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Rodo, X, Pascual M, Doblas-Reyes FJ, Gershunov A, Stone DA, Giorgi F, Hudson PJ, Kinter J, Rodriguez-Arias MA, Stenseth NC, Alonso D, Garcia-Serrano J, Dobson AP.  2013.  Climate change and infectious diseases: Can we meet the needs for better prediction? Climatic Change. 118:625-640.   10.1007/s10584-013-0744-1   AbstractWebsite

The next generation of climate-driven, disease prediction models will most likely require a mechanistically based, dynamical framework that parameterizes key processes at a variety of locations. Over the next two decades, consensus climate predictions make it possible to produce forecasts for a number of important infectious diseases that are largely independent of the uncertainty of longer-term emissions scenarios. In particular, the role of climate in the modulation of seasonal disease transmission needs to be unravelled from the complex dynamics resulting from the interaction of transmission with herd immunity and intervention measures that depend upon previous burdens of infection. Progress is also needed to solve the mismatch between climate projections and disease projections at the scale of public health interventions. In the time horizon of seasons to years, early warning systems should benefit from current developments on multi-model ensemble climate prediction systems, particularly in areas where high skill levels of climate models coincide with regions where large epidemics take place. A better understanding of the role of climate extremes on infectious diseases is urgently needed.

OrtizBevia, MJ, Perez-Gonzalez I, Alvarez-Garcia FJ, Gershunov A.  2010.  Nonlinear estimation of El Nino impact on the North Atlantic winter. Journal of Geophysical Research-Atmospheres. 115   10.1029/2009jd013387   AbstractWebsite

The differences in the teleconnections forced by different El Nino events (Ninos) can be partly explained by the intrinsic nonlinearity of the atmospheric response. In the present study, we segregate the responses of the North Atlantic to strong from those to moderate Ninos and compare nonlinear and linear estimates. El Nino forcing is represented by the tropical Pacific sea surface temperature anomalies, and the North Atlantic atmospheric response is represented by sea level pressure anomalies in the region. To gain insight into the evolution of El Nino teleconnections in a future climate, linear and nonlinear analyses are carried out on the corresponding data fields in the control and scenario simulations of a climate model experiment. This experiment presents, in its control version, realistic teleconnections. In the observational analysis, the nonlinear method performs only slightly better than the linear one. However, in the analysis of the interannual variability by a long control experiment of a realistic climate simulation, the nonlinear estimate improves significantly with respect to the linear one. The analysis of the corresponding scenario experiment points to an intensification of the (negative) surface pressure anomalies associated with the Ninos in the west European sector in a future climate. This feature is related to the important stratospheric anomalies in the same region, revealed by previous studies.