The changing atmospheric water cycle in polar regions in a warmer climate

We have examined the atmospheric water cycle of both Polar Regions, pole wards of 60°N and 60°S, using the ERA-Interim re-analysis and high-resolution simulations with the ECHAM5 model for both the present and future climate based on the IPCC, A1B scenario, representative of the last three decades of the 21st century. The annual precipitation in ERA-Interim amounts to ~17000 km3 and is more or less the same in the Arctic and the Antarctic, but it is composed differently. In the Arctic the annual evaporation is some 8000 km3 but some 3000 km3 less in the Antarctica where the net horizontal transport is correspondingly larger. The net water transport of the model is more intense than in ERA-Interim, in the Arctic the difference is 2.5% and in the Antarctic it is 6.2%. Precipitation and net horizontal transport in the Arctic has a maximum in August and September. Evaporation peaks in June and July. The seasonal cycle is similar in Antarctica with the highest precipitation in the austral autumn. The largest net transport occurs at the end of the major extra-tropical storm tracks in the Northern Hemisphere such as the eastern Pacific and eastern north Atlantic. The variability of the model is virtually identical to that of the re-analysis and there are no changes in variability between the present climate and the climate at the end of the 21st century when normalized with the higher level of moisture. The changes from year to year are substantial with the 20 and 30-year records being generally too short to identify robust trends in the hydrological cycle. In the A1B climate scenario the strength of the water cycle increases by some 25% in the Arctic and by 19% in the Antarctica, as measured by annual precipitation. The increase in the net horizontal transport is 29% and 22% respectively, and the increase in evaporation correspondingly less. The net transport follows closely the Clausius-Clapeyron relation. There is 2 a minor change in the annual cycle of the Arctic atmospheric water cycle with the maximum transport and precipitation occurring later in the year. There is a small imbalance of some 4-6% between the net transport and precipitation minus evaporation. We suggest that this is mainly due to the fact the transport is calculated from instantaneous 6-hourly data while precipitation and evaporation is accumulated over a 6 hour period. The residual difference is proportionally similar for all experiments and hardly varies from year to year.

Patterns of nucleotide substitution in angiosperm cpDNA trnL (UAA)-trnF (GAA) regions

Patterns of substitution in chloroplast encoded trnL_F regions were compared between species of Actaea (Ranunculales), Digitalis (Scrophulariales), Drosera (Caryophyllales), Panicoideae (Poales), the small chromosome species clade of Pelargonium (Geraniales), each representing a different order of flowering plants, and Huperzia (Lycopodiales). In total, the study included 265 taxa, each with > 900-bp sequences, totaling 0.24 Mb. Both pairwise and phylogeny-based comparisons were used to assess nucleotide substitution patterns. In all six groups, we found that transition/transversion ratios, as estimated by maximum likelihood on most-parsimonious trees, ranged between 0.8 and 1.0 for ingroups. These values occurred both at low sequence divergences, where substitutional saturation, i.e., multiple substitutions having occurred at the same (homologous) nucleotide position, was not expected, and at higher levels of divergence. This suggests that the angiosperm trnL-F regions evolve in a pattern different from that generally observed for nuclear and animal mtDNA (transitional/transversion ratio > or = 2). Transition/transversion ratios in the intron and the spacer region differed in all alignments compared, yet base compositions between the regions were highly similar in all six groups. A>-C transversions were significantly less frequent than the other four substitution types. This correlates with results from studies on fidelity mechanisms in DNA replication that predict A<->T and G<->C transversions to be least likely to occur. It therefore strengthens confidence in the link between mutation bias at the polymerase level and the actual fixation of substitutions as recorded on evolutionary trees, and concomitantly, in the neutrality of nucleotide substitutions as phylogenetic markers.

Another look at the relative importance of sectors and regions in determining property returns

This paper re-examines the relative importance of sector and regional effects in determining property returns. Using the largest property database currently available in the world, we decompose the returns on individual properties into a national effect, common to all properties, and a number of sector and regional factors. However, unlike previous studies, we categorise the individual property data into an ever-increasing number of property-types and regions, from a simple 3-by-3 classification, up to a 10 by 63 sector/region classification. In this way we can test the impact that a finer classification has on the sector and regional effects. We confirm the earlier findings of previous studies that sector-specific effects have a greater influence on property returns than regional effects. We also find that the impact of the sector effect is robust across different classifications of sectors and regions. Nonetheless, the more refined sector and regional partitions uncover some interesting sector and regional differences, which were obscured in previous studies. All of which has important implications for property portfolio construction and analysis.

The core-periphery model with three regions and more

We determine the properties of the core-periphery model with three regions and compare our results with those of the standard 2-region model. The conditions for the stability of dispersion and concentration are established. As in the 2-region model, dispersion and concentration can be simultaneously stable. We show that the 3-region (2-region) model favours the concentration (dispersion) of economic activity. Furthermore, we provide some results for the n-region model. We show that the stability of concentration of the 2-region model implies that of any model with an even number of regions.