Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate

Abstract

[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.

Contributions of different bias‐correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes

“…According to earlier work [Alexander et al, 2006;Sillmann et al, 2013aSillmann et al, , 2013b, these indices are categorized into four groups: (1) absolute indices, as exemplified by the hottest day (TXx) or maximum 1 day precipitation (Rx1day); (2) threshold indices that count the number of days when a temperature or precipitation threshold is exceeded, such as frost days (FDs) or the number of heavy precipitation days (R10mm); (3) duration indices that describe, for instance, the maximum length of a warm spell (WSDI) or a dry spell (CDD); and (4) percentile indices that describe the exceedance rate above or below the 10th or 90th percentile calculated from the 1961-1990 base period, such as cool nights (TN10p) or extremely wet days (R99p). The percentiles were calculated as described in Zhang et al [2005], using the bootstrapping method to eliminate possible inhomogeneities at the boundaries between the base and out-of-base periods due to sampling error.…”

“…Historical and future changes in the frequency, intensity, and duration of climate extremes have been increasingly studied in the last few decades using daily weather observation networks, global climate model (GCM) simulations [Frich et al, 2002;Alexander et al, 2006;Donat et al, 2013;Sillmann et al, 2013aSillmann et al, , 2013b, and regional climate model simulations [Iizumi et al, , 2012. At the global scale, GCM-projected climate extremes are translated into water scarcity, human health burden, food production loss, and degradation of ecosystem services using GCM output as the input of impact models (e.g., hydrologic and crop models).…”

Observed changes in temperature extremes over Asia and their attribution

“…Sillmann et al (2013a, b) calculated the ETCCDI indices for some of the CMIP5 simulations of the twentieth century (historical simulations) and the twenty-first century under different representative concentration pathways (RCPs) emission scenarios. Data from these indices available from Sillmann et al (2013a) are used in this study. Indices data for simulations that were not computed by Sillmann et al (2013a), but whose daily temperature data were available at the time of analysis, were computed using the same software.…”

Temperature and precipitation changes over the Loess Plateau between 1961 and 2011, based on high-density gauge observations

“…Considering the consistency of temperature and precipitation over a long timeframe, we focused on the period between 1961 and 2011 in this study. (Sillmann et al, 2013) that were used in this study. Among these, the first fourteen are indices for temperature extremes, and the remainder are indices for precipitation extremes.…”