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Multi‐year predictability of temperature and precipitation in multiple climate models
Author(s) -
Jia Liwei,
DelSole Timothy
Publication year - 2012
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2012gl052778
Subject(s) - predictability , climatology , precipitation , environmental science , forcing (mathematics) , sea surface temperature , surface air temperature , coupled model intercomparison project , climate model , mean radiant temperature , atmospheric sciences , climate change , meteorology , geology , mathematics , geography , statistics , oceanography
This paper explicitly identifies patterns in the ocean and land surface that are predictable on multi‐year time scales in multiple climate models. The patterns are identified by maximizing the Average Predictability Time (APT) of surface air temperature and precipitation in control simulations from the recently available fifth phase of the Coupled Model Intercomparison Project (CMIP5). Because the patterns are identified from control runs, the predictability arises from internal dynamics that occur in the absence of interannual variations of anthropogenic and natural forcing. The most predictable component of annual mean surface air temperature is shown to have significant predictability as long as 3–20 years, depending on model. Over land, two components of surface air temperature are verified to be significantly predictable on multi‐year time scales, with one component deriving predictability from the persistence of temperature over the oceans, and the other deriving predictability from evolving ENSO patterns. Annual mean land precipitation is shown to be significantly predictable for 2–4 years in multiple models. These results contradict the widely held belief that temperature and precipitation over land is unpredictable beyond seasonal time scales.