El Nino/Southern Oscillation response to global warming

Towards ecosystem-based techniques for tipping point detection

An ecosystem shifts to an alternative stable state when a threshold of accumulated pressure (i.e. direct impact of environmental change or human activities) is exceeded. Detecting this threshold in empirical data remains a challenge because ecosystems are governed by complex interlinkages and feedback loops between their components and pressures. In addition, multiple feedback mechanisms exist that can make an ecosystem resilient to state shifts. Therefore, unless a broad ecological perspective is used to detect state shifts, it remains questionable to what extent current detection methods really capture ecosystem state shifts and whether inferences made from smaller scale analyses can be implemented into ecosystem management. We reviewed the techniques currently used for retrospective detection of state shifts detection from empirical data. We show that most techniques are not suitable for taking a broad ecosystem perspective because approximately 85% do not combine intervariable non-linear relationships and high-dimensional data from multiple ecosystem variables, but rather tend to focus on one subsystem of the ecosystem. Thus, our perception of state shifts may be limited by methods that are often used on smaller data sets, unrepresentative of whole ecosystems. By reviewing the characteristics, advantages, and limitations of the current techniques, we identify methods that provide the potential to incorporate a broad ecosystem-based approach. We therefore provide perspectives into developing techniques better suited for detecting ecosystem state shifts that incorporate intervariable interactions and high-dimensionality data.

Hidden high-risk states identification from routine urban traffic

One of the core risk management tasks is to identify hidden high-risk states that may lead to system breakdown, which can provide valuable early warning knowledge. However, due to the high dimensionality and nonlinear interactions embedded in large-scale complex systems like urban traffic, it remains challenging to identify hidden high-risk states from huge system state space where over 99% of possible system states are not yet visited in empirical data. Based on the maximum entropy model, we infer the underlying interaction network from complicated dynamical processes of urban traffic and construct the system energy landscape. In this way, we can locate hidden high-risk states that may have never been observed from real data. These states can serve as risk signals with a high probability of entering hazardous minima in the energy landscape, which lead to huge recovery cost. Our findings might provide insights for complex system risk management.

Climate-related health impact indicators for public health surveillance in a changing climate: a systematic review and local suitability analysis

Climate change challenges public health. Effective management of climate-related health risks relies on robust public health surveillance (PHS) and population health indicators. Despite existing global and country-specific indicators, their integration into local PHS systems is limited, impacting decision-making. We conducted a systematic review examining population health indicators relevant to climate change impacts and their suitability for national PHS systems. Guided by a registered protocol, we searched multiple databases and included 41 articles. Of these, 35 reported morbidity indicators, and 39 reported mortality indicators. Using Chile as a case study, we identified three sets of indicators for the Chilean PHS. The high-priority set included vector-, food-, and water-borne diseases, as well as temperature-related health outcomes indicators due to their easy integration into existing PHS systems. This review highlights the importance of population health indicators in monitoring climate-related health impacts, emphasising the need for local contextual factors to guide indicator selection.

Funding

This research project was partly funded by ANID Chile and University College London. None of these sources had any involvement in the research conceptualisation, design, or interpretation of the results.

Mega El Niño instigated the end-Permian mass extinction

The ultimate driver of the end-Permian mass extinction is a topic of much debate. Here, we used a multiproxy and paleoclimate modeling approach to establish a unifying theory elucidating the heightened susceptibility of the Pangean world to the prolonged and intensified El Niño events leading to an extinction state. As atmospheric partial pressure of carbon dioxide doubled from about 410 to about 860 ppm (parts per million) in the latest Permian, the meridional overturning circulation collapsed, the Hadley cell contracted, and El Niños intensified. The resultant deforestation, reef demise, and plankton crisis marked the start of a cascading environmental disaster. Reduced carbon sequestration initiated positive feedback, producing a warmer hothouse and, consequently, stronger El Niños. The compounding effects of elevated climate variability and mean state warming led to catastrophic but diachronous terrestrial and marine losses.

Increasing ocean wave energy observed in Earth's seismic wavefield since the late 20th century

Ocean waves excite continuous globally observable seismic signals. We use data from 52 globally distributed seismographs to analyze the vertical component primary microseism wavefield at 14-20 s period between the late 1980s and August 2022. This signal is principally composed of Rayleigh waves generated by ocean wave seafloor tractions at less than several hundred meters depth, and is thus a proxy for near-coastal swell activity. Here we show that increasing seismic amplitudes at 3σ significance occur at 41 (79%) and negative trends occur at 3σ significance at eight (15%) sites. The greatest absolute increase occurs for the Antarctic Peninsula with respective acceleration amplitude and energy trends ( ± 3σ) of 0.037 ± 0.008 nm s-2y-1 (0.36 ± 0.08% y-1) and 4.16 ± 1.07 nm2 s-2y-1 (0.58 ± 0.15% y-1), where percentage trends are relative to historical medians. The inferred global mean near-coastal ocean wave energy increase rate is 0.27 ± 0.03% y-1 for all data and is 0.35 ± 0.04% y-1 since 1 January 2000. Strongly correlated seismic amplitude station histories occur to beyond 50∘ of separation and show regional-to-global associations with El Niño and La Niña events.

Hysteresis of the El Niño-Southern Oscillation to CO2 forcing

El Niño-Southern Oscillation (ENSO) is the strongest interannual climate variability with far-reaching socioeconomic consequences. Many studies have investigated ENSO-projected changes under future greenhouse warming, but its responses to plausible mitigation behaviors remain unknown. We show that ENSO sea surface temperature (SST) variability and associated global teleconnection patterns exhibit strong hysteretic responses to carbon dioxide (CO2) reduction based on the 28-member ensemble simulations of the CESM1.2 model under an idealized CO2 ramp-up and ramp-down scenario. There is a substantial increase in the ensemble-averaged eastern Pacific SST anomaly variance during the ramp-down period compared to the ramp-up period. Such ENSO hysteresis is mainly attributed to the hysteretic response of the tropical Pacific Intertropical Convergence Zone meridional position to CO2 removal and is further supported by several selected single-member Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations. The presence of ENSO hysteresis leads to its amplified and prolonged impact in a warming climate, depending on the details of future mitigation pathways.

Stochastic resonance in climate reddening increases the risk of cyclic ecosystem extinction via phase-tipping

Human activity is leading to changes in the mean and variability of climatic parameters in most locations around the world. The changing mean has received considerable attention from scientists and climate policy makers. However, recent work indicates that the changing variability, that is, the amplitude and the temporal autocorrelation of deviations from the mean, may have greater and more imminent impact on ecosystems. In this paper, we demonstrate that changes in climate variability alone could drive cyclic predator-prey ecosystems to extinction via so-called phase-tipping (P-tipping), a new type of instability that occurs only from certain phases of the predator-prey cycle. We construct a mathematical model of a variable climate and couple it to two self-oscillating paradigmatic predator-prey models. Most importantly, we combine realistic parameter values for the Canada lynx and snowshoe hare with actual climate data from the boreal forest. In this way, we demonstrate that critically important species in the boreal forest have increased likelihood of P-tipping to extinction under predicted changes in climate variability, and are most vulnerable during stages of the cycle when the predator population is near its maximum. Furthermore, our analysis reveals that stochastic resonance is the underlying mechanism for the increased likelihood of P-tipping to extinction.

Origins of Barents-Kara sea-ice interannual variability modulated by the Atlantic pathway of El Niño-Southern Oscillation

Winter Arctic sea-ice concentration (SIC) decline plays an important role in Arctic amplification which, in turn, influences Arctic ecosystems, midlatitude weather and climate. SIC over the Barents-Kara Seas (BKS) shows large interannual variations, whose origin is still unclear. Here we find that interannual variations in winter BKS SIC have significantly strengthened in recent decades likely due to increased amplitudes of the El Niño-Southern Oscillation (ENSO) in a warming climate. La Niña leads to enhanced Atlantic Hadley cell and a positive phase North Atlantic Oscillation-like anomaly pattern, together with concurring Ural blocking, that transports Atlantic ocean heat and atmospheric moisture toward the BKS and promotes sea-ice melting via intensified surface warming. The reverse is seen during El Niño which leads to weakened Atlantic poleward transport and an increase in the BKS SIC. Thus, interannual variability of the BKS SIC partly originates from ENSO via the Atlantic pathway.

Assessing Sumatran Peat Vulnerability to Fire under Various Condition of ENSO Phases Using Machine Learning Approaches

In recent decades, catastrophic wildfire episodes within the Sumatran peatland have contributed to a large amount of greenhouse gas emissions. The El-Nino Southern Oscillation (ENSO) modulates the occurrence of fires in Indonesia through prolonged hydrological drought. Thus, assessing peatland vulnerability to fires and understanding the underlying drivers are essential to developing adaptation and mitigation strategies for peatland. Here, we quantify the vulnerability of Sumatran peat to fires under various ENSO conditions (i.e., El-Nino, La-Nina, and Normal phases) using correlative modelling approaches. This study used climatic (i.e., annual precipitation, SPI, and KBDI), biophysical (i.e., below-ground biomass, elevation, slope, and NBR), and proxies to anthropogenic disturbance variables (i.e., access to road, access to forests, access to cities, human modification, and human population) to assess fire vulnerability within Sumatran peatlands. We created an ensemble model based on various machine learning approaches (i.e., random forest, support vector machine, maximum entropy, and boosted regression tree). We found that the ensemble model performed better compared to a single algorithm for depicting fire vulnerability within Sumatran peatlands. The NBR highly contributed to the vulnerability of peatland to fire in Sumatra in all ENSO phases, followed by the anthropogenic variables. We found that the high to very-high peat vulnerability to fire increases during El-Nino conditions with variations in its spatial patterns occurring under different ENSO phases. This study provides spatially explicit information to support the management of peat fires, which will be particularly useful for identifying peatland restoration priorities based on peatland vulnerability to fire maps. Our findings highlight Riau’s peatland as being the area most prone to fires area on Sumatra Island. Therefore, the groundwater level within this area should be intensively monitored to prevent peatland fires. In addition, conserving intact forests within peatland through the moratorium strategy and restoring the degraded peatland ecosystem through canal blocking is also crucial to coping with global climate change.

Future intensification of extreme Aleutian low events and their climate impacts

Extreme Aleutian Low (AL) events have been associated with major ecosystem reorganisations and unusual weather patterns in the Pacific region, with serious socio-economic consequences. Yet, their future evolution and impacts on atmosphere-ocean interactions remain uncertain. Here, a large ensemble of historical and future runs from the Community Earth System Model is used to investigate the evolution of AL extremes. The frequency and persistence of AL extremes are quantified and their connection with climatic variables is examined. AL extremes become more frequent and persistent under the RCP8.5 scenario, associated with changes in precipitation and air temperature patterns over North America. Future changes in AL extremes also increase the variability of the sea surface temperature and net heat fluxes in the Kuroshio Extension, the most significant heat and energy flux region of the basin. The increased frequency and persistence of future AL extremes may potentially cause substantial changes in fisheries and ecosystems of the entire Pacific region as a knock-on effect.

Impact of Meteorological Factors and Southern Oscillation Index on Scrub Typhus Incidence in Guangzhou, Southern China, 2006-2018

Background: Scrub typhus was epidemic in the western Pacific Ocean area and East Asia, scrub typhus epidemic in densely populated areas in southern China. To better understand the association between meteorological variables, Southern Oscillation Index (SOI), and scrub typhus incidence in Guangzhou was benefit to the control and prevention.

Methodology/Principal Findings: We collected weekly data for scrub typhus cases and meteorological variables in Guangzhou, and Southern Oscillation Index from 2006 to 2018, and used the distributed lag non-linear models to evaluate the relationships between meteorological variables, SOI and scrub typhus. The median value of each variable was set as the reference. The high-risk occupations were farmer (51.10%), house worker (17.51%), and retiree (6.29%). The non-linear relationships were observed with different lag weeks. For example, when the mean temperature was 27.7°C with1-week lag, the relative risk (RR) was highest as 1.08 (95% CI: 1.01–1.17). The risk was the highest when the relative humidity was 92.0% with 9-week lag, with the RR of 1.10 (95% CI: 1.02–1.19). For aggregate rainfall, the highest RR was 1.06 (95% CI: 1.03–1.11), when it was 83.0 mm with 4-week lag. When the SOI was 19 with 11-week lag, the highest RR was 1.06 (95% CI: 1.01–1.12). Most of the extreme effects of SOI and meteorological factors on scrub typical cases were statistically significant.

Conclusion/Significance: The high-risk occupations of scrub typhus in Guangzhou were farmer, house worker, and retiree. Meteorological factors and SOI played an important role in scrub typhus occurrence in Guangzhou. Non-linear relationships were observed in almost all the variables in our study. Approximately, mean temperature, and relative humidity positively correlated to the incidence of scrub typhus, on the contrary to atmospheric pressure and weekly temperature range (WTR). Aggregate rainfall and wind velocity showed an inverse-U curve, whereas the SOI appeared the bimodal distribution. These findings can be helpful to facilitate the development of the early warning system to prevent the scrub typhus.

IASI-Derived Sea Surface Temperature Data Set for Climate Studies

Sea surface temperature (SST) is an essential climate variable, that is directly used in climate monitoring. Although satellite measurements can offer continuous global coverage, obtaining a long-term homogeneous satellite-derived SST data set suitable for climate studies based on a single instrument is still a challenge. In this work, we assess a homogeneous SST data set derived from reprocessed Infrared Atmospheric Sounding Interferometer (IASI) level-1 (L1C) radiance data. The SST is computed using Planck's Law and simple atmospheric corrections. We assess the data set using the ERA5 reanalysis and the EUMETSAT-released IASI level-2 SST product. Over the entire period, the reprocessed IASI SST shows a mean global difference with ERA5 close to zero, a mean absolute bias under 0.5°C, with a SD of difference around 0.3°C and a correlation coefficient over 0.99. In addition, the reprocessed data set shows a stable bias and SD, which is an advantage for climate studies. The interannual variability and trends were compared with other SST data sets: ERA5, Hadley Centre's SST (HadISST), and NOAA's Optimal Interpolation SST Analysis (OISSTv2). We found that the reprocessed SST data set is able to capture the patterns of interannual variability well, showing the same areas of high interannual variability (>1.5°C), including over the tropical Pacific in January corresponding to the El Niño Southern Oscillation. Although the period studied is relatively short, we demonstrate that the IASI data set reproduces the same trend patterns found in the other data sets (i.e., cooling trend in the North Atlantic, warming trend over the Mediterranean).

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.

Hydrological cycle changes under global warming and their effects on multiscale climate variability

Despite a globally uniform increase in the concentrations of emitted greenhouse gases, radiatively forced surface warming can have significant spatial variations. These define warming patterns that depend on preexisting climate states and through atmospheric and oceanic dynamics can drive changes of the hydrological cycle with global-scale feedbacks. Our study reviews research progress on the hydrological cycle changes and their effects on multiscale climate variability. Overall, interannual variability is expected to become stronger in the Pacific and Indian Oceans and weaker in the Atlantic. Global monsoon rainfall is projected to increase and the wet season to lengthen despite a slowdown of atmospheric circulation. Strong variations among monsoon regions are likely to emerge, depending on surface conditions such as orography and land-sea contrast. Interdecadal climate variability is expected to modulate the globally averaged surface temperature change with pronounced anomalies in the polar and equatorial regions, leading to prolonged periods of enhanced or reduced warming. It is emphasized that advanced global observations, regional simulations, and process-level investigations are essential for improvements in understanding, predicting, and projecting the modes of climate variability, monsoon sensitivity, and energetic fluctuations in a warming climate.

Pathogen-Specific Impacts of the 2011-2012 La Niña-Associated Floods on Enteric Infections in the MAL-ED Peru Cohort: A Comparative Interrupted Time Series Analysis

Extreme floods pose multiple direct and indirect health risks. These risks include contamination of water, food, and the environment, often causing outbreaks of diarrheal disease. Evidence regarding the effects of flooding on individual diarrhea-causing pathogens is limited, but is urgently needed in order to plan and implement interventions and prioritize resources before climate-related disasters strike. This study applied a causal inference approach to data from a multisite study that deployed broadly inclusive diagnostics for numerous high-burden common enteropathogens. Relative risks (RRs) of infection with each pathogen during a flooding disaster that occurred at one of the sites-Loreto, Peru-were calculated from generalized linear models using a comparative interrupted time series framework with the other sites as a comparison group and adjusting for background seasonality. During the early period of the flood, increased risk of heat-stable enterotoxigenic E. coli (ST-ETEC) was identified (RR = 1.73 [1.10, 2.71]) along with a decreased risk of enteric adenovirus (RR = 0.36 [0.23, 0.58]). During the later period of the flood, sharp increases in the risk of rotavirus (RR = 5.30 [2.70, 10.40]) and sapovirus (RR = 2.47 [1.79, 3.41]) were observed, in addition to increases in transmission of Shigella spp. (RR = 2.86 [1.81, 4.52]) and Campylobacter spp. (RR = 1.41 (1.01, 1.07). Genotype-specific exploratory analysis reveals that the rise in rotavirus transmission during the flood was likely due to the introduction of a locally atypical, non-vaccine (G2P[4]) strain of the virus. Policy-makers should target interventions towards these pathogens-including vaccines as they become available-in settings where vulnerability to flooding is high as part of disaster preparedness strategies, while investments in radical, transformative, community-wide, and locally-tailored water and sanitation interventions are also needed.

Climate change and harmful benthic microalgae

Sea surface temperatures in the world's oceans are projected to warm by 0.4-1.4 °C by mid twenty-first century causing many tropical and sub-tropical harmful dinoflagellate genera like Gambierdiscus, Fukuyoa and Ostreopsis (benthic harmful algal bloom species, BHABs) to exhibit higher growth rates over much of their current geographic range, resulting in higher population densities. The primary exception to this trend will be in the tropics where temperatures exceed species-specific upper thermal tolerances (30-31 °C) beyond which growth slows significantly. As surface waters warm, migration to deeper habitats is expected to provide refuge. Range extensions of several degrees of latitude also are anticipated, but only where species-specific habitat requirements can be met (e.g., temperature, suitable substrate, low turbulence, light, salinity, pH). The current understanding of habitat requirements that determine species distributions are reviewed to provide fuller understanding of how individual species will respond to climate change from the present to 2055 while addressing the paucity of information on environmental factors controlling small-scale distribution in localized habitats. Based on the available information, we hypothesized how complex environmental interactions can influence abundance and potential range extensions of BHAB species in different biogeographic regions and identify sentinel sites appropriate for long-term monitoring programs to detect range extensions and reduce human health risks.

Climate Change in the Tropics: Ecological and Evolutionary Responses at Low Latitudes

Climate change is affecting every ecosystem on Earth. Though climate change is global in scope, literature reviews on the biotic impacts of climate change have focused on temperate and polar regions. Tropical species have distinct life histories and physiologies, and ecological communities are assembled differently across latitude. Thus, tropical species and communities may exhibit different responses to climate change compared with those in temperate and polar regions. What are the fingerprints of climate change in the tropics? This review summarizes the current state of knowledge on impacts of climate change in tropical regions and discusses research priorities to better understand the ways in which species and ecological communities are responding to climate change in the most biodiverse places on Earth.

Nonlinear response of Northern Hemisphere stratospheric polar vortex to the Indo-Pacific warm pool (IPWP) Niño

Variations in tropical sea surface temperatures (SST) have pronounced impacts on the stratospheric polar vortex, with the role of El Niño being the focus of much research interest. However, the Indo-Pacific warm pool (IPWP), which is the warmest body of seawater in the world, has received less attention. The IPWP has been warming in recent years. This paper presents for the first time the remarkable nonlinearity in Northern Hemisphere (NH) stratospheric circulation and temperature response to IPWP warming (the so-called IPWP Niño) in boreal winter. The magnitude of NH stratospheric vortex weakening is strong and significant in case of moderate IPWP Niño, but is weak and insignificant in strong IPWP Niño case. This phenomenon is robust in both the historical simulations and observations. An idealized model experiments forced with linear varying SST forcing in the IPWP region isolate the nonlinearities arising from IPWP Niño strength. Westward extension of precipitation into the Maritime Continent drives attenuation and westward shift of extratropical waves during strong IPWP Niño events. Linear wave interference analysis reveals this leads to weak interference between the climatological and anomalous stationary waves and thereby a weak response of the stratospheric vortex. These findings imply a distinct stratospheric vortex response to the IPWP Niño, and provide extended implications for the surface climate in the NH.

Does climate variability influence the demography of wild primates? Evidence from long-term life-history data in seven species

Earth's rapidly changing climate creates a growing need to understand how demographic processes in natural populations are affected by climate variability, particularly among organisms threatened by extinction. Long-term, large-scale, and cross-taxon studies of vital rate variation in relation to climate variability can be particularly valuable because they can reveal environmental drivers that affect multiple species over extensive regions. Few such data exist for animals with slow life histories, particularly in the tropics, where climate variation over large-scale space is asynchronous. As our closest relatives, nonhuman primates are especially valuable as a resource to understand the roles of climate variability and climate change in human evolutionary history. Here, we provide the first comprehensive investigation of vital rate variation in relation to climate variability among wild primates. We ask whether primates are sensitive to global changes that are universal (e.g., higher temperature, large-scale climate oscillations) or whether they are more sensitive to global change effects that are local (e.g., more rain in some places), which would complicate predictions of how primates in general will respond to climate change. To address these questions, we use a database of long-term life-history data for natural populations of seven primate species that have been studied for 29-52 years to investigate associations between vital rate variation, local climate variability, and global climate oscillations. Associations between vital rates and climate variability varied among species and depended on the time windows considered, highlighting the importance of temporal scale in detection of such effects. We found strong climate signals in the fertility rates of three species. However, survival, which has a greater impact on population growth, was little affected by climate variability. Thus, we found evidence for demographic buffering of life histories, but also evidence of mechanisms by which climate change could affect the fates of wild primates.

Does climate variability influence the demography of wild primates? Evidence from long-term life-history data in seven species

Earth's rapidly changing climate creates a growing need to understand how demographic processes in natural populations are affected by climate variability, particularly among organisms threatened by extinction. Long-term, large-scale, and cross-taxon studies of vital rate variation in relation to climate variability can be particularly valuable because they can reveal environmental drivers that affect multiple species over extensive regions. Few such data exist for animals with slow life histories, particularly in the tropics, where climate variation over large-scale space is asynchronous. As our closest relatives, nonhuman primates are especially valuable as a resource to understand the roles of climate variability and climate change in human evolutionary history. Here, we provide the first comprehensive investigation of vital rate variation in relation to climate variability among wild primates. We ask whether primates are sensitive to global changes that are universal (e.g., higher temperature, large-scale climate oscillations) or whether they are more sensitive to global change effects that are local (e.g., more rain in some places), which would complicate predictions of how primates in general will respond to climate change. To address these questions, we use a database of long-term life-history data for natural populations of seven primate species that have been studied for 29-52 years to investigate associations between vital rate variation, local climate variability, and global climate oscillations. Associations between vital rates and climate variability varied among species and depended on the time windows considered, highlighting the importance of temporal scale in detection of such effects. We found strong climate signals in the fertility rates of three species. However, survival, which has a greater impact on population growth, was little affected by climate variability. Thus, we found evidence for demographic buffering of life histories, but also evidence of mechanisms by which climate change could affect the fates of wild primates.

Impact of El Niño Southern Oscillation on infectious disease hospitalization risk in the United States

Although the global climate is changing at an unprecedented rate, links between weather and infectious disease have received little attention in high income countries. The "El Niño Southern Oscillation" (ENSO) occurs irregularly and is associated with changing temperature and precipitation patterns. We studied the impact of ENSO on infectious diseases in four census regions in the United States. We evaluated infectious diseases requiring hospitalization using the US National Hospital Discharge Survey (1970-2010) and five disease groupings that may undergo epidemiological shifts with changing climate: (i) vector-borne diseases, (ii) pneumonia and influenza, (iii) enteric disease, (iv) zoonotic bacterial disease, and (v) fungal disease. ENSO exposure was based on the Multivariate ENSO Index. Distributed lag models, with adjustment for seasonal oscillation and long-term trends, were used to evaluate the impact of ENSO on disease incidence over lags of up to 12 mo. ENSO was associated more with vector-borne disease [relative risk (RR) 2.96, 95% confidence interval (CI) 1.03-8.48] and less with enteric disease (0.73, 95% CI 0.62-0.87) in the Western region; the increase in vector-borne disease was attributable to increased risk of rickettsioses and tick-borne infectious diseases. By contrast, ENSO was associated with more enteric disease in non-Western regions (RR 1.12, 95% CI 1.02-1.15). The periodic nature of ENSO may make it a useful natural experiment for evaluation of the impact of climatic shifts on infectious disease risk. The impact of ENSO suggests that warmer temperatures and extreme variation in precipitation events influence risks of vector-borne and enteric disease in the United States.

Exceptionally strong easterly wind burst stalling El Niño of 2014

Intraseasonal wind bursts in the tropical Pacific are believed to affect the evolution and diversity of El Niño events. In particular, the occurrence of two strong westerly wind bursts (WWBs) in early 2014 apparently pushed the ocean-atmosphere system toward a moderate to strong El Niño--potentially an extreme event according to some climate models. However, the event's progression quickly stalled, and the warming remained very weak throughout the year. Here, we find that the occurrence of an unusually strong basin-wide easterly wind burst (EWB) in June was a key factor that impeded the El Niño development. It was shortly after this EWB that all major Niño indices fell rapidly to near-normal values; a modest growth resumed only later in the year. The easterly burst and the weakness of subsequent WWBs resulted in the persistence of two separate warming centers in the central and eastern equatorial Pacific, suppressing the positive Bjerknes feedback critical for El Niño. Experiments with a climate model with superimposed wind bursts support these conclusions, pointing to inherent limits in El Niño predictability. Furthermore, we show that the spatial structure of the easterly burst matches that of the observed decadal trend in wind stress in the tropical Pacific, suggesting potential links between intraseasonal wind bursts and decadal climate variations.

The interacting effects of food, spring temperature, and global climate cycles on population dynamics of a migratory songbird

Although long-distance migratory songbirds are widely believed to be at risk from warming temperature trends, species capable of attempting more than one brood in a breeding season could benefit from extended breeding seasons in warmer springs. To evaluate local and global factors affecting population dynamics of the black-throated blue warbler (Setophaga caerulescens), a double-brooded long-distance migrant, we used Pradel models to analyze 25 years of mark-recapture data collected in New Hampshire, USA. We assessed the effects of spring temperature (local weather) and the El Niño Southern Oscillation index (a global climate cycle), as well as predator abundance, insect biomass, and local conspecific density on population growth in the subsequent year. Local and global climatic conditions affected warbler populations in different ways. We found that warbler population growth was lower following El Niño years (which have been linked to poor survival in the wintering grounds and low fledging weights in the breeding grounds) than La Niña years. At a local scale, populations increased following years with warm springs and abundant late-season food, but were unaffected by spring temperature following years when food was scarce. These results indicate that the warming temperature trends might have a positive effect on recruitment and population growth of black-throated blue warblers if food abundance is sustained in breeding areas. In contrast, potential intensification of future El Niño events could negatively impact vital rates and populations of this species.

Target observations for improving initialization of high-impact ocean-atmospheric environmental events forecasting

In this paper, we emphasize the importance of accurate initial conditions in predicting high-impact ocean-atmospheric environmental events, such as El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), tropical cyclone (TC), and Kuroshio large meander (KLM), by reviewing recent progresses toward target observations for improving the initialization of these events forecasting. Since field observations are costly and will never be dense enough to fully cover the vast space of these events, it is necessary to develop methodologies that guide the design of efficient and effective observation strategy. Of particular interest is a method called conditional non-linear optimal perturbation (CNOP), which has been shown to be very useful in determining the sensitive areas for target observations applicable to the predictions of ENSO, IOD, TC, and KLM. Further studies are needed to understand the predictability of these events under the influence of climate change, and to explore the possibility of implementing field programs of target observations. These studies are challenging but are crucially important for improving our forecast skill of the high-impact ocean-atmospheric environmental events, and thus for disaster prevention, climate change mitigation, and sustainable socio-economic development.

El Niño Southern Oscillation (ENSO) enhances CO2 exchange rates in freshwater Marsh ecosystems in the Florida everglades

This research examines the relationships between El Niño Southern Oscillation (ENSO), water level, precipitation patterns and carbon dioxide (CO₂) exchange rates in the freshwater wetland ecosystems of the Florida Everglades. Data was obtained over a 5-year study period (2009–2013) from two freshwater marsh sites located in Everglades National Park that differ in hydrology. At the short-hydroperiod site (Taylor Slough; TS) and the long-hydroperiod site (Shark River Slough; SRS) fluctuations in precipitation patterns occurred with changes in ENSO phase, suggesting that extreme ENSO phases alter Everglades hydrology which is known to have a substantial influence on ecosystem carbon dynamics. Variations in both ENSO phase and annual net CO₂ exchange rates co-occurred with changes in wet and dry season length and intensity. Combined with site-specific seasonality in CO₂ exchanges rates, El Niño and La Niña phases magnified season intensity and CO₂ exchange rates at both sites. At TS, net CO₂ uptake rates were higher in the dry season, whereas SRS had greater rates of carbon sequestration during the wet season. As La Niña phases were concurrent with drought years and extended dry seasons, TS became a greater sink for CO₂ on an annual basis (−11 to −110 g CO₂ m⁻² yr⁻¹) compared to El Niño and neutral years (−5 to −43.5 g CO₂ m⁻² yr⁻¹). SRS was a small source for CO₂ annually (1.81 to 80 g CO₂ m⁻² yr⁻¹) except in one exceptionally wet year that was associated with an El Niño phase (−16 g CO₂ m⁻² yr⁻¹). Considering that future climate predictions suggest a higher frequency and intensity in El Niño and La Niña phases, these results indicate that changes in extreme ENSO phases will significantly alter CO₂ dynamics in the Florida Everglades.

Status and challenges for conservation of small mammal assemblages in South America

South America spans about 44° latitude, covers almost 18 million km(2) , and is second only to Africa in continental mammal species richness. In spite of this richness, research on the status of this fauna and on the nature and magnitude of contemporary threats remains limited. Distilling threats to this diverse fauna at a continental scale is challenging, in part because of the limited availability of rigorous studies. Recognizing this constraint, we summarize key threats to small mammals in South America, emphasizing the roles of habitat loss and degradation, direct persecution, and the increasing threat of climate change. We focus on three regional 'case studies': the tropical Andes, Amazonia and adjacent lowland regions, and the southern temperate region. We close with a brief summary of recent findings at our long-term research site in north-central Chile as they pertain to projected threats to this fauna. Habitat alteration is a pervasive threat that has been magnified by market forces and globalization (e.g. extensive agricultural development in Amazonia), and threatens increasing numbers of populations and species. Climate change poses even greater threats, from changes in rainfall and runoff regimes and resulting changes in vegetative structure and composition to secondary influences on fire dynamics. It is likely that many changes have yet to be recognized, but existing threats suggest that the future may bring dramatic changes in the distribution of many mammal taxa, although it is not clear if key habitat elements (vegetation) will respond as rapidly as climatic factors, leading to substantial uncertainty. Climate change is likely to result in 'winners' and 'losers' but available information precludes detailed assessment of which species are likely to fall into which category. In the absence of long-term monitoring and applied research to characterize these threats more accurately, and to develop strategies to reduce their impacts, managers already are being faced with daunting challenges. As the line between 'pure' and 'applied' research blurs in the face of converging interests of scientists and society we hope that solutions to these critical issues will be incorporated in addressing anticipated conservation crises.

Phenological changes in the southern hemisphere

Current evidence of phenological responses to recent climate change is substantially biased towards northern hemisphere temperate regions. Given regional differences in climate change, shifts in phenology will not be uniform across the globe, and conclusions drawn from temperate systems in the northern hemisphere might not be applicable to other regions on the planet. We conduct the largest meta-analysis to date of phenological drivers and trends among southern hemisphere species, assessing 1208 long-term datasets from 89 studies on 347 species. Data were mostly from Australasia (Australia and New Zealand), South America and the Antarctic/subantarctic, and focused primarily on plants and birds. This meta-analysis shows an advance in the timing of spring events (with a strong Australian data bias), although substantial differences in trends were apparent among taxonomic groups and regions. When only statistically significant trends were considered, 82% of terrestrial datasets and 42% of marine datasets demonstrated an advance in phenology. Temperature was most frequently identified as the primary driver of phenological changes; however, in many studies it was the only climate variable considered. When precipitation was examined, it often played a key role but, in contrast with temperature, the direction of phenological shifts in response to precipitation variation was difficult to predict a priori. We discuss how phenological information can inform the adaptive capacity of species, their resilience, and constraints on autonomous adaptation. We also highlight serious weaknesses in past and current data collection and analyses at large regional scales (with very few studies in the tropics or from Africa) and dramatic taxonomic biases. If accurate predictions regarding the general effects of climate change on the biology of organisms are to be made, data collection policies focussing on targeting data-deficient regions and taxa need to be financially and logistically supported.

Short-term effect of El Niño-Southern Oscillation on pediatric hand, foot and mouth disease in Shenzhen, China

Hand, foot and mouth disease (HFMD) was an emerging viral infectious disease in recent years in Shenzhen. The underlying risk factors have not yet been systematically examined. This study analyzed the short-term effect of El Niño-Southern Oscillation on pediatric HFMD in Shenzhen, China. Daily count of HFMD among children aged below 15 years old, Southern Oscillation Index (SOI), and weather variables were collected to construct the time series. A distributed lag non-linear model was applied to investigate the effect of daily SOI on pediatric HFMD occurrence during 2008-2010. We observed an acute effect of SOI variation on HFMD occurrence. The extremely high SOI (SOI = 45, with 0 as reference) was associated with increased HFMD, with the relative risk (RR) being 1.66 (95% Confidence Interval [CI]: 1.34-2.04). Further analyses of the association between HFMD and daily mean temperature and relative humidity supported the correlation between pediatric HFMD and SOI. Meteorological factors might be important predictors of pediatric HFMD occurrence in Shenzhen.

Improved El Nino forecasting by cooperativity detection

Although anomalous episodic warming of the eastern equatorial Pacific, dubbed El Niño by Peruvian fishermen, has major (and occasionally devastating) impacts around the globe, robust forecasting is still limited to about 6 mo ahead. A significant extension of the prewarning time would be instrumental for avoiding some of the worst damages such as harvest failures in developing countries. Here we introduce a unique avenue toward El Niño prediction based on network methods, inspecting emerging teleconnections. Our approach starts from the evidence that a large-scale cooperative mode--linking the El Niño basin (equatorial Pacific corridor) and the rest of the ocean--builds up in the calendar year before the warming event. On this basis, we can develop an efficient 12-mo forecasting scheme, i.e., achieve some doubling of the early-warning period. Our method is based on high-quality observational data available since 1950 and yields hit rates above 0.5, whereas false-alarm rates are below 0.1.

Cogs in the endless machine: lakes, climate change and nutrient cycles: a review

Lakes have, rather grandly, been described as sentinels, integrators and regulators of climate change (Williamson et al., Limnol. Oceanogr. 2009; 54: 2273-82). Lakes are also part of the continuum of the water cycle, cogs in a machine that processes water and elements dissolved and suspended in myriad forms. Assessing the changes in the functioning of the cogs and the machine with respect to these substances as climate changes is clearly important, but difficult. Many other human-induced influences, not least eutrophication, that impact on catchment areas and consequently on lakes, have generally complicated the recording of recent change in sediment records and modern sets of data. The least confounded evidence comes from remote lakes in mountain and polar regions and suggests effects of warming that include mobilisation of ions and increased amounts of phosphorus. A cottage industry has arisen in deduction and prediction of the future effects of climate change on lakes, but the results are very general and precision is marred not only by confounding influences but by the complexity of the lake system and the infinite variety of possible future scenarios. A common conclusion, however, is that warming will increase the intensity of symptoms of eutrophication. Direct experimentation, though expensive and still unusual and confined to shallow lake and wetland systems is perhaps the most reliable approach. Results suggest increased symptoms of eutrophication, and changes in ecosystem structure, but in some respects are different from those deduced from comparisons along latitudinal gradients or by inference from knowledge of lake behaviour. Experiments have shown marked increases in community respiration compared with gross photosynthesis in mesocosm systems and it may be that the most significant churnings of these cogs in the earth-air-water machine will be in their influence on the carbon cycle, with possibly large positive feedback effects on warming.

Developing higher resolution climate change scenarios for agricultural risk assessment: progress, challenges and prospects

Climate change presents perhaps the greatest economic and environmental challenge we have ever faced. Climate change and its associated impacts, adaptation and vulnerability have become the focus of current policy, business and research. This paper provides invaluable information for those interested in climate change and its impacts. This paper comprehensively reviews the advances made in the development of regional climate change scenarios and their application in agricultural impact, adaptation and vulnerability assessment. Construction of regional climate change scenarios evolved from the application of arbitrary scenarios to the application of scenarios based on general circulation models (GCMs). GCM-based climate change scenarios progressed from equilibrium climate change scenarios to transient climate change scenarios; from the use of direct GCM outputs to the use of downscaled GCM outputs; from the use of single scenarios to the use of probabilistic climate change scenarios; and from the application of mean climate change scenarios to the application of integrated climate change scenarios considering changes in both mean climate and climate variability.

Tropical cloud forest climate variability and the demise of the Monteverde golden toad

Widespread amphibian extinctions in the mountains of the American tropics have been blamed on the interaction of anthropogenic climate change and a lethal pathogen. However, limited meteorological records make it difficult to conclude whether current climate conditions at these sites are actually exceptional in the context of natural variability. We use stable oxygen isotope measurements from trees without annual rings to reconstruct a century of hydroclimatology in the Monteverde Cloud Forest of Costa Rica. High-resolution measurements reveal coherent isotope cycles that provide annual chronological control and paleoclimate information. Climate variability is dominated by interannual variance in dry season moisture associated with El Niño Southern Oscillation events. There is no evidence of a trend associated with global warming. Rather, the extinction of the Monteverde golden toad (Bufo periglenes) appears to have coincided with an exceptionally dry interval caused by the 1986-1987 El Niño event.