Global warming is caused by the emission of greenhouse gases. According to the Intergovernmental Panel on Climate Change (IPCC), by 2019 the temperature had risen by 1.1 degrees Celsius compared to pre-industrial levels. However, at the same time, the combustion of fossil fuels emits aerosols, in the form of pollutant particles such as soot or sulfuric acid, which cool our climate. They reflect sunlight and also increase the reflectivity of clouds. According to the IPCC, the presence of aerosols in the atmosphere caused the climate to be 0.5 degrees Celsius cooler in 2019 than without them. Other effects such as land use change also play a role.

Study documents widespread evidence of this reduced cooling climate effect

In a new international analysis, Professor Johannes Quaas, a meteorologist at Leipzig University, and colleagues from across Europe, China, and the US have now documented robust evidence of this effect on the climate of improved air quality. “We analysed data from NASA’s Terra and Aqua satellites. They have been providing comprehensive satellite observations of the Earth since the year 2000, measuring incoming and outgoing radiation, but also clouds and aerosol pollution. The latter has decreased significantly across North America, Europe and East Asia since 2000,” says Professor Johannes Quaas, lead author of the study, which was initiated in a meeting by the two European research projects CONSTRAIN and FORCES.

Reduction in aerosol-induced cooling increases warming due to CO₂ since 2000 by up to 50 per cent

This has also reduced the cooling effect of aerosols. Compared to the year 2000, it has led to an increase in the warming effect that is up to 50 per cent of the one by CO₂ increases in the same period. This means an acceleration of the drivers of global warming compared to the previous period. “Our study should not be interpreted to mean that we should now be emitting more aerosols to cool the climate. On the contrary: aerosols are harmful to human health and the environment, which is why we need to keep reducing emissions,” Quaas concludes. And it is why air quality legislation has become increasingly stringent since the 1970s and is being implemented by more and more countries. Professor Quaas and his colleagues on the new study stress the ever more urgent need for rapid and strong reductions in greenhouse gas emissions.

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Materials provided by Universität Leipzig. Note: Content may be edited for style and length.

The forecasts come from a model developed by the researchers that combines measures of accumulated annual and perennial grass vegetation that is potential fire fuel with recent weather and climate data. When integrated, this information can be translated into maps showing the likelihood of a large wildfire — greater than 1,000 acres — across the Great Basin. These forecasts also can be scaled down to predict fire risk for counties or even single pastures.

Great Basin rangeland fire probability maps for the rangeland fire season, roughly June through September, are posted on the Rangeland Analysis Platform. Two new research articles discussing details and utility of the new model are part of a special issue of Rangeland Ecology & Management. The first paper used vegetation data from the Rangeland Analysis Platform alongside historical fire data from the Monitoring Trends in Burn Severity dataset to build fire-prediction models for the Great Basin using 32 years of historical weather, vegetation, and fire data. The second paper extends the fire probability work from analysis into how practitioners can use this information to make decisions for the fire season.

“Accurate forecasting of rangeland fires depends, in large part, on our ability to quantify the buildup of grassy fuels from the previous year,” explained rangeland scientist Chad S. Boyd with the ARS Range and Meadow Forage Management Research Unit in Burns, Oregon, and a co-leader in developing the model.

Forest fires commonly only need an ignition event, such as a lightning strike, at most forest locations because they generally have enough vegetative fuels to burn every year.

“But with rangeland fires, whether there is enough fuel to feed a large-scale fire really varies from place to place and year to year across the Great Basin,” Boyd added.

Until recently, rangeland fire forecasting was limited by not having accurate measurements of how much annual and perennial grasses and forbs were produced each year. But satellite technology and remote sensing have made precise data readily accessible.

Joe T. Smith, a research scientist with the University of Montana and model designer co-lead, said, “I was a little surprised that accumulation of herbaceous plants from the previous growing season was the best predictor of large range fires. This gives new emphasis to fighting the spread of fast-growing invasive annual grasses like cheatgrass in the Great Basin.”

While woody shrubs and trees like juniper or pinyon-pine don’t really increase the odds of a large fire occurring, their place in the fire landscape still matters. Shrubs burn hotter, causing more extreme fire behavior that puts lives and property at risk and makes wildfires difficult to suppress.

Unlike weather and topography, which are major influences over rangeland fires, people can directly and preemptively manage available fuel. These new fire probability maps will help land managers prioritize where it makes the most sense to invest in fuel management.

“The maps can be used alongside other planning tools to decide where to focus limited resources before the fire season begins,” Boyd explained. “They also can help target annual efforts to reduce fine fuels like grasses, which can lessen the impacts of fire on the region’s wildlife and working lands.”

Archeologists in the early 20th century posited the volcano erupted around 1500 BCE, during the Egyptian New Kingdom period, and created a history around this assumption. But beginning in the 1970s, advances in radiocarbon dating have thrown that timeline into chaos, with many experts insisting the eruption happened as much as 100-plus years earlier.

Sturt Manning, Distinguished Professor of Arts and Sciences in Classical Archaeology in the College of Arts and Sciences, is hoping to settle one of modern archaeology’s longstanding disputes. By parsing the available data and combining it with cutting-edge statistical analysis, he has zeroed in on a much narrower range of dates for the eruption: approximately 1609-1560 BCE, during the preceding Second Intermediate Period of Egypt, when the Hyksos — a Canaanite-origin dynasty — controlled Lower Egypt. While not yet a precise date, to the year, for resolving the big-picture question of the correct historical period, the finding clarifies many years of debate.

“This has been the single most contested date in Mediterranean history for over 40 years,” said Manning, who directs the Cornell Tree-Ring Laboratory. “It’s been one of these endless disputes, to the point where people just say, ‘there’s a problem here, we can’t solve it, let’s move on.’ I’m hoping with this paper people may suddenly go, ‘You know what, this actually limits and defines the problem in a way that we’ve never been able to do before, and narrows it down to where, usefully, we can say it’s in the Second Intermediate Period. So we should start writing a different history.'”

For Manning, the Thera eruption has been like Mount Everest — a challenge that he has wanted to tackle since early in his career. Accurately dating the event has become more feasible in recent years with the increased sophistication of Bayesian statistical analysis, enabling chronological modeling that can integrate massive amounts of data and archaeological observations to better define the probability parameters for an unknown event.

The parameters have been fairly well understood for years, Manning said, thanks to the extensive geological and archaeological research that’s been conducted. The missing piece of the puzzle has been an often-raised concern that volcanic carbon dioxide emissions could have contaminated organic samples from Thera and caused incorrect age assessments.

Last spring, Manning realized he could solve the problem by looking elsewhere — hundreds of kilometers away from Thera — to regions of the Aegean Sea that experienced the tsunami effects caused by the eruption. Manning incorporated dates obtained for these episodes into his model to test for, and discount, the volcanic carbon dioxide caveat. On Thera itself, he also spotted the importance of a short but clearly observed gap in time between the abandonment of the town at Akrotiri and the huge eruption, and he incorporated this previously overlooked constraint into the modeling.

“It’s been observed for years that there is some short interval in the archaeological sequence between when the city of Akrotiri was abandoned by its human population and before its burial under meters of pumice from the eruption. Even though several hectares have been excavated, no human skeletons have been found, so clearly people had warning of impending peril and left. No one’s allowed for that in the past,” Manning said. “By putting that extra qualification in, we tighten the statistical analysis.”

The modeling identified the most likely range of dates for the eruption to be: between about 1609-1560 BCE (95.4% probability), or about 1606-1589 BCE (68.3% probability).

The new timeline synchronizes the civilizations of the eastern Mediterranean while also ruling out several ancillary theories, such as the idea that the Thera eruption was responsible for destroying Minoan palaces on the coast of Crete as the first excavator of Akrotiri, Spyridon Marinatos, proposed in 1939.

“That seems not to be the case,” Manning said. “Because when we date the destruction levels on Crete, they seem to be upwards of a century later.”

Because his analysis pegs the Thera eruption earlier than the original proposed date, but not as early as radiocarbon dating had initially suggested, Manning hopes the new timeline may be more palatable for experts on both sides of the long-running debate.

“This demonstrates, as with so much of science, that people have to make hypotheses based on the initial information, but as you get more and more information and better analysis, you revise and refine,” he said. “In this case, the answer seems to be between the original position and the first radiocarbon indications pointing as much as 100-150 years earlier. Hopefully this new analysis, based on a large dataset and a recent, better-defined radiocarbon calibration curve released in 2020, should be more palatable for the overall archaeological and historical fields. It changes the historical context, but at the same time, is not trying to push things quite so far out of the envelope.”

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Materials provided by Cornell University. Original written by David Nutt, courtesy of the Cornell Chronicle. Note: Content may be edited for style and length.

But new research from the University of Georgia shows people who rely on algorithms for assistance with language-related, creative tasks didn’t improve their performance and were more likely to trust low-quality advice.

Aaron Schecter, an assistant professor in management information systems at the Terry College of Business, had his study “Human preferences toward algorithmic advice in a word association task” published this month in Scientific Reports. His co-authors are Nina Lauharatanahirun, a biobehavioral health assistant professor at Pennsylvania State University, and recent Terry College Ph.D. graduate and current Northeastern University assistant professor Eric Bogert.

The paper is the second in the team’s investigation into individual trust in advice generated by algorithms. In an April 2021 paper, the team found people were more reliant on algorithmic advice in counting tasks than on advice purportedly given by other participants.

This study aimed to test if people deferred to a computer’s advice when tackling more creative and language-dependent tasks. The team found participants were 92.3% more likely to use advice attributed to an algorithm than to take advice attributed to people.

“This task did not require the same type of thinking (as the counting task in the prior study) but in fact we saw the same biases,” Schecter said. “They were still going to use the algorithm’s answer and feel good about it, even though it’s not helping them do any better.”

Using an algorithm during word association

To see if people would rely more on computer-generated advice for language-related tasks, Schecter and his co-authors gave 154 online participants portions of the Remote Associates Test, a word association test used for six decades to rate a participant’s creativity.

“It’s not pure creativity, but word association is a fundamentally different kind of task than making a stock projection or counting objects in a photo because it involves linguistics and the ability to associate different ideas,” he said. “We think of this as more subjective, even though there is a right answer to the questions.”

During the test, participants were asked to come up with a word tying three sample words together. If, for example, the words were base, room and bowling, the answer would be ball.

Participants chose a word to answer the question and were offered a hint attributed to an algorithm or a hint attributed to a person and allowed to change their answers. The preference for algorithm-derived advice was strong despite the question’s difficulty, the way the advice was worded, or the advice’s quality.

Participants taking the algorithm’s advice were also twice as confident in their answers as people who used the person’s advice. Despite their confidence in their answers, they were 13% less likely than those who used human-based advice to choose correct answers.

“I’m not going say the advice was making people worse, but the fact they didn’t do any better yet still felt better about their answers illustrates the problem,” he said. “Their confidence went up, so they’re likely to use algorithmic advice and feel good about it, but they won’t necessarily be right.

Should you accept autocorrect when writing an email?

“If I have an autocomplete or autocorrect function on my email that I believe in, I might not be thinking about whether it’s making me better. I’m just going to use it because I feel confident about doing it.”

Schechter and colleagues call this tendency to accept computer-generated advice without an eye to its quality as automation bias. Understanding how and why human decision-makers defer to machine learning software to solve problems is an important part of understanding what could go wrong in modern workplaces and how to remedy it.

“Often when we’re talking about whether we can allow algorithms to make decisions, having a person in the loop is given as the solution to preventing mistakes or bad outcomes,” Schecter said. “But that can’t be the solution if people are more likely than not to defer to what the algorithm advises.”

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Materials provided by University of Georgia. Original written by J. Merritt Melancon. Note: Content may be edited for style and length.