Decadal interactions between the western tropical Pacific and the North Atlantic Oscillation

Kucharski, Fred; Molteni, Franco; Bracco, Annalisa

doi:10.1007/s00382-005-0085-5

Cited by 182 publications

(133 citation statements)

References 27 publications

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“…We examine these issues in the International Centre for Theoretical Physics climate model (27,28), which has been used for a number of climate problems (29,30). Here a triangular truncation at total wavenumber 30 (T30) version is used, roughly equivalent to a 3.75°× 3.75°spatial resolution.…”

Section: Climate Model Setupmentioning

confidence: 99%

Considerations for parameter optimization and sensitivity in climate models

Neelin

Bracco

Luo

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

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108

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Climate models exhibit high sensitivity in some respects, such as for differences in predicted precipitation changes under global warming. Despite successful large-scale simulations, regional climatology features prove difficult to constrain toward observations, with challenges including high-dimensionality, computationally expensive simulations, and ambiguity in the choice of objective function. In an atmospheric General Circulation Model forced by observed sea surface temperature or coupled to a mixed-layer ocean, many climatic variables yield rms-error objective functions that vary smoothly through the feasible parameter range. This smoothness occurs despite nonlinearity strong enough to reverse the curvature of the objective function in some parameters, and to imply limitations on multimodel ensemble means as an estimator of global warming precipitation changes. Low-order polynomial fits to the model output spatial fields as a function of parameter (quadratic in model field, fourth-order in objective function) yield surprisingly successful metamodels for many quantities and facilitate a multiobjective optimization approach. Tradeoffs arise as optima for different variables occur at different parameter values, but with agreement in certain directions. Optima often occur at the limit of the feasible parameter range, identifying key parameterization aspects warranting attention-here the interaction of convection with free tropospheric water vapor. Analytic results for spatial fields of leading contributions to the optimization help to visualize tradeoffs at a regional level, e.g., how mismatches between sensitivity and error spatial fields yield regional error under minimization of global objective functions. The approach is sufficiently simple to guide parameter choices and to aid intercomparison of sensitivity properties among climate models.climate model optimization | metamodeling | precipitation bias and sensitivity I nterest in systematic parameter sensitivity and optimization has been developing both in the context of global average climate sensitivity associated with increased greenhouse gases and the effort to improve the model climatology (1-7). Some of this work has focused on variations with parameter of a climate sensitivity defined by the change of global average surface temperature under doubled CO 2 , some on optimizing the simulation of current climate features by tuning parameter values. Here we examine related questions in sensitivity and optimization, with a particular interest in precipitation. Despite capturing large-scale features, simulations of precipitation in current climate are subject to considerable regional-scale bias (8-12). A common experience is that the simulated climatology exhibits high sensitivity to parameterization changes in certain respects but nonetheless proves difficult to constrain toward observations. At the same time, predicted changes in seasonal precipitation under global warming exhibit striking disagreement among models (13-15). This manifestation of sensitivi...

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Section: Climate Model Setupmentioning

confidence: 99%

Considerations for parameter optimization and sensitivity in climate models

Neelin

Bracco

Luo

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

108

120

View full text Add to dashboard Cite

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“…[6] We analyze two ensembles realized with the ICTP AGCM [Molteni, 2003;Bracco et al, 2004;Kucharski et al, 2006] in its 8-layer configuration and T30 horizontal resolution coupled in the Indian basin to MICOM version 2.9 [Bleck et al, 1992] in a regional domain with 20 vertical levels and 1°Â 1°horizontal resolution. This model has been intensively tested and despite its coarse resolution reproduces the monsoon climatology and variability very well , and the ENSO-IMR teleconnection, essential in this study, close to the theoretical potential predictability limit (see K2007 for a discussion).…”

Section: Model and Experimental Set-upmentioning

confidence: 99%

Atlantic forced component of the Indian monsoon interannual variability

Kucharski

Bracco

Yoo

et al. 2008

Geophysical Research Letters

195

157

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[1] The Indian monsoon interannual variability is modulated by the El Niño Southern Oscillation (ENSO), with a drier than normal monsoon season usually preceding peak El Niño conditions, and vice versa for La Niña phase. Pacific sea surface temperature (SST) anomalies, however, are not the only player. Building upon our recent discovery that atmospheric teleconnections between the tropical Atlantic and the Indian basin contributed to the weakening of the ENSO-monsoon anticorrelation during the '80s and '90s, we investigate the role of south equatorial Atlantic SSTs in forcing the Indian monsoon rainfall (IMR). Using two observational data sets and two ensembles of simulations we show that the residual in the IMR time series for observed and modeled data, obtained by subtracting the ENSO-forced component of the IMR that is linearly related to the NINO34 index, is significantly correlated with south equatorial Atlantic SSTs. Our results have important implications for seasonal forecast efforts.

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“…Results of multi-decadal simulations performed with the current 8-level and a previous 7-level version of the model are discussed in Kucharski et al (2006) and Bracco et al (2004), respectively.…”

Section: Model Configurations and Climatologymentioning

confidence: 99%