Tropical ocean teleconnections with gross primary productivity of monsoon-Asia

The intricate oceanic climate interactions with terrestrial primary production of Asian ecosystems exert crucial social-economical-environmental repercussions. Yet, a holistic understanding of tropical sea surface temperature (SST) anomalies associated with the gross primary productivity (GPP) variations of monsoon-Asia remains constrained. This study provides a statistical framework demonstrating how SST perturbations in the tropics influence GPP fluctuations in monsoon-Asia by modulating hydrothermal conditions of different climate system components. Observation evidence explicitly illustrated the characteristic anomalous SST signatures of positive and negative GPP anomalies in South and Southeast Asia during June-August. The SST anomalies of the central-eastern tropical Pacific showed a robust negative impact on the GPP variability of South-Asia. The GPP alterations in maritime-Southeast-Asia exhibited strong connections with SST anomalies of the western Pacific (positive) and eastern equatorial Pacific (negative). The oceanic signals in the GPP variability of South-Asia and maritime-Southeast-Asia mirrored canonical El Niño and La Niña patterns. The detected SST-GPP link is feasible through large-scale atmospheric circulation variability and the consequent regional modulation of heat and moisture fluxes. The anomalous strengthening (weakening) of Walker cell enhances (reduces) water availability to plants for photosynthesis during the La Niña (El Niño) phase of the ENSO cycle and thus elevates (lowers) GPP in South-Asia and Maritime-southeast-Asia. In contrast, the enhanced GPP anomaly in mainland-Southeast-Asia depicts signs of canonical La Niña and Indian Ocean subtropical dipole (IOSD) teleconnections. The positive impact of IOSD was through the modulation of the Mascarene High and the consequent impact on the monsoon. Meanwhile, decreased GPP bears the imprint of El Niño Modoki and warm tropical Indian Ocean SSTs. The atmospheric teleconnections demonstrated the delayed impact of El Niño Modoki on GPP variability through the Indian Ocean capacitor effect. Our findings could be instrumental in forecasting the probable effects on vegetation growth in monsoon-Asia associated with high-frequency tropical oceanic changes.

Reconstruction of ENSO variability using the standardized growth index of a Tridacna shell from Yongshu Reef, South China Sea

El Niño-Southern Oscillation (ENSO) is the strongest signal of global interannual climate anomaly and reconstructing past ENSO variations using high-resolution paleoclimate archives can improve our understanding of ENSO variability, as well as improve our ability to predict future climate changes. Here, a daily resolution standardized growth index (SGI) was established using a giant clam (Tridacna spp.) shell specimen MD2 (life span: 1994-2013 CE), collected from the Yongshu Reef, southern South China Sea (SCS). The cross-spectral and correlation analysis indicated that the SGI variation of MD2 was strongly influenced by ENSO variability on an interannual timescale. Tridacna spp. is in symbiosis with zooxanthellae, and its growth index is usually modulated by the photosynthetic efficiency of zooxanthellae. During the El Niño (La Niña) period, the convective anomalies stimulated in western Pacific would increase (decrease) the effective solar radiation on Yongshu Reef, and in turn influence the photosynthesis rate of zooxanthellae and enzyme activity for the calcification site and thus the SGI of giant clam MD2. The SGI can explain 54.7 % of ENSO variance, demonstrating the potential for Tridacna SGI in ENSO reconstruction. Compared with conventional ENSO reconstruction using high-resolution geochemical proxies, the method of giant clam SGI is rapid and economical.

A distinct and reproducible teleconnection pattern over North America during extreme El Niño events

El Niño-Southern Oscillation (ENSO) teleconnections are an important predictability source for extratropical seasonal climate forecasts. Previous studies suggest that the ENSO teleconnection pattern depends on the ENSO phase (El Niño vs. La Niña) and/or Sea Surface Temperature (SST) pattern (central Pacific vs. eastern Pacific El Niño events). Observations and ensemble simulations with the CNRM-CM6.1 atmospheric general circulation model indicate that only extreme El Niño events (e.g. 1982-1983, 1997-1998, 2015-2016) display a statistically significant eastward shift relative to the well-known Pacific-North American teleconnection pattern that occurs during both central and eastern Pacific moderate El Niño or during La Niña. This specific teleconnection pattern emerges when equatorial SST anomalies are both eastward-shifted and sufficiently large to exceed the deep atmospheric convection threshold over most of the eastern Pacific, resulting in a basin-wide reorganization of tropospheric heat sources. It yields> 0.5 std wet conditions over Western United States (74% likelihood) as well as> 0.5 std warm anomalies over Canada and the Northern United States (71% likelihood), with more consistency across events and ensemble members than for any other El Niño or La Niña type. These findings hold important implications for the seasonal forecasting of El Niño's impacts on the North American climate.

Recent increases in tropical cyclone rapid intensification events in global offshore regions

Rapid intensification (RI) is an essential process in the development of strong tropical cyclones and a major challenge in prediction. RI in offshore regions is more threatening to coastal populations and economies. Although much effort has been devoted to studying basin-wide temporal-spatial fluctuations, variations of global RI events in offshore regions remain uncertain. Here, we show that compared with open oceans, where the annual RI counts do not show significant changes, offshore areas within 400 km of the coastline have experienced a significant increase in RI events, with the count tripling from 1980 to 2020. Furthermore, thermodynamic environments present more favorable conditions for this trend, and climate models show that global ocean warming has enhanced such changes. This work yields an important finding that an increasing threat of RI in coastal regions has occurred in the preceding decades, which may continue under a future warming climate.

Seasonally-reversed trends in the subtropical Northwestern Pacific linked to asymmetric AMO influences

This study identifies seasonally-reversed trends in Kuroshio strength and sea surface temperatures (SSTs) within the western North Pacific (WNP) since the 1990s, specifically in the 22° N-28° N region. These trends are characterized by increases during summer and decreases during winter. The seasonally-reversed trends are a result of the asymmetric responses of the WNP to a shift towards the positive phase of the Atlantic multidecadal oscillation (AMO) around the same period. The positive AMO induces an anomalous descent over the North Pacific during summer, leading to the direct strengthening of the gyre. However, during winter, it triggers an anomalous descent over the tropical Pacific, which excites a poleward wavetrain impacting the WNP and causing gyre weakening. The associated responses of the East Asian monsoon and China Coastal Current contribute to the observed seasonally-reversed SST trends. It is noteworthy that the seasonally-reversed trends in gyre strength and SSTs are predominantly observed north of 20° N in the WNP. This limitation arises because the anomalous cyclone within the winter poleward wavetrain is located north of this latitude boundary. Specifically, the clearest trends in gyre strength are observed in the northern segment of the Kuroshio, while the manifestation of SST trends in the Taiwan Strait could potentially be attributed to the influence and enhancement of the East Asian monsoon and the China Coastal Current. Due to the limited length of observational data, statistical significance of some of the signals discussed is rather limited. A CESM1 pacemaker experiments is further conducted to confirm the asymmetric responses of the North Pacific to the AMO between the summer and winter seasons.

Increasingly negative tropical water-interannual CO2 growth rate coupling

Terrestrial ecosystems have taken up about 32% of the total anthropogenic CO2 emissions in the past six decades1. Large uncertainties in terrestrial carbon-climate feedbacks, however, make it difficult to predict how the land carbon sink will respond to future climate change2. Interannual variations in the atmospheric CO2 growth rate (CGR) are dominated by land-atmosphere carbon fluxes in the tropics, providing an opportunity to explore land carbon-climate interactions3-6. It is thought that variations in CGR are largely controlled by temperature7-10 but there is also evidence for a tight coupling between water availability and CGR11. Here, we use a record of global atmospheric CO2, terrestrial water storage and precipitation data to investigate changes in the interannual relationship between tropical land climate conditions and CGR under a changing climate. We find that the interannual relationship between tropical water availability and CGR became increasingly negative during 1989-2018 compared to 1960-1989. This could be related to spatiotemporal changes in tropical water availability anomalies driven by shifts in El Niño/Southern Oscillation teleconnections, including declining spatial compensatory water effects9. We also demonstrate that most state-of-the-art coupled Earth System and Land Surface models do not reproduce the intensifying water-carbon coupling. Our results indicate that tropical water availability is increasingly controlling the interannual variability of the terrestrial carbon cycle and modulating tropical terrestrial carbon-climate feedbacks.

North Atlantic oscillation controls multidecadal changes in the North Tropical Atlantic-Pacific connection

By exciting subtropical teleconnections, sea surface temperature (SST) anomalies in the North Tropical Atlantic (NTA) during boreal spring can trigger El Niño-Southern Oscillation (ENSO) events in the following boreal winter, thereby providing a precursor for ENSO predictability. However, this NTA-ENSO connection is not stationary, and it varies considerably over multidecadal timescales, which cannot be directly explained by the Atlantic multidecadal oscillation or the global warming trend. Here we show that multidecadal changes in the NTA-ENSO connection are principally controlled by multidecadal variability associated with the North Atlantic Oscillation (NAO). During the positive phase of the NAO, the amplification of the NTA impact on ENSO mainly arises from strengthening of the boreal spring mean precipitation over the equatorial Atlantic and enhancement of the persistence of NTA SST anomalies, which enhance the NTA influence by exciting stronger and more persistent subtropical teleconnections. Our findings show that multidecadal variability of the NAO is key to understanding the impacts of the NTA SST on the tropical Pacific Ocean.

Greenhouse warming and internal variability increase extreme and central Pacific El Niño frequency since 1980

El Niño has been recorded to change its properties since the 1980s, characterized by more common extreme El Niño and Central Pacific (CP) El Niño events. However, it is still unclear whether such change is externally forced or part of the natural variability. Here, we find that the frequency of the extreme and CP El Niño events also increased during the period 1875-1905, when the anthropogenic CO2 concentration was relatively lower, but with a positive phase of the Atlantic Multidecadal Oscillation (AMO). Models and palaeoclimate proxies reveal that a positive AMO enhances the zonal sea surface temperature gradient in the CP, which strengthens zonal advective feedback, favoring extreme and CP El Niño development. Moreover, we estimate that internal variability contributed to ~65% of the increasingly extreme and CP El Niño events, while anthropogenic forcing has made our globe experience ~1 more extreme and ~2 more CP events over the past four decades.

Modes and Mechanisms of Pacific Decadal-Scale Variability

The modes of Pacific decadal-scale variability (PDV), traditionally defined as statistical patterns of variance, reflect to first order the ocean's integration (i.e., reddening) of atmospheric forcing that arises from both a shift and a change in strength of the climatological (time-mean) atmospheric circulation. While these patterns concisely describe PDV, they do not distinguish among the key dynamical processes driving the evolution of PDV anomalies, including atmospheric and ocean teleconnections and coupled feedbacks with similar spatial structures that operate on different timescales. In this review, we synthesize past analysis using an empirical dynamical model constructed from monthly ocean surface anomalies drawn from several reanalysis products, showing that the PDV modes of variance result from two fundamental low-frequency dynamical eigenmodes: the North Pacific-central Pacific (NP-CP) and Kuroshio-Oyashio Extension (KOE) modes. Both eigenmodes highlight how two-way tropical-extratropical teleconnection dynamics are the primary mechanisms energizing and synchronizing the basin-scale footprint of PDV. While the NP-CP mode captures interannual- to decadal-scale variability, the KOE mode is linked to the basin-scale expression of PDV on decadal to multidecadal timescales, including contributions from the South Pacific.

A large-scale view of marine heatwaves revealed by archetype analysis

Marine heatwaves can have disastrous impacts on ecosystems and marine industries. Given their potential consequences, it is important to understand how broad-scale climate variability influences the probability of localised extreme events. Here, we employ an advanced data-mining methodology, archetype analysis, to identify large scale patterns and teleconnections that lead to marine extremes in certain regions. This methodology is applied to the Australasian region, where it identifies instances of anomalous sea-surface temperatures, frequently associated with marine heatwaves, as well as the broadscale oceanic and atmospheric conditions associated with those extreme events. Additionally, we use archetype analysis to assess the ability of a low-resolution climate model to accurately represent the teleconnection patterns associated with extreme climate variability, and discuss the implications for the predictability of these impactful events.

Nonstationary footprints of ENSO in the Mekong River Delta hydrology

The Mekong River Delta (MRD) is an essential agricultural area for the worldwide rice supply. Floods and droughts triggered by El Niño southern oscillation (ENSO) have been threatening sustenance in the MRD. Sustainable food supplies require understanding the response of the MRD hydrology to the changing ENSO behaviour in recent decades. Here, we reconstructed the annual rainfall maxima in the MRD using the oceanic paleoclimate proxy from coral skeletons and compared them with ENSO indexes. Annual minima of coral-based seawater oxygen isotope (δ¹⁸O_sw) correlated with annual rainfall maxima, which allowed to extend rainfall data from 1924 to the recent. The annual rainfall maxima based on δ¹⁸O_sw negatively correlated with the central Pacific El Niño index. This suggested that La Niña and central Pacific El Niño events lead to heavy and light rainy seasons. The heavy rainy season had more serious impacts in recent decades, which likely increases the flood risk. In contrast, the frequency and rainfall amount of the light rainy season has not changed significantly, although a catastrophic drought has hit the MRD. Our finding concludes that the impact of the ENSO event on MRD hydrology is inconsistent in the past century.

Southern Indian Ocean Dipole as a trigger for Central Pacific El Niño since the 2000s

Despite decades of effort, predicting the El Niño-Southern Oscillation (ENSO) since the 2000s has become increasingly challenging. This is due to the weaker coupling between the ENSO and well-known precursors in tropical ocean basins, particularly in the Indian Ocean. Here we show that the Southern Indian Ocean Dipole (SIOD), which is characterized by an east-west-oriented sea surface temperature dipole pattern over the southern Indian Ocean, has become a key precursor of Central Pacific El Niño since the 2000s with a 14-month lead. The role of the SIOD in the subsequent year's ENSO is distinctive from the equatorial Indian Ocean Dipole mode in that it prolongs the ENSO period. The westward-shifted ENSO has sustained simultaneous SIOD events for longer periods since the 2000s, which leads to weak but persistent westerly anomalies over the western Pacific. This eventually results in the development of the Central Pacific El Niño in the subsequent year.

Emergence of changing Central-Pacific and Eastern-Pacific El Niño-Southern Oscillation in a warming climate

El Niño-Southern Oscillation (ENSO) features strong warm events in the eastern equatorial Pacific (EP), or mild warm and strong cold events in the central Pacific (CP), with distinct impacts on global climates. Under transient greenhouse warming, models project increased sea surface temperature (SST) variability of both ENSO regimes, but the timing of emergence out of internal variability remains unknown for either regime. Here we find increased EP-ENSO SST variability emerging by around 2030 ± 6, more than a decade earlier than that of CP-ENSO, and approximately four decades earlier than that previously suggested without separating the two regimes. The earlier EP-ENSO emergence results from a stronger increase in EP-ENSO rainfall response, which boosts the signal of increased SST variability, and is enhanced by ENSO non-linear atmospheric feedback. Thus, increased ENSO SST variability under greenhouse warming is likely to emerge first in the eastern than central Pacific, and decades earlier than previously anticipated.

Seasonal Evolution of Chlorophyll-a in the North Indian Ocean Associated with the Indian Ocean Dipole and Two Types of El Niño Events

To investigate the main modes of interannual variation of chlorophyll-a (Chla) with seasonal evolution and its variation cycle in the North Indian Ocean based on satellite-derived products during 1998–2016, a season-reliant empirical orthogonal function (S-EOF) analysis and power spectrum analysis based on Fourier transform are applied in the study. The first three dominate modes reveal distinct Chla variability, as the S-EOF1 features by one dipole pattern have a negative anomaly in the central western Indian Ocean and a positive anomaly off the Java–Sumatra coasts, which is mainly synchronously associated with the climate indices of the positive Indian Ocean dipole (IOD) and eastern Pacific El Nino (EP-El Niño). The S-EOF2 indicates a tripolar structure with positive anomalies located in the central Indian Ocean surrounded by two negative anomalies, which is one year behind a positive IOD and EP-El Niño event. The S-EOF3 exhibits a different dipole distribution, with a positive anomaly in the central west and a negative anomaly in the southeast, synchronized or lagging behind the central Pacific El Nino (CP-El Niño). Moreover, regarding the correlation between the main modes of interannual variation and the IOD and El Nino events, the dynamic parameters (such as SST, SLA, rain, and wind) of the tropical Indo-Pacific Ocean are discussed using time-delay correlation and linear regression analysis to explain the key factors and possible influencing mechanism of the joint seasonal and interannual variations of Chla in the northern Indian Ocean.

Multi-year El Niño events tied to the North Pacific Oscillation

Multi-year El Niño events induce severe and persistent floods and droughts worldwide, with significant socioeconomic impacts, but the causes of their long-lasting behaviors are still not fully understood. Here we present a two-way feedback mechanism between the tropics and extratropics to argue that extratropical atmospheric variability associated with the North Pacific Oscillation (NPO) is a key source of multi-year El Niño events. The NPO during boreal winter can trigger a Central Pacific El Niño during the subsequent winter, which excites atmospheric teleconnections to the extratropics that re-energize the NPO variability, then re-triggers another El Niño event in the following winter, finally resulting in persistent El Niño-like states. Model experiments, with the NPO forcing assimilated to constrain atmospheric circulation, reproduce the observed connection between NPO forcing and the occurrence of multi-year El Niño events. Future projections of Coupled Model Intercomparison Project phases 5 and 6 models demonstrate that with enhanced NPO variability under future anthropogenic forcing, more frequent multi-year El Niño events should be expected. We conclude that properly accounting for the effects of the NPO on the evolution of El Niño events may improve multi-year El Niño prediction and projection.

The causes of long-lasting behaviors of multi-year El Niño are still not fully understood. Here, the authors find that persistent two-way teleconnections between the North Pacific Oscillation and the tropical Pacific constitute a key source of multi-year El Niño.

The Influence of Teleconnections on the Precipitation in Baluchistan

Precipitation plays a vital role in the economies of agricultural countries, such as Pakistan. Baluchistan is the largest province in Pakistan (in terms of land) and it is facing reoccurring droughts due to changing precipitation patterns. The landscape of the province consists of rugged terrain, mountains, hills, and valleys. The torrential rains lead to devastating flash floods due to the topography of the province, which has proven to be more catastrophic in nature. It is quite intriguing to observe the changing precipitation patterns in Baluchistan. Precipitation has become less frequent but intense, resulting in flash floods and landslides, as well as damage to agriculture, infrastructure, trade, environment, and the ecosystem. Baluchistan is under a drought warning and is already facing a water crisis. This study was performed on monthly precipitation time series data obtained from the Pakistan Meteorological Department (PMD) for determining trends in precipitation from 41 years of data (1977 to 2017) over 13 selected stations in Baluchistan. Due to the non-linear nature of the precipitation data, a non-parametric Mann–Kendall (MK) test was used to determine the increasing or decreasing trends in precipitation on a monthly basis. Large-scale atmospheric circulation and climate indices that affected precipitation were considered to determine their influence on precipitation. Statistical techniques of the partial Mann–Kendall (PMK) and Pearson correlation were applied to each station to ascertain the influence on precipitation due to climatic indices.

An ISOMAP Analysis of Sea Surface Temperature for the Classification and Detection of El Niño & La Niña Events

Isometric feature mapping (ISOMAP) is a nonlinear dimensionality reduction method used for extracting features from spatiotemporal data. The traditional principal component analysis (PCA), a linear dimensionality reduction method, measures the distance between two data points based on the Euclidean distance (line segment), which cannot reflect the actual distance between the data points in a nonlinear space. By contrast, the ISOMAP measures the distance between two data points based on the geodesic distance, which more closely reflects the actual distance by the view of tracing along the local linearity in the original nonlinear structure. Thus, ISOMAP-reconstructed data points can reflect the features of real structures and can be classified more accurately than traditional PCA-reconstructed data points. Moreover, these ISOMAP-reconstructed data points can be used for cluster analysis by emphasizing the differences among the points more than those by the traditional PCA. In this study, sea surface temperature (SST) data points reconstructed using the traditional PCA and ISOMAP were compared. The classification based on these reconstructed SST points was tested using the Niño 3.4 index, which labels El Niño, La Niña, or normal events. The mean differences from the ISOMAP data points were larger than those from the traditional PCA data points. The ISOMAP not only helped differentiate the points in two different events but also provided better difference measurement of the points belonging to the same class (e.g., 82/83 and 97/98 El Niño events). On examining the evolution of the leading three temporal eigen components of the SST PCA, or especially the SST ISOMAP, we found that the trajectories were similar to the Lorenz 63 model on a phase space figure. This implies that NWP perturbations can be traced using the ISOMAP to measure growing unstable behaviors. Spatial eigenmodes (empirical orthogonal function) between the traditional PCA and ISOMAP were also determined and compared herein.

Assessment of Quarterly, Semiannual and Annual Models to Forecast Monthly Rainfall Anomalies: The Case of a Tropical Andean Basin

Rainfall forecasting is essential to manage water resources and make timely decisions to mitigate adverse effects related to unexpected events. Considering that rainfall drivers can change throughout the year, one approach to implementing forecasting models is to generate a model for each period in which the mechanisms are nearly constant, e.g., each season. The chosen predictors can be more robust, and the resulting models perform better. However, it has not been assessed whether the approach mentioned above offers better performance in forecasting models from a practical perspective in the tropical Andean region. This study evaluated quarterly, semiannual and annual models for forecasting monthly rainfall anomalies in an Andean basin to show if models implemented for fewer months outperform accuracy; all the models forecast rainfall on a monthly scale. Lagged rainfall and climate indices were used as predictors. Support vector regression (SVR) was used to select the most relevant predictors and train the models. The results showed a better performance of the annual models mainly due to the greater amount of data that SVR can take advantage of in training. If the training of the annual models had less data, the quarterly models would be the best. In conclusion, the annual models show greater accuracy in the rainfall forecast.

The increased frequency of combined El Niño and positive IOD events since 1965s and its impacts on maritime continent hydroclimates

The Indian and Pacific Oceans surround the Maritime Continent (MC). Major modes of sea surface temperature variability in both oceans, including the Indian Ocean Dipole (IOD) and El Niño–Southern Oscillation (ENSO), can strongly affect precipitation on the MC. The prevalence of fires in the MC is closely associated with precipitation amount and terrestrial water storage in September and October. Precipitation and terrestrial water storage, which is a measurement of hydrological drought conditions, are significantly modulated by Indian Ocean Dipole (IOD) and El Niño events. We utilize long-term datasets to study the combined effects of ENSO and the IOD on MC precipitation during the past 100 years (1900–2019) and find that the reductions in MC precipitation and terrestrial water storage are more pronounced during years when El Niño and a positive phase of the IOD (pIOD) coincided. The combined negative effects are produced mainly through an enhanced reduction of upward motion over the MC. Coincident El Niño-pIOD events have occurred more frequently after 1965. However, climate models do not project a higher occurrence of coincident El Niño-pIOD events in a severely warming condition, implying that not the global warming but the natural variability might be the leading cause of this phenomenon.

Revisiting the Contrasting Response of Polar Stratosphere to the Eastern and Central Pacific El Niños

El Niño Southern Oscillation (ENSO) invokes the release of a large amount of heat and moisture into the tropical atmosphere, inducing circulation anomalies. The circulation response to ENSO propagates both horizontally poleward and vertically into the stratosphere. Here, we investigate the remote response of the polar stratosphere to ENSO using reanalysis data, along with composite and regression analysis. In particular, we focus on inter-event variability resulting from two ENSO types (the Eastern Pacific (EP) and the Central Pacific (CP) El Niño) and the inter-hemispheric difference in the ENSO responses. Consistent with previous results, we show that ENSO is associated with a weakening in the stratospheric polar vortex but emphasize that the polar stratosphere response strongly depends on the ENSO types, differs between the hemispheres, and changes from the lower to middle stratosphere. The main inter-hemispheric asymmetry manifests in response to the EP El Niño, which is not significant in the Southern Hemisphere, while CP events are associated with pronounced weakening in the polar vortex in both hemispheres. The weakening in the stratospheric polar vortex arguably results from the intensification in the wave flux from the troposphere into the stratosphere and is accompanied by increased heat transport. The latter causes stratospheric warming in the Artic and Antarctic and slows zonal currents. The response of the lower stratosphere circulation to ENSO is approximately the opposite to that of the middle stratosphere.

A Review of the Role of the Oceanic Rossby Waves in Climate Variability

In this paper, the role of oceanic Rossby waves in climate variability is reviewed, as well as their dynamics in tropical oceans and at mid-latitudes. For tropical oceans, both the interactions between equatorial Rossby and Kelvin waves, and off-equatorial Rossby waves are privileged. The difference in the size of the basins induces disparities both in the forcing modes and in the dynamics of the tropical waves, which form a single quasi-stationary wave system. For Rossby waves at mid-latitudes, a wide range of periods is considered, varying from a few days to several million years when very-long-period Rossby waves winding around the subtropical gyres are hypothesized. This review focuses on the resonant forcing of Rossby waves that seems ubiquitous: the quasi-geostrophic adjustment of the oceans favors natural periods close to the forcing period, while those far from it are damped because of friction. Prospective work concentrates on the resonant forcing of dynamical systems in subharmonic modes. According to this new concept, the development of ENSO depends on its date of occurrence. Opportunities arise to shed new light on open issues such as the Middle Pleistocene transition.

From Phase Transition to Interdecadal Changes of ENSO, Altered by the Lower Stratospheric Ozone

This paper shows more evidence for the existing spatial–temporal synchronization of the air surface temperature and pressure within the lower stratospheric ozone, which is unevenly distributed over the globe. The focus of this study is put on the region of formation and manifestation of El Niño Southern Oscillation (ENSO). Statistical analysis of data (covering the period 1900–2019) displays a well pronounced covariance of ozone at 70 hPa with (i) Nino3.4 index, (ii) air surface temperature, and (iii) sea level pressure, in each grid-point with spatial resolution of 5° in latitude and longitude. The ozone impact could be found at different time scales—from interannual (altering the ENSO phase transition) to interdecadal. Moreover, the centers of action of ozone on the sea level temperature and pressure are positioned at different places, depending on the temporal scale of variability—from the tropical Central Pacific—at interannual and interdecadal, to extratropics—at subdecadal time scales. We show also that positive ozone anomalies at 70 hPa trigger a cooling of the sea surface, with a delay of 9 record’s time intervals. The ozone depletion, on the other side, is followed by a sea level warming with a delay of 1–2 record’s time intervals.

Multi–Proxy Reconstruction of Drought Variability in China during the Past Two Millennia

Drought imposes serious challenges to ecosystems and societies and has plagued mankind throughout the ages. To understand the long-term trend of drought in China, a series of annual self-calibrating Palmer drought severity indexes (scPDSI), which is a semi-physical drought index based on the land surface water balance, were reconstructed during AD 56~2000. Multi-proxy records of tree-ring width and stalagmite oxygen isotope δ18O were used for this reconstruction, along with random forest regression. The spatiotemporal characteristics of the reconstruction results were analyzed, and comparisons were made with previous studies. Results showed that (1) China witnessed a drought-based state during the past 2000 years (mean value of scPDSI was −0.3151), with an average annual drought area of 85,000 km2; 4 wetting periods, i.e., the Han Dynasty (AD 56~220), the Tang Dynasty (AD 618~907), the Ming Dynasty (AD 1368~1644), and the Qing Dynasty (AD 1644~1912); and 2 drying periods, i.e., the Era of Disunity (AD 221~580) and the Song Dynasty (AD 960~1279). (2) Three different alternating fluctuation dry-wet modes (i.e., interannual, multidecadal, and centennial scales) in China were all significantly (p-value < 0.001) correlated with the amplitude and frequency of temperature in the Northern Hemisphere. (3) According to the spatial models disassembled from the rotated empirical orthogonal function, China was divided into nine dry-wet regions: northwestern China, Xinjiang, southwestern China, southeastern China, the Loess plateau, central China, southwestern Tibet, eastern China, and northeastern China. (4) The random forest (RF) was found to be accurate and stable for the reconstruction of drought variability in China compared with linear regression.

Seasonal Temperature Extremes in the North Eurasian Regions Depending on ENSO Phase Transitions

Seasonal anomalies of surface air temperature were analyzed for the North Eurasian regions in mid-latitudes using long-term data from the end of the 19th century with an assessment of El Niño Southern Oscillation (ENSO) effects. In particular, temperature anomalies in the spring–summer months for the European (ER) and Asian (AR) Russian regions for different phase transitions of the ENSO phenomena were estimated using the Niño3, Niño3.4 and Niño4 indices. The largest frequency of the extremely high-temperature and drought conditions in spring–summer months in ER was detected for years starting in the El Niño phase with the transition to the La Niña phase at the end of the year. Such conditions were realized in ER in summer 2010 (“Russian heatwave”). The corresponding largest frequency of high temperature in AR was obtained for conditions with the continuation of the El Niño phase during the whole year. Such conditions in AR were noted, in particular, in the summer of 2015, with an extremely high temperature and extremely low precipitation in the Lake Baikal basin.

Does the El Niño-Southern Oscillation Affect the Combined Impact of the Atlantic Multidecadal Oscillation and Pacific Decadal Oscillation on the Precipitation and Surface Air Temperature Variability over South America?

Previous studies have shown that the Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO) have combined effects on the precipitation (PRP) variability over South America. The combined impacts have been assessed considering four mean states as the averages of the variable anomalies during sub-periods overlapping time intervals of the PDO and AMO phases. Since these sub-periods include years under El Niño-Southern Oscillation (ENSO) extremes, the extent to which these years’ occurrence affects the averaged anomaly patterns during different mean states is investigated. The analyses are done for the PRP and surface air temperature (SAT) during the austral winter (June to August) and summer (December to February) of the 1901–2014 period using a composite technique. The nonlinear ENSO response in each mean state for a variable corresponds to the sum of the anomaly composites of the El Niño and La Niña events. In each mean state, the nonlinear PRP and SAT anomalies are not negligible and show similar patterns of the corresponding mean state, with larger magnitudes. For both seasons and all mean states, these similarities are more pronounced for SAT than for PRP. Thus, the ENSO variability affects the mean state’s PRP and SAT anomaly patterns in different ways. As far as we know, analyses of the nonlinear ENSO response of the South American climate during distinct mean states were not performed before. Our results also indicate that the ENSO variability should be considered in the studies of the low-frequency modes and their effects on the mean state over South America. The results presented could be relevant for climate monitoring and modeling studies.

Possible Causes of the Occurrence of a Rare Antarctic Sudden Stratospheric Warming in 2019

A minor Antarctic sudden stratospheric warming (SSW) with the strongest circulation changes since the first major SSW over the Antarctic was recorded in 2002 occurred in early September 2019. The diagnosis demonstrates two possible causes of this SSW. First, the tropical central Pacific warming is identified, which enhanced the amplitude of tropospheric planetary wavenumber 1 (W1) in the extratropics on the seasonal time scale. Second, the impact of intraseasonal convection anomalies similar to previous studies is also suggested here. The enhanced deep convection over the South Pacific Convergence Zone (SPCZ) in late August–early September excited a Rossby wave train to deepen an anomalous ridge, which significantly and persistently strengthened the tropospheric W1. The central Pacific warming and intraseasonal convection anomalies jointly provided the conditions for the occurrence of the Antarctic SSW in 2019 on different time scales. On the other hand, the difference of the stratospheric state between the Antarctic SSWs in 2019 and 2002 may be an important reason why the 2019 event did not meet the major SSW criteria. The stratospheric state before the 2019 SSW event is somewhat not as ideal as that of the 2002 event. Vertical planetary waves are, hence, more difficult to enter into the polar stratosphere, making it more difficult to trigger major events.

The North Equatorial Countercurrent East of the Dateline, Its Variations and Its Relationship to the El Niño Event

Using forty years (1978–2017) of Ocean Reanalysis System 4 (ORAS4) dataset, the purpose of this study is to investigate the fluctuation of the North Equatorial Countercurrent (NECC) to the east of the dateline in relation to the presence of three kinds of El Niño events. From spring (MAM) through summer (JJA), we found that the NECC was stronger during the Eastern Pacific El Niño (EP El Niño) and the MIX El Niño than during the Central Pacific El Niño (CP El Niño). When it comes to winter (DJF), on the other hand, the NECC was stronger during the CP and MIX El Niño and weaker during the EP El Niño. This NECC variability was affected by the fluctuations of thermocline depth near the equatorial Pacific. Moreover, we also found that the seasonal southward shift of the NECC occurred between winter and spring, but the shift was absent during the CP and MIX El Niño events. This meridional shift was strongly affected by the local wind stress.

Why Australia was not wet during spring 2020 despite La Niña

The austral spring climate of 2020 was characterised by the occurrence of La Niña, which is the most predictable climate driver of Australian springtime rainfall. Consistent with this La Niña, the Bureau of Meteorology's dynamical sub-seasonal to seasonal forecast system, ACCESS-S1, made highly confident predictions of wetter-than-normal conditions over central and eastern Australia for spring when initialised in July 2020 and thereafter. However, many areas of Australia received near average to severely below average rainfall, particularly during November. Possible causes of the deviation of rainfall from its historical response to La Niña and causes of the forecast error are explored with observational and reanalysis data for the period 1979-2020 and real-time forecasts of ACCESS-S1 initialised in July to November 2020. Several compounding factors were identified as key contributors to the drier-than-anticipated spring conditions. Although the ocean surface to the north of Australia was warmer than normal, which would have acted to promote rainfall over northern Australia, it was not as warm as expected from its historical relationship with La Niña and its long-term warming trend. Moreover, a negative phase of the Indian Ocean Dipole mode, which typically acts to increase spring rainfall in southern Australia, decayed earlier than normal in October. Finally, the Madden-Julian Oscillation activity over the equatorial Indian Ocean acted to suppress rainfall across northern and eastern Australia during November. While ACCESS-S1 accurately predicted the strength of La Niña over the Niño3.4 region, it over-predicted the ocean warming to the north of Australia and under-predicted the strength of the November MJO event, leading to an over-prediction of the Australian spring rainfall and especially the November-mean rainfall.

El Niño Modoki can be mostly predicted more than 10 years ahead of time

The 2014-2015 "Monster"/"Super" El Niño failed to be predicted one year earlier due to the growing importance of a new type of El Niño, El Niño Modoki, which reportedly has much lower forecast skill with the classical models. In this study, we show that, so far as of today, this new El Niño actually can be mostly predicted at a lead time of more than 10 years. This is achieved through tracing the predictability source with an information flow-based causality analysis, which has been rigorously established from first principles during the past 16 years (e.g., Liang in Phys Rev E 94:052201, 2016). We show that the information flowing from the solar activity 45 years ago to the sea surface temperature results in a causal structure resembling the El Niño Modoki mode. Based on this, a multidimensional system is constructed out of the sunspot number series with time delays of 22-50 years. The first 25 principal components are then taken as the predictors to fulfill the prediction, which through causal AI based on the Liang-Kleeman information flow reproduces rather accurately the events thus far 12 years in advance.

A Process-Oriented Approach to Identify Evolutions of Sea Surface Temperature Anomalies with a Time-Series of a Raster Dataset

It is important to consider where, when, and how the evolution of sea surface temperature anomalies (SSTA) plays significant roles in regional or global climate changes. In the comparison of where and when, there is a great challenge in clearly describing how SSTA evolves in space and time. In light of the evolution from generation, through development, and to the dissipation of SSTA, this paper proposes a novel approach to identifying an evolution of SSTA in space and time from a time-series of a raster dataset. This method, called PoAIES, includes three key steps. Firstly, a cluster-based method is enhanced to explore spatiotemporal clusters of SSTA, and each cluster of SSTA at a time snapshot is taken as a snapshot object of SSTA. Secondly, the spatiotemporal topologies of snapshot objects of SSTA at successive time snapshots are used to link snapshot objects of SSTA into an evolution object of SSTA, which is called a process object. Here, a linking threshold is automatically determined according to the overlapped areas of the snapshot objects, and only those snapshot objects that meet the specified linking threshold are linked together into a process object. Thirdly, we use a graph-based model to represent a process object of SSTA. A node represents a snapshot object of SSTA, and an edge represents an evolution between two snapshot objects. Using a number of child nodes from an edge’s parent node and a number of parent nodes from the edge’s child node, a type of edge (an evolution relationship) is identified, which shows its development, splitting, merging, or splitting/merging. Finally, an experiment on a simulated dataset is used to demonstrate the effectiveness and the advantages of PoAIES, and a real dataset of satellite-SSTA is used to verify the rationality of PoAIES with the help of ENSO’s relevant knowledge, which may provide new references for global change research.

Climate variability of the East Asian winter monsoon and associated extratropical-tropical interaction: a review

The East Asian winter monsoon (EAWM) is one of the most important climate systems during boreal winter. It has a large meridional extent and provides a vital bridge between the mid-to-high latitudes and the tropics. This paper reviews the latest research on the climate variability of the EAWM, with particular emphasis on the associated processes of extratropical-tropical interaction. Some new results have been achieved in understanding the variation of the EAWM on interdecadal, interannual, and intraseasonal time scales, such as the interdecadal strengthening in the early 21st century, the unstable impact of El Niño-South Oscillation (ENSO) on the EAWM, and the intraseasonal strong cold events in East Asia. In addition, understanding of the extratropical-tropical interactions related to the EAWM has improved in several aspects, including the impacts of the EAWM on ENSO-related climate effects, and the variation of regional Hadley circulation over the western Pacific and its climate impacts in the Asia-Australia region. Moreover, recent work has systematically evaluated the capacity of climate models to simulate the EAWM, including its climatology, interannual to interdecadal variations, and its relationship with ENSO. Finally, some scientific issues regarding our understanding of the EAWM are proposed for future investigation.

Interannual variability of the frequency of MJO phases and its association with two types of ENSO

In this study, we reexamine the effect of two types of El Niño Southern Oscillation (ENSO) modes on Madden Julian Oscillation (MJO) activity in terms of the frequency of MJO phases. Evaluating all-season data, we identify two dominant zonal patterns of MJO frequency exhibiting prominent interannual variability. These patterns are structurally similar to the Wheeler and Hendon (Mon. Weather Rev. 132:1917–1932, 2004) RMM1 and RMM2 spatial patterns. The first pattern explains a higher frequency of MJO activity over the Maritime Continent and a lower frequency over the central Pacific Ocean and the western Indian Ocean, or vice versa. The second pattern is associated with a higher frequency of MJO active days over the eastern Indian Ocean and a lower frequency over the western Pacific, or vice versa. We find that these two types of MJO frequency patterns are related to the central Pacific and eastern Pacific ENSO modes. From the positive to the negative ENSO (central Pacific or eastern Pacific) phases, the respective MJO frequency patterns change their sign. The MJO frequency patterns are the lag response of the underlying ocean state. The coupling between ocean and atmosphere is exceedingly complex. The first MJO frequency pattern is most prominent during the negative central-Pacific (CP-type) ENSO phases (specifically during September–November and December-February seasons). The second MJO frequency pattern is most evident during the positive eastern-Pacific (EP-type) ENSO phases (specifically during March–May, June–August and September–November). Different zonal circulation patterns during CP-type and EP-type ENSO phases alter the mean moisture distribution throughout the tropics. The horizontal convergence of mean background moisture through intraseasonal winds are responsible for the MJO frequency anomalies during the two types of ENSO phases. The results here show how the MJO activity gets modulated on a regional scale in the presence of two types of ENSO events and can be useful in anticipating the seasonal MJO conditions from a predicted ENSO state.

Typhoon storm surge in the southeast Chinese mainland modulated by ENSO

In the past decade (2010-2019), the annual maximum typhoon storm surge (AMTSS) accounted for 46.6% of the total direct economic loss caused by marine disasters in Chinese mainland, but its prediction in advance is challenging. By analyzing records of 23 tide-gauge stations, we found that the AMTSSs in Shanghai, Zhejiang and Fujian show significant positive correlations with the El Niño-Southern Oscillation (ENSO). For the 1987-2016 period, the maximum correlation is achieved at Pingtan station, where correlation coefficient between the AMTSS and Niño-3.4 is 0.55. The AMTSS occurring in El Niño years are stronger than those in non-El Niño years by 9-35 cm in these areas. Further analysis suggests that a developing El Niño can greatly modulate the behaviors of Northwest Pacific typhoons. Strong typhoons tend to make landfall in southeast China with stronger intensities and northward shifted landfall positions. This study indicates that the modulation effect by ENSO may provide potential predictability for the AMTSS, which is useful for the early alert and reduction of storm surge damages.

Impacts of teleconnection patterns on South America climate

Oceanic heat sources disturb the atmosphere, which, to come back to its initial state, disperses waves. These waves affect the climate in remote regions, characterizing the teleconnection patterns. In this study, we describe eight teleconnection patterns that affect South America climate: the El Niño-Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), the Atlantic Multidecadal Oscillation (AMO), the Tropical Atlantic Dipole (TAD), the South Atlantic Dipole (SAD), the Southern Annular Mode (SAM), the Madden-Julian Oscillation (MJO), and the Indian Ocean Dipole (IOD). Precipitation and winds at 850-hPa anomalies, considering these teleconnection patterns in ENSO neutral periods, are also presented. Overall, southeastern South America and the north sector of the North and Northeast regions of Brazil are the most affected areas by the teleconnection patterns. In general, there is a precipitation dipole pattern between these regions during each teleconnection pattern.

Determination of a New Coastal ENSO Oceanic Index for Northern Peru

In 2017, extreme rainfall events occurred in the northern portion of Peru, causing nearly 100,000 victims, according to the National Emergency Operations Center (COEN). This climatic event was attributed to the occurrence of the El Niño Southern Oscillation (ENSO). Therefore, the main objective of this study was to determine and differentiate between the occurrence of canonical ENSO, with a new type of ENSO called “El Niño Costero” (Coastal El Niño). The polynomial equation method was used to analyze the data from the different types of existing ocean indices to determine the occurrence of ENSO. It was observed that the anomalies of sea surface temperature (SST) 2.5 °C (January 2016) generated the “Modoki El Niño” and that the anomaly of SST −0.3 °C (January 2017) generated the “Modoki La Niña”; this sequential generation generated El Niño Costero. This new knowledge about the sui generis origin of El Niño Costero, based on the observations of this analysis, will allow us to identify and obtain important information regarding the occurrence of this event. A new oceanic index called the Pacific Regional Equatorial Index (PREI) was proposed to follow the periodic evolution and forecast with greater precision a new catastrophic event related to the occurrence of El Niño Costero and to implement prevention programs.

Subsurface Temperature Estimation from Sea Surface Data Using Neural Network Models in the Western Pacific Ocean

Estimating the ocean subsurface thermal structure (OSTS) based on multisource sea surface data in the western Pacific Ocean is of great significance for studying ocean dynamics and El Niño phenomenon, but it is challenging to accurately estimate the OSTS from sea surface parameters in the area. This paper proposed an improved neural network model to estimate the OSTS from 0–2000 m from multisource sea surface data including sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), and sea surface wind (SSW). In the model experiment, the rasterized monthly average data from 2005–2015 and 2016 were selected as the training and testing set, respectively. The results showed that the sea surface parameters selected in the paper had a positive effect on the estimation process, and the average RMSE value of the ocean subsurface temperature (OST) estimated by the proposed model was 0.55 °C. Moreover, there were pronounced seasonal variation signals in the upper layers (the upper 200 m), however, this signal gradually diminished with increasing depth. Compared with known estimation models such as the random forest (RF), the multiple linear regression (MLR), and the extreme gradient boosting (XGBoost), the proposed model outperformed these models under the data conditions of the paper. This research can provide an advanced artificial intelligence technique for estimating subsurface thermohaline structure in major sea areas.

Mean sea surface temperature changes influence ENSO-related precipitation changes in the mid-latitudes

El Niño profoundly impacts precipitation in high-population regions. This demands an advanced understanding of the changes in El Niño-induced precipitation under the future global warming scenario. However, thus far, consensus is lacking regarding future changes in mid-latitude precipitation influenced by El Niño. Here, by analyzing the Coupled Model Intercomparison Project simulations, we show that future precipitation changes are tightly linked to the response of each type of El Niño to the tropical Pacific mean sea surface temperature (SST) change. A La Niña-like mean SST change intensifies basin-wide El Niño events causing approximately 20% more precipitation over East Asia and North America via enhancing moisture transport. Meanwhile, an El Niño-like mean SST change generates more frequent eastern Pacific El Niño events, enhancing precipitation in North American. Our findings highlight the importance of the mean SST projection in selectively influencing the types of El Niño and their remote impact on precipitation.

El Niño-driven precipitation profoundly affects the mid-latitudes, but how this impact changes in the future is uncertain. Here, the authors show that changes in the tropical Pacific sea surface temperatures causes an increase in rainfall linked to El Niño events of about 20% over East Asia and North America.

Statistical physics approaches to the complex Earth system

Global warming, extreme climate events, earthquakes and their accompanying socioeconomic disasters pose significant risks to humanity. Yet due to the nonlinear feedbacks, multiple interactions and complex structures of the Earth system, the understanding and, in particular, the prediction of such disruptive events represent formidable challenges to both scientific and policy communities. During the past years, the emergence and evolution of Earth system science has attracted much attention and produced new concepts and frameworks. Especially, novel statistical physics and complex networks-based techniques have been developed and implemented to substantially advance our knowledge of the Earth system, including climate extreme events, earthquakes and geological relief features, leading to substantially improved predictive performances. We present here a comprehensive review on the recent scientific progress in the development and application of how combined statistical physics and complex systems science approaches such as critical phenomena, network theory, percolation, tipping points analysis, and entropy can be applied to complex Earth systems. Notably, these integrating tools and approaches provide new insights and perspectives for understanding the dynamics of the Earth systems. The overall aim of this review is to offer readers the knowledge on how statistical physics concepts and theories can be useful in the field of Earth system science.

Recent migration of tropical cyclones toward coasts

Poleward migrations of tropical cyclones have been observed globally, but their impact on coastal areas remains unclear. We investigated the change in global tropical cyclone activity in coastal regions over the period 1982-2018. We found that the distance of tropical cyclone maximum intensity to land has decreased by about 30 kilometers per decade, and that the annual frequency of global tropical cyclones increases with proximity to land by about two additional cyclones per decade. Trend analysis reveals a robust migration of tropical cyclone activity toward coasts, concurrent with poleward migration of cyclone locations as well as a statistically significant westward shift. This zonal shift of tropical cyclone tracks may be mainly driven by global zonal changes in environmental steering flow.

Effect of Various Types of ENSO Events on Moisture Conditions in the Humid and Subhumid Tropics

Moisture anomaly conditions within humid and subhumid tropics that are associated with different types of El Niño and La Niña phenomena are described and analyzed with a focus on their spatial distribution and seasonal variability. Five dryness indices (Keetch–Byram Drought Index, Weighted Anomaly Standardized Precipitation Index, Standardized Precipitation Index, Palmer Drought Severity Index, and Percent of Normal Precipitation) were derived from ECMWF (European Centre for Medium-Range Weather Forecasts) fifth generation reanalysis (ERA5) reanalysis and University Corporation for Atmospheric Research (UCAR) datasets for the period from 1979 to 2019. Cross-correlation analysis was used to evaluate the relationships between the El Niño Southern Oscillation (ENSO) and selected dryness indices. To describe the seasonal variability of the ENSO–surface moisture relationships, the composite maps of dryness indices in different seasons were analyzed. The results showed a significant heterogeneity of the ENSO-induced moisture anomaly conditions both within and across various geographical regions. Four main areas in humid and subhumid tropics with the maximum effects of El Niño/La Niña events on the surface moisture conditions were found: Southeast Asia and Australia, Eastern and South Africa, Northeastern and Eastern South America, and Central America. It was shown that the effects of La Niña were usually opposite to those of El Niño, while the responses to the two types of El Niño differed mostly in the moisture anomaly intensity and its spatial patterns.

The probabilistic backbone of data-driven complex networks: an example in climate

Complex systems often exhibit long-range correlations so that typical observables show statistical dependence across long distances. These teleconnections have a tremendous impact on the dynamics as they provide channels for information transport across the system and are particularly relevant in forecasting, control, and data-driven modeling of complex systems. These statistical interrelations among the very many degrees of freedom are usually represented by the so-called correlation network, constructed by establishing links between variables (nodes) with pairwise correlations above a given threshold. Here, with the climate system as an example, we revisit correlation networks from a probabilistic perspective and show that they unavoidably include much redundant information, resulting in overfitted probabilistic (Gaussian) models. As an alternative, we propose here the use of more sophisticated probabilistic Bayesian networks, developed by the machine learning community, as a data-driven modeling and prediction tool. Bayesian networks are built from data including only the (pairwise and conditional) dependencies among the variables needed to explain the data (i.e., maximizing the likelihood of the underlying probabilistic Gaussian model). This results in much simpler, sparser, non-redundant, networks still encoding the complex structure of the dataset as revealed by standard complex measures. Moreover, the networks are capable to generalize to new data and constitute a truly probabilistic backbone of the system. When applied to climate data, it is shown that Bayesian networks faithfully reveal the various long-range teleconnections relevant in the dataset, in particular those emerging in El Niño periods.

Advanced Rainfall Trend Analysis of 117 Years over West Coast Plain and Hill Agro-Climatic Region of India

In this paper, the rainfall trend of the West Coast Plain and Hill Agro-Climatic Region is analyzed for 117 years (1901–2017). This region is a globally recognized biodiversity hotspot and known for one of the highest rainfall receiving regions in India. Rainfall grid dataset is used for the analysis of rainfall trends on monthly, seasonal, and decadal time scales. Modified Mann–Kendall’s test, Linear Regression, Innovative Trend Analysis, Sen’s Slope test, Weibull’s Recurrence Interval, Pearson’s Coefficient of Skewness, Consecutive Disparity Index, Kurtosis, and some other important statistical techniques are employed for trend analysis. Results indicate that the rainfall trend is significant in January, July, August, September as well as the Winter season. Among all the significant trends, January and July showed a decreasing rainfall trend. July has the highest contribution (30%) among all the obtained monotonic trend to annual rainfall and coincidentally has the highest trend magnitude. August and September months with a combined contribution of 30% to annual rainfall, show an increasing monotonic trend with high magnitude whereas Winter season shows a monotonic decreasing rainfall trend with comparatively low magnitudes. Decadal analysis along with the study of recurrence interval of excess and deficit years helps to understand the decadal rhythm of trend and the magnitude of extreme monthly and seasonal events. Skewness reveals that rainfall dataset of all the periodic results is right-skewed and the recurrence interval also supports the skewness results. Sharply decreasing rainfall in July and rising rainfall in August and September is predictive of the impact on agriculture, biodiversity and indicates the rainfall regime shift in the region.

Teleconnection between the Asian Polar Vortex and surface PM2.5 in China

Atmospheric fine particulate matter (PM_2.5) pollutions are of particular concern because of their direct and indirect harm to humans and organisms. China has suffered from severe air pollution for the past ten years, related to heavy pollution emissions and compounded by the effects of atmospheric circulation. This study applied statistical methods, observational data of ground pollutants, and meteorological data to analyze the impact of large-scale atmospheric circulations on PM_2.5 pollution over China. Empirical orthogonal function (EOF) analysis was used to evaluate the main PM_2.5 patterns and total contributions of the leading four EOFs. The results indicate that the total contributions of the leading four EOFs accounted for 50.5% of the total variance, reflecting four main types of PM_2.5 pollution, namely, overall pollution phase, north–south phase, east–west phase and north–center–south phase, with contributions of 28.4%, 9.7%, 6.5% and 5.9%, respectively. We selected indices of the Asian Polar Vortex (APV) to analyze the impact of large-scale atmospheric circulations on PM_2.5 pollution over China. The most pronounced APV control occurred in Beijing and its surroundings, specifically, along the Bohai Sea and the Northeast Plain.

ENSO diversity driving low-frequency change in mesoscale activity off Peru and Chile

Transient mesoscale oceanic eddies in Eastern Boundary Upwelling Systems are thought to strongly affect key regional scale processes such as ocean heat transport, coastal upwelling and productivity. Understanding how these can be modulated at low-frequency is thus critical to infer their role in the climate system. Here we use 26 years of satellite altimeter data and regional oceanic modeling to investigate the modulation of eddy kinetic energy (EKE) off Peru and Chile by ENSO, the main mode of natural variability in the tropical Pacific. We show that EKE tends to increase during strong Eastern Pacific (EP) El Niño events along the Peruvian coast up to northern Chile and decreases off central Chile, while it is hardly changed during Central Pacific El Niño and La Niña events. However the magnitude of the EKE changes during strong EP El Niño events is not proportional to their strength, with in particular the 1972/1973 El Niño event standing out as an extreme event in terms of EKE increase off Peru reaching an amplitude three times as large as that during the 1997/1998 El Niño event, and the 2015/2016 El Niño having instead a weak impact on EKE. This produces decadal changes in EKE, with a similar pattern than that of strong EP El Niño events, resulting in a significant negative (positive) long-term trend off Peru (central Chile).

Monthly Rainfall Anomalies Forecasting for Southwestern Colombia Using Artificial Neural Networks Approaches

Improving the accuracy of rainfall forecasting is relevant for adequate water resources planning and management. This research project evaluated the performance of the combination of three Artificial Neural Networks (ANN) approaches in the forecasting of the monthly rainfall anomalies for Southwestern Colombia. For this purpose, we applied the Non-linear Principal Component Analysis (NLPCA) approach to get the main modes, a Neural Network Autoregressive Moving Average with eXogenous variables (NNARMAX) as a model, and an Inverse NLPCA approach for reconstructing the monthly rainfall anomalies forecasting in the Andean Region (AR) and the Pacific Region (PR) of Southwestern Colombia, respectively. For the model, we used monthly rainfall lagged values of the eight large-scale climate indices linked to the El Niño Southern Oscillation (ENSO) phenomenon as exogenous variables. They were cross-correlated with the main modes of the rainfall variability of AR and PR obtained using NLPCA. Subsequently, both NNARMAX models were trained from 1983 to 2014 and tested for two years (2015–2016). Finally, the reconstructed outputs from the NNARMAX models were used as inputs for the Inverse NLPCA approach. The performance of the ANN approaches was measured using three different performance metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Pearson’s correlation (r). The results showed suitable forecasting performance for AR and PR, and the combination of these ANN approaches demonstrated the possibility of rainfall forecasting in these sub-regions five months in advance and provided useful information for the decision-makers in Southwestern Colombia.

Impact of Indo-Pacific Climate Variability on Rice Productivity in Bihar, India

The impact of Indo-Pacific climate variability in the South Asian region is very pronounced and their impact on agriculture is very important for the Indian subcontinent. In this study, rice productivity, climatic factors (Rainfall, Temperature and Soil Moisture) and associated major Indo-Pacific climate indices in Bihar were investigated. Bihar is one of the major rice-producing states of India and the role of climate variability and prevailing climate indices in six events (between 1991–2014) with severer than −10% rice productivity are analyzed. The Five-year moving average, Pearson’s Product Moment Correlation, Partial Correlation, Linear Regression Model, Mann Kendall Test, Sen’s Slope and some other important statistical techniques were used to understand the association between climatic variables and rice productivity. Pearson’s Product Moment Correlation provided an overview of the significant correlation between climate indices and rice productivity. Whereas, Partial Correlation provided the most refined results on it and among all the climate indices, Niño 3, Ocean Niño Index and Southern Oscillation Index are found highly associated with years having severer than −10% decline in rice productivity. Rainfall, temperature and soil moisture anomalies are analyzed to observe the importance of climate factors in rice productivity. Along with the lack of rainfall, lack of soil moisture and persistent above normal temperature (especially maximum temperature) are found to be the important factors in cases of severe loss in rice productivity. Observation of the dynamics of ocean-atmosphere coupling through the composite map shows the Pacific warming signals during the event years. The analysis revealed a negative (positive) correlation of rice productivity with the Niño 3 and Ocean Niño Index (Southern Oscillation Index).

Nonstationary El Niño teleconnection on the post-summer upwelling off Vietnam

Summer upwelling has often been observed off Vietnam in the South China Sea (SCS). Occasional disappearance of the upwelling has attracted much attention because it modulates the regional climate and harms surrounding fisheries. Fluctuations of the East Asian monsoon associated with El Niño are considered responsible for the weakened or abolished upwelling. However, analyses of observations performed in the present study were equivocal in terms of the dominant influence of El Niño. Based on long-term sea surface temperature data, we demonstrated that weak upwelling off Vietnam occurs more frequently during periods of accelerated global warming compared to warming hiatus periods. Warming signals in the Indian Ocean vanished relatively quickly during the hiatus period. The accompanying easterly anomalies south of the anomalous anticyclone (AAC) in the northwestern Pacific were also weakened, reducing the impact of the El Niño teleconnection on the SCS summer monsoon and thus preserving the regular post-summer upwelling off Vietnam during warming hiatus periods.

Use of Teleconnections to Predict Western Australian Seasonal Rainfall Using ARIMAX Model

Increased demand for engineering propositions to forecast rainfall events in an area or region has resulted in developing different rainfall prediction models. Interestingly, rainfall is a very complicated natural system that requires consideration of various attributes. However, regardless of the predictability performance, easy to use models have always been welcomed over the complex and ambiguous alternatives. This study presents the development of Auto–Regressive Integrated Moving Average models with exogenous input (ARIMAX) to forecast autumn rainfall in the South West Division (SWD) of Western Australia (WA). Climate drivers such as Indian Ocean Dipole (IOD) and El Nino Southern Oscillation (ENSO) were used as predictors. Eight rainfall stations with 100 years of continuous data from two coastal regions (south coast and north coast) were selected. In the south coast region, Albany (0,1,1) with exogenous input DMIOct–Nino3Nov, and Northampton (0,1,1) with exogenous input DMIJan–Nino3Nov were able to forecast autumn rainfall 4 months and 2 months in advance, respectively. Statistical performance of the ARIMAX model was compared with the multiple linear regression (MLR) model, where for calibration and validation periods, the ARIMAX model showed significantly higher correlations (0.60 and 0.80, respectively), compared to the MLR model (0.44 and 0.49, respectively). It was evident that the ARIMAX model can predict rainfall up to 4 months in advance, while the MLR has shown strict limitation of prediction up to 1 month in advance. For WA, the developed ARIMAX model can help to overcome the difficulty in seasonal rainfall prediction as well as its application can make an invaluable contribution to stakeholders’ economic preparedness plans.

Interannual to Decadal Variations of Submesoscale Motions around the North Pacific Subtropical Countercurrent

The outputs from a submesoscale permitting hindcast simulation from 1990 to 2016 are used to investigate the interannual to decadal variations of submesoscale motions. The region we focus on is the subtropical Northwestern Pacific including the subtropical countercurrent. The submesoscale kinetic energy (KE) is characterized by strong interannual and decadal variability, displaying larger magnitudes in 1996, 2003, and 2015, and smaller magnitudes in 1999, 2009, 2010, and 2016. These variations are partially explained by those of the available potential energy (APE) release at submesoscale driven by mixed layer instability in winter. Indeed, this APE release depends on the mixed layer depth and horizontal buoyancy gradient, both of them modulated with the Pacific Decadal Oscillation (PDO). As a result of the inverse KE cascade, the submesoscale KE variability possibly leads to interannual to decadal variations of the mesoscale KE (eddy KE (EKE)). These results show that submesoscale motions are a possible pathway to explain the impact associated with the PDO on the decadal EKE variability. The winter APE release estimated from the Argo float observations varies synchronously with that in the simulation on the interannual time scales, which suggests the observation capability to diagnose the submesoscale KE variability.