The Science of Doom

What controls the frequency of tropical cyclones?

Here’s an interesting review paper from 2021 on one aspect of tropical cyclone research, by a cast of luminaries in the field – Tropical Cyclone Frequency by Adam Sobel and co-authors.

Plain language summaries are a great idea and this paper has one:

In this paper, the authors review the state of the science regarding what is known about tropical cyclone frequency. The state of the science is not great. There are around 80 tropical cyclones in a typical year, and we do not know why it is this number and not a much larger or smaller one.

We also do not know much about whether this number should increase or decrease as the planet warms – thus far, it has not done much of either on the global scale, though there are larger changes in some particular regions.

No existing theory predicts tropical cyclone frequency.

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One Look at the Effect of Higher Resolution Models

In #15 we looked at one issue in modeling tropical cyclones (TCs). Current climate models have actual biases in their simulation of ocean temperature. When we run simulations with and without these errors there are large changes in the total energy of TCs.

In this article we’ll look at another issue – model resolution. Because TCs are fast and small scale, climate models at current resolution struggle to model them.

It’s a well known problem in climate modeling, and not at all a surprise to anyone who understands the basics of mathematical modeling.

This is another paper referenced by the 6th Assessment Report (AR6): “Impact of Model Resolution on Tropical Cyclone Simulation Using the HighResMIP–PRIMAVERA Multimodel Ensemble”, by Malcolm Roberts and co-authors from 2020.

The key science questions addressed in this study are the following:

1) Are there robust impacts of higher resolution on explicit tropical cyclone simulation across the multi- model ensemble using different tracking algorithms?

2) What are the possible processes responsible for any changes with resolution?

3) How many ensemble members are needed to assess the skill in the interannual variability of tropical cyclones?

In plain English:

• They review the results of a number of climate models each at their standard resolution and then at a higher resolution
• When they find a difference, what is the physics responsible? What’s missing from the lower resolution model that “kicks in” with the higher resolution model?
• How many runs of the same model with slightly different initial conditions are needed before we start to see the year to year variability that we see in reality?

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In #1-#6 of the “Extreme Weather” series we looked at trends in Tropical Cyclones (TCs) from the perspective of chapter 11 of the 6th assessment report of the IPCC (AR6). The six parts were summarized here.

The report breaks up each type of extreme weather, reviews recent trends and then covers attribution and future projections.

Both attribution and future projections rely primarily on climate models. We looked at some of the ideas of attribution in the “Natural Variability, Attribution and Climate Models” series.

AR6 has a section: Model Evaluation, on p. 1587, before it moves into Detection and Attribution, Event Attribution.

How good are models at reproducing Tropical Cyclones?

Accurate projections of future TC activity have two principal requirements: accurate representation of changes in the relevant environmental factors (e.g., sea surface temperatures) that can affect TC activity, and accurate representation of actual TC activity in given environmental conditions.

Suppose in the future we had a model that was amazing at reproducing tropical cyclones when a variety of climate metrics were accurately reproduced. However, if the climate model didn’t reproduce these metrics reliably we still wouldn’t get a reliable answer about future trends in tropical cyclones.

As a result tropical cyclones are a major modeling challenge.

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Overview of Chapter 3 of the IPCC 6th Assessment Report

The periodic IPCC assessment reports are generally good value for covering the state of climate science. I’m taking about “Working group 1 – the Physical Science Basis”, which in the case of the 6th assessment report (AR6) is 12 chapters.

They are quite boring compared with news headlines. Boring and dull is good if you want to find out about real climate.

If you prefer reading about the end of days then you’ll need to stick to press releases.

Here’s a quick summary of Chapter 3 – “Human Influence on the Climate System”. Chapter 3 naturally follows on from Chapter 2 – “Changing State of the Climate System”.

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In #9 we looked at an interesting paper (van Oldenborgh and co-authors from 2013) assessing climate models. They concluded that climate models were over-confident in projecting the future, at least from one perspective which wouldn’t be obvious to a newcomer to climate.

Their perspective was to assess spatial variability of climate models’ simulations and compare them to reality. If they got the spatial variation reasonably close then maybe we can rely on their assessment of how the climate might change over time.

Why is that?

One idea behind this thinking is to consider a coin toss:

• If you flip 100 coins at the same time you expect around 50 heads and 50 tails. Spatial.
• If you flip one coin 100 times you expect 50 heads and 50 tails. Time.

There’s no strong reason to make this parallel with climate models on spatial and time dimensions but climate is full of challenging problems where we have limited visibility. We could give up, but we just have the one planet so all ideas are welcome.

In the paper they touched on ideas that often come up in modeling studies:

• assessing natural variability by doing lots of runs of the same climate model and seeing how they vary
• comparing the results of different climate models

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How do we know that a change in the climate, for example, rainfall trends in recent decades, is due to human activity like burning fossil fuels? How do we know it’s not natural variability?

This is the question we’ve started to look at in the first four parts of this series.

When it comes to attribution there are 1000s of papers considering the attribution of various trends in climate metrics to human activity (primarily burning fossil fuels).

Here’s what the 6th assessment report (AR6) says about attribution in plain English:

We need to make some assumptions: observed changes are due to a simple addition of forced changes (e.g effects from more CO2 in the atmosphere) and natural variability; and we can work out natural variability

Another way to write the second part:

If we don’t understand natural variability then our assessment could well be wrong.

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In #1 we looked at natural variability – how the climate changes over decades and centuries before we started burning fossil fuels in large quantities. So clearly many past trends were not caused by burning fossil fuels. We need some method to attribute (or not) a recent trend to human activity. This is where climate models come in.

In #3 we looked at an example of a climate model producing the right value of 20th century temperature trends for the wrong reason.

The Art and Science of Climate Model Tuning is an excellent paper by Frederic Hourdin and a number of co-authors. It got a brief mention in Models, On – and Off – the Catwalk – Part Six – Tuning and Seasonal Contrasts. One of the co-authors is Thorsten Mauritsen who was the lead author of Tuning the Climate of a Global Model, looked at in another old article, and another co-author is Jean-Christophe Golaz, lead author of the paper we looked at in #3.

They explain that there are lots of choices to make when building a model:

Each parameterization relies on a set of internal equations and often depends on parameters, the values of which are often poorly constrained by observations. The process of estimating these uncertain parameters in order to reduce the mismatch between specific observations and model results is usually referred to as tuning in the climate modeling community.

Anyone who has dealt with mathematical modeling understands this – some parameters are unknown, or they might have a broad range of plausible values

An interesting comment:

There may also be some concern that explaining that models are tuned may strengthen the arguments of those claiming to question the validity of climate change projections. Tuning may be seen indeed as an unspeakable way to compensate for model errors.

The authors are advocating for more transparency on this topic..

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