Application of a stochastic weather generator to assess climate change impacts in a semi-arid climate: The Upper Indus Basin

Assessing local climate change impacts requires downscaling from Global Climate Model simulations. Here, a stochastic rainfall model (RainSim) combined with a rainfall conditioned weather generator (CRU WG) have been successfully applied in a semi-arid mountain climate, for part of the Upper Indus Basin (UIB), for point stations at a daily time-step to explore climate change impacts. Validation of the simulated time-series against observations (1961–1990) demonstrated the models’ skill in reproducing climatological means of core variables with monthly RMSE of <2.0 mm for precipitation and ⩽0.4 °C for mean temperature and daily temperature range. This level of performance is impressive given complexity of climate processes operating in this mountainous context at the boundary between monsoonal and mid-latitude (westerly) weather systems. Of equal importance the model captures well the observed interannual variability as quantified by the first and last decile of 30-year climatic periods. Differences between a control (1961–1990) and future (2071–2100) regional climate model (RCM) time-slice experiment were then used to provide change factors which could be applied within the rainfall and weather models to produce perturbed ‘future’ weather time-series. These project year-round increases in precipitation (maximum seasonal mean change:+27%, annual mean change: +18%) with increased intensity in the wettest months (February, March, April) and year-round increases in mean temperature (annual mean +4.8 °C). Climatic constraints on the productivity of natural resource-dependent systems were also assessed using relevant indices from the European Climate Assessment (ECA) and indicate potential future risk to water resources and local agriculture. However, the uniformity of projected temperature increases is in stark contrast to recent seasonally asymmetrical trends in observations, so an alternative scenario of extrapolated trends was also explored. We conclude that interannual variability in climate will continue to have the dominant impact on water resources management whichever trajectory is followed. This demonstrates the need for sophisticated downscaling methods which can evaluate changes in variability and sequencing of events to explore climate change impacts in this region

Perturbing a Weather Generator using change factors derived from Regional Climate Model simulations

The purpose of this paper is to provide a method for perturbing Weather Generators (WGs) for future decades and to assess its effectiveness. Here the procedure is applied to the WG implemented within the UKCP09 package and assessed using data from a Regional Climate Model (RCM) simulation which provides a significant "climate change" between a control run period and a distant future. The WG is normally calibrated on observed data. For this study, data from an RCM control period (1961-1990) was used, then perturbed using the procedure. Because only monthly differences between the RCM control and scenario periods are used to perturb the WG, the direct daily RCM scenario may be considered as unseen data to assess how well the perturbation procedure reproduces the direct RCM simulations for the future

New estimates of future changes in extreme rainfall across the UK using regional climate model integrations. 1. Assessment of control climate

Widespread major flood events in both the UK and Europe over the last decade have focussed attention on perceived increases in rainfall intensities. The changing magnitude of such events may have significant impacts upon many sectors, particularly those associated with flooding, water resources and the insurance industry. Here, two methods are used to assess the performance of the HadRM3H model in the simulation of UK extreme rainfall: regional frequency analysis and individual grid box analysis. Both methods use L-moments to derive extreme value distributions of rainfall for 1-, 2-, 5- and 10-day events for both observed data from 204 sites across the UK (1961–1990) and gridded ∼50 km by 50 km data from the control climate integration of HadRM3H. Despite differences in spatial resolution between the observed and modelled data, HadRM3H provides a good representation of extreme rainfall at return periods of up to 50 years in most parts of the UK. Although the east–west rainfall gradient tends to be exaggerated, leading to some overestimation of extremes in high elevation western areas and an underestimation in eastern ‘rain shadowed’ regions, this suggests that the regional climate model will also have skill in predicting how rainfall extremes might change under enhanced greenhouse conditions

Climate scenarios and decision making under uncertainty

Climate scenarios provide an essential foundation for research on the impacts of climate change on the built environment and for the identification of appropriate adaptation measures. They are, however, subject to uncertainties in the underlying greenhouse gas emissions and concentration scenarios as well as a range of scientific uncertainties associated with climate modelling and the natural variability of climate. These uncertainties provide a major motive for the current move towards probabilistic climate scenarios a move which is also supported from the decision-making perspective. Examples of probabilistic scenarios constructed for variables and UK locations of interest for built environment research are presented here. The need to consider other uncertainties potentially important sub-grid scale processes such as the urban heat island effect and the influence of natural variability in non-stationary series of weather extremes is demonstrated. Consideration is also given to aspects of decision making under uncertainty focusing on the timing of upgrade of the Thames tidal defences and the extent to which managers integrate climate change information into long-term projects in the built environment. A major recommendation from the case-study examples presented here is the need for improved communication between climate scenario developers, and scenario users and decision makers in the built environment sector