Clouds throw curveball at scientists trying to predict climate
By
Brooks Hays, UPI
Published Jun 26, 2020 7:21 PM EDT
June 24 (UPI) -- Efforts to improve the precision with which climate models simulate cloud processes have yielded more realistic models. New research suggests these efforts have also introduced greater uncertainty, according to a study published Wednesday in the journal Science Advances.
When the latest generation of climate models started producing results last year, researchers noticed that several models were predicting higher amounts of warming than previous models. The results of the new models inspired news headlines that suggested global warming might be worse than previously thought.
As researchers with the Coupled Model Intercomparison Project, CMIP6, soon found out, a few of the latest generation of models predicted smaller levels of warming than previous models. To identify the cause of this uncertainty, CMIP6 researchers decided some historical context was needed.
One way to measure and compare the predictions of different climate models is by calculating the equilibrium climate sensitivity, or ECS.
Researchers say that incorporating clouds into climate models has offered more realistic predictions, but that the predictions are also less certain. (NOAA/UPI)
"It's kind of an abstract measure, but it's one these metrics that has been around for a long time," Gerald Meehl, a senior scientist at the National Center for Atmospheric Research, told UPI.
Essentially, scientists double the CO2 in a model and let the simulation run its course until the climate stabilizes. Each model -- and each new generation of models -- produces a narrow range of warming, between 1.5 to 4.5 degrees Celsius, or 2.7 to 8.1 degrees Fahrenheit.
"This kind of range has been out there for some time, and with each successive generation of models has produced about the same range in terms of degrees," Meehl said. "With the latest generation of models, the average warming has stayed roughly the same, but the range has gotten bigger than ever -- at both the low and the high end."
When Meehl and his colleagues asked members of the groups responsible for the 39 new CMIP6 models why they thought the ECS value got bigger, most of them pointed to clouds.
To improve the accuracy of the latest generation of climate models, scientists have worked hard to simulate small-scale cloud processes. But these efforts have introduced a variety of new interactions between clouds and tiny particles called aerosols -- interactions that can produce contradictory results.
"For example, if you have polluted air, particularly sulfur dioxide, that can influence clouds. Sulfur dioxide is emitted from cars and factories, and it goes into the air and forms sulfate aerosols," Meehl said. "When you see the sky and it looks orange and hazy, chances are that a lot of that is caused by an abundance of sulfate aerosols."
According to Meehl, these aerosols operate as cloud condensation nuclei. When these aerosols seed clouds, they seed clouds with a lot more tiny droplets.
"That increased number of small droplets makes the cloud brighter, and it's going to reflect more sunlight and have a cooling effect," Meehl said.
But this phenomena, now rendered more precisely in climate models, can also yield the opposite effect.
"On the other hand, you've formed all these droplets in the sky, but the aerosols absorb some sunlight, warm the air, and evaporate some of the droplets and that reduces the amount of clouds," Meehl said. "That allows a little more sun into the system, and now you have a warming effect."
Cloud-aerosol interactions are just one example of new simulated intricacies that offer both greater realism and greater uncertainty. According to Meehl, there are a variety of interacting processes involving a variety of different cloud types at different altitudes.
"With more interacting processes, your level of uncertainty can go up," he said.
But ECS isn't the only way to test and compare climate models. Most climate modelers prefer to use transient climate response, or TCR.
"You increase CO2 at 1 percent per year, compounded, until the time you double the amount of carbon dioxide, which is usually about 70 years," Meehl said.
TCR works on a smaller timescale and works more like actual climate change. When scientists calculated the TCR range for the newest generation of climate models, they got the same average warming value but a smaller range.
Meehl and his colleagues shared the ECS and TCR values produced by the latest CMIP6 models in the new paper.
In addition to putting the latest generation of climate models into historical context, Meehl hopes the new study will inspire cloud modeling improvements.
"We're doing a better job of simulating the clouds themselves, but now we have these different feedbacks that give you more uncertainty," he said.
Now that researchers have highlighted this uncertainty, Meehl hopes climate research institutions and the climate modeling community will work to address the issue by directing more funds to relevant observational and analysis programs.
"You can't simulate what you don't understand," Meehl said.
And to understand how exactly clouds will effect climate and vice versa, in the future, scientists need more robust observational programs and better satellite measurements.
Report a Typo
News / Weather News
Clouds throw curveball at scientists trying to predict climate
By Brooks Hays, UPI
Published Jun 26, 2020 7:21 PM EDT
Partner Content
June 24 (UPI) -- Efforts to improve the precision with which climate models simulate cloud processes have yielded more realistic models. New research suggests these efforts have also introduced greater uncertainty, according to a study published Wednesday in the journal Science Advances.
When the latest generation of climate models started producing results last year, researchers noticed that several models were predicting higher amounts of warming than previous models. The results of the new models inspired news headlines that suggested global warming might be worse than previously thought.
As researchers with the Coupled Model Intercomparison Project, CMIP6, soon found out, a few of the latest generation of models predicted smaller levels of warming than previous models. To identify the cause of this uncertainty, CMIP6 researchers decided some historical context was needed.
One way to measure and compare the predictions of different climate models is by calculating the equilibrium climate sensitivity, or ECS.
Researchers say that incorporating clouds into climate models has offered more realistic predictions, but that the predictions are also less certain. (NOAA/UPI)
"It's kind of an abstract measure, but it's one these metrics that has been around for a long time," Gerald Meehl, a senior scientist at the National Center for Atmospheric Research, told UPI.
Essentially, scientists double the CO2 in a model and let the simulation run its course until the climate stabilizes. Each model -- and each new generation of models -- produces a narrow range of warming, between 1.5 to 4.5 degrees Celsius, or 2.7 to 8.1 degrees Fahrenheit.
"This kind of range has been out there for some time, and with each successive generation of models has produced about the same range in terms of degrees," Meehl said. "With the latest generation of models, the average warming has stayed roughly the same, but the range has gotten bigger than ever -- at both the low and the high end."
When Meehl and his colleagues asked members of the groups responsible for the 39 new CMIP6 models why they thought the ECS value got bigger, most of them pointed to clouds.
To improve the accuracy of the latest generation of climate models, scientists have worked hard to simulate small-scale cloud processes. But these efforts have introduced a variety of new interactions between clouds and tiny particles called aerosols -- interactions that can produce contradictory results.
"For example, if you have polluted air, particularly sulfur dioxide, that can influence clouds. Sulfur dioxide is emitted from cars and factories, and it goes into the air and forms sulfate aerosols," Meehl said. "When you see the sky and it looks orange and hazy, chances are that a lot of that is caused by an abundance of sulfate aerosols."
Related:
According to Meehl, these aerosols operate as cloud condensation nuclei. When these aerosols seed clouds, they seed clouds with a lot more tiny droplets.
"That increased number of small droplets makes the cloud brighter, and it's going to reflect more sunlight and have a cooling effect," Meehl said.
But this phenomena, now rendered more precisely in climate models, can also yield the opposite effect.
"On the other hand, you've formed all these droplets in the sky, but the aerosols absorb some sunlight, warm the air, and evaporate some of the droplets and that reduces the amount of clouds," Meehl said. "That allows a little more sun into the system, and now you have a warming effect."
Cloud-aerosol interactions are just one example of new simulated intricacies that offer both greater realism and greater uncertainty. According to Meehl, there are a variety of interacting processes involving a variety of different cloud types at different altitudes.
"With more interacting processes, your level of uncertainty can go up," he said.
But ECS isn't the only way to test and compare climate models. Most climate modelers prefer to use transient climate response, or TCR.
"You increase CO2 at 1 percent per year, compounded, until the time you double the amount of carbon dioxide, which is usually about 70 years," Meehl said.
TCR works on a smaller timescale and works more like actual climate change. When scientists calculated the TCR range for the newest generation of climate models, they got the same average warming value but a smaller range.
Meehl and his colleagues shared the ECS and TCR values produced by the latest CMIP6 models in the new paper.
In addition to putting the latest generation of climate models into historical context, Meehl hopes the new study will inspire cloud modeling improvements.
"We're doing a better job of simulating the clouds themselves, but now we have these different feedbacks that give you more uncertainty," he said.
Now that researchers have highlighted this uncertainty, Meehl hopes climate research institutions and the climate modeling community will work to address the issue by directing more funds to relevant observational and analysis programs.
"You can't simulate what you don't understand," Meehl said.
And to understand how exactly clouds will effect climate and vice versa, in the future, scientists need more robust observational programs and better satellite measurements.
Report a Typo