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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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In #1 we took a brief look at Natural Variation – climate varies from decade to decade, century to century. In #2 we took a brief look at attribution from “simple” models and from climate models (GCMs).

Here’s an example of the problem of “what do we make of climate models?”

I wrote about it on the original blog – Opinions and Perspectives – 6 – Climate Models, Consensus Myths and Fudge Factors. I noticed the paper I used in that article came up in Hourdin et al 2017, which in turn was referenced from the most recent IPCC report, AR6.

So this is the idea from the paper by Golaz and co-authors in 2013.

They ran a climate model over the 20th century – this is a standard thing to do to test a climate model on lots of different metrics. How well does the model reproduce our observations of trends?

In this case it was temperature change from 1900 to present.

In one version of the model they used a parameter value (related to aerosols and clouds) that is traditional but wrong, in another version they used the best value based on recent studies, and they added another alternate value.

What happens?

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In #1 we looked at some examples of natural variability – the climate changes from decade to decade, century to century and out to much longer timescales.

How sure are we that any recent changes are from burning fossil fuels, or other human activity?

In some scientific fields we can run controlled experiments but we just have the one planet. So instead we need to use our knowledge of physics.

In an attempt to avoid a lengthy article I’m going to massively over-simplify.

“Simple Physics”

Some concepts in climate can be modeled by what I’ll call “simple physics”. It often doesn’t look simple.

Let’s take adding CO2 to the atmosphere. We can do this in a mathematical model. If we “keep everything else the same” in a given location we can calculate the change in energy the planet emits to space for more CO2. Less energy is emitted to space with more CO2 in the atmosphere.

The value varies in different locations, but we just calculate it in lots of places and take the average.

As less energy is leaving the planet (but the same amount is still being absorbed by the sun) the planet warms up.

In our model, we can keep increasing the temperature of the planet in our model until the energy emitted to space is back to what it was before. The planetary energy budget is back in balance.

So we’ve calculated a new surface temperature for, say, a doubling of CO2.

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In the last set of articles we’ve looked at past trends in extreme weather, following the flow of chapter 11 from the 6th assessment report of the IPCC.

How do we know the cause of any changes?

In recent years in most of the media everything that changes is “climate change” which is implicitly or explicitly equated with burning fossil fuels, i.e., adding CO2 into the atmosphere. It’s a genius catchphrase from a marketing point of view, not so helpful for scientific understanding.

I used to prefer the term “anthropogenic global warming” but it has its flaws as well, as some recent trends are believed to be anthropogenic but not from adding CO2 into the atmosphere. An example is changes that result from reduced aerosols in the atmosphere as a result of burning less biomass.

I’ll generally try and stay with “anthropogenic” or “from more CO2”, but there’s no copy editor, so let’s see.

Lots of changes in past climate metrics are simply natural variability. Understanding and quantifying natural variability is a big topic and our knowledge is always going to be imperfect.

For example, there were multi-decadal megadroughts in North America and Europe in the past 1000 years. They were probably “unprecendented” for their time, but clearly weren’t caused by burning fossil fuels.

Here is a reconstruction of the drought index over 1000 years of western North America..

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Global temperature has been rising since around 1900, and CO2 is the principal cause. The physics behind the inappropriately-named “greenhouse effect” is certain, so burning fossil fuels, which adds CO2 to the atmosphere, is certain to increase the surface temperature. I’ve written many articles on that topic on the original blog and shown how the equations are derived (see Notes).

So it should be no surprise to find that there are more extreme hot days and less extreme cold days.

If the temperature goes up, then the number of days with a temperature above say 35°C (86°F) or 40°C (95°F) – or whatever number you want to pick – will increase.

Likewise, the number of days with a temperature below say -10°C (14°F) or -20°C (-4°F) – again, pick a number for your region – will decrease.

Here’s a graph of global land and ocean temperature, extracted from a larger graph in chapter 2 of AR6 (see the Notes below for the full map of changes):

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In #10-#13 we looked at recent trends in droughts.

After covering Tropical Cyclones in #1-#6 it was worth doing a summary in part because AR6’s summary missed some good news from the report itself.

In #7-#9 we looked at extreme rainfall and floods and the summary was important because AR6’s summary missed some good news.

I’ve included the full text from p. 1575 below in the notes.

If you check the table in the notes section of #11 we can see that there were 8 regions identified with a rainfall deficit and 6 regions identified with a rainfall increase. These are also listed in the report section before the summary. However, the summary section only says:

There is medium confidence in increases in precipitation deficits in a few regions of Africa and South America.

Of course, people can read the section before and find out that there were places with a rainfall increase (a decrease in droughts). But anyone limiting themselves to only the “summary” would miss out on this good news.

On soil moisture droughts – agricultural and ecological droughts – they say:

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In #12 we saw that soil moisture droughts – agricultural and ecological droughts – have increased globally.

I’ve been following the flow of AR6 in their discussion of recent trends. They do go on to discuss hydrological droughts without much that’s definitive so perhaps we’ll have a brief look at that in another article, as I’m something of a completionist.

But there’s something important missing from the drought section.

Are plants dying? If not, is there really an increase in soil moisture drought?

Here’s a question from Alexis Berg & Justin Sheffield (2018) to put the problem in a broader context. Here and in all the other papers quoted, bold text is my change:

The notion that a warmer climate leads to a drier land surface, i.e., increased water stress, driven overwhelming by the effect of warmer temperatures on evaporative demand, appears, however, inconsistent with paleo-evidence and vegetation reconstructions for different colder and warmer past climates.

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In #11 we looked at “droughts from rainfall”, i.e., rainfall deficits. There were regional variations but no obvious global trend.

In fact, we expect more rainfall as the earth warms (warm air holds more moisture) so a question to return to in a later article is why there hasn’t been an obvious increase in rainfall, i.e., why there hasn’t been a reduction in “droughts from rainfalls”.

We can measure rainfall. Think – a little bucket that fills up with rain and someone comes around every day, takes a measurement, and writes that number down in a notebook. Of course, the measurement is often more sophisticated in recent times. But we can all appreciate it’s a measurement that can easily be taken.

The other side of soil moisture is evaporation. If it’s hotter, all other things being equal, we expect more evaporation and so on the margins, more places in droughts.

The probem? There is no simple instrument we can stick in the ground next to the rainfall bucket to measure evaporation.

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In #10 we saw an overview which included the idea that a rainfall deficit is one part of a soil moisture deficit. But it’s the part we can measure.

AR6 says, p.1573:

Global studies generally show no significant trends in SPI [drought index for rainfall] time series (Orlowsky and Seneviratne, 2013; Spinoni et al., 2014), and in derived drought frequency and severity data (Spinoni et al., 2019), with very few regional exceptions.

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