Climate change and infectious diseases: Can we meet the needs for better prediction?

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.     

Spatial trends in heavy metals and nitrogen deposition in Navarra (Northern Spain) based on moss analysis

A biomonitoring survey using the moss species Hypnum cupressiforme Hedw. was performed in the North of Navarra (Spain) in 2006. The levels of V, Cr, Mn, Ni, Cu, Zn, As, Zr, Cd, Hg and Pb, and the total nitrogen content were determined in the samples by means of ICP-MS, CV-AA, and the Kjeldahl method. PCA analysis showed a differentiation between lithogenic (V, Cr, Mn, Ni, As and Zr) and anthropogenic elements (Mn, Cu, Zn, Cd, Hg and Pb). Spatial distribution maps were drawn using the kriging method, in order to identify the most affected areas and the main pollution sources. A similar spatial distribution was observed for the elements belonging to each group separated by the PCA, showing an important contribution from metal industries located in the Basque Country, as well as influence of long-range transboundary pollution processes. Background levels were also determined for the study area, along with the contamination factor for the different elements analysed. Mosses seemed to be good biomonitors of N deposition in areas of accumulation.     

Added-value from initialization in predictions of Atlantic multi-decadal variability

Identifying regions sensitive to external radiative changes, including anthropogenic (sulphate aerosols and greenhouse gases) and natural (volcanoes and solar variations) forcings, is important to formulate actionable information at multi-year time-scales. Internally-generated climate variability can overcome this radiative forcing, especially at regional level, so that detecting the areas for this potential dominance is likewise critical for decadal prediction. This work aims to clarify where each contribution has the largest effect on North Atlantic sea surface temperature (SST) predictions in relation to the Atlantic multi-decadal variability (AMV). Initialized decadal hindcasts and radiatively-forced historical simulations from the fifth phase of the Climate Model Intercomparison Project are analysed to assess multi-year skill of the AMV. The initialized hindcasts reproduce better the phase of the AMV index fluctuations. The radiatively-forced component consists of a residual positive trend, although its identification is ambiguous. Initialization reduces the inter-model spread when estimating the level of AMV skill, thus reducing its uncertainty. Our results show a skilful performance of the initialized hindcasts in capturing the AMV-related SST anomalies over the subpolar gyre and Labrador Sea regions, as well as in the eastern subtropical basin, and the inability of the radiatively-forced historical runs to simulate the horseshoe-like AMV signature over the North Atlantic. Initialization outperforms empirical predictions based on persistence beyond 1–4 years ahead, suggesting that ocean dynamics play a role in the AMV predictability beyond the thermal inertia. The initialized hindcasts are also more skilful at reproducing the observed AMV teleconnection to the West African monsoon. The impact of the start date frequency is also described, showing that the standard of 5-year interval between start dates yields the main features of the AMV skill that are robustly detected in hindcasts with yearly start date sampling. This work updates previous studies, complementing them, and concludes that skilful initialized multi-model forecasts of the AMV-related climate variability can be formulated, improving uninitialized projections, until 3–6 years ahead.