Exploring the impact of CMIP5 model biases on the simulation of North Atlantic decadal variability

Instrumental observations, paleoproxies, and climate models suggest significant decadal variability within the North Atlantic subpolar gyre (NASPG). However, a poorly sampled observational record and a diversity of model behaviors mean that the precise nature and mechanisms of this variability are u...

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Bibliographic Details
Published in:Geophysical research letters 2015-07, Vol.42 (14), p.5926-5934
Main Authors: Menary, Matthew B., Hodson, Daniel L. R., Robson, Jon I., Sutton, Rowan T., Wood, Richard A., Hunt, Jonathan A.
Format: Article
Language:English
Subjects:
Assimilation
Climate
Climate models
cmip5
Computer simulation
Control systems
Control theory
decadal predictions
decadal variability
Density
Feedback
Labrador
Marine
Mathematical models
Meteorology
Methods
north atlantic
Ocean temperature
Ocean-atmosphere interaction
resolution
Salinity
Salinity effects
Salinity variations
Simulation
Spectra
subpolar gyre
Temperature
Temperature effects
Variability
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Summary:Instrumental observations, paleoproxies, and climate models suggest significant decadal variability within the North Atlantic subpolar gyre (NASPG). However, a poorly sampled observational record and a diversity of model behaviors mean that the precise nature and mechanisms of this variability are unclear. Here we analyze an exceptionally large multimodel ensemble of 42 present‐generation climate models to test whether NASPG mean state biases systematically affect the representation of decadal variability. Temperature and salinity biases in the Labrador Sea covary and influence whether density variability is controlled by temperature or salinity variations. Ocean horizontal resolution is a good predictor of the biases and the location of the dominant dynamical feedbacks within the NASPG. However, we find no link to the spectral characteristics of the variability. Our results suggest that the mean state and mechanisms of variability within the NASPG are not independent. This represents an important caveat for decadal predictions using anomaly assimilation methods. Key Points Climate model biases systematically affect diagnosed mechanisms of variability Decadal predictions cannot be assumed to be independent of the mean state North Atlantic biases, density drivers, feedbacks, and resolution are linked
ISSN:0094-8276
1944-8007
DOI:10.1002/2015GL064360