Xuguang Sun

Under global warming, North Atlantic sea surface temperatures (SSTs) have warmed highly non-uniformly, yet the role of evolving SST spatial structure in driving large-scale atmospheric reorganizations remains poorly understood. Here, we reveal a pronounced meridional reversal in North Atlantic SST trends around the mid-2000s: accelerated mid-latitude warming replaced earlier subpolar-dominated warming, effectively flipping the basin-wide meridional SST gradient. Focusing on internal climate variability, this oceanic shift was accompanied by a coherent atmospheric cascade, marked by a poleward migration of the North Atlantic mid-latitude westerly jet, a reorganization of Rossby waveguide pathways, and a pronounced shift of the Eurasian teleconnection pattern. Critically, the extreme cold event hotspots exhibited a progressive southward displacement across Eurasia throughout the successive research phases. These changes reflect a multi-stage reorganization of hemispheric circulation modulated by the continuous dynamic evolution of internal atmospheric variability distinct from the global warming signal. Our results suggest that robust climate attribution and future risk assessment must explicitly account for the continuous dynamic evolution of spatial SST patterns and their localized gradients, rather than relying on static basin-mean metrics.

Northwest China (NWC) is warming far faster than the global average and is highly vulnerable to climate change. Spring‐summer temperature anomalies in NWC critically affect ecosystems and socioeconomics, yet their cross‐seasonal coherence and mechanisms remain unclear. Using ERA5 data (1959–2025), Season‐reliant Empirical Orthogonal Function analysis identifies the Persistent Uniform Warming Regime (PUWR) as the dominant spring‐summer mode, featuring distinct interannual and interdecadal variability. Climate variability associated with PUWR exhibit phase asymmetry and timescale dependence: during its warm phase, PUWR increases extreme heat events and reduces interannual spring precipitation on the interannual timescale, but increases spring precipitation on the interdecadal timescale; conversely, its cold phase amplifies interdecadal extreme cold events. The PUWR warm phase is driven by persistent local high‐pressure anomalies, which induce adiabatic subsidence and diabatic heating in spring and warm advection in summer on the interannual timescale, while diabatic heating dominates in both seasons on the interdecadal timescale. These high‐pressure systems are embedded within atmospheric teleconnection wave trains emanating from midlatitude North Atlantic and North Pacific, driven by persistent sea surface temperature gradients. On the interannual timescale, two distinct wave trains propagate meridionally and zonally, respectively. On the interdecadal timescale, the wave trains are linked to the Atlantic Multidecadal Oscillation and Pacific Decadal Oscillation, exhibiting meridional propagation in spring and zonal propagation in summer. All observed wave trains are well reproduced in linear baroclinic model experiments with combined midlatitude vorticity forcing. This study systematically elucidates the multiscale characteristics and mechanisms of PUWR, advancing our understanding of temperature variability and supporting improved regional climate prediction in NWC.

Seasonal droughts and floods are among the world’s most severe natural disasters, threatening socioeconomic development and human life and property, making accurate seasonal prediction a key need for disaster risk reduction. Yet, the high complexity of precipitation anomalies driven by multi-scale variations, multiple factors, and atmospheric nonlinear chaos, which makes such forecasts a global challenge. Climate modes (e.g., ENSO), as major drivers of seasonal precipitation anomalies, are essential predictors. Systematically assessing their contributions to predictability and developing prediction methods based on their influence are therefore of great scientific and practical value for understanding and forecasting seasonal droughts and floods.Addressing seasonal rainfall prediction challenges, the SMART, the acronym for Singular predictable climate Modes (SM) and Anomalous Relative Tendency (ART), climate prediction principle is proposed by Prof. Xiu-Qun Yang from Nanjing University. It contains 4 major steps: 1. Online temporal-scale separation (ART); 2. Extracting optimal singular climate modes (SM); 3. Constructing SMART model based on SM and ART; 4. Predicting with ART and Recent Background Anomalous (RBA). This study develops two ensemble prediction methods based on SMART principle, which combine the impacts of climate modes on China’s flood-season precipitation anomalous relative tendencies (ART) with multiple artificial intelligence (AI) models and multi-parameter perturbation scheme, including the SMART Optimal combined Multiple AI Method (OMAI) and SMART Ensemble AI Method (EAI). These two methods demonstrate significant predictive skill improvements over MME direct predictions. For example, using 160 stations historical precipitation data in China and historical circulation datasets, multiple key tropical and extratropical climate modes affecting to the ART of China’s flood-season (JJA) precipitation are extracted by SVD method. The SMART-OMAI method integrates these modes with multiple AI models, while SMART-EAI incorporates multi-parameter perturbations with LSTM model. Independent validation for flood-season precipitation anomalies in China during 1994-2016 via these two methods, yields PS scores of 76.5 and 76.4, respectively over 5% higher than dynamical-statistical models (73.2) and over 20% better than direct MME direct predictions (63.5). Anomaly correlation coefficients reach 0.16 and 0.14, marking qualitative improvements over MME direct predictions (0.01), with notable temporal correlation enhancements in North China and Northeast China. By merging the physical basis of the climate modes with non-linear predictive strengths of AI models, these two AI-based prediction methods offer a scientifically robust and practical solution, that is, a SMART solution for seasonal rainfall prediction in China.

This study examines the cross‐seasonal influence of the Arctic Oscillation (AO) on winter El Niño, using atmospheric and oceanic reanalysis data sets. It has been revealed that the linkage between positive AO and subsequent El Niño is closest during spring, indicating the seasonal dependent and selective impact of AO on ENSO. Composition analyses illustrate that positive spring AO is often associated with a dipole circulation anomaly over the North Pacific. The corresponding cyclonic circulation anomaly in the subtropical North Pacific initiates the Pacific Meridional Mode (PMM), which persists and evolves through the wind‐evaporation‐SST (WES) feedback from, triggering the westerly anomalies in the tropical western Pacific and subsequently triggering the onset of El Niño. Further clustering analysis reveals that the positive spring AO events exhibit two distinct spatial patterns: Type1 with prominent positive SLP anomaly over the midlatitude North Pacific and Type2 with the positive SLP anomaly over the North Atlantic. Both types can trigger El Niño by stimulating the subtropical cyclone anomaly in the North Pacific but through different mechanisms. In Type1 events, high‐pressure anomalies in the midlatitude North Pacific trigger southward‐propagating atmospheric waves, generating the cyclone anomalies in the subtropical North Pacific. Although in Type2 events, the AO‐related circulation anomalies in the North Atlantic stimulate teleconnection wave trains that cross the Eurasian continent and propagate southward to the subtropical North Pacific, inducing the cyclone anomalies. However, Type1 events occur more frequently and are more closely linked to El Niño, highlighting the significant role of air‐sea coupling processes in the North Pacific in triggering El Niño.

Arctic Oscillation (AO) is the dominant mode of atmospheric circulation in the extratropical Northern Hemisphere regions. The spatial pattern of the AO Arctic center affects the extent of polar cold air outbreaks southward. However, the underlying nature and causes of its interdecadal variation remain unclear. Utilizing ERA5 reanalysis data, this study identifies two distinct spatial patterns of the wintertime AO Arctic center through the K‐means clustering method, which alternate over different decade periods. The Double‐trough pattern generates a tripolar temperature pattern of “cold Arctic‐warm Eurasia‐cold Tibetan Plateau” through a Rossby wave train during 1960–1997/2013–2024. While the Single‐trough pattern leads to a dipolar temperature pattern in 1998–2012. Furthermore, interdecadal variations in North Atlantic sea surface temperature meridional gradient act as an influencing factor in shaping the spatial pattern of the AO Arctic center. This research aids the understanding and prediction of climate anomalies using AO signals within various decadal contexts.

Prior research extensively investigates the delayed influence of El Niño‐Southern Oscillation (ENSO) events on subsequent summer climates, with persistent sea surface temperature anomalies (SSTAs) in remote tropical oceans serving as crucial pathways. This study unveils a previously overlooked midlatitude pathway. During the developing winter, El Niño events induce basin‐scale cold SSTAs in the central North Pacific, which can persist into the following summer. These anomalies significantly influence early‐summer atmospheric circulation by enhancing atmospheric baroclinicity and transient eddy activities. Primarily driven by transient eddy vorticity forcing, an equivalent barotropic geopotential low anomaly emerges over the North Pacific. Enhanced by the southwesterly winds of the atmospheric low, tropical moisture is transported farther northeastward in the early summer, resulting in increased rainfall in the Pacific Northwest region. By elucidating this meridional pathway, our study advances the understanding of ENSO's delayed impacts and associated dynamical processes, in which the midlatitude oceanic feedbacks are emphasized.

The long‐term memory of the ocean makes sea surface temperature anomalies (SSTAs) become a significant predictor for the subsequent atmosphere, and the tropical ocean is primarily regarded as a major source of atmospheric anomalies. While in North Pacific, the local midlatitude SSTAs also have large contributions but have not been adequately considered yet. We discover a strong connection between the Kuroshio‐Oyashio Extension (KOE) SSTAs in spring and the local atmospheric circulation anomalies in following summer at interannual timescale, wherein, the spring KOE SSTAs are generally independent of tropical ocean, and they are primarily induced by the concurrent atmospheric anomalies via surface heat flux and ocean dynamic processes. The spring KOE SSTAs can persist to summer, and then generate nearly reversed whole‐layer atmospheric circulation anomalies in their north side through both diabatic heating and atmospheric transient eddy forcing. Consequently, precipitation anomalies in Pan‐Pacific regions are distinctly modulated from spring to summer.

Although the summers of 2021 and 2022 are both in the two successive La Niña decaying stages and under the same climate background of negative Pacific Decadal Oscillation (PDO) phase and global warming trend, they exhibit significantly different and even opposite precipitation patterns in the Yangtze River Valley (YRV) as well as in the Indian monsoon region (IMR). In contrast to the abundant precipitation and lower temperature in the YRV in summer 2021, in summer 2022 the YRV experiences severe drought and extremely high temperatures, which is also accompanied by Mega‐floods in the IMR. This study identifies the joint influence of sea surface temperature anomalies (SSTAs) in Niño4 and Barents Sea (BS) regions as the underlying cause for the contrast YRV precipitation anomalies in the summers of 2021 and 2022. Specifically, the cold SSTAs in both Niño4 and BS regions in summer 2021 favor stronger and southward shifted western North Pacific subtropical high (WNPSH), leading to more precipitation in the YRV, which is however generally reversed but more intense in summer 2022 because of the synergistic effect of cold Niño4 and warm BS SSTAs. Moreover, the induced extreme precipitation in the IMR in summer 2022, which is absent in summer 2021 due to the offsetting effect of cold SSTAs in both Niño4 and BS regions, in turn further strengthens the anomalous atmospheric circulations via its released large diabatic heating and serves as a relay pathway for the dramatic drought and heat wave in the YRV.

Eurasian teleconnection pattern (EU) and its two variants (EU1 and EU2) are the representative wintertime atmospheric teleconnections over Eurasian continent. They are mainly indicative of the local features and closely related to other teleconnection patterns. What are the major interannual climate modes over mid‐high latitude Eurasia in boreal winter is still an open question. With the ERA5 reanalysis data sets after removing the linear impact of El Niño‐Southern Oscillation (ENSO), three wintertime climate modes over mid‐high latitude Eurasia are identified by the first three empirical orthogonal function (EOF) modes of the anomalous relative tendency (ART) of 500 hPa geopotential height. They approximately explain 75% of the interannual variance in total. The three climate modes have combined features of EU‐like patterns with Arctic Oscillation (AO), North Atlantic Oscillation (NAO) and West Atlantic (WA) teleconnections, respectively, and they are named EU‐AO, EU‐NAO and EU‐WA climate modes accordingly. All the three climate modes originate mainly from the North Atlantic and demonstrate clear Rossby wave trains downstream to East Asia along the great circle route, and they can be primarily stimulated and maintained by positive air‐sea feedback over North Atlantic regarding to the obvious North Atlantic tripole‐like sea surface temperature patterns. Interannual climate variations over the most Eurasian continent are strongly linked to and well reproduced by the three ENSO‐independent climate modes, which can be applied as the important signals for monitoring and predicting winter interannual climate variabilities over mid‐high latitude Eurasia.