Time of emergence of climate signals

The time at which the signal of climate change emerges from the noise of natural climate variability (Time of Emergence, ToE) is a key variable for climate predictions and risk assessments. Here we present a methodology for estimating ToE for individual climate models, and use it to make maps of ToE for surface air temperature (SAT) based on the CMIP3 global climate models. Consistent with previous studies we show that the median ToE occurs several decades sooner in low latitudes, particularly in boreal summer, than in mid-latitudes. We also show that the median ToE in the Arctic occurs sooner in boreal winter than in boreal summer. A key new aspect of our study is that we quantify the uncertainty in ToE that arises not only from inter-model differences in the magnitude of the climate change signal, but also from large differences in the simulation of natural climate variability. The uncertainty in ToE is at least 30 years in the regions examined, and as much as 60 years in some regions. Alternative emissions scenarios lead to changes in both the median ToE (by a decade or more) and its uncertainty. The SRES B1 scenario is associated with a very large uncertainty in ToE in some regions. Our findings have important implications for climate modelling and climate policy which we discuss.

Targeted observations of a polar low in the Norwegian sea

A developing polar low is targeted with dropsonde observations to improve the forecast of its landfall. Accurately forecasting a polar low's strength and location remains a challenge; polar lows form over the ocean in poorly observed regions, therefore initial condition errors may contribute significantly to forecast error. The targeted polar low formed in the Norwegian Sea on 3 March 2008, during the Norwegian IPY-THORPEX field campaign. Two flights, six hours apart, released dense networks of dropsondes into a sensitive region covering the polar low and Arctic front to its west. The impact of the targeted observations is assessed using the limited-area Met Office Unified Model and three-dimensional variational (3D-Var) data assimilation scheme. Forecasts were verified using ECMWF analysis data, which show good agreement with both dropsonde data from a flight through the mature polar low, and 10 m QuikSCAT winds. The impact of the targeted data moved southwards with the polar low as it developed and then hit the Norwegian coast after 24 hours. The results show that the forecast of the polar low is sensitive to the initial conditions; targeted observations from the first flight did not improve the forecast, but those from the second flight clearly improved the forecast polar low position and intensity. However, caution should be applied to attributing the forecast improvement to the assimilation of the targeted observations from a single case-study, especially in this case as the forecast improvement is moderate relative to the spread from an operational ensemble forecast

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.

Technological change and the challenges for regional development: building social capital in less-favoured regions

The relevance of regional policy for less favoured regions (LFRs) reveals itself when policy-makers must reconcile competitiveness with social cohesion through the adaptation of competition or innovation policies. The vast literature in this area generally builds on an overarching concept of ‘social capital’ as the necessary relational infrastructure for collective action diversification and policy integration, in a context much influenced by a dynamic of industrial change and a necessary balance between the creation and diffusion of ‘knowledge’ through learning. This relational infrastructure or ‘social capital’ is centred on people’s willingness to cooperate and ‘envision’ futures as a result of “social organization, such as networks, norms and trust that facilitate action and cooperation for mutual benefit” (Putnam, 1993: 35). Advocates of this interpretation of ‘social capital’ have adopted the ‘new growth’ thinking behind ‘systems of innovation’ and ‘competence building’, arguing that networks have the potential to make both public administration and markets more effective as well as ‘learning’ trajectories more inclusive of the development of society as a whole. This essay aims to better understand the role of ‘social capital’ in the production and reproduction of uneven regional development patterns, and to critically assess the limits of a ‘systems concept’ and an institution-centred approach to comparative studies of regional innovation. These aims are discussed in light of the following two assertions: i) learning behaviour, from an economic point of view, has its determinants, and ii) the positive economic outcomes of ‘social capital’ cannot be taken as a given. It is suggested that an agent-centred approach to comparative research best addresses the ‘learning’ determinants and the consequences of social networks on regional development patterns. A brief discussion of the current debate on innovation surveys has been provided to illustrate this point.

The impact of a seasonally ice free Arctic Ocean on the temperature, precipitation and surface mass balance of Svalbard

The observed decline in summer sea ice extent since the 1970s is predicted to continue until the Arctic Ocean is seasonally ice free during the 21st Century. This will lead to a much perturbed Arctic climate with large changes in ocean surface energy flux. Svalbard, located on the present day sea ice edge, contains many low lying ice caps and glaciers and is expected to experience rapid warming over the 21st Century. The total sea level rise if all the land ice on Svalbard were to melt completely is 0.02 m. The purpose of this study is to quantify the impact of climate change on Svalbard’s surface mass balance (SMB) and to determine, in particular, what proportion of the projected changes in precipitation and SMB are a result of changes to the Arctic sea ice cover. To investigate this a regional climate model was forced with monthly mean climatologies of sea surface temperature (SST) and sea ice concentration for the periods 1961–1990 and 2061–2090 under two emission scenarios. In a novel forcing experiment, 20th Century SSTs and 21st Century sea ice were used to force one simulation to investigate the role of sea ice forcing. This experiment results in a 3.5 m water equivalent increase in Svalbard’s SMB compared to the present day. This is because over 50 % of the projected increase in winter precipitation over Svalbard under the A1B emissions scenario is due to an increase in lower atmosphere moisture content associated with evaporation from the ice free ocean. These results indicate that increases in precipitation due to sea ice decline may act to moderate mass loss from Svalbard’s glaciers due to future Arctic warming.

Does regional infrastructure investment stimulate extra housing and commercial building supply? Long-run evidence across the English regions

This paper uses long-term regional construction data to investigate whether increases infrastructure investment in the English regions leads to subsequent rises in housebuilding and new commercial property, using time series modeling. Both physical (roads and harbours) and social infrastructure (education and health) impacts are investigated across nine regions in England. Significant effects for physical infrastructure are found across most regions and, also, some evidence of a social infrastructure effect. The results are not consistent across regions, which may be due to geographical differences and to network and diversionary effects. However, the results do suggest that infrastructure does have some impact but follows differential lag structures. These results provide a test of the hypothesis of the economic benefits of infrastructure investment in an approach that has not been used before.

Style analysis in real estate markets: beyond the sectors and regions dichotomy

While style analysis has been studied extensively in equity markets, applications of this valuable tool for measuring and benchmarking performance and risk in a real estate context are still relatively new. Most previous real estate studies on this topic have identified three investment categories (rather than styles): sectors, administrative regions and economic regions. However, the low explanatory power reveals the need to extend this analysis to other investment styles. We identify four main real estate investment styles and apply a multivariate model to randomly generated portfolios to test the significance of each style in explaining portfolio returns. Results show that significant alpha performance is significantly reduced when we account for the new investment styles, with small vs. big properties being the dominant one. Secondly, we find that the probability of obtaining alpha performance is dependent upon the actual exposure of funds to style factors. Finally we obtain that both alpha and systematic risk levels are linked to the actual characteristics of portfolios. Our overall results suggest that it would be beneficial for real estate fund managers to use these style factors to set benchmarks and to analyze portfolio returns.

The relative importance of sectors v's regions in determining property returns

A number of studies have investigated the benefits of sector versus regional diversification within a real estate portfolio without explicitly quantify the relative benefits of one against the other. This paper corrects this omission by adopting the approach of Heston and Rouwenhorst (1994) and Beckers, Connor and Curds (1996) on a sample of 187 property data points using annual data over the period 1981-1995. The general conclusion of which is the sector diversification explains on average 22% of the variability of property returns compared with 8% for administratively defined regions. A result in line with previous work. Implying that sector diversification should be the first level of analysis in constructing and managing the real estate portfolio. However, unlike previous work functionally defined regions provide less of an explanation of regional diversification than administrative regions. Which may be down to the weak definition of economic regions employed in this study.

Automatic prediction of functional site regions in low-resolution protein structures

World-wide structural genomics initiatives are rapidly accumulating structures for which limited functional information is available. Additionally, state-of-the art structural prediction programs are now capable of generating at least low resolution structural models of target proteins. Accurate detection and classification of functional sites within both solved and modelled protein structures therefore represents an important challenge. We present a fully automatic site detection method, FuncSite, that uses neural network classifiers to predict the location and type of functionally important sites in protein structures. The method is designed primarily to require only backbone residue positions without the need for specific side-chain atoms to be present. In order to highlight effective site detection in low resolution structural models FuncSite was used to screen model proteins generated using mGenTHREADER on a set of newly released structures. We found effective metal site detection even for moderate quality protein models illustrating the robustness of the method.

The nature of Arctic polar vortices in chemistry-climate models

The structure of the Arctic stratospheric polar vortex in three chemistry–climate models (CCMs) taken from the CCMVal-2 intercomparison is examined using zonal mean and geometric-based methods. The geometric methods are employed by taking 2D moments of potential vorticity fields that are representative of the polar vortices in each of the models. This allows the vortex area, centroid location and ellipticity to be determined, as well as a measure of vortex filamentation. The first part of the study uses these diagnostics to examine how well the mean state, variability and extreme variability of the polar vortices are represented in CCMs compared to ERA-40 reanalysis data, and in particular for the UMUKCA-METO, NIWA-SOCOL and CCSR/NIES models. The second part of the study assesses how the vortices are predicted to change in terms of the frequency of sudden stratospheric warmings and their general structure over the period 1960–2100. In general, it is found that the vortices are climatologically too far poleward in the CCMs and produce too few large-scale filamentation events. Only a small increase is observed in the frequency of sudden stratospheric warming events from the mean of the CCMVal-2 models, but the distribution of extreme variability throughout the winter period is shown to change towards the end of the twentyfirst century.

The impact of 21st century Arctic sea ice decline on the climate and mass balance of the Greenland ice sheet and Svalbard’s glaciers and ice caps

The Arctic is a region particularly susceptible to rapid climate change. General circulation models (GCMs) suggest a polar amplification of any global warming signal by a factor of about 1.5 due, in part, to sea ice feedbacks. The dramatic recent decline in multi-year sea ice cover lies outside the standard deviation of the CMIP3 ensemble GCM predictions. Sea ice acts as a barrier between cold air and warmer oceans during winter, as well as inhibiting evaporation from the ocean surface water during the summer. An ice free Arctic would likely have an altered hydrological cycle with more evaporation from the ocean surface leading to changes in precipitation distribution and amount. Using the U.K. Met Office Regional Climate Model (RCM), HadRM3, the atmospheric effects of the observed and projected reduction in Arctic sea ice are investigated. The RCM is driven by the atmospheric GCM HadAM3. Both models are forced with sea surface temperature and sea ice for the period 2061-2090 from the CMIP3 HadGEM1 experiments. Here we use an RCM at 50km resolution over the Arctic and 25km over Svalbard, which captures well the present-day pattern of precipitation and provides a detailed picture of the projected changes in the behaviour of the oceanic-atmosphere moisture fluxes and how they affect precipitation. These experiments show that the projected 21stCentury sea ice decline alone causes large impacts to the surface mass balance (SMB) on Svalbard. However Greenland’s SMB is not significantly affected by sea ice decline alone, but responds with a strongly negative shift in SMB when changes to SST are incorporated into the experiments. This is the first study to characterise the impact of changes in future sea ice to Arctic terrestrial cryosphere mass balance.