Testing and improving ENSO models by process using transfer functions

MacMynowski, Douglas G.; Tziperman, Eli

doi:10.1029/2010gl044050

Cited by 14 publications

(28 citation statements)

References 22 publications

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“…8 in Delworth et al (1993) for an earlier version of the GFDL model, where here we have slightly adjusted the sinking region used in the calculation, consistent with the mean streamfunction for the current simulation. Transfer function analysis (MacMynowski and Tziperman 2010;MacMartin et al 2013, and supplementary material) in Fig. 4c complements this finding by showing that the phase relationship between variables is consistent across a range of frequencies: temperature and density anomalies lead AMOC by 1 /4 cycle (908), while salinity anomalies lag AMOC by 1 /4 cycle, not just at the spectral peak that dominates the calculation of correlation, but across a wide frequency band.…”

Section: Characteristics Of the Variabilitysupporting

confidence: 55%

Suppression of Atlantic Meridional Overturning Circulation Variability at Increased CO2

2016

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Multidecadal variability in the Atlantic meridional overturning circulation (AMOC) is shown to differ significantly between the 4 3 CO 2 and preindustrial control simulations of the GFDL Earth System Model, version 2M (ESM2M) general circulation model (GCM). In the preindustrial simulation, this model has a peak in the power spectrum of both AMOC and northward heat transport at latitudes between 268 and 508N. In the 4 3 CO 2 simulation, the only significant spectral peak is near 608N. Understanding these differences is important for understanding the effect of future climate change on climate variability, as well as for providing insight into the physics underlying AMOC variability. Transfer function analysis demonstrates that the shift is predominantly due to a shift in the internal ocean dynamics rather than a change in stochastic atmospheric forcing. Specifically, the reduction in variance from 268 to 458N is due to an increased stratification east of Newfoundland that results from the shallower and weaker mean overturning. The reduced AMOC variance that accompanies the reduced mean value of the AMOC at 4 3 CO 2 differs from predictions of simple box models that predict a weaker circulation to be closer to a stability bifurcation point and, therefore, be accompanied by amplified variability. The high-latitude variability in the 4 3 CO 2 simulation is related to the advection of anomalies by the subpolar gyre, distinct from the variability mechanism in the control simulation at lower latitudes. The 4 3 CO 2 variability has only a small effect on midlatitude meridional heat transport, but does significantly affect sea ice in the northern North Atlantic.

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Section: Characteristics Of the Variabilitysupporting

confidence: 55%

Suppression of Atlantic Meridional Overturning Circulation Variability at Increased CO2

2016

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“…The specific methodology used in this study, borrowed from control engineering (e.g., Astrom and Murray 2008), is transfer function analysis, which describes a dynamic process that relates two variables in the frequency domain. Recently, MacMynowski andTziperman (2010, 2012, manuscript submitted to Philos. Trans.…”

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confidence: 99%

Frequency Domain Multimodel Analysis of the Response of Atlantic Meridional Overturning Circulation to Surface Forcing

2013

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The dynamics of the Atlantic meridional overturning circulation (AMOC) vary considerably among different climate models; for example, some models show clear peaks in their power spectra while others do not. To elucidate these model differences, transfer functions are used to estimate the frequency domain relationship between surface forcing fields, including sea surface temperature, salinity, and wind stress, and the resulting AMOC response. These are estimated from the outputs of the Coupled Model Intercomparison Project phase 5 (CMIP5) and phase 3 (CMIP3) control runs for eight different models, with a specific focus on Geophysical Fluid Dynamics Laboratory Climate Model, version 2.1 (GFDL CM2.1), and the Community Climate System Model, version 4 (CCSM4), which exhibit rather different spectral behavior. The transfer functions show very little agreement among models for any of the pairs of variables considered, suggesting the existence of systematic model errors and that considerable uncertainty in the simulation of AMOC in current climate models remains. However, a robust feature of the frequency domain analysis is that models with spectral peaks in their AMOC correspond to those in which AMOC variability is more strongly excited by high-latitude surface perturbations that have periods corresponding to the frequency of the spectral peaks. This explains why different models exhibit such different AMOC variability. These differences would not be evident without using a method that explicitly computes the frequency dependence rather than a priori assuming a particular functional form. Finally, transfer functions are used to evaluate two proposed physical mechanisms for model differences in AMOC variability: differences in Labrador Sea stratification and excitation by westward-propagating subsurface Rossby waves.

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“…We applied the transfer function as an additional tool to estimate the process dynamics of ENSO [9] and to analyse AMOC variability [10]. Here, we (i) provide more detail on the transfer function estimation and error bounds, as well as guidelines on its use, and (ii) extend the previous results to consider additional processes involved in ENSO dynamics motivated by the delayed oscillator.…”

Section: Introductionmentioning

confidence: 98%

“…relationship between two variables) which will be analysed from appropriate model and data time series in §3. We present and analyse the six transfer functions for TAO array observations and several models, including GFDL CM2.1 as in MacMynowski & Tziperman [9] but using significantly more data, and also CCSM4 [21], and the Cane-Zebiak model.…”

Section: T Rkmentioning

confidence: 99%

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Using transfer functions to quantify El Niño Southern Oscillation dynamics in data and models

MacMartin

Tziperman

2014

Proc. R. Soc. A.

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Transfer function tools commonly used in engineering control analysis can be used to better understand the dynamics of El Niño Southern Oscillation (ENSO), compare data with models and identify systematic model errors. The transfer function describes the frequency-dependent input–output relationship between any pair of causally related variables, and can be estimated from time series. This can be used first to assess whether the underlying relationship is or is not frequency dependent, and if so, to diagnose the underlying differential equations that relate the variables, and hence describe the dynamics of individual subsystem processes relevant to ENSO. Estimating process parameters allows the identification of compensating model errors that may lead to a seemingly realistic simulation in spite of incorrect model physics. This tool is applied here to the TAO array ocean data, the GFDL-CM2.1 and CCSM4 general circulation models, and to the Cane–Zebiak ENSO model. The delayed oscillator description is used to motivate a few relevant processes involved in the dynamics, although any other ENSO mechanism could be used instead. We identify several differences in the processes between the models and data that may be useful for model improvement. The transfer function methodology is also useful in understanding the dynamics and evaluating models of other climate processes.

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Testing and improving ENSO models by process using transfer functions

Cited by 14 publications

References 22 publications

Suppression of Atlantic Meridional Overturning Circulation Variability at Increased CO2

Suppression of Atlantic Meridional Overturning Circulation Variability at Increased CO2

Frequency Domain Multimodel Analysis of the Response of Atlantic Meridional Overturning Circulation to Surface Forcing

Using transfer functions to quantify El Niño Southern Oscillation dynamics in data and models

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