Researchers at Princeton University have found that the climate models used by scientists to project the future conditions of our planet underestimate the cooling effect that clouds have on a daily or even hourly basis, especially on the ground.
The researchers report in the journal Nature Communications that models tend to factor in too much of the sun’s daily heat, resulting in hotter and drier conditions than might actually occur. The researchers found that inaccuracies in accounting for the diurnal or daily cloud cycle did not appear to invalidate climate projections, but they did increase the margin of error of a crucial tool scientists use to understand how climate change will affect us.
“It’s important to get the right result for the right reason,” said the corresponding author. Amilcare PorporatoProfessor of civil and environmental engineering and the Princeton Environmental Institute. “These errors can affect other changes, such as the projection of fewer and weaker storms. We hope that our results will be useful for improving cloud modeling, which would improve the calibration of climate models and make the results much more reliable.”
Porporato and first author Jun Yin, a postdoctoral research associate in civil and environmental engineering, found that failing to accurately capture the daily cloud cycle has patterns showing the sun bombarding Earth with one or two watts of light. additional energy per square meter. It is estimated that the increase in carbon dioxide in the atmosphere since the beginning of the industrial era produces an additional 3.7 watts of energy per square meter. “The error here is half of that, so in that sense it becomes substantial,” Porporato said.
Yin and Porporato began their study after attending a seminar on cloud cover and climate sensitivity. “The speaker talked a lot about where the clouds are, but not when,” Yin said. “We thought the timing was just as important and were surprised to find there were fewer studies about it.”
Clouds change from hour to hour and day to day. Climate models do a good job of capturing average cloud cover, Yin said, but they miss important peaks in actual cloud cover. These peaks can have a dramatic effect on day-to-day conditions, such as early afternoon during the hottest part of the day.
“Climate scientists have the clouds, but they miss the moment,” Porporato said. “There is a strong sensitivity between the daily cloud cycle and the temperature. It’s like a person putting on a blanket at night or using an umbrella during the day. If you miss that, it makes a huge difference.”
The researchers used both reanalysis data and satellite images from 1986 to 2005 to calculate average diurnal cloud cycles in each season around the world. The reanalysis (above) shows (from left to right) the mean (mean), standard deviation (amplitude) and phase (timing) of global cloud cover by season. The color scale indicates low (blue) to high (red) coverage, amplitude and synchronization. Most previous models suggest clouds are thickest over land in the early morning, but the Princeton study showed that cloud cover peaks more frequently in the afternoon.
The researchers used satellite images from 1986 to 2005 to calculate average diurnal cloud cycles in each season around the world. Yin analyzed cloud cover at three-hour intervals, examining more than 6,000 points on the globe measuring 175 miles by 175 miles each.
Yin and Porporato compared the averages they obtained to those of nine climate models used by climatologists. The majority of models have the thickest cover occurring in the morning on earth, rather than in the early afternoon when clouds shield Earth from the sun’s most intense heat. “A small difference in timing can have a large radiative impact,” Yin said.
Researchers plan to explore the effect of different types of clouds on climate model projections, as well as how cloud cycles influence the Earth’s annual temperature variation, particularly with respect to concerns extreme precipitation.
Gabriel Katul, a professor of hydrology and micrometeorology at Duke University, said “the importance is quite high” of accurately modeling the daily cloud cycle. Katul was not involved in the research but knows it well.
The cloud cycle may indicate gaps in the characterization of surface heating and atmospheric water vapor, both of which are necessary for cloud formation, he said. These two factors also govern how the lowest part of Earth’s atmosphere – known as the atmospheric boundary layer – interacts with the planet’s surface.
“Modeling boundary layer growth and collapse is fraught with difficulty because it involves complex processes that need to be oversimplified in climate models,” Katul said. “Thus, exploring the timing of cloud formation and cloud thickness is important at the diurnal scale precisely because these time scales are most relevant to boundary layer dynamics and the exchange of heat and water vapor between the surface and the atmosphere.
With respect to clouds, climate models have typically focused on mechanisms, spatial areas and timescales – such as air pollution and microphysics, hundreds of square kilometers and seasons, respectively – which are larger and more widespread, Katul said. “There are practical reasons why the data model comparisons were conducted in such a way as to mask diurnal cloud variation,” he said. “The diurnal variation was masked somewhat by the fact that much of the climate model performance was reported on longer-term, larger-scale averages.”
However, by capturing the timing and thickness of the daily cloud cycle on a global scale, Yin and Porporato have provided scientists with a tool to confirm whether climate models are correctly depicting cloud formation and cloud interaction. and the atmosphere.
“The global coverage and focus on both ‘timing’ and ‘amount’ is remarkable. As far as I know, this is the first study to explore this variety of patterns in such a consistent way” , said Katul. “I am sure that this type of work will provide new perspectives for improving cloud representation. I would not be surprised to see this highly cited paper in future IPCC [U.N. Intergovernmental Panel on Climate Change] reports.”
The paper, “Diurnal Cloud Cycle Bias in Climate Models,” was published online Dec. 22 by Nature Communications. The work was supported by the Agricultural Research Service of the United States Department of Agriculture (#58-6408-3-027); the National Science Foundation (grant numbers EAR-1331846, EAR-1316258, and EAR-1338694); and the Duke University Wireless Intelligent Sensor Networks (WISeNet) Program (Grant No. DGE-1068871).
