Accounting for global-mean warming and scaling uncertainties in climate change impact studies: application to a regulated lake system

Abstract

A probabilistic assessment of climate change and related impacts should consider a large range of potential future climate scenarios. State-ofthe-art climate models, especially coupled atmosphere-ocean general circulation models and Regional Climate Models (RCMs) cannot, however, be used to simulate such a large number of scenarios. This paper presents a methodology for obtaining future climate scenarios through a simple scaling methodology. The projections of several key meteorological variables obtained from a few regional climate model runs are scaled, based on different global-mean warming projections drawn in a probability distribution of future global-mean warming. The resulting climate change scenarios are used to drive a hydrological and a water management model to analyse the potential climate change impacts on a water resources system. This methodology enables a joint quantification of the climate change impact uncertainty induced by the global-mean warming scenarios and the regional climate response. It is applied to a case study in Switzerland, a water resources system formed by three interconnected lakes located in the Jura Mountains. The system behaviour is simulated for a control period (19611990) and a future period (20702099). The potential climate change impacts are assessed through a set of impact indices related to different fields of interest (hydrology, agriculture and ecology). The results obtained show that future climate conditions will have a significant influence on the performance of the system and that the uncertainty induced by the inter-RCM variability will contribute to much of the uncertainty of the prediction of the total impact. This research has been conducted within the 20012004 EU funded project SWURVE.

Climate change and hydropower production in the Swiss Alps: quantification of potential impacts and related modelling uncertainties

“…negatively All results are conditioned by the underlying modelling assumptions and by the data used in long-term climate projections, namely the global-mean warming probability density function given by Wigley and Raper (2001) and the regional scaling relationships derived from 19 regional climate models according to the methodology of Hingray et al (2007b). These two data sources incorporate the maximum amount of scientific knowledge currently available in the area of climate modelling.…”

“…These statistics are the absolute change of seasonal mean temperature, the ratio of corresponding standard deviations, the ratio of seasonal mean daily precipitation and the ratio of corresponding coefficients of variation. The methodology presented by Hingray et al (2007b) is used to sample the entire range of possible regional climate statistics under the global-mean warming probability distribution of Wigley and Raper (2001) and the scaling distribution of Hingray et al, (2007a). Figure 5 illustrates the resulting distribution of future mean monthly temperature and precipitation for the case study catchment, together with the mean monthly temperature and precipitation observed for the control period.…”

Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling

“…Luo et al, 2005;Trinka et al, 2005), and emerging studies in the use of probabilistic scenarios in climate change impact assessment on water related activities. For example, Hingray et al (2007b) applied probabilistic climate change scenarios for regional temperature and precipitation (pdfs) developed in Hingray et al (2007a) to examine the impacts on the water resources of a regulated lake system in Switzerland. They concluded that the uncertainty resulting from the climate model (i.e.…”

A framework for the quantitative assessment of climate change impacts on water-related activities at the basin scale

“…Coherently with other studies (e.g. Hingray et al, 2007), we demonstrate that one major source of uncertainty lays in the climatic model. Nonetheless the indication that climate change will have negative impacts on water use clearly emerges.…”

“…Besides this distinction, some studies Schaefli et al, 2007) seem to indicate that the choice of the GCM is the most critical. However, as for the RCM scenarios generated in the PRUDENCE project and used in this analysis, Hingray et al (2007) show that variability among RCMs is comparable to the variability induced by the GCM choice. Following these considerations and for brevity's sake, in this paper we will focus only on the RCM variability.…”