Uncertainties In Future Projections Of Extreme Rainfall: The Role Of Climate Model, Emission Scenario And Randomness

Climate change has resulted in substantial variations in annual extreme rainfall quantiles in different durations and return periods. Predicting the future changes in extreme rainfall quantiles is essential for various water resources design, assessment, and decision making purposes. Current Predictions of future rainfall extremes, however, exhibit large uncertainties. According to extreme value theory, rainfall extremes are rather random variables, with changing distributions around different return periods; therefore there are uncertainties even under current climate conditions. Regarding future condition, our large-scale knowledge is obtained using global climate models, forced with certain emission scenarios. There are widely known deficiencies with climate models, particularly with respect to precipitation projections. There is also recognition of the limitations of emission scenarios in representing the future global change. Apart from these large-scale uncertainties, the downscaling methods also add uncertainty into estimates of future extreme rainfall when they convert the larger-scale projections into local scale. The aim of this research is to address these uncertainties in future projections of extreme rainfall of different durations and return periods. We plugged 3 emission scenarios with 2 global climate models and used LARS-WG, a well-known weather generator, to stochastically downscale daily climate models’ projections for the city of Saskatoon, Canada, by 2100. The downscaled projections were further disaggregated into hourly resolution using our new stochastic and non-parametric rainfall disaggregator. The extreme rainfall quantiles can be consequently identified for different durations (1-hour, 2-hour, 4-hour, 6-hour, 12-hour, 18-hour and 24-hour) and return periods (2-year, 10-year, 25-year, 50-year, 100-year) using Generalized Extreme Value (GEV) distribution. By providing multiple realizations of future rainfall, we attempt to measure the extent of total predictive uncertainty, which is contributed by climate models, emission scenarios, and downscaling/disaggregation procedures. The results show different proportions of these contributors in different durations and return periods.

Development Of Climate Change Information Database And Its Use In Civic Consciousness Enlightenment

GCM outputs such as CMIP3 are available via network access to PCMDI web site. Meteorological researchers are familiar with the usage of the GCM data, but the most of researchers other than meteorology such as agriculture, civil engineering, etc., and general people are not familiar with the GCM. There are some difficulties to use GCM; 1) to download the enormous quantity of data, 2) to understand the GCM methodology, parameters and grids. In order to provide a quick access way to GCM, Climate Change Information Database has been developed. The purpose of the database is to bridge the users and meteorological specialists and to facilitate the understanding the climate changes. The resolution of the data is unified, and climate change amount or factors for each meteorological element are provided from the database. All data in the database are interpolated on the same 80km mesh. Available data are the present-future projections of 27 GCMs, 16 meteorological elements (precipitation, temperature, etc.), 3 emission scenarios (A1B, A2, B1). We showed the summary of this database to residents in Toyama prefecture and measured the effect of showing and grasped the image for the climate change by using the Internet questionary survey. The persons who feel a climate change at the present tend to feel the additional changes in the future. It is important to show the monitoring results of climate change for a citizen and promote the understanding for the climate change that had already occurred. It has been shown that general images for the climate change promote to understand the need of the mitigation, and that it is important to explain about the climate change that might occur in the future even if it did not occur at the present in order to have people recognize widely the need of the adaptation.