Exploring metrics of extreme daily precipitation in a large ensemble of regional climate model simulations

Abstract

The ability of a large ensemble of 15 state-of-the-art regional climate models (RCMs) to simulate precipitation extremes was investigated. The 99th, 99.9th and 99.99th percentiles of daily precipitation in the models were compared with those in the recently released E-OBS observational database for winter, spring, summer and autumn. The majority of the models overestimated the values of the precipitation extremes compared with E-OBS, on average by approximately 38%, but some models exceeded 50%. To measure model performance, a simple metric is proposed that averages a nonlinear function of the seasonal biases over the European area. The sensitivity of the metric to different assumptions in the construction and the quality of the observational data was explored. Generally, low sensitivities of the metric to spatial and seasonal averaging were found. However, large sensitivities to potential biases in the observational database were found. An alternative metric that measures the spatial pattern of the extremes (which is not sensitive to a potential constant offset in the observational data) was further explored. With this metric, the ranking between the models changed substantially. However, the 2 models with the worst score in the standard metric also displayed the worst scores with this alternative metric. Finally, the regional climate models displayed the largest biases compared with E-OBS in areas where the underlying station density used in E-OBS is low, thus suggesting that data quality is indeed an important issue. In summary, the results show that: (1) there is no metric that guarantees an objective and precise ranking or weighting of the models, (2) by exploring different metrics it nevertheless appears possible to indentify models that perform consistently worse than other models, and (3) the observational data quality should be considered when designing and interpreting metrics.

A comprehensive benchmarking system for evaluating global vegetation models

“…Shukla et al, 2006: Hall andQu, 2006). In recent years, there has been considerable effort spent on the development of standard metrics for climate-model evaluation (Taylor, 2001;Gleckler et al, 2008: Lenderink, 2010Moise and Delage, 2011;Yokoi et al, 2011). In comparison, there has been little quantitative assessment of DGVM performance under recent conditions.…”

Projected Changes in Flood-Generating Precipitation Extremes Over the Czech Republic in High-Resolution Regional Climate Models

“…metoffice.com, 2004metoffice.com, -2009metoffice.com, ) and CECILIA (http://www.ceciliaeu.org, 2006metoffice.com, -2009. Although surface air temperature and precipitation amounts have been the variables most frequently examined in outputs of climate models, increasing attention has been paid to their extreme values (Fowler and Ekström, 2009;Pongracz et al, 2009;Ballester et al, 2010;Lenderink, 2010).…”

Weight assignment in regional climate models

“…f 4 : extremes in terms of re-occurrence periods for temperature and precipitation Consideration of the far tails of distributions (99th, 99.9th and 99.99th percentiles) provides additional information to the full PDF information as captured in f 3 . We evaluate such extremes in 2 ways: by considering daily precipitation extremes taken directly from the empirically deduced PDFs (Lenderink 2010) and by using generalized extreme-value theory (Buonomo unpubl.) for daily precipitation and daily maximum/ minimum temperatures.…”