CHASE-PL Climate Projection dataset over Poland – bias adjustment of EURO-CORDEX simulations

The CHASE-PL (Climate change impact assessment for selected sectors in Poland) Climate Projections – Gridded Daily Precipitation and Temperature dataset 5 km (CPLCP-GDPT5) consists of projected daily minimum and maximum air temperatures and precipitation totals of nine EURO-CORDEX regional climate model outputs bias corrected and downscaled to a 5 km  ×  5 km grid. Simulations of one historical period (1971–2000) and two future horizons (2021–2050 and 2071–2100) assuming two representative concentration pathways (RCP4.5 and RCP8.5) were produced. We used the quantile mapping method and corrected any systematic seasonal bias in these simulations before assessing the changes in annual and seasonal means of precipitation and temperature over Poland. Projected changes estimated from the multi-model ensemble mean showed that annual means of temperature are expected to increase steadily by 1 °C until 2021–2050 and by 2 °C until 2071–2100 assuming the RCP4.5 emission scenario. Assuming the RCP8.5 emission scenario, this can reach up to almost 4 °C by 2071–2100. Similarly to temperature, projected changes in regional annual means of precipitation are expected to increase by 6 to 10 % and by 8 to 16 % for the two future horizons and RCPs, respectively. Similarly, individual model simulations also exhibited warmer and wetter conditions on an annual scale, showing an intensification of the magnitude of the change at the end of the 21st century. The same applied for projected changes in seasonal means of temperature showing a higher winter warming rate by up to 0.5 °C compared to the other seasons. However, projected changes in seasonal means of precipitation by the individual models largely differ and are sometimes inconsistent, exhibiting spatial variations which depend on the selected season, location, future horizon, and RCP. The overall range of the 90 % confidence interval predicted by the ensemble of multi-model simulations was found to likely vary between −7 % (projected for summer assuming the RCP4.5 emission scenario) and +40 % (projected for winter assuming the RCP8.5 emission scenario) by the end of the 21st century. Finally, this high-resolution bias-corrected product can serve as a basis for climate change impact and adaptation studies for many sectors over Poland. The CPLCP-GDPT5 dataset is publicly available at http://dx.doi.org/10.4121/uuid:e940ec1a-71a0-449e-bbe3-29217f2ba31d

Teachers, school choice and competition: Lock-in effects within and between sectors

© Author(s) 2016. Neoliberal forces since the latter part of the 20th century have ushered in greater devolution in state schooling systems, producing uneven effects on the working conditions of teachers, commonly the largest segment of the public sector workforce. Within this context, this paper examines secondary teachers' working conditions as they relate to the restructuring of the professional landscape that school choice reforms bring. Drawing illustrations from a qualitative study of teachers' working experiences in the lowest socio-economic status schools, through the 'middle band', to the most prestigious and affluent in a metropolitan city in Australia, this paper finds that teachers develop skill-sets that are context specific, creating possible 'lock-in effects' within but also between sectors. Moreover, various work arrangement issues seem to reinforce the lock-in effects by making changes between sectors risky and unattractive. We postulate that inter- and intra-sectoral differences, which are exacerbated through school choice processes, have the potential to reinforce and deepen the lock-in effects on teachers, with possible consequences for their future career mobility

Governance reform in context: Welfare sector professionals’ working and employment conditions

This article addresses the relationship between profession, organisation and spatial (geographical) setting, more specifically the relationship between welfare sector professionals’ conditions for work amidst governance change. In previous research, the conditions for welfare sector professionals’ work have largely been studied without taking the employing organisations or the local and regional situation into consideration. In this article, the authors question and seek to counteract this de-contextualised approach. They do so by showing that the circumstances of the specific workplace context are essential in understanding welfare sector professionals’ working conditions, especially so in current governance contexts characterised to varying degrees by marketisation, via processes and structures which facilitate choice, competition, privatisation and devolution. This line of argument is illustrated in relation to how upper secondary teachers in Sweden experience their conditions for work and employment in eight schools across three different ‘market types’. The authors contend that whilst different conditions in different workplaces can to some extent always be expected, current governance agendas in the welfare sector seem to exacerbate these differences. The article’s theoretical contribution, therefore, is in the privileging of local contextual dynamics. The authors suggest a stronger emphasis on spatially-informed frames of reference in future studies of conditions for welfare sector professionals

On using principal components to represent stations in empirical-statistical downscaling

We test a strategy for downscaling seasonal mean temperature for many locations within a region, based on principal component analysis (PCA), and assess potential benefits of this strategy which include an enhancement of the signal-to-noise ratio, more efficient computations, and reduced sensitivity to the choice of predictor domain. These conditions are tested in some case studies for parts of Europe (northern and central) and northern China. Results show that the downscaled results were not highly sensitive to whether a PCA-basis or a more traditional strategy was used. However, the results based on a PCA were associated with marginally and systematically higher correlation scores as well as lower root-mean-squared errors. The results were also consistent with the notion that PCA emphasises the large-scale dependency in the station data and an enhancement of the signal-to-noise ratio. Furthermore, the computations were more efficient when the predictands were represented in terms of principal components

On using principal components to represent stations in empirical-statistical downscaling

We test a strategy for downscaling seasonal mean temperature for many locations within a region, based on principal component analysis (PCA), and assess potential benefits of this strategy which include an enhancement of the signal-to-noise ratio, more efficient computations, and reduced sensitivity to the choice of predictor domain. These conditions are tested in some case studies for parts of Europe (northern and central) and northern China. Results show that the downscaled results were not highly sensitive to whether a PCA-basis or a more traditional strategy was used. However, the results based on a PCA were associated with marginally and systematically higher correlation scores as well as lower root-mean-squared errors. The results were also consistent with the notion that PCA emphasises the large-scale dependency in the station data and an enhancement of the signal-to-noise ratio. Furthermore, the computations were more efficient when the predictands were represented in terms of principal components

Simple and approximate estimations of future precipitation return values

We present estimates of future 20-year return values for 24 h precipitation based on multi-model ensembles of temperature projections and a crude method to quantify how warmer conditions may influence precipitation intensity. Our results suggest an increase by as much as 40–50 % projected for 2100 for a number of locations in Europe, assuming the high Representative Concentration Pathway (RCP) 8.5 emission scenario. The new strategy was based on combining physical understandings with the limited information available, and it utilised the covariance between the mean seasonal variations in precipitation intensity and the North Atlantic saturation vapour pressure. Rather than estimating the expected values and interannual variability, we tried to estimate an upper bound for the response in the precipitation intensity based on the assumption that the seasonal variations in the precipitation intensity are caused by the seasonal variations in temperature. Return values were subsequently derived from the estimated precipitation intensity through a simple and approximate scheme that combined the 1-year 24 h precipitation return values and downscaled annual wet-day mean precipitation for a 20-year event. The latter was based on the 95th percentile of a multi-model ensemble spread of downscaled climate model results. We found geographical variations in the shape of the seasonal cycle of the wet-day mean precipitation which suggest that different rain-producing mechanisms dominate in different regions. These differences indicate that the simple method used here to estimate the response of precipitation intensity to temperature was more appropriate for convective precipitation than for orographic rainfall