What can we do to keep up with tracking climate impacts?
The Intergovernmental Panel on Climate Change sixth assessment report or the IPCC AR6 provides more accurate and longer-range data than ever before. What does it all mean? The projections are reducing uncertainty and generating confidence in the conclusions, but strong regional differences mean effects can vary significantly across the world. Determining how shifting, large-scale averages affect local events matters greatly in predicting societal impacts and insured losses. What can we do to prepare and focus on the impact without getting lost in noise? In this premiere episode of Fo[RE]sight, Guy Carpenter experts Kieran Bhatia and Sam Phibbs share their insights into this multilayered issue.
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Transcript
Pia Welch: I'm Pia Welch with Guy Carpenter. Welcome to this episode of Fo[RE]sight: a Guy Carpenter podcast series bringing you unmatched insights on trending challenges and our solutions delivered by Guy Carpenter experts on the vanguard of thought leadership within the reinsurance industry. Today, Guy Carpenter's Kieran Bhatia, Vice President, North American Perils Advisory, and Sam Phibbs, Vice President, International Catastrophe Advisory, will discuss themes regarding physical risk and climate change. Over to you, Sam.
Sam Phibbs: Thank you, Pia. It's great to be here with Kieran to discuss the physical risk associated with climate change. And I think the best place to start is the IPCC report released last year. This is the latest comprehensive effort by the World Scientists to summarize the physical risk basis for climate change. And in case you're wondering, IPCC stands for the Intergovernmental Panel on Climate Change.
And this report is part of their sixth assessment. They only come out every six to eight years, so it's really significant when they're published. Kieran, it would be great to hear what jumped out at you from this report.
Kieran Bhatia: Thanks, Sam. Yeah, it's a tough question, but I will do my best. Last year's report was almost 4,000 pages long. So there will be a disclaimer that there is plenty of interesting new conclusions to choose from, but, I will really gravitate to one word here, and that's confidence. There is higher confidence in the significance of past temperature changes, higher confidence in the human impacts on those global temperature changes, and higher confidence in the projected temperature ranges for the future. Now, that word confidence, there's a lot going on behind the scenes. That confidence comes from more accurate and longer observations of the recent past—and what I mean by that is, we have new data sets coming out that are recreations of the recent history of the Earth's atmosphere and oceans.
And one great example of that is the global tropical cyclone dataset that recently came out, that was from a group from University of Wisconsin. And that dataset has a longer spatially and temporally homogeneous intensity dataset than we've ever seen before. And that allows us to do trend analysis, knowing that the observations across the world are all done consistently.
So that anything we see in the historical records are not data artifacts. And there is actually some science that comes behind that and we can really study it carefully. Now, for the more distant past, we have something called paleoclimate records—which are getting better and better—and paleoclimate refers to things like tree rings, ice cores and other long, historical records are coming from this data.
Now, the recent decade of 2011-2020 was more likely than not the warmest in the roughly 125,000 years. And that confidence in that statement comes from the fact that these paleoclimate records are getting better. Now, it's important to note that this period 125,000 years ago, the warming was much more gradual than we're seeing today, and that was really caused by Earth's changing orbital cycles around the sun and not as related to greenhouse gas as the recent changes that we've seen. Now, finally, the new climate model simulations that are coming out at higher resolutions are leading to improved understanding of human influence on a wider range of climate variables, including weather and climate extremes.
And when I say resolution, I mean when a climate model is developed, you split the world up into grid cells. And these grid cells are where you solve the equations of motion. Those grid cells are getting smaller and smaller, and hence we say the resolution is getting higher. And finally, those consistencies in those results between the model projections and recent observations, they're reducing the range of possible warming outcomes for a particular scenario when we project to the future. So compared to the last IPCC report for a given warming scenario, the range of model projections of warming are getting smaller and therefore we have a higher confidence in that range.
Sam Phibbs: Thanks, Kieran. And I just want to add on a final comment with respect to the projections that you mentioned. I believe this report is a real vindication of previous efforts by the IPCC. There's projections of how true, and now, as you've mentioned, they're reducing the uncertainty further. So this should give us real confidence in their conclusions, not just for temperature, but also for extreme weather and make everybody take them very seriously.
Kieran Bhatia: Exactly. Now, thanks to that improved modeling and observational capabilities we just discussed, the chapter that came out, this IPCC report on weather and climate related extremes was particularly interesting. The chapter began with a quote, which said "It is an established fact that human induced greenhouse gas emissions have led to an increased frequency and/or intensity of some weather and climate extremes since pre-industrial time."
Now, this is definitely one of the higher confidence statements I've ever seen in an IPCC report. And I'm wondering from you, Sam, what sort of weather and extremes are they referencing here? What are the hazards and perils that are most affected by climate change?
Sam Phibbs: So that's a great question. Weather and climate extremes cause huge losses for the insurance industry, so this is really one of the most important questions for us. And to answer your question, I want to start with a distinction between our confidence climate change is affecting a peril and the impact that that change will have naturally as climate change is causing a power to change particularly quickly.
It's going to have a large impact, but it also makes that change easier to observe and model, increasing our confidence in the change. But our confidence doesn't just depend on that; there are several other factors at play. To go through a few: firstly, how well do we understand the physical mechanism driving the change, which is often related to whether there is a direct link between greenhouse gases and the hazard?
Secondly, affecting our confidence is our ability to model the peril. For example, for small events, they are not particularly well-represented in global climate models, which are often quite low resolution. And thirdly, the length and quality of our observations, which is impacted by a number of factors, including the frequency of the hazard in question.
Kieran Bhatia: Interesting! So, now we know what drives the recent higher confidence and changes to perils. What perils do you see changing the most?
Sam Phibbs: Let me give a few examples. So, heatwaves are virtually certain to get worse in the future. The physical mechanism is extremely simple as it's related to temperature increase. We have an excellent understanding of how increased emissions relate to global surface temperature increases, and we have long, high-quality records of temperature and substantial modeling to back up the theory.
Thus far, heatwaves haven't caused large insured losses to property, although there are potential implications for life insurance and indirect effects and properties such as subsidence.
So my next example is flash flooding. Obviously, this does cause substantial loss to property and, like heat waves, we have a relatively simple theory as to why flash flooding will increase. Warmer air holds more moisture and therefore for intense rainfall events, we can expect them to scale in the same way. There is also a lot of modeling to back up this theory. However, our records of extreme rainfall are substantially less reliable than that of temperature, adding somewhat to the uncertainty. But overall for flash flooding, we're pretty confident that it will get worse for the majority of regions.
And the final peril example that I want to talk about is severe convective storm. This includes hail and tornadoes. The occurrence of these storms depend on specific atmospheric conditions, some of which climate change will make less likely, some of which they'll make more likely. In addition, these storms are small, which means that observational records are limited and modeling them is very tricky as well. So overall, we don't have a great understanding of how these storms would change in the future.
But crucially, that doesn't mean they won't change in a meaningful and impactful way. And hence, me wanting to make the distinction between our confidence and the impact peril will have.
Kieran Bhatia: Sam, thanks, that really highlights how nuanced the interconnections are between climate change and extreme weather. Now, to hammer this point home, I'll bring up another weather phenomena that is particularly interesting to me: hurricanes—because they are arguably the largest catastrophe loss driver globally. Now, depending on the region and hazard associated with these storms, there is a different level of confidence on how climate change may affect them.
For example, this IPCC report boosted our confidence in the projections that hurricane landfalls would, on average, produce more rain and storm surge. However, the changes to storm frequency and the local changes to storms are very uncertain. This just emphasizes the importance in leveraging evidence that we do have and not using blanket statements within a particular hazard, or overall with all the hazards, when it comes to climate change and weather extremes.
Sam Phibbs: I couldn't agree more, Kieran. In the insurance industry, we care a lot about risk in specific regions. So I'm wondering how climate change will impact regional and local scale versus the big global picture.
Kieran Bhatia: Yeah, so it's always a good starting point to discuss global changes over long timescales because that allows us to separate the signal from the noise or the influence of climate change from the natural, year-to-year variability and weather. But at the end of the day, though, it's these shifting large-scale averages manifesting themselves in local events that matter for societal impacts and insured losses.
Now, climate change displays strong regional differences, and we must account for those when discussing impacts. For example, the Arctic has warmed at more than twice the global rate over the last 50 years. Some areas, like the Mediterranean, have received more droughts, while others, like the Eastern United States, have gotten much wetter. And then the Gulf of Mexico has seen rapid sea-level rise and subsidence, while the northwest United States has seen much smaller changes in sea level.
Now there's reasons for all that, but the important point here is climate change's influence on atmospheric and oceanic patterns will naturally lead to climate change affecting regions differently. And as a result, global change metrics like 1.5 degrees Celsius of temperature change over pre-industrial times are a good guide for tracking changes, but not great for local impacts.
Sam Phibbs: Thanks, Kieran. Clearly, there's going to be substantial regional differences from climate change. In the insurance industry, tail risk is really driven by just a few major loss events. So is it possible to connect individual acute events to these large-scale climate changes?
Kieran Bhatia: Sam, I'm going to use wise words from a good man and going to reference your previous comments here. For the hazards where we have a good physical understanding and can model the hazard effectively at the local scale, we can connect the global climate changes to an increase in the probability of more extreme events at that local scale. In other words, when extreme weather occurs, slower, seemingly innocuous changes to the global climate can lead to more local tail events and hazards not seen before.
For example, let's use a tropical cyclone making landfall in the New York City area and the subway flooding as a case study. A bit of an oversimplification here, but the subway flooding represents a binary outcome where losses can exponentially change. In situation A, the subway floods badly and there could be billions of dollars of losses. In situation B, it doesn't, and there are much smaller losses observed.
Now, there have been about four storms in the last 70 years that missed flooding the subway by about two feet. And projections suggest that in 30 to 50 years, sea-level rise would make all these storms likely flood the subway. There is also evidence that these significant risk thresholds exist across the world for a number of perils, not just sea-level rise. And there's evidence that these significant risk thresholds exist across the world for a number of perils. And the science suggests that with climate change, there is a larger probability of these thresholds being passed as we go into the future.
Sam Phibbs: Thanks, Kieran. That's a really nice illustrative example of how climate change can impact an individual type of event. I want to add on a final point in terms of individual events, there's been real demand to understand the role of climate change in recent extreme events, and this has led to a remarkable rise in attribution science. To explain this simply, we can almost never say whether climate change caused an event or didn't cause an event, but we often talk about it loading the dice for severe events occurring.
Now we're starting to quantify how much is loading the dice by the individual events. And this is really an interesting quantification of climate change. Now, that's not to say that every single historic event has a climate change role. And indeed, there's been some studies on European windstorms that found that there's a negligible role for climate change.
Kieran Bhatia: OK, so we've talked now a bit about the evolving physical risk landscape from a hazard perspective. But how can we quantify the impact of climate change in the past and the future for the insurance industry? When do we lean on climate model output and when are catastrophe models the most useful?
Sam Phibbs: The overlap between climate modeling and catastrophe modeling is an intriguing and rapidly growing field. Over the last decade, climate models and their output have gone from being used entirely in an academic setting to being increasingly integrated into multiple industries, including the insurance industry. And at the same time, climate models have progressed enormously in terms of their representation of physical processes.
However, climate model output is not a panacea for predicting physical climate risk. Let's take hurricanes as an example again. When we model the hurricane risk in the insurance industry, we use a catastrophe model with at least 10,000 years of high-resolution plausible hurricanes to build an accurate probabilistic view of the present day risk. In contrast, climate models are typically run for hundreds of years, and they struggle to accurately represent intense storms.
But it is not simply the case of choosing the right situation to use climate-model output versus catastrophe-model output. It's essential that we integrate them to develop a financial view on how the catastrophic risk is changing. And this is some of the work we've been carrying out here at Guy Carpenter.
But Kieran, perhaps I've been being too harsh on climate models. You've been more recently in academia than me, so you've seen some of the latest updates. What am I missing in terms of leveraging climate-model output?
Kieran Bhatia: Sam, I think you bring up a lot of great points here. However, I'll bring up a term that I love to use for discussing climate change: inflection point.
Now, this term is relevant to this conversation in two ways—I would argue that we have observed inflection point in the climate research modeling community. Some global models are being run now at 1 kilometer resolution or less; we have ensemble simulations of thousands of years, and the spike in computing power and academic capital working on these climate scale problems has led to major advances. Now what we need is industry/academic partnerships to get the right climate-model output in the right format to the reinsurance and insurance industry to leverage.
Now, the second inflection point I'll talk about is in the climate: we haven't seen rates of change in temperatures in over 60 million years like this. Recent warming has occurred about 10 times faster than the warming at the end of an ice age. Now, this has major implications for catastrophe models, which are statistically driven on the past. In other words, they're based on historical observations, which means catastrophe models that are not recently updated could be potentially miscalculating the level of risk for a particular peril that's changing quickly.
Also, as our climate changes are expected to accelerate, it will become increasingly important to incorporate events and years unlike those we haven't seen before. Because we know, from events like Hurricane Harvey and Hurricane Sandy, these are physically possible. This is why a reliable and high-resolution climate model is a necessity to complement the catastrophe models. They will provide plausible outcomes that have not yet been seen before, but are driven by the physics of the atmosphere and ocean and enhance output from catastrophe models.
Sam Phibbs: Thanks, Kieran. It's great to get an update on the progress in the scientific community, and it sounds like there's a lot of further work to do in terms of integration between climate models and catastrophe models. It's been a pleasure discussing climate change and physical risk with you today. And with that, I'll pass back to Pia
Pia Welch: I'm Pia Welch, and thank you to Kieran Bhatia and Sam Phibbs of Guy Carpenter for sharing your insights on how the physical risk landscape continues to evolve. Anyone wanting to learn more or who would like to engage with a Guy Carpenter expert directly should visit www.guycarp.com and click on "Explore Solutions." Please look for the next episodes in our series, as we address additional themes connected with climate, cyber and other key issues affecting the reinsurance environment.
And thank you to our audience for sharing this time with us and listening to Fo[RE]sight: a Guy Carpenter podcast series.