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Ralph, FM, Wilson AM, Shulgina T, Kawzenuk B, Sellars S, Rutz JJ, Lamjiri MA, Barnes EA, Gershunov A, Guan B, Nardi KM, Osborne T, Wick GA.  2019.  ARTMIP-early start comparison of atmospheric river detection tools: how many atmospheric rivers hit northern California's Russian River watershed? Climate Dynamics. 52:4973-4994.   10.1007/s00382-018-4427-5   AbstractWebsite

Many atmospheric river detection tools (ARDTs) have now been developed. However, their relative performance is not well documented. This paper compares a diverse set of ARDTs by applying them to a single location where a unique 12-year-long time-series from an atmospheric river observatory at Bodega Bay, California is available. The study quantifies the sensitivity of the diagnosed number, duration, and intensity of ARs at this location to the choice of ARDT, and to the choice of reanalysis data set. The ARDTs compared here represent a range of methods that vary in their use of different variables, fixed vs. percentile-based thresholds, geometric shape requirements, Eulerian vs. Lagrangian approaches, and reanalyses. The ARDTs were evaluated first using the datasets documented in their initial publication, which found an average annual count of 19 +/- 7. Applying the ARDTs to the same reanalysis dataset yields an average annual count of 19 +/- 4. Applying a single ARDT to three reanalyses of varying grid sizes (0.5 degrees, 1.0 degrees-2.5 degrees) showed little sensitivity to the choice of reanalysis. While the annual average AR event count varied by about a factor of two (10-25 per year) depending on the ARDT, average AR duration and maximum intensity varied by less than +/- 10%, i.e., 24 +/- 2h duration; 458 +/- 44kg m(-1) s(-1) maximum IVT. ARDTs that use a much higher threshold for integrated vapor transport were compared separately, and yielded just 1-2 ARs annually on average. Generally, ARDTs that include either more stringent geometric criteria or higher thresholds identified the fewest AR events.

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.