While AP’s Seth Borenstein cites opinions of activist scientists in erroneously claiming that ‘climate change’ increasing atmospheric moisture is the main driver for winter weather events, it turns out that El Niño patterns are strongly associated with winter weather impacts. – Anthony
NOAA research finds new way to identify which El Niño events will have biggest impact on U.S. winter weather
Weather forecasters have long known that El Niño events can throw seasonal climate patterns off kilter, particularly during winter months. Now, new research from NOAA and the University of Washington suggests that a different way to detect El Niño could help forecasters predict the unusual weather it causes.
A network of buoys that spans the Pacific, the TAO-Triton array, observes conditions in the upper ocean and is essential for forecasting El Niño months in advance, and for monitoring it as it grows and decays. A new study, just published in the February issue of the Journal of Climate, describes an atmospheric El Niño signal that is very strongly associated with U.S. winter weather impacts. Ed Harrison, Ph.D. of the NOAA Pacific Marine Environmental Laboratory in Seattle and Andrew Chiodi, Ph.D., of the NOAA Joint Institute for the Study of the Atmosphere and Ocean at the University of Washington, co-authored the paper.
“When it comes to El Niño’s weather impacts, we are always looking for ways to improve our forecasting skill,” said Harrison. “Our goal is to extract the most useful information to predict El Niño seasonal weather anomalies.”
Harrison and Chiodi looked at all El Niño events that were identified by sea surface temperature measurements since 1979. They then examined satellite imagery for these events and found that a subset of the events showed a sharp dip in heat radiating from the tops of deep convective clouds, an indicator known as outgoing long-wave radiation or OLR. When comparing the El Niño events to historical weather records, the scientists found that the El Niño events with drops in OLR were the ones most likely to play havoc with winter weather.
They also found that El Niño events with no corresponding drop in OLR did not produce statistically significant anomalies in weather patterns. The dip in heat from deep convective clouds usually occurred before winter, so the timing of the signal could help forecasters improve winter seasonal outlooks, the scientists said.
“By sorting El Niño events into two categories, one with OLR changes and one without, forecasters may be able to produce winter seasonal outlooks with more confidence than previously thought possible,” Harrison said.
El Niño refers to a warming of waters along the equator in the Eastern Pacific Ocean. Through its influence on the atmosphere, El Niño shifts tropical rainfall patterns which causes further shifts in weather around the globe, including milder winters in western Canada and parts of the northern United States and wetter winters in the some southern states.
Industry sectors from energy and construction to transportation and tourism are keenly interested in how El Niño will affect their costs. El Niño-influenced weather can affect fuel oil demand, travel delays, and retail sales. Better accuracy in El Niño predictions could help industry to prepare for its impacts more efficiently.
The paper is:
El Niño Impacts on Seasonal U.S. Atmospheric Circulation, Temperature, and Precipitation Anomalies: The OLR-Event Perspective
Andrew M. Chiodi, Joint Institute for the Study of the Ocean and Atmosphere, University of Washington, Seattle, Washington
Don E. Harrison, NOAA/Pacific Marine Environmental Laboratory, Seattle, Washington
This study shows that, since 1979 when outgoing longwave radiation (OLR) observations became reliably available, most of the useful U.S. seasonal weather impact of El Niño events is associated with the few events identified by the behavior of outgoing longwave radiation (OLR) over the eastern equatorial Pacific (“OLR–El Niño events”). These events produce composite seasonal regional weather anomalies that are 95% statistically significant and robust (associated with almost all events). Results also show that there are very few statistically significant seasonal weather anomalies, even at the 80% level, associated with the non-OLR–El Niño events. A major enhancement of statistical seasonal forecasting skill over the contiguous United States appears possible by incorporating these results. It is essential to respect that not all events commonly labeled as El Niño events lead to statistically useful U.S. seasonal forecast skill.
h/t to WUWT reader Chris Calderon