Extreme Rainfall at Multiple Scales in Historical and Future Climate: How do Extremes From the Canadian CRCM5 Large-Ensemble Scale in Space and Time?
Abstract
The characterization of extreme rainfall across various spatial and temporal scales is critical to predict precipitation response to climate change. This represents, however, a major challenge, since high uncertainties linked to deficiencies of available datasets (temporal and spatial coverage of series, measurement errors, etc.) may affect the estimations.An interesting option to evaluate extreme rainfall climatology is thus the use of high-resolution Regional Climate Models (RCMs) which provide long simulated series with complete spatial coverage. Based on large ensembles of high-resolution RCM simulations, the assessment of the extreme variability across a broad range of spatio-temporal scales and climate periods is also possible. However, despite steady improvements achieved in last decades, several issues remain in RCM simulations, especially for sub-daily rainfall. It is thus crucial to evaluate state-of-the-art models before their use.As a part of the ClimEx project (http://www.climex-project.org/), a large ensemble of 50 members of the Canadian RCM v5 (CRCM5) has been produced at 0.11° for part of North America and Europe for the period 1950-2100 under RCP 8.5. In the present study the CRCM5 Large-Ensemble (CRCM5-LE) is used to assess the variability of rainfall extremes across a wide range of spatial and temporal scales for the North American domain and the 150-year simulation period. The RCM evaluation is carried out by comparing CRCM5-LE Annual Maxima Series (AMS) with meteorological stations and three gridded datasets: a convection-permitting 13-year WRF simulation; the bias-corrected satellite CMORPH dataset; and the MSWEP v2 dataset. The aim is to evaluate the ability of the models to simulate extremes and describe the dependence of extreme statistics on data resolution and source. The evolution of simulated extremes under climate change is then assessed by analyzing the change of AM probability distributions over time. To this end, CRCM5-LE members are pooled to define long AMS for durations between 1 h and 3 days and estimate high return period quantiles over short periods of a few years. The main goal is to assess how different precipitation regimes respond to climate warming over different time-horizons and to explore the impact of natural climate variability at local and regional scales
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFMGC32B..06I
- Keywords:
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- 1616 Climate variability;
- GLOBAL CHANGEDE: 1626 Global climate models;
- GLOBAL CHANGEDE: 1637 Regional climate change;
- GLOBAL CHANGE