Abstract
Abstract. Regional climate models (RCMs) are valuable tools to evaluate impacts of climate change (CC) at regional scale. However, as the size of the area of interest decreases, the ability of a RCM to simulate extreme precipitation events decreases due to the spatial resolution. Thus, it is difficult to evaluate whether a RCM bias on localized extreme precipitation is caused by the spatial resolution or by a misrepresentation of the physical processes in the model. Thereby, it is difficult to trust the CC impact projections for localized extreme precipitation. Stochastic spatial disaggregation models can bring the RCM precipitation data at a finer scale and reduce the bias caused by spatial resolution. In addition, disaggregation models can generate an ensemble of outputs, producing an interval of possible values instead of a unique discrete value. The objective of this work is to evaluate whether a stochastic spatial disaggregation model applied on annual maximum daily precipitation (i) enables the validation of a RCM for a period of reference, and (ii) modifies the evaluation of CC impacts over a small area. Three simulations of the Canadian RCM (CRCM) covering the period 1961–2099 are used over a small watershed (130 km2) located in southern Québec, Canada. The disaggregation model applied is based on Gibbs sampling and accounts for physical properties of the event (wind speed, wind direction, and convective available potential energy – CAPE), leading to realistic spatial distributions of precipitation. The results indicate that disaggregation has a significant impact on the validation. However, it does not provide a precise estimate of the simulation bias because of the difference in resolution between disaggregated values (4 km) and observations, and because of the underestimation of the spatial variability by the disaggregation model for the most convective events. Nevertheless, disaggregation illustrates that the simulations used mostly overestimated annual maximum precipitation depth in the study area during the reference period. Also, disaggregation slightly increases the signal of CC compared to the RCM raw simulations, highlighting the importance of spatial resolution in CC impact evaluation of extreme events.
Highlights
Extreme precipitation events may cause disasters, such as flooding, dam failure, soil erosion, and landslide, which may have substantial social, economic and environmental impacts
The objective of this work is to evaluate whether a stochastic spatial disaggregation model applied on annual maximum daily precipitation (i) enables the validation of a Regional climate models (RCMs) for a period of reference, and (ii) modifies the evaluation of climate change (CC) impacts over a small area
While an increase of the mean global temperature is expected from climate model projections (IPCC, 2007), there is uncertainty associated with precipitation change
Summary
Extreme precipitation events may cause disasters, such as flooding, dam failure, soil erosion, and landslide, which may have substantial social, economic and environmental impacts. As for instance dam building or development of new habitable zone, a good knowledge of the occurrence of extreme events is required to properly evaluate the risk (i.e., the expected cost of damage caused by extreme precipitation). In a context of climate change (CC), past impact studies made with a stationary climate assumption must be reconsidered. There is a consensus in the scientific community about the existence of CC (IPCC, 2007). While an increase of the mean global temperature is expected from climate model projections (IPCC, 2007), there is uncertainty associated with precipitation change.
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