El Niño-Southern Oscillation complexity

Unraveling the role of the western North Pacific circulation anomaly in modulating Indian summer monsoon rainfall variability beyond ENSO

The Indian summer monsoon (ISM) rainfall interannual variability is known to be strongly linked to the El-Niño-Southern Oscillation (ENSO). This linear relationship is the primary factor in controlling the interannual variation in ISM precipitation. However, there are many outlier cases, and such deviations pose significant challenges in seasonal prediction over this region. Here we show that such challenges can be attributed to anomalous atmospheric pressure patterns in the Western North Pacific (WNP) region. The anticyclonic circulation anomaly over WNP region causes the easterly wind toward the Indian subcontinent, leading to positive precipitation anomalies with stronger low-level moist convergence, while the cyclonic circulation decreases ISM precipitation. The linear baroclinic model simulation results further support that the WNP circulation pattern can serve as an independent factor for forecasting precipitation over India. The WNP circulation anomaly play the crucial role generating ISM precipitation particularly for July and September. Our study suggests that the role of the WNP circulation anomaly should be carefully considered as the secondary prevailing mechanism on the subseasonal timescale during the boreal summer in addition to the ENSO signal.

Future increase in extreme El Niño supported by past glacial changes

El Niño events, the warm phase of the El Niño-Southern Oscillation (ENSO) phenomenon, amplify climate variability throughout the world1. Uncertain climate model predictions limit our ability to assess whether these climatic events could become more extreme under anthropogenic greenhouse warming2. Palaeoclimate records provide estimates of past changes, but it is unclear if they can constrain mechanisms underlying future predictions3-5. Here we uncover a mechanism using numerical simulations that drives consistent changes in response to past and future forcings, allowing model validation against palaeoclimate data. The simulated mechanism is consistent with the dynamics of observed extreme El Niño events, which develop when western Pacific warm pool waters expand rapidly eastwards because of strongly coupled ocean currents and winds6,7. These coupled interactions weaken under glacial conditions because of a deeper mixed layer driven by a stronger Walker circulation. The resulting decrease in ENSO variability and extreme El Niño occurrence is supported by a series of tropical Pacific palaeoceanographic records showing reduced glacial temperature variability within key ENSO-sensitive oceanic regions, including new data from the central equatorial Pacific. The model-data agreement on past variability, together with the consistent mechanism across climatic states, supports the prediction of a shallower mixed layer and weaker Walker circulation driving more frequent extreme El Niño genesis under greenhouse warming.

Similar teleconnection patterns of ENSO-NAO and ENSO-precipitation in Colombia: linear and non-linear relationships

The Central-Pacific (CP) and Eastern-Pacific (EP) types of El Niño-Southern Oscillation (ENSO) and their ocean-atmosphere effect cause diverse responses in the hydroclimatological patterns of specific regions. Given the impact of ENSO diversity on the North Atlantic Oscillation (NAO), this study aimed to determine the relationship between the ENSO-NAO teleconnection and the ENSO-influenced precipitation patterns in Colombia during the December-February period. Precipitation data from 1981 to 2023, obtained from the Climate Hazards Group (CHIRPS), were analyzed using nine ENSO and NAO indices spanning from 1951 to 2023. Using Pearson's correlation and mutual information (MI) techniques, nine scenarios were devised, encompassing the CP and EP ENSO events, neutral years, and volcanic eruptions. The results suggest a shift in the direction of the ENSO-NAO relationship when distinguishing between the CP and EP events. Higher linear correlations were observed in the CP ENSO scenarios (r > 0.65) using the MEI and BEST indices, while lower correlations were observed when considering EP events along with the Niño 3 and Niño 1.2 indices. MI show difference in relationships based on the event type and the ENSO index used. Notably, an increase in the non-linear relationship was observed for the EP scenarios with respect to correlation. Both teleconnections followed a similar pattern, exhibiting a more substantial impact during CP ENSO events. This highlights the significance of investigating the impacts of ENSO on hydrometeorological variables in the context of adapting to climate change, while acknowledging the intricate diversity inherent to the ENSO phenomenon.

Coral Sr/Ca-SST reconstruction from Fiji extending to ~1370 CE reveals insights into the Interdecadal Pacific Oscillation

The southwestern tropical Pacific is a key center for the Interdecadal Pacific Oscillation (IPO), which regulates global climate. This study introduces a groundbreaking 627-year coral Sr/Ca sea surface temperature reconstruction from Fiji, representing the IPO's southwestern pole. Merging this record with other Fiji and central tropical Pacific records, we reconstruct the SST gradient between the southwestern and central Pacific (SWCP), providing a reliable proxy for IPO variability from 1370 to 1997. This reconstruction reveals distinct centennial-scale temperature trends and insights into Pacific-wide climate impacts and teleconnections. Notably, the 20th century conditions, marked by simultaneous basin-scale warming and weak tropical Pacific zonal-meridional gradients, deviate from trends observed during the past six centuries. Combined with model simulations, our findings reveal that a weak SWCP gradient most markedly affects IPO-related rainfall patterns in the equatorial Pacific. Persistent synchronous western and central Pacific warming rates could lead to further drying climate across the Coral Sea region, adversely affecting Pacific Island nations.

On the need to integrate interannual natural variability into coastal multihazard assessments

The co-occurrence of multiple hazards can either exacerbate or mitigate risks. The interrelationships between multiple hazards greatly depend on the spatiotemporal scale and can be difficult to detect from large to local scales. In this paper, we identified coastal regions worldwide where the leading tropical (El Niño-Southern Oscillation, ENSO) and polar (Arctic Oscillation, AO; Southern Annular Mode, SAM) modes of climate variability simultaneously modify the seasonal conditions of multiple hazards, including the near-surface wind speed and swell and wind-sea wave powers. We classified the results at the national and municipal levels, with a focus on multiple hazards simultaneously occurring in space and time. The results revealed that the ENSO modulates multiple hazards, affecting approximately 40% of coastal countries, while the polar annular modes affect approximately 30% of coastal countries. The ENSO induced a greater diversity of multiple hazards, with Asian countries (e.g., Indonesia experienced increases of + 2% in wind and + 7% in swell) and countries in the Americas (e.g., Peru exhibited increases of + 1.5% in wind and + 6% in wind-sea) the most notably affected. The SAM imposed a greater influence on swells in the eastern countries of ocean basins (+ 2.5% in Chile) than in other countries, while the influence of the AO was greater in Norway and the UK (+ 12% for wind-sea and 8% for swell). Low-lying islands exhibited notable variations in pairwise hazards between phases and seasons. Our results could facilitate the interpretation of multihazard interactions and pave the way for a wide range of potential implementations of different coastal industries.

Maternal preconceptional and prenatal exposure to El Niño Southern Oscillation levels and child mortality: a multi-country study

El Niño Southern Oscillation (ENSO) has been shown to relate to the epidemiology of childhood infectious diseases, but evidence for whether they increase child deaths is limited. Here, we investigate the impact of mothers' ENSO exposure during and prior to delivery on child mortality by constructing a retrospective cohort study in 38 low- and middle-income countries. We find that high levels of ENSO indices cumulated over 0-12 lagged months before delivery are associated with significant increases in risks of under-five mortality; with the hazard ratio ranging from 1.33 (95% confidence interval [CI], 1.26, 1.40) to 1.89 (95% CI, 1.78, 2.00). Child mortality risks are particularly related to maternal exposure to El Niño-like conditions in the 0th-1st and 6th-12th lagged months. The El Niño effects are larger in rural populations and those with unsafe sources of drinking water and less education. Thus, preventive interventions are particularly warranted for the socio-economically disadvantaged.

Triboelectric Basalt Textiles Efficiently Operating within an Ultrawide Temperature Range

With the continuous upsurge in demand for wearable energy, nanogenerators are increasingly required to operate under extreme environmental conditions. Even though they are at the cutting edge of technology, nanogenerators have difficulty producing high-quality electrical output at very extreme temperatures. Here, a triboelectric basalt textile (TBT) with an ultrawide operational temperature range (from -196 to 520 °C) is created employing basalt material as the main body. The output power density of the TBT, in contrast to most conventional nanogenerators, would counterintuitively rise by 2.3 times to 740.6 mW m-2 after heating to 100 °C because the high temperature will enhance the material's interface polarization and electronic kinetic energy. The TBT retains ≈55% of its initial electrical output even after heating in the flame of an alcohol lamp (520 °C). Surprisingly, the TBTs output voltage may retain over 85% of its initial value even after submerging in liquid nitrogen. The TBTs exceptional resistance to heat and cold indicates its possible use in high and low latitudes, high altitudes, deserts, and even space settings.

Assessing desertification vulnerability and mitigation strategies in northern Nigeria: A comprehensive approach

Desertification constitutes a grave threat to the environmental and socio-economic stability of desertification frontline states in Northern Nigeria. From 2003 to 2020, this research comprehensively analyzes desertification vulnerability, integrating parameters such as NDVI, LST, TVDI, MSAVI, and Albedo. Key factors contributing to land degradation are identified, along with the spatial patterns and trends of desertification over the two-decade period. The consequences are profound, with Northern Nigeria's ecosystem experiencing a steady decline in vegetation cover. Agriculture, vital to the region's economy, faces increased aridity and reduced arable land, jeopardizing food security. Diminishing water resources exacerbates scarcity issues, placing additional strain on communities. These environmental changes lead to severe socio-economic implications, including displacement, loss of livelihoods, and heightened vulnerability to climate-related risks. Urgent, comprehensive, and strategic interventions are imperative. Policy recommendations underscore revising and enforcing land use regulations, promoting sustainable agricultural practices, and establishing monitoring systems to guide decision-making. This research contributes practical strategies to enhance the resilience of desertification frontline states, safeguard livelihoods, and align with Nigeria's sustainable development objectives. Findings from the study indicate that only a tiny percentage (6.7 %) of the study area remains unaffected by desertification. Moreover, 13.3 % exhibit light vulnerability, 20 % demonstrate moderate exposure, and 60 % fall into the severe (26.7 %) and compelling (33.3 %) vulnerability categories. These statistics underscore the gravity of desertification in the study area, emphasizing the urgent need for effective mitigation measures to address its impact comprehensively.

Possible shift in controls of the tropical Pacific surface warming pattern

Changes in the sea surface temperature (SST) pattern in the tropical Pacific modulate radiative feedbacks to greenhouse gas forcing, the pace of global warming and regional climate impacts. Therefore, elucidating the drivers of the pattern is critically important for reducing uncertainties in future projections. However, the causes of observed changes over recent decades, an enhancement of the zonal SST contrast coupled with a strengthening of the Walker circulation, are still debated. Here we focus on the role of external forcing and review existing mechanisms of the forced response categorized as either an energy perspective that adopts global and hemispheric energy budget constraints or a dynamical perspective that examines the atmosphere-ocean coupled processes. We then discuss their collective and relative contributions to the past and future SST pattern changes and propose a narrative that reconciles them. Although definitive evidence is not yet available, our assessment suggests that the zonal SST contrast has been dominated by strengthening mechanisms in the past, but will shift towards being dominated by weakening mechanisms in the future. Finally, we present opportunities to resolve the model-observations discrepancy regarding the recent trends.

Data-driven modeling of equatorial atmospheric waves: The role of moisture and nonlinearity on global-scale instabilities and propagation speeds

The equatorial region of the Earth's atmosphere serves as both a significant locus for phenomena, including the Madden-Julian Oscillation (MJO), and a source of formidable complexity. This complexity arises from the intricate interplay between nonlinearity and thermodynamic processes, particularly those involving moisture. In this study, we employ a normal mode decomposition of atmospheric reanalysis ERA-5 datasets to investigate the influence of nonlinearity and moisture on amplitude growth, propagation speed, and mode coupling associated with equatorially trapped waves. We focus our analysis on global-scale baroclinic Kelvin and Rossby waves, recognized as crucial components contributing to the variability of the MJO. We examine the dependence of wave amplitudes on the background moisture field in the equatorial region, as measured by total column water vapor. Our analysis demonstrates the crucial role of moisture in exciting these waves. We further investigate the dependence of the propagation speed of the waves on their amplitudes and the background moisture field. Our analysis reveals a robust correlation between the phase speed of the normal modes and their corresponding amplitude, whereas a weaker correlation is found between the eigenmodes' phase speed and the moisture field. Hence, our findings suggest that moisture plays a role in exciting the global-scale Rossby-Kelvin structure of the MJO. In this context, the propagation speed of the eigenmodes is mainly influenced by their amplitudes, underscoring the significant role of nonlinearity in wave propagation.

Explainable El Niño predictability from climate mode interactions

The El Niño-Southern Oscillation (ENSO) provides most of the global seasonal climate forecast skill1-3, yet, quantifying the sources of skilful predictions is a long-standing challenge4-7. Different sources of predictability affect ENSO evolution, leading to distinct global effects. Artificial intelligence forecasts offer promising advancements but linking their skill to specific physical processes is not yet possible8-10, limiting our understanding of the dynamics underpinning the advancements. Here we show that an extended nonlinear recharge oscillator (XRO) model shows skilful ENSO forecasts at lead times up to 16-18 months, better than global climate models and comparable to the most skilful artificial intelligence forecasts. The XRO parsimoniously incorporates the core ENSO dynamics and ENSO's seasonally modulated interactions with other modes of variability in the global oceans. The intrinsic enhancement of ENSO's long-range forecast skill is traceable to the initial conditions of other climate modes by means of their memory and interactions with ENSO and is quantifiable in terms of these modes' contributions to ENSO amplitude. Reforecasts using the XRO trained on climate model output show that reduced biases in both model ENSO dynamics and in climate mode interactions can lead to more skilful ENSO forecasts. The XRO framework's holistic treatment of ENSO's global multi-timescale interactions highlights promising targets for improving ENSO simulations and forecasts.

Northwestern Pacific Oceanic circulation shaped by ENSO

The intricate currents of the Northwest Pacific Ocean, with strong manifestations along the westside rim, connect tropical and subtropical gyres and significantly influence East Asian and global climates. The El Niño/Southern Oscillation (ENSO) originates in the tropical Pacific Ocean and disrupts this ocean circulation system. However, the spatiotemporal dependence of the impact of ENSO events has yet to be elucidated because of the complexities of both ENSO events and circulation systems, as well as the increased availability of observational data. We thus combined altimeter and drifter observations to demonstrate the distinct tropical and subtropical influences of the circulation system on ENSO diversity. During El Niño years, the North Equatorial Current, North Equatorial Countercurrent, Mindanao Current, Indonesian Throughflow, and the subtropical Kuroshio Current and its Extension region exhibit strengthening, while the tropical Kuroshio Current weakens. The tropical impact is characterized by sea level changes in the warm pool, whereas the subtropical influence is driven by variations in the wind stress curl. The tropical and subtropical influences are amplified during the Centra Pacific El Niño years compared to the Eastern Pacific El Niño years. As the globe warms, these impacts are anticipated to intensify. Thus, strengthening observation systems and refining climate models are essential for understanding and projecting the enhancing influences of ENSO on the Northwest Pacific Oceanic circulation.

Revealing trends and persistent cycles of non-autonomous systems with autonomous operator-theoretic techniques

An important problem in modern applied science is to characterize the behavior of systems with complex internal dynamics subjected to external forcings. Many existing approaches rely on ensembles to generate information from the external forcings, making them unsuitable to study natural systems where only a single realization is observed. A prominent example is climate dynamics, where an objective identification of signals in the observational record attributable to natural variability and climate change is crucial for making climate projections for the coming decades. Here, we show that operator-theoretic techniques previously developed to identify slowly decorrelating observables of autonomous dynamical systems provide a powerful means for identifying nonlinear trends and persistent cycles of non-autonomous systems using data from a single trajectory of the system. We apply our framework to real-world examples from climate dynamics: Variability of sea surface temperature over the industrial era and the mid-Pleistocene transition of Quaternary glaciation cycles.

Spatiotemporal characteristics of meteorological drought events in 34 major global river basins during 1901-2021

Meteorological drought is a crucial driver of various types of droughts; thus, identifying the spatiotemporal characteristics of meteorological drought at the basin scale has implications for ecological and water resource security. However, differences in drought characteristics between river basins have not been clearly elucidated. In this study, we identify and compare meteorological drought events in 34 major river basins worldwide using a three-dimensional drought-clustering algorithm based on the standardised precipitation evapotranspiration index on a 12-month scale from 1901 to 2021. Despite synchronous increases in precipitation and potential evapotranspiration (PET), with precipitation increasing by more than three times the PET, 47 % (16/34) of the basins showed a tendency towards drought in over half their basin areas. Drought events occurred frequently, with more than half identified as short-term droughts (lasting less than or equal to three months). Small basins had a larger drought impact area, with major drought events often originating from the basin boundaries and migrating towards the basin centre. Meteorological droughts were driven by changes in sea surface temperature (SST), especially the El Niño Southern Oscillation (ENSO) or other climate indices. Anomalies in SST and atmospheric circulation caused by ENSO events may have led to altered climate patterns in different basins, resulting in drought events. These results provide important insights into the characteristics and mechanisms of meteorological droughts in different river basins worldwide.

Data-driven dimensionality reduction and causal inference for spatiotemporal climate fields

We propose a data-driven framework to describe spatiotemporal climate variability in terms of a few entities and their causal linkages. Given a high-dimensional climate field, the methodology first reduces its dimensionality into a set of regionally constrained patterns. Causal relations among such patterns are then inferred in the interventional sense through the fluctuation-response formalism. To distinguish between true and spurious responses, we propose an analytical null model for the fluctuation-dissipation relation, therefore allowing us for uncertainty estimation at a given confidence level. We showcase the methodology on the sea surface temperature field from a state-of-the-art climate model. The usefulness of the proposed framework for spatiotemporal climate data is demonstrated in several ways. First, we focus on the correct identification of known causal relations across tropical basins. Second, we show how the methodology allows us to visualize the cumulative response of the whole system to climate variability in a few selected regions. Finally, each pattern is ranked in terms of its causal strength, quantifying its relative ability to influence the system's dynamics. We argue that the methodology allows us to explore and characterize causal relations in spatiotemporal fields in a rigorous and interpretable way.

Investigation of the Correlation between Enterovirus Infection and the Climate Factor Complex Including the Ping-Year Factor and El Niño-Southern Oscillation in Taiwan

Enterovirus infection and enterovirus infection with severe complications (EVSC) are critical issues in several aspects. However, there is no suitable predictive tool for these infections. A climate factor complex (CFC) containing several climate factors could provide more effective predictions. The ping-year factor (PYF) and El Niño-Southern Oscillation (ENSO) are possible CFCs. This study aimed to determine the relationship between these two CFCs and the incidence of enterovirus infection. Children aged 15 years and younger with enterovirus infection and/or EVSC were enrolled between 2007 and 2022. Each year was categorized into a ping-year or non-ping-year according to the PYF. Poisson regression was used to evaluate the associations between the PYF, ENSO, and the incidence of enterovirus infection. Compared to the ping-year group, the incidence rate of enterovirus infection, the incidence rate of EVSC, and the ratio of EVSC in the non-ping-year group were 1.24, 3.38, and 2.73 times higher, respectively (p < 0.001). For every one-unit increase in La Niña, the incidence rate of enterovirus infection decreased to 0.96 times (p < 0.001). Our study indicated that CFCs could be potential predictors for enterovirus infection, and the PYF was more suitable than ENSO. Further research is needed to improve the predictive model.

Augmented human thermal discomfort in urban centers of the Arabian Peninsula

Anthropogenic climate change has amplified human thermal discomfort in urban environments. Despite the considerable risks posed to public health, there is a lack of comprehensive research, evaluating the spatiotemporal changes in human thermal discomfort and its characteristics in hot-hyper arid regions, such as the Arabian Peninsula (AP). The current study analyzes spatiotemporal changes in human thermal discomfort categories and their characteristics in AP, using the newly developed high-resolution gridded ERA5-HEAT (Human thErmAl comforT) dataset for the period 1979-2022. In addition, the study assesses the interplay between the Universal Thermal Climate Index (UTCI) and El Niño-Southern Oscillation (ENSO) indices for the study period. The results reveal a significant increase in human thermal discomfort and its characteristics, with higher spatial variability in the AP region. The major urban centers in the southwestern, central, and southeastern parts of AP have experienced significant increases in human thermal discomfort (0.4-0.8 °C), with higher frequency and intensity of thermal stress during the study period. The temporal distribution demonstrates a linear increase in UTCI indices and their frequencies and intensities, particularly from 1998 onward, signifying a transition towards a hotter climate characterized by frequent, intense, and prolonged heat stress conditions. Moreover, the UTCI and ENSO indices exhibit a dipole pattern of correlation with a positive (negative) pattern in the southwestern (eastern parts) of AP. The study's findings suggest that policymakers and urban planners need to prioritize public health and well-being in AP's urban areas, especially for vulnerable groups, by implementing climate change adaptation and mitigation strategies, and carefully designing future cities to mitigate the effects of heat stress.

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.

Seasonal acoustic presence of marine mammals at the South Orkney Islands, Scotia Sea

Increased knowledge about marine mammal seasonal distribution and species assemblage from the South Orkney Islands waters is needed for the development of management regulations of the commercial fishery for Antarctic krill (Euphausia superba) in this region. Passive acoustic monitoring (PAM) data were collected during the autumn and winter seasons in two consecutive years (2016, 2017), which represented highly contrasting environmental conditions due to the 2016 El Niño event. We explored differences in seasonal patterns in marine mammal acoustic presence between the two years in context of environmental cues and climate variability. Acoustic signals from five baleen whale species, two pinniped species and odontocete species were detected and separated into guilds. Although species diversity remained stable over time, the ice-avoiding and ice-affiliated species dominated before and after the onset of winter, respectively, and thus demonstrating a shift in guild composition related to season. Herein, we provide novel information about local marine mammal species diversity, community structure and residency times in a krill hotspot. Our study also demonstrates the utility of PAM data and its usefulness in providing new insights into the marine mammal habitat use and responses to environmental conditions, which are essential knowledge for the future development of a sustainable fishery management in a changing ecosystem.

Orographic amplification of El Niño teleconnections on winter precipitation across the Intermountain West of North America

A large proportion of western North America experiences regular water stress, compounded by high seasonal and interannual variability. In the Intermountain West region, the El Niño/Southern Oscillation (ENSO) is a critical control on winter precipitation, but the nature of this signal is entangled with a combination of orographic effects and long-term climate trends. This study employs a spatially distributed, nonlinear spline model to isolate ENSO impacts from these other factors using gauge-based observations starting in 1871. In contrast to previous modelling approaches, our approach uses original gauge data, without shortening the record to accommodate a common period. This enables more detailed separation of ENSO effects from the confounding influence of topography and long-term trends, whereas the longer time frame permits more robust correlation with the ENSO signal. Here we show that the complex topography of the Intermountain West exaggerates the underlying ENSO signal, producing a 2.3-5.8 times increase in the range of ENSO-induced precipitation changes along high-elevation western slopes relative to lower elevations. ENSO effects on winter precipitation can be as large as ± 100 mm at high elevations. Further, our approach reveals that the previously recognized dipolar pattern of positive (negative) association of ENSO with precipitation in the south (north) manifests as an incremental relationship in the south but as a near-binary switch in effects between El Niño and La Niña in the north. The location and extent of the strongest precipitation differences vary during the positive and negative ENSO phases within each region. The intricacies of these spatial- and elevation-based modulations of ENSO impacts are especially informative for the northern centre of this dipole, where ENSO-precipitation relationships have previously been difficult to resolve.

Advanced time-lagged effects of drought on global vegetation growth and its social risk in the 21st century

Extensive studies have demonstrated the restricting effect of past and present drought conditions on vegetation growth over the past three decades. However, the underlying mechanism of the impact of prior drought on vegetation growth - along with the magnitude of its impact over the rest of the 21st century - remains uncertain. Herein, we examined the evolution and characteristics of global vegetation growth and drought for both baseline (1982-2014) and future (2015-2100) periods under four representative pathways using the gross primary productivity (GPP) and the Standardized Precipitation Evapotranspiration Index from the CMIP6. Further, we investigated the time-lagged effects of drought on vegetation growth and the intensity of population and economy exposure to drought by identifying drought-threatened areas under four emission scenarios. The results show that, at the end of the 21st century, the global terrestrial GPP will experience an increasing trend under four scenarios, especially in SSP5-8.5, with a growth rate of 0.032 kg C m-2/decade, which is 10 times higher than that in SSP1-2.6. From the SSP1-2.6 to the SSP5-8.5 scenario, the SPEI change rates are -0.03, -0.01, -0.017, and -0.018/decade, respectively, indicating that the intensity of global drought events will rise with increases in CO2 emissions. 28.3%, 24.7%, 30.4%, and 35% of global land exhibit downward mean time-lagged months in four scenarios, especially in the middle-high latitudes of the northern hemisphere (>45°N), indicating an advanced response of vegetation to drought. Nearly 8, 9.1, 12.9, and 11.5 billion people - valued at 94,138 (SSP1-2.6), 976,020 (SSP2-4.5), 526,595 (SSP3-7.0), and 204,728 (SSP5-8.5) billion US$, respectively - will be threatened by continuous drought. Globally, the population and economy exposure to moderate and extreme drought zones is larger, and the economic risk from extreme droughts is 8 times greater under the high emissions scenario than the low emissions scenario.

Southern Hemisphere dominates recent decline in global water availability

Global land water underpins livelihoods, socioeconomic development, and ecosystems. It remains unclear how water availability has changed in recent decades. Using an ensemble of observations, we quantified global land water availability over the past two decades. We show that the Southern Hemisphere has dominated the declining trend in global water availability from 2001 to 2020. The significant decrease occurs mainly in South America, southwestern Africa, and northwestern Australia. In the Northern Hemisphere, the complex regional increasing and decreasing trends cancel each other, resulting in a negligible hemispheric trend. The variability and trend in water availability in the Southern Hemisphere are largely driven by precipitation associated with climate modes, particularly the El Niño-Southern Oscillation. This study highlights their dominant role in controlling global water availability.

Overemphasized role of preceding strong El Niño in generating multi-year La Niña events

Previous studies have emphasized the significance of a strong El Niño preceding La Niña (LN) in the formation of multi-year LN events due to the slow recharge-discharge ocean heat content process. However, observational analyses from 1900 to 2022 reveal that the majority (64%) of multi-year LN events did not necessitate a preceding strong El Niño to generate their second LN, suggesting an overemphasis on traditional views. Instead, here we show that a negative phase of the North Pacific Meridional Mode (PMM) during spring, when the first LN begins to decay, activates the mechanism responsible for triggering another LN and producing a multi-year event. The westward extension of the first LN's cold anomalies, which interact directly with the eastern edge of the western Pacific warm pool, is highlighted as a crucial factor in the occurrence of a negative PMM. Additionally, the PMM mechanism can create a third LN, leading to triple-dip events.

Complex interactions of ENSO and local conditions buffer the poleward shift of migratory fish in a subtropical seascape

Ocean warming is associated with the tropicalization of fish towards higher latitudes. However, the influence of global climatic phenomena like the El Niño Southern Oscillation (ENSO) and its warm (El Niño) and cold (La Niña) phases on tropicalization has been overlooked. Understanding the combined effects of global climatic forces together with local variability on the distribution and abundance of tropical fish is essential for building more accurate predictive models of species on the move. This is particularly important in regions where ENSO-related impacts are known to be major drivers of ecosystem change, and is compounded by predictions that El Niño is becoming more frequent and intense under current ocean warming. In this study, we used long-term time series of monthly standardized sampling (August 1996 to February 2020) to investigate how ocean warming, ENSO and local environmental variability influence the abundance of an estuarine dependent tropical fish species (white mullet Mugil curema) at subtropical latitudes in the southwestern Atlantic Ocean. Our work revealed a significant increasing trend in surface water temperature in shallow waters (<1.5 m) at estuarine and marine sites. However, against our initial expectation, we did not observe an increasing trend in the abundance of this tropical mullet species. Generalized Additive Models revealed complex, non-linear relationships between species abundance and environmental factors operating at large (ENSO's warm and cold phases), regional (freshwater discharge in the coastal lagoon's drainage basin) and local (temperature and salinity) scales across the estuarine marine gradient. These results demonstrate that fish responses to global climate change can be complex and multifaceted. More specifically, our findings suggested that the interaction among global and local driving forces dampen the expected effect of tropicalization for this mullet species in a subtropical seascape.

Key tropical crops at risk from pollinator loss due to climate change and land use

Insect pollinator biodiversity is changing rapidly, with potential consequences for the provision of crop pollination. However, the role of land use-climate interactions in pollinator biodiversity changes, as well as consequent economic effects via changes in crop pollination, remains poorly understood. We present a global assessment of the interactive effects of climate change and land use on pollinator abundance and richness and predictions of the risk to crop pollination from the inferred changes. Using a dataset containing 2673 sites and 3080 insect pollinator species, we show that the interactive combination of agriculture and climate change is associated with large reductions in insect pollinators. As a result, it is expected that the tropics will experience the greatest risk to crop production from pollinator losses. Localized risk is highest and predicted to increase most rapidly, in regions of sub-Saharan Africa, northern South America, and Southeast Asia. Via pollinator loss alone, climate change and agricultural land use could be a risk to human well-being.

Enhanced multi-year predictability after El Niño and La Niña events

Several aspects of regional climate including near-surface temperature and precipitation are predictable on interannual to decadal time scales. Despite indications that some climate states may provide higher predictability than others, previous studies analysing decadal predictions typically sample a variety of initial conditions. Here we assess multi-year predictability conditional on the phase of the El Niño-Southern Oscillation (ENSO) at the time of prediction initialisation. We find that predictions starting with El Niño or La Niña conditions exhibit higher skill in predicting near-surface air temperature and precipitation multiple years in advance, compared to predictions initialised from neutral ENSO conditions. This holds true in idealised prediction experiments with the Community Climate System Model Version 4 and to a lesser extent also real-world predictions using the Community Earth System Model and a multi-model ensemble of hindcasts contributed to the Coupled Model Intercomparison Project Phase 6 Decadal Climate Prediction Project. This enhanced predictability following ENSO events is related to phase transitions as part of the ENSO cycle, and related global teleconnections. Our results indicate that certain initial states provide increased predictability, revealing windows of opportunity for more skillful multi-year predictions.

Reflections on the impact and response to the Peruvian 2017 Coastal El Niño event: Looking to the past to prepare for the future

Climate-related phenomena in Peru have been slowly but continuously changing in recent years beyond historical variability. These include sea surface temperature increases, irregular precipitation patterns and reduction of glacier-covered areas. In addition, climate scenarios show amplification in rainfall variability related to the warmer conditions associated with El Niño events. Extreme weather can affect human health, increase shocks and stresses to the health systems, and cause large economic losses. In this article, we study the characteristics of El Niño events in Peru, its health and economic impacts and we discuss government preparedness for this kind of event, identify gaps in response, and provide evidence to inform adequate planning for future events and mitigating impacts on highly vulnerable regions and populations. This is the first case study to review the impact of a Coastal El Niño event on Peru's economy, public health, and governance. The 2017 event was the third strongest El Niño event according to literature, in terms of precipitation and river flooding and caused important economic losses and health impacts. At a national level, these findings expose a need for careful consideration of the potential limitations of policies linked to disaster prevention and preparedness when dealing with El Niño events. El Niño-related policies should be based on local-level risk analysis and efficient preparedness measures in the face of emergencies.

Increased occurrences of consecutive La Niña events under global warming

Most El Niño events occur sporadically and peak in a single winter^1-3, whereas La Niña tends to develop after an El Niño and last for two years or longer^4-7. Relative to single-year La Niña, consecutive La Niña features meridionally broader easterly winds and hence a slower heat recharge of the equatorial Pacific^6,7, enabling the cold anomalies to persist, exerting prolonged impacts on global climate, ecosystems and agriculture^8-13. Future changes to multi-year-long La Niña events remain unknown. Here, using climate models under future greenhouse-gas forcings¹⁴, we find an increased frequency of consecutive La Niña ranging from 19 ± 11% in a low-emission scenario to 33 ± 13% in a high-emission scenario, supported by an inter-model consensus stronger in higher-emission scenarios. Under greenhouse warming, a mean-state warming maximum in the subtropical northeastern Pacific enhances the regional thermodynamic response to perturbations, generating anomalous easterlies that are further northward than in the twentieth century in response to El Niño warm anomalies. The sensitivity of the northward-broadened anomaly pattern is further increased by a warming maximum in the equatorial eastern Pacific. The slower heat recharge associated with the northward-broadened easterly anomalies facilitates the cold anomalies of the first-year La Niña to persist into a second-year La Niña. Thus, climate extremes as seen during historical consecutive La Niña episodes probably occur more frequently in the twenty-first century.

Influence of El Niño on the variability of global shoreline position

Coastal zones are fragile and complex dynamical systems that are increasingly under threat from the combined effects of anthropogenic pressure and climate change. Using global satellite derived shoreline positions from 1993 to 2019 and a variety of reanalysis products, here we show that shorelines are under the influence of three main drivers: sea-level, ocean waves and river discharge. While sea level directly affects coastal mobility, waves affect both erosion/accretion and total water levels, and rivers affect coastal sediment budgets and salinity-induced water levels. By deriving a conceptual global model that accounts for the influence of dominant modes of climate variability on these drivers, we show that interannual shoreline changes are largely driven by different ENSO regimes and their complex inter-basin teleconnections. Our results provide a new framework for understanding and predicting climate-induced coastal hazards.

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.

Middle Pleistocene re-organization of Australian Monsoon

The sensitivity of the Australian Monsoon to changing climate boundary conditions remains controversial due to limited understanding of forcing processes and past variability. Here, we reconstruct austral summer monsoonal discharge and wind-driven winter productivity across the Middle Pleistocene Transition (MPT) in a sediment sequence drilled off NW Australia. We show that monsoonal precipitation and runoff primarily responded to precessional insolation forcing until ~0.95 Ma, but exhibited heightened sensitivity to ice volume and pCO₂ related feedbacks following intensification of glacial-interglacial cycles. Our records further suggest that summer monsoon variability at the precessional band was closely tied to the thermal evolution of the Indo-Pacific Warm Pool and strength of the Walker circulation over the past ~1.6 Myr. By contrast, productivity proxy records consistently tracked glacial-interglacial variability, reflecting changing rhythms in polar ice fluctuations and Hadley circulation strength. We conclude that the Australian Monsoon underwent a major re-organization across the MPT and that extratropical feedbacks were instrumental in driving short- and long-term variability.

Population exposure to drought severities under shared socioeconomic pathways scenarios in India

As a widespread natural hazard, droughts impact several aspects of human society adversely. Thus, the present study aims to answer the following research questions; (i) What are the expected variabilities in different drought conditions over India in the future? (ii) How the population exposure to drought varies under different climate change and population scenarios? (iii) How is the total exposure attributed to the individual exposure (climate, population, and interaction) in future climate change scenarios? In this sense, the study is performed under four Shared Socioeconomic Pathways scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) using thirteen Global Climate Models from Coupled Model Intercomparison Project Phase 6 and Standardized Precipitation Evapotranspiration Index as a drought indicator. The future period is divided into two parts i.e., 2023-2061 (T1) and 2062-2100 (T2), and compared with the historical period during 1967-2005. The results show that the severe (56 % to 72 % of the area) and extreme (99 % of the area) droughts are likely to increase under all the scenarios for 3-month scale conditions, respectively. The drought intensity is projected to increase under 3-and 12-month scale drought conditions. The population exposure to the extreme drought severity is anticipated to increase for both the drought conditions and the highest exposure is noticed under the SSP3-7.0 scenario. The significant contribution from climate or interaction effects is observed in the case of 3- and 9-month scale extreme drought conditions. The present study necessitates a call for effective measures to alleviate the risk, especially in the high-risk areas of India.

A self-attention-based neural network for three-dimensional multivariate modeling and its skillful ENSO predictions

Large biases and uncertainties remain in real-time predictions of El Niño-Southern Oscillation (ENSO) using process-based dynamical models; recent advances in data-driven deep learning algorithms provide a promising mean to achieve superior skill in the tropical Pacific sea surface temperature (SST) modeling. Here, a specific self-attention-based neural network model is developed for ENSO predictions based on the much sought-after Transformer model, named 3D-Geoformer, which is used to predict three-dimensional (3D) upper-ocean temperature anomalies and wind stress anomalies. This purely data-driven and time-space attention-enhanced model achieves surprisingly high correlation skills for Niño 3.4 SST anomaly predictions made 18 months in advance and initiated beginning in boreal spring. Further, sensitivity experiments demonstrate that the 3D-Geoformer model can depict the evolution of upper-ocean temperature and the coupled ocean-atmosphere dynamics following the Bjerknes feedback mechanism during ENSO cycles. Such successful realizations of the self-attention-based model in ENSO predictions indicate its great potential for multidimensional spatiotemporal modeling in geoscience.

A modified extreme event-based synchronicity measure for climate time series

Extreme event-based synchronicity is a specific measure of similarity of extreme event-like time series. It is capable to capture the nonlinear interactions between climatic extreme events. In this study, we proposed a modified extreme event-based synchronicity measure that incorporates two types of extreme events (positive and negative) simultaneously in climate anomalies to characterize the synchronization and time delays. Statistical significance of the modified extreme event synchronization measure is tested by Monte-Carlo simulations. The applications of the modified extreme event-based synchronicity measure on synthetic time series verified that it was superior to the traditional event synchronicity measure. Both synchronous and antisynchronous features between climate time series could be captured by the modified measure. It is potentially applied in investigating the interrelationship between climate extremes and climate index or constructing complex networks of climate variables. In addition, this modified extreme event-based synchronicity measure could be easily applied to other types of time series just by identifying the extreme events properly.

Probabilistic projections of El Niño Southern Oscillation properties accounting for model dependence and skill

The El Niño - Southern Oscillation (ENSO) is a dominant mode of global climate variability. Nevertheless, future multi-model probabilistic projections of ENSO properties have not yet been made. Main roadblocks that have been hindering making these projections are climate model dependence and difficulty in quantifying historical model performance. Dependence is broadly defined as similarity between climate model output, assumptions, or physical parameterizations. Here, we propose a unifying metric of relative model performance, based on the probability density function (PDF) of ENSO paths. This metric is applied to assess the overall skill of Climate Model Intercomparison Project phase 6 (CMIP6) climate models at capturing ENSO. We then perform future multi-model probabilistic projections of changes in ENSO properties (from years 1850-1949 to 2040-2099) under the shared socioeconomic pathway scenario SSP585, accounting for model skill and dependence. We find that future ENSO will likely be more seasonally locked (89% chance), and have a longer period (67% chance). Yet, the jury is still out on future ENSO amplification. Our method reduces uncertainty by up to 37% compared to a simple approach ignoring model dependence and skill.

El Niño-Southern Oscillation affects the species-level temporal variation in seed and leaf fall in a mixed temperate forest

El Niño-Southern Oscillation (ENSO), the variation between anomalously cold (La Niña) and warm conditions (El Niño), is one of the most prominent large-scale climate patterns with worldwide effects. Elevated seed and leaf fall has been found at the positive phase of ENSO (El Niño) in tropical forests. However, how seed and leaf fall respond to ENSO at species level is understudied, especially in temperate forests. In this study, we monitored seed and leaf fall at the species-level at 150 points across a 25-ha temperate forest in northeastern China over a span of 12 years. Using time series and wavelet analyses, we assessed three hypotheses: 1) temperate tree species' seed and leaf fall are strongly, but differently, correlated with ENSO and, 2) community synchrony in seed and leaf occurred both at seasonal and ENSO scales; finally, 3) local climatic modulated the effects of ENSO on seed and leaf fall. We found that ENSO was significantly correlated with seed and leaf fall of all species, although correlation strength varied across species (r = 0.206-0.658). Specifically, ENSO indices (ENSO12 or ENSO34) accounted for the most variation in seed and leaf fall of Acer pseudo-sieboldianum (40 % and 34 %, respectively) and ranged 4 %-31 % in all other species. Leaf fall was synchronous with ENSO cycles with a period of 2-7 years, but community synchrony of seed fall was only detected at seasonal scales. ENSO influenced seed fall of Fraxinus mandshurica and Tilla amurensis by mediating rainfall and relative humidity, respectively, highlighting the interactive effects of local climate and ENSO. Our findings highlight the potential effects of ENSO on ecosystems outside of tropical regions and improve our ability to predict regeneration dynamics and nutrient cycling of temperate forests under the context of global change.

Physics captured by data-based methods in El Niño prediction

On average once every four years, the Tropical Pacific warms considerably during events called El Niño, leading to weather disruptions over many regions on Earth. Recent machine-learning approaches to El Niño prediction, in particular, Convolutional Neural Networks (CNNs), have shown a surprisingly high skill at relatively long lead times. In an attempt to understand this high skill, we here use data from distorted physics simulations with the intermediate-complexity Zebiak-Cane model to determine what aspects of El Niño physics are represented in a specific CNN-based classification method. We find that the CNN can adequately correct for distortions in the ocean adjustment processes, but that the machine-learning method has far more trouble in dealing with distortions in upwelling feedback strength.

Thermal coupling of the Indo-Pacific warm pool and Southern Ocean over the past 30,000 years

The role of the tropical Pacific Ocean and its linkages to the southern hemisphere during the last deglacial warming remain highly controversial. Here we explore the evolution of Pacific horizontal and vertical thermal gradients over the past 30 kyr by compiling 340 sea surface and 7 subsurface temperature records, as well as one new ocean heat content record. Our records reveal that La Niña-like conditions dominated during the deglaciation as a result of the more intense warming in the western Pacific warm pool. Both the subsurface temperature and ocean heat content in the warm pool rose earlier than the sea surface temperature, and in phase with South Pacific subsurface temperature and orbital precession, implying that heat exchange between the tropical upper water column and the extratropical Southern Ocean facilitated faster warming in the western Pacific. Our study underscores the key role of the thermal coupling between the warm pool and the Southern Ocean and its relevance for future global warming.

The mechanism of the last deglacial global warming is key for future climate. Here, the authors shed light on the pivotal role of the thermal coupling between the western Pacific warm pool and the Southern Ocean.

El Niño frequency threshold controls coastal biotic communities

El Niño has profound influences on ecosystem dynamics. However, we know little about how it shapes vertebrate faunal community composition over centennial time scales, and this limits our ability to forecast change under projections of future El Niño events. On the basis of correlations between geological records of past El Niño frequency and the species composition of bird and fish remains from a Baja California bone deposit that spans the past 12,000 years, we documented marked faunal restructuring when major El Niño events occurred more than five times per century. This tipping point has implications for the past and future ecology of eastern Pacific coastal environments.

Holocene centennial variability in sea surface temperature and linkage with solar irradiance

The climate periodically fluctuates on various time scales, however, there remains a lack of consensus on the centennial-scale variabilities and associated driving force. A continuous high-resolution sea surface temperature (SST) record allows for the detection of centennial-scale fluctuations. This study presents a high-resolution SST record covering the last 10,000 years based on the analysis of the alkenone unsaturation index in marine sediment cores off the southwest coast of the Korean Peninsula. Alkenone SST's spectral and wavelet analysis revealed significant periodicities of 414, 190, 135, 102, and 89 years at a > 90% confidence level. These cycles exhibit extreme proximity to the solar activity cycles of 353, 206 (Suess/de Vries cycles), 130, and 104–87 years (Gleissberg cycles), suggesting that the multidecadal to centennial variations in SST are linked to solar forcing. To the best of our knowledge, this is the first high-resolution Holocene SST record that all solar activity cycles on centennial scale match, suggesting centennial-scale variability in the climate system and illustrating the role of solar activity on SST change in the mid-latitude region of the Northern Hemisphere.

Regional Responses of the Northern Hemisphere Subtropical Jet Stream to Reduced Arctic Sea Ice Extent

The effect of Arctic sea ice loss on the boreal winter regional trends of wind speed and latitudinal position of the Northern Hemisphere subtropical jet stream (STJ) in 1980–2012 is investigated. Two sets of global simulations with reduced Arctic sea ice extent are analyzed: simulations that, south of 70 N, use a climatological annual cycle of the sea surface temperature (SST) and a second set that uses full SST variability. Results with the climatological SST have a significant but weak response of the STJ wind speed and latitudinal position to the warmer Arctic: the wind speed generally decreases and the jet core is displaced equatorward. However, in the realistic SST simulations, the effect of Arctic warming is only slightly evident in a small equatorward shift of the jet over the Atlantic basin. Over the Pacific basin the STJ is mostly driven by tropical and mid-latitude SST variability, with little influence from the Arctic region. A weakening and poleward shift of the STJ that is observed in the realistic SST simulations over the Pacific basin is attributed to negative SST trends in the tropical Pacific and the consequent weakening of the mid-latitude meridional gradient of geopotential height in the upper troposphere.

Advances and challenges of operational seasonal prediction in Pacific Island Countries

Seasonal climate forecasts play a critical role in building a climate-resilient society in the Pacific Island Countries (PICs) that are highly exposed to high-impact climate events. To assist the PICs National Meteorological and Hydrological Services in generating reliable national climate outlooks, we developed a hybrid seasonal prediction system, the Pacific Island Countries Advanced Seasonal Outlook (PICASO), which has the strengths of both statistical and dynamical systems. PICASO is based on the APEC Climate Center Multi-Model Ensemble (APCC-MME), tailored to generate station-level rainfall forecasts for 49 stations in 13 countries by applying predictor optimization and the large-scale relationship-based Bayesian regression approaches. Overall, performance is improved and further stabilized temporally and spatially relative to not only APCC-MME but also other existing operational prediction systems in the Pacific. Gaps and challenges in operationalization of the PICASO system and its incorporation into operational climate services in the PICs are discussed.

Systematic multi-scale decomposition of ocean variability using machine learning

Multi-scale systems, such as the climate system, the atmosphere, and the ocean, are hard to understand and predict due to their intrinsic nonlinearities and chaotic behavior. Here, we apply a physics-consistent machine learning method, the multi-resolution dynamic mode decomposition (mrDMD), to oceanographic data. mrDMD allows a systematic decomposition of high-dimensional data sets into time-scale dependent modes of variability. We find that mrDMD is able to systematically decompose sea surface temperature and sea surface height fields into dynamically meaningful patterns on different time scales. In particular, we find that mrDMD is able to identify varying annual cycle modes and is able to extract El Nino-Southern Oscillation events as transient phenomena. mrDMD is also able to extract propagating meanders related to the intensity and position of the Gulf Stream and Kuroshio currents. While mrDMD systematically identifies mean state changes similarly well compared to other methods, such as empirical orthogonal function decomposition, it also provides information about the dynamically propagating eddy component of the flow. Furthermore, these dynamical modes can also become progressively less important as time progresses in a specific time period, making them also state dependent.

NoLiTiA: An Open-Source Toolbox for Non-linear Time Series Analysis

In many scientific fields including neuroscience, climatology or physics, complex relationships can be described most parsimoniously by non-linear mechanics. Despite their relevance, many neuroscientists still apply linear estimates in order to evaluate complex interactions. This is partially due to the lack of a comprehensive compilation of non-linear methods. Available packages mostly specialize in only one aspect of non-linear time-series analysis and most often require some coding proficiency to use. Here, we introduce NoLiTiA, a free open-source MATLAB toolbox for non-linear time series analysis. In comparison to other currently available non-linear packages, NoLiTiA offers (1) an implementation of a broad range of classic and recently developed methods, (2) an implementation of newly proposed spatially and time-resolved recurrence amplitude analysis and (3) an intuitive environment accessible even to users with little coding experience due to a graphical user interface and batch-editor. The core methodology derives from three distinct fields of complex systems theory, including dynamical systems theory, recurrence quantification analysis and information theory. Besides established methodology including estimation of dynamic invariants like Lyapunov exponents and entropy-based measures, such as active information storage, we include recent developments of quantifying time-resolved aperiodic oscillations. In general, the toolbox will make non-linear methods accessible to the broad neuroscientific community engaged in time series processing.

Variability of ENSO Forecast Skill in 2-Year Global Reforecasts Over the 20th Century

In order to explore temporal changes of predictability of El Niño Southern Oscillation (ENSO), a novel set of global biennial climate reforecasts for the historical period 1901-2010 has been generated using a modern initialized coupled forecasting system. We find distinct periods of enhanced long-range skill at the beginning and at the end of the twentieth century, and an extended multi-decadal epoch of reduced skill during the 1930s-1950s. Once the forecast skill extends beyond the first spring barrier, the predictability limit is much enhanced and our results provide support for the feasibility of skillful ENSO forecasts up to 18 months. Changes in the mean state, variability (amplitude), persistence, seasonal cycle and predictability suggest that multi-decadal variations in the dynamical characteristics of ENSO rather than the data coverage and quality of the observations have primarily driven the reported non-monotonic skill modulations.

Analysis of ENSO-Driven Variability, and Long-Term Changes, of Extreme Precipitation Indices in Colombia, Using the Satellite Rainfall Estimates CHIRPS

Climate change includes the change of the long-term average values and the change of the tails of probability density functions, where the extreme events are located. However, obtaining average values are more straightforward than the high temporal resolution information necessary to catch the extreme events on those tails. Such information is difficult to get in areas lacking sufficient rain stations. Thanks to the development of Satellite Precipitation Estimates with a daily resolution, this problem has been overcome, so Extreme Precipitation Indices (EPI) can be calculated for the entire Colombian territory. However, Colombia is strongly affected by the ENSO (El Niño—Southern Oscillation) phenomenon. Therefore, it is pertinent to ask if the EPI’s long-term change due to climate change is more critical than the anomalies due to climate variability induced by the warm and cold phases of ENSO (El Niño and La Niña, respectively). In this work, we built EPI annual time series at each grid-point of the selected Satellite Precipitation Estimate (CHIRPSv2) over Colombia to answer the previous question. Then, the Mann-Whitney-Wilcoxon test was used to compare the samples drawn in each case (i.e., change tests due to both long-term and climatic variability). After performing the analyses, we realized that the importance of the change depends on the region analyzed and the considered EPI. However, some general conclusions became evident: during El Niño years (La Niña), EPI’s anomaly follows the general trend of reduction -drier conditions- (increase; -wetter conditions-) observed in Colombian annual precipitation amount, but only on the Pacific, the Caribbean, and the Andean region. In the Eastern plains of Colombia (Orinoquía and Amazonian region), EPI show a certain insensitivity to change due to climatic variability. On the other hand, EPI’s long-term changes in the Pacific, the Caribbean, and the Andean region are spatially scattered. Still, long-term changes in the eastern plains have a moderate spatial consistency with statistical significance.

Long-Term Landsat-Based Monthly Burned Area Dataset for the Brazilian Biomes Using Deep Learning

Fire is a significant agent of landscape transformation on Earth, and a dynamic and ephemeral process that is challenging to map. Difficulties include the seasonality of native vegetation in areas affected by fire, the high levels of spectral heterogeneity due to the spatial and temporal variability of the burned areas, distinct persistence of the fire signal, increase in cloud and smoke cover surrounding burned areas, and difficulty in detecting understory fire signals. To produce a large-scale time-series of burned area, a robust number of observations and a more efficient sampling strategy is needed. In order to overcome these challenges, we used a novel strategy based on a machine-learning algorithm to map monthly burned areas from 1985 to 2020 using Landsat-based annual quality mosaics retrieved from minimum NBR values. The annual mosaics integrated year-round observations of burned and unburned spectral data (i.e., RED, NIR, SWIR-1, and SWIR-2), and used them to train a Deep Neural Network model, which resulted in annual maps of areas burned by land use type for all six Brazilian biomes. The annual dataset was used to retrieve the frequency of the burned area, while the date on which the minimum NBR was captured in a year, was used to reconstruct 36 years of monthly burned area. Results of this effort indicated that 19.6% (1.6 million km2) of the Brazilian territory was burned from 1985 to 2020, with 61% of this area burned at least once. Most of the burning (83%) occurred between July and October. The Amazon and Cerrado, together, accounted for 85% of the area burned at least once in Brazil. Native vegetation was the land cover most affected by fire, representing 65% of the burned area, while the remaining 35% burned in areas dominated by anthropogenic land uses, mainly pasture. This novel dataset is crucial for understanding the spatial and long-term temporal dynamics of fire regimes that are fundamental for designing appropriate public policies for reducing and controlling fires in Brazil.