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The Indian Ocean tripole (IOT) is an independent mode of ocean–atmosphere circulation centered on the tropical Indian Ocean. This study explores the physical mechanisms of the IOT affecting the western United States climate variation during the boreal summer. We find that the IOT is significantly correlated with both western United States summer surface temperature and precipitation anomalies. During positive IOT events, the westerly wind anomalies over the northern Indian Ocean are intensified by two cross-equator airflows over the tropical eastern Indian Ocean and the east coast of Africa. The resulting convergence of air over the northern Bay of Bengal–Indochina Peninsula–northern South China Sea (NBB–IP–NSCS) region (15°–25°N, 80°–125°E) exacerbates the surplus precipitation there. Serving as a heat source, these NBB–IP–NSCS precipitation anomalies can excite a circumglobal teleconnection (CGT)-like pattern that propagates eastward from west-central Asia toward North America along the Asia subtropical westerly jet, further influencing local circulation anomalies. Development of strong anticyclonic circulation over the western United States enhances descending motion and divergence there, resulting in negative precipitation anomalies. This circulation anomaly also induces the diabatic heating anomalies through allowing more solar radiation to reach the ground surface, further increasing the surface temperature anomalies. Meanwhile, the increased tropospheric temperature also raises local surface temperatures by modulating the adiabatic air expansion and compression. Ultimately, the CGT-like pattern associated with NBB–IP–NSCS precipitation anomalies sets up an atmospheric bridge by which the IOT can impact summer climate in the western United States.

This study focuses on regional extreme precipitation (REP) in North China. We found a trend turning in summer (July and 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 and the Western Pacific Subtropical High Pressure (WPSH) in 2003–2020. Rains in North China are accompanied by a west-east low-high dipole (LHD) at upper troposphere. During the REP, the high of the LHD at Northeast China (the NEH) is strongly amplified from a pre-existing stationary ridge at 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 Ural Blockings (UB) and leading to a change of the Rossby wave propagation path from along the subtropical wave guide 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, providing the background conditions for further westward-northward expanding of the daily WPSH under the influence of the NEH before the REP day leading to strong moisture transport from north Pacific and increased intensity and probability of extreme precipitation over southern North China.

This study focuses on regional extreme precipitation (REP) in North China. We found a trend turning in summer (July and August) REP frequencies and intensities from a decrease trend in 1961-2002 to an increase trend in 2003-2020, accompanying which, the extreme rain belt shows a southward shift, and the connections of the REP with Ural blocking and with the western Pacific subtropical high (WPSH) are enhanced in 2003-2020. The low and high pressures at west and east of North China (the low-high dipole, LHD) leads to northward moisture transport, air rising and rain there. During the REP the high pressure of the LHD at Northeast China (the NEH) is strongly amplified through the forcing of Rossby wave energy propagating along the zonal subpolar/subtropical wave guide over Eurasian on the preexisting northeast Asia stationary ridge. In 2003-2020, the enhancement of Ural stationary ridge favors the development of Ural blockings (UB), and leads to a change of the eastward Rossby wave propagating path from along the subtropical wave guide in 1961-2002 to along the polar wave guide in 2003-2020, which connects the UB and the LHD, leads to a strengthened-and- northeastward-shift/southwestward-shift of the low/high of the LHD. The JA mean WPSH expands more westward-northward in 2003-2020 than in 1961-2002. It provides a condition for a further westward-northward expanding of the daily WPSH under the influence of the NEH before the REP, which brings strong moisture from north Pacific and results in a strengthening of extreme rain over southern North China.

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.

The Victoria mode (VM) is the second dominant sea surface temperature mode in the North Pacific, forced by North Pacific Oscillation–like extratropical atmospheric variability. Observational studies have shown that the boreal spring VM is closely connected to the following winter El Niño, with the VM efficiently acting as a precursor signal to El Niño events. This study evaluates the relationship of the spring VM with subsequent winter El Niño in the preindustrial simulations of phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). We found that most CMIP5 and CMIP6 models can simulate the basic characteristics of the VM reasonably well. The current CMIP6 models simulate the VM–El Niño connections more realistically as compared to the earlier CMIP5 models. The analysis further suggests that the improved capability of the CMIP6 models to simulate the VM–El Niño relationship is because the CMIP6 models are better able to capture the VM-related surface air–sea thermodynamic coupling process over the subtropical/tropical Pacific and the seasonal evolution of VM-related anomalous subsurface ocean temperature in the equatorial Pacific.

Numerical seasonal forecasts in Earth science always contain forecast errors that cannot be eliminated by improving the ability of the numerical model. Therefore, correction of model forecast results is required. Analog correction is an effective way to reduce model forecast errors, but the key question is how to locate analogs. In this paper, we updated the local dynamical analog (LDA) algorithm to find analogs and depicted the process of model error correction as the LDA correction scheme. The LDA correction scheme was first applied to correct the operational seasonal forecasts of sea surface temperature (SST) over the period 1982–2018 from the state-of-the-art coupled climate model named NCEP Climate Forecast System, version 2. The results demonstrated that the LDA correction scheme improves forecast skill inmany regions as measured by the correlation coefficient and root-mean-square error, especially over the extratropical eastern Pacific and tropical Pacific, where the model has high simulation ability. El Niño–Southern Oscillation (ENSO) as the focused physics process is also improved. The seasonal predictability barrier of ENSO is in remission, and the forecast skill of central Pacific ENSO also increases due to the LDA correction method. The intensity of the ENSOmature phases is improved.Meanwhile, the ensemble forecast results are corrected, which proves the positive influence from this LDA correction scheme on the probability forecast of cold and warm events. Overall, the LDA correction scheme, combining statistical and model dynamical information, is demonstrated to be readily integrable with other advanced operational models and has the capability to improve forecast results.

This study investigates the connection between the North Pacific Victoria mode (VM) during the boreal spring [February–April (FMA)] and the following boreal winter [January–March (JFM)] rainfall over South China (SC). The VM is defined as the second empirical orthogonal function mode (EOF2) of sea surface temperature (SST) anomalies (SSTAs) in the North Pacific poleward of 20°N. It is found that the boreal spring VM has a significant positive correlation with the following winter rainfall over SC. Analyses indicate that a strong positive VM during spring can induce El Niño during the following winter via an air–sea interaction, resulting in the generation of an anomalous anticyclone over the western North Pacific (WNPAC). The anomalous southwesterlies along the southeast coast of East Asia associated with the WNPAC favor an abundant supply of water vapor and anomalous ascending motion over SC. As a result, winter rainfall over SC increases. A linear regression model based on the VM shows that the VM can act as an effective predictor of winter rainfall over SC about 1 year in advance. It also has a higher prediction skill than ENSO in predicting winter rainfall over SC.

This paper examines the differences in the troposphere-stratosphere coupling during extreme and moderate El Niño events by considering the wave geometry of the stratospheric vortex, using two-dimensional moment diagnostics to measure the geometry of the vortex. Based on reanalysis data for 1979–2020 and sensitivity runs with the Whole Atmosphere Community Climate Model version 4 (WACCM4), we demonstrate that the Arctic polar vortex exhibits larger variability throughout the late winter to the early spring season in terms of the vortex geometry during extreme El Niño than that during moderate El Niño. Although both equatorward displacement and splitting of the vortex are detected during moderate and extreme El Niño events, split vortex events show larger occurrences than displaced vortex events in the case of extreme El Niño. This is in agreement with stronger tropospheric responses to extreme El Niño manifested in zonal mean signals in the tropospheric jet stream and surface temperature. The geometric characteristics of the vortex, being incoherent with the zonal mean results, show that disturbed vortex during extreme El Niño penetrate deeper into the troposphere, while anomalies associated with moderate El Niño do not decent far below the tropopause.

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.

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.

Using the observational data and the Coupled Model Intercomparison Project Phase 5 (CMIP5) models to show the influence of the North Pacific Victoria mode (VM) on the Madden–Julian Oscillation (MJO) are examined in this analysis. The results show that the February–April (FMA) VM had a significant influence on the development and propagation of the MJO over the equatorial central–western Pacific (ECWP) during spring (March–May) between 1979 and 2017. Specifically, MJO development was favored more by positive VM events than negative VM events. One probably description for these complicated connections is that the SST gradient anomalies associated with positive VM events enhance the convergence of low-level over the ECWP, which, combined with the warm SST anomalies (SSTAs) in the equatorial central Pacific that lead to a boost in the Kelvin wave anomalies, results in the enhanced MJO activity over the ECWP. These conclusions indicate that the VM is an important factor in MJO diversity.

Trend turning (or trend change) is a type of structural change that is common in climate data, and methods for detecting it in time series with multiple turning‐points need to be developed. A recently developed method for this, the running slope difference (RSD) t‐test, examines trend differences in sub‐series of the sample time series to identify the trend turning‐points. In this paper, we use Monte Carlo simulation to evaluate this method's detection ability. Evaluation results show the method to be an effective tool for detecting trend turning time series and identify three major advantages of the RSD t‐test: ability to detect multiple turning‐points, capacity to detect all three types of trend turning, and great performance of reducing false alarm rate.

Numerical forecasts always have associated errors. Analogue correction methods combine numerical simulations with statistical analyses to reduce model forecast errors. However, identifying appropriate analogues remains a challenging task. Here, we use the Local Dynamical Analog (LDA) method to locate analogues and correct model forecast errors. As an example, an ENSO model forecast error correction experiment confirms that the LDA method locates more dynamical analogues of states of interest and better corrects forecast errors than do other methods. This is because the LDA method ensures similarity of the initial states and the evolution of both states. In addition, the LDA method can be applied using a scalar time series, which reduces the complexity of the dynamical system. Model forecast error correction using the LDA method provides a new approach to correcting state-dependent model errors and can be readily integrated with other advanced models.

Global mean surface air temperature (GMT) rose roughly 0.85 °C from 1880 to 2012 (IPCC 2013), attributing mainly to an increase in atmospheric greenhouse gases. For different decadal timescale periods in the past 100 years, the warming rate of different periods may significantly different. For example, IPCC AR1 (1990) point out that GMT between 1910-1940 and 1975-1990 are significantly warming, meanwhile GMT stay nearly constant between 1940 and early 1970. The phenomenon of two nearby periods showing significantly different trends is knowing as trend turning, this phenomenon is common in climate time series and crucial when climate change is investigated. However, the available detection methods for climate trend turnings are relatively few, especially for the methods which have the ability of detecting multiple trend turnings. We propose a new methodology named as the running slope difference (RSD) t-test to detect multiple trend turnings. This method employs a t-distributed statistic of slope difference to test the sub-series trends difference of the time series, thereby identify the turning-points. We compare the RSD t-test method with some other existing trend turning detection methods with an idealized time series case and several climate time series cases. And we also report the Monte Carlo simulation used to evaluate this method’s detection ability. Results show that the RSD t-test method is an effective tool for detecting trend turning in time series, and this method has three major advantages: ability to detect multiple turning-points, capacity to detect all three types of trend turning, and great performance of avoiding false alarm.

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.

This study examines the differences between the North American dipole (NAD) and the North American Oscillation (NAO) in terms of their spatial structure, temporal variations, and climate impacts. The results indicate that the sea level pressure anomalies associated with the NAD are located in more western and southern areas than those associated with the NAO, and that the NAD has its own temporal variability. In addition, the NAD has a greater influence on sea surface temperature (SST) and precipitation anomalies in the northern tropical Atlantic (NTA) than the NAO does in the North Atlantic. In the tropical Pacific, the NAD tends to be more effective in forcing SST warming during spring in the northeastern subtropical Pacific (NESP). This can extend equatorward to reach the equatorial central Pacific in the autumn, finally leading to a central Pacific (CP)-type El Ni&ntilde, o event. In contrast, the NAO induces only weak SST warming over the NESP, so that a CP-type El Ni&ntilde, o event does not occur. Additional analysis indicates that the influence of the NAO can pass to the tropical Pacific only when the NAD and NAO have the same sign, suggesting that the NAD may serve as an important bridge linking the NAO to El Ni&ntilde, o&ndash, Southern Oscillation (ENSO).

Vector quantities, e.g., vector winds, play an extremely important role in climate systems. The energy and water exchanges between different regions are strongly dominated by wind, which in turn shapes the regional climate. Thus, how well climate models can simulate vector fields directly affects model performance in reproducing the nature of a regional climate. This paper devises a new diagram, termed the vector field evaluation (VFE) diagram, which is a generalized Taylor diagram and able to provide a concise evaluation of model performance in simulating vector fields. The diagram can measure how well two vector fields match each other in terms of three statistical variables, i.e., the vector similarity coefficient, root mean square length (RMSL), and root mean square vector difference (RMSVD). Similar to the Taylor diagram, the VFE diagram is especially useful for evaluating climate models. The pattern similarity of two vector fields is measured by a vector similarity coefficient (VSC) that is defined by the arithmetic mean of the inner product of normalized vector pairs. Examples are provided, showing that VSC can identify how close one vector field resembles another. Note that VSC can only describe the pattern similarity, and it does not reflect the systematic difference in the mean vector length between two vector fields. To measure the vector length, RMSL is included in the diagram. The third variable, RMSVD, is used to identify the magnitude of the overall difference between two vector fields. Examples show that the VFE diagram can clearly illustrate the extent to which the overall RMSVD is attributed to the systematic difference in RMSL and how much is due to the poor pattern similarity.