Predictability Barrier Research Articles

The Role of Tropical Atlantic in ENSO Predictability Barrier

AbstractThis paper investigates the impacts of tropical Atlantic on El Niño‐Southern Oscillation (ENSO) predictability through an empirical dynamical model‐ Linear Inverse Model (LIM). By selectively including or excluding the coupling between tropical Atlantic and tropical Pacific in LIM, we find that the tropical Atlantic dynamics significantly improve the Eastern Pacific (EP)‐ENSO prediction and weaken the EP‐ENSO predictability barrier (PB), with the Equatorial Atlantic (EA) mode playing a more important role than the Tropical North Atlantic (TNA) mode. The tropical Atlantic impacts on Central Pacific (CP)‐ENSO predictability and PB are relatively smaller. Consistent with observations, the tropical Atlantic can weaken PB in most CMIP6 models. The evolution of the tropical Atlantic optimum initial structures confirms the important influence of the EA mode on the eastern tropical Pacific. Therefore, the tropical Atlantic dynamics, especially the EA mode, should be appropriately considered to improve the prediction of EP‐ENSO and weaken the EP‐ENSO PB.

A Nonlinear Cause for the Seasonal Predictability Barrier of SST Anomaly in the Tropical Pacific

AbstractThe seasonal Predictability Barrier (PB) of Sea Surface Temperature Anomaly (SSTA) is characterized by a rapid loss of prediction skills at a particular season in dynamic models. Here, the connection between seasonal PB and the inherent nonlinearity of SSTA was investigated using a statistical method known as Sample Entropy (SamEn). When the SamEn value is large, the chaotic degree of SSTA is high. In the Niño 3 and Niño 3.4 regions, the chaotic degree of SSTA was high in the spring; in the Niño 4 region, it was high in the summer. This result was consistent with the known PB occurrence season in these regions. The month when the chaotic degree of SSTA peaked moved westward longitudinally from March to June. This spatial‐temporal variation of the chaotic degree was consistent with that of the low SSTA variance and PB occurrence timing. Specifically, when the variance of SSTA was low, the low‐amplitude background SSTA signal was more dominant than the El Niño/Southern Oscillation (ENSO)‐related signal, the chaotic degree of SSTA was high, and the PB phenomenon occurred. Hence, the results indicated that the background SSTA signal was more chaotic than the ENSO‐related signal and the seasonal PB may result from the inherent low predictability of the chaotic background SSTA signal. Furthermore, the correlation coefficient between SSTA and shortwave flux anomaly showed similar variation along the longitude compared with the chaotic degree, which suggested that the seasonal connection between SSTA and atmospheric forcing may be responsible for the spatial‐temporal variation of the chaotic degree.

Season-dependent predictability barrier for two types of El Ni\xf1o revealed by an approach to data analysis for predictability

The real-time prediction skill for El Niño-Southern Oscillation has not improved steadily during the twenty-first century. One important reason is the season-dependent predictability barrier (PB), and another is due to the diversity of El Niño. In this paper, an approach to data analysis for predictability is developed to investigate the season-dependent PB phenomena of two types of El Niño events by using the monthly mean data of the preindustrial control (“pi-Control”) runs from several coupled model outputs in CMIP5 experiments. The results find that predictions for Central Pacific El Niño (CP-El Niño) suffered from summer PB, whereas those for Eastern Pacific El Niño (EP-El Niño) are mainly interfered with by spring PB. The initial errors most frequently causing PB for CP- and EP-El Niño are revealed and they emphasize that the initial sea temperature accuracy in the Victoria mode (VM) region in the North Pacific is more important for better predictions of the intensity of the CP-El Niño, whereas that in the subsurface layer of the west equatorial Pacific and the surface layer of the southeast Pacific is of more concern for better predictions of the structure of CP-El Niño. However, for EP-El Niño, the former is indicated to modulate the structure of the event, whereas the latter is shown to be more effective in predictions of the intensity of the event. Obviously, for predicting which type of El Niño will occur, more attention should be paid to the initial sea temperature accuracy in not only the subsurface layer of the west equatorial Pacific and the surface layer of the southeast Pacific but also the region covered by the VM-like mode in the North Pacific. This result provided guidance aiming at how to initialize model in predictions of El Niño types.

A Theory for the Seasonal Predictability Barrier: Threshold, Timing, and Intensity

AbstractA theory is developed in a stochastic climate model for understanding the general features of the seasonal predictability barrier (PB), which is characterized by a band of maximum decline in autocorrelation function phase-locked to a particular season. Our theory determines the forcing threshold, timing, and intensity of the seasonal PB as a function of the damping rate and seasonal forcing. A seasonal PB is found to be an intrinsic feature of a stochastic climate system forced by either seasonal growth rate or seasonal noise forcing. A PB is generated when the seasonal forcing, relative to the damping rate, exceeds a modest threshold. Once generated, all the PBs occur in the same calendar month, forming a seasonal PB. The PB season is determined by the decline of the seasonal forcing as well as the delayed response associated with damping. As such, for a realistic weak damping, the PB season is locked close to the minimum SST variance under the seasonal growth-rate forcing, but after the minimum SST variance under the seasonal noise forcing. The intensity of the PB is determined mainly by the amplitude of the seasonal forcing. The theory is able to explain the general features of the seasonal PB of the observed SST variability over the world. In the tropics, a seasonal PB is generated mainly by a strong seasonal growth rate, whereas in the extratropics a seasonal PB is generated mainly by a strong seasonal noise forcing. Our theory provides a general framework for the understanding of the seasonal PB of climate variability.