Managing the inconceivable: participatory assessments of impacts and responses to extreme climate change

A comprehensive understanding of the implications of extreme climate change requires an in-depth exploration of the perceptions and reactions of the affected stakeholder groups and the lay public. The project on “Atlantic sea level rise: Adaptation to imaginable worst-case climate change” (Atlantis) has studied one such case, the collapse of the West Antarctic Ice Sheet and a subsequent 5–6 m sea-level rise. Possible methods are presented for assessing the societal consequences of impacts and adaptation options in selected European regions by involving representatives of pertinent stakeholders. Results of a comprehensive review of participatory integrated assessment methods with a view to their applicability in climate impact studies are summarized including Simulation-Gaming techniques, the Policy Exercise method, and the Focus Group technique. Succinct presentations of these three methods are provided together with short summaries of relevant earlier applications to gain insights into the possible design options. Building on these insights, four basic versions of design procedures suitable for use in the Atlantis project are presented. They draw on design elements of several methods and combine them to fit the characteristics and fulfill the needs of addressing the problem of extreme sea-level rise. The selected participatory techniques and the procedure designs might well be useful in other studies assessing climate change impacts and exploring adaptation options.     

Detection of a 50-day oscillation in the Wolf number

Резюме Вaрuaцuu с nерuо?rt;ом около 50 ?rt;неŭ (коmорые ?rt;aвно обнaружены в uзмененuях nро?rt;олжumельносmu суmок u во мно uх меmеороло uческuх велuчuнaх, особенно в моменmе колuчесmвa ?rt;вuженuя amмосферы) былu рaскрыmы в солнечноŭ aкmuвносmu, оnuсaнноŭ чuслом Вольфa.      

Estimating groundwater discharge to surface waters using heat as a tracer in low flux environments: the role of thermal conductivity

Analytical modelling of heat transport was used to address effects of uncertainty in thermal conductivity on groundwater–surface water exchange. In situ thermal conductivities and temperature profiles were measured in a coastal lagoon bed where groundwater is known to discharge. The field site could be divided into three sediment zones where significant spatial changes in thermal conductivity on metre to centimetre scale show that spatial variability connected to the sediment properties must be considered. The application of a literature‐based bulk thermal conductivity of 1.84 Wm^?1 °C^?1, instead of field data that ranged from 0.62 to 2.19 W m^?1 °C^?1, produced a mean overestimation of 2.33 cm d^?1 that, considering the low fluxes of the study area, represents an 89% increase and up to a factor of 3 in the most extreme cases. Incorporating the uncertainty due to sediment heterogeneities leads to an irregular trend of the flux distribution from the shore towards the lagoon. The natural variability of the thermal conductivity associated with changes in the sediment composition resulted in a mean variation of ±0.66 cm d^?1 in fluxes corresponding to a change of ±25.4%. The presence of organic matter in the sediments, a common situation in the near‐shore areas of surface water bodies, is responsible for the decrease of thermal conductivity. The results show that the natural variability of sediment thermal conductivity is a parameter to be considered for low flux environments, and it contributes to a better understanding of groundwater–surface water interactions in natural environments. Copyright © 2015 John Wiley & Sons, Ltd.     

Depositional rates and dating techniques of modern deposits in the Brno reservoir (Czech Republic) during the last 70 years

Facies analysis, magnetic susceptibility, and analysis of grain size, TOC content and isotopes (¹³⁷Cs, ²¹⁰Pb, ²²⁶Ra, ²²⁸Ra, and ²³⁸U concentrations) were used to determine the history of the modern deposits of the Brno reservoir. The sedimentary succession can be subdivided into two main units. The lower unit is formed predominantly by medium- to coarse-grained silty sands and is interpreted as a fluvial succession deposited before the Svratka River was dammed. The upper unit consists of brownish planar laminated silts and rarely of clayey or sandy silts and is interpreted as a product of the reservoir deposition. The concentrations of ²³⁸U reflect the history of uranium mining in the upper part of the Svratka River catchment. As a consequence, ²¹⁰Pb radionuclide concentrations cannot be used for establishing a sediment chronology. Concentrations of ¹³⁷Cs show two marked peaks, the upper of which is attributed to the Chernobyl reactor accident in 1986, and the lower one is attributed to the maximum rate of atomic weapons testing in 1963. From these peaks, mean depositional rates of 3.2 cm year⁻¹ for the time period of 1986–2007 and of 3.4 cm year⁻¹ between 1963 and 1986 are calculated. Based on the known age of the reservoir, which was constructed in 1939, we can also calculate mean depositional rate for the time period of 1939–1963, which is 3.1 cm year⁻¹.     

Pre-processing of a sample of multi-scene and multi-date Landsat imagery used to monitor forest cover changes over the tropics

In support to the Remote Sensing Survey of the global Forest Resource Assessment 2010, the TREES-3 project has processed more than 12,000 Landsat TM and ETM+ data subsets systematically distributed over the tropics. The project aims at deriving area estimates of tropical forest cover change for the periods 1990-2000-2005. The paper presents the pre-processing steps applied in an operational and robust manner to this large amount of multi-date and multi-scene imagery: conversion to top-of-atmosphere reflectance, cloud and cloud shadow detection, haze correction and image radiometric normalization. The results show that the haze correction algorithm has improved the visual appearance of the image and significantly corrected the digital numbers for Landsat visible bands, especially the red band. The impact of the normalization procedures (forest normalization and relative normalization) was assessed on 210 image pairs: in all cases the correlation between the spectral values of the same land cover in both images was improved. The developed automatic pre-processing chain provided a consistent multi-temporal data set across the tropics that will constitute the basis for an automatic object-based supervised classification.     

Climate change scenarios of precipitation extremes in Central Europe from ENSEMBLES regional climate models

The study examines future scenarios of precipitation extremes over Central Europe in an ensemble of 12 regional climate model (RCM) simulations with the 25-km resolution, carried out within the European project ENSEMBLES. We apply the region-of-influence method as a pooling scheme when estimating distributions of extremes, which consists in incorporating data from a ‘region’ (set of gridboxes) when fitting an extreme value distribution in any single gridbox. The method reduces random variations in the estimates of parameters of the extreme value distribution that result from large spatial variability of heavy precipitation. Although spatial patterns differ among the models, most RCMs simulate increases in high quantiles of precipitation amounts when averaged over the area for the late-twenty-first century (2070–2099) climate in both winter and summer. The sign as well as the magnitude of the projected change vary only little for individual parts of the distribution of daily precipitation in winter. In summer, on the other hand, the projected changes increase with the quantile of the distribution in all RCMs, and they are negative (positive) for parts of the distribution below (above) the 98% quantile if averaged over the RCMs. The increases in precipitation extremes in summer are projected in spite of a pronounced drying in most RCMs. Although a rather general qualitative agreement of the models concerning the projected changes of precipitation extremes is found in both winter and summer, the uncertainties in climate change scenarios remain large and would likely further increase considerably if a more complete ensemble of RCM simulations driven by a larger suite of global models and with a range of possible scenarios of the radiative forcing is available.     

Climate Change driven evolution of hazards to Europe’s transport infrastructure throughout the twenty-first century

Air circulation due to the urban heat island (UHI) effect can influence the dispersion of air pollutants in a metropolis. This study focusses on the influence of the UHI effect on particulate matter (PM; including PM_2.5 and PM_2.5–10) between May and September 2010–2012 in the Taipei basin. Meteorological and PM data were obtained from the sites, owned by the governmental authorities. The analysis was carried out using t test, relative indices (RIs), Pearson product–moment correlation and stepwise regression. The results show that the RI values for PM were the highest at moderate UHI intensity (MUI; 2 °C ≤ UHI < 4 °C) rather than at strong UHI intensity (SUI; 4 °C ≤ UHI) during the peak time for anthropogenic emissions (20:00 LST). Neither the accumulation of PM nor the surface convergence occurred in the hot centre, as shown by the case study. At MUI, more than 89 % of the synoptic weather patterns showed that the weather was clear and hot or that the atmosphere was stable. The variation in PM was associated with horizontal and vertical air dispersion. Poor horizontal air dispersion, with subsidence, caused an increase in PM at MUI. However, the updraft motion diluted the PM at SUI. The stepwise regression models show that the cloud index and surface air pressure determined the variation in PM_2.5–10, while cloud index, wind speed and mixing height influenced the variation in PM_2.5. In conclusion, a direct relationship between UHI effect and PM was not obvious.     

Covariance-Based Variable Selection for Compositional Data

Omitting variables in compositional data analysis may lead to a substantial change in results from that of multivariate statistical analysis. In particular, this is the case for principal component analysis and the compositional biplot, where both the interpretation of loadings and scores of the remaining subcomposition are affected. A stepwise procedure is introduced that allows for a reduction of the original composition to a subcomposition by avoiding a substantial change of the information, like those carried by the compositional biplot. The subcomposition is easier to handle and interpret. Numerical results give evidence of the usefulness of the procedure.