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Accurately estimating decadal predictability limits (PLs) is essential for advancing long-term climate predictions and understanding decadal-scale variability. This study combines the optimal local dynamic analog (OLDA) algorithm with the nonlinear local Lyapunov exponent (NLLE) method to estimate decadal PLs of oceanic and atmospheric variables, using long-term reanalysis datasets. Results demonstrate that the OLDA algorithm can enhance identification of analog states and improve PL estimation. The decadal PLs of sea surface temperature (SST) show regional and seasonal differences, with zonal mean values ranging from 8 to 17 years, and higher values in boreal summer and autumn, especially in the Northern Hemisphere and Southern Ocean. Sea level pressure (SLP) decadal PLs range from 8 to 11 years, exhibiting patchy distribution and seasonal variation. The global mean PL of SLP reaches about 10 years in boreal spring and 9 years in other seasons. SLP and SST PL distributions differ across seasons, reflecting the complexity of ocean–atmosphere interactions. Decadal PLs of major climate modes were also estimated, e.g., decadal PL of the SST Inter-Hemispheric Dipole (SSTID) is ~ 17 years, Atlantic Multidecadal Oscillation (AMO) ~ 14 years, Pacific Decadal Oscillation (PDO) ~ 13 years, North Atlantic Oscillation (NAO) ~ 16 years, Northern Hemisphere Annular Mode (NAM) ~ 11 years, and Southern Hemisphere Annular Mode (SAM) ~ 15 years. These modes display distinct predictability patterns and seasonal variations, highlighting their unique roles in regional climate dynamics. These findings enhance our understanding of decadal-scale predictability. [ABSTRACT FROM AUTHOR]

This study investigated the influence of interannual variations in tropical Indian Ocean tripole (IOT) on the surface air temperature (SAT) over the western Tibetan Plateau (TP) during boreal summer. During the positive phase of the IOT, two northward cross-equatorial airflows are induced over the tropical eastern and western Indian Ocean. These airflows reinforce the ascending motion over the southern tropical Asia (15°–25°N, 80°–125°E), increasing local precipitation, as confirmed by observations and simulations by the Community Atmosphere Model. The upper-level Asian Continental Meridional Teleconnection (ACMT) pattern is excited by the latent heat released from precipitation and transmits signals from the southern tropical Asia to the western TP, leading to the positive geopotential height anomalies and anomalous anticyclones over there. Upper-level circulation anomalies over the western TP enhance atmospheric thickness through adiabatic processes, consequently elevating local SAT. The ACMT associated with precipitation anomalies thus serves as an atmospheric bridge connecting the IOT and the SAT variations over the western TP. [ABSTRACT FROM AUTHOR]

Winter precipitation anomalies in South China (SC) frequently result in severe disasters. However, the evaluation of prediction performance and distinctions between positive precipitation anomaly events (PPA, wet condition) and negative precipitation anomaly events (NPA, dry condition) in current operational models remains incomplete. This study employed the Climate Forecast System version 2 (CFSv2) to assess winter precipitation prediction accuracy in SC from 1983 to 2021. Differences in predicting PPA and NPA events and the underlying physical mechanisms were explored. The results indicate that CFSv2 can effectively predict interannual variations in winter precipitation in SC, as there is a significant time correlation coefficient of 0.68 (0.62) between observations and predictions, with a lead time of 0 (3) months. The model revealed an intriguing asymmetry in prediction skills: PPA outperformed NPA in both deterministic and probabilistic prediction. The higher predictability of PPA, as indicated by the perfect model correlation and signal-to-noise ratio, contributed to its superior prediction performance when compared to NPA. Physically, tropical signals from the ENSO and extratropical signals from the Arctic sea ice anomaly, were found to play pivotal roles in this asymmetric feature. ENSO significantly impacts PPA events, whereas NPA events are influenced by a complex interplay of factors involving ENSO and Arctic sea ice, leading to low NPA predictability. The capability of the model to replicate Arctic sea ice signals is limited, but it successfully predicts ENSO signals and reproduces their related circulation responses. This study highlights the asymmetrical features of precipitation prediction, aiding in prediction models improvement. [ABSTRACT FROM AUTHOR]

This study focuses on regional extreme precipitation (REP) in North China. We found a trend turning in summer (July to August) REP frequencies and intensities from a decrease trend in 1961–2002 to an increase trend in 2003–2020, accompanied by a southward shift of the extreme rain belt, and an enhanced connection with the Ural blocking (UB) and the Western Pacific Subtropical High Pressure (WPSH) in 2003–2020. Rains in North China are accompanied by a west–east low–high dipole at upper troposphere. During the REP, the high of the low–high dipole at Northeast China (the NEH) is strongly amplified from a pre-existing stationary ridge over northeast Asia under the influence of eastward propagating Rossby wave energies along the subpolar/subtropical wave guide over Eurasia. For the REP years, an enhanced stationary ridge over the Ural Mountains in the period 2003–2020 replaces the stationary Ural trough in the period 1961–2002, favouring the development of the UB and leading to a change of the Rossby wave propagation path from along the subtropical waveguide in 1961–2002 to along the polar wave guide in 2003–2020. Therefore, a connection between the NEH and the UB forms, which may lead to a higher probability of extreme precipitation in North China since blocking is a major source of strong circulation anomalies. The mean summer WPSH expands more westward-northward in 2003–2020 than in 1961–2002, which provides a background conditions for a further westward-northward expanding of the daily WPSH under the influence of the NEH leading to a strong moisture transport from north Pacific. As a result, the intensity and probability of extreme precipitation over southern North China increase. [ABSTRACT FROM AUTHOR]

This study investigates the role of perturbation potential energy (PPE) in energetic connection between the South China Sea summer monsoon (SCSSM) and Indian Ocean dipole (IOD). When the SCSSM is strong during boreal summer, the higher and lower PPE anomalies controlled primarily by the diabatic heating correspond to negative and positive energy conversion, favoring the ascending and descending motions over western North Pacific (WNP) and southern Maritime Continent (SMC), respectively. This implies the existence of the regional Hadley circulation. This regional Hadley circulation-induced lower southeasterly wind anomalies reduce the local sea surface temperature (SST) anomalies over the tropical southeastern Indian Ocean via the wind–evaporation–SST and wind–thermocline–SST feedbacks, increasing the zonal SST gradient over the tropical Indian Ocean. Thus, a positive IOD event develops in boreal summer, and verse vice. Although the SCSSM decays during boreal autumn, the increased gradient of the PPE anomalies intensifies the anomalous Walker circulation over the tropical Indian Ocean, providing positive feedback that allows the IOD to mature. Consequently, the PPE dipole over WNP and SMC serves as an energetic bridge between the SCSSM and IOD. [ABSTRACT FROM AUTHOR]

El Niño and the Southern Oscillation (ENSO) have a worldwide impact on seasonal to yearly climate. However, there are decadal variations in the seasonal prediction skill of ENSO in dynamical and statistical models; in particular, ENSO prediction skill has declined since 2000. The shortcomings of models mean that it is very important to study ENSO seasonal predictability and its decadal variation using observational/reanalysis data. Here we quantitatively estimate the seasonal predictability limit (PL) of ENSO from 1900 to 2015 using Nonlinear local Lyapunov exponent (NLLE) theory with an observational/reanalysis dataset and explore its decadal variations. The mean PL of sea surface temperature (SST) is high in the central/eastern tropical Pacific and low in the western tropical Pacific, reaching 12–15 and 7–8 months, respectively. The PL in the tropical Pacific varies on a decadal timescale, with an interdecadal standard deviation of up to 2 months in the central tropical Pacific that has similar spatial structure to the mean PL. Taking the PL of SST in the Niño 3.4 region as representative of the PL in the central/eastern tropical Pacific, there are clearly higher values in the 1900s, mid-1930s, mid-1960s, and mid-1990s, and lower values in the 1920s, mid-1940s, and mid-2010s. Meanwhile, the PL of SST in the Niño 6 region—whose average value is 7 months—is in good agreement with the PL of most regions in the western tropical Pacific, with higher values in the 1910s, 1940s, and 1980s and lower values in the 1930s, 1950s, and mid-1990s. In the framework of NLLE theory, the PL is determined by the error growth rate (representing the dissipation rate of the predictable signal) and the saturation value of relative error (representing predictable signal intensity). We reveal that the spatial structure of the mean PL in the tropical Pacific is determined mainly by the error growth rate. The decadal variability of PL is affected more by the variation of the saturation value of relative error in the equatorial Pacific, whereas the error growth rate cannot be ignored in the PL of some regions. As an important source of predictability in ENSO dynamics, the relationship between warm water volume and SST in the Niño 3.4 region has a critical role in the decadal variability of PL in the tropical Pacific through the error growth rate and saturation value of relative error. This strong relationship reduces the error growth rate in the initial period and increases the saturated relative error, contributing to the high PL. [ABSTRACT FROM AUTHOR]

The global mean surface air temperature (GMST) shows multidecadal variability over the period of 1910–2013, with an increasing trend. This study quantifies the contribution of hemispheric surface air temperature (SAT) variations and individual ocean sea surface temperature (SST) changes to the GMST multidecadal variability for 1910–2013. At the hemispheric scale, both the Goddard Institute for Space Studies (GISS) observations and the Community Earth System Model (CESM) Community Atmosphere Model 5.3 (CAM5.3) simulation indicate that the Northern Hemisphere (NH) favors the GMST multidecadal trend during periods of accelerated warming (1910–1945, 1975–1998) and cooling (1940–1975, 2001–2013), whereas the Southern Hemisphere (SH) slows the intensity of both warming and cooling processes. The contribution of the NH SAT variation to the GMST multidecadal trend is higher than that of the SH. We conduct six experiments with different ocean SST forcing, and find that all the oceans make positive contributions to the GMST multidecadal trend during rapid warming periods. However, only the Indian, North Atlantic, and western Pacific oceans make positive contributions to the GMST multidecadal trend between 1940 and 1975, whereas only the tropical Pacific and the North Pacific SSTs contribute to the GMST multidecadal trend between 2001 and 2013. The North Atlantic and western Pacific oceans have important impacts on modulating the GMST multidecadal trend across the entire 20th century. Each ocean makes different contributions to the SAT multidecadal trend of different continents during different periods. [ABSTRACT FROM AUTHOR]

Here, we explored in depth the relationship among the deterministic prediction skill, the probabilistic prediction skill and the potential predictability. This was achieved by theoretical analyses and, in particular, by an analysis of long-term ensemble ENSO hindcast over 161 years from 1856 to 2016. First, a nonlinear monotonic relationship between the deterministic prediction skill and the probabilistic prediction skill, derived by theoretical analysis, was examined and validated using the ensemble hindcast. Further, the co-variability between the potential predictability and the deterministic prediction skill was explored in both perfect model assumption and actual model scenario. On these bases, we investigated the relationship between the potential predictability and probabilistic prediction skill from both the practice of ENSO forecast and theoretical perspective. The results of the study indicate that there are nonlinear monotonic relationships among these three kinds of measures. The potential predictability is considered to be a good indicator for the actual prediction skill in terms of both the deterministic measures and the probabilistic framework. The relationships identified here exhibit considerable significant practical sense to conduct predictability researches, which provide an inexpensive and moderate approach for inquiring prediction uncertainties without the requirement of costly ensemble experiments. [ABSTRACT FROM AUTHOR]

Nonlinear local Lyapunov vectors (NLLVs) are the nonlinear extension of the Lyapunov vectors (LVs) based on linear error growth theory. As a development of bred vectors (BVs), NLLVs retain the time-saving, simply-applied and flow-dependent advantages of BVs. However, unlike BVs, NLLVs correspond not only to the leading LV but also to other orthogonal LVs. In this paper, NLLVs are applied to the Zebiak-Cane (ZC) coupled model. First, using the analysis data from the ensemble Kalman filter, we explore the effect of the parameters of the breeding process on calculating the NLLVs. It is found that the statistical properties of NLLVs are not very sensitive to the breeding parameters. However, the higher NLLVs (i.e., excluding NLLV1) show temporal randomness. Then, we study the characteristics of the spatial structures and growth rates of different NLLVs. The different NLLVs each have a certain probability of being the fastest error growth direction and together construct the error growth subspace of the ZC model. Compared with BVs, the NLLVs have some advantages in terms of the relationship between the generated error growth subspace and the analysis errors. The NLLVs also have higher local dimensionality than the BVs. NLLVs, as initial ensemble perturbations, are applied to the ensemble prediction of ENSO in a perfect environment. Compared with the results obtained using ensembles employing the random perturbation technique and the BV method, the present results demonstrate the advantages of using the NLLV method in ensemble forecasts. [ABSTRACT FROM AUTHOR]