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Thursday's release of NOAA’s Climate Prediction Center's (CPC) temperature and precipitation outlooks for meteorological spring, which spans March, April and May, indicates that a persistent ...
El Niño–Southern Oscillation (ENSO) is a global climate phenomenon that emerges from variations in winds and sea surface temperatures over the tropical Pacific Ocean. Those variations have an irregular pattern but do have some semblance of cycles. The occurrence of ENSO is not predictable.
A wetter southern tier and a drier northern tier in an outlook for this winter from the Climate Prediction Center have all the fingerprints of an El Niño winter.
The multivariate ENSO index, abbreviated as MEI, is a method used to characterize the intensity of an El Niño Southern Oscillation (ENSO) event. Given that ENSO arises from a complex interaction of a variety of climate systems, MEI is regarded as the most comprehensive index for monitoring ENSO since it combines analysis of multiple meteorological and oceanographic components.
Climate Prediction Center monitors and forecasts short-term climate fluctuations and provides information on the effects climate patterns can have on the nation. Environmental Modeling Center develops and improves numerical weather, climate, hydrological and ocean prediction through a broad program in partnership with the research community.
“An event of this strength would potentially be in the top five of El Niño events since 1950,” NOAA states in the report. ... with a transition to ENSO-neutral favored during April-June 2024 ...
In 1970, various federal weather and climate functions were consolidated into the National Weather Service (NWS) and placed in a new agency called the National Oceanic and Atmospheric Administration (NOAA). In the 1980s the National Weather Service established the Climate Prediction Center, known at the time as the Climate Analysis Center (CAC).
The NOAA Earth System Research Laboratory produces official ENSO forecasts, and Experimental statistical forecasts using a linear inverse modeling (LIM) method [34] [35] to predict the PDO, LIM assumes that the PDO can be separated into a linear deterministic component and a non-linear component represented by random fluctuations.