Abstract

A number of studies have attempted to explain the cause of decadal variability in the tropical Pacific and explore its possible link to decadal variability in the midlatitude Pacific. To investigate some of the current theories of Pacific decadal variability, a linear, wind-driven model, designed to simulate only baroclinic wave dynamics, was forced with wind stress anomalies in the Pacific Ocean basin from 1945 through 1992. An analysis technique designed to isolate the decadal/interdecadal scale variability from interannual ENSO variability was performed on the model's thermocline depth anomaly (TDA).

It was found that the temporal and spatial patterns of the observed tropical decadal sea surface temperatures are consistent with our modeled TDA. Furthermore, restricting the wind forcing to within 5° of the equator does not substantially alter the decadal/interdecadal variability of the equatorial region. The authors conclude that the observed decadal variability in the low-latitude Pacific is primarily a linear dynamical response to tropical wind forcing and does not directly require an oceanic link to the midlatitudes. The question of how tropical wind anomalies are generated is not addressed.

In addition, it is shown that in model scenarios where the wind forcing is restricted to the western equatorial Pacific, the 1976–77 climate shift is still clearly visible as a dominant feature of tropical decadal variability. The temporal decadal signal of the model-generated TDA is more pronounced during the eastern equatorial upwelling season (July–September) than in the boreal winter. This is consistent with the observed seasonal bias in tracer and SST data from the eastern equatorial Pacific.

Abstract

The seasonal and interannual predictability of ENSO variability in a version of the Zebiak–Cane coupled model is examined in a perturbation experiment. Instead of assuming that the model is “perfect,” it is assumed that a set of optimal initial conditions exists for the model. These states, obtained through a nonlinear minimization of the misfit between model trajectories and the observations, initiate model forecasts that correlate well with the observations. Realistic estimates of the observational error magnitudes and covariance structures of sea surface temperatures, zonal wind stress, and thermocline depth are used to generate ensembles of perturbations around these optimal initial states, and the error growth is examined. The error growth in response to subseasonal stochastic wind forcing is presented for comparison.

In general, from 1975 to 2002, the large-scale uncertainty in initial conditions leads to larger error growth than continuous stochastic forcing of the zonal wind stress fields. Forecast ensemble spread is shown to depend most on the calendar month at the end of the forecast rather than the initialization month, with the seasons of greatest spread corresponding to the seasons of greatest anomaly variance. It is also demonstrated that during years with negative (and rapidly decaying) Niño-3 SST anomalies (such as the time period following an El Niño event), there is a suppression of error growth. In years with large warm ENSO events, the ensemble spread is no larger than in moderately warm years. As a result, periods with high ENSO variance have greater potential prediction utility.

In the realistic range of observational error, the ensemble spread has more sensitivity to the initial error in the thermocline depth than to the sea surface temperature or wind stress errors. The thermocline depth uncertainty is the principal reason why initial condition uncertainties are more important than wind noise for ensemble spread.

Abstract

The Zebiak–Cane (ZC) model for simulation of the El Niño–Southern Oscillation is shown to be capable of producing sequences of variability that exhibit shifts in the time-mean state of the eastern equatorial Pacific that resemble observations of tropical Pacific decadal variability. The model's performance in predicting these shifts is compared to two naive forecasting strategies. It is found that the ZC model consistently outperforms the two naive forecasts that serve as a null hypothesis in assessing the significance of results. Forecasts initialized during anomalously warm and anomalously cold decades are shown to have the highest predictability.

These modeling results suggest that, to a moderate extent, the state of the tropical Pacific in one decade can predetermine its time-mean state in the following decade. However, even in this idealized context decadal forecasting skill is modest. Results are discussed in the context of their implications for the ongoing debate over the origin of decadal variations in the Pacific.

Abstract

An ensemble seasonal hindcast approach is used to investigate the development of the equatorial Pacific Ocean cold sea surface temperature (SST) bias and its characteristic annual cycle in the Community Earth System Model, version 1 (CESM1). In observations, eastern equatorial Pacific SSTs exhibit a warm phase during boreal spring and a cold phase during late boreal summer–autumn. The CESM1 climatology shows a cold bias during both warm and cold phases. In our hindcasts, the cold bias during the cold phase develops in less than 6 months, whereas the cold bias during the warm phase takes longer to emerge. The fast-developing cold-phase cold bias is associated with too-strong vertical advection and easterly wind stress over the eastern equatorial region. The antecedent boreal summer easterly wind anomalies also appear in atmosphere-only simulations, indicating that the errors are intrinsic to the atmosphere component. For the slower-developing warm-phase cold bias, we find that the too-cold SSTs over the equatorial region are associated with a slowly evolving upward displacement of subsurface ocean zonal currents and isotherms that can be traced to the ocean component.

Abstract

The objective of near-term climate prediction is to improve our fore-knowledge, from years to a decade or more in advance, of impactful climate changes that can in general be attributed to a combination of internal and externally forced variability. Predictions initialized using observations of past climate states are tested by comparing their ability to reproduce past climate evolution with that of uninitialized simulations in which the same radiative forcings are applied. A new set of decadal prediction (DP) simulations has recently been completed using the Community Earth System Model (CESM) and is now available to the community. This new large-ensemble (LE) set (CESM-DPLE) is composed of historical simulations that are integrated forward for 10 years following initialization on 1 November of each year between 1954 and 2015. CESM-DPLE represents the “initialized” counterpart to the widely studied CESM Large Ensemble (CESM-LE); both simulation sets have 40-member ensembles, and they use identical model code and radiative forcings. Comparing CESM-DPLE to CESM-LE highlights the impacts of initialization on prediction skill and indicates that robust assessment and interpretation of DP skill may require much larger ensembles than current protocols recommend. CESM-DPLE exhibits significant and potentially useful prediction skill for a wide range of fields, regions, and time scales, and it shows widespread improvement over simpler benchmark forecasts as well as over a previous initialized system that was submitted to phase 5 of the Coupled Model Intercomparison Project (CMIP5). The new DP system offers new capabilities that will be of interest to a broad community pursuing Earth system prediction research.

Abstract

The correspondence between mean sea surface temperature (SST) biases in retrospective seasonal forecasts (hindcasts) and long-term climate simulations from five global climate models is examined to diagnose the degree to which systematic SST biases develop on seasonal time scales. The hindcasts are from the North American Multimodel Ensemble, and the climate simulations are from the Coupled Model Intercomparison Project. The analysis suggests that most robust climatological SST biases begin to form within 6 months of a realistically initialized integration, although the growth rate varies with location, time, and model. In regions with large biases, interannual variability and ensemble spread is much smaller than the climatological bias. Additional ensemble hindcasts of the Community Earth System Model with a different initialization method suggest that initial conditions do matter for the initial bias growth, but the overall global bias patterns are similar after 6 months. A hindcast approach is more suitable to study biases over the tropics and subtropics than over the extratropics because of smaller initial biases and faster bias growth. The rapid emergence of SST biases makes it likely that fast processes with time scales shorter than the seasonal time scales in the atmosphere and upper ocean are responsible for a substantial part of the climatological SST biases. Studying the growth of biases may provide important clues to the causes and ultimately the amelioration of these biases. Further, initialized seasonal hindcasts can profitably be used in the development of high-resolution coupled ocean–atmosphere models.