New weather forecasts show human impact

Weather forecasts give us an idea of what to expect during the day: how hot it will get, anticipated wind speeds, the likelihood of rain. What if the next time you opened the weather app on your phone, it also showed you just how much human activity is responsible for the conditions that we are experiencing? Climate physicists at the University of Oxford have developed a technique for determining exactly that, expanding our understanding of how we impact the planet.

In a new paper published in Nature Communications, researchers showed for the first time how existing weather forecasts can be used to show how human activity like greenhouse gas emissions can cause and intensify severe weather events-a breakthrough in the world of extreme event attribution.

A new way to attribute human impact

Attempts to determine how human activity affects weather events have been around for decades, though the field of extreme event attribution is relatively new. It was first mentioned in a 2011 "State of the Climate" bulletin published by the American Meteorological Society, in which researchers concluded that climate change played a role in causing or exacerbating five of six extreme weather events studied.

According to Dr. Nicholas Leach, a climate scientist at the University of Oxford who led the U.S. leg of the new study, attribution is typically done in one of two ways. The first is to use historical data or observations and applied statistical models to try to assess the likelihood that a certain weather event may have changed due to human activity. The other approach is to use climate models, which Leach described as "really big numerical models that try to simulate a representation of the Earth's climate as accurately as possible." These models are run once to try to replicate the real-world event, then run again with human influence like greenhouse gas emissions removed to try to understand the impact those influences have on the event.

Leach and his team took a different approach. "We used the same models that we trust to forecast the weather," he said. "Weather forecast models, at the very fundamental level, are very similar to climate models," but are run in different ways. Leach explained that weather models are typically much higher resolution, meaning it takes into account much more complex information. This, according to Leach, makes forecast models better at simulating the physical processes that lead to extreme weather events.

Attribution applied

To test this technique, Leach and the other researchers decided to run the model for the 2021 Pacific Northwest heat wave. The event, which lasted nearly two weeks, saw peak temperatures of over 121 degrees Fahrenheit and resulted in more than 1,400 deaths related to heat exposure.

The Oxford researchers chose this event for two reasons. First, heat waves are considered the easiest weather event to attribute to human activity. Secondly, even with modern climate modeling techniques, this event stood out as being more extreme than models could have predicted.

"What's notable in this paper is the presentation of evidence related to a specific heat wave event, which was rather unusual," Lloyd Treinish, Distinguished Engineer and Chief Scientist of Environmental Modelling and Climate and Weather at IBM, explained.

"Individual weather events and their characteristics are not directly due to planetary warming. Determining why specific events that are considered extreme, unusual or not typical for a region and season is difficult," he said. "Teasing out those factors versus those driven by climate change is complex. It is relatively easier for precipitation versus a heat wave."

Using the European Centre's Integrated Forecasting System, Leach and his team took the operational forecast model, which is a simulation of the Earth's atmosphere and its behavior that accounts for meteorological data. This data includes temperature, humidity, wind speed and atmospheric pressure, among other factors, to produce a forecast for a specific location during a particular band of time.

The researchers modified the operational model to remove human effects, which meant cooling down ocean temperatures and reducing greenhouse gases in the atmosphere. They ran that model and compared it to the operational model, which allowed them to "quantify the downstream impact from human inputs on that extreme weather event," according to Leach.

The technique revealed that human influence on the climate made the heat wave at least eight times more likely-and found the current rate of climate change makes the likelihood of another similar event double every 20 years.

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Beyond heat waves

While heat waves present a reliable test case for attribution models, the researchers at Oxford believe their new technique can be used to determine human impact for other types of extreme weather events, as well.

In fact, a study from the same team published earlier this year in the journal Environmental Research: Climate used the same technique to determine how human behavior affected Storm Eunice, a severe windstorm that blew through the United Kingdom in 2022, hitting wind speeds of 122 miles per hour, knocking out power for 1.4 million homes and causing 17 deaths.

Using their forecast models, the researchers were able to determine that the conditions caused by climate change intensified the storm by as much as 26%.

"This demonstrates that this method isn't just applicable to heat waves, which are seen as being the easiest type of extreme weather to attribute to climate change," Leach said. "Impacts from climate change are generally much harder to kind of disentangle from a mid-latitude windstorm, and we successfully applied it to that."

The fact this technique has already been used for attribution on a windstorm suggests it may have potential to perform operational extreme attribution, which is essentially a nearly real-time determination of how human activity is affecting a weather event.

This is important not just to understand how our behaviors affect the weather that we're experiencing, but to be able to deliver that information when most people are paying attention. "Generally, people are most interested in extreme weather immediately following the event and interest tends to fade over time," Leach said. Achieving operational attribution would allow scientists to share with the public how human activity is affecting weather while the world is rapt by a storm.

Leach argued that the method his team utilized moves the scientific community closer to operational attribution, as it makes use of weather models that already run routinely every day to produce forecasts.

"We suggest that with this approach, you can take an almost identical model setup to what is used to produce a routine operational forecast and modify it to produce forecasts of a counterfactual world without human influence on the climate in order to do operational attribution," he said.

Another potential application that Leach sees for this method is for impact attribution, which is a branch of the same school of science that focuses on the social, economic and ecological impacts of weather events. For example, with a heat wave, impact attribution would try to determine how many excess deaths can be attributed to the conditions of climate change that made the conditions worse.

Leach said that weather forecast models are already built into some impact modeling chains that are used for emergency management services. "In theory, you could get that extra step toward the impact, just by the fact that you're using a weather forecast model that is already built into this modeling chain."

More to consider

While the Oxford team's method offers new insight into extreme event attribution, the reality is that weather models can only simulate so much. The primary conditions that these models account for include greenhouse gas emissions and its major effects, like rising global temperatures.

But, as Treinish points out, there are many ways that human activity impacts weather conditions that typically can't be accounted for in weather models. "Human activity that changes land use and land cover can have an effect on weather in the short term, such as the built infrastructure in a city," he explained. "There are also intentional activities such as cloud seeding to influence the formation of precipitation."

Some of these impacts may be captured in future models, as researchers continue to try to improve resolution and take into account the many factors that may change how weather is experienced. Leach noted that advancements in artificial intelligence and machine learning technology could, in theory, allow physicists to run higher-resolution forecast models at less computational cost, which would allow researchers to run more simulations and better understand the underlying physics of these events.

Still, the ability to attribute the impact of human activity to a particular weather event presents an advancement in our understanding of climate change and how we address it. "It provides further evidence of the impact of climate change, and both quantifies the impact and reduces the associated uncertainty," Treinish explained. "Such information can help inform policymakers among others."

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