Skillful Seasonal Prediction of North American Summertime Heat Extremes
Journal
Journal of Climate
Journal Volume
35
Journal Issue
13
Pages
4331-4345
Date Issued
2022
Author(s)
Jia L
DELWORTH T.L
Kapnick S
Yang X
JOHNSON N.C
Cooke W
Lu F
Harrison M
Rosati A
Zeng F
Mchugh C
WITTENBERG A.T
Zhang L
Murakami H
Abstract
This study shows that the frequency of North American summertime (June-August) heat extremes is skillfully predicted several months in advance in the newly developed Geophysical Fluid Dynamics Laboratory (GFDL) Seamless System for Prediction and Earth System Research (SPEAR) seasonal forecast system. Using a statistical optimization method, the average predictability time, we identify three large-scale components of the frequency of North American summer heat extremes that are predictable with significant correlation skill. One component, which is related to a secular warming trend, shows a continent-wide increase in the frequency of summer heat extremes and is highly predictable at least 9 months in advance. This trend component is likely a response to external radiative forcing. The second component is largely driven by the sea surface temperatures in the North Pacific and North Atlantic and is significantly correlated with the central U.S. soil moisture. The second component shows largest loadings over the central United States and is significantly predictable 9 months in advance. The third component, which is related to the central Pacific El Niño, displays a dipole structure over North America and is predictable up to 4 months in advance. Potential implications for advancing seasonal predictions of North American summertime heat extremes are discussed. © 2022 American Meteorological Society.
Subjects
Extreme events; North America; Seasonal forecasting
Other Subjects
Atmospheric radiation; Earth system models; Forecasting; Oceanography; Soil moisture; Earth systems; Extreme events; Geophysical fluid dynamics laboratories; North america; North American; Prediction systems; Seamless system; Seasonal forecasting; Seasonal prediction; Summer heat; Surface waters; climate prediction; El Nino; extreme event; optimization; soil moisture; summer; United States
Type
journal article