[1] This paper provides a first overview of the performance of state-of-the-art global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) in simulating climate extremes indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), and compares it to that in the previous model generation (CMIP3). For the first time, the indices based on daily temperature and precipitation are calculated with a consistent methodology across multimodel simulations and four reanalysis data sets (ERA40, ERA-Interim, NCEP/NCAR, and NCEP-DOE) and are made available at the ETCCDI indices archive website. Our analyses show that the CMIP5 models are generally able to simulate climate extremes and their trend patterns as represented by the indices in comparison to a gridded observational indices data set (HadEX2). The spread amongst CMIP5 models for several temperature indices is reduced compared to CMIP3 models, despite the larger number of models participating in CMIP5. Some improvements in the CMIP5 ensemble relative to CMIP3 are also found in the representation of the magnitude of precipitation indices. We find substantial discrepancies between the reanalyses, indicating considerable uncertainties regarding their simulation of extremes. The overall performance of individual models is summarized by a "portrait" diagram based on root-mean-square errors of model climatologies for each index and model relative to four reanalyses. This metric analysis shows that the median model climatology outperforms individual models for all indices, but the uncertainties related to the underlying reference data sets are reflected in the individual model performance metrics.

[1] This study provides an overview of projected changes in climate extremes indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The temperature-and precipitation-based indices are computed with a consistent methodology for climate change simulations using different emission scenarios in the Coupled Model Intercomparison Project Phase 3 (CMIP3) and Phase 5 (CMIP5) multimodel ensembles. We analyze changes in the indices on global and regional scales over the 21st century relative to the reference period 1981-2000. In general, changes in indices based on daily minimum temperatures are found to be more pronounced than in indices based on daily maximum temperatures. Extreme precipitation generally increases faster than total wet-day precipitation. In regions, such as Australia, Central America, South Africa, and the Mediterranean, increases in consecutive dry days coincide with decreases in heavy precipitation days and maximum consecutive 5 day precipitation, which indicates future intensification of dry conditions. Particularly for the precipitation-based indices, there can be a wide disagreement about the sign of change between the models in some regions. Changes in temperature and precipitation indices are most pronounced under RCP8.5, with projected changes exceeding those discussed in previous studies based on SRES scenarios. The complete set of indices is made available via the ETCCDI indices archive to encourage further studies on the various aspects of changes in extremes.

[1] In this study, we present the collation and analysis of the gridded land-based dataset of indices of temperature and precipitation extremes: HadEX2. Indices were calculated based on station data using a consistent approach recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices, resulting in the production of 17 temperature and 12 precipitation indices derived from daily maximum and minimum temperature and precipitation observations. High-quality in situ observations from over 7000 temperature and 11,000 precipitation meteorological stations across the globe were obtained to calculate the indices over the period of record available for each station. Monthly and annual indices were then interpolated onto a 3.75 Â 2.5 longitude-latitude grid over the period 1901-2010. Linear trends in the gridded fields were computed and tested for statistical significance. Overall there was very good agreement with the previous HadEX dataset during the overlapping data period. Results showed widespread significant changes in temperature extremes consistent with warming, especially for those indices derived from daily minimum temperature over the whole 110 years of record but with stronger trends in more recent decades. Seasonal results showed significant warming in all seasons but more so in the colder months. Precipitation indices also showed widespread and significant trends, but the changes were much more spatially heterogeneous compared with temperature changes. However, results indicated more areas with significant increasing trends in extreme precipitation amounts, intensity, and frequency than areas with decreasing trends.Citation: Donat, M. G., et al. (2013), Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: The HadEX2 dataset,

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[1] The objective of the present study is to develop efficient estimation methods for the use of the GEV distribution for quantile estimation in the presence of nonstationarity. Parameter estimation in the nonstationary GEV model is generally done with the maximum likelihood estimation method (ML). In this work, we develop the generalized maximum likelihood estimation method (GML), in which covariates are incorporated into parameters. A simulation study is carried out to compare the performances of the GML and the ML methods in the case of the stationary GEV model (GEV0), the nonstationary case with a linear dependence of the location parameter on covariates (GEV1), the nonstationary case with a quadratic dependence on covariates (GEV2), and the nonstationary case with linear dependence in both location and scale parameters (GEV11). Simulation results show that the GLM method performs better than the ML method for all studied cases. The nonstationary GEV model is also applied to a case study to illustrate its potential. The case study deals with the annual maximum precipitation at the Randsburg station in California, and the covariate process is taken to be the Southern Index Oscillation.