dealt at least one ace in a two-card hand is about 14.8%.4

Let’s say that I stack the combined deck by adding 1 additional ace — raising the total from 24 to 25 total aces. Now, what are the chances of getting at least one ace in a two-card hand? The chances have now increased to 15.3%.5

I next add five more aces, for a total of six. Now, in a two-card hand, the chances of receiving at least one ace increases to 18.2%.

We thus have three different decks in our thought experiment:

1. a standard combined 6×52 deck,
2. a stacked deck with 1 additional ace — a ~4.1% increase in aces,
3. a stacked deck with 6 additional aces — a massive 25% increase in aces.

We can next ask, if we did not know that two of the decks were stacked — (2) and (3) — how many hands would we have to play to have a certain degree of confidence that the deck was actually stacked?

To reach a 50% confidence level that the 6×52 card deck was stacked with one additional ace, we would need to play 99 two-card hands. For the stacked deck with 6 additional aces, we’d need just 22 two-card hands.

The table below shows results for different levels of confidence for each of the two stacked decks.

 
Let’s take the recommended IPCC threshold for detection of change of 90%, and let’s also assume that we play 3 hands per year (comparable to the average number of major hurricanes in the Atlantic every year). In this example to detect a ~4% increase in aces at a 90% level of confidence would require more than 100 years (=329/3).6 Detection of a 25% increase in aces would require almost 25 years (=~73/3).

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