The damping of gravity water-waves due to percolation

The problem of viscous damping of gravity water-waves over a permeable sea bed is re-examined. A generalized Darcy's law (Dagan, 1979) is adopted to describe the oscillatory flow motions in the sea bed. Boundary layers are required in the sea bed adjacent to boundaries due to the discontinuity of medium property. A boundary-layer-Poincaré method is used to derive the damping rate and to obtain in addition the small wave number shift due to viscosity. The damping rate is shown to increase with permeability for small permeability and then to decrease with permeability for large permeability. Laboratory results (Savage, 1953) are used to verify the theory.     

Yearly comparisons of the martian polar caps: 1999-2003 Mars Orbiter Camera observations

The Mars Global Surveyor Mars Orbiter Camera wide-angle cameras were used to obtain images of the north and south seasonal and residual polar caps between 1999 and 2003. Wide-angle red camera images were used in assembling mosaics of the north and south polar recessions and regression rates were measured and compared. There are small variations in the north polar recession between 2000 and 2002, especially between LS=7° and LS=50°, however there is no evidence for the plateau in the recession curves that has been observed in some prior years. The south polar recession changes very little from year to year, and the 2001 dust storm had little if any effect on the average cap recession that year. Albedo values of the geographic north pole were measured using wide-angle red and blue camera images, and the residual south polar cap configuration was compared between the three years observed by MOC. The albedo of the geographic north pole generally varies between 0.5 and 0.6 as measured from MOC wide-angle red camera images. There were only minor variations near the edges of the residual south polar cap between the three years examined.     

Alignment between nationally determined contributions and the sustainable development goals for West Africa

The UN Sustainable Development Goals (SDGs) and the 2015 Paris Agreement are two of the most important policy frameworks of the twenty-first century. However, the alignment of national commitments linked to them has not yet been analysed for West African states. Such analyses are vital to avoid perverse outcomes if states assess targets and develop SDG implementation plans, and Nationally Determined Contributions (NDC) under the Paris Agreement, without integrated planning and cross-sectoral alignment. This article provides a situation analysis guided by the following questions: (a) Which priority sectors are mentioned in relation to adaptation and mitigation in West African NDCs? (b) Are the NDCs of West African states well aligned with the SDGs? (c) What are the co-benefits of NDCs in contributing towards the SDGs? and (d) How are West African states planning to finance actions in their NDCs? The study uses iterative content analysis to explore key themes for adaptation and mitigation within NDCs of 11 West African states and their alignment to selected SDGs. A national multi-stakeholder workshop was held in Ghana to examine the co-benefits of the NDCs in contributing towards the SDGs and their implementation challenges. Results show that agriculture and energy are priority sectors where NDCs have pledged significant commitments. The analysis displays good alignment between mitigation and adaptation actions proposed in NDCs and the SDGs. These represent opportunities that can be harnessed through integration into national sectoral policies. However, cross-sectoral discussions in Ghana identify significant challenges relating to institutional capacity, a lack of coordination among institutions and agencies, and insufficient resources in moving towards integrated implementation of national planning priorities to address successfully both NDC priorities and the SDGs.

Key policy insights

• Positive alignments between West African NDCs and SDGs present opportunities for mutual benefits that can advance national development via a more climate resilient pathway.

• NDCs of West African states can provide mutual benefits across the water–energy–food nexus, such as through climate-smart agriculture and low carbon energy technologies.

• Ghanaian multi-sectoral insights show the need to empower national coordinating bodies to overcome misalignments across different sectors.

     

Choice of conodont Idiognathodus simulator （sensu stricto） as the event marker for the base of the global Gzhelian Stage （Upper Pennsylvanian Series,Carboniferous System）

We propose that the level at which the conodont species Idiognathodus simulator （Ellison 1941） （sensu stricto） first appears be selected to mark the base of the Gzhelian Stage, because we believe that this is the optimal level by which this boundary can be correlated. This taxon has a short range and a wide distribution, as shown by correlation of glacial-eustatic cyclothems across the Kasimovian-Gzhelian boundary interval among Midcontinent North America and the Moscow and Donets basins of eastern Europe, based on scale of the cyclothems along with several aspects of biostrati- graphy. Outside of these areas, I. simulator （sensu stricto） is known also from other parts of the U.S., and is reported from the southern Urals and south-central China in its expected position between other widespread taxa. Its first appearance is consistent with the current ammonoid placement of the boundary （first appearance of Shumardites cuyleri）, and it is also compatible with certain aspects of the distribution of Eurasian fusulinid faunas （e.g., lectotype ofRauserites rossicus）.     

Using the Twentieth Century Reanalysis to assess climate variability for the European wind industry

We characterise the long-term variability of European near-surface wind speeds using 142 years of data from the Twentieth Century Reanalysis (20CR), and consider the potential of such long-baseline climate data sets for wind energy applications. The low resolution of the 20CR would severely restrict its use on its own for wind farm site-screening. We therefore perform a simple statistical calibration to link it to the higher-resolution ERA-Interim data set (ERAI), such that the adjusted 20CR data has the same wind speed distribution at each location as ERAI during their common period. Using this corrected 20CR data set, wind speeds and variability are characterised in terms of the long-term mean, standard deviation and corresponding trends. Many regions of interest show extremely weak trends on century timescales, but contain large multidecadal variability. Since reanalyses such as ERAI are often used to provide the background climatology for wind farm site assessments, but contain only a few decades of data, our results can be used as a way of incorporating decadal-scale wind climate variability into such studies, allowing investment risks for wind farms to be reduced.     

Earthquake prediction: the null hypothesis

The null hypothesis in assessing earthquake predictions is often, loosely speaking, that the successful predictions are chance coincidences. To make this more precise requires specifying a chance model for the predictions and/or the seismicity. The null hypothesis tends to be rejected not only when the predictions have merit, but also when the chance model is inappropriate. In one standard approach, the seismicity is taken to be random and the predictions are held fixed. 'Conditioning' on the predictions this way tends to reject the null hypothesis even when it is true, if the predictions depend on the seismicity history. An approach that seems less likely to yield erroneous conclusions is to compare the predictions with the predictions of a 'sensible' random prediction algorithm that uses seismicity up to time t to predict what will happen after time t. The null hypothesis is then that the predictions are no better than those of the random algorithm. Significance levels can be assigned to this test in a more satisfactory way, because the distribution of the success rate of the random predictions is under our control. Failure to reject the null hypothesis indicates that there is no evidence that any extra-seismic information the predictor uses (electrical signals for example) helps to predict earthquakes.