Andrew M. Moore

Stochastic forcing of ENSO by the intraseasonal oscillation

Abstract Using the ideas of generalized linear stability theory, the authors examine the potential role that tropical variability on synoptic–intraseasonal timescales can play in controlling variability on seasonal–interannual timescales. These ideas are investigated using an intermediate coupled ocean–atmosphere model of the El Nino–Southern Oscillation (ENSO). The variability on synoptic–intraseasonal timescales is treated as stochastic noise that acts as a forcing function for variability at ENSO timescales. The spatial structure is computed that the stochastic noise forcing must have in order to enhance the variability of the system on seasonal–interannual timescales. These structures are the so-called stochastic optimals of the coupled system, and they bear a good resemblence to variability that is observed in the real atmosphere on synoptic and intraseasonal timescales. When the coupled model is subjected to a stochastic noise forcing composed of the stochastic optimals, variability on seasonal–inte...

Impacts of the 2015–2016 El Niño on the California Current System: Early assessment and comparison to past events

The 2015–2016 El Nino is by some measures one of the strongest on record, comparable to the 1982–1983 and 1997–1998 events that triggered widespread ecosystem change in the northeast Pacific. Here we describe impacts of the 2015–2016 El Nino on the California Current System (CCS) and place them in historical context using a regional ocean model and underwater glider observations. Impacts on the physical state of the CCS are weaker than expected based on tropical sea surface temperature anomalies; temperature and density fields reflect persistence of multiyear anomalies more than El Nino. While we anticipate El Nino-related impacts on spring/summer 2016 productivity to be similarly weak, their combination with preexisting anomalous conditions likely means continued low phytoplankton biomass. This study highlights the need for regional metrics of El Ninos effects and demonstrates the potential to assess these effects before the upwelling season, when altered ecosystem functioning is most apparent.

Assimilation of subsurface thermal data into a simple ocean model for the initialization of an intermediate tropical coupled ocean-atmosphere forecast model

Abstract An adjoint variational assimilation technique is used to assimilate observations of both the oceanic state and wind stress data into an intermediate coupled ENSO prediction model. This method of initialization is contrasted with the more usual method, which uses only wind stress data to establish the initial state of the ocean. It is shown that ocean temperature data has a positive impact on the prediction skill in such models. On the basis of hindcasts for the period 1982–91, it is shown that NIN03 SST anomaly correlations greater than 0.7 can be obtained for hindcasts of duration up to 13 months and greater than 0.6 up to 16 months. There are also clear indications of skill at two years.

The Nonnormal Nature of El Niño and Intraseasonal Variability

Abstract The idea that intraseasonal variability in the tropical west Pacific can act as an effective means of stochastically forcing ENSO episodes is explored. Using the ideas of generalized linear stability theory as they apply to nonnormal dynamical systems, the physical attributes of the coupled ocean–atmosphere system in the Tropics that allow perturbations with structures that are dissimilar to ENSO to act as precursors for ENSO episodes are examined. Using a coupled ocean–atmosphere model, two particularly important factors are identified that contribute to the nonnormality of the coupled system: nonsolar atmospheric heating directly related to SST changes, and the dissimilarity between the equatorial ocean wave reflection process at eastern and western boundaries. The latter is intrinsic to the dynamics of the ocean, while the former is related to the presence of the west Pacific warm pool and its relationship with the Walker circulation.

Reliability of ENSO Dynamical Predictions

Abstract In this study, ensemble predictions were constructed using two realistic ENSO prediction models and stochastic optimals. By applying a recently developed theoretical framework, the authors have explored several important issues relating to ENSO predictability including reliability measures of ENSO dynamical predictions and the dominant precursors that control reliability. It was found that prediction utility (R), defined by relative entropy, is a useful measure for the reliability of ENSO dynamical predictions, such that the larger the value of R, the more reliable the prediction. The prediction utility R consists of two components, a dispersion component (DC) associated with the ensemble spread and a signal component (SC) determined by the predictive mean signals. Results show that the prediction utility R is dominated by SC. Using a linear stochastic dynamical system, SC was examined further and found to be intrinsically related to the leading eigenmode amplitude of the initial conditions. This...

A New Method for Determining the Reliability of Dynamical ENSO Predictions

Determination of the reliability of particular ENSO forecasts is of particular importance to end users. Theoretical arguments are developed that indicate that the amplitudes of slowly decaying (or growing) normal modes of the coupled system provide a useful measure of forecast reliability. Historical forecasts from a skillful prediction model together with a series of ensemble predictions from a ‘‘perfect model’’ experiment are used to demonstrate that these arguments carry over to the practical prediction situation. In such a setting it is found that the amplitude of the dominant normal mode, which strongly resembles the observed ENSO cycle, is a potentially useful index of reliability. The fact that this index was generally lower in the 1970s than the 1980s provides an explanation for why many coupled models performed better in the latter decade. It does not, however, explain the low skill of some coupled models in the early 1990s as the index defined here was then moderate.

An Adjoint Sensitivity Analysis of the Southern California Current Circulation and Ecosystem

Adjoint methods of sensitivity analysis were applied to the California Current using the Regional Ocean Modeling Systems (ROMS) with medium resolution, aimed at diagnosing the circulation sensitivity to variations in surface forcing. The sensitivities of coastal variations in SST, eddy kinetic energy, and baroclinic instability of complex time-evolving flows were quantified. Each aspect of the circulation exhibits significant interannual and seasonal variations in sensitivity controlled by mesoscale circulation features. Central California SST is equally sensitive to wind stress and surface heat flux, but less so to wind stress curl, displaying the greatest sensitivity when upwelling-favorable winds are relaxing and the least sensitivity during the peak of upwelling. SST sensitivity is typically 2‐4 times larger during summer than during spring, although larger variations occur during some years. The sensitivity of central coast eddy kinetic energy to surface forcing is constant on average throughout the year. Perturbations in the wind that align with mesoscale eddies to enhance the strength of the circulation by local Ekman pumping yield the greatest sensitivities. The sensitivity of the potential for baroclinic instability is greatest when nearshore horizontal temperature gradients are largest, and it is associated with variations in wind stress concentrated along the core of the California Current. The sensitivity varies by a factor of ;1.5 throughout the year. A new and important aspect of this work is identification of the complex flow dependence and seasonal dependence of the sensitivity of the ROMS California Current System (CCS) circulation to variations in surface forcing that was hitherto not previously appreciated.

A Comparison of the Influence of Additive and Multiplicative Stochastic Forcing on a Coupled Model of ENSO

Abstract A currently popular idea is that El Nino–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 Nino 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 stochast...

The Calculation of Climatically Relevant Singular Vectors in the Presence of Weather Noise as Applied to the ENSO Problem

An efficient technique for the extraction of climatically relevant singular vectors in the presence of weather noise is presented. This technique is particularly relevant to the analysis of coupled general circulation models where the fastest growing modes are connected with weather and not climate. Climatic analysis, however, requires that the slow modes relevant to oceanic adjustment be extracted, and so effective techniques are required to essentially filter the stochastic part of the system. The method developed here relies on the basic properties of the evolution of first moments in stochastic systems. The methodology for the climatically important ENSO problem is tested using two different coupled models. First, the method using a stochastically forced intermediate coupled model for which exact singular vectors are known is tested. Here, highly accurate estimates for the first few singular vectors are produced for the associated dynamical system without stochastic forcing. Then the methodology is applied to a relatively complete coupled general circulation model, which has been shown to have skill in the prediction of ENSO. The method is shown to converge rapidly with respect to the expansion basis chosen and also with respect to ensemble size. The first climatic singular vector calculated shows some resemblance to that previously extracted by other authors using observational datasets. The promising results reported here should hopefully encourage further investigation of the methodology in a range of coupled models and for a range of physical problems where there exists a clear separation of timescales.

The Role of Air–Sea Interaction in Controlling the Optimal Perturbations of Low-Frequency Tropical Coupled Ocean–Atmosphere Modes

Abstract In this paper the structure and dynamics of the optimal perturbations of tropical low-frequency coupled ocean–atmosphere oscillations relevant to El Nino–Southern Oscillation (ENSO) are explored. These optimal perturbations yield information about potential precursors for ENSO events, and about the fundamental dynamical processes that may control perturbation growth and limit the predictability of interannual variability. The present study uses a hierarchy of hybrid coupled models. Each model is configured for the tropical Pacific Ocean and shares a common ocean general circulation model. Three different atmospheric models are used: a statistical model, a dynamical model, and a combination of a dynamical model and boundary layer model. Each coupled model possesses a coupled ocean–atmosphere eigenmode oscillation with a period of the order of several years. The properties of these various eigenmodes and their corresponding adjoint eigenmodes are explored. The optimal perturbations of each coupled ...