Search

A currently popular idea is that El Niño–Southern Oscillation (ENSO) can be viewed as a linear deterministic system forced by noise representing processes with periods shorter than ENSO. Also, there is observational evidence to suggest that the Madden–Julian oscillation (MJO) acts to trigger and/or amplify the warm phase of ENSO in this way. The feedback of the slower process, ENSO, to higher-frequency atmospheric phenomena, of which a large part of the variability in the intraseasonal band is due to the MJO, has received little attention. This paper considers the hypothesis that the probability of an El Niño event is modified by high MJO activity and that, in turn, the MJO is regulated by ENSO activity. If this is indeed the case, then viewing ENSO as a low-frequency oscillation forced by additive stochastic noise would not present a complete picture. This paper tests the above hypothesis using a stochastically forced intermediate coupled model by allowing ENSO to directly influence the stochastic forcing. The model response to a variety of stochastic forcing types is found to be sensitive to the type of forcing applied. When the model is operated beyond its intrinsic Hopf bifurcation, its probability distribution function (PDF) is fundamentally altered when the stochastic forcing is changed from additive to multiplicative. The model integration period also influences the shape of the PDF, which is also compared to the PDF derived from observations. It is found that multiplicative stochastic forcing reproduces some measures of the observations better than the additive stochastic forcing. [ABSTRACT FROM AUTHOR]

Constraining the low-frequency (LF) behavior of general circulation models (GCMs) requires reliable observational estimates of LF variability. This two-part paper presents multiproxy reconstructions of Niño-3.4 sea surface temperature over the last millennium, applying two techniques [composite plus scale (CPS) and hybrid regularized expectation maximization (RegEM) truncated total least squares (TTLS)] to a network of tropical, high-resolution proxy records. This first part presents the data and methodology before evaluating their predictive skill using frozen network analysis (FNA) and pseudoproxy experiments. The FNA results suggest that about half of the Niño-3.4 variance can be reconstructed back to A.D. 1000, but they show little LF skill during certain intervals. More variance can be reconstructed in the interannual band where climate signals are strongest, but this band is affected by dating uncertainties (which are not formally addressed here). The CPS reliably estimates interannual variability, while LF fluctuations are more faithfully reconstructed with RegEM, albeit with inevitable variance loss. The RegEM approach is also tested on representative pseudoproxy networks derived from two millennium-long integrations of a coupled GCM. The pseudoproxy study confirms that reconstruction skill is significant in both the interannual and LF bands, provided that sufficient variance is exhibited in the target Niño-3.4 index. It also suggests that FNA severely underestimates LF skill, even when LF variability is strong, resulting in overly pessimistic performance assessments. The centennial-scale variance of the historical Niño-3.4 index falls somewhere between the two model simulations, suggesting that the network and methodology presented here would be able to capture the leading LF variations in Niño-3.4 for much of the past millennium, with the caveats noted above. [ABSTRACT FROM AUTHOR]

In this paper the extensive integrations produced for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) are used to examine the relationship between ENSO and monsoons at interannual and decadal time scales. The study begins with an analysis of the monsoon simulation in the twentieth-century integrations. Six of the 18 models were found to have a reasonably realistic representation of monsoon precipitation climatology. For each of these six models SST and anomalous precipitation evolution along the equatorial Pacific during El Niño events display considerable differences when compared to observations. Out of these six models only four [Geophysical Fluid Dynamics Laboratory Climate Model versions 2.0 and 2.1 (GFDL_CM_2.0 and GFDL_CM_2.1), Meteorological Research Institute (MRI) model, and Max Planck Institute ECHAM5 (MPI_ECHAM5)] exhibit a robust ENSO–monsoon contemporaneous teleconnection, including the known inverse relationship between ENSO and rainfall variations over India. Lagged correlations between the all-India rainfall (AIR) index and Niño-3.4 SST reveal that three models represent the timing of the teleconnection, including the spring predictability barrier, which is manifested as the transition from positive to negative correlations prior to the monsoon onset. Furthermore, only one of these three models (GFDL_CM_2.1) captures the observed phase lag with the strongest anticorrelation of SST peaking 2–3 months after the summer monsoon, which is partially attributable to the intensity of the simulated El Niño itself. The authors find that the models that best capture the ENSO–monsoon teleconnection are those that correctly simulate the timing and location of SST and diabatic heating anomalies in the equatorial Pacific and the associated changes to the equatorial Walker circulation during El Niño events. The strength of the AIR-Niño-3.4 SST correlation in the model runs waxes and wanes to some degree on decadal time scales. The overall magnitude and time scale for this decadal modulation in most of the models is similar to that seen in observations. However, there is little consistency in the phase among the realizations, suggesting a lack of predictability of the decadal modulation of the monsoon–ENSO relationship. The analysis was repeated for each of the four models using results from integrations in which the atmospheric CO2 concentration was raised to twice preindustrial values. From these “best” models in the double CO2 simulations there are increases in both the mean monsoon rainfall over the Indian subcontinent (by 5%–25%) and in its interannual variability (5%–10%). For each model the ENSO–monsoon correlation in the global warming runs is very similar to that in the twentieth-century runs, suggesting that the ENSO–monsoon connection will not weaken as global climate warms. This result, though plausible, needs to be taken with some caution because of the diversity in the simulation of ENSO variability in the coupled models that have been analyzed. Implications of the present results for monsoon prediction are discussed. [ABSTRACT FROM AUTHOR]