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The North Pacific sea surface temperature (SST) has a profound climatic influence. The El Niño‐Southern Oscillation (ENSO) significantly impacts the North Pacific SST; however, the influence of the distinct phases of ENSO on SST predictability remains unclear. To overcome the model limitations, this study assessed SST predictability under diverse ENSO phases using reanalysis. The predictability limit of the North Pacific SST under La Niña (8.4 months) is longer than that under Neutral (5.9 months) and El Niño (5.5 months) conditions, which unveils asymmetry. This asymmetry mirrors contemporary multimodal prediction skills. Error growth dynamics reveal La Niña's robust signal strength with a slow error growth rate, in contrast to El Niño's weaker signal and faster error growth. There exhibits intermediate signal strength and elevated error growth in Neutral condition. Physically, predictability signal strength aligns with SST variability, whereas the error growth rate correlates with atmospheric‐ocean heating anomalies. La Niña, which induces positive heating anomalies, minimizes the impact of atmospheric noise, resulting in lower error growth. The result is beneficial for improving North Pacific SST predictions. The North Pacific sea surface temperature (SST) significantly impacts global climate, particularly in East Asia and the United States. Accurate prediction of North Pacific SST, which is crucial for seasonal and interannual forecasting, remains a focal point in atmospheric and oceanic research. This study investigates how the El Niño‐Southern Oscillation (ENSO) influences SST predictability in the North Pacific during different phases (El Niño, La Niña, Neutral). Using reanalysis data, this research introduces the concept of predictability limit (PL), revealing an asymmetry in the PL response: La Niña extends the PL to 8.4 months, El Niño shortens it to 5.5 months, and Neutral falls in 5.9 months. This asymmetry aligns with the existing prediction skills. Examining the error growth dynamics, La Niña exhibits a robust SST signal with slower error growth, whereas El Niño displays a weaker signal with faster error growth. Neutral, meanwhile, shows a high signal strength but large error growth. This study sheds light on these differences in SST predictability and provides valuable insights for refining North Pacific SST predictions through model advancements. El Niño‐Southern Oscillation phases asymmetrically impact North Pacific sea surface temperature (SST) predictability: El Niño (5.5 months), La Niña (8.4 months), Neutral (5.9 months)Error growth dynamics vary: La Niña displays strong signals with slow growth, El Niño exhibits weak signals and rapid growthA positive heating anomaly from La Niña reduces atmospheric noise impact, resulting in a smaller error growth rate of the North Pacific SST El Niño‐Southern Oscillation phases asymmetrically impact North Pacific sea surface temperature (SST) predictability: El Niño (5.5 months), La Niña (8.4 months), Neutral (5.9 months) Error growth dynamics vary: La Niña displays strong signals with slow growth, El Niño exhibits weak signals and rapid growth A positive heating anomaly from La Niña reduces atmospheric noise impact, resulting in a smaller error growth rate of the North Pacific SST

Rice (Oryza sativaL.) production in southern China (SC) is important to Chinese food security and sensitive to climate change. This study investigates the contrasting effects of eastern Pacific (EP) El Niño and central Pacific (CP) El Niño on early rice yields in SC during boreal early summer (May–June–July, or MJJ) for the first time. During EP El Niño events, early rice yields usually exhibit a moderate increase across the entire SC region, whereas during CP El Niño events the yields decrease in about two‐thirds of the region. The increase and decrease of yields correspond to .86 and 1.54% of the mean total yield during the study period, respectively. The effects of EP and CP El Niño events on early rice yields are significantly different over nearly half of the harvested areas in SC. Their difference mainly derives from the variability in rainfall associated with the two types. During MJJ, EP events are accompanied by reduced rainfall across SC, which increases early rice yields. In contrast, CP events are accompanied by increased rainfall in northeastern SC, resulting in unfavorable growing conditions. Although temperature affects early rice yields in SC, it is not the major contributor to the yield differences. Our findings highlight the importance of investigating the impacts of both types of El Niño on crop yields separately; and provide a possible climatological mechanism, linking the different locations of sea surface temperature anomalies (SSTAs) with SC early rice yields differences, through atmospheric teleconnection effects on local climatic factors.

Variations in the sea surface temperature (SST) field in both the North Pacific (represented by the Victoria mode [VM]) and the South Pacific (represented by the South Pacific quadrapole [SPQ] mode) are related to the state of the El Niño–Southern Oscillation (ENSO) three seasons later. Here with the aid of observational data and numerical experiments, we demonstrate that both VM and SPQ SST forcing can influence the development of ENSO events through a similar air‐sea coupling mechanism. By comparing ENSO amplitudes induced by the VM and SPQ, as well as the percentages of strong ENSO events followed by the VM and SPQ events, we find that the VM and SPQ make comparable contributions and therefore have similar levels of importance to ENSO. Additional analysis indicates that although VM or SPQ SST forcing alone may serve as a good predictor for ENSO events, it is more effective to consider their combined influence. A prediction model based on both VM and SPQ indices is developed, which is capable of yielding skillful forecasts for ENSO at lead times of three seasons. The extratropical North and South Pacific Oceans make comparable contributions to the development of ENSOThe extratropical North and South Pacific precursor signals may be used together to better predict the ENSO events

A nonlinear local Lyapunov exponent method based on monthly sea surface temperature data is employed to explore the predictability limit of warm El Niño–Southern Oscillation (ENSO) events. Results using observational data show an asymmetry of the predictability limit between the developing and decaying stages of the warm ENSO phase. To wit, predictability of the developing stage of warm ENSO events is found to approach a limit of 10 months, less than that of the mature and decaying stages. This asymmetrical predictability limit is also found in a long climate model simulation and may explain the asymmetry in operational forecast skill of warm ENSO events. Through exploring the error growth rate as represented by nonlinear local Lyapunov exponent and the instantaneous error growth rate, it is shown that error growth, especially during the first 8‐month lead forecasts, is the primary contributor to the asymmetry of the predictability limit of warm ENSO events. El Niño events have a characteristic development and decay cycle, whereby most events start developing in late boreal spring, peak in boreal winter, and subsequently decay. Model skill in operational forecasting of El Niño events depends on whether the forecast is done during the developing or decaying stage of the event. This study shows that this asymmetry in forecast skill is associated with an asymmetry in the inherent predictability limits of the developing and decay stages of warm El Niño–Southern Oscillation events. These inherent predictability limits stem from the fact that small perturbations of a dynamical system may grow into large forecast errors after a period of time. The warm ENSO phase exhibits an asymmetry in its predictability limit between the developing and decaying stageThe asymmetrical inherent predictability limit explains the asymmetry of operational forecast skill for warm ENSO eventsNonlinear error growth, especially during the first 8‐month lead forecasts, contributes to the asymmetry of the predictability limit

We use short‐range ensemble forecasts and ensemble clustering analysis to study the factors affecting the intensification of Hurricane Patricia (2015). Convection‐permitting ensemble forecasts are classified into two groups: 10 spin‐down (SPD) members and 10 spin‐up (SPU) members with intensification rates of <0 and >0 m s−1for the first 6 hr, respectively. Ensemble clustering analysis found that the wind–pressure relationship was incorrect in the SPD group, indicating that the SPD issue may be partly caused by an imbalance in the initial minimum sea‐level pressure (MSLP) and the maximum wind speed (MWS). The SPD issue appears to be related to three main points: (a) a weaker upper‐level warm core; (b) a drier inner core at low levels; and (c) a dry, cold air intrusion at mid‐levels. In contrast, the SPU group has a stronger upper‐level warm core and a relatively wet inner core at lower levels, as well as a relatively strong secondary circulation. These favorable initial conditions in the SPU group, combined with a greater updraft and stronger convection around the eyewall, result in more latent heating around or in the eyewall that favors the intensification of the tropical cyclone. Comparisons between the SPD and SPU groups suggest that the analyzed ensemble could not accurately capture the relationship between the initial MSLP and MWS, which, combined with the unfavorable thermodynamic conditions at the initial time, resulted in the incorrect evolution of the intensity. Therefore, improving the initial conditions appears to be an effective way to address the SPD issue. Intensity spin‐down (SPD) has been identified as a major issue in numerical hurricane models because maximum wind speed can decrease significantly and artificially in the first few hours of a simulation, thereby degrading the remainder of the intensity forecast. Ensemble clustering analysis was performed to explore the role of the thermodynamic structure of the inner core in predicting the intensification of Hurricane Patricia (2015). Convection‐permitting ensemble forecasts are classified into SPD and spin‐up (SPU) groups based on the intensification rates for the first 6 hr. Several statistically significant differences between the SPD and SPU members were found in the wind–pressure relationship, the upper‐level warm core, moisture in the inner core, and secondary circulation. Particularly, the SPD issue may be partly caused by an imbalance in the initial wind–pressure relationship. Moreover, it also appears to be related to three main points: a weaker upper‐level warm core; a drier inner core at low levels; and a dry, cold air intrusion at mid‐levels. However, the intensification of the hurricane was sensitive to the upper‐level warm core, moisture in the inner core, and secondary circulation. Overall, thermodynamic structure of the inner core plays a key role in predicting the intensification of Hurricane Patricia (2015). The spin‐down issue may be partly caused by an imbalance in the initial wind–pressure relationshipIntensification of the hurricane was sensitive to the upper‐level warm core, moisture in the inner core, and secondary circulationThe thermodynamic structure of the inner core plays a key role in predicting the intensification of Hurricane Patricia (2015) The spin‐down issue may be partly caused by an imbalance in the initial wind–pressure relationship Intensification of the hurricane was sensitive to the upper‐level warm core, moisture in the inner core, and secondary circulation The thermodynamic structure of the inner core plays a key role in predicting the intensification of Hurricane Patricia (2015)

This study investigates the influence of the interannual variability of Indian Ocean tripole (IOT) on summertime cold extremes in Central Siberia. During positive IOT phases, two cross‐equatorial airflows are induced over the tropical eastern and western Indian Ocean. These strengthen ascending motion over the southern tropical Asia (80°–125°E, 15°–25°N), increasing precipitation in situ, as evidenced in observations and simulations by using Community Atmosphere Model. Serving as a heat source, the induced upper‐level Asian continent meridional teleconnection (ACMT) pattern transports the signals from southern tropical Asia into Central Siberia. Positive upper‐level geopotential height anomalies over Central Siberia induced by ACMT favor more solar radiation to reach the surface and raise local surface temperatures through modulating the tropospheric air expansion/compression, further reducing the extreme cold days. Consequently, the ACMT induced by latent heat fluxes associated with precipitation anomalies acts as an atmospheric bridge that links the IOT to Central Siberia cold extremes. The variability of temperature extremes in Central Siberia can lead to the tremendous social and economic losses. Understanding what drives and controls extreme temperatures over central Siberia is therefore important. This study found that the Indian Ocean tripole (IOT) mode, a unique interannual variability, has an important influence on summertime cold extremes over Central Siberia. Based on observations and numerical simulations, we conclude that precipitation anomalies over the southern tropical Asia region are strengthened by two cross‐equatorial airflows over the tropical eastern and western Indian Ocean. An upper‐level teleconnection induced by the precipitation related to the IOT transports the signal from southern tropical Asia into Central Siberia, affecting local circulation. The perturbed upper‐level circulations over the Central Siberia thus decrease the local cold extreme days. Improving our understanding of extreme temperatures in Central Siberia may strengthen forest protection and reduce local socioeconomic losses. Summertime cold extremes in Central Siberia are closely associated with the Indian Ocean tripole (IOT) patternAnomalous upper‐level geopotential height over Central Siberia inhibits local cold extremesAsian continent meridional teleconnection plays an atmospheric bridge between IOT and Central Siberia cold extremes Summertime cold extremes in Central Siberia are closely associated with the Indian Ocean tripole (IOT) pattern Anomalous upper‐level geopotential height over Central Siberia inhibits local cold extremes Asian continent meridional teleconnection plays an atmospheric bridge between IOT and Central Siberia cold extremes

Numerical forecasts always have associated errors. Analog correction methods combine numerical simulations with statistical analyses to reduce model forecast errors. However, identifying appropriate analogs remains a challenging task. Here, we use the Local Dynamical Analog (LDA) method to locate analogs and correct model forecast errors. As an example, an El Niño–Southern Oscillation (ENSO) intermediate coupled model forecast error correction experiment confirms that the LDA method locates high quality analogs of states of interest and improves the model forecast performance, which is due to the initial and evolution information included in the LDA method. In addition, the LDA method can be applied using a scalar time series, which reduces the complexity of the dynamical system. The LDA method is a promising method to locate dynamic analogs and can be applied to existing numerical models and forecast results. Earth‐science models are important tools in the analysis of physical processes and in forecasts of future conditions. However, numerical models always contain forecast errors. Model forecast error in historical data may appear again. Thus, the historical model forecast error can be used to correct the forecast results of focused states, which can reduce the model forecast error without building the new numerical model. The key question is how to locate suitable historical model forecast errors for the focused states. In this paper, we use the Local Dynamical Analog (LDA) method to locate the model forecast error and firstly correct the model forecast results. In the ENSO prediction experiment by an intermediate coupled model, the LDA is proved the advantage over other analog‐locate methods to find analogs and improve the whole forecast skill and the ENSO event forecast. The improvement from the LDA method in the root squared mean error skill is significant, and the forecast intensity of ENSO events is closer to observation than that of the uncorrected forecast. Analogs based on historical forecast can be used to estimate model forecast error and correct model forecastsThe Local Dynamical Analog method can locate high quality analogs based on the initial and evolutional informationAnalog correction using the Local Dynamical Analog improves ENSO forecast skill, significantly in root‐mean‐squared error skill