A field experiment to recreate species rich hay meadows using regional seed mixtures

Species rich semi-natural grasslands are an important but threatened habitat throughout Europe and much of the former area has been lost since the 1950s. However, in some countries large areas have been preserved and the demand for meadow recreation by sowing seed mixtures is increasing. In the White Carpathians Protected Landscape Area (Czech Republic) the use of commercial seed mixtures is undesirable and the use of regional mixtures has been investigated. The costs for seeding large areas are high and lower cost techniques are needed. In 1999 a field experiment was set up to investigate the establishment of hay meadow vegetation comparing sowing a regional mixture all over a plot with sowing narrow 2.5 In strips of regional seed mixtures into a matrix of a commercial grass mixture or into natural regeneration. The results after five seasons showed good establishment of the sown species in the meadow treatment. Spread of sown species from the sown strips into the surrounding matrix occurred but the cover of species was lower in the commercial grass matrix compared with the natural regeneration matrix. Colonisation of some plots by unsown desirable grassland species from adjacent grassland habitats also occurred, but more species colonised the natural regeneration matrix than the commercial grasses or the sown meadow matrix itself. Overall, the results indicate that, in appropriate situations, sown strips can provide a lower cost but slower and longer-term alternative to field scale sowing of regional seed mixtures for recreation of hay meadow vegetation. (C) 2007 Elsevier Ltd. All rights reserved.

Seasonal pattern of regional carbon balance in the central Rocky Mountains from surface and airborne measurements

[1] High-elevation forests represent a large fraction of potential carbon uptake in North America, but this uptake is not well constrained by observations. Additionally, forests in the Rocky Mountains have recently been severely damaged by drought, fire, and insect outbreaks, which have been quantified at local scales but not assessed in terms of carbon uptake at regional scales. The Airborne Carbon in the Mountains Experiment was carried out in 2007 partly to assess carbon uptake in western U.S. mountain ecosystems. The magnitude and seasonal change of carbon uptake were quantified by (1) paired upwind-downwind airborne CO2 observations applied in a boundary layer budget, (2) a spatially explicit ecosystem model constrained using remote sensing and flux tower observations, and (3) a downscaled global tracer transport inversion. Top-down approaches had mean carbon uptake equivalent to flux tower observations at a subalpine forest, while the ecosystem model showed less. The techniques disagreed on temporal evolution. Regional carbon uptake was greatest in the early summer immediately following snowmelt and tended to lessen as the region experienced dry summer conditions. This reduction was more pronounced in the airborne budget and inversion than in flux tower or upscaling, possibly related to lower snow water availability in forests sampled by the aircraft, which were lower in elevation than the tower site. Changes in vegetative greenness associated with insect outbreaks were detected using satellite reflectance observations, but impacts on regional carbon cycling were unclear, highlighting the need to better quantify this emerging disturbance effect on montane forest carbon cycling.

Exploring the role of reanalysis data in simulating regional wind generation variability over Northern Ireland

As wind generation increases, system impact studies rely on predictions of future generation and effective representation of wind variability. A well-established approach to investigate the impact of wind variability is to simulate generation using observations from 10 m meteorological mast-data. However, there are problems with relying purely on historical wind-speed records or generation histories: mast-data is often incomplete, not sited at a relevant wind generation sites, and recorded at the wrong altitude above ground (usually 10 m), each of which may distort the generation profile. A possible complimentary approach is to use reanalysis data, where data assimilation techniques are combined with state-of-the-art weather forecast models to produce complete gridded wind time-series over an area. Previous investigations of reanalysis datasets have placed an emphasis on comparing reanalysis to meteorological site records whereas this paper compares wind generation simulated using reanalysis data directly against historic wind generation records. Importantly, this comparison is conducted using raw reanalysis data (typical resolution ∼50 km), without relying on a computationally expensive “dynamical downscaling” for a particular target region. Although the raw reanalysis data cannot, by nature of its construction, represent the site-specific effects of sub-gridscale topography, it is nevertheless shown to be comparable to or better than the mast-based simulation in the region considered and it is therefore argued that raw reanalysis data may offer a number of significant advantages as a data source.

Conditioning model output statistics of regional climate model precipitation on circulation patterns

Dynamical downscaling of Global Climate Models (GCMs) through regional climate models (RCMs) potentially improves the usability of the output for hydrological impact studies. However, a further downscaling or interpolation of precipitation from RCMs is often needed to match the precipitation characteristics at the local scale. This study analysed three Model Output Statistics (MOS) techniques to adjust RCM precipitation; (1) a simple direct method (DM), (2) quantile-quantile mapping (QM) and (3) a distribution-based scaling (DBS) approach. The modelled precipitation was daily means from 16 RCMs driven by ERA40 reanalysis data over the 1961–2000 provided by the ENSEMBLES (ENSEMBLE-based Predictions of Climate Changes and their Impacts) project over a small catchment located in the Midlands, UK. All methods were conditioned on the entire time series, separate months and using an objective classification of Lamb's weather types. The performance of the MOS techniques were assessed regarding temporal and spatial characteristics of the precipitation fields, as well as modelled runoff using the HBV rainfall-runoff model. The results indicate that the DBS conditioned on classification patterns performed better than the other methods, however an ensemble approach in terms of both climate models and downscaling methods is recommended to account for uncertainties in the MOS methods.

Urban retrofitting: identifying disruptive and sustaining technologies using performative and foresight techniques

Cities, which are now inhabited by a majority of the world's population, are not only an important source of global environmental and resource depletion problems, but can also act as important centres of technological innovation and social learning in the continuing quest for a low carbon future. Planning and managing large-scale transitions in cities to deal with these pressures require an understanding of urban retrofitting at city scale. In this context performative techniques (such as backcasting and roadmapping) can provide valuable tools for helping cities develop a strategic view of the future. However, it is also important to identify ‘disruptive’ and ‘sustaining’ technologies which may contribute to city-based sustainability transitions. This paper presents research findings from the EPSRC Retrofit 2050 project, and explores the relationship between technology roadmaps and transition theory literature, highlighting the research gaps at urban/city level. The paper develops a research methodology to describe the development of three guiding visions for city-regional retrofit futures, and identifies key sustaining and disruptive technologies at city scale within these visions using foresight (horizon scanning) techniques. The implications of the research for city-based transition studies and related methodologies are discussed.

An investigation into the impact of using various techniques to estimate Arctic surface air temperature anomalies

Time series of global and regional mean Surface Air Temperature (SAT) anomalies are a common metric used to estimate recent climate change. Various techniques can be used to create these time series from meteorological station data. The degree of difference arising from using five different techniques, based on existing temperature anomaly dataset techniques, to estimate Arctic SAT anomalies over land and sea ice were investigated using reanalysis data as a testbed. Techniques which interpolated anomalies were found to result in smaller errors than non-interpolating techniques relative to the reanalysis reference. Kriging techniques provided the smallest errors in estimates of Arctic anomalies and Simple Kriging was often the best kriging method in this study, especially over sea ice. A linear interpolation technique had, on average, Root Mean Square Errors (RMSEs) up to 0.55 K larger than the two kriging techniques tested. Non-interpolating techniques provided the least representative anomaly estimates. Nonetheless, they serve as useful checks for confirming whether estimates from interpolating techniques are reasonable. The interaction of meteorological station coverage with estimation techniques between 1850 and 2011 was simulated using an ensemble dataset comprising repeated individual years (1979-2011). All techniques were found to have larger RMSEs for earlier station coverages. This supports calls for increased data sharing and data rescue, especially in sparsely observed regions such as the Arctic.