Joan Ballester

Association of Kawasaki disease with tropospheric wind patterns.

The causal agent of Kawasaki disease (KD) remains unknown after more than 40 years of intensive research. The number of cases continues to rise in many parts of the world and KD is the most common cause of acquired heart disease in childhood in developed countries. Analyses of the three major KD epidemics in Japan, major non-epidemic interannual fluctuations of KD cases in Japan and San Diego, and the seasonal variation of KD in Japan, Hawaii, and San Diego, reveals a consistent pattern wherein KD cases are often linked to large-scale wind currents originating in central Asia and traversing the north Pacific. Results suggest that the environmental trigger for KD could be wind-borne. Efforts to isolate the causative agent of KD should focus on the microbiology of aerosols.

Long-term projections and acclimatization scenarios of temperature-related mortality in Europe

The steady increase in greenhouse gas concentrations is inducing a detectable rise in global temperatures. The sensitivity of human societies to warming temperatures is, however, a transcendental question not comprehensively addressed to date. Here we show the link between temperature, humidity and daily numbers of deaths in nearly 200 European regions, which are subsequently used to infer transient projections of mortality under state-of-the-art high-resolution greenhouse gas scenario simulations. Our analyses point to a change in the seasonality of mortality, with maximum monthly incidence progressively shifting from winter to summer. The results also show that the rise in heat-related mortality will start to completely compensate the reduction of deaths from cold during the second half of the century, amounting to an average drop in human lifespan of up 3-4 months in 2070-2100. Nevertheless, projections suggest that human lifespan might indeed increase if a substantial degree of adaptation to warm temperatures takes place.

Tropospheric winds from northeastern China carry the etiologic agent of Kawasaki disease from its source to Japan

Significance Kawasaki disease (KD), the leading cause of acquired heart disease in children worldwide, has remained a mystery for more than 40 y. No etiological agent has yet been identified. By using simulations with the flexible particle dispersion model from different Japanese cities from each single high (low) KD incidence day, the source region KD is retrieved in cereal croplands in northeastern China. We infer the incubation time for KD ranges from 6 h to 2 d, thus favoring an antigenic or toxic exposure as the trigger. Candida sp. is reported as the dominant fungal species collected aloft (54% of all fungal DNA clones) demonstrating the potential for human disease in aerosols transported by wind currents traveling long distances. Evidence indicates that the densely cultivated region of northeastern China acts as a source for the wind-borne agent of Kawasaki disease (KD). KD is an acute, coronary artery vasculitis of young children, and still a medical mystery after more than 40 y. We used residence times from simulations with the flexible particle dispersion model to pinpoint the source region for KD. Simulations were generated from locations spanning Japan from days with either high or low KD incidence. The postepidemic interval (1987–2010) and the extreme epidemics (1979, 1982, and 1986) pointed to the same source region. Results suggest a very short incubation period (<24 h) from exposure, thus making an infectious agent unlikely. Sampling campaigns over Japan during the KD season detected major differences in the microbiota of the tropospheric aerosols compared with ground aerosols, with the unexpected finding of the Candida species as the dominant fungus from aloft samples (54% of all fungal strains). These results, consistent with the Candida animal model for KD, provide support for the concept and feasibility of a windborne pathogen. A fungal toxin could be pursued as a possible etiologic agent of KD, consistent with an agricultural source, a short incubation time and synchronized outbreaks. Our study suggests that the causative agent of KD is a preformed toxin or environmental agent rather than an organism requiring replication. We propose a new paradigm whereby an idiosyncratic immune response, influenced by host genetics triggered by an environmental exposure carried on winds, results in the clinical syndrome known as acute KD.

A New Extratropical Tracer Describing the Role of the Western Pacific in the Onset of El Niño: Implications for ENSO Understanding and Forecasting

Abstract A complex empirical orthogonal function analysis was applied to sea surface temperature data in the southern high-latitude Pacific to identify and isolate primary processes related to the onset of El Nino (EN) events. Results were compared to those of a lead–lag composite analysis of a new tracer of EN events in the southern high-latitude Pacific, the Ross–Bellingshausen (RB) dipole. Both techniques successfully isolate the main low-frequency features in the interaction among the tropical and southern extratropical Pacific during the onset of recent eastward-propagating EN events. Particularly, positive RB peaks were followed by EN events around 9 months later, on average. In turn, RB maxima were anticipated by local warm anomalies in the western tropical Pacific a year in advance, which enhance local convection and upper-troposphere divergence and generate an anomalous wave train extending eastward and poleward in the southern extratropics. In addition, circulation changes lead to a warm SST reg...

Improving the long-lead predictability of El Niño using a novel forecasting scheme based on a dynamic components model

El Niño (EN) is a dominant feature of climate variability on inter-annual time scales driving changes in the climate throughout the globe, and having wide-spread natural and socio-economic consequences. In this sense, its forecast is an important task, and predictions are issued on a regular basis by a wide array of prediction schemes and climate centres around the world. This study explores a novel method for EN forecasting. In the state-of-the-art the advantageous statistical technique of unobserved components time series modeling, also known as structural time series modeling, has not been applied. Therefore, we have developed such a model where the statistical analysis, including parameter estimation and forecasting, is based on state space methods, and includes the celebrated Kalman filter. The distinguishing feature of this dynamic model is the decomposition of a time series into a range of stochastically time-varying components such as level (or trend), seasonal, cycles of different frequencies, irregular, and regression effects incorporated as explanatory covariates. These components are modeled separately and ultimately combined in a single forecasting scheme. Customary statistical models for EN prediction essentially use SST and wind stress in the equatorial Pacific. In addition to these, we introduce a new domain of regression variables accounting for the state of the subsurface ocean temperature in the western and central equatorial Pacific, motivated by our analysis, as well as by recent and classical research, showing that subsurface processes and heat accumulation there are fundamental for the genesis of EN. An important feature of the scheme is that different regression predictors are used at different lead months, thus capturing the dynamical evolution of the system and rendering more efficient forecasts. The new model has been tested with the prediction of all warm events that occurred in the period 1996–2015. Retrospective forecasts of these events were made for long lead times of at least two and a half years. Hence, the present study demonstrates that the theoretical limit of ENSO prediction should be sought much longer than the commonly accepted “Spring Barrier”. The high correspondence between the forecasts and observations indicates that the proposed model outperforms all current operational statistical models, and behaves comparably to the best dynamical models used for EN prediction. Thus, the novel way in which the modeling scheme has been structured could also be used for improving other statistical and dynamical modeling systems.

[Accepted Manuscript] Seasonal forecasting and health impact models: challenges and opportunities.

After several decades of intensive research, steady improvements in understanding and modeling the climate system have led to the development of the first generation of operational health early warning systems in the era of climate services. These schemes are based on collaborations across scientific disciplines, bringing together real‐time climate and health data collection, state‐of‐the‐art seasonal climate predictions, epidemiological impact models based on historical data, and an understanding of end user and stakeholder needs. In this review, we discuss the challenges and opportunities of this complex, multidisciplinary collaboration, with a focus on the factors limiting seasonal forecasting as a source of predictability for climate impact models.

Seasonal forecasting and health impact models: challenges and opportunities

After several decades of intensive research, steady improvements in understanding and modeling the climate system have led to the development of the first generation of operational health early warning systems in the era of climate services. These schemes are based on collaborations across scientific disciplines, bringing together real‐time climate and health data collection, state‐of‐the‐art seasonal climate predictions, epidemiological impact models based on historical data, and an understanding of end user and stakeholder needs. In this review, we discuss the challenges and opportunities of this complex, multidisciplinary collaboration, with a focus on the factors limiting seasonal forecasting as a source of predictability for climate impact models.

On the dynamical mechanisms explaining the western Pacific subsurface temperature buildup leading to ENSO events

Despite steady progress in the understanding of El Nino–Southern Oscillation (ENSO) in the past decades, questions remain on the exact mechanisms explaining the heat buildup leading to the onset of El Nino (EN) events. Here we use an ensemble of ocean and atmosphere assimilation products to identify mechanisms that are consistently identified by all the data sets and that contribute to the heat buildup in the western Pacific 18 to 24 months before the onset of EN events. Meridional and eastward heat advection due to equatorward subsurface mass convergence and transport along the equatorial undercurrent are found to contribute to the subsurface warming at 170°E–150°W. In the warm pool, instead, surface horizontal convergence and downwelling motion have a leading role in subsurface warming. The picture emerging from our results highlights a sharp dynamical transition at 170°E near the level of the thermocline.

Sensitivity of El Niño intensity and timing to preceding subsurface heat magnitude

Despite extensive ongoing efforts on improving the long-term prediction of El Niño-Southern Oscillation, the predictability in state-of-the-art operational schemes remains limited by factors such as the spring barrier and the influence of atmospheric winds. Recent research suggests that the 2014/15 El Niño (EN) event was stalled as a result of an unusually strong basin-wide easterly wind burst in June, which led to the discharge of a large fraction of the subsurface ocean heat. Here we use observational records and numerical experiments to explore the sensitivity of EN to the magnitude of the heat buildup occurring in the ocean subsurface 21 months in advance. Our simulations suggest that a large increase in heat content during this phase can lead to basin-wide uniform warm conditions in the equatorial Pacific the winter before the occurrence of a very strong EN event. In our model configuration, the system compensates any initial decrease in heat content and naturally evolves towards a new recharge, resulting in a delay of up to one year in the occurrence of an EN event. Both scenarios substantiate the non-linear dependency between the intensity of the subsurface heat buildup and the magnitude and timing of subsequent EN episodes.

Heat advection processes leading to El Niño events as depicted by an ensemble of ocean assimilation products

The oscillatory nature of El Nino-Southern Oscillation results from an intricate superposition of near-equilibrium balances and out-of-phase disequilibrium processes between the ocean and the atmosphere. The main objective of the present work is to perform an exhaustive spatiotemporal analysis of the upper ocean heat budget in an ensemble of state-of-the-art ocean assimilation products. We put specific emphasis on the ocean heat advection mechanisms, and their representation in individual ensemble members and in the different stages of the ENSO oscillation leading to EN events. Our analyses consistently show that the initial subsurface warming in the western equatorial Pacific is advected to the central Pacific by the equatorial undercurrent, which, together with the equatorward advection associated with anomalies in both the meridional temperature gradient and circulation at the level of the thermocline, explains the heat buildup in the central Pacific during the recharge phase. We also find that the recharge phase is characterized by an increase of meridional tilting of the thermocline, as well as a southward upper-ocean cross-equatorial mass transport resulting from Ekman-induced anomalous vertical motion in the off-equatorial regions. Although differences between data sets are generally small, and anomalies tend to have the same sign, the differences in the magnitude of the meridional term are seen to be key for explaining the different propagation speed of the subsurface warming tendency along the thermocline. The only exception is GECCO, which does not produce the patterns of meridional surface Ekman divergence (subsurface Sverdrup convergence) in the western and central equatorial Pacific.