Interannual variation of seasonal-mean tropical convection over the Indo-Pacific region is primarily controlled by El Niño–Southern Oscillation (ENSO). For example, during El Niño winters, seasonal-mean convection around the Maritime Continent becomes weaker than normal, while that over the central to eastern Pacific is strengthened. Similarly, subseasonal convective activity, which is associated with the Madden–Julian oscillation (MJO), is influenced by ENSO. The MJO activity tends to extend farther eastward to the date line during El Niño winters and contract toward the western Pacific during La Niña winters. However, the overall level of MJO activity across the Maritime Continent does not change much in response to the ENSO. It is shown that the boreal winter MJO amplitude is closely linked with the stratospheric quasi-biennial oscillation (QBO) rather than with ENSO. The MJO activity around the Maritime Continent becomes stronger and more organized during the easterly QBO winters. The QBO-related MJO change explains up to 40% of interannual variation of the boreal winter MJO amplitude. This result suggests that variability of the MJO and the related tropical–extratropical teleconnections can be better understood and predicted by taking not only the tropospheric circulation but also the stratospheric mean state into account. The seasonality of the QBO–MJO link and the possible mechanism are also discussed.

The Madden–Julian oscillation (MJO), the dominant mode of tropical intraseasonal variability, provides a major source of tropical and extratropical predictability on a subseasonal time scale. This study conducts a quantitative evaluation of the MJO prediction skill in state-of-the-art operational models, participating in the subseasonal-to-seasonal (S2S) prediction project. The relationship of MJO prediction skill with model biases in the mean moisture fields and in the longwave cloud–radiation feedbacks is also investigated. The S2S models exhibit MJO prediction skill out to a range of 12 to 36 days. The MJO prediction skills in the S2S models are affected by both the MJO amplitude and phase errors, with the latter becoming more important at longer forecast lead times. Consistent with previous studies, MJO events with stronger initial MJO amplitude are typically better predicted. It is found that the sensitivity to the initial MJO phase varies notably from model to model. In most models, a notable dry bias develops within a few days of forecast lead time in the deep tropics, especially across the Maritime Continent. The dry bias weakens the horizontal moisture gradient over the Indian Ocean and western Pacific, likely dampening the organization and propagation of the MJO. Most S2S models also underestimate the longwave cloud–radiation feedbacks in the tropics, which may affect the maintenance of the MJO convective envelope. The models with smaller bias in the mean horizontal moisture gradient and the longwave cloud–radiation feedbacks show higher MJO prediction skills, suggesting that improving those biases would enhance MJO prediction skill of the operational models.

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Background We aimed to investigate the effects of ambient respiratory viral infections in the general population on rheumatoid arthritis (RA) development. Methods Data of weekly incident RA (2012–2013) were obtained from the Korean National Health Insurance claims database, and those of weekly observations on eight respiratory viral infections were obtained from the Korea Centers for Disease Control and Prevention database. We estimated the percentage change in incident RA associated with ambient mean respiratory viral infections using a generalized linear model, after adjusting for time trend, air pollution, and meteorological data. Results A total of 24,117 cases of incident RA (mean age 54.7 years, 18,688 [77.5%] women) were analyzed. Ambient respiratory viral infections in the population were associated with a higher number of incident RA over time, and its effect peaked 6 or 7 weeks after exposure. Among the 8 viruses, parainfluenza virus (4.8% for 1% respiratory viral infection increase, 95% CI 1.6 to 8.1, P = .003), coronavirus (9.2%, 3.9 to 14.8, P < .001), and metapneumovirus (44%, 2.0 to 103.4, P = .038) were associated with increased number of incident RA. The impact of these respiratory viral infections remained significant in women (3.8%, 12.1%, and 67.4%, respectively, P < .05) and in older patients (10.7%, 14.6%, and 118.2%, respectively, P < .05). Conclusions Ambient respiratory viral infections in the population were associated with an increased number of incident RA, especially in women and older patients, suggesting that respiratory viral infections can be a novel environmental risk factor for the development of RA. Electronic supplementary material The online version of this article (10.1186/s13075-019-1977-9) contains supplementary material, which is available to authorized users.