UK Food Standards Agency cis-monounsaturated fatty acid workshop report

The UK Food Standards Agency convened a group of expert scientists to review current research investigating the optimal dietary intake for n-9 cis-monounsaturated fatty acids (MUFA). The aim was to review the mechanisms underlying the reported beneficial effects of MUFA on CHD risk, and to establish priorities for future research. The issue of optimal MUFA intake is contingent upon optimal total fat intake; however, there is no consensus of opinion on what the optimal total fat intake should be. Thus, it was recommended that a large multi-centre study should look at the effects on CHD risk of MUFA replacement of saturated fatty acids in relation to varying total fat intakes; this study should be of sufficient size to take account of genetic variation, sex, physical activity and stage of life factors, as well as being of sufficient duration to account for adaptation to diets. Recommendations for studies investigating the mechanistic effects of MUFA were also made. Methods of manipulating the food chain to increase MUFA at the expense of saturated fatty acids were also discussed.

Atmospheric blocking and mean biases in climate models

Models often underestimate blocking in the Atlantic and Pacific basins and this can lead to errors in both weather and climate predictions. Horizontal resolution is often cited as the main culprit for blocking errors due to poorly resolved small-scale variability, the upscale effects of which help to maintain blocks. Although these processes are important for blocking, the authors show that much of the blocking error diagnosed using common methods of analysis and current climate models is directly attributable to the climatological bias of the model. This explains a large proportion of diagnosed blocking error in models used in the recent Intergovernmental Panel for Climate Change report. Furthermore, greatly improved statistics are obtained by diagnosing blocking using climate model data corrected to account for mean model biases. To the extent that mean biases may be corrected in low-resolution models, this suggests that such models may be able to generate greatly improved levels of atmospheric blocking.