February 28, 2003
Berkeley Lab Science Beat Berkeley Lab Science Beat
How the temperature of the South Pacific Ocean affects snowfall in the American West
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Jiming Jin and Norman Miller of Berkeley Lab's Earth Sciences Division, in collaboration with Soroosh Sorooshian at the University of Arizona, are using a computer model to understand the link between winter and spring snowfall in the Western United States and the El Niño Southern Oscillation (ENSO).


In a study funded by NASA, the researchers found that the higher and lower sea surface temperatures in the tropical Pacific that characterize El Niño, a recurring warming of equatorial waters, and La Niña, a corresponding cooling, change wind patterns in mid-latitudes in winter and spring. This shifts the way moist air gets transported in the atmosphere and directly affects Western U.S. precipitation and snow accumulation.

Almost 75 to 85 percent of water resources in the Western U.S come from snow that accumulates in the winter and early spring and melts as runoff in spring and summer. Understanding the link between the Western snowpack and temperature changes in the tropical Pacific may make it possible to predict future snowfall rates, a great help to citizens and policy makers alike.

The El Niño Southern Oscillation marks a seesaw shift in surface air pressure between Darwin, Australia, and the South Pacific Island of Tahiti. When the pressure is high at Darwin it is low at Tahiti and vice versa. El Niño and its sister event, La Niña, are the extreme phases of this southern oscillation, with El Niño referring to a warming of the eastern tropical Pacific and La Niña to a cooling.

During El Niño, sea-surface temperatures in the eastern equatorial Pacific are warmer than usual (top), while during La Niña the same region is cooler.

Scientists' ability to forecast future snowfall and water availability in the West depends on continual updating and refinement of computer climate models, which in turn depend on a better understanding of the connections among global and regional processes.

"If the computer climate models can accurately describe the processes that connect ENSO and snowpack in the Western U.S., then the model can be used to predict the impact of ENSO on snowfall in those areas," said Jin. "In addition, the model can give us more detailed information than observations, which can lead to a further understanding of those observed processes."

The researchers entered over 45 years of data, from 1949 to 1995, into their computer climate model. They included observed global sea-surface temperatures, wind data, the amount of water contained in snowpacks for the beginning of the first four months of each year from over 300 western U.S. field sites, and precipitation and surface air temperature observations.

The data showed that Washington, Oregon, Idaho, and Montana experienced decreased precipitation during weak El Niños and increased precipitation during the opposite La Niña phase.

  Snowpack in the Sierra affects water resources throughout California's urban areas and Central Valley agricultural regions. Lake Tahoe is at center, right; San Francisco Bay and Monterey Bay are visible low on the coast.  

During strong El Niño episodes, stronger winter and spring precipitation was found south of Sacramento, including parts of California, Nevada, Utah, Colorado and all of Arizona and New Mexico. During strong La Niña events, however, the researchers did not find any changes to precipitation patterns in the western U.S.

The computer model matched well with actual observations, including the effects of both El Niño and La Niña — except when it came to weak ENSO episodes. During those events, the mid-latitude atmosphere in the model reacted too strongly to the shifts in tropical Pacific sea-surface temperatures, and moist air masses from that region moved incorrectly in the model.

Nevertheless, the model clearly showed that different intensities of ENSO episodes have differing effects on western U.S. snowfall. The researchers hope to fine-tune the model's responses in the future.

Jin and Miller are currently developing new snow assimilation techniques that show improved forecast skill, which they hope will make water allocation decisions more accurate and cost efficient. Their research may yield a forecast tool that greatly benefits citizens and water resource managers in the Western U.S.

Jin, Miller, and Sorooshian presented their results at the 83rd Annual Meeting of the American Meteorological Society in Long Beach, Calif., on February 11, 2003.

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