We are in a period of turbulent weather, that’s clear. Extremes of temperature, rainfall, drought, storm frequency, and glacier loss are occurring (among other things). Climate scientists have concluded that the trend toward increasingly extreme weather is due to the effects of “greenhouse gases” (GHGs), such as carbon dioxide (CO2) and methane, which have been produced primarily by the extraction and burning of fossil fuels.
But, as the scientists have been telling us for decades, it hasn’t been possible to say that any given weather event is due to the effects of those gases. It has been impossible to tease out what part is due to the natural variability of the weather from what part is due to the GHGs.
That is now changing. Thanks to better computer-based models of the weather systems (including the influence of the oceans, changing ice cover, and so on) we can now begin to assign blame in more detail.
Weather attribution science. A new book called Angry Weather, by Friederike Otto, has just appeared in the Kendal library. It outlines the new discipline of weather attribution science. There are now massive computer models of the climate, dealing with far more data than the computers could handle a decade ago. The models make use of copious satellite data, but also decades of historical data from weather stations, tide gauges, ship’s logs, and the like.
These models can reproduce our actual weather fairly precisely, and can provide useful predictions for more than a week ahead.
The models also have a new capability that is potentially a game changer: they can show what our weather would be like if the GHGs from fossil fuels were removed from the atmosphere. This capability makes it possible to compare the likelihood of an actual weather event in our current climate with the likelihood of the same event if GHG-induced climate change had not occurred. The difference between the two probabilities represents the role that can be attributed to climate change. That kind of calculation is the basis of the new field of weather attribution science.
Getting results while it is still news. Otto describes the early weather attribution studies, done beginning in 2004. They attempted to reconstruct important storms and heat waves for which detailed atmospheric data was available. Their objective was to assign a probability: how much likelier was this event, given the increased level of GHGs in the atmosphere? Because they were published in peer-reviewed journals with long publishing cycles, they came out many months after the event they described, and they got little media attention.
Otto’s group decided a new approach was needed, one that would produce results within a few days, when the extreme weather event was still in the news. Only in that way could they get the public’s attention, they reasoned. They took the approach of publishing their methods in peer-reviewed journals in advance, then applying them immediately to storms as they occurred. This was controversial in scientific circles, and the fossil-fuel industry initiated a high-profile PR campaign attempting to sow doubt. But the team persisted.
The first case studies were done during the summer of 2015. The first was a European heat wave that the team determined was between two times and six times more likely than it would have been without climate change. The second case study was tropical storm Desmond that same summer. It produced massive flooding in the UK. Otto’s team determined the climate change had made it 40 percent more likely.
A major confirmation of the team’s approach came in 2016, when the US National Academy of Sciences published a report endorsing their methods.
Houston and Harvey. Woven through several chapters of the book is a chronology of the catastrophic flooding of Houston, Texas, by Hurricane Harvey in August 2017. As Otto’s team watched from Europe, reports on the flooding came in. On August 26 through 28, Houston received over 41 inches of rain. Within a few days, the climate models showed that climate change had made such a storm at 2-4 times as likely, at a minimum.
Sometimes climate change has a role, sometimes it doesn’t. In some ways, it is just as important to know that a given event has not been made more likely by climate change. In 2014, the area around Sao Paolo, Brazil, experienced a drought. An attribution study showed that climate change had not made it more likely. In this case, increased heat had led to more evaporation, but that had not caused major drying because, given the local climate, the evaporation had resulted an equal amount of rainfall. So the drought was attributed to natural variability. Climate change had not increased its likelihood.
There are also weather events to which attribution studies just can’t be applied. These are extremely local events, such as tornadoes and hailstorms. In mountain ranges, the weather can vary greatly within a mile or two. Data is not yet available on a small enough scale to model events of these kinds.
Holding polluters accountable. One of the most significant aspects of weather attribution is that it promises to make it possible to assign blame for weather disasters, at least to some degree. Here’s an example of how the argument goes: suppose we know that climate change has doubled the probability of a given type of storm, and suppose we know that the degree of climate change in general is due to specific types of pollution (such as GHGs); and additionally, suppose we know how much of that GHG was produced by a given company.
Weather attribution and climate science can provide the first two, and there are industrial records to document the third. For example, a study of GHG emissions from 1751 to 2010 showed that just 90 companies were responsible for 63 percent of the emissions during that period.
We can combine all that information with the cost of the damage due to the storm. Suppose the storm caused $20 billion in damage. Since, in this example, we have determined that storms of this kind are twice as likely to occur due to climate change, we can say that half of the destruction they cause would have been avoided if climate change had not occurred. That leads to the conclusion that it is reasonable to attribute $10 billion of the damage from this particular storm to climate change.
The final step is to determine the responsibility of a specific company. Suppose we were to determine that a company such as ExxonMobil or Shell produced 1% of the GHGs in our atmosphere. In that case, if the storm caused $20 billion in damages, and $10 billion was due to climate change, then $100 million (1%) of that would be assignable to that company. Whoever paid the bills for recovery (governments, insurance companies, private individuals) could sue for restitution.
$100 million would not be a large cost for most of the large polluters, but it would be a start. And when you consider that the damage from Hurricane Harvey cost $125 billion (not the $20 billion in my example), the costs could add up in a hurry.
So far, the courts have not upheld such suits, but that is likely to change. Otto devotes a chapter to an analysis of how the suits are changing. Until recently, the suits have been primarily about the future suffering of today’s young people, and they have had only limited success. Weather attribution makes it possible to sue for actual losses, already incurred, from storms and rising sea levels. We can expect to start seeing many court battles based on these ideas.
A challenging but worthwhile book. Angry Weather is not particularly easy reading. Although it is well written and accessible to the non-scientist, and there are no equations or scientific jargon, it is attempting to explain a very complicated process—the study of how our actual climate works, and how it would work if the extra GHGs from industrialization were not present. That makes the book a challenging read.
It is a translation from the German, and I sense that there are places where clarity may have suffered in the translation process.
Still, it is a book that rewards a careful reading. You will come away with an appreciation how far climate science has come, and how we are finally able to say what role GHGs had in a given weather event and track that back to the industries — and the companies — responsible.