ALBERT

Description: Assessing the influence of climate on the incidence of Plasmodium falciparum malaria worldwide and how it might impact local malaria dynamics is complex and extrapolation to other settings or future times is controversial. This is especially true in the light of the particularities of the short- and long-term immune responses to infection. In sites of epidemic malaria transmission, it is widely accepted that climate plays an important role in driving malaria outbreaks. However, little is known about the role of climate in endemic settings where clinical immunity develops early in life. To disentangle these differences among high- and low-transmission settings we applied a dynamical model to two unique adjacent cohorts of mesoendemic seasonal and holoendemic perennial malaria transmission in Senegal followed for two decades, recording daily P. falciparum cases. As both cohorts are subject to similar meteorological conditions, we were able to analyze the relevance of different immunological mechanisms compared with climatic forcing in malaria transmission. Transmission was first modeled by using similarly unique datasets of entomological inoculation rate. A stochastic nonlinear human–mosquito model that includes rainfall and temperature covariates, drug treatment periods, and population variability is capable of simulating the complete dynamics of reported malaria cases for both villages. We found that under moderate transmission intensity climate is crucial; however, under high endemicity the development of clinical immunity buffers any effect of climate. Our models open the possibility of forecasting malaria from climate in endemic regions but only after accounting for the interaction between climate and immunity.

Description: A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project—integration with public health needs, a common forecasting framework, shared and standardized data, and open participation—can help advance infectious disease forecasting beyond dengue.

Description: The ClimaDat station at Gredos (GIC3) has been continuously measuring atmospheric (dry air) mixing ratios of carbon dioxide (CO2) and methane (CH4), as well as meteorological parameters, since November 2012. In this study we investigate the atmospheric variability of CH4 mixing ratios between 2013 and 2015 at GIC3 with the help of co-located observations of 222Rn concentrations, modelled 222Rn fluxes and modelled planetary boundary layer heights (PBLHs). Both daily and seasonal changes in atmospheric CH4 can be better understood with the help of atmospheric concentrations of 222Rn (and the corresponding fluxes). On a daily timescale, the variation in the PBLH is the main driver for 222Rn and CH4 variability while, on monthly timescales, their atmospheric variability seems to depend on emission changes. To understand (changing) CH4 emissions, nocturnal fluxes of CH4 were estimated using two methods: the radon tracer method (RTM) and a method based on the EDGARv4.2 bottom-up emission inventory, both using FLEXPARTv9.0.2 footprints. The mean value of RTM-based methane fluxes (FR_CH4) is 0.11 mg CH4 m−2 h−1 with a standard deviation of 0.09 or 0.29 mg CH4 m−2 h−1 with a standard deviation of 0.23 mg CH4 m−2 h−1 when using a rescaled 222Rn map (FR_CH4_rescale). For our observational period, the mean value of methane fluxes based on the bottom-up inventory (FE_CH4) is 0.33 mg CH4 m−2 h−1 with a standard deviation of 0.08 mg CH4 m−2 h−1. Monthly CH4 fluxes based on RTM (both FR_CH4 and FR_CH4_rescale) show a seasonality which is not observed for monthly FE_CH4 fluxes. During January–May, RTM-based CH4 fluxes present mean values 25 % lower than during June–December. This seasonal increase in methane fluxes calculated by RTM for the GIC3 area appears to coincide with the arrival of transhumant livestock at GIC3 in the second half of the year.

Description: Recent studies arising from both statistical analysis and dynamical disease models indicate that there is a link between incidence of cholera, a paradigmatic waterborne bacterial disease (WBD) endemic to Bangladesh, and the El Niño–Southern Oscillation (ENSO). However, a physical mechanism explaining this relationship has not yet been established. A regionally coupled, or “pacemaker,” configuration of the Center for Ocean–Land–Atmosphere Studies atmospheric general circulation model is used to investigate links between sea surface temperature in the central and eastern tropical Pacific and the regional climate of Bangladesh. It is found that enhanced precipitation tends to follow winter El Niño events in both the model and observations, providing a plausible physical mechanism by which ENSO could influence cholera in Bangladesh. The enhanced precipitation in the model arises from a modification of the summer monsoon circulation over India and Bangladesh. Westerly wind anomalies over land to the west of Bangladesh lead to increased convergence in the zonal wind field and hence increased moisture convergence and rainfall. This change in circulation results from the tropics-wide warming in the model following a winter El Niño event. These results suggest that improved forecasting of cholera incidence may be possible through the use of climate predictions.

Description: Recent studies arising from both statistical analysis and dynamical disease models indicate that there is a link between the incidence of cholera, a paradigmatic waterborne bacterial illness endemic to Bangladesh, and the El Niño–Southern Oscillation (ENSO). Cholera incidence typically increases following boreal winter El Niño events for the period 1973–2001. Observational and model analyses find that Bangladesh summer rainfall is enhanced following winter El Niño events, providing a plausible physical link between El Niño and cholera incidence. However, rainfall and cholera incidence do not increase following every winter El Niño event. Substantial variations in Bangladesh precipitation also occur in simulations in which identical sea surface temperature (SST) anomalies are prescribed in the central and eastern tropical Pacific. Bangladesh summer precipitation is thus not uniquely determined by forcing from the tropical Pacific, with significant implications for predictions of cholera risk. Nonparametric statistical analysis is used to identify regions of SST anomalies associated with variations in Bangladesh rainfall in an ensemble of pacemaker simulations. The authors find that differences in the response of Bangladesh summer precipitation to winter El Niño events are strongly associated with the persistence of warm SST anomalies in the central Pacific. Also there are significant differences in the SST patterns associated with positive and negative Bangladesh rainfall anomalies, indicating that the response is not fully linear. SST anomalies in the Indian Ocean also modulate the influence of the tropical Pacific, with colder Indian Ocean SST tending to enhance Bangladesh precipitation relative to warm Indian Ocean SST for identical conditions in the central and eastern tropical Pacific. This influence is not fully linear. Forecasts of Bangladesh rainfall and cholera risk may thus be improved by considering the Niño-3 and Niño-4 indices separately, rather than the Niño-3.4 index alone. Additional skill may also be gained by incorporating information on the southeast Indian Ocean and by updating the forecast with information on the evolution of the SST anomalies into spring.

Description: Recent studies arising from both statistical analysis and dynamical disease models demonstrate a link between the incidence of cholera, a paradigmatic waterborne bacterial illness endemic to Bangladesh, and the El Niño–Southern Oscillation (ENSO). The physical significance of this relationship was investigated by examining links between the regional climate of Bangladesh and western Pacific sea surface temperatures (SST) associated with ENSO using a pacemaker configuration of the Center for Ocean–Land–Atmosphere Studies atmospheric general circulation model. The global SST response to ENSO SST anomalies in the western Pacific alone is found to be relatively weak and unrealistic when compared to observations, indicating that the global response to ENSO is driven primarily by anomalies in the central and eastern tropical Pacific. Despite the weak global response to western Pacific SST anomalies, however, a signal is found in summer rainfall over India and Bangladesh. Specifically, reduced rainfall typically follows winter El Niño events. In the absence of warm SST anomalies in the eastern Pacific, cold anomalies in the western Pacific produce a La Niña–like response in the model circulation. Cold SST anomalies suppress convection over the western Pacific. Large-scale convergence shifts into the eastern Indian Ocean and modifies the summer monsoon circulation over India and Bangladesh. The probabilistic relationship between Bangladesh rainfall and SST is also explored using a nonparametric statistical technique. Decreased rainfall is strongly associated with cold SST in the western Pacific, while associations between SST and enhanced rainfall are substantially weaker. Also found are strong associations between rainfall and SST in the Indian Ocean in the absence of differences in forcing from the western Pacific. It thus appears that the Indian Ocean may represent an independent source of predictability for the monsoon and cholera risk. Likewise, under certain circumstances, the western Pacific may also exert a significant influence on Bangladesh rainfall and cholera risk.

Description: 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 Niño (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 region in the central tropical Pacific, which is then strengthened by suppressed equatorial easterlies. Convection thus starts to move to the central Pacific and so the Walker circulation weakens, activating the positive Bjerknes feedback that ultimately leads to the development of an EN event. These results highlight the enormous potential of the interaction between the tropics and this high-latitude region in the Southern Hemisphere to increase El Niño–Southern Oscillation understanding and to improve the long-lead prediction skill of EN phenomenon.

Description: The differences in boreal summer (June–August) monthly-mean rainfall estimates over the Indian Ocean region in five research-quality products are examined for the period 1979–2003. Two products derived from the merged satellite and surface observations are considered: the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) and the Global Precipitation Climatology Project (GPCP). In addition, three products derived solely from rain gauge observations are considered: the Chen et al. product; the Indian Meteorological Department (IMD) product; and a new, objectively analyzed product based on the Climate Anomaly Monitoring System (CAMS) dataset. Significant discrepancies have been found between the different products across the entire Indian Ocean region, with the greatest disagreement over Burma and neighboring Bangladesh. These differences appear to be primarily due to the absence of reported rain gauge data for Burma and differences in the algorithms used to merge the satellite microwave emission and scattering data in coastal regions. Representations of rainfall across much of the eastern Indian Ocean region would likely be improved by the identification and inclusion of reporting stations from Burma and a refinement of the techniques used for merging microwave data. The differences among the five products are sufficient to affect both quantitative and qualitative conclusions drawn about rainfall, particularly over Bangladesh and Burma. Consequently, the results of precipitation studies in this region will depend, in some cases, on the choice of the data product, including such basic questions as to whether a given summer was wet or dry. Of particular note is that the apparent relationship between rainfall and ENSO can depend on the choice of the data product.