Polar ice, atmospheric water vapor biggest drivers of variation among
climate models

Date:
October 7, 2020
Source:
Florida State University
Summary:
Researchers have found varying projections on global warming trends
put forth by climate change scientists can be explained by differing
models' predictions regarding ice loss and atmospheric water vapor.



FULL STORY ==========================================================================
A Florida State University researcher is part of a team that has found
varying projections on global warming trends put forth by climate change scientists can be explained by differing models' predictions regarding
ice loss and atmospheric water vapor.


==========================================================================
The work will help climate scientists reconcile various models to improve
their accuracy, said Florida State University Meteorology Professor Ming
Cai, one of the authors of the study published in Nature Communications .

Climate scientists agree that the Earth's surface temperature is warming,
but the details of exactly where and by how much are less clear. A
worst-case climate change scenario (known as the "Representative
Concentration Pathway 8.5") predicted a likely increase in average
global temperatures of about 2.6 degrees Celsius to 4.8 degrees Celsius
(or about 4.7 degrees Fahrenheit to 8.6 degrees Fahrenheit) by 2100.

"This uncertainty limits our ability to foresee the severity of the global warming impacts on nature and human civilization," Cai said. "The more information we have about the effects of climate change around the world,
the better prepared we will be." The difference in those conclusions
would mean the difference between a sea level rise of about a half-meter
to close to one meter, for example.

As scientists around the world have studied the climate, they have
developed their own models. Although the major components of these
climate models are based on the same general physical principles, such
as conservations of energy and mass, they still differ from one another
in many details, which is what leads to a range of conclusions about
something like the future average global temperature.



========================================================================== "What are the best ways to represent those details in a climate
model?" Cai said. "That's something that climate science is still
working to answer. The model gets into the 'art' part of science."
The researchers investigated the variability among 25 climate models
that participated in the United Nations' Intergovernmental Panel on
Climate Change.

They found that climate models that predicted higher average temperatures
for the Earth's surface overall also yielded results that showed more
polar ice loss and more water vapor in the atmosphere.

"We found that these two factors explain close to 99 percent of the
difference in global-mean warming forecasts among these 25 climate
models," Cai said. "Our findings suggest that variability among climate
models could be significantly reduced by narrowing the uncertainty in
models simulating ice-albedo and water vapor feedbacks." The research
also found that cloud cover is less important than scientists previously thought for explaining variation among models.

These models are tools for making forecasts for things like sea
level rise, flood risk, the viability of crops and wildlife and other considerations.

"Knowing that polar ice and water vapor in the atmosphere are the most important drivers of variability in different climate models will help
climate scientists further refine those models," Cai said.

Researchers from Sun Yat-sen University and Southern Marine Science
and Engineering Guangdong Laboratory in China, Science Systems and
Applications Inc. in Hampton, Virginia, and NASA contributed to this
study.

This research was funded in part by the U.S. National Science Foundation,
NASA and the National Natural Science Foundation of China.


========================================================================== Story Source: Materials provided by Florida_State_University. Original
written by Bill Wellock. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Xiaoming Hu, Hanjie Fan, Ming Cai, Sergio A. Sejas, Patrick
Taylor, Song
Yang. A less cloudy picture of the inter-model spread in future
global warming projections. Nature Communications, 2020; 11 (1)
DOI: 10.1038/ s41467-020-18227-9 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/10/201007145307.htm

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