Optimally growing initial errors of El Niño events in the CESM

Abstract

The optimally growing initial errors (OGEs) of El Niño events are found in the Community Earth System Model (CESM) by the conditional nonlinear optimal perturbation (CNOP) method. Based on the characteristics of low-dimensional attractors for ENSO (El Niño Southern Oscillation) systems, we apply singular vector decomposition (SVD) to reduce the dimensions of optimization problems and calculate the CNOP in a truncated phase space by the differential evolution (DE) algorithm. In the CESM, we obtain three types of OGEs of El Niño events with different intensities and diversities and call them type-1, type-2 and type-3 initial errors. Among them, the type-1 initial error is characterized by negative SSTA errors in the equatorial Pacific accompanied by a negative west–east slope of subsurface temperature from the subsurface to the surface in the equatorial central-eastern Pacific. The type-2 initial error is similar to the type-1 initial error but with the opposite sign. The type-3 initial error behaves as a basin-wide dipolar pattern of tropical sea temperature errors from the sea surface to the subsurface, with positive errors in the upper layers of the equatorial eastern Pacific and negative errors in the lower layers of the equatorial western Pacific. For the type-1 (type-2) initial error, the negative (positive) temperature errors in the eastern equatorial Pacific develop locally into a mature La Niña (El Niño)-like mode. For the type-3 initial error, the negative errors in the lower layers of the western equatorial Pacific propagate eastward with Kelvin waves and are intensified in the eastern equatorial Pacific. Although the type-1 and type-3 initial errors have different spatial patterns and dynamic growing mechanisms, both cause El Niño events to be underpredicted as neutral states or La Niña events. However, the type-2 initial error makes a moderate El Niño event to be predicted as an extremely strong event.

Error Evolutions and Analyses on Joint Effects of SST and SL via Intermediate Coupled Models and Conditional Nonlinear Optimal Perturbation Method

“…Similarly, our OGE patterns also corresponded to non-seasonal OGEs [45,46]. In particular, Xu et al [28] obtained three types of OGEs with distinct El Niño events in CESM, which were somewhat similar to the winter mode of SSTA-OGE (Figure 6c), autumn mode of Joint-OGE (Figure 8b) and spring mode of Joint-OGE (Figure 8d). The comprehensive OGE patterns imply the validity of our experimental methods and are reliable and valuable supports for dynamic analyses.…”

“…The maximum iteration step (Maxit) is set to 50, which can ensure the convergence and efficiency of SPG2. The integrating time of ICM (lead time) is set to 12 months for ENSO, generally going through 12 months from the growth phase to the mature phase [28].…”

“…Further, identifying critical regions for intensive observation is also highly necessary to reduce the initial field error and minimize the forecast error [23][24][25]. The CNOP method [26] is used to study the optimal growth initial error (OGE) [27][28][29], which represents a perturbation under a given physical constraint, resulting in the largest nonlinear error evolution at the prediction time. IOCAS ICM (intermediate coupled model, developed at the Institute of Oceanology, Chinese Academy of Sciences) [9] stands out as one of the prediction models listed in Figure 1, which has provided ENSO forecasting for IRI/CPC since August 2015 and successfully predicted a strong El Niño event in 2015/2016 [10,11] and consecutive third La Niña events in 2020/2021, 2021/2022 and 2022/2023 [12,13].…”

“…Further, identifying critical regions for intensive observation is also highly necessary to reduce the initial field error and minimize the forecast error [23][24][25]. The CNOP method [26] is used to study the optimal growth initial error (OGE) [27][28][29], which represents a perturbation under a given physical constraint, resulting in the largest nonlinear error evolution at the prediction time.…”

Probabilistic Prediction of ENSO Over the Past 137 Years Using the CESM Model

“…The retrospective forecasts of ENSO were conducted by the CESM 1.2, which is a fully coupled model that has been widely used in ENSO research (Bellenger et al., 2014; Hu et al., 2019; Hurrell et al., 2013; Xu et al., 2021; Zhou et al., 2021). The atmospheric component used in this study was the Community Atmosphere Model version 4 (CAM4), with a grid resolution of 0.9° × 1.25°.…”